Positive Family Functioning 29 September 2010 Final Report by Access Economics Pty Limited for Department of Families, Housing, Community Services and Indigenous Affairs © Access Economics Pty Limited This work is copyright. The Copyright Act 1968 permits fair dealing for study, research, news reporting, criticism or review. Selected passages, tables or diagrams may be reproduced for such purposes provided acknowledgment of the source is included. Permission for any more extensive reproduction must be obtained from Access Economics Pty Limited through the contact officer listed for this report. Disclaimer While every effort has been made to ensure the accuracy of this document and any attachments, the uncertain nature of economic data, forecasting and analysis means that Access Economics Pty Limited is unable to make any warranties in relation to the information contained herein. Access Economics Pty Limited, its employees and agents disclaim liability for any loss or damage which may arise as a consequence of any person relying on the information contained in this document and any attachments. Access Economics Pty Limited ABN 82 113 621 361 www.AccessEconomics.com.au CANBERRA Level 1 9 Sydney Avenue Barton ACT 2600 MELBOURNE Level 27 150 Lonsdale Street Melbourne VIC 3000 SYDNEY Suite 1401, Level 14 68 Pitt Street Sydney NSW 2000 T: +61 2 6175 2000 F: +61 2 6175 2001 T: +61 3 9659 8300 F: +61 3 9659 8301 T: +61 2 9376 2500 F: +61 2 9376 2501 For information on this report please contact
Lynne Pezzullo T: 61‐2‐6175 2000 E: [email protected] Report prepared by Lynne Pezzullo Penny Taylor Scott Mitchell Laze Pejoski Khoa Le Anam Bilgrami 2 Positive Family Functioning Contents Acknowledgements.........................................................................................................................i Glossary ii Executive Summary .........................................................................................................................i 1 Introduction ........................................................................................................................ 5 2 Methodological overview ................................................................................................... 6 2.1 Definition of family functioning and the outcomes of interest ............................................. 6 2.2 Data review............................................................................................................................ 7 2.3 Literature review.................................................................................................................. 10 2.4 Concepts and data underlying lifetime costing ................................................................... 10 2.5 Model construction.............................................................................................................. 11 2.6 Cost benefit/cost effectiveness analysis (CBA/CEA) and the process for selecting interventions for analysis..................................................................................................... 18 3 Findings from the LSAC data investigation ....................................................................... 21 3.1 LSAC variables ...................................................................................................................... 21 3.2 LSAC analysis........................................................................................................................ 24 4 Findings from the ATP data investigation......................................................................... 31 4.1 ATP variables........................................................................................................................ 31 4.2 ATP analysis ......................................................................................................................... 34 5 The costing model............................................................................................................. 38 5.1 Health outcomes.................................................................................................................. 38 5.2 Productivity outcomes ......................................................................................................... 44 5.3 Criminality outcomes ........................................................................................................... 45 5.4 Lifetime costing framework ................................................................................................. 49 6 Cost benefit analysis ......................................................................................................... 52 6.1 Communities for children (CfC)............................................................................................ 52 6.2 Positive Parenting Program (PPP) ........................................................................................ 56 6.3 Reconnect ............................................................................................................................ 62 6.4 Shocking all variables – the value of PFF ............................................................................. 69 7 Conclusions ....................................................................................................................... 71 References................................................................................................................................... 77 Appendix A : LSAC children and future addictive behaviours .................................................... 85 Appendix B : Literature review sources, ATP ............................................................................. 88 Appendix C : LSAC regression outcomes .................................................................................... 91 Appendix D : LSAC variable specification ................................................................................. 104 Appendix E : Mean and standard deviation of LSAC variables................................................. 108 Appendix F : ATP variable interpretation.................................................................................. 111 Appendix G : ATP Regression outcomes ................................................................................... 118 Appendix H : Mean and standard deviation of ATP variables................................................... 128 Appendix I : Detailed costing methodology and tables ........................................................... 133 Charts Chart 3.1 : Explanatory power of regressions, by age................................................................ 26 Chart A.1 : Differences in cognitive development by socio‐economic status............................ 86 Tables Table 2.1 : Characteristics of family functioning domains ........................................................... 6 Table 2.2 : FF variables used for each intervention .................................................................... 16 Table 3.1 : Regression constructs in LSAC .................................................................................. 23 Table 4.1 : Regression constructs................................................................................................ 32 Table 5.1 : Summary of total costs of obesity (2010)................................................................. 39 Table 5.2 : Summary of total costs of anxiety and depression (2010) ....................................... 41 Table 5.3 : Summary of total costs of daily smoking (2010) ...................................................... 42 Table 5.4 : Summary of total costs of alcohol abuse (2010) ...................................................... 43 Table 5.5 : Summary of total costs of illicit drug use (2010)...................................................... 44 Table 5.6 : Estimated effects* (%) of year 12 and undergraduate completion* on probability of participation and average earnings ............................................................................ 45 Table 5.7 : Social costs of crime by cost type (2010) ................................................................. 46 Table 5.8 : Net recurrent expenditure on criminal courts (2008‐09) and % criminal court finalisations by court type .......................................................................................... 47 Table 5.9 : Total real net operating expenditure on prisons and community corrections in Australia in 2008‐09.................................................................................................... 48 Table 5.10 : Discounted lifetime costs of adverse health outcomes (a) (2010 dollars)............. 49 Table 5.11 : Discounted lifetime costs of adverse productivity outcomes (a) (2010 dollars).... 50 Table 5.12 : Discounted lifetime costs of criminality outcomes (a) (2010 dollars).................... 51 Table 6.1 : Communities for Children target areas and related LSAC variables ......................... 54 Table 6.2 : Outcomes of Communities for Children variables used in this report...................... 55 Table 6.3 : Outcomes of CfC as delivered over 2004‐05 to 2007‐08 .......................................... 56 Table 6.4 : PPP target areas and LSAC variables ......................................................................... 58 Table 6.5 : Impact of large scale Queensland PPP trial ............................................................... 58 Table 6.6 : Impact of large scale Western Australian PPP trial ................................................... 59 Table 6.7 : Improvement in means scores of European PPP trial .............................................. 60 Table 6.8 : Outcomes of PPP variables used in this analysis....................................................... 60 Positive Family Functioning Table 6.9 : PPP costs ($US) .......................................................................................................... 61 Table 6.10 : Outcomes of PPP as delivered over 2004‐05 to 2007‐08 in Queensland................ 61 Table 6.11 : ATP variables associated with the Reconnect Program .......................................... 66 Table 6.12 : Estimate of effect size — young person’s perception of their ability to manage family conflict before Reconnect and now................................................................. 67 Table 6.13 : Estimate of effect size — young person’s perception of their family’s ability to manage family conflict before Reconnect and now ................................................... 68 Table 6.14 : Outcomes of the Reconnect Program, as delivered over 2004‐05 to 2008‐09....... 69 Table 6.15 : The value of PFF....................................................................................................... 69 Table 7.1 : Summary of costs and benefits of modelled interventions ...................................... 72 Table B.1 : Antisocial behaviour ................................................................................................. 88 Table B.2 : Anxiety and depression ............................................................................................ 88 Table B.3 : Smoking .................................................................................................................... 89 Table B.4 : Alcohol...................................................................................................................... 89 Table B.5 : Illicit drug use ........................................................................................................... 89 Table B.6 : Productivity .............................................................................................................. 90 Table B.7 : Overweight/obesity.................................................................................................. 90 Table C.1 : Obesity B1 (ahs23c2) ................................................................................................ 91 Table C.2 : Obesity B2 (bcbmi) ................................................................................................... 92 Table C.3 : Obesity B3 (ccbmi).................................................................................................... 92 Table C.4 : Obesity K2 (dcbmi) ................................................................................................... 93 Table C.5 : Obesity K3 (ecbmi).................................................................................................... 93 Table C.6 : Productivity B1 (awlrnoi) .......................................................................................... 94 Table C.7 : Productivity B2 (bwlrnoi).......................................................................................... 94 Table C.8 : Productivity B3 (cwlrnoi) .......................................................................................... 95 Table C.9 : Productivity K2 (dwlrnoi).......................................................................................... 95 Table C.10 : Productivity K3 (ewlrnoi) ........................................................................................ 96 Table C.11 : Anxiety and depression B1 (apedsgc)..................................................................... 96 Table C.12 : Anxiety and depression B2 (bpedsef)..................................................................... 97 Table C.13 : Anxiety and depression B3 (cpedsef) ..................................................................... 97 Table C.14 : Anxiety and depression K2 (daemot) ..................................................................... 98 Table C.15 : Anxiety and depression K3 (eaemot) ..................................................................... 98 Table C.16 : Antisocial B1 (apedsgc)........................................................................................... 99 Table C.17 : Antisocial B2 (babitp) ............................................................................................. 99 Table C.18 : Antisocial B3 (caconda) ........................................................................................ 100 Table C.19 : Antisocial K2 (daconda) ........................................................................................ 100 Table C.20 : Antisocial K3 (eaconda) ........................................................................................ 101 Table C.21 : Addictions B1 (apedsgc) ....................................................................................... 101 Table C.22 : Addictions B2 (babitp) .......................................................................................... 102 Table C.23 : Addictions B3 (casdqta) ........................................................................................ 102 Table C.24 : Addictions K2 (dasdqta)........................................................................................ 103 Table C.25 : Addictions K3 (easdqta)........................................................................................ 103 Table D.1 : Standard LSAC variables......................................................................................... 104 Table D.2 : Combinations of LSAC variables............................................................................. 106 Illustrative distribution of categorical variables........................................................................ 109 Table G.1 : Underengagement predictor variables................................................................... 118 Table G.2 : Completion of high school(a)(b) ............................................................................. 118 Table G.3 : Completion of high school (only) v University degree(a)(b) ................................... 119 Table G.4 : Body mass index at 23‐24 years(a)(b)..................................................................... 120 Table G.5 : ATP anxiety/depression 2002 crosstabulation ....................................................... 122 Table G.6 : Logistic regression results — child age 19‐20 years (year 2002)(a)(b) ................... 122 Table G.7 : Logistic regression results — child age 19‐20 years (year 2002)(a)(b) ................... 124 Table I.1 : Obesity prevalence rates and estimated obese people (number) in 2010 ............. 136 Table I.2 : Anxiety and depression prevalence rates and estimated people with anxiety and depression (number) in 2010 ................................................................................... 136 Table I.3 : Smoking prevalence rates and estimated current daily smokers in 2010............... 137 Table I.4 : Prevalence rates for tobacco‐caused diseases and conditions(a)........................... 138 Table I.5 : Prevalence rates ‐ drinking at risky‐high risk(a) levels of long term health harm and estimated risky‐high risk drinkers in 2010................................................................ 138 Table I.6 : Prevalence rates for recent use(a) of illicit drugs and estimated recent users in 2010139 Table I.7 : Offender age‐gender prevalence profile and estimated offenders in 2008‐09 ...... 139 Table I.8 : Prisoner age‐gender prevalence rates and estimated prisoners in 2009 ............... 140 Table I.9 : Annual per‐person costs of obesity ‐ males (in 2010 dollars) ................................. 140 Table I.10 : Annual per‐person costs of obesity ‐ females (in 2010 dollars) ............................ 141 Table I.11 : Annual per‐person costs of anxiety and depression ‐ males (in 2010 dollars)...... 142 Table I.12 : Annual per‐person costs of anxiety and depression ‐ females (in 2010 dollars) .. 142 Table I.13 : Annual per‐person costs of current daily smoking ‐ males (in 2010 dollars) ........ 143 Table I.14 : Annual per‐person costs of current daily smoking ‐ females (in 2010 dollars)..... 143 Table I.15 : Annual per‐person costs of alcohol abuse ‐ males (in 2010 dollars)..................... 144 Table I.16 : Annual per‐person costs of alcohol abuse ‐ females (in 2010 dollars).................. 144 Positive Family Functioning Table I.17 : Annual per‐person costs of illicit drug abuse ‐ males (in 2010 dollars)................. 145 Table I.18 : Annual per‐person costs of illicit drug abuse ‐ females (in 2010 dollars) ............. 145 Table I.19 : Age‐gender employment in the general population ............................................. 146 Table I.20 : Age‐gender average weekly earnings(a) for the general population ($) .............. 146 Table I.21 : Annual costs of year 12 non‐completion ($ 2010) ................................................ 147 Table I.22 : Annual costs of undergraduate degree non‐completion ($ 2010)........................ 147 Table I.23 : Age‐specific undergraduate non‐completion rates in 2007.................................. 148 Table I.24 : Annual policing cost per offender ($) .................................................................... 149 Table I.25 : Annual court system cost per offender ($)............................................................ 149 Table I.26 : Annual prison system cost per prisoner ($)........................................................... 149 Table I.27 : Annual per‐person societal costs of crime for males ($)....................................... 150 Table I.28 : Annual per‐person societal costs of crime for females ($).................................... 150 Table I.29 : Crime under‐reporting multipliers and derived probabilities ............................... 151 Table I.30 : Probabilities of court action on reported crimes .................................................. 152 Table I.31 : Court finalisation outcome probabilities............................................................... 153 Table I.32 : Custodial sentence probabilities in guilty verdict court cases .............................. 153 Figures Figure 2.1 : Approximate age of study cohorts and bridging the current information gap......... 8 Figure 2.2 : Diagram of data map................................................................................................. 9 Figure 2.3 : Conceptual map for valuing costs of NFF................................................................ 10 Figure 2.4 : Incidence versus prevalence approach .................................................................... 10 Figure 2.5 : Cost effectiveness analysis model map.................................................................... 17 Figure 2.6 : CEA model pathway for interventions .................................................................... 18 Figure 6.1 : Value of PFF by benefit type, 2010 (total $5.4 billion), $bn and % total ................. 70 Positive Family Functioning Acknowledgements Access Economics would like to acknowledge with gratitude the expert knowledge and inputs provided by members of the Expert Reference Group for this project. Professor Ann Sanson Department of Paediatrics, University of Melbourne, ARC/NHMRC Research Network Coordinator, Australian Research Alliance for Children and Youth Brian Babington Chief Executive Officer, Families Australia Carol Ey Branch Manager, Research and Analysis Branch, Department of Families, Housing, Community Services and Indigenous Affairs Dr Lance Emerson Chief Executive Officer, Australian Research Alliance for Children and Youth (ARACY) Dr Marian Esler Section Manager, Research Section, Family and Child Support Policy Branch, Department of Families, Housing, Community Services and Indigenous Affairs Dr Matthew Gray Deputy Director, Australian Institute of Family Studies Megan Shipley Research Section, Family and Child Support Policy Branch, Department of Families, Housing, Community Services and Indigenous Affairs Paula Mance Research Projects and Publications Section, Department of Families, Housing, Community Services and Indigenous Affairs Rachel Henry Research Section, Family and Child Support Policy Branch, Department of Families, Housing, Community Services and Indigenous Affairs Professor Stephen Zubrick Co‐Director for Developmental Health, Curtin University of Technology Access Economics would like to acknowledge in particular the staff of the Australian Institute of Family Studies (AIFS), who analysed the Australian Temperament Project (ATP) data and modelled those regressions. Apart from Dr Gray, particular thanks go to Dr Ben Edwards and Dr Diana Smart. Glossary ABS Australian Bureau of Statistics AE‐DEM Access Economics Demographic Model AEM Access Economics Macroeconomics Model AIFS Australian Institute of Family Studies AIHW Australian Institute of Health and Welfare AWE average weekly earnings ATP Australian Temperament Project B1, B2, B3 Baby cohort, waves 1, 2 and 3 in LSAC BOD burden of disease CALD Culturally and Linguistically Diverse CBA cost benefit analysis CEA cost effectiveness analysis CFC Communities for Children DALY disability adjusted life year DCBA disease cost‐burden analysis DEEWR Department of Education, Employment and Workplace Relations DOFD Department of Finance and Deregulation DSM Diagnostic and Statistical Manual DSP Disability Support Pension DWL deadweight loss FAHCSIA Australian Government Department of Families, Housing, Community Services and Indigenous Affairs FF family functioning FRS family relationship services GP general practitioner HILDA Household Income and Labour Dynamics in Australia ICD‐10 International Classification of Diseases (10th revision) K1, K2, K3 Kindy cohort, waves 1, 2 and 3 in LSAC LSAC Longitudinal Study of Australian Children LSAY Longitudinal Study of Australian Youth NHS National Health Survey NDHS National Drug Strategy Household Survey NPV net present value Positive Family Functioning NSA Newstart Allowance NFF negative family functioning MBS Medicare Benefits Schedule OLS Ordinary Least Squares PBS Pharmaceutical Benefits Scheme PC Productivity Commission PFF positive family functioning PPP Postive Parenting Program PS Parenting Scale PSOC Parental Sense of Competency PUP Parents under Pressure QOL quality of life REACH Responding Early Assisting Children SCRGSP Steering Committee for the Review of Government Service Provision SDAC Survey of Disability, Ageing and Carers (ABS) SDQ Strengths and Difficulties Questionnaire SES socioeconomic status SFCS Stronger Families and Communities Strategy TILA Transition to Independent Living Allowance VSLY value of a statistical life year WHO World Health Organization W1,2,3 waves 1,2,3 of LSAC YLD year(s) of healthy life lost due to disability YLL year(s) of healthy life lost due to premature mortality Positive Family Functioning Executive Summary Access Economics was commissioned by the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) to quantify, in economic terms, the value of ‘goods and services’ provided by positive family functioning (PFF) and to conduct a cost benefit analysis (CBA) to establish the returns to government and society for investments made in supporting family functioning (FF). This report follows a scoping study, also conducted by Access Economics, to establish the methodology for the project. The scoping study explains the equivalence of measuring the value of PFF as the costs of negative family functioning (NFF). This study was overseen by a panel of experts. Methods FF is defined through a variety of domains – emotional, governance, cognitive, physical, intra‐
familial and social (Table 2.1). Literature review revealed three broad areas of outcomes associated with FF. ■
Health outcomes were observed through the occurrence of anxiety and depression, obesity and substance abuse (smoking, alcohol and drug abuse) later in life. These are associated with health expenditures, productivity losses (through lower workforce participation and premature death), other financial costs, and loss of quality of life (QoL) (measured in disability adjusted life years or DALYs). ■
Productivity outcomes were reflected in secondary and tertiary educational achievement completion, flowing on to impact lifetime earnings. ■
Social outcomes were primarily measured through negative manifestations – antisocial behaviour such as delinquency and crime, resulting in criminal justice system costs. Two longitudinal studies — the Longitudinal study of Australian Children (LSAC) aged up to 9 years and the Australian Temperament Project (ATP) for older children were selected to analyse the relationship between FF and child outcomes. Regression analysis was conducted to establish relationships between ‘transition’ health, productivity and social outcome variables in LSAC and FF variables. The latter were selected on the basis of literature evidence, after controlling for other factors such as socioeconomic status (SES). Transition variables were carefully selected to match similar or identical ATP variables. Further regression analysis was undertaken using the transition variables, together with ATP FF and control independent variables, to establish relationships with ATP ‘interim’ health, productivity and social outcomes in early adulthood. The interim outcomes were then used to predict lifetime health, productivity and social costs, based on an extensive costing process utilising multiple data sources (Chapter 5). Findings The net present value (NPV) of benefits from intervening in childhood and adolescence to prevent poor outcomes later in life are substantial, despite the fact that such intervention incurs costs today but discounted benefits are realised a long time into the future. ■
In total, the potential NPV of benefits to be realised is in the order of $5.4 billion per annum in 2010 dollars. This can be considered the cost of NFF currently, or the value of PFF gains possible. Over half these gains (53% or $2.9 billion) are productivity gains, with a further 22% ($1.2 billion) of the benefits deriving from savings from fewer i addictions. Fewer cases of anxiety and depression would save $0.6 billion (11%), while lower rates of criminality and antisocial behaviour would accrue $0.5 billion (10%). A reduction in obesity would save $0.3 billion per annum (5% of the total) ‐ Figure i. Figure i: Value of PFF by benefit type, 2010 (total $5.4 billion), $bn and % total 261 , 5%
1,176 , 22%
547 , 10%
2,882 , 53%
581 , 11%
Obesity
Productivity
Anxiety and depression
Anti‐social
Addictions
Source: Access Economics calculations. Note: Shares may not sum to 100% due to rounding. There are also marked social and economic benefits if cost effective prevention programs can be identified and implemented. This analysis has focused on three interventions selected on the bases of a range of criteria (section 2.6) ■
The Communities for Children program, targeting pre‐school and primary school aged children, is one of the major Australian Government investments in families. The program improves outcomes in various FF areas including hostile parenting, parenting self‐efficacy, parent mental health, quality of the home learning environment, parental relationship conflict, child total emotional and behavioural problems, childhood overweight, receptive vocabulary achievement and verbal ability. The benefit:cost ratio for this program was estimated as 4.8:1, a 377% return on investment. ■
The Positive Parenting Program is one of the best evaluated FF programs for younger children. The program improves FF outcomes in parental sense of competency, the dyadic adjustment scale, the Strengths and Difficulties Questionnaire (SDQ) emotional and conduct scales, the Eyberg Child Behaviour Intensity score, parental depression, parental laxness, parental over‐reactivity, and parental verbosity. The benefit:cost ratio for this program was estimated as 13.8:1, a substantial 1,283% return on investment. ■
The Reconnect program targets an older cohort of children and was found to improve outcomes in school bonding and conflictual relationships, with proxied effect sizes estimated for attachment to parents and harsh parenting. The benefit:cost ratio for this program was estimated as 1.8:1, an 81% return on investment. Costs and benefits are summarised in Table ii. ii Positive Family Functioning Table ii: Summary of costs and benefits of modelled interventions CfC PPP Reconnect Program cost ($m)* Unit cost ($) 113.6 19.7 112.1 840/child aged 0‐5 34/child aged 2‐12 3,800/person aged 12‐21 Benefit ($m, lifetime NPV) 541.4 272.4 202.8 Benefit:cost ratio 4.76 13.82 1.81 Source: Access Economics calculations. * Costs estimated over 2004‐05 to 2007‐08 except for Reconnect which extends to 2008‐09. Many of the family ‘inputs’ incorporated in the analysis were found to be statistically significant explanators of child outcomes with the relationship consistent with that predicted by the literature. ■
Obesity was explained by key drivers such as previous obesity, parental obesity, lack of child persistence, and parent‐child conflict. ■
Anxiety and depression were dependent on previous emotional problems, difficult temperament, lower socioeconomic status (SES), harsh discipline, parental anxiety/depression, alienation from parents and lack of child persistence. ■
Smoking in young adulthood (19‐20 years) was determined by previous smoking in adolescence, parental permission to smoke at home and a conflictual parent‐teenager relationship. Alcohol abuse (binge drinking) in young adulthood was dependent on teen bingeing, lack of parental monitoring, father drinking and initiating drinking at an older age (over 15 compared to 14 or younger). Illicit drug use in 23‐24 year olds was dependent on the child’s temperament, lack of parental monitoring, and mother smoking. ■
Predisposition to smoking, alcohol abuse and illicit drug use was established in early years by parental smoking, temperament, harsh and/or inconsistent discipline, poor family cohesion and parental anxiety depression. ■
Productivity was driven by previous learning outcomes, consistent discipline, temperament, socioeconomic status, parent education and, in adolescence, persistence, relationship quality/warmth, parental monitoring and a positive attitude to school. ■
Antisocial behaviour and outcomes were determined by child lack of persistence, previous social/conduct problems and, importantly, were largely influenced by early life FF variables such as poor family cohesion, harsh discipline, parental smoking and low SES, along with parental anxiety/depression and the child’s temperament. The greatest value in this project has been primarily to showcase how a broad, quantitative approach to social policy evaluation can work. With better quality data in the future, there is scope to refine and continue to develop the modelling and elaborate on findings further. The scope of this project has been both ambitious and challenging but, we believe, the methods developed and many findings and insights are of global significance. The novelty of the research inspires further work in this field that we hope can be used to triangulate these findings internationally as well as continue to enhance the evidence base in Australia. Access Economics iii Positive Family Functioning 1
Introduction Access Economics was commissioned by the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) to quantify, in economic terms, the value of ‘goods and services’ provided by positive family functioning (PFF) and to conduct a cost benefit analysis (CBA) and/or cost effectiveness analysis (CEA) to establish the returns to government and society for investments made in supporting family functioning. This report follows a scoping study, also conducted by Access Economics. The scoping study in 2009 determined: ■
the feasibility of quantifying, in economic terms, the value of ‘goods and services’ provided by PFF; ■
■
a method for measuring the benefits of PFF; and a method to conduct CBA and CEA of interventions to improve FF. The methods developed in the scoping study form the basis for the analysis in this report. The scoping study explains the equivalence of measuring the value of PFF as the costs of negative family functioning (NFF). Both the scoping study and this full study were overseen by a panel of experts, as listed in the acknowledgements section of each report. In addition, this study was undertaken with assistance from the Australian Institute of Family Studies (AIFS), which analysed the Australian Temperament Project (ATP) data. The structure of this report is as follows. ■
■
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■
■
■
The methodology is explained in chapter 2. The findings from the investigation of the Longitudinal Survey of Australian Children (LSAC) are outlined in chapter 3. The findings from the investigation of the ATP are detailed in chapter 4. The costing model is described in 5. In chapter 6, the interventions analysed and the CEA results are explained. Conclusions are elaborated in chapter 7. Detailed information is provided in the Appendices in relation to the methods, LSAC and ATP investigations, and the costing model. 5 2
Methodological overview 2.1 Definition of family functioning and the outcomes of interest The focus of the scoping study was on the outcomes of family functioning for the child, without pursuing any family ‘ideal’ or promoting any specific type of family structure. Children are not able to explicitly control their family environment and they are often viewed as the main victims of NFF. The scoping study identified that while no simple definition of PFF exists, consistent themes (or ‘domains’) of FF emerged from the literature. These were developed and agreed, in consultation with the Expert Reference Group for the scoping study and with FaHCSIA. These domains provide an overarching definition of the FF environment. A summary is provided below. Family functioning (FF) – positive and negative – is defined through a variety of emotional attributes, family governance frameworks, cognitive engagement and development characteristics, physical health habits, intra‐familial relationships and social connectedness. PFF is characterised by emotional closeness, warmth, support and security; well‐communicated and consistently applied age‐
appropriate expectations; stimulating and educational interactions; the cultivation and modelling of physical health promotion strategies; high quality relationships between all family members; and involvement of family members in community activities. The domains of FF are not mutually exclusive, but interact, complement each other and co‐
exist. Table 2.1: Characteristics of family functioning domains Domain Characteristics / Proxies Emotional Closeness of parent‐child relationships, warmth, responsiveness, sensitivity, perceived parental and family support as well as healthy open communication, and security/safety. Governance Establishment of age‐appropriate rules, expectations and consistency Engagement and cognitive development Reading and verbal engagement, quality time fostering the development of educational, language and interaction skills. Physical health Healthy/unhealthy physical activities or environments as well as access – including in‐utero – to specific products (e.g. fruit and vegetables, cigarettes and alcohol). Intra‐familial relationships (dyadic family relationships) Quality of relationships between all members of the family. For example sibling rivalries, parent‐child relationships as well as the health of the parents’ relationship. Social connectivity Involvement of parents and children in activities outside of the family unit (e.g. school, community service, volunteer work). Also includes relationships with extended family and work/life balance. Source: Access Economics in consultation with the Expert Reference Group and FaHCSIA. 6 Positive Family Functioning The literature review for the scoping study revealed three broad areas of outcomes associated with FF. ■
Health outcomes were mostly observed through the occurrence of mental illnesses such as anxiety and depression later in life, but also included eating disorders, health behaviours (e.g. unsafe sex, physical inactivity, overweight and obesity) and substance abuse (e.g. smoking, alcohol and drug abuse), with the consequent physical impacts of these risk factors on morbidity and mortality outcomes. ■
Productivity outcomes were reflected in rates of labour force participation, employment and hourly wage rates, with a number of intermediate measures reported in the literature, such as reduced levels of literacy and numeracy and other measures of educational achievement. ■
Social outcomes were measured primarily through their negative manifestations –
involvement in antisocial behaviour such as delinquency, and the probability of criminal behaviour during youth and later in life. In contrast it was harder to quantitatively associate PFF with positive manifestations such as the quality of inter‐personal relationships and future community contributions. The criteria used to select the specific outcomes for analysis in this report were: ■
■
each outcome domain was covered (health, productivity and social); ■
the outcomes were able to be measured by the data sets used as the basis for analysis (see below). outcomes were associated with high economic and social costs, including burden of disease (BoD), based on prevalence and/or existing studies of costs and BoD; In this report, the correlation between FF is thus examined for the following outcomes: ■
■
■
productivity (school completion and completion of an undergraduate university degree); health (anxiety and/or depression, obesity, smoking, drinking, illicit drug use); and social (antisocial behaviour). While the literature provided valuable insights into potential linkages between FF and child outcomes, the following cautions apply. ■
Many of the concepts are difficult to capture and measure and there is a mosaic of different and overlapping instruments and metrics. ■
■
Statistical techniques can be used to determine correlation rather than causation. While measures of intermediate outcomes are available, it is difficult to convert these to final outcomes without lengthy study timeframes. For example, intermediate outcomes such as numeracy and literacy cannot easily be converted into specific types of jobs and lifetime earnings streams. 2.2 Data review Two longitudinal studies — the Longitudinal study of Australian Children (LSAC) and the Australian Temperament Project (ATP) were selected to analyse the correlation between FF and child outcomes. The Longitudinal Survey of Australian Youth (LSAY) and the Household, Income and Labour Dynamics in Australia (HILDA) were also investigated but the LSAY did not 7 collect information on FF variables and HILDA is limited to relationships between the family environment and parent’s participation in the labour force. ■
LSAC has the advantage of containing a breadth of data, valuable for testing confounding factors. However, a disadvantage is the relatively short timeframe of data collection as the eldest participants from the child cohorts are currently 10‐11 years of age. ■
ATP is currently the only study in Australia that allows the determination of long term impacts of FF on health, economic and social outcomes as the most recent data for participants in this study were collected at the age of 23‐24 years. However, measures of FF and parenting have only been recorded since the participants were in their early teens, with no measures during infancy or early childhood. Variables from the ATP and LSAC within each of FF domains were mapped to the specific outcomes selected for analysis so that the likelihood of one of the events of interest occurring could be established across different age groups (Figure 2.1) and linked with outcomes. The ability to join information from LSAC and ATP is limited by slightly different methods in each data set of measuring FF and differences in the generations (ATP children were teenagers during the 1990s, whereas LSAC children are growing up during the new century). There are, however, conceptual consistencies in measurement of FF between the two data sets. Figure 2.1: Approximate age of study cohorts and bridging the current information gap LSAC (1)
LSAC (2)
Current Age Cohort Data Gap
ATP
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Approximate Age of Cohort in 2009
LSAC (1) is the Birth cohort and LSAC (2) is the Kindy cohort. Ideally, one or two more waves of information from the LSAC is required to successfully bridge the gap in ages between the two studies, to enable mapping outcomes for children aged 9 years to those aged 13 years. This approach is considered acceptable, given the alternative is to wait some years for additional study waves to be completed. Figure 2.2 shows how LSAC provides the dependent (end‐period ‘transition’) variables and independent (early and mid‐period family functioning and control) variables in regression analysis of children aged up to 9 years. ■
End‐period transition variables are those that relate to observations in the oldest groups i.e. the third wave of the Kindy cohort in LSAC. Early and mid‐period FF and control variables relate to observations in all the other age groups (the first and second wave observations and the third wave for the birth cohort). ATP data then provide the dependent (end‐period ‘interim outcome’) variables and independent (early and mid‐period family functioning and control) variables in regression analysis of people aged up to 25 years. 8 Positive Family Functioning ■
End‐period interim outcome variables are those that relate to observations in the oldest groups of ATP relevant to the cost data and the circumstance. For example, highest level of education completed, illicit drug use and body mass index were assessed at age 23‐24 years, while other variables (completing year 12, smoking, binge drinking, anxiety/depression, and anti‐social behaviour) were assessed at adulthood (18‐19 years). Early and mid‐period FF and control variables relate to younger age observations. The interim outcomes can then be used to predict, in an Excel model, lifetime health costs, lifetime productivity losses, social costs of crime and disability adjusted life years (DALYs). The impact of childhood programs can be modelled by ‘shocking’ the LSAC variables, while adolescent interventions 1 can be modelled by ‘shocking’ the ATP variables. ‘Shocking’ the model refers to changing input parameters from one level to another, simulating the effects of an intervention, and then observing the consequent change in model outputs (health, productivity and social outcomes). Figure 2.2: Diagram of data map CfC, PPP
LSAC ‐ early and mid‐period
shock FF variables
control variables (confounding)
Regression analysis
LSAC ‐ end‐period
Transition variables
ATP ‐ early and mid‐period
FF variables
control variables (confounding)
shock
Reconnect
Regression analysis
ATP ‐ end‐period
Interim outcomes
Excel model
Lieftime health costs
Productivity (lifetime earnings)
Social (lifetime costs of crime)
DALYs
In laymen’s terms, LSAC was used to establish the impact of FF on children up to the age of 9 years, at which age transitional outcomes from childhood were spliced into the ATP analysis by matching this set of transitional outcomes across the two datasets. Health, productivity and social interim outcomes in early adulthood thus depended on FF during adolescence as well as the transitional outcomes from childhood. From the interim outcomes in early adulthood, lifetime cost impacts were predicted using a variety of other datasets. Given the identification of the ATP and LSAC databases as providing adequate data to establish the relationship between family functioning and outcomes, collaborative relationships were established with AIFS researchers and FaHCSIA data specialists. Both were important participants in the project. Furthermore, AIFS currently manages the ATP data set and access arrangements require that AIFS undertake any in depth analysis. 1
The Communities for Children (CfC) and Positive Parenting Program (PPP) were selected for younger children, while the Reconnect program was selected for adolescents (see Section 2.6). 9 2.3 Literature review Evidence from the literature was used to edify the selection of family functioning inputs from the ATP and LSAC that would be likely to be correlated with outcomes. The findings are briefly outlined in chapter 3, and chapter 4, and evidence is summarised in Appendix A and Appendix B. 2.4 Concepts and data underlying lifetime costing Costs were attached to each outcome as depicted in Figure 2.3. Figure 2.3: Conceptual map for valuing costs of NFF Emotional
Health
Health system expenditures
Governance
Productivity losses
Cognitive
Productivity
Criminality costs
Physical
Social/criminality
DWLs
Intra‐familial
Other financial impacts
Social
Burden of disease
Source: Access Economics. Blue – health impacts. Red – productivity impacts. Green – Social/criminality impacts. An incidence or ‘life time’ costing approach was adopted in line with the aetiology of lagged outcomes – i.e. ‘lifetime’ costs (hazard model). An incidence approach is distinguished from a prevalence approach in Figure 2.4. Figure 2.4: Incidence versus prevalence approach 2000
A*
2009
B*
A
B
B**
C
C*
Incidence costs (2009) = C + present value of C*
Prevalence costs (2009) = A + B + C
2018
Note: The years are illustrative and do not relate to this analysis. The lifetime costs associated with each outcome include the standard cost categories for disease cost burden analysis from the health economics literature: ■
10 Direct health costs – estimated with cost data sourced primarily from the Australian Institute of Health and Welfare (AIHW) data, National Hospital Cost Data Collection (Department of Health and Ageing), the Pharmaceutical Benefits Scheme (PBS), the Positive Family Functioning Medicare Benefits Schedule (MBS) and epidemiological data sourced from the Australian Bureau of Statistics (ABS) National Health Survey (NHS), AIHW and other specific epidemiological studies reported in the peer reviewed literature. ■
Costs of crime – estimated with data primarily from the Steering Committee for the Review of Government Service Provision (SCRGSP) Report on Government Services, and reports from the Australian Institute of Criminology and ABS. . ■
Productivity costs – are estimated using the human capital approach and reflect reduced labour force participation and absenteeism due to the outcomes selected. Parameters and labour force data were drawn primarily from the ABS and reports by the Productivity Commission (PC), as well as peer reviewed literature. ■
Burden of disease (BoD) – was estimated using DALYs and determined using the same disability weights and methodology used by the AIHW (Begg et al, 2007). Monetary values were estimated for the BoD using the value of a statistical life year from DOFD (2009). ■
Other financial costs – include costs associated with the provision of informal care (unpaid care provided by family and friends), health aids and appliances, deadweight losses (DWLs) (efficiency losses which arise due to transfer payments). The main sources of these estimates are previous studies by Access Economics, and the ABS Survey of Disability, Ageing and Carers (SDAC) (ABS, 2004), and Lattimore et al (1997). Further detail on costing methods, cost categories and data sources is provided in Appendix I. 2.5 Model construction This section provides an outline of the model developed to investigate the benefits of PFF and the costs and benefits of the three family functioning programs selected for analysis. The net benefits for each program were derived under a scenario with the intervention compared to a ‘base case’ without the intervention. ■
The costs for the CfC program, PPP and Reconnect programs are reported in chapter 6 and represent a direct input into the cost benefit analysis. ■
The benefits are based on the extent to which each intervention improves FF and reduces its associated costs. The effectiveness of each intervention is reported in chapter 6 while the associated costs (health, productivity and social) are detailed in chapter 5. The underlying principle of the model (developed in Microsoft Excel 2007) is that outcomes (dependent variables in the regression analyses) change as a result of the intervention affecting early and mid‐period family functioning (independent) variables in the regression. The size of the intervention is a direct function of the effectiveness of the program on impacted functioning variables (chapter 6) and the size of the coefficients derived in the regressions (Appendix C and Appendix G). A simple example illustrates the model construction using the impact of a 'shock' to consistent discipline on anxiety in children aged 4‐5 (so the analysis starts with the B3 cohort of this age – incidentally the same age as the K1 cohort). The ‘shock’ in this example was derived from PPP improvements in ‘parental laxness’ and mapped directly to parental consistent discipline as both in practice are aspects of parental consistency (Table 6.20). 11 The 'shock' can be viewed as changing the input parameters (e.g. parental consistent discipline) from one level to another, simulated by the effects of an intervention. Before the shock, the multivariate regressions for anxiety are given below for age group 4‐5 (B3), 6‐7 (K2) and 8‐9 (K3), respectively. In each case, i represents the observational child in the dataset (so anxiety is that of the child, and discipline is towards that child). Where: B3 = Cohort of children aged 4‐5 in the ("Baby") group B2 = Cohort of children aged 2‐3 in the ("Baby") group = Beta coefficient for 'Anxiety' in group B2 = Beta coefficient for 'Consistent discipline' in group B3 = Regression error term Where: K2 = Cohort of children aged 6‐7 in the ("Kindergarten") group B3 = Cohort of children aged 4‐5 in the ("Baby") group = Beta coefficient for 'Anxiety' in group B3 = Beta coefficient for 'Consistent discipline' in group K2 = Regression error term Where: K3 = Cohort of children aged 8‐9 in the ("Kindergarten") group K2 = Cohort of children aged 6‐7 in the ("Kindergarten") group = Beta coefficient for 'Anxiety' in group K2 = Beta coefficient for 'Consistent discipline' in group K3 = Regression error term 12 Positive Family Functioning As a result of the intervention, the new level of childhood anxiety for children aged 4‐5 includes changes in parental consistent discipline. The change in parental consistent discipline from the shock was calculated by multiplying the effectiveness of the program by the average consistent discipline value for children aged 4‐5. Where: = New 'Anxiety' value after intervention = (mean consistent discipline x effectiveness of program) = Beta coefficient for 'Anxiety' in group B2 = Beta coefficient for 'Consistent discipline' in group B3 B3 = Cohort of children aged 4‐5 in the ("Baby") group B2 = Cohort of children aged 2‐3 in the ("Baby") group = Regression error term As a result of the shock, the consequent change in the model output (Anxiety') is given by a percentage change. Where: = Percentage change in 'Anxiety' = New 'Anxiety' value after intervention = Baseline 'Anxiety' value B3 = Cohort of children aged 4‐5 in the ("Baby") group B2 = Cohort of children aged 2‐3 in the ("Baby") group Consequently, the direct percentage change in anxiety levels for children in the 4‐5 age group is captured through to the next LSAC age group (6‐7 year group) through the use of the lagged dependent variable changing by the intervention effectiveness. 13 Where: = Percentage change in 'Anxiety' = New 'Anxiety' value after intervention = Baseline 'Anxiety' mean value B3 = Cohort of children aged 4‐5 in the ("Baby") group B2 = Cohort of children aged 2‐3 in the ("Baby") group Where: = New 'Anxiety' value after intervention in the previous age group = New 'Anxiety' lagged dependent variable = as above = Beta coefficient for 'Anxiety' in group B2 = Beta coefficient for 'Consistent discipline' in group B3 K2 = Cohort of children aged 6‐7 in the ("Kindergarten") group B3 = Cohort of children aged 4‐5 in the ("Baby") group = Regression error term Following this pattern, Anxiety’’K2,i and Anxiety’K3,i are similarly calculated. The change in the final period of LSAC compared with the actual outcome is then included in the ATP multinomial logit regression i.e. the new anxiety levels for K3 children aged 9 (the final regression in the LSAC analysis) is fed directly into the ATP logistic regression for children aged 13 years. 14 Positive Family Functioning Where: = New 'Anxiety' value in children aged 8‐9 in the ("Kindergarten") group = Base 'Anxiety' value in children aged 8‐9 in the ("Kindergarten") group This effectively assumes that no change occurs in outcomes between the ages of 10 to 12. As mentioned before, this approach is considered acceptable, given the alternative is to wait some years for additional study waves to be completed. The multinomial logit regression models were used to analyse the impact of an intervention on the probability of each family functioning outcome for children aged 13‐23 years. Unlike the multivariate linear regressions, the results presented in the logit regression represent the probability of an outcome for a person with ‘average’ attributes. Like the LSAC multivariate linear regressions, the interventions were modelled as a deviation from the mean of a particular explanatory variable (e.g. anxiety). The baseline logistic regression is given below, where e is a mathematical constant, i is again each child observed (in ATP this time), βs are again coefficients, and εs are again error terms. X is the sample mean. After the percentage change from anxiety in the 8‐9 year old age group, the new logistic regression is given, with changes to the lagged dependent variable. In using the multinomial logit model, coefficient estimates are not directly interpretable so do not provide the same type of information as coefficients from an Ordinary Least Squares (OLS) model. A more natural way of interpreting results from a multinomial logit model is to determine the impact on the probability of an outcome by changing the variables that would be impacted by the intervention while holding all others constant. The impact of the intervention on the probability can therefore be represented by: The impact of the intervention was therefore measured as the difference between the probability of an outcome for an average person with and without the intervention. As such, the model projects the probabilistic change in the outcome as a result of the intervention and calculates the economic benefits attached (detailed in chapter 5). The net economic gains 15 between the scenario and the ‘base case’ (no intervention) can therefore be evaluated to determine the intervention’s overall return on investment using a dollar value. Table 2.2 summarises all the FF variables in the model for each intervention modelled (i.e. found to be significant in the regression analysis reported later on). A conceptual map of the model is provided in Figure 2.5. Table 2.2: FF variables used for each intervention CfC PPP Reconnect Hostile parenting Hostile parenting Harsh parenting Parenting self‐efficacy Parental self‐efficacy Consistent parental discipline Attachment to parents Parental warmth* Parental relationship conflict Family cohesion Conflictual relationships Parent mental health Parental anxiety and depression Child total emotional and behavioural problems (SDQ) SDQ total score, SDQ emotional score, SDQ conduct problems Quality of the home learning environment Home learning environment* Receptive vocabulary achievement and verbal ability Inductive reasoning School bonding (positive affect towards school); Under‐engagement (not in education or training and not employed)* Child overweight Source: Access Economics (2010). Note: See Chapters 3, 4 and 6 for derivation. * These variables are in the Reconnect model but the effect size was estimated as zero. 16 Positive Family Functioning Figure 2.5: Cost effectiveness analysis model map Source: Access Economics (2010) 17 2.6 Cost benefit/cost effectiveness analysis (CBA/CEA) and the process for selecting interventions for analysis The concept of CEA modelling of FF interventions is outlined in Figure 2.6. All analyses compare the outcomes for children with the interventions, against outcomes for children without the intervention. The efficacy of a selected intervention in improving FF is derived from previous evaluations of the programs. The Excel model is then used to explore how this improvement in FF (the ‘shock’) reduces lifetime costs (in dollars and DALYs). These lifetime benefits can then be compared with the intervention costs, in net present value (NPV) terms. Figure 2.6: CEA model pathway for interventions Intervention
Improvement in family functioning in Yr x (efficacy)
Cost in 09‐10$
Reduces lifetime costs (NPV in Yr x, discounted DALYs saved )
Benefit in 09‐10$
DALYs saved
CBA
CEA
$:$ Benefit:Cost ratio
$/DALY saved
As noted in Section 2.2, ‘shocking’ the model for CBA and CEA involves comparing what happens in the absence of an intervention (the status quo), with what would happen if a particular target population received an intervention. The intervention improves FF based on evaluated effectiveness of the program, which in turn improves transition and/or interim outcomes (based on the coefficients derived from the modelling). Better outcomes are associated with lower costs, so the NPV of the benefits (lower costs of NFF) can then be compared with the costs of the intervention. Benefits minus costs provide the ‘net benefit’ in dollars, while benefits divided by costs provide the ‘benefit:cost ratio’. The process of selecting appropriate FF interventions commenced in the scoping study, when a preliminary assessment was undertaken of 12 types of interventions: 1.
family assistance and income support payments; 2.
family relationship services (FRS); 3.
Stronger Families and Communities Strategy, including Communities for Children and Invest to Grow; 4.
Positive Parenting Program (PPP); 5.
Early childhood education; 6.
Peel Child Health Survey; 7.
Responding Early Assisting Children (REACh); 8.
Reconnect; 9.
Youthlinx; 10. Transition to Independent Living Allowance (TILA); 11. SureStart; and 12. Parents under Pressure (PuP). 18 Positive Family Functioning For this full study, criteria were established in the first Reference Group meeting for assessing appropriate interventions for CEA. These four criteria were: 1.
Model data: the LSAC and ATP datasets will be able to accommodate ‘shocks’ to the intervention; 2.
Target age: the interventions will target different age groups (e.g. pre‐school, primary school, youth) 3.
Reach: the interventions have ‘reach’ i.e. they effectively target relevant (disadvantaged) groups; and 4.
Efficacy data: adequate information is available from Australian (preferably) or international sources in order to provide an indication of the efficacy of interventions. A follow‐up meeting with Steve Zubrick identified three further criteria. 5.
Specificity: Interventions specifically target family functioning, rather than indirectly affect it (e.g. income supplementation can assist with FF, but is in essence a poverty alleviation method). 6.
Sustainability: Interventions are current, and likely to continue into the future. 7.
Relevance: Interventions have strong connections to or relevance for FaHCSIA. We also reviewed literature provided by the Reference Group – Karoly et al (2007) and Wise et al (2005). The interventions selected from this process are summarised below. 2.6.2
Communities for children (CfC) CfC meets all criteria for the CEA. 1.
Model data: LSAC FF data can be linked with CfC interventions. 2.
Target age: CfC is targeted to pre‐school and primary school age children. 3.
Reach: CfC targets all Australian population sub‐groups. 4.
Efficacy data: CfC has been evaluated with efficacy outcomes that can be imputed to the Excel model. 5.
Specificity: CfC aims to improve family functioning and outcomes for children. 6.
Sustainability: The CfC program has forward funding. 7.
Relevance: CfC is a FaHCSIA program. Conclusion: CfC is one of the major Australian Government investments in families. It has already been shown to be efficacious, and the CEA evaluation can also determine at what cost its effective outcomes are achieved. 2.6.3
Positive Parenting Program PPP meets all criteria for the CEA. 1.
Model data: LSAC FF data can be linked with PPP interventions. 2.
Target age: PPP is targeted to pre‐school and primary school age children. 3.
Reach: PPP targets all Australian population sub‐groups. 4.
Efficacy data: PPP has been evaluated with efficacy outcomes that can be imputed to the Excel model. 19 5.
6.
7.
Specificity: PPP aims to improve family functioning and outcomes for children. Sustainability: The PPP program is successful and growing. Relevance: PPP is a program relevant to FaHCSIA core business. Conclusion: PPP is one of the best evaluated programs targeted at improving family functioning and outcomes for younger children. While its efficacy is well‐
proven, there are fewer studies on its cost effectiveness and this CEA can also act as a tool to test/triangulate the power of the model. 2.6.4
Reconnect In the Scoping Study Reference Group and in the 13 January Reference Group meeting the Reconnect program was identified as being a good candidate for CEA. Again it meets all criteria for the CEA. 1.
Model data: ATP FF data can be linked with Reconnect interventions. 2.
Target age: Reconnect targets youth aged 12‐18 (who are homeless or at risk of homelessness) and their families. 3.
Reach: Reconnect targets all Australian population sub‐groups. 2 4.
Efficacy data: Reconnect has been evaluated with efficacy outcomes that can be imputed to the Excel model (based on two longitudinal studies). 5.
Specificity: Reconnect aims to improve family functioning and outcomes for high school aged children. 6.
Sustainability: The Reconnect program is funded into the future. 7.
Relevance: Reconnect is a FaHCSIA program. Conclusion: Reconnect provides a complementary intervention targeted at an the older cohort of children, which has been evaluated as effective, but where nothing is yet known regard cost effectiveness. 2
Indigenous youth comprised 9% of the respondents to the longitudinal survey that formed a key element of the 2003 evaluation by the Australian Government Department of Family and Community Services (FACS, 2003:34) and ‘No differences exist between entering and exiting clients in relation to country of birth or language background’ (FACS, 2003:35). Youth were represented from all jurisdictions, from both sexes and with varying levels of case complexity. 20 Positive Family Functioning 3
Findings from the LSAC data investigation LSAC consists of two cohorts ‘B’ (for Baby) and ‘K’ (for Kindergarten). Each of these cohorts has three sets of time data ‐‘waves’ of survey data taken at two yearly intervals. Information is collected through self‐reporting and also observational measures (such as parent‐child interactions). Child outcomes are measured as: behavioural and emotional adjustment; language and cognitive development; and social competence. LSAC contains specific research questions. One question focuses particularly on the impacts of family relationships, composition and dynamics on child outcomes, and changes to these over time. The question includes the analysis of: ■
■
■
■
■
■
■
■
the size and make‐up of family; the involvement of extended family; roles of family members; character of parental relationships and level of conflict in the family; parenting practices; child’s temperament; impact of family break‐up and re‐formation; and family coping strategies, particularly in times of stress. 3.1 LSAC variables Evidence from the literature was used to edify the selection of family functioning inputs from the ATP and LSAC that would be likely to be correlated with outcomes. The evidence is summarised in Appendix B. In most cases, the choice of relevant LSAC variables to represent these general literature categories in the model is fairly straightforward. ■
■
■
Appendix C provides a complete list of LSAC variables included in the model. Appendix D provides a detailed description of each individual variable. Appendix E provides a distribution of LSAC variables. Cases that involved more consideration are discussed below. ■
In some cases, concepts linked in the literature to negative outcomes for children — either as control variables or family functioning variables — were not available from the datasets. In the LSAC, no variables were identified for child abuse, school bonding, enmeshment, or negative conflictual relationships between children and parents. In addition, some literature concepts were not relevant to LSAC children such as study child’s alcohol consumption or smoking. ■
For the obesity regression, the category ‘Sedentary activities’ is not derived directly from the literature discussed in Appendix B. The FF variable used here (e‐tainment) measures total hours per week of electronic entertainment: using computers, playing video games and watching television. The concept is that too much sedentary activity contributes to obesity. 21 ■
Also, under obesity, the control variable ‘needs extra medical care’, while a direct LSAC variable, is not derived directly from the literature in Appendix B. The rationale here is that some children may be physically unable to exercise, which could in turn contribute to weight gains. ■
Under productivity, academic competency (learning outcomes) is derived from the literature but, given the young age of the children involved, academic competency could vary considerably within the two‐year span of each wave. Accordingly, the LSAC variable ‘study child age in months’ was added as a control variable. ■
For the very young cohorts (particularly B1 and sometimes B2 and older), there were no distinct measures of addictions, antisocial behaviour, or anxiety and depression. In some cases the best proximate dependant variable was the same (such as apedsgc for the B1 cohort across all three regressions). Since the only impact of these early cohorts is to feed into older age groups as a general indicator of previous problems, this was considered sensible, since the same predisposing factor can be a risk for two different outcomes later. Note that for B2, babitp is used as the dependent variable with identical independent variables except that parental overprotection is used to predict addiction but not antisocial behaviour, based on the literature (Emmelkamp and Heeres, 1988), which reported that parental overprotection was a significant factor in adult drug addiction, hence its inclusion there. ■
Parental anxiety and depression has been recoded from an average to a sum of component measures. ■
The literature category ‘socio‐economic status’ is proxied by the LSAC variables household income 3 and highest level of parent education 4 . ■
Alcohol consumption has been recoded from six categories down to three categories – abstain, safe and unsafe. ■
In an effort to obtain a ‘cradle to grave’ coverage for the full model, where the same dependant variable for the regressions is not available for all age groups, close substitutes are used. There were also issues around sample size. The initial sample for both cohorts was around 5,000 children. After excluding children who have not participated in all three waves, the sample size is reduced to between 4,000 and 4,500. Using standard regressions, for any given variable, participants with nil responses are excluded. That is, the variable with the most missing observations determines the sample size for the regression. Thus, there is a trade off between more variables (greater explanatory power) and fewer variables (larger sample size). Preference has been given here to greater explanatory power, but this does limit sample sizes to between 2,000 and 3,000 observations. ■
Certain participants are more likely to drop out as the study proceeds or to be non‐
responders (which will affect sample size and bias in the analysis sample) – e.g. people with low socioeconomic status (SES), Indigenous Australians and those from non‐English speaking backgrounds. To account for this, sample weightings were employed. 3
For some groups, weekly income had to be used, but for compatibility this was converted to annual income. 4
Highest level is ‘post secondary’ (which includes the majority of parents, so there may be a lack of precision in the high end of the scale). 22 Positive Family Functioning ■
The model did not adjust for complex survey design, as this is not usually an issue unless location‐specific factors are important. ■
Also, to maintain sample size, the primary carer was used to represent parents, rather than having separate (and thus smaller) equations for mothers and fathers. This is also consistent with the ATP, which generally does not differentiate parents by gender. However, to allow for gender differences, sex of parent was included as a control variable. ■
The LSAC variable ‘family cohesion’ was used as a proxy for the literature category ‘marital conflict’. LSAC also measures ‘argumentative relationships’, although this variable is not asked of sole parent families, who are an important subsample to retain in the modelling. ■
Scales from the Strengths and Difficulties Questionnaire (SDQ) were utilised from LSAC in relation to anxiety and depression, anti‐social behaviour and addictions (Table 3.3). Table 3.3: Regression constructs in LSAC Literature Concept LSAC Measure LSAC Variable Name Dependent variables# Obesity Body mass index *cbmi (ahs23c2) Productivity Learning Outcome Index *wlrnoi Anxiety and depression SDQ emotional symptoms scale *aemot (apedsgc, bpedsef, cpedsef)1 Antisocial SDQ conduct problems scale *aconda (apedsgc, babitp) Addictions SDQ total score *asdqta (apedsgc, babitp) FF variables Harsh discipline Hostile parenting scale *ahostc (caang K1) Parental monitoring / supervision Importance of monitoring / supervising child *pa08a1 Relationship quality / warmth Parental warmth scale *awarm Inductive reasoning Inductive reasoning scale *aireas Inconsistent discipline Consistent parenting scale *acons Parental self efficacy Global rating of self‐efficacy *pa01a Family cohesion Family ability to get along with each other *re06a Eating behaviour (obesity) Food diary (fat) *hfat Eating behaviour (obesity) Food diary (sugary drink) *hsdrnk Exercise (obesity)‡ Choice of physical activity in free time *hb14c4 Sedentary activities (obesity) Hours per week electronic entertainment† *he06b1, *he06c1, *he07b2,*egweek (combo) Parental involvement in education (productivity) Home (education) activities index *ahact Parental involvement in education (productivity) Involvement in class activities *assc 23 Literature Concept LSAC Measure LSAC Variable Name Parental smoking (addictions, anxiety & depression, antisocial) Frequency of cigarette smoking *hb15a1 Parental alcohol (addictions, anxiety & depression, antisocial) Alcohol consumption groups *balcgp Overprotection (anxiety & depression, addictions) Parental overprotection scale *aoverp Control variables Temperament Temperament *se06 Parental anxiety & depression K‐6 depression scale *ak6 Gender Sex of study child zf02m1 2
Socioeconomic status Household annual income *hinc (*fn05) Parental education level Highest level of education completed† *fd08a1, *fd08b2 (combo) Parent gender Sex of primary carer zf02m2 Parental body mass index (obesity) primary carer body mass index *abmi Disability (obesity)‡ Special health care needs *hs14d Age (productivity)‡ study child age in months *scagem Notes: In the left column, variables in normal font are universal, variables in italics are particular to the individual regressions in brackets. In the right column, variables outside brackets are those used mostly or exclusively and variables in brackets are those used when the main variable was not available. Appendix D provides greater description of these variables. # Previous wave of the dependent variable is also used as a control variable. † = LSAC variables that have been combined to form new variables. ‡ = variable not directly suggested by literature search. * Where a variable code contains a wildcard (*) it covers all age groups (represented by first letters a,b,c,d,e) unless another age‐specific variable code is included next to the variable name. 1 caemot would have been preferred, but yielded nonsensical results (R‐squared of 0 in Stata and 1 in Excel). 2 Household annual income was categorical, converted to dollars per year. 3.2 LSAC analysis The LSAC presents a number of methodological issues for time series analysis. As noted in the preamble to chapter 3, the Baby and Kindergarten cohorts each have three waves of time data taken at two yearly intervals. ■
While this should yield a total of six time series observations, the two cohorts overlap at ages 4‐5 years (intentionally), leaving only five time series observations. ■
Moreover, as each cohort of children are entirely distinct populations, this creates conceptual difficulties in splicing the two together to create one ‘continuous’ time series. The situation is further complicated by the fact that, despite having over 16,000 variables, the great majority of them are not common across all wave/cohort groups. This is to some extent unavoidable given the great differences between newborns and nine year olds, but it still makes it difficult to track variables continuously over time periods. 24 Positive Family Functioning Accordingly, a number of approaches were investigated for modelling LSAC data. A hypothetical obesity regression is used here to illustrate the differences between these approaches. ■
1) a straightforward longitudinal analysis. This derives averages across each total age group (e.g. average obesity for 4‐5 year olds) and uses them as regression variables. However, this approach was not satisfactory for a number of reasons, including the lack of suitable variables which covered all age groups. ■
2) a semi‐panel approach, combining elements of longitudinal and cross sectional. Illustratively, obesity in wave (W) 3 is a function of obesity in W2, obesity in W1, diet in W3, diet in W2, diet in W1. This had the advantage that if the same variable cannot be found for diet across all groups, it does not matter, as different diet variables can be used for each age group. On the other hand, the number of variables with significant explanatory power was not high, and most of them belonged to W3. Also, because effective sample size for regression analysis was limited to the smallest variable among any of the constituent age groups, the number of observations was usually around 1,500, which was low. ■
3) a stepped time series model. Illustratively, obesity in W2 is a function of W2 diet, other W2 control and FF variables, and previous period (W1) obesity. While explanatory power was good for the older groups, and average for intermediate ages, it was very poor for the B1 infants group (Chart 3.1). A further issue with this approach is that the K1 group could not be utilised, as they lacked a link to previous periods. 5 Fortunately, this age group (four to five year olds) is the only one covered with two sets of survey data, so B3 could be used instead to represent this age group. While none of the methods were optimal, method three was adopted as it had the least shortcomings. Outcomes for individual regressions are discussed below. 5
For the 6‐7 year old group (K2), the coefficient for the lagged variable is derived from regressing against the K1 4‐5 year olds, but in the final regression the values used for lagged obesity are from the B3 4‐5 year olds to enable continuity with B2 and B1. 25 Chart 3.1: Explanatory power of regressions, by age 0.90 Obesity
Productivity
Anxiety
Antisocial
Addictions
0.80 0.70 R squared
0.60 0.50 0.40 0.30 0.20 0.10 ‐
0‐1
2‐3
4‐5
6‐7
8‐9
Age Cohort
3.2.2
Obesity Overall, obesity had the best explanatory power (R2) among the regressions, ranging from .08 for B1, up to 0.77 for K3. As in all regressions, B1 was lowest and K3 highest in terms of explanatory power. As with all regressions, the lagged dependant variable (in this case, last period’s obesity) was significant for every age group. Previous obesity also had the largest coefficients. Parental obesity was also always significant. Female gender for the child was a risk for the B1 cohort, while male gender was a risk for K2 and K3. Male gender of the primary carer was a risk factor in two cohorts. Interestingly, fatty foods, electronic entertainment and exercise tended not to be significant, and when they were, occasionally had an unexpected sign in the younger cohorts (when the children were aged 2 and 4). When children are young, body mass index is a less reliable indicator and this may explain why some of the findings are not intuitive. Moreover, children in the younger cohorts may be less likely to use electronic entertainment and eat fatty foods. It should also be noted that the model captures children’s preference to being active or not, not to actual measures of physical activity. 3.2.3
Productivity The explanatory power (R2) for productivity regressions ranged from .07 to .51. 26 Positive Family Functioning Previous learning index outcomes were always significant, as were consistent discipline, temperament (easy temperament effect), SES and parent education 6 . SES, however, had an unexpected sign for the B1 cohort. This may reflect that higher income is associated with returning to work which may, in turn, be associated with less ability to spend time the baby and poorer early learning outcomes as a consequence. Both temperament and child gender had consistently strong coefficients. Being a girl was an advantage up to the K2 age group, when being a boy was significant (neither gender was significant for K3). Other variables also sometimes had unexpected signs – such as parental warmth (B2, B3, K2), parental monitoring (K2), and parental involvement in education (K3). This is discussed further in Section 3.2.7. 3.2.4
Anxiety and depression Anxiety and depression, as measured by the LSAC’s Strengths and Difficulties Questionnaire (SDQ) emotional symptoms scale had explanatory power ranging from 0.01 (B1) to 0.36 (K3). Previous emotional problems was always significant with the expected sign, while difficult temperament also had a strong impact and SES had a significant impact in some age groups, mostly with the expected sign. Harsh discipline/hostile parenting and parental anxiety/depression were significant. Family cohesion and over‐protection also sometimes had unexpected signs. Note that some outcome measures used are coded in less than intuitive directions. For example, while in the PEDS global concerns item (apedsgc and *pedsef), lower scores represent concern, but for the SDQ high scores equate with more problems (i.e. poor functioning). This is important to understand when interpreting the coefficients in Appendix C. 3.2.5
Anti‐social behaviour Antisocial behaviour, as measured by the LSAC’s SDQ conduct problems scale, had explanatory power ranging from 0.01 (B1) to 0.50 (K3). Previous conduct problems, family cohesion, harsh discipline, parental smoking, low SES and the child’s temperament were always significant with the expected signs. Difficult temperament had consistently large coefficients, along with parental anxiety/depression in the B2 cohort. Parental efficacy, parental anxiety/depression, gender (with risk for boys), and household income were mostly significant. Consistent discipline was mostly significant but with an unexpected sign in the K2 cohort. Inductive reasoning was significant for children aged 4 years and older, but with an unexpected sign. It may be that parents are more likely to use inductive reasoning with children who have conduct issues (i.e. reverse causation). Parental alcohol consumption had 6
The scale of parental education is provided in Appendix D. 27 an unexpected sign in the two younger cohorts cases where it was significant, possibly due to the same issue7. 3.2.6
Addictions ‘Addictions’ is a composite measure covering the literature concepts of smoking, drinking and drug use. It is rare for children in the LSAC age groups to smoke, drink or take other drugs. Thus, some LSAC variable needed to be found as an intermediate outcome that could act as a pointer to future addictive problems when the children reach ATP age and later adulthood. There is abundant literature showing clear links between poor FF inputs in childhood and crime and addiction outcomes in adulthood. However, because such literature is frequently based on contiguous longitudinal surveys, there is no focus on transitional outcomes in nine year old children, as is needed here to bridge between the LSAC and the ATP. There is evidence from paediatric and neurological studies (Shonkoff et al, 2010) to suggest that anti‐social behaviour in primary school children may be linked to both addictions and anti‐
social behaviour in adulthood, with stress as the common factor. Continual environmental stress – such as that occasioned by NFF – can cause structural changes in the brain that strengthen negative emotions such as fear and aggression while simultaneously diminishing the forward‐thinking and reasoning capacities which normally moderate reactions to such emotions. While longitudinal studies on the influence of high stress have only been covered in animals so far, retrospective studies show that people who experienced trauma as children are much more likely as adults to smoke, drink and use drugs (Felitti et al, 1998) and become criminals (Currie and Tekin, 2006). High levels of childhood stress can manifest as psychological and emotional dysregulation in childhood (Evans and Kim, 2007). Thus, for the purposes of regression modelling, ‘addictions’ are represented by the LSAC variable ‘total SDQ score’. A detailed rationale for this choice can be found in Appendix A. The addictions regression, thus defined, had explanatory power ranging from 0.01 (B1) to 0.62 (K3). No variables were significant in the youngest B1 cohort. In the four older cohorts, previous total SDQ problems, harsh discipline, inconsistent discipline, family cohesion, smoking, temperament, gender (risk for boys), and parental anxiety/depression were always significant with the expected signs. Temperament and parental anxiety/depression consistently had the largest coefficients. Parental warmth and overprotection were mostly significant with expected signs, while SES was sometimes significant with the expected sign. Inductive reasoning was sometimes significant, but with unexpected signs – possibly for the same reasons as for antisocial behaviour (e.g. reverse causation). Parental alcohol consumption had an unexpected sing in the B2 and B3 cohorts also. 7 7
Reverse causality with alcohol has also been discussed in the literature. That is, parents of problem children may use alcohol as a coping mechanism, as reported by Pelham and Lang (1999): “Studies strongly support the assumption that the deviant child behaviours that represent major chronic interpersonal stressors for parents of ADHD children are associated with increased parental alcohol consumption. Studies also have demonstrated that parenting hassles may result in increased alcohol consumption in parents of "normal" children.” However, this does not explain the significant inverse relationship. Another possible reason, although we could find no literature 28 Positive Family Functioning 3.2.7
General observations A number of variables in the LSAC analysis had unexpected signs. Family cohesion and parental warmth may be in this category because of their skewed distributions; LSAC data indicates that a large proportion of people report that they are warm parents and their family has good cohesion. Inductive reasoning usually has an unexpected sign. In the LSAC, ‘inductive reasoning’ asks parents if they ‘explain to children why they are being disciplined / talk it over’. Note that most studies of the PPP include a variable to measure parental verbosity, which has a consistent negative effect on behaviour. It is possible that ‘inductive reasoning’ is in fact capturing ‘verbosity’ instead. In other words, the parent may perceive a detailed explanation, while the child may perceive ‘going on about it’ (at worst, nagging or harassment). It may also be that inductive reasoning is positively associated with conduct and total emotional problems because parents are more likely to use this parenting technique with children with difficult behaviour. Parental involvement in education at home was negatively associated for the K3 cohort but positively correlated for the children aged 2 and 4 as expected. It should be noted that the measure changes (due to developmental differences) between the cohorts. When the children are younger, the measure includes activities such as reading stories, drawing, music and singing. These activities are likely to have a link to educational outcomes. In the K3 cohort, there are only three home activities questions – everyday activities (e.g. cooking, caring for pets), outdoor activities (e.g. swimming) and an adult in the family reading to the child. The link between caring for pets, swimming and learning outcomes may not be as clear as activities such as drawing and music. By the age of 8‐9 a child may spend more time reading to themselves and/or their parents than vice versa, particularly higher performing children. Another possible explanation of the unexpected sign in the older cohort is reverse causation. That is, if a child has learning difficulties, a parent may have to spend more time helping them. ■
Indeed, in general it should be noted that these analyses are largely cross‐sectional so direction/causation cannot be perfectly tested, although to some extent this is overcome by including lagged dependent variables in subsequent stages of the modelling. However, in some cases (notably this variable and inductive reasoning), rather than the parents’ behaviour causing the child’s problem, it may be that the parents’ behaviour is in reaction to the child. Parental alcohol consumption often has an unexpected sign, being significantly negatively associated with anxiety and depression (K2, K3), anti‐social behaviour (B2, B3) and addictions (B2, B3). This might possibly be due to reverse causation together with other factors in these toddler and pre‐school cohorts. Finally, income sometimes has unexpected signs. This may be improved by standardising for household size. That is, a household with higher income may not have better outcomes than another household with lower total income, if income per child is in the first house is actually supporting this conjecture, might be that parents of toddlers and pre‐schoolers drink either little or nothing, rather than a lot. It is a time of life when mothers may be pregnant or breastfeeding and when the young family is not as involved in social activities. If children have difficult behaviours, parents may find themselves even less likely to be able to participate in social functions, whereas if children are well‐behaved, they may be more able to relax and enjoy a drink (without abusing). However, this conjecture is purely speculative. 29 lower than income per child in the second house. In two wave 1 regression variables, income coefficients had an unexpected sign – for B1 productivity and obesity. We think this can be explained as follows. For the babies, the obesity metric is in fact weight – so in this case the result is as expected – higher income is associated with heavier babies (low SES with underweight babies). As income goes up, learning in babies may be negatively impacted by mothers returning to work earlier (Ruhm, 2000), which could explain the productivity finding. Readers should note that some variables are reverse coded in the LSAC database, including parental depression, parental highest education achieved and family cohesion — and at first glance may appear to have unexpected signs. 30 Positive Family Functioning 4
Findings from the ATP data investigation 4.1 ATP variables Access Economics and the Australian Institute of Family Studies (AIFS) worked to specify appropriate models of family functioning based on the literature and variables available from the Australian Temperament Project (ATP). ATP variables were mapped to family functioning domains (emotional, governance, engagement and cognitive development, physical health, intra‐familial relationships and social connectivity — and to outcomes (in the domains of health, productivity and criminal behaviour). A literature review was also conducted to inform the selection of family functioning and other variables from the ATP likely to be correlated with the selected outcomes. Statistical analysis determined the final model specifications, and only variables found to be significantly correlated with outcomes were included in the CEA model. In the literature, positive outcomes for young people were commonly associated with authoritative parenting models. Based on the two dimensions of parenting in Baumrind’s categorisation (see Wise 2003) — authoritative parenting is characterised by high levels of both acceptance (which entails emphasis by parents on warmth and the expression of affection) and control (which can be divided into control based on the use of inductive reasoning and control based on parents’ use of power). Authoritative parenting is to be distinguished from alternatives often associated with poorer outcomes for children — authoritarian parenting (high on control and low on acceptance), permissive parenting (high on warmth and low on control) and neglectful parenting (low on both control and acceptance) (see Wise 2003). Other features of parenting or family functioning commonly identified in the literature as influencing outcomes for children include: ■
■
■
■
■
family conflict and family cohesion; marital relationships; sibling relationships; child abuse (including physical, sexual and emotional abuse) and neglect; and parental smoking and/or drinking. The tables in Appendix B provide a summary of the literature reviewed. Confounding variables available in the ATP and included in the analysis included: ■
■
■
the child’s innate temperament (persistence, reactivity and approach); parents’ education (as a proxy for socioeconomic status); family stress (e.g. death of a parent or divorce) — some of which may reflect family functioning (such as the circumstances surrounding marriage breakup). 31 In rare cases, concepts linked in the literature to outcomes for children — either as control variables or family functioning variables — were not available from the datasets. For example, in the ATP we were unable to allow for parental mental health — mother’s post natal depression or parent’s anxiety/depression linked to child’s anxiety/depression (Rapee 1997, van Gastel et al, 2009) — or parent’s body mass index as a factor linked to child’s overweight or obesity outcomes (Wardle et al, 2001, Danielzik et al, 2004). Family income was another variable not available from the ATP. However, in some cases, proxies were found, for example, in place of family income, the ATP parameter: ‘each resident parent’s highest education level attained’. Positive affect towards school was selected as an indicator of school bonding based on AIFS advice about the findings of previous research. The initial regression constructs investigated are summarised in Table 4.4. It is important to note that ‘family functioning’ cannot always be isolated from other influences such as environmental factors or child temperament, and is difficult to measure. The ATP variables selected inevitably reflect a mixture of family functioning and other factors and are at times difficult to interpret as either reflecting family functioning or some other factor external to the family. In addition, there is the potential for omitted variable bias where variables do not completely capture the concept being measured or where there are missing variables within the ATP. The statistical relationships reported here need to be viewed in this context. Table 4.4: Regression constructs Productivity Interim outcome (dependent) variables  Underengagement (children not employed and not studying)  Did not complete Yr 12  Completed Yr 12, Post secondary (non‐University), University degree Family functioning variables  Harsh discipline  Parental monitoring/supervision  Inconsistent discipline  Attachment to parents  Negative conflictual parent‐child relationship  School bonding  Academic competence at an earlier age  Parental education level  Temperament Overweight/ obesity  Body mass index (normal or underweight/overweight/obese)  Attachment to parents  Negative conflictual parent‐child relationship  Inconsistent discipline  Harsh discipline  Teenager eating behaviours  Parental monitoring/supervision  Inductive reasoning  Family status (i.e. ‘intact’)  Parental education level 32 Positive Family Functioning Anxiety and/or depression  Anxiety and/or depression  Attachment to parents  Relationship quality/warmth  Harsh discipline  Enmeshment  Experience of child abuse/neglect  Inductive reasoning  Marital relationship  Stressful life event  Temperament Alcohol/ smoking  Alcohol consumption including binge drinking  Parental management of teen substance use  Daily Smoking  Inductive reasoning  Attachment to parents  Harsh discipline  Parental monitoring/supervision  Inconsistent discipline  Negative conflictual parent‐child relationship  Parental smoking  Temperament Illicit drug use  Illicit drug use including marijuana  Experience of child abuse/neglect  marital relationship (conflict)  Attachment to parents  Parent adolescent conflict  Parental monitoring/supervision  Mother/Father smoking and drinking habits  Temperament Antisocial behaviour  3 or more antisocial acts in the past year including illicit drugs  Relationship with parents  Attachment to parents  Family cohesion  Relationship quality/warmth  Harsh discipline  Parental monitoring/supervision  Inconsistent discipline  Marital relationship  History of parental separation and its effect  Parental smoking  Parental use of alcohol  Temperament 33 4.2 ATP analysis The statistical analysis was undertaken by AIFS using ordinary least squares, logistic and multinomial regressions. The variables are described in Appendix F and details of each of the regression outcomes are in Appendix G. The means and standard deviations of the regression variables are in Appendix H. In summary: ■
Variables were constructed at various child ages depending on the most appropriate sample size for that variable in any given year. ■
In some instances, composite variables were constructed which included data for children at a range of different ages. Inter‐correlations were computed to assist decisions about the viability of using across‐time variables. Composite variables were only constructed for variables which were highly correlated across time. ■
■
Tests for multicollinearity suggest this was not a problem in any of the final equations. The initial ATP sample comprised 2,443 families from urban and rural areas of Victoria in 1983, approximately two‐thirds of whom are still participating after 24 years. However, the sample sizes for the regression analyses were determined by the samples available for all of the variables included in the regression analysis — and were affected by missing data and drop outs over time. The most recent wave of the ATP for which data are available (2006) reports on ATP children at the age of 23‐24 years. While regression analysis was conducted on the transitional outcomes for children aged 19‐20 as well as 23‐24, dependent variables for children aged 19‐20 were generally preferred on the basis that family influences were likely to be stronger at this age. Exceptions are obesity, completion of a university degree and illicit drug use. ■
For the first two (obesity and completion of a university degree), dependent variables were not available for this age group. ■
For illicit drug use, the regression equation for the older group was preferred because one of the independent variables was of an unexpected sign (see Appendix G). 4.2.1
Productivity A logistic regression was also conducted for under‐engagement (children not employed and not studying versus other children) but none of the predictor variables was significant. The sample size for this dependent variable was a lot less — 574 children of whom only 58 children were not employed and not studying. Completed high school versus did not complete A logistic regression was conducted comparing children who completed secondary school (year 12) with those who did not. The responses are based on young adult report at age 19‐
20 years. There were 687 children in the sample, 65 of whom did not complete high school. The Nagelkerke R2 was 0.309. The following predictor variables were significant: ■
34 Relationship warmth (inverse relationship with completion of high school); Positive Family Functioning ■
Parental monitoring/supervision of teen’s activities/associates, teenager report 17‐18 years (an increase in supervision was associated with an increased chance of completing school); ■
Mother’s education (higher educational achievement was associated with a greater chance of the child completing high school); ■
The child’s academic competence at 11‐12 years based on teacher report (greater academic competence was associated with a greater likelihood of completing school); ■
School bonding 8 (better bonding was associated with a greater likelihood of completing school); and ■
Persistent temperament style, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years (persistence was associated with a greater likelihood of completing school). Completed high school versus completed university degree In the sample of 595 young adults at age 23‐24 included in a logistic regression, 143 had completed high school only, and 277 had completed a university degree. The Nagelkerke R2 was 0.276. The following predictor variables were significant: ■
Attachment to parents child age 17‐18 (children with higher levels of attachment are more likely to complete a university degree than only completing high school); ■
Relationship warmth (greater warmth is associated with a greater likelihood of only completing high school); ■
Father’s education (higher educational achievement of the father was associated with a greater chance of the child completing a university degree) ■
School bonding 9 (better bonding was associated with a greater likelihood of completing a university degree). Obesity 4.2.2
In the ATP, body mass index is generated from self report (rather than actual measurements) at 15‐16 years and 23‐24 years. The sample size was 513, with 44 obese, 128 overweight and the rest underweight or normal. A logistic regression analysis comparing the chance of being overweight or obese at age 23‐24 compared with ‘normal’ or underweight found the following significant variables. The Nagelkerke R2 was 0.518. ■
Overweight 
Body mass index in 1998 (child age 15‐16) (a high index at age 15‐16 is associated with a greater chance of being overweight at age 23‐24); 8
9
positive affect towards school, teenager report 15‐16 years positive affect towards school, teenager report 15‐16 years 35 ■
4.2.3

Father’s education (lower levels of education are associated with a greater chance of being overweight at age 23‐24); and 
A persistent temperament style (composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years) is associated with a lower chance of being overweight at age 23‐24. Obesity 
Body mass index in 1998 (child age 15‐16) (a high index at age 15‐16 is associated with a greater chance of being obese at age 23‐24); 
A negative/conflictual parent‐teenager relationship, (parent report 17‐18 years), is associated with a greater chance of obesity at age 23‐24; 
Father’s education (lower levels of education are associated with a greater chance of obesity at age 23‐24); and 
A persistent temperament style (composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years) is associated with a lower chance of obesity at age 23‐24. Anxiety and/or depression Anxiety and depression were combined for this analysis. The ATP scales for anxiety and depression are based on Lovibond and Lovibond (1995), which lead to similar prevalence results to the Diagnostic and Statistical Manual of Mental Disorders (DSM IV) used more broadly in Australia. A logistic regression comparing young adults aged 19‐20 who were at risk of being in the clinical range for anxiety and/or depression with those who were not was conducted using a sample of 642 children, 166 of whom were anxious and/or depressed. The Nagelkerke R2 was 0.213. Anxiety and/or depression at age 19‐20 was significantly positively associated with: ■
■
■
4.2.4
alienation of the child from his or her parents; not having a persistent temperament; and previous experience of anxiety/depression. Drinking A logistic regression was conducted using a sample of 580 young adults aged 19‐20, divided into those who spent 1‐4 days per month binge drinking (245 young adults), those who spent 5 or more days per month binge drinking (196 young adults) and those who did not binge drink (0 days per month). The Nagelkerke R2 was 0.121. Binge drinking on 1‐4 days per month at age 19‐20 was significantly positively associated with drinking at age 13‐14 years. Binge drinking on 5 or more days per month at age 19‐20 was significantly positively associated with: ■
■
36 drinking at age 13‐14 years; less parental monitoring at age 15‐16; Positive Family Functioning ■
■
4.2.5
father drinking, (composite mean score of parent report at 13‐14, and 17‐18 years); and a higher age at which the teenager was first allowed to drink at home (this variable has two categories — the teen was allowed to drink at home at 15 years or older or the teen was allowed to drink at home at 14 years or younger — (based on parent report at 17‐
18 years). Smoking A logistic regression was conducted on a sample of 830 young adults aged 19‐20 years, 121 of whom smoked daily. The Nagelkerke R2 was 0.297. Daily smoking in this group was significantly positively associated with: ■
■
■
■
4.2.6
Smoking at age 13‐14 years; Being allowed to smoke at home; Negative conflictual parent‐child relationship at age 17‐18; and Mother smoking. Illicit drugs A regression was based on a sample of 664 young adults aged 23‐24 years who reported the number of differing types of illicit drugs used on one or more day/s in past month (range 0‐4). The R2 was 0.076. Independent variables which were significantly positively associated with the dependent variable were: ■
■
■
4.2.7
Less parental supervision/monitoring at age 17‐18; A mother who smoked; and An approaching temperament. Antisocial behaviour A logistic regression was conducted using 755 young adults aged 19‐20, 112 of whom reported that they were involved in 3 or more differing types of antisocial activities in past 12 months (including illicit substance use). Tne Nagelkerke R2 was 0.145. Antisocial behaviour in 1996 at age 13‐14 years and not having a persistent temperament were the only independent variables significantly positively associated with antisocial behaviour. 37 5
The costing model As explained in the introductory chapter, FF was defined as having six domains, and variables from the ATP and LSAC within each of these domains were mapped to specific outcomes. The outcomes were drawn from three domains: health (anxiety and/or depression, obesity, smoking, alcohol abuse and illicit drug use), productivity (not completing year 12 and not completing a university undergraduate degree) and social/criminality (antisocial behaviour). The cost of each adverse outcome was then estimated (recall Figure 2.3). This chapter describes the method for estimating the social and economic costs associated with adverse outcomes attributable to NFF. The costs of each outcome include: ■
Health costs – of five main conditions: tobacco use, alcohol abuse, illicit drug use, obesity, and anxiety and depression. These were sourced from the Australian Institute of Health and Welfare (AIHW) data and Australian Bureau of Statistics (ABS) National Health Survey (NHS) primarily. ■
Costs of crime – using data sources from the PC’s Report on Government Services for 2009, as well as reports from the Australian Institute of Criminology. ■
Productivity costs – estimates of reduced participation and absenteeism related to health impacts are based on Access Economics cost of illness methods, plus other sources for alcohol and illicit drugs. Productivity costs associated with school completion, TER and post‐secondary education outcomes are from the PC and other sources. ■
Burden of disease (BoD)– was determined using the AIHW Begg et al (2007) BoD study for the year 2003 (with extrapolations modelled to future years) and the value of a statistical life year (based on DOFD, 2009). ■
Other financial costs – these include costs associated with the provision of informal care, aids and appliances, deadweight efficiency losses and various other costs for people with long term substance abuse and other health problems. The main sources comprise previous studies by Access Economics and the Survey of Disability, Ageing and Carers (SDAC) 2003 (ABS, 2004). Further detail on costing methods, cost categories and data sources is provided in Appendix I. 5.1 Health outcomes Analysis of the LSAC and ATP data‐sets led to the identification of the following main health conditions and behaviours associated with NFF: ■
■
■
■
■
38 anxiety and depression; obesity; current daily smoking; alcohol abuse; and illicit drug abuse. Positive Family Functioning In line with Access Economics’ Disease Cost Burden Analysis (DCBA) framework, health outcomes are associated with health system costs, productivity losses, DWLs, other financial costs and BoD. For anxiety and depression, obesity and smoking, past Access Economics studies were used to collate total costs in 2010 and calculate per‐person costs. For alcohol abuse and drug abuse, Collins and Lapsley (2008) was used. Total reported costs were disaggregated and matched, as closely as possible, with Access Economics’ DCBA categories. It is noted that there are limitations matching costs from Collins and Lapsley (2008) with the DCBA categories, due to differences in costing methodologies. However, Collins and Lapsley (2008) is the most recent and relevant source for costs of drug abuse in Australia and is thus employed. This report can be referred to for details on the methodology it uses. 5.1.1
Obesity Obesity is the accumulation of excessive body fat, defined here as a body mass index of over 30 for adults. For children and adolescents aged 2 to 18 years, a set of age‐gender specific thresholds was used. The cost of obesity was estimated for this project using Access Economics (2008) — that report included the costs of type 2 diabetes, cardiovascular disease, osteoarthritis and cancer attributable to obesity. The 2008 costs were inflated to 2010 using a health inflation rate of 3.5% per annum (based on historical averages reported by the AIHW). and 3% per annum for non health expenditure (Table 5.5). Table 5.5: Summary of total costs of obesity (2010) Cost type Cost ($m) Health system expenditure 2,098 Productivity losses 3,850 Other financial costs(a) 2,860 Total financial costs 8,808 BoD (DALYs x VSLY) 52,935 Total costs 61,743 (a)‘Other financial’ category includes DWL from transfers, carer costs and other indirect costs. Source: Access Economics (2008 and 2010 calculations) Obesity prevalence rates were also drawn from Access Economics (2008). The rates were applied to 2010 population projections from Access Economics Demographic Model (AE‐Dem). Total costs were divided by the estimated number of obese people in 2010 (around 4 million) to obtain a per‐person annual cost for each cost type. The age‐gender prevalence profile for obesity was then applied to these per‐person costs to further refine costs into per‐person annual costs by age and gender. To ensure per‐person costs multiplied by estimated obese people summed to the correct total, a minor level adjustment downwards was applied while preserving the prevalence profile. 39 5.1.2
Anxiety and depression Anxiety and depression are defined consistent with the clinical definitions used for prevalence studies as follows: ■
Anxiety disorders are those in which ‘anxiety is a predominant feature’ (American Psychiatric Association, 2009) such as panic disorder, obsessive‐compulsive disorder, a phobia, or generalised anxiety disorder. ■
Depression and related affective disorders are defined in the International Classification of Diseases Tenth Revision (ICD‐10) by the World Health Organization (WHO, 2010) as a lowering of mood, reduction of energy, and decrease in activity. Capacity for enjoyment, interest, and concentration is reduced, and marked tiredness after even minimum effort is common. Sleep is usually disturbed and appetite diminished. Self‐
esteem and self‐confidence are almost always reduced and, even in the mild form, some ideas of guilt or worthlessness are often present. The lowered mood varies little from day to day, is unresponsive to circumstances and may be accompanied by so‐called ‘somatic’ symptoms, such as loss of interest and pleasurable feelings, waking in the morning several hours before the usual time, depression worst in the morning, marked psychomotor retardation, agitation, loss of appetite, weight loss, and loss of libido. Cost data were drawn from Access Economics (2009) — a study of the costs of six categories of mental illness in the 12 to 25 years age group including substance abuse disorders, anxiety disorders, affective disorders, bipolar disorder, schizophrenia and other mental illness. The costs were adjusted so that they applied to all age groups by: ■
applying 2003 age‐gender prevalence rates, relative risks of mortality attributable to anxiety and depression and applying the relevant disability weights for anxiety and depression from Begg et al (2007); ■
expanding productivity losses to incorporate all age groups using NHS data for 2004‐05 (special request from the ABS for Access Economics, 2009) ‐ parameters for employment/productivity effects of all mental illness were used to proxy anxiety and depression; ■
adjusting carer costs to apply to the prevalent group of those with anxiety and depression; and ■
applying average annual government welfare payment rates in 2009 (Centrelink, 2009) to total recipients with mental illness. The prevalence profile for anxiety and/or depression was obtained from Begg et al (2007), which grouped anxiety and depression as one disease category due to a high degree of co‐
morbidity and similarity in psychological and drug treatment between these conditions. The number of people with anxiety and/or depression was calculated by applying the prevalence rates to 2010 population projections from the AE‐Dem. The total aggregated cost of anxiety and depression in 2010 is presented in Table 5.6 by cost type. The 2009 costs were inflated to 2010 using a health inflation rate of 3.5% per annum (based on historical averages reported by the AIHW)) and 3% per annum for non health expenditure. 40 Positive Family Functioning Table 5.6: Summary of total costs of anxiety and depression (2010) Cost type Costs ($m) Health system expenditure 3,818 Productivity losses 17,992 Other financial costs(a) 3,491 Total financial costs 25,301 BoD (DALYs x VSLY) 41,162 Total costs 66,463 (a)‘Other financial’ category includes DWL from transfers, carer costs and other indirect costs. Source: Access Economics (2008 and 2010 calculations) Total costs were divided by the estimated number of people with anxiety and/or depression in 2010 (around 2 million) to obtain a per‐person annual cost for each cost type. The age‐gender prevalence profile for anxiety and depression was then applied to these per‐person costs. Adjustments were made to ensure per‐person costs summed to the relevant total. 5.1.3
Smoking A person is defined as being a smoker if he or she currently smokes on a daily basis. Others e.g. ex‐smokers or people smoking less frequently are excluded. The costs were estimating using the findings of Access Economics (2007), adjusted as follows: ■
removing the stipulation of having a mental illness and being a smoker from all relevant costs to obtain total costs of current daily smoking rather than ‘excess’ costs; ■
applying 2007 age‐gender prevalence rates for current daily smoking for those without a mental illness from Access Economics (2007); ■
calculating relative risks of mortality attributable to current daily smoking for all age groups using Begg et al (2007); ■
adjusting BoD estimates to incorporate average disability weights for tobacco‐caused diseases for males and females using unpublished data from the AIHW requested for preparation of Access Economics (2007); ■
calculating productivity losses using NHS 2004‐05 data (special request from the ABS) for all current daily smokers; and ■
applying welfare payment rates (Centrelink, 2009) to estimated recipients with tobacco‐
caused cancer (Access Economics, 2006a) and tobacco‐caused cardiovascular illness (Access Economics 2006b) in 2005. Two separate prevalence profiles were applied to estimate per‐person costs of daily smoking. The first profile employed prevalence rates for current daily smoking for people aged 18 and over, from Access Economics (2007). This prevalence profile displays falling prevalence rates with higher age groups and was applied to estimate per‐person productivity costs, DWLs from transfers, carer costs and indirect costs. This profile was also applied to AE‐Dem 2010 population estimates to calculate estimated current daily smokers in 2010. The second prevalence profile employed prevalence rates for tobacco‐attributable diseases and conditions. This profile displays higher prevalence rates for older age groups, and was 41 applied to health system and BoD costs. An increasing‐prevalence profile was deemed to be more suitable for these categories due to the cumulative nature of health deficits from smoking with increasing age (Hubbard et al, 2009). Thus, annual health expenditures and loss of wellbeing costs are likely to increase with age. Prevalence data for this second profile was obtained from the AIHW via a special data request in 2004 for Access Economics’ past study (2007). Prevalence rates were calculated by dividing the 2004 prevalent cases of total tobacco‐caused diseases per age group by the 2004 population in that age group (from AE‐Dem). The total aggregated cost of current daily smoking in 2010 is presented in Table 5.7 by cost type. The 2005 costs were inflated to 2010 using a health inflation rate of 3.5% per annum (based on historical averages reported by the AIHW ) and 3% per annum for non health expenditure. Table 5.7: Summary of total costs of daily smoking (2010) Cost ($m) Cost type Health system expenditure 1,779 Productivity losses 12,400 Other financial costs(a) 2,261 Total financial costs 16,440 BoD (DALYs x VSLY) 167,197 Total costs 183,637 (a)‘Other financial’ category includes DWL from transfers, carer costs and other indirect costs. Source: Access Economics (2008 and 2010 calculations) Total productivity and other financial costs were divided by the estimated number of smokers in 2010 (2.9 million) to obtain a per‐person annual cost for these categories. The smoker age‐
gender prevalence profile was then applied to these per‐person costs to further refine costs into per‐person annual costs by age and gender. Total health system costs and the value of the BoD were divided by estimated prevalent cases of tobacco‐caused disease in 2010 (692,523) to obtain a per‐person annual cost for these categories. The tobacco‐caused disease prevalence profile was then applied to these per‐
person costs to further refine costs into per‐person annual costs by age and gender. Adjustments were made to ensure per‐person costs summed to the relevant total. 5.1.4
Alcohol abuse Collins and Lapsley (2008) note that the definition of ‘abuse’ in relation to alcohol is problematic, with the National Alcohol Strategy showing no preference or separation between the terms ‘misuse’ and ‘abuse’. For the purposes of this report, alcohol abuse is defined as consumption of alcohol at a level that produces sufficient risk of long‐term harm to health. This definition was taken from the 2004 National Drug Strategy Household Survey (NDHS) (AIHW, 2005). The categories of ‘risky’ drinking and ‘high risk’ drinking were summed. ‘Risky’ drinking is defined by the AIHW as 29 to 42 Positive Family Functioning 42 drinks per week for males and 15 to 28 drinks per week for females. ‘High risk’ drinking is defined as 43+ drinks per week for males and 29+ drinks per week for females. Short term risk was not included as the data were insufficient in LSAC and ATP to identify short term risk (e.g. from bingeing). The primary data source for costs was Collins and Lapsley (2008). The prevalence profile for long‐term risky drinking was obtained from the 2004 NDHS (AIHW, 2005). These rates were applied to AE‐Dem 2010 population projections to estimate the number of people affected in 2010. The total aggregated costs of alcohol abuse in 2010 are in Table 5.8. The 2004‐05 costs were inflated to 2010 using a health inflation rate of 3.5% per annum (based on historical averages reported by the AIHW) and 3% per annum for non health expenditure. Notably, crime costs were excluded to avoid double‐counting — the costs of crime related to substance abuse are included in section 5.3. The estimates of the BoDfrom alcohol abuse appear small when compared to those for smoking, anxiety and depression, and obesity. This is partly due to limitations in matching the Collins and Lapsley study categories with Access Economics disease cost burden analysis categories due to differences in study methodologies. Table 5.8: Summary of total costs of alcohol abuse (2010) Cost type Costs ($m) Health system expenditure 2,347 Productivity losses 4,102 Other financial costs(a) 4,511 Total financial costs 10,960 BoD (loss of life + pain and suffering) 5,204 Total costs 16,163 (a)‘Other financial’ category includes accidents not elsewhere included, fires not elsewhere included and abusive consumption costs. Source: Collins and Lapsley (2008) – costs adapted to Access Economics DCBA categories Total costs were divided by the estimated number of people with the condition in 2010 (1.8 million) to obtain a per‐person annual cost for each cost type. The age‐gender prevalence profile was then applied to these costs to develop a set of per‐person annual costs by age and gender group. Adjustments were made to ensure per‐person costs summed to the relevant total. 5.1.5
Illicit drug abuse Illicit drug abuse in this report is defined to include all illicit drugs (e.g. marijuana), consistent with the source of the cost data (Collins and Lapsley, 2008). The prevalence profile for recent use of illicit drugs was obtained from the 2004 NDHS (AIHW 2005), with recent use defined as use within the last 12 months. Recent drug users in 2010 were calculated by applying prevalence rates to the 2010 population estimates from the AE‐
Dem. 43 The total aggregated cost of illicit drug abuse in 2010 is in Table 5.9. The 2004‐05 costs were inflated to 2010 using a health inflation rate of 3.5% per annum (based on historical averages reported by the AIHW) and 3% per annum for non health expenditure. As with alcohol abuse, the costs of crime associated with illicit drug abuse were excluded to avoid double‐counting. Table 5.9: Summary of total costs of illicit drug use (2010) Cost type Costs ($m) Health system expenditure 240 Productivity losses 1,912 Other financial costs(a) 1,647 Total financial costs 3,798 BoD (loss of life + pain and suffering from road accidents) 1,477 Total costs 5,275 (a)‘Other financial’ category includes accidents not elsewhere included, fires not elsewhere included and abusive consumption costs. Source: Collins and Lapsley (2008) – costs adapted to Access Economics DCBA categories Total costs were divided by estimated people with the condition in 2010 (around 2.8 million) to obtain a per‐person annual cost for each cost type. The age‐gender prevalence profile was then applied to these costs to develop a set of per‐person annual costs by age and gender group. Adjustments were made to ensure per‐person costs summed to the relevant total. 5.2 Productivity outcomes Based on analysis of the LSAC and ATP data‐sets, and parameters available from the literature, the loss of earnings from not completing year 12 and not completing a tertiary (undergraduate) degree (if completed year 12) were able to be costed. Human capital theory supports the idea that people with higher education levels display higher labour productivity, as proxied by their earnings (Forbes et al, 2010). Additionally, those with higher education are more likely to participate in the workforce, due to higher labour demand for these workers, and labour supply factors such as expectations of higher wages and derivation of greater utility from the social and intellectual stimulation work provides attracting these workers to the labour force (La Plagne et al, 2007). Consequently, there are costs (in the form of forgone income) associated with non‐completion of year 12 and/or undergraduate studies. A number cost components for productivity losses were estimated as follows: ■
44 Parameters for the effect of not completing year 12 and/or a university degree on employment and productivity were drawn from La Plagne et al (2007) and Forbes et al (2010). La Plagne et al (2007), through econometric modelling, estimated the effects of different educational attainment levels on the probability of participation in the labour force, relative to the baseline of year 11 completion only. A more recent study (Forbes et al 2010) estimated the impact of different education levels on average hourly wages Positive Family Functioning (taken as an indicator of labour productivity), relative to the baseline of year 11 completion. Parameters from these studies are presented in Table 5.10. ■
Differences in participation depending on educational attainment were used as a proxy for differences in employment. This difference was combined with average weekly earnings (AWE) (ABS, 2009b) and employment rates (ABS, 2009a) for each age‐gender group to estimate lost earnings. The ABS AWE data were for 2008 and were inflated to the end of 2009 using labour price index data (ABS, 2010a). ■
Wage differences were used as a proxy for the impact of educational attainment on labour productivity. These calculations were also based on ABS AWE (ABS, 2009b) and employment rate data (ABS, 2009a). ■
DWLs arising from foregone earnings were estimated by calculating taxation revenue foregone by applying the average personal income tax rate and average indirect taxation rate to foregone earnings. The tax rates for 2010 (19.48% and 11.68% respectively) were drawn from Access Economics’ Macroeconomic Model (AEM). DWLs were then calculated by applying a DWL rate of 28.75% of each tax dollar foregone (Lattimore, 1997; PC, 2003) and Access Economics estimates of Federal Government administration costs). Table 5.10: Estimated effects* (%) of year 12 and undergraduate completion* on probability of participation and average earnings Effect on probability of participation* Effect on average earnings* Males 5.7% 12.8% Females 7.7% 10.1% Undergraduate degree Males 8.7% 38.4% Females 16.4% 36.7% Level of attainment Year 12 * Relative to the baseline of year 11 completion Source: Productivity Commission ‐ La Plagne et al (2007) and Forbes et al (2010) The proportion of the population not completing an undergraduate degree was based on unpublished data on undergraduate award course completions for 2007 from the Department of Education, Employment and Workplace Relations (DEEWR) via a special data request in 2009. 5.3 Criminality outcomes Four broad categories of costs were included: ■
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societal costs ‐ due to the impacts on victims and society; policing costs; court system costs; and prison system costs. 45 The costs were matched with the relevant probability e.g. of the crime being prosecuted and resulting in a conviction and prison sentence. Societal costs 5.3.1
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medical costs for victims of violent interpersonal crimes; productivity losses for hospitalised and non‐hospitalised victims; the costs of pain, suffering and lost quality of life (Rollings, 2008); property‐related costs incurred by individuals and businesses due to property damaged/destroyed and non‐recovered stolen property; and other costs such as cost of volunteer services and those costs non‐separable into the above categories. Estimates of societal costs were taken from an Australian Institute of Criminology report (Rollings, 2008). Rollings (2008) quantified societal costs for the following types of crime: ■
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homicide; assault; sexual assault; robbery – including individual, commercial, burglary, thefts of vehicles, thefts from vehicles, shop theft and other theft; criminal damage; arson; fraud; and drug offences. The total social cost in 2010 is in Table 5.11. The 2005 costs from Rollings (2008) were inflated to 2010 using a health inflation rate of 3.5% per annum (based on historical averages reported by the AIHW) and 3% per annum for non health expenditure. Societal costs were divided by the number of offenders in 2008‐09 (344,274) to determine a set of per offender costs by cost type (Table 5.11). Table 5.11: Social costs of crime by cost type (2010) Cost type Total Costs ($m) Per offender ($) 791 2,296 Lost output (productivity) 3,392 9,853 Costs of pain and suffering (intangible costs) 3,467 10,072 Property damage/loss 9,819 28,521 Other(a) 7,298 21,197 Total societal costs 24,767 71,939 Medical costs (a)Those costs non‐separable into above categories Source: Rollings (2008) 46 Positive Family Functioning 5.3.2
Policing costs Policing costs are defined here as recurrent government expenditures on crime‐related policing services in a year. Data on policing expenditures were obtained from SCRGSP (2010) and adjusted down by 30% consistent with Rollings (2008) to account for the fact that not all police time is spent on crime. A prevalence profile of offenders was developed using ABS data on offenders in Australia (ABS 2009c; 2010b). There were approximately 344,274 total offenders in Australia for all crime types in 2008‐09 (ABS, 2010b). An age‐gender distribution of offenders based on 2007‐08 data (ABS, 2009c) was applied to this figure to estimate offenders by age‐gender group. A set of offender prevalence rates was developed by dividing these offender estimates by population estimates from the AE‐Dem. 5.3.3
Court system costs Court system costs are defined here as recurrent government expenditures for all levels of criminal court: ■
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Supreme courts; District/County courts; Magistrate courts; and Children’s courts. Data on total recurrent expenditures on criminal courts were obtained from SCRGSP (2010). This publication also contained estimates of recurrent expenditures per finalisation for each court level. Total recurrent expenditure on criminal courts in Australia in 2008‐09 and recurrent expenditure per court finalisation is presented in Table 5.12, by court level. A weighted average net recurrent expenditure per finalisation for all courts was calculated by applying weights based on the percentage of total criminal court finalisations at each court level. These weights are presented in the last column of Table 5.12. Table 5.12: Net recurrent expenditure on criminal courts (2008‐09) and % criminal court finalisations by court type Criminal court Net recurrent expenditure ($m ) Net recurrent expenditure per court finalisation ($) % of court finalisations Supreme courts 85 15,118 0.6% District/County courts 204 7,553 3.0% Magistrate courts 327 414 89.2% Children’s courts 32 503 7.0% Total 85 ‐ 100.0% Source: Access Economics calculations using data from (SCRGSP, 2010) 47 The offender prevalence profile was applied to the weighted average expenditure per finalisation estimate to develop per‐offender annual court costs based on age‐gender group. Prison costs 5.3.4
Prison costs are defined here as real net operating expenditures by the government on prisoners in a year. Data on real net operating expenditure per prisoner were obtained from SCRGSP (2010). This contained figures for real net operating expenditure on prisons and community corrections in Australia in 2008‐09. An age‐gender profile of prisoners was developed using data on prisoners in Australia from the ABS (2009d). A set of prisoner prevalence rates was developed by dividing prisoner estimates in each age‐gender group by age‐gender population estimates from the AE‐Dem. Total real net operating expenditure on prisons and community corrections in Australia in 2008‐09 is presented in Table 5.13. Overall, total real net operating expenditure on prisons and community corrections was approximately $2.5 billion in 2008‐09. This figure was divided by the number of prisoners in 2008‐09 (29,319) to determine a cost per prisoner of approximately $85,064. Table 5.13: Total real net operating expenditure on prisons and community corrections in Australia in 2008‐09 Amount ($’000) Expenditure type Prisons 2,140,154 Community corrections 353,823 Total real net operating expenditure 2,493,977 Source: (SCRGSP, 2010) The prisoner prevalence profile was applied to the cost per prisoner to attain a set of per‐
prisoner annual prison system costs by age‐gender group. 5.3.5
Probabilities of incurring costs Since not all crimes incur all cost types (i.e. dependent on visibility of crime, severity), probabilities based on crime type were applied to costs within the final costing model. All crimes were assumed to attract the societal costs mentioned in section 5.3.1, regardless of whether they were reported. However a schematic set of probabilities was developed to determine whether a crime attracted policing, court and prison system costs. This set included: ■
probability of a crime being reported to the police (thus attracting a policing system cost); ■
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probability of court action on a reported crime (thus attracting a court system cost); probability of a ‘guilty verdict’ in a court finalisation; and probability of a guilty verdict involving a custodial sentence (thus attracting a prison system cost). Crime types were based on ABS (2010b) for the application of probabilities as follows: 48 Positive Family Functioning ■
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homicide and related offences; acts intended to cause injury; sexual assault and related offences; dangerous or negligent acts endangering people; abduction/harassment/other offences against the person; robbery, extortion and related offences; unlawful entry with intent; theft and related offences; fraud, deception and related offences; illicit drug offences; prohibited/regulated weapons and explosives offences; property damage and environmental pollution; public order offences; and offences against justice. 5.4 Lifetime costing framework The lifetime costing framework was based on the application of each set of per‐head annual costs by age‐gender, across an estimated lifetime. Total lifetime costs were dependent on an assumed age of onset for the condition and assumed life‐span over which costs would apply. Summation of these costs across life‐spans gave a total lifetime, undiscounted cost from the presence of that NFF outcome. Application of a discount rate across the lifespan allowed the estimation of discounted lifetime costs (net present values) in 2010 dollars. The assumed age of onset for all health, productivity and criminality outcomes, excluding undergraduate degree non‐completion, was 19 years. For undergraduate degree non‐
completion, the assumed age of onset was 21 years. Lifetime costs were discounted back to 2010 at an annual real discount rate of 7%, consistent with the Australian Government’s choice of discount rate for assessing regulatory interventions (Australian Government, 2010). Assumed life spans were based on life expectancies for males and females taken from ABS life tables (2009e). The costs are summarised in the tables below. Table 5.14: Discounted lifetime costs of adverse health outcomes (a) (2010 dollars) Cost type Obesity Males Females Health system 1,968 1,510 Productivity losses 3,617 2,777 49 Other financial(b) 2,687 2,063 BoD 49,728 38,186 Total 57,999 44,536 Anxiety and depression Health system 8,739 6,340 Productivity losses 40,749 29,445 Other financial(b) 7,959 5,766 BoD 95,075 69,219 Total 152,522 110,771 Health system 6,596 6,440 Productivity losses 58,075 62,149 Other financial(b) 10,588 11,331 BoD 576,799 563,150 Total Alcohol abuse 652,059 643,069 Health system 14,780 16,199 Productivity losses 26,017 28,497 Other financial(c) 28,610 31,338 Smoking BoD 33,006 36,153 Total 102,413 112,188 904 1,045 Productivity losses 7,337 8,461 Other financial(b) 6,320 7,288 BoD 5,671 6,540 Total 20,232 23,334 Illicit drug abuse Health system (a) Assuming an age of onset of 19 years, life expectancies from ABS and 7% discount rate (b) ‘Other financial’ category for obesity, anxiety and depression and smoking includes DWL from transfers, carer costs and other indirect costs. (c) ‘Other financial’ category for alcohol abuse and drug abuse includes accidents not elsewhere included, fires not elsewhere included and abusive consumption costs. Source: Access Economics calculations. Table 5.15: Discounted lifetime costs of adverse productivity outcomes (a) (2010 dollars) Cost type Males Year 12 non‐completion Females 95,865 Total productivity losses(b) Undergraduate non‐completion 312,768 Total productivity losses(c) 54,963 198,282 (a) Including costs from lower employment/participation, productivity and DWL due to lower taxation revenue. (b) Assuming an age of onset of 19 years, life expectancies from ABS and 7% discount rate. (c) Assuming an age of onset of 21 years, life expectancies from ABS and 7% discount rate. Source: Access Economics calculations. 50 Positive Family Functioning Table 5.16: Discounted lifetime costs of criminality outcomes (a) (2010 dollars) Males Cost type Policing costs 99,278 Total discounted costs Court system costs Females 85,439 Total discounted costs 9,087 8,512 Prison system costs Total discounted costs Societal costs 928,696 865,222 Medical 15,869 13,660 Lost output 68,092 58,611 Intangible(b) 69,604 59,913 Property damage 197,108 169,664 Other(c) 146,493 126,096 Total discounted costs 497,166 427,945 (a) Assuming an age of onset of 19 years, life expectancies from ABS and 7% discount rate. (b) ‘Intangible’ costs are those due to pain, suffering and lost QoL (Rollings, 2008). (c) Those costs non‐separable into above categories. Source: Access Economics calculations. 51 6
Cost benefit analysis The Communities for Children (CfC), Positive Parenting Program (PPP) and Reconnect programs were selected for analysis. The reasons for selection of these is explained in section 2.6. 6.1 Communities for children (CfC) This section is based on the evaluation of the Stronger Families and Communities Strategy report (FaHCSIA, 2009). CfC is one of the major Australian Government investments in families. It has already been shown to be efficacious, and the CEA evaluation can also determine at what cost its effective outcomes are achieved. The CfC program was an initiative funded by the Australian Government from 2004–2009. The program aimed to: ■
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improve coordination of services for children 0 to 5 years and their families; identify and provide services to address unmet needs; build community capacity to engage in service delivery; and improve the community context in which children grow up. Under the CfC, FaHCSIA funded non‐government organisations as ‘Facilitating Partners’ in 45 disadvantaged geographic areas around Australia to develop and implement a whole‐of‐
community approach to enhancing early childhood development. CfC was one of three models of service delivery funded under the Australian Government’s Stronger Families and Communities Strategy (SFCS) 2004–2009. Most of the outcomes targeted by CfC are included as LSAC variables within Access Economics’ model (Table 6.17). The main exception is building child‐friendly communities, which is not directly related to family functioning. FaHCSIA (2009) presented the results of the evaluation of the short‐run impacts of the CfC initiative on child, family and community outcomes. The evaluation study was based on a three‐wave longitudinal study of 2,202 families living in 10 sites that had a CfC program and five sites that did not have a CfC program but were in other ways comparable to the CfC sites (contrast sites). The study had a large sample, representing 42 per cent of the population of 2 year‐old children in 10 CfC sites and five contrast sites at wave 1. Follow up (wave 3) was conducted in 2008, approximately one year after CfC program activities were under way. The effects of the CfC initiative were estimated using statistical techniques that allowed child, family and community outcomes in the CfC sites to be compared to what they would have been in the absence of the CfC intervention (using outcomes in the contrast sites). The outcomes measures related to four priority areas: ■
52 healthy young families—child injuries requiring medical attention, child and parent physical health, children’s experiences of emotional and behavioural problems, children’s prosocial behaviour, children being overweight, and parents’ mental health; Positive Family Functioning ■
supporting families and parents—harsh parenting, parenting self‐efficacy or self‐
confidence, parent relationship conflict, and living in a jobless household; ■
early learning and care—children’s receptive vocabulary achievement and verbal ability, and the quality of the home learning environment; and ■
child‐friendly communities—parents’ involvement in community service activities, the level of support parents receive from others to raise children, the quality of the neighbourhood as a place to raise children, parents’ sense of community social cohesion, their perception of the quality of facilities in the community, and the level of unmet service needs. The overall conclusion is that, on balance, there is evidence that CfC had the following positive impacts: ■
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fewer children were living in a jobless household; parents reported less hostile or harsh parenting practices; and parents felt more effective in their roles as parents. At initial follow up, any impact of CfC was expected to be small, given that the evaluation was of short‐run effects and the intervention was intended to have an impact on all families living in CfC sites and not just those who directly accessed services. As expected, for many of the outcome measures, the estimated impact of CfC was not statistically significant, which is not surprising given that any effects were likely to be small in the short‐run. Depending on the statistical model estimated, between two‐thirds and three‐quarters of the outcome variables indicated a positive, although not necessarily statistically significant, effect. ■
Although non‐significance means that it is not possible to say with a high level of confidence that the individual effect was not different from zero, the skewed pattern of results towards positive effects provides support for the conclusion that CfC has had some positive impacts in the short‐run. ■
The effect sizes of the CfC impacts on all outcomes were small, but can be considered positive relative to what was observed in the early phase of Sure Start (a large‐scale area‐based initiative in the United Kingdom). The current results were also comparable in size to those found in the later impacts evaluation of Sure Start, where 3 year‐old children were exposed to more developed programs from birth. ■
Also important is the extent to which CfC outcomes compare with alternative early childhood interventions that target specific client groups and seek to enhance child outcomes through other processes, such as centre‐based programs, home visiting programs, case management interventions and parenting programs. Wise et al (2005) found that most studies reported effect sizes on parenting and child outcomes that were negligible to small. It should also be noted that most of these evaluations measured outcomes for children who were directly enrolled in the program, whereas CfC is aimed at improving outcomes for children in the whole community. The fact that the effect sizes of CfC were comparable to, if not greater than, many alternative early childhood interventions, and that these effects were evident irrespective of whether parents and children in the CfC communities had actually received services, seems to point towards an additional effect over and above the provision of new, stand‐alone services, 53 possibly as the result of a better coordinated local system of early childhood services and/or other enhancements to the community context in which children develop. 6.1.1
CfC outcomes Three of the five LSAC categories used as interim variables in this report can be used as proxies for variables modelled in FaHCSIA (2009): emotional and social problems, overweight (obesity) and vocabulary and verbal ability (learning outcomes). Four more CfC variables ‐ hostile parenting (harsh discipline), parental self‐efficacy, parental mental health (depression) and parental relational conflict (family cohesion) are used as inputs to the other two interim outcomes. The last CfC variable, quality of the home learning environment (parental involvement in education at home) is used as an input into the learning outcome interim variable (Table 6.17). Table 6.17: Communities for Children target areas and related LSAC variables Priority area Outcome LSAC variable modelled Healthy young families Child injuries ‐ Child physical health ‐ Child emotional and behavioural development *asdqta Child overweight *cbmi Parent general health ‐ Parent mental health *ak6 Supporting families and parents Hostile parenting *ahostc Parenting self‐efficacy *pa01a Parental conflict *re06a Living in jobless household ‐ Early learning and care Vocabulary and verbal achievement *wlrnoi Home learning environment *ahactd Child‐friendly communities Various ‐ Note: Variables in bold are mapped to LSAC dependent variables. Variables in normal font (not bold) are explanatory variables. Variables with dashes do not occur in our model as the literature review did not support them as being significant for our model. Source: FaHCSIA (2009). All of the relevant outcomes of the CfC, as modelled in FaHCSIA (2009) had the expected signs (that is, they were all improvements.) The LSAC sample includes 364 children who were also participants in the CfC program. The data were drawn from the K1 cohort to match these children. By comparing CfC effect sizes against LSAC averages for the same variables, it is possible to estimate the magnitude of the average improvements under the program (Table 6.18). 54 Positive Family Functioning Most of the substantial effects from CfC were improvements to parenting 10 . The smallest effects were on child outcomes – but they were still all positive. Table 6.18: Outcomes of Communities for Children variables used in this report CfC outcomes Domain LSAC standard deviations1 Improvement Hostile parenting ‐0.14 1.28 ‐10.9% Parenting self‐efficacy 0.11 0.84 13.1% Parent mental health ‐0.07 0.56 ‐12.5% Quality of the home learning environment 0.02 0.56 3.6% Parental relationship conflict ‐0.02 0.84 ‐2.4% Child total emotional and behavioural problems (SDQ) ‐0.04 4.68 ‐0.9% Child overweight ‐0.04 1.59 ‐2.5% Receptive vocabulary achievement and verbal ability ‐0.19 9.73 ‐2.0% Source: FaHCSIA (2009). Note: Variables in bold are mapped to LSAC dependent variables. Variables in normal font (not bold) are explanatory variables. 1 Obtained from LSAC database observations on CfC children. 6.1.2
CfC costs The cost of funding CfC over the four years from 2004‐05 to 2008‐09 was $142 million. The majority of this investment was spent on service delivery, with Community Partners (the local service provider that delivered the services/activities) receiving 60% of the funding, Facilitating Partners receiving 7% of the funding and 3% was used for local evaluation. The remaining 30% was for community resource funding (development, implementation, project management and community development). Based on the number of 0 to 5 year olds in each CfC site in 2006 (n=28,810), $840 was spent on each 0 to 5 year‐old child living in the CfC communities between 2004–05 and 2007–08. This four year period was thus selected for the costing – estimated as 4/5ths of $142 million or $113.6 million. 6.1.3
Cost benefit results The difference between the economic benefits and costs under a scenario with the intervention and one without was estimated by using an incidence approach i.e. the difference in the number of people affected by each outcome. Under the CFC, the resulting reductions in the incidence of negative outcomes and the increases and improvements in positive outcomes are shown in Table 6.19, along with the intervention effect in percentage terms. The net economic gains between the scenario and the ‘base case’ (no intervention) were evaluated to determine the intervention’s overall return on investment. 10
Defining substantial here as roughly a 2% change. 55 The total financial benefits represent discounted social and economic savings associated with improvements in outcomes attributable to CFC over the life of each individual. Consequently, the benefits were estimated to be $541.4 million, or $135.4 million per annum (assessed over 2004‐05 to 2007‐2008). Table 6.19: Outcomes of CfC as delivered over 2004‐05 to 2007‐08 Intervention effect* (change in incidence rate compared with no intervention) Difference in the number of people affected ‐22.44% ‐8,546 Productivity (yr 12 completion) 1.42% 804 Anxiety and Depression ‐0.87% ‐46 Antisocial ‐0.01% ‐1 Addictions ‐0.5% ‐47 Obesity Source: Access Economics (2010). * The intervention as modelled targets children aged 0‐1 years. Given the cost of funding over four years 2004–05 to 2007–08 was $113.6 million (Section 6.1.2), the average annual cost of the program was therefore $28.4 million per year. As a result, the total benefit cost ratio of the CfC program was calculated to be 4.77, suggesting a 377% return on investment from the CfC program. 6.2 Positive Parenting Program (PPP) PPP is one of the best evaluated programs targeted at improving family functioning and outcomes for younger children. While its efficacy is well‐proven, there are fewer studies on its cost effectiveness and this CEA can also act as a tool to test/triangulate the power of the model. Developed at the University of Queensland, the PPP (also referred to as ‘Triple‐P’) is a multi‐
level system of parenting and family support. It can be provided individually, in a group, or a self‐directed format. It incorporates five levels of intervention on a tiered continuum of increasing strength for parents of children and adolescents from birth to age 16. The multi‐
disciplinary nature of the program allows utilisation of the existing professional workforce in the task of promoting competent parenting. The program targets five different developmental periods from infancy to adolescence. Within each developmental period, the reach of the intervention can vary from being very broad (targeting an entire population) to quite narrow (targeting only high‐risk children). The PPP enables practitioners to determine the scope of the intervention given their own service priorities and funding. The aims of PPP 11 are: ■
To promote the development, growth, health and social competencies of children and young people. 11
http://www26.triplep.net/?pid=65 56 Positive Family Functioning ■
To promote the development of non‐violent, protective and nurturing environments for children. ■
To promote the independence and health of families by enhancing parents' knowledge, skills and confidence. ■
To enhance the competence, resourcefulness and self‐sufficiency of parents in raising their children. ■
To reduce the incidence of child abuse, mental illness, behavioural problems, delinquency and homelessness. PPP is prevention oriented, multi‐disciplinary and has five levels. The program has been developed through over 20 years of clinical trials. Five different developmental periods are targeted at each level – infants, toddlers, preschoolers, primary school aged children and teenagers. The program aims to promote parental competence and enable parents to become independent problem solvers. Five key principles of parenting – safe, engaging environment; positive learning environment; assertive discipline; reasonable expectations and taking care of self as parent. The program has five levels, as outlined below. ■
Level 1 – provides universal access to parenting information through print and electronic media. This level aims to increase community awareness of parenting resources, encourage parent participation in PPP and create a sense of optimism. ■
Level 2 – One or two primary health care sessions that provides ‘anticipatory developmental guidance’ to parents who have children with mild behaviour problems. Parenting tip sheets and videotapes are commonly used at this level. ■
Level 3 – Four primary care intervention sessions for parents of children with mild to moderate behaviour problems. The sessions provide active skills training for parents. ■
Level 4 – Intensive program of eight to ten sessions that are either individual sessions, group based or self‐directed. This level is for parents of children with more severe behaviour problems. ■
Level 5 – An enhanced behavioural family intervention program for parents whose difficulties are complicated by other issues such as relationship conflict and parental depression. This level therefore targets not only parenting skills but also distressing parental emotional reactions (including depression, anger and stress) via cognitive behavioural techniques. 6.2.1
PPP outcomes PPP has been the subject of dozens of peer‐reviewed journal articles over the years. The majority of the LSAC regression dependent variables in this analysis have been reported directly in Australian PPP studies (Table 6.20). ■
PPP targets childhood problem solving skills (which is similar to the dependant variable in the productivity regression (learning outcomes). While the literature search did not uncover any quantitative reporting for this outcome, several input variables (or common psychological variables that can be used as proxies for input variables) were reported. ■
PPP also targets health, of which obesity is a factor. Again, no studies were found that reported on obesity directly, but a number of input variables have been studied. 57 Table 6.20: PPP target areas and LSAC variables PPP target LSAC measure LSAC variable Parental Sense of Competency (PSOC) Parental self‐efficacy Dyadic Adjustment Scale Family cohesion *re06a SDQ emotional score SDQ emotional score *aemot SDQ conduct problems SDQ conduct problems *aconda ECBI SDQ total score *asdqta Parental depression Parental anxiety and depression Parental laxness1 Consistent parental discipline *pa01a Hostile parenting *fhostc Inductive reasoning *areas Parental overreactivity1 1
Parental verbosity *apa01a *ak6 Note: Text in bold indicates LSAC regression dependent variables. 1 – these three variables are often reported upon collectively as the Parenting Scale (PS). Sanders et al (2008) conducted a large scale (n = 3,000) controlled evaluation of all five stages of PPP for four to seven year old Australian children with follow up at two years. The authors found (Table 6.21) that PPP produced substantial improvements in SDQ emotional difficulties (by 17.6%) and SDQ total difficulties (by 21.6%) in children, and significant reductions in parental depression (26.2%). All of these worsened, or got no better, in the control groups. Table 6.21: Impact of large scale Queensland PPP trial PPP % Clinical Control OR 95%CI SDQ emotional % Clinical OR Tarones χ2 95%CI Sig Pre 15.3 1 0.653 12.1 1 0.909 4.969* 0.026 Post 12.6 0.803* 0.988 13.4 1.128 1.398 SDQ conduct problems Pre 18.7 1 0.685 16.8 1 0.661 0.036 0.85 16 0.828 1.001 14 .806* 0.822 Post SDQ total difficulties Pre 13.9 1 0.608 9.7 1 0.855 4.783* 0.029 Post 10.9 .757* 0.941 10.4 1.085 1.37 Parental depression Pre 26.70 1 .570 19.10 1 0.802 7.673* 0.006 Post 19.70 0.676 .802 18.60 0.963 1.157 Parenting consistency Pre 91.10 1 .808 86.10 1 0.915 Post 91.40 1.04 1.34 87.50 1.13 1.397 Parental confidence Pre 99.70 1 0.148 99.50 1 0.263 Post 99.40 0.491 1.634 99.30 0.694 1.827 Note: * indicates significant difference (at p=0.05) between intervention and control groups. Source: Sanders et al (2008). 58 0.243 0.622 0.193 0.661 Positive Family Functioning De Graaf et al (2008) conducted a meta analysis on the impact of PPP Level 4 on two parenting factors. The first, Parenting Scale (PS) is a composite of laxness (inconsistent discipline), over‐reactivity (harsh parenting) and verbosity (inductive reasoning) 12 . The second, Parental Sense of Competency (PSOC), approximates the LSAC variable Parental self‐efficacy. The authors found weighted average effect sizes of 0.68 for PS and 0.65 for PSOC, both of which were stated to be large. Zubrick et al (2005) conducted a large, community based Level Four PPP trial in Western Australia with over 800 participants in the intervention group and the control group. This was not a randomised trial, as the intervention was targeted towards at risk families (as measured by likelihood of having clinically significant issues), but it was a controlled trial. Results were reported immediately after the intervention and at 12 and 24 months. In all cases there were significant positive results directly after the intervention, and most of these gains were still preserved after two years. Not all of the measures used in the study had direct LSAC equivalents that used in our model, so closest substitutes were chosen. On this basis, child social and emotional problems, parental depression and family cohesion all recorded improvements equivalent to ten percent or better. As the authors reported means for the intervention group, the implied improvement in (Table 6.22) is simply the estimated intervention effect as a proportion of the mean. Table 6.22: Impact of large scale Western Australian PPP trial Attribute Estimated Relevant LSAC Mean (% in intervention effect equivalent clinical range) at 24 months Implied improvement Parental depression Parental depression 16.2 4.41 27.2% Marital satisfaction Family Cohesion 10.0 0.73# 7.3% Parental laxness Inconsistent discipline 39.6 0.38 1.0% Parental over‐reactivity Hostile parenting 69.5 0.33 0.5% Parental verbosity Inductive reasoning 40.6 0.29 0.7% Eyberg Child Behaviour Intensity (ECBI) SDQ total problems 121.6* 12.92 10.6% Notes: *ECBI mean is a score, not percent in clinical range, # Abbreviated Dyadic Adjustment Scale. Source: Zubrick et al (2005). While the focus of this report is on Australian parenting, several studies have demonstrated that PPP is effective in other cultures. For example, Bodenmann et al (2008) in a smaller European controlled trial (n = 50 families) found significant improvements in parenting, parental esteem and child misbehaviour, most of which were maintained after 12 months. 12
‘Inductive reasoning’ as recorded by LSAC often had counter‐intuitive results, so it is possible that what parents think of as inductive reasoning may in fact be closer to what clinicians would describe as verbosity. 59 Table 6.23: Improvement in means scores of European PPP trial Topic Pre‐trial Post‐trial After 12 months % change PS laxness 13.5 13.2 12.3 12.3% PS over reactivity 24.7 18.7 21.0 15.0% Parental self efficacy 26.9 28.7 28.8 7.1% ECBI problems 117.2 104.7 99.9 14.8% Dyadic Adjustment Scale* 106.3 111.0 109.6 3.1% Note *measures quality of the couple’s relationship, approximates LSAC family cohesion. Source: Bodenmann et al (2007). Table 6.24 shows the outcomes of PPP variables that were used in the modelling. Variables from Queensland study were used where available (SDQ emotional and total scores, parental anxiety and depression), as this was the largest study and Australian. Variables from the Western Australian study were the second preference (used for family cohesion, parental laxness, parental over‐reactivity and parental verbosity), as this study was also Australian, and larger than the European study. Note, however, that the effect sizes for laxness and over‐
reactivity were much smaller in the Western Australian study than in the European study. The European study provided the inputs for parental self‐efficacy. The SDQ conduct problems score was based on the total score, since the emotional score was less than the total score so it is probably that the conduct score at least reflected the total score (or more). Table 6.24: Outcomes of PPP variables used in this analysis PPP outcome Model concept Specific variable % improvement QLD WA EU Parental Sense of Competency (PSOC) Parental self‐
efficacy *apa01a 7.1% n/s n/a 7.1% Dyadic Adjustment Scale Family cohesion *re06a 7.3% n/a 7.3% 3.1% SDQ emotional score SDQ emotional score *aemot 28.4% 28.4% n/a n/a SDQ conduct problems SDQ conduct problems *aconda 28.8% n/s n/a n/a ECBI SDQ total score *asdqta 28.8% 28.8% 10.6% 14.8% Parental depression Parental anxiety and *ak6 depression 23.6% 23.6% 27.2% Parental laxness Consistent parental discipline *pa01a 1.0% n/s% 1.0% 12.3% Parental over‐
reactivity Hostile parenting *fhostc 0.5% n/a 0.5% 15.0% Parental verbosity Inductive reasoning *areas 0.7% n/a 0.7% n/a Source: Based on Table 6.21, Table 6.22 and Table 6.23. QLD=Queensland. WA = Western Australia. 60 n/a Positive Family Functioning PPP costs 6.2.2
Wise et al (2005) report that cost effectiveness analyses of PPP have found that that its costs per person range from 75c at Level 1 to $422.45 at Level 4. Foster et al (2007) reported the following estimated costs of PPP, by level, in the United States (Table 6.25). As an overall average, the authors estimated the costs of PPP were $US11.27 per child. Table 6.25: PPP costs ($US) Level Provider costs Attending training Preparation Total Grand Total Primary care 707 500 32 532 1,239 Participant costs L4 Group 719 726 44 769 1,488 L4 Standard 740 800 57 857 1,598 L5 Enhanced 660 587 18 605 1,266 Mihalopoulos et al (2007) state that the cost of implementing PPP in Queensland to 572,701 children aged between 2 and 12 years of age (315,378 families) was A$19.7 million (Level 1, $240,000; Level 2, $5.8 million; Level 3, $5.7 million; Level 4, $4.4 million; Level 5, $3.6 million) with an average cost of $34 per child. Cost benefit results 6.2.3
Similar to CfC in section 6.1.3, the difference between the economic benefits and costs under a scenario with the PPP intervention and one without was estimated using an incidence approach, and was based on the costs from the Queensland study as presented above. The change in outcomes and intervention effects are shown in Table 6.26. The net economic gains between the scenario and the ‘base case’ (no intervention) were evaluated to determine the intervention’s overall return on investment. The benefits were estimated to be $68.1 million per year (assessed over 2004‐05 to 2007‐2008). Table 6.26: Outcomes of PPP as delivered over 2004‐05 to 2007‐08 in Queensland Obesity Productivity (yr 12 completion) Anxiety and Depression Antisocial Addictions Intervention effect* (change in incidence rate compared with no intervention) 0.00% 0.00% ‐0.69% ‐0.13% ‐1.02% Difference in the number of people affected 0 0 ‐161 ‐96 ‐266 Source: Access Economics (2010). * The intervention as modelled targets children aged 4‐5 years. 61 Given the cost of the intervention in Queensland over the four years 2004–05 to 2007–08 was $19.7 million, the average annual cost of the program was therefore $4.925 million. The total benefit cost ratio was therefore calculated to be 13.83, suggesting a substantial 1,283% return on investment from the PPP program. 6.3 Reconnect The Reconnect program aims to assist ■
young people aged 12 to 18 years who are homeless, or at risk of homelessness, and their families. This includes a number of specialist Reconnect services including Indigenous, Culturally and Linguistically Diverse (CALD), Mental Health, Gay, Lesbian, Bisexual, Transgender, Intersex and Newly Arrived Youth specialists; and ■
young people aged 12‐21 years who have arrived in Australia in the previous five years, focussing on people entering Australia on humanitarian visas and family visas, and who are homeless or at risk of homelessness through the Newly Arrived Youth Specialist Services. The objective of Reconnect is to assist young people to stabilise their living situation 13 and improve their level of engagement with family, work, education, training and their local community through: ■
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using family focussed early intervention strategies to achieve family reconciliation; improving coordination and integration of services delivered by government and the community sector; and working with Centrelink, young people, and their parents to ensure that income support at the ‘away from home’ rate (where appropriate) is available to young people who are properly entitled to it (FaHCSIA 2003a). Reconnect services include counselling, group work, mediation and practical support and are provided to the whole family. Reconnect providers also 'buy in' services to target individual needs of clients, such as specialised mental health services. Services are provided where the client is most comfortable, such as in homes, schools, and other sites in the community. The program is funded by FaHCSIA and services are delivered by (generally) non‐government organisations. Participants may be self‐referred or referred from a range of sources, including: schools, education and training organisations; parents or family; community agencies; Centrelink; juvenile justice; police; child protection agencies; accommodation services; specialist services, such as English language centres; and state/territory community service departments. 14 13
The aim is to achieve family reconciliation, wherever practicable, between homeless young people, or those at risk of homelessness
and their family. Family reconciliation outcomes include: the young person returns home; ongoing positive family relationships are
created which provide the young person with emotional and physical support; reconciling the young person with other family members
e.g. grandparents or siblings; both parent (s) and the young person accept that independence is appropriate for the young person; and
establishing a viable support system for the independent young person that includes a member of his/her family
(http://www.fahcsia.gov.au/sa/housing/funding/reconnect/recon_operation_guidelines/Pages/2_overview.aspx accessed August 2010). 14
http://www.fahcsia.gov.au/sa/housing/funding/reconnect/recon_operation_guidelines/Pages/2_overview.aspx accessed August 2010 62 Positive Family Functioning Reconnect services are located in areas of high need having regard to factors such as the size of the adolescent population, indicators of socio‐economic disadvantage, particularly vulnerable population groups such as Indigenous people or newly arrived refugees or immigrants from non‐English speaking backgrounds, the range of other services in a region and geographic location. As a result, there are Reconnect services located in major metropolitan centres, rural towns and more remote locations (FaHCSIA 2003). Reconnect services are small, typically employing two to three practitioners with some administrative support. Reconnect services are located in disadvantaged communities throughout Australia. In 2008‐09, there were 101 services, and around 5,215 cases 15 . The data available suggests activity each year between 2004‐05 and 2008‐09 was as follows: ■
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6.3.1
2004‐05: 6,301 cases; 2005‐06: 6,025 cases; 2006‐07: 5,890 cases; 2007‐08: 6,078 cases; and 2008‐09: 5,215 cases. Reconnect outcomes Two evaluative studies were conducted on Reconnect in 2001 and 2002 (reported in 2003) — a community study investigating service structures and whether these had changed in accordance with Reconnect objectives, and a longitudinal survey of Reconnect clients which aimed to assess the potential benefits of Reconnect on the lives of young people and their parents. The community study (FaHCSIA 2003a) investigated 12 Reconnect services over a one‐year period (end 2001 to end 2002) to assess their contribution to an improvement in community capacity for early intervention around youth homelessness. The 12 services were selected to be reflective of the broader Reconnect program, and were located in remote, rural and urban locations and included specifically targeted as well as generalist services. The study concluded that the 12 services had a significant impact, relative to their own capacity, on building community capacity for early intervention for youth homelessness in three key ways: ■
by building community infrastructure for early intervention for example, by improving access to services and through training of service providers, community members, parents and clients; ■
by strengthening service networks, collaboration and resource sharing between agencies; and ■
through assisting other organisations to have a greater focus on effective early intervention. 15
When a client engages with a service, a case is commenced for that client. The case work will involve providing whatever services that client needs. The case can continue to deal with the initial issues and/or any issues identified while working with the client. The case is closed when: a) all issues are resolved, ie as mutually agreed by the worker and the client, or b) can be closed if either the worker or the client feels that is appropriate. c) If a client does not have contact with the service for over 30 days, the case can also be closed. A new case would be opened if the client comes back to the service after the previous case has been closed. A new case cannot be created if there is an open case for that client. 63 The longitudinal survey (FaHCSIA 2003b) was sent to selected clients at two points in time, around 10 months apart. The first wave of the survey commenced in November 2001 and the second wave was administered in October 2002. At each time point, both new clients and clients who were exiting Reconnect were surveyed. 1,001 cases participated — 516 new clients and 485 whose support period had ended. Cases could consist of a young person only, adult(s) only (in most cases, parents), or both a young person and adult(s). ■
In the first wave of the survey, responses were received for 455 cases, making an overall case response rate of 45.5%. The response rate for exiting cases (46.0%) was slightly higher than for entering cases (45.0%). ■
In the second survey wave, responses were received for 260 cases—this represents a wave 2 response rate of 57.1%. The response rate for entering cases (59.1%) was higher than for exiting cases (55.2%). The longitudinal survey concluded: ■
There was no difference in the extent to which entering and exiting clients were participating in employment or education at wave 1. As entering clients are used as a de facto control group, this suggests that Reconnect intervention has little immediate impact on improving young people’s participation in education or employment. ■
Self‐reported school performance, interest in school, perceived importance of school subjects, expectations of educational attainment, and psychological sense of school membership did not vary significantly across entering and exiting client groups. It is difficult, therefore, to conclude that Reconnect significantly improves clients’ connectedness to employment or education using the measures studied. ■
There was a significant improvement across time in the extent to which young people felt liked and respected at school, which could not be explained by sex, age or case complexity. It is difficult to conclusively attribute this to Reconnect. However, clients were also asked separately about the impact of Reconnect, and the finding from the longitudinal survey is consistent with young clients’ self‐reported impact of Reconnect on how they feel about themselves, their school, and their ability to deal with their teachers. ■
Young people responding to wave 2 of the survey reported improvements in their own ability to manage family conflict. About two thirds of respondents rated their ability in this area as poor or very poor before entering the Reconnect program, but just 16% felt their ability was poor or very poor after receiving Reconnect support. The proportion who felt they had good or very good skills in managing family conflict increased from 12% before Reconnect to 44% after Reconnect. Young people also reported that their family, as a whole, was better able to manage conflict after Reconnect. Those rating their family’s abilities as good or very good increased from 14% to 37%. Exiting clients reported higher levels of improvement than entering clients, suggesting that improvements in conflict management are lasting, and indeed increase over time. Similar levels of improvement in conflict management were also reported by parents. ■
Respondents reported positive shifts in communication between family members. About 7% of young people and 2% of parents felt their family communicated very well before receiving Reconnect support. Comparable figures reported at the time of the wave 2 survey were 17% and 13%. No significant difference was found in changes in communication between entering and exiting clients. 64 Positive Family Functioning ■
Among young people, although there was no significant impact of Reconnect found for family closeness or attachment to parents (measured by three scales of Trust and communication, Alienation, and Relationship with father), parents as a whole experienced a significant decline in feelings of ‘depression and anxiety’ across the two waves. There were no statistically significant differences between exiting and entering clients, or between wave 1 and wave 2 clients in response to scales administered relating to ‘depression and anxiety’ and ‘self‐worth and coping’. ■
The extent to which parents felt their child was likely to engage in undesirable social behaviours was also examined. There was no difference in the assessments of entering and exiting parents, suggesting little, if any, impact by Reconnect services. However, it should be noted that little improvement was to be made. Average scores for both entering and exiting parents on the index of parental‐assessed likelihood of children engaging in undesirable behaviours were very low (2.39 and 2.35, respectively out of a possible 10). The final report (FaHCSIA, 2003c) concluded that: ■
There was improvement in the stability of young people’s living situations — Young people living in temporary situations fell from 16.5% at Reconnect’s initial intervention to 5% at exit from the services. Reconnect intervention also increased the stability of young people’s living situations in relation to parents—young people living with parents increased from 57.5% at the start of support to 62% after support, an increase which was found across all age categories. ■
There was improvement in young people’s ability to manage conflict — as reported for the longitudinal survey above. ■
There were improvements in communication within families — Young people and parents reported improvements in communication following the Reconnect intervention. The proportion of young people who felt their family communicated well increased from 22% before Reconnect intervention to 41% afterwards. Parents’ reported improvement was more pronounced: the proportion of parents reporting their family communicated well or very well increased from 11% before Reconnect intervention to 42% at the second survey. Parents reported feeling increased closeness with their children and less alienation after Reconnect intervention, although this effect was not sustained over time. ■
Improvements in young people’s attitudes to school — The majority of young people using Reconnect services were in full‐time education (76%) — a participation level which was unchanged by Reconnect intervention. However, the evaluation found that, for the majority of young people using Reconnect, experience of school was not a happy one. Reconnect clients tend to change school frequently: 52% of young people had been at their current school for less than two years. Just over one third of young people (36%) reported being bullied, 18% saying that it happened on ‘most days’; more than half (53%) of the young people surveyed reported hating school often or almost always; 43% reported having been suspended and 10% having been expelled. The longitudinal study found a significant improvement over time in the extent to which young people felt liked and respected at school. ■
Improvements in engagement with education and employment — The proportion of young people employed full time or part time increased from 2% at the start of support to 5% at completion. The proportion of young people who were not in education, 65 training or employment dropped from 15% at program entry to 11% at exit. Education participation rates and unemployed (looking for work) status remained unchanged. However, in the light of the findings on case complexity of Reconnect clients, maintaining young people’s participation in education can be viewed as a positive achievement. ■
Engagement with community — although self‐reported improvements in young people’s engagement with the community were found, other measures used in the longitudinal study to track increases in participation in community activities found no significant improvements over time. ■
Clients’ views of outcomes — Three quarters of young people and parents reported overall improvement in the situation that led them to Reconnect. More than half of young people (55%) and parents (52%) attributed ‘a lot’ of this improvement to the Reconnect intervention. ATP data for the Reconnect cost effectiveness analysis Based on the findings of the evaluation, ATP variables relevant to the Reconnect program were selected (see Table 6.27). The method for estimating the effect sizes is described after the table. Table 6.27: ATP variables associated with the Reconnect Program Evaluation outcome variable ATP variable(s) tested in regressions Effect size on FF variable Young people's attitudes to school (school changes, wagging, suspension or expulsion, hated school, curriculum irrelevant, teachers not respect students) School bonding (positive affect towards school) 18.6% Young people's ability to manage conflict Conflictual relationships 55% Communication within families Attachment to parents, harsh parenting and family closeness 37% Engagement with education and employment Conservatively not modelled Under‐engagement (young adult not in education or training and not in employment) Source: Australian Government Department of Family and Community Services (2003) ■
66 School bonding: The longitudinal survey of Reconnect clients measured clients’ sense of belonging to school (or students’ psychological membership in the school) across two dimensions (FaHCSIA, 2003b:53): ‘Respect and affect’ (school membership concerning how students get along with, and are viewed by, their peers) and ‘belongingness’ (a more internal measure of the extent to which a given student feels they ‘fit in’ to the school. There was a significant difference in mean scores on the ‘respect and affect’ scale across time (scores on this scale increased from wave 1 to wave 2), across all categories of client status, age, sex and case complexity. However, the effect size was not reported. While scores on the ‘belongingness scale’ did not vary significantly between the two waves, there was a significant effect for cases classed as medium Positive Family Functioning complexity 16 where the mean increased from 5.06 in wave 1 to 6.00 in wave 2. The effect size was therefore modelled as 6/5.06‐1 = 18.6%. ■
Conflictual relationships: The longitudinal survey found that young people perceived an improvement in their ability to manage family conflict before Reconnect and when surveyed (‘now’) (FaHCSIA, 2003b). While the extent of the change was derived and reported, the units were not clear (see the reporting around the analysis of variance, FaHCSIA. 2003b:70). Hence, we used data from the report shown in Table 6.28 to estimate an effect size of 55% (3.326/2.144‐1). Table 6.28: Estimate of effect size — young person’s perception of their ability to manage family conflict before Reconnect and now Likert scale categories Before Reconnect % survey respondents Now Score multiplied by % % survey respondents Score multiplied by % Very poor 1 35.0% 0.350 6.8% 0.068 Poor 2 30.7% 0.614 9.3% 0.186 OK 3 22.7% 0.681 39.5% 1.185 Good 4 8.6% 0.344 33.3% 1.332 Very good 5 3.1% 0.155 11.1% 0.555 2.144 3.326 Weighted average Source: FaHCSIA (2003b:76) and Access Economics calculations. ■
Communication within families and family closeness. For ‘attachment to parents’ specifically, the longitudinal survey found no significant difference between the scores for young people who were entering Reconnect and those who were exiting Reconnect. 17 However, good communication increased significantly, by 86% (22% to 41%). Good communication is considered likely to be related to the finding that the family’s ability to resolve conflict improved, and is also thought to reduce harsh parenting, to the extent that such parenting is indicative of and associated with poorer communication. As such, we have used a proxy for attachment and harsh parenting based on a method similar to that for conflictual relationship, where the child’s perception of their family’s ability to manage conflict is utilised as a proxy for intra‐
familial attachment, in the absence of a more specific direct effect size and given the significant improvement in communication and parental wellbeing found. We used data from the report shown in Table 6.29 to estimate an effect size of 37% (3.070/2.234‐1). 16
Complexity was measured as low (34.8% of respondents), medium (30.3% of respondents) or high (34.9% of respondents). Case managers measured complexity according to conflict with authority, physical or emotional violence, sexual abuse, mental illness, substance abuse, disability, child protection, poverty, homelessness or living situation, living skills, and identity conflict. 17
Analysis of variance was used to examine whether Reconnect clients experienced a change in their perceived parent‐child attachment over time, but for young people no significant differences were found. The nature of the reporting did not allow use of the means in the model. 67 Table 6.29: Estimate of effect size — young person’s perception of their family’s ability to manage family conflict before Reconnect and now Likert scale categories Before Reconnect % survey respondents Now Score multiplied by % % survey respondents Score multiplied by % Very poor 1 34.0% 0.335 11.6% 0.116 Poor 2 27.4% 0.548 15.9% 0.318 OK 3 25.6% 0.768 36.0% 1.080 Good 4 9.2% 0.368 27.4% 1.096 Very good 5 4.3% 0.215 9.2% 0.460 2.234 3.070 Weighted average Source: FaHCSIA (2003b:76) and Access Economics calculations. Engagement with education and employment was conservatively not modelled. Although this interim outcome variable improved 27% (4%/15%), the connection was not through family functioning variables per se. A case could be made in evaluating Reconnect overall, to include this benefit as well. However, as this analysis is focused on the interventions that lead to FF improvements which in turn lead to improvements in interim outcomes, the direct benefits of Reconnect that bypass the FF domain have been excluded from this analysis. 6.3.2
Reconnect costs The budget for Reconnect is around $20 million per year (FaHCSIA portfolio Budget papers from 2001‐02 to 2006‐07). Unpublished data from FaHCSIA suggest the average cost per case across all services and all cases in 2008‐09 was $3,800, i.e. an average of $22.4 million per annum over all cases 2004‐05 to 2008‐09. The distribution of costs per case across each service was skewed, with 50% of services having a cost per case less than $4,000 and 92% with a cost per case less than $10,000. This is likely to reflect location and case mix, together with other factors. 6.3.3
Cost benefit results Similar to CfC and PPP, the difference between the economic benefits and costs with and without Reconnect was estimated using an incidence approach, with the change in outcomes and intervention effects shown in Table 6.30. The net economic gains between the scenario and the ‘base case’ (no intervention) were evaluated to determine the intervention’s overall return on investment. Benefits were estimated to be $40.6 million per year (assessed over 2004‐05 to 2008‐09). 68 Positive Family Functioning Table 6.30: Outcomes of the Reconnect Program, as delivered over 2004‐05 to 2008‐09 Obesity Productivity (yr 12 completion) Anxiety and Depression Antisocial Addictions Intervention effect (change in incidence rate compared with no intervention) ‐6.73% 13.68% 0% 0% ‐1.18% Difference in the number of people affected ‐377 1,690 0 0 ‐18 Source: Access Economics (2010). * The intervention as modelled targets children aged 12‐13 years. With the cost of Reconnect estimated to be $22.4 million per year (Section 6.3.2), this yields a benefit cost ratio of 1.81, suggesting an 81% return on investment from the Reconnect program. 6.4 Shocking all variables – the value of PFF A final economic shock was specified which set the values of all the consistently significant 18 FF variables to their optimum values, some being maxima and others minima 19 – depending on the specific relationship to the health, productivity and social outcome variables and the coding of the variables. By setting the transition variables in ATP to their optimum values, this equated to optimal FF in the early age groups. The target population was set as the population of Australian children entering adulthood in 2010 (313,577 people turning 19) of whom, in the absence of the shock, 6.5% would be obese, 41.9% would have completed secondary school, 8.0% would have anxiety/depression, 5.2% would exhibit antisocial behaviours, and 19.2% would have addictions (4.3% abusing alcohol, 2.3% using illicit drugs and 12.6% smoking). The effect on each outcome from optimal FF and the value of the benefit is shown in Table 6.31 and Figure 6.7. Table 6.31: The value of PFF PFF NPV lifetime cost per case effectiveness*
% people Benefit $m
Obesity $51,268 ‐24.75% 6.55% 260.6 Productivity $75,414 29.06% 41.93% 2,881.7 Anxiety and depression $131,647 ‐17.66% 7.97% 581.1 Anti‐social $462,556 ‐7.28% 5.18% 546.7 Addictions $258,883 ‐7.53% 19.23% 1,175.5 5,445.7 Total Source: Access Economics calculations. * The maximum that PFF can affect each outcome. 18
19
Variables shoes coefficient signs changed across different age groups were excluded from the shock. For ATP variables, maxima and minima were proxied by taking two standard deviations from the mean. 69 Figure 6.7: Value of PFF by benefit type, 2010 (total $5.4 billion), $bn and % total 261 , 5%
1,176 , 22%
547 , 10%
2,882 , 53%
581 , 11%
Obesity
Productivity
Anxiety and depression
Anti‐social
Addictions
Source: Access Economics calculations. Note: Shares may not sum to 100% due to rounding. The outcomes for health, productivity and social costs were then compared to the base case of no shock to estimate the total value of gains from PFF in 2010 dollar NPV terms as $5.4 billion per annum. Of the total annual benefits: ■
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productivity gains comprise the largest component (52.9% or $2.9 billion per annum); savings from fewer addictions comprise the second largest component (21.6% or $1.2 billion); savings from fewer cases of anxiety and depression are 10.7% or $0.6 billion; reduced costs of criminality comprise 10.0% or $0.5 billion; and reduced costs of obesity represent 4.8% or $0.3 billion per annum. 70 Positive Family Functioning 7
Conclusions The scope of this project has been both ambitious and challenging but, we believe, the methods developed and many findings and insights are of global significance. The novelty of the research inspires further work in this field that we hope can be used to triangulate these findings internationally as well as continue to enhance the evidence base in Australia. Key findings The main finding of this report is that positive family functioning is economically and socially valuable, and that value can be quantitatively measured using extensive and detailed ‘cost of illness’ methodology traditionally adopted to analyse interventions in the health sector. By demonstrating and quantifying the value of PFF, it is then also possible to systematically and consistently evaluate interventions employed by the Government and the community to enhance FF in Australia. Value of PFF: The potential benefits of intervening in childhood and adolescence to prevent poor outcomes later in life are substantial, even (or despite) the fact that such intervention incurs costs today as a down‐payment for discounted benefits realised a long time into the future. ■
In total, the potential NPV of benefits to be realised is in the order of $5.4 billion per annum in 2010 dollars. This can be considered the cost of NFF currently, or the value of PFF gains possible. Over half these gains (53%) are productivity gains, with a further 21% of the benefits deriving from savings from fewer addictions. Fewer cases of anxiety and depression would save $0.6 billion (11%), while lower rates of criminality and antisocial behaviour would accrue $0.5 billion (10%). A reduction in obesity would save $0.3 billion per annum (5% of the total). ■
The benefits accrue from: 
health benefits (from lower levels obesity, anxiety and depression, and lower rates of smoking, alcohol abuse and illicit drug abuse); 
productivity benefits (from improvements in lifetime earnings due to better secondary and tertiary education outcomes); and 
social benefits (from lower rates of criminality and reduced court and prison system costs). Value of interventions to enhance FF: There are clearly marked social and economic benefits to be gained if cost effective prevention programs can be identified and implemented. This analysis has focused on three interventions selected on the basis that they specifically target FF in a mixture of age groups, they have reach, relevance and sustainability, and they have been previously evaluated and shown to be efficacious in the short term in improving particular aspects of FF. Moreover, these particular FF aspects were ones that, in the main, were able to be measured in our analysis of FF variables in LSAC and ATP, and were, at least in part, found to be significant factors ultimately influencing to improved health, productivity and social outcomes in early adulthood – with lifelong impacts. ■
The Communities for Children program, targeting pre‐school and primary school aged children, is one of the major Australian Government investments in families. The 71 program has been shown to improve outcomes in various FF areas including areas significant in our analysis as impacting lifelong outcomes – namely, hostile parenting, parenting self‐efficacy, parent mental health, quality of the home learning environment, parental relationship conflict, child total emotional and behavioural problems, childhood overweight, receptive vocabulary achievement and verbal ability. The benefit:cost ratio for this program was estimated as 4.8:1, a 377% return on investment. ■
The Positive Parenting Program is one of the best evaluated programs targeted at improving FF and outcomes for younger children. The program has been shown to improve outcomes in FF areas significant in our analysis – namely, parental sense of competency, the dyadic adjustment scale, the SDQ emotional and conduct scales, the Eyberg Child Behaviour Intensity score, parental depression, parental laxness, parental over‐reactivity, and parental verbosity. The benefit:cost ratio for this program was estimated as 13.8:1, a substantial 1,283% return on investment. ■
The Reconnect program targets an older cohort of children and was found to improve outcomes in school bonding and conflictual relationships, with proxied effect sizes estimated for attachment to parents and harsh parenting. No impact was modelled for the program for under‐engagement (in education and employment) and the home learning environment, which may be conservative, since these impacts were found to be direct, rather than occurring through FF variables. The benefit:cost ratio for this program was estimated as 1.8:1, an 81% return on investment. Costs and benefits are summarised in Table 7.32. Table 7.32: Summary of costs and benefits of modelled interventions CfC PPP Reconnect 113.6 19.7 112.1 840/child aged 0‐5 34/child aged 2‐12 3,800/person aged 12‐21 Benefit ($m, lifetime NPV) 541.4 272.4 202.8 Benefit:cost ratio 4.76 13.82 1.81 Program cost ($m)* Unit cost ($) Source: Access Economics calculations. * Costs estimated over 2004‐05 to 2007‐08 except for Reconnect which extends to 2008‐09. Building the evidence base – critical assets and challenges While evidence of the success of family programs is slowly growing (Nation et al, 2003; Manning et al, 2010; Katz, 2009; Aos et al, 2004; Durlak and Wells, 1997; Hawkins et al, 2009; Masse and Barnett, 2002; Schweinhart et al, 1993, Wise et al, 2005; Weissberg et al, 2003) there has historically been little investment in rigorous economic evaluation in the sector. Wise et al (2005) concluded there are specific gaps in evidence of the long term benefits and cost effectiveness of programs. The analysis in this report provides further confirmation that current economic methods for comparing interventions to improve FF provide useful tools to assist in policy choices but that ongoing emphasis needs to continue to supply evidence of individual program efficacy and effectiveness in Australia, and development of appropriate data sets and collection of longitudinal data to feed in to policy making and program development. Moreover, program evaluation needs to have a greater focus on the inputs required for economic analysis. 72 Positive Family Functioning The gap in economic evaluation has meant that many program evaluations do not measure or report concepts required for economic analysis. An example is the Reconnect program for which the evaluation found substantial program related benefits (FaHCSIA, 2003b) but which did not report well on some of the key effect sizes necessary for quantitative economic analysis. The LSAC and ATP data sets are rich and offer many avenues for research. Their complexity can potentially diminish their accessibility. There are important implications for governments in continuing to fund longitudinal data sets so that quality and accessibility endure through the maintenance of specialist and corporate knowledge about these data sets. Those managing the ATP and LSAC were extremely important collaborators for this project. Advice and inputs from the experts on the Reference Group for this project were also critical to the success of the analysis. ‘ ■
Family functioning’ can be difficult to measure and LSAC and ATP variables reflect a mixture of FF and other factors which, at times, can be difficult to interpret. In addition, there is the potential for omitted variable bias where variables do not completely capture the concept being measured or where there are missing variables within the LSAC and ATP. ■
There were a number of obstacles to overcome with the LSAC dataset, to try to consistently capture desired information (such as depression/anxiety, antisocial behaviour or addiction tendencies in babies, toddlers or pre‐schoolers). This largely arises due to the vast developmental differences between newborns and nine year olds, and means that many of the LSAC regressions had to use three different versions of the dependent variable. 20 ■
Also, there were a few unexpected but statistically significant results. Most of these could be explained on reflection, while a small remainder were less intuitive (see Section 3.2.7). ■
There is a four year gap between the oldest LSAC children and the youngest ATP children for whom FF data are available. To transition between the LSAC and the ATP, FF variables remain unchanged over this gap in the modelling. ■
Another difficulty encountered was attempting to map the impact of parenting in childhood on addictive behaviour (use of alcohol, nicotine and other drugs). LSAC has childhood FF data, but no drug use data, while the ATP has drug use data, but no childhood FF data. ■
In the ATP, variables measuring family income, and parental anxiety/depression are not available. Parents’ educational achievement was used as a proxy for the former in this analysis and its significance for child outcomes was demonstrated via the statistical analysis. Findings from the econometric analysis Not surprisingly, many of the family ‘inputs’ incorporated in the analysis were found to be statistically significant explanators of child outcomes with the relationship consistent with that predicted by the literature. 20
(For example, anxiety and depression is proxied by apedsgc in 0 to 1 year olds, babitp in 2 to 3 year olds, and caemot in 4 to 5 year olds). 73 ■
Obesity was explained by key drivers such as previous obesity, parental obesity, lack of child persistence, and parent‐child conflict. ■
Anxiety and depression were dependent on previous emotional problems, difficult temperament, lower SES, harsh discipline, parental anxiety/depression, alienation from parents and lack of child persistence. ■
Smoking in young adulthood (19‐20 years) was determined by previous smoking in adolescence, parental permission to smoke at home and a conflictual parent‐teenager relationship. Alcohol abuse (binge drinking) in young adulthood was dependent on teen bingeing, lack of parental monitoring, father drinking and initiating drinking at an older age (over 15 compared to 14 or younger). Illicit drug use in 23‐24 year olds was dependent on the child’s temperament, lack of parental monitoring, and mother smoking. ■
Predisposition to smoking, alcohol abuse and illicit drug use was established in early years by parental smoking, temperament, harsh and/or inconsistent discipline, poor family cohesion and parental anxiety depression. ■
Productivity was driven by previous learning outcomes, consistent discipline, temperament, socioeconomic status, parent education and, in adolescence, persistence, relationship quality/warmth, parental monitoring and a positive attitude to school. ■
Antisocial behaviour and outcomes were determined by child lack of persistence, previous social/conduct problems and, importantly, were largely influenced by early life FF variables such as poor family cohesion, harsh discipline, parental smoking and low SES, along with parental anxiety/depression and the child’s temperament. There were, however, some notable exceptions. ■
An ATP variable denoting parental ‘warmth’ was inversely related to high school outcomes for children, but can potentially be understood in the context of being task‐
focused, which may come across as less ‘warm’ but (without harshness) may achieve better high school outcomes. ■
None of the ATP FF variables were significant explanators of antisocial behaviour. Areas for further research It would be desirable in the future to bridge the gap between the age of children in the most recent wave of the LSAC and the youngest age of children involved in the ATP for which FF variables are available. A repeat analysis might be conducted in five years time, re‐estimating equations and triangulating findings. In addition, some of the transition variables available for matching LSAC with ATP participants are not perfectly matched in this analysis, and there might be scope to address this in future waves of LSAC, to provide a more seamless transition between the two datasets. Moreover, probabilistic analysis around the realisation of future benefits would be an important further step, but is out of scope here. However, the finding in relation to the PPP suggests the model constructed for the project provides a sound basis for development, since this finding is consistent with other CBA/CEA studies of this program. 74 Positive Family Functioning Due to the complexities of the analysis and issues such as potential two‐way causation for some variables, we caution readers in making overly strong statements about conclusions presented. Rather, the greatest value in this project has been primarily to showcase how a broad, quantitative approach to social policy evaluation can work. With better quality data in the future, there is scope to refine and continue to develop the modelling and elaborate on findings further. We commend the ongoing research focus of FaHCSIA, which is proving so valuable in Australia and globally to bring an evidence basis fully in focus in social policy decision making, even in areas previously deemed unquantifiable or intangible. 75 Positive Family Functioning References Access Economics 2009a, Positive family functioning ‐ scoping study, Report for the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA). ‐
2009b, The economic impact of youth mental illness and the cost effectiveness of early intervention, Report for Orygen Research Centre. ‐
2008, The growing cost of obesity in 2008: three years on, Report for Diabetes Australia. ‐
2007, Smoking and mental illness: costs, Report for SANE Australia. ‐
2006a, Cost of cancer in New South Wales, Report for the Cancer Council of NSW. ‐
2006b, The economic costs of obesity, Report for Diabetes Australia. American Psychiatric Association 2009, Diagnostic and Statistical Manual of Mental Disorders (DSM) IV, http://psych.org/MainMenu/Research/DSMIV.aspx. Aos S, Lieb R, Mayfield J, Miller M, Pennucci A 2004, Benefits and costs of prevention and early intervention programs for youth, Olympia: Washington State Institute for Public Policy. Arria AM, Kuhn V, Caldeira KM, O'Grady KE, Vincent KB, Wish ED 2008, ‘High school drinking mediates the relationship between parental monitoring and college drinking: a longitudinal analysis’, Subst Abuse Treat Prev Policy, 7:3:6. Australian Bureau of Statistics 2010a, Labour price index, Australia, March 2010, ABS Cat No 6345.0. ‐
2010b, Recorded Crime – Offenders 2008‐09, ABS Cat No 4519.0. ‐
2010c, Criminal Courts Australia 2008‐09, ABS Cat No 4513.0. ‐
2009a, Australian Labour Market Statistics, Australia, July, 2009, ABS Cat No 6105.0. ‐
2009b, Employee earnings, benefits and trade union membership, Australia, August 2009, ABS Cat No 6310.0. ‐
2009c, Recorded Crime – Offenders, selected states and territories, 2007‐08, ABS Cat No 4519.0. ‐
2009d, Prisoners in Australia, ABS Cat No 4517.0. ‐
2009e, Life tables – Australia, 2006‐2008, ABS Cat No 3302.0.55.001. ‐
2004, Survey of Disability, Ageing and Carers: Summary of Findings, Australia Cat No 4430.0, September, and associated data cubes. Australian Government 2010, Best Practice Regulation Handbook, Canberra. 77 Australian Government Department of Family and Community Services, 2003, ’I'm looking at the future’, Evaluation Report of Reconnect, Final Report, October. Australian Institute of Health and Welfare (AIHW) 2005, 2004 National drug strategy household survey: detailed findings, AIHW Cat No PHE 66, Drug Statistics Series, No 16. Bahr SJ, Hoffmann JP, Yang X 2005, ’Parental and Peer Influences on the Risk of Adolescent Drug Use’, The Journal of Primary Prevention, 26(6):529‐551. Begg S, Vos T, Barker B, Stevenson C, Stanley L, Lopez AD 2007, The burden of disease and injury in Australia 2003, Canberra, AIHW PHE 82. Berthelsen D, and Walker S (2008) ‘Parents’ involvement in their children’s education’ Family Matters, No 79, Australian Institute of Family Studies. Betts, J; Gullone E, Sabura Allen J, An examination of emotion regulation, temperament, and parenting style as potential predictors of adolescent depression risk status: A correlational study British Journal of Developmental Psychology, Volume 27, Number 2, June 2009 , pp. 473‐485(13) Bierut LJ, Dinwiddie SH, Begleiter H, Crowe RR, Hesselbrock V, Nurnberger JI Jr, Porjesz B, Schuckit MA, Reich T 1998, ‘Familial Transmission of Substance Dependence: Alcohol, Marijuana, Cocaine and Habitual Smoking’, Archives of General Psychiatry, 55:982‐988. Bodenmann G, Cina A, Ledermann T, Sanders R 2008, ‘The efficacy of the Triple P‐Positive Parenting Program in improving parenting and child behaviour: a comparison with two other treatment conditions.’ Behaviour and Research Therapy, 46: 411‐427. Bögelsand SM and Brechman‐Toussaint ML 2006, Family issues in child anxiety: Attachment, family functioning, parental rearing and beliefs Clinical Psychology Review Volume 26, Issue 7, November 2006, Pages 834‐856 Brechting EH 2004, ‘Family Environment and Substance Use in Adolescent Males’, University of Kentucky Electronic Theses, Available (Online) URL: http://hdl.handle.net/10225/458 (Accessed: 19 February, 2010) Brens C, Johnson ME, Freemon M 2004, ‘Childhood Abuse History and Substance Use Among Men and Women Receiving Detoxification Services’, The American Journal of Drug and Alcohol Abuse, 30(4):799 – 821. Brown AM, Whiteside SP 2008, Relations among perceived parental rearing behaviors, attachment style, and worry in anxious children. J Anxiety Disord;22(2):263‐
72. Epub 2007 Feb 23. Bruce AE, Cole DA, Dallaire DH, Jacquez FM, Pineda AQ, LaGrange B 2006, Relations of parenting and negative life events to cognitive diatheses for depression in children. J Abnorm Child Psychol. Jun;34(3):321‐33. Epub 2006 May 17. Bureau of Transport Economics 2000, Road Crash Costs in Australia: Report 102, Bureau of Transport Economics: Canberra. 78 Positive Family Functioning Campbell K, and Crawford D 2001, ‘Family Food Environments as Determinants of Preschool‐
aged Children’s Eating Behaviours: Implications for Obesity Prevention Policy, A Review’, Australian Journal of Nutrition and Dietetics, 58(1):19–26. Campbell K, Crawford D, Jackson M, Cashel K, Worsley A, Gibbons K, Birch LL 2002, ‘Family Food Environments of 5‐6‐year‐old‐children: Does socioeconomic status make a difference?’, Asia Pacific Journal of Clinical Nutrition, 11:S553–S561. Catalano R, Fleming C, Haggerty K, Abbott R 2007, The Relationships between the Family Environment during Adolescence and Self‐reported Crime in Emerging Adulthood: Results from the Raising Healthy Children, Presented at the American Society of Criminology, Atlanta Marriott Marquis, Atlanta, Georgia, Nov 14. Centrelink 2009, A guide to Australian Government payments, Department of Families, Housing, Community Services and Indigenous Affairs and Department of Education, Employment and Workplace Relations, Canberra. Clark DB, Kirisci L, Mezzich A, Chung T 2008, ‘Parental supervision and alcohol use in adolescence: developmentally specific interactions’ J Dev Behav Pediatr, 29(4):285‐92. Cohen DA, and Rice J 1997, ‘Parenting styles, adolescent substance use, and academic achievement’ J Drug Educ. 27(2):199‐211. Cohen DA, Richardson J, LaBree L 1994, ‘Parenting behaviours and the onset of smoking and alcohol use: a longitudinal study’, Pediatrics,. September (3):368‐75. Collins DJ, Lapsley HM 2008, The costs of tobacco, alcohol and illicit drug abuse to Australian society in 2004/05, Department of Health and Ageing: Canberra. Currie J, Tekin E 2006, Does child abuse cause crime? Working Paper No. 12171, National Bureau of Economic Research, USA. Danielzik S, Czerwinski‐Mast M, Langnase K, Dilba B, Muller MJ 2004, ‘Parental Overweight, Socioeconomic Status and High Birth Weight are the Major Determinants of Overweight and Obesity in 5‐7 y‐old Children: Baseline Data of the Kiel Obesity Prevention Study (KOPS)’, International Journal of Obesity, 28:1494–1502. De Graaf I, Speetjens P, Smit F, de Wolf M, Tavecchio L 2008, ‘Effectiveness of the Triple P Positive Parenting Program on Parenting: A Meta‐ Analysis’. Family Relations, 57: 553‐
566 Department of Finance and Deregulation (DOFD) 2009, Best Practice Regulation Guidance Note: Value of statistical life, http://www.finance.gov.au/obpr/docs/ValuingStatisticalLife.pdf. Dick DM, Pagan JL, Viken R, Purcell S, Kaprio J, Pulkkinen L, Rose RJ 2007,‘Changing environmental influences on substance use across development’, Twin Res Hum Genet. 10(2):315‐26. Dornbusch SM, Ritter PL, Leiderman PH, Roberts DF, Fraleigh MJ 1987, ‘The relation of parenting style to adolescent school performance’ Child Development, 58: 1244‐1257. 79 Drevets WC, Price JL, Simpson JR Jr, et al 1997, ‘Subgenual prefrontal cortex abnormalities in mood disorders’ Nature 386(6627):824‐827. Dube SR, Felitti VJ, Dong M, Chapman DP, Giles WH 2003, ‘Childhood Abuse, Neglect, and Household Dysfunction and the Risk of Illicit Drug Use: The Adverse Childhood Experiences Study’, American Academy of Paediatrics, 111(3):564 – 572. Durlak JA, Wells AM 1997, ’Primary prevention mental health programs for children and adolescents: a meta‐analytical review‘, American Journal of Community Psychology, 25(2):115‐152. Emmelkamp PM, Heeres H 1988, ‘Drug addiction and parental rearing style: a controlled study’. The International Journal of Addictions, 23(2):207‐16. Evans GW, Kim P 2007, ‘Childhood poverty and health: cumulative risk exposure and stress dysregulation’ Psychol Sci. 18(11):953‐957. FaHCSIA 2009, Stronger families in Australia study: the impact of Communities for Children, Occasional Paper No 25. ‐
2003a, Report of the Reconnect Longitudinal Study: Building Community Capacity for Early Intervention, Report prepared by RPR Consulting, April. ‐
2003b, Longitudinal survey of ReConnect clients, Statistical report of the Longitudinal Survey of Reconnect Clients, Prepared by RPR Consulting, September. ‐
2003c, ‘I’m looking at the future’ Evaluation Report of Reconnect, Final report, October. Farrington DP 1995, ‘The Twelfth Jack Tizard Memorial Lecture, The Development of Offending and Antisocial Behaviour from Childhood: Key Findings from the Cambridge Study in Delinquent Development/Child Psychol.’ Psychiat. 360(6):929‐064. Forbes M, Barker A, Turner S 2010, The effects of education and health on wages and productivity, Productivity Commission Staff Working Paper, Melbourne. Foster E, Prinz R, Sanders M, Shapiro C 2007, ‘The costs of a public health infrastructure for delivering parenting and family support’ Children and Youth Services Review 30:493‐501. Felitti VJ, Anda RF, Nordenberg D et al 1998, ‘Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: the adverse childhood experiences (ACE) study’ Am J Prev Med, 14(4):245‐258. Fergusson DM, Horwood LJ 2002, ‘Male and female offending trajectories’, Development and Psychopathology, 14:159‐177. Francis DD, Meaney MJ (1999) ‘Maternal care and the development of stress responses’ Curr Opin Neurobiol, 9(1):128‐134. Gable S, and Lutz S 2000, ‘Household, Parent and Child Contributions to Childhood Obesity’, Family Relations, 49(3):293–300. 80 Positive Family Functioning Garasky S, Stewart SD, Gundersen C, Lohman BJ, Eisenmann JC 2009, ‘Family Stressors and Child Obesity’, Social Science Research, (38):755–766. Gibson LY, Byrne SM, Davis EA, Blair E, Jacob P, Zubrick SR 2007, ‘The Role of Family and Maternal Factors in Childhood Obesity’, Medical Journal of Australia, 186(11):591‐595. Hawkins JD, Oesterle S, Brown EC, Arthur MW, Abbott RD, Fagan AA, Catalano RF 2009, ’Results of a type 2 translational research trial to prevent adolescent drug use and delinquency: A test of Communities that Care‘, Archives of Pediatrics and Adolescent Medicine, 163 (9):789‐798. Heider D, Bernert S, Matschinger H, Haro JM, Alonso J, Angermeyer MC; ESEMeD/MHEDEA 2000 Investigators Parental bonding and suicidality in adulthood..Aust N Z J Psychiatry. 2007 Jan;41(1):66‐73. Hubbard RE, Searle SD, Mitnitski A, Rockwood K 2009, ‘Effect of smoking on the accumulation of deficits, frailty and survival in older adults: A secondary analysis from the Canadian study of health and aging’, The Journal of Nutrition, Health and Aging, 13(5):468‐472. Isgor C, Kabbaj M, Akil H, Watson SJ (2004) ‘Delayed effects of chronic variable stress during peripubertaljuvenile period on hippocampal morphology and on cognitive and stress axis functions in rats’ Hippocampus, 14(5):636‐648. Karoly LA, Greenwood PW, Everingham SS, Hoube J, Kilburn MR, Rydell CP, Sanders M, Chiesa J 2007, Investing in Our Children: What We Know and Don't Know About the Costs and Benefits of Early Childhood Interventions, RAND Monograph Report last accessed 25 January at http://www.rand.org/pubs/monograph_reports/MR898/index.html Katz DL 2009, ’School‐based interventions for health promotion and weight control: Not just waiting on the world to change‘, Annual Review of Public Health, 30:253‐272. Kaufman D, Banerji MA, Shorman I et al 2007, ‘Early life stress and the development of obesity and insulin resistance in juvenile bonnet macaques’ Diabetes, 56(5):1382‐1386. Kilpatrick DG, Acierno R, Saunders B, Resnick HS, Best CL 2000, ‘Risk factors for adolescent substance abuse and dependence: data From a national sample’, Journal of Consulting and Clinical Psychology, 68(1):19–30. Kishiyama MM, Boyce WT, Jimenez AM, Perry LM, Knight RT 2009, ‘Socioeconomic disparities affect prefrontal function in children’ J Cogn Neurosci. 21(6):1106‐15. Kratt P, Reynolds K, Shewchuk R 2000, The Role of Availability as a Moderator of Family Fruit and Vegetable Consumption, Health Education Behaviour, 27:471‐484. La Plagne P, Glover M, Shomos A 2007, The effects of health and education on labour force participation’, Productivity Commission Staff Working Paper, Melbourne. Latendresse SJ, Rose RJ, Viken RJ, Pulkkinen L, Kaprio J, Dick DM 2008, ‘Parenting mechanisms in links between parents' and adolescents' alcohol use behaviors’ Alcohol Clin Exp Res, 32(2):322‐30. 81 Lattimore R 1997, Research and development fiscal incentives in Australia: impacts and policy lessons, Paper presented to the OECD Conference on Policy Evaluation in Innovation, Paris, 26 27 June, 81:574‐577. Letcher P, Smart D, Sanson A, Toumbourou JW, 2009, Trajectories of internalising behaviour, Social Development, 18, 3, 2009 Lissau I, and Sorensen TIA 1994, ‘Parental neglect during childhood and increased risk of obesity in young adulthood’ The Lancet, 343:324–27. Lovibond PF, Lovibond SH 1995, ‘The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories’ Behaviour Research and Therapy, 33:335‐343. Manning M, Homel R, Smith C 2010, ‘A meta‐analysis of the effects of early developmental prevention programs in at‐risk populations on non‐health outcomes in adolescence’, Children and Youth Services Review, 32:506‐519. Masse LN, Barnett WS 2002, A Benefit Cost Analysis of the Abecedarian Early Childhood Intervention, National Institute for Early Education Research: New Brunswick, http://nieer.org/resources/research/AbecedarianStudy.pdf. McLoyd VC 1998, ‘Socioeconomic disadvantage and child development’ Am Psychol, 53(2):185‐
204. Marmot M (chair) 2010, Fair society, healthy lives, Report of the Strategic Review of Health Inequalities in England post‐2010, UK Government. Mihalopoulos C, Sanders M, Turner K, Murphy‐Brennan M, Carter R 2007 ‘Does the Triple‐P Positive Parenting Program represent value for money?’ Australian and New Zealand Journal of Psychiatry, 41: 239‐246. Miller P, Mulvey C, Martin N 2001, ‘Genetic and environmental contributions to educational attainment in Australia’, Economics of Education Review, 20(3):211‐224. Moens E, Braet C, Bosmans G, Rosseel Y 2009, ‘Unfavourable family characteristics and their associations with childhood obesity: a cross‐sectional study’, European Eating Disorders Review, 17:315–323. Moens E, Braet C, Soetens B 2007, ‘Observations of family functioning at mealtime: a comparison between families of children with and without overweight’, Journal of Pediatric Psychology, 32(1):52–63. Nation M, Crusto C, Wandersman A, Kumpfer KL, Seybolt D, Morrissey‐Kane E, Davino K 2003, ‘What works in prevention: Principles of effective prevention programs’, American Psychologist, 58(6/7): 449‐456. Pelham WE Jr, Lang AR 1999, ‘Can your children drive you to drink? Stress and parenting in adults interacting with children with ADHD’. Alcohol Research and Health. 23(4):292‐8. 82 Positive Family Functioning Pires P and Jenkins JM 2007, ‘A growth curve analysis of the joint influences of parenting affect, child characteristics and deviant peers on adolescent illicit drug use’, Journal of Youth Adolescence, 36:169‐183. Plomin R, Fulker DW, Corley R, DeFries JC 1997, Nature, nurture, and cognitive development from 1 to 16 Years: a parent‐offspring adoption study, Association for Psychological Science. Prior M, Sanson A, Smart D, Oberklaid F 2000, Pathways from infancy to adolescence: Australian Temperament Project 1983‐2000, Australian Institute of Family Studies Research Report No 4. Productivity Commission 2003, Evaluation of the Pharmaceutical Industry Investment Program, Research Report, AusInfo, Canberra. Rapee RMPotential role of childrearing practices in the development of anxiety and depression. Clin Psychol Rev. 1997;17(1):47‐67. Radziszewska B, Richardson JL, Dent CW, Flay BR 1996, ‘Parenting style and adolescent depressive symptoms, smoking, and academic achievement: Ethnic, gender, and SES differences’ Journal of Behavioural Medicine, 19(3):289‐305. Rollings K 2008, Counting the costs of crime in Australia: a 2005 update, Australian Institute of Criminology, Research and Public Policy Series No 91, Canberra. Ruhm CJ 2000, ‘Parental leave and child health’. Journal of Health Economics, 19(6):931‐60. Sanders M, Ralph A, Sofrnoff K, Gardiner P, Thompson R, Dwyer S, Bidwell K 2008, ‘Every family: a population approach to emotional problems in children making the transition to school’ J Primary Prevent, 29(3):197‐222. Savage JS, Fisher JO, Birch LL 2007, ‘Parental Influence on Eating Behaviour’, Journal of Law, Medicine and Ethics, 35(1):22–34. Schweinhart LJ, Barnes HV, Weikhart DP 1993, Significant Benefits, The High/Scope Perry Pre‐
School Study through Age 27, Ypsilanti: HighScope Press. Scragg R, Laugesen M 2007, ‘Influence of smoking by family and best friend on adolescent tobacco smoking, results from the 2002 New Zealand national survey of Year 10 students’, Aust N Z J Public Health, 31:217‐223. Shonkoff P, Boyce T, McEwen B (2010) ‘Neuroscience, molecular biology and the childhood roots of health disparities: building a new framework for health promotion and disease prevention’ JAMA 301(21):2252‐2259. Smith CA, Ireland TO, Thornberry TP 2005, ‘Adolescent maltreatment and its impact on young adult antisocial behaviour’ Child Abuse and Neglect, 29:1099–1119. Steering Committee for the Review of Government Service Provision (SCRGSP) 2010, Report on Government Services 2010, Productivity Commission, Canberra. 83 Steinberg L, Lamborn SD, Dornbusch SM, Darling N 1992, ‘Impact of parenting practices on adolescent achievement: authoritative parenting, school involvement, and encouragement to succeed’, Child Dev. 63(5):1266‐81. Taylor P, Moore P, Pezzullo L, Tucci J, Goddard C, De Bortoli L 2008, The cost of child abuse in Australia, Access Economics, Australian Childhood Foundation and Child Abuse Prevention Research Australia: Melbourne. van Gastel W, Legerstee JS, Ferdinand RF 2009 The role of perceived parenting in familial aggregation of anxiety disorders in children. J Anxiety Disord. Jan;23(1):46‐
53. Epub 2008 Mar 26. Vassallo S, Smart D, Sanson A 2002, Patterns and precursors of adolescent antisocial behaviour, The first report of a collaborative partnership between the Australian Institute of Family Studies and Crime Prevention Victoria, Department of Justice, December. Wardle J, Guthrie C, Sanderson S, Birch L, Plomin R 2001, ‘Food and Activity Preferences in Children of Lean and Obese Parents’, International Journal of Obesity, 25:971–977. Weatherburn D 2001, ‘What causes crime?’ Contemporary issues in crime and justice No 54, NSW Bureau of Crime Statistics and Research, February. Webb JA, Bray JH, Getz JG, Adams G 2002, ‘Gender, perceived parental monitoring, and behavioral adjustment: influences on adolescent alcohol use’. Am J Orthopsychiatry July 72(3):392‐400. Weissberg RP, Kumpfer KL, Seligman MEP 2003, ‘Prevention that works for children and youth’, American Psychologist, 58(6/7): 425‐432. Wise S 2003, An overview of the development in diverse families study, Australian Institute of Family Studies Research Paper No 30, January. Wise S, da Silva L, Webster E, Sanson A 2005, The Efficacy of Early Childhood Interventions, Report prepared for the Australian Government Department of Family and Community Services, Australian Institute of Family Studies. World Health Organization 2010, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, available at http://apps.who.int/classifications/apps/icd/icd10online/ accessed 22 September 2010. Zubrick S, Ward K, Silburn S, Lawrence D, Williams A, Blair E, Robertson D, Sanders M 2005, ‘Prevention of child behaviour problems through universal implementation of a group behavioural family intervention’, Prevention Science, 6(4):287‐304. 84 Positive Family Functioning Appendix A: LSAC children and future addictive behaviours As indicated in the scoping study, there are numerous studies showing that the impact of family functioning variable x is that children who experience it will become adults with outcome y. For example, parents who have substance abuse problems (tobacco, alcohol, drugs) are likely to see their children grow up to have similar abuse problems (e.g. Latendresse et al, 2008). However, there are some unusual aspects of the current study that are not directly covered by the literature. For example, the LSAC has parents who drink and smoke, but only little children, who do not; the ATP has teenagers who may drink and smoke, but no record of whether their parents did so when they were little. Our approach was thus to identify some factor that is: ■
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■
■
a consequence of poor family functioning; contributes to addictive behaviour in adulthood; has symptoms that are recorded in young children (LSAC); and those symptoms are also recorded in teens / young adults (ATP). The factor identified was stress (allostatic load). It is caused by (among other factors) poor parenting. Stress causes changes in brain structure and functioning which, in the short term, can make children more prone to anti‐social behaviour (reported in both the LSAC and the ATP). In the long‐term, these changes make adults more prone to engage in anti‐social behaviour and/or become addicted. Because there is no one piece of literature that makes all these connections, a potted summary is laid out below. Recent work by paediatricians and neuroscientists (Shonkoff et al, 2010) shows that childhood stress – such as that caused by poor FF – can have permanent effects that cause deleterious outcomes in adulthood. And, of relevance to this study, these childhood stresses also cause negative social and emotional outcomes, of the types that are captured by the LSAC and the ATP. In the brain, under such high ‘allostatic loads’ the amygdala grows more neurons, causing the brain to generate more fear and anxiety. Simultaneously, the prefrontal cortex loses neurons, reducing the brain’s ability to plan and prioritise – and also to keep the amygdala’s negative emotions in check. Studies of animals show that such early, stress‐related changes in brain circuitry can persist into adult life and alter emotional states, decision‐making capacities and processes which in humans contribute to substance abuse, aggression, obesity and stress‐related mental disorders (Isgor et al, 2004 and Kaufman at al, 2007). ■
The extent to which such temperaments are genetic or environmental is debated. Animal studies have shown that higher levels of environmental stress can develop into a generational susceptibility to stress through epigenetic changes in DNA methylation (Francis and Meaney, 1999). 85 No longitudinal studies have been conducted on the influence of high stress in humans. However, retrospective studies have shown strong associations between traumatic childhoods and adverse adult outcomes including alcoholism, drug abuse, obesity, smoking, depression and other mental illnesses, (Felitti et al, 1998). Further, adults with depression also exhibit smaller prefrontal cortexes (Drevets, 1997). Recent work shows that there are systematic differences in brain circuitries between children of high and low SES (Kishiyama, 2009). In particular, low SES children have smaller prefrontal cortexes, which appear to be the brain’s way of protecting itself from chronic stress, but may affect their intellectual development — low SES children who have high cognitive (Q) scores at 22 months of age, by 78 months fall below the Q scores of high SES children who started with low 22‐month scores (Chart A.1). Also, adults who were low SES children also have greater amygdale activity than others. Chart A.1: Differences in cognitive development by socio‐economic status Source: Marmot (2010). It is also well documented that children with low SES are i) exposed to high degrees of conflictive / punitive parental behaviour and ii) have more prevalent mental disorders and substance abuse (McLoyd, 1998). ■
It well known that low SES groups have higher disease‐specific and all‐cause morbidity and mortality rates that cannot be explained just by material deprivation, illiteracy or lower access to health services. Experts are beginning to consider that the long‐term effects of childhood stress account for much of this hitherto unexplained gap. ■
If so, an important implication is that interventions to reduce childhood stress may be more effective than trying to target risky health behaviour later in life. For example, in 86 Positive Family Functioning the UK only 15% of men with professional qualifications smoke, but over 40% of unskilled workers do – despite quit campaigns and punitive taxation. Childhood stress appears to also have an impact on adult criminality. This stands to reason as childhood stress increases both anger and aggressiveness, while reducing reasoning and control mechanisms. Empirically, there is a strong link between childhood stressful events and committing crime as an adult. Currie and Tekin (2006) at the US National Bureau of Economic Research used a large national sample to examine the effects of childhood maltreatment (including neglect as well as abuse) on adult crime. They found that: ■
maltreatment approximately doubled the probability of engaging in many types of crime (far greater than other effects such as unemployment, low wages, or access to guns); ■
low SES children are both more likely to be mistreated and suffer more damaging effects; ■
sexual abuse (arguably the most stressful form of abuse) appears to have the largest negative effects, and ■
the probability of engaging in crime increases with the experience of multiple forms of maltreatment. Finally, and of direct relevance for this study, high levels of childhood stress manifest in psychological and emotional dysregulation in chronically stressed children (Evans and Kim, 2007). The variable chosen to capture social and emotional difficulties is the Strengths and Difficulties Questionnaire (SDQ) Total Score, which is a composite measure of social and emotional development. A higher SDQ score represents more difficulties. AIFS advised that this LSAC measure is compatible with ATP measures. 87 Appendix B: Literature review sources, ATP Table B.1: Antisocial behaviour Variable Evidence FF variables Relationship with parents Vassallo et al (2002) Attachment to parents Weatherburn (2001), Catalano et al (2007), Vassallo et al (2002) Family cohesion Vassallo et al (2002) Relationship quality/warmth Harsh discipline Parental monitoring/supervision Inconsistent discipline Farrington (1995), Prior et al (2000), Vassallo et al (2002) Control variables Marital relationship History of parental separation and its effect Vassallo et al (2002) Fergusson and Horwood (2002) Parental smoking Parental alcohol Fergusson and Horwood (2002), Vassallo et al (2002) Temperament AIFS advised (from previous ATP analysis) Social and emotional difficulties Shonkoff et al (2010) and others, see discussion above Table B.2: Anxiety and depression Variable Evidence FF variables Attachment to parents Prior et al (2000) Relationship quality/warmth Inductive reasoning Harsh discipline Unpublished work by Primose Letcher at University of Melbourne and AIFS Heider et al (2007), Betts et al (2009), Rapee (1997), Brown and Whiteside (2008), Bruce et al (2006) Enmeshment Unpublished work by Primose Letcher at University of Melbourne and AIFS Betts at al (2009), Rapee (1997), Bogels and Bergman‐Toussaint (2006) Experience of child abuse/neglect (retrospective) Evidence cited in Taylor et al (2008) Control variables Marital relationship Bogels and Bergman‐Toussaint (2006) Stressful life event Bruce et al (2006) Temperament Betts et al (2009), Bogels and Bergman‐Toussaint (2006), Prior et al (2000) * Parental anxiety/depression Rapee (1997), van Gastel et al (2009) Gender Letcher et al (2009) * Not available in the ATP 88 Positive Family Functioning Table B.3: Smoking Variables Evidence Parental management of teen substance use Scragg and Laugesen (2007) Inductive reasoning Harsh discipline Attachment to parents Parental monitoring/supervision Negative conflictual relationship Inconsistent discipline Dick et al (2007) Cohen et al (1994) Control variables Parental smoking Latendresse et al (2008) Social and emotional difficulties Shonkoff et al (2010) and others, see discussion above Temperament Clark et al (2008) Table B.4: Alcohol Variables Evidence FF Parental management of teen drinking Latendresse et al (2008) Inductive reasoning Attachment to parents Harsh discipline Parental monitoring/supervision Inconsistent discipline Negative conflictual relationship Latendresse et al (2008), Dick et al (2007), Arria et al (2008), Webb et al (2002), Cohen et al (1994) Control variables Parental use of alcohol Latendresse et al (2008) Social and emotional difficulties Shonkoff et al (2010) and others, see discussion above Temperament Clark et al (2008) Table B.5: Illicit drug use Variables Evidence Family functioning Experience of child abuse/neglect (retrospective) Dube et al (2003), Brens et al (2004), Kilpatrick et al, (2000), Smith et al, (2005), Pires and Jenkins, (2007) Attachment to parents Parent adolescent conflict Brechting (2004) Parental monitoring/supervision Bahr et al (2005) Marital relationship (conflict — proxy for witnessing violence) Control Dube et al (2003), Kilpatrick et al, (2000) 89 Variables Evidence Mother/Father smoking and drinking habits Dube et al (2003), Brechting (2004), Kilpatrick et al (2000), Bierut et al (1998) Social and emotional difficulties Shonkoff et al (2010) and others, see discussion above Temperament AIFS advised Table B.6: Productivity Productivity variables Family functioning variables Harsh discipline Parental monitoring/supervision Inconsistent discipline Attachment to parents Negative conflictual relationship Dornbusch et al (1987), Steinberg et al (1992), Cohen and Rice (1997) Radziszewska et al (1996), Berthelsen and Walker (2008) Parental involvement in education Control variables Academic and social progress at school School bonding Parental education level Academic competence and reading Plomin et al (1997) and Miller et al (2001) Temperament AIFS advised Table B.7: Overweight/obesity Variables Evidence FF variables Attachment to parents Negative conflictual relationship Inconsistent discipline Harsh discipline Parental monitoring/supervision Inductive reasoning Teenager eating behaviours Moens et al (2009), Savage et al (2007), Campbell and Crawford (2001); Campbell et al (2002), Gabel and Lutz (2000), Kratt et al (2000), Lissau and Sorensen (1994); Moens et al (2009) Control variables Family status (single parent household etc) Gabel and Lutz, 2000; Gibson et al. 2007 Socioeconomic status Garasky et al, (2009), Campbell et al, (2002), Danielzik et al, (2004), Gabel and Lutz, (2000) 90 Positive Family Functioning Appendix C: LSAC regression outcomes Results in bold are significant. Results in bold red are significant, but have unexpected signs. Several variables, including parental depression are reverse coded in the LSAC database. Given the experimental nature of this exercise, significance was chosen to equal 0.10 in order to cast the net relatively broadly for possible substantiative FF effects. In the tables in this appendix, p values of ‘‐‘ represent values less than 0.001 (which is how Stata reports the output). As K1 has no antecedents, lagged dependent variables cannot be derived, thus B3 (for which prior observations are available) was chosen instead to represent this 4‐5 year age group. Temperament does not have a clearly expected sign and is not coloured red. Where temperament is significant, for normally coded dependent variables, a negative sign indicates easy temperament is operative, and a positive sign indicates difficult temperament. ■
It could be argued that an easy temperament should lead to better outcomes. But conversely, if difficult temperament results from perfectionism, for example, that child might do better academically than a child with an easy temperament but less attention to detail. Similarly, a fussy eater (difficult temperament) may be less prone to obesity than an easy‐going omnivore. Table C.1: Obesity B1 (ahs23c2) Variable
Harsh discipline
Relationship quality / warmth
Parental efficacy
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Parent BMI
Needs extra medical care
Constant
Coef.
aahost
0.08
aawarm ‐ 0.17
apa01a
0.03
ase06i
‐ 0.00
aak6
0.05
zf02m1 ‐ 0.72
afn05
0.08
acomboed 0.00
zf02m2 ‐ 0.03
aabmi
0.02
ahs14d
0.05
_cons
9.96
Std. Err
0.03
0.08
0.04
0.06
0.06
0.06
0.00
0.03
0.32
0.01
0.09
0.83
t
2.53
‐ 2.02
0.89
‐ 0.02
0.84
‐ 11.64
1.88
0.06
‐ 0.10
3.10
0.58
11.98
P>|t|
0.01
0.04
0.37
0.98
0.40
‐
0.06
0.95
0.92
0.00
0.56
‐
[95% Conf Intervals]
0.02 0.13
‐ 0.33 ‐ 0.01
‐ 0.04 0.10
‐ 0.13 0.12
‐ 0.07 0.16
‐ 0.84 ‐ 0.60
‐ 0.00 0.17
‐ 0.06 0.06
‐ 0.66 0.60
0.01 0.03
‐ 0.13 0.24
8.33 11.59 R2=0.08, F = 14.44, No. observations = 2,155. 91 Table C.2: Obesity B2 (bcbmi) Variable
Coef.
Prev dep var
ahs23c2 0.32
Harsh discipline
bahost
0.00
Parental monitoring / supervision bpa08a1 0.00
Relationship quality / warmth
bawarm 0.12
Inductive reasoning
baireas ‐ 0.03
Parental efficacy
bpa01a
0.03
Family cohesion
bre06a ‐ 0.02
fatty foods
bhfat
‐ 0.07
Sugary Drinks
bhsdrnk ‐ 0.03
Exercise
bhb14c4 0.08
E‐tainment
betain
‐ 0.00
Temperament
bse06a ‐ 0.08
Parental anxiety & depression
bak6
0.01
Gender
zf02m1 ‐ 0.05
Socioeconomic status
bhinc
‐ 0.05
Parental education level
bcomboe ‐ 0.08
Parent gender
zf02m2 0.59
Parent BMI
babmi
0.06
Needs extra medical care
bhs14d ‐ 0.01
Constant
_cons
11.25
R2=0.15, F = 13.49, No. observations = 2,156. Std. Err
0.03
0.03
0.07
0.09
0.06
0.04
0.05
0.04
0.03
0.05
0.00
0.06
0.07
0.07
0.00
0.03
0.24
0.01
0.09
0.93
t
12.36
0.05
‐
1.25
‐ 0.48
0.58
‐ 0.50
‐ 1.69
‐ 0.97
1.64
‐ 0.64
‐ 1.34
0.11
‐ 0.66
‐ 1.57
‐ 2.33
2.45
7.20
‐ 0.14
12.13
P>|t|
‐
0.96
1.00
0.21
0.63
0.56
0.62
0.09
0.33
0.10
0.52
0.18
0.92
0.51
0.12
0.02
0.01
‐
0.89
‐
[95% Conf Intervals]
0.27 0.37
‐ 0.06 0.06
‐ 0.14 0.14
‐ 0.07 0.30
‐ 0.15 0.09
‐ 0.06 0.11
‐ 0.11 0.07
‐ 0.15 0.01
‐ 0.09 0.03
‐ 0.02 0.17
‐ 0.01 0.01
‐ 0.21 0.04
‐ 0.14 0.15
‐ 0.19 0.09
‐ 0.11 0.01
‐ 0.14 ‐ 0.01
0.12 1.07
0.04 0.07
‐ 0.18 0.16
9.43 13.07 Table C.3: Obesity B3 (ccbmi) Variable
Prev dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Parental self efficacy
Family cohesion
fatty foods
Sugary Drinks
Exercise
E‐tainment
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Parent BMI
Needs extra medical care
Constant
R2=0.45, F = 50.58 No. observations = 2,955. 92 Coef.
bcbmi
0.68
cahostb 0.02
cpa08a1 0.00
cawarm ‐ 0.02
caireas
0.05
cacons ‐ 0.02
cpa01a
0.08
cre06a
0.02
chfat
‐ 0.02
chsdrnkb 0.01
chb14c4 ‐ 0.05
cetain
‐ 0.00
cse06a
0.01
cak6
0.06
zf02m1 ‐ 0.01
chinc
0.01
ccomboed‐ 0.01
zf02m2 0.22
cabmi
0.05
chs14d ‐ 0.13
_cons
3.36
Std. Err
0.02
0.02
0.02
0.07
0.05
0.05
0.03
0.04
0.03
0.02
0.04
0.00
0.05
0.06
0.05
0.00
0.03
0.20
0.01
0.10
0.84
t
28.13
1.00
0.20
‐ 0.25
1.02
‐ 0.46
2.36
0.61
‐ 0.60
0.47
‐ 1.30
‐ 1.86
0.18
1.14
‐ 0.19
0.56
‐ 0.44
1.08
8.06
‐ 1.38
4.03
P>|t|
‐
0.32
0.84
0.80
0.31
0.64
0.02
0.54
0.55
0.64
0.19
0.06
0.86
0.26
0.85
0.58
0.66
0.28
‐
0.17
‐
[95% Conf Intervals]
0.63
‐ 0.02
‐ 0.04
‐ 0.15
‐ 0.04
‐ 0.12
0.01
‐ 0.05
‐ 0.07
‐ 0.03
‐ 0.13
‐ 0.01
‐ 0.09
‐ 0.05
‐ 0.12
‐ 0.02
‐ 0.07
‐ 0.18
0.03
‐ 0.32
1.73
0.73
0.07
0.05
0.11
0.14
0.07
0.15
0.09
0.03
0.05
0.03
0.00
0.10
0.17
0.10
0.04
0.05
0.62
0.06
0.06
5.00 Positive Family Functioning Table C.4: Obesity K2 (dcbmi) Var
Prev dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Parental self efficacy
Family cohesion
fatty foods
Sugary Drinks
Exercise
E‐tainment
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Parent BMI
Needs extra medical care
Constant
Coef.
ccbmi
1.01
dahostc ‐ 0.03
dpa08a1 0.09
dawarm ‐ 0.01
daireas 0.10
dacons ‐ 0.10
dpa01a 0.07
dre06a
0.01
dhfat
0.03
dhsdrnk 0.09
dhb14c4 0.05
detain
‐ 0.00
dse06a
0.05
dak6
‐ 0.06
zf02m1 0.09
dhinc
‐ 0.00
dcomboed 0.04
zf02m2 0.14
dabmi
0.03
dhs14d
0.08
cons
‐ 1.85
Std. Err
0.03
0.02
0.06
0.07
0.05
0.05
0.04
0.04
0.03
0.03
0.04
0.00
0.05
0.05
0.05
0.00
0.03
0.17
0.01
0.10
0.74
t
31.56
‐ 1.25
1.52
‐ 0.10
1.80
‐ 2.19
1.90
0.35
1.06
3.31
1.47
‐ 1.23
0.99
‐ 1.23
1.69
‐ 0.11
1.34
0.83
5.15
0.84
‐ 2.50
P>|t|
‐
0.21
0.13
0.92
0.07
0.03
0.06
0.73
0.29
0.00
0.14
0.22
0.32
0.22
0.09
0.92
0.18
0.41
‐
0.40
0.01
R2=0.67, F = 98.73 No. observations = 2,701. [95% Conf Intervals]
0.95 1.07
‐ 0.08 0.02
‐ 0.02 0.19
‐ 0.14 0.13
‐ 0.01 0.20
‐ 0.19 ‐ 0.01
‐ 0.00 0.15
‐ 0.07 0.10
‐ 0.03 0.09
0.03 0.14
‐ 0.02 0.13
‐ 0.01 0.00
‐ 0.05 0.15
‐ 0.17 0.04
‐ 0.01 0.19
‐ 0.05 0.04
‐ 0.02 0.10
‐ 0.19 0.47
0.02 0.05
‐ 0.11 0.27
‐ 3.30 ‐ 0.40 Table C.5: Obesity K3 (ecbmi) Var
Prev dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Parental self efficacy
Family cohesion
fatty foods
Sugary Drinks
Exercise
E‐tainment
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Parent BMI
Needs extra medical care
Constant
Coef.
dcbmi
1.14
eahostc ‐ 0.01
epa08a1 0.03
eawarm 0.02
eaireas ‐ 0.03
eacons ‐ 0.06
epa01a ‐ 0.02
ere06a ‐ 0.01
ehfat
0.03
ehsdrnkb 0.02
ehb14c4 ‐ 0.09
eetain
‐ 0.00
ese06a
0.05
eak6
0.08
zf02m1 0.10
ehinc
‐ 0.02
ecomboed 0.04
zf02m2 0.28
eabmi
0.03
ehs14d
0.11
_cons
‐ 2.86
R2=0.77, F = 326.88 No. observations = 3,018. Std. Err
0.02
0.02
0.03
0.06
0.04
0.05
0.03
0.03
0.03
0.02
0.04
0.00
0.05
0.05
0.05
0.00
0.03
0.14
0.01
0.07
0.68
t
72.98
‐ 0.55
0.91
0.37
‐ 0.66
‐ 1.22
‐ 0.46
‐ 0.23
1.18
1.00
‐ 2.37
‐ 0.99
0.92
1.40
1.79
‐ 0.97
1.55
1.97
5.83
1.64
‐ 4.20
P>|t|
‐
0.58
0.37
0.71
0.51
0.22
0.64
0.82
0.24
0.32
0.02
0.32
0.36
0.16
0.07
0.33
0.12
0.05
‐
0.10
‐
[95% Conf Intervals]
1.11 1.17
‐ 0.06 0.03
‐ 0.03 0.08
‐ 0.09 0.13
‐ 0.11 0.06
‐ 0.15 0.03
‐ 0.08 0.05
‐ 0.08 0.06
‐ 0.02 0.08
‐ 0.02 0.07
‐ 0.16 ‐ 0.02
‐ 0.01 0.00
‐ 0.05 0.14
‐ 0.03 0.18
‐ 0.01 0.20
‐ 0.06 0.02
‐ 0.01 0.09
0.00 0.56
0.02 0.05
‐ 0.02 0.25
‐ 4.19 ‐ 1.52 93 Table C.6: Productivity B1 (awlrnoi) Category
Var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Parental anxiety & depression
Gender
Age
Socioeconomic status
Parental education level
Parent gender
Constant
Coef.
aahost
0.22
aawarm 5.66
apa01a 0.52
aak6
‐ 0.35
zf02m1 0.66
ascagem 0.00
afn05
‐ 0.64
acomboed 0.05
zf02m2 0.08
_cons
72.30
Std. Err
t
0.16 1.34
0.51 11.17
0.21 2.41
0.34 ‐ 1.04
0.35 1.86
0.08 ‐
0.00 ‐ 2.58
0.18 0.29
1.44 0.05
4.01 18.03
P>|t|
0.18
‐
0.02
0.30
0.06
1.00
0.01
0.77
0.96
‐
[95% Conf Intervals]
‐ 0.10 0.54
4.67 6.66
0.10 0.94
‐ 1.01 0.31
‐ 0.03 1.36
‐ 0.16 0.16
‐ 1.13 ‐ 0.16
‐ 0.30 0.40
‐ 2.75 2.91
64.44 80.16
R2=0.07, F = 21.80 No. observations = 3,162. Table C.7: Productivity B2 (bwlrnoi) Category
Var
Learning outcome index
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Parental self efficacy
Family cohesion
Parental involvement in education
Temperament
Parental anxiety & depression
Gender
Age
Socioeconomic status
Parental education level
Parent gender
Constant
Coef.
awlrnoi 0.22
bahost ‐ 0.01
bpa08a1 ‐ 0.39
bawarm ‐ 0.98
baireas 3.17
bpa01a ‐ 0.37
bre06a ‐ 0.53
bahact
2.57
bse06a ‐ 2.15
bak6
1.30
zf02m1 3.04
bscagem ‐ 0.00
bhinc
0.92
bcomboe ‐ 0.42
zf02m2 2.03
_cons
58.06
Std. Err
0.02
0.16
0.45
0.54
0.38
0.26
0.28
0.39
0.38
0.42
0.38
0.07
0.00
0.22
1.39
5.42
t
10.67
‐ 0.04
‐ 0.88
‐ 1.81
8.36
‐ 1.38
‐ 1.90
6.63
‐ 5.70
3.07
7.99
‐ 0.06
4.99
‐ 1.92
1.46
10.72
P>|t|
‐
0.97
0.38
0.07
‐
0.17
0.06
‐
‐
0.00
‐
0.95
‐
0.06
0.15
‐
[95% Conf Intervals]
0.18
‐0.31
‐1.28
‐2.04
2.43
‐0.88
‐1.08
1.81
‐2.89
0.47
2.30
‐0.15
0.00
‐0.86
‐0.70
47.44
0.26
0.30
0.49
0.08
3.92
0.15
0.02
3.33
‐ 1.41
2.13
3.79
0.14
0.56
0.01
4.75
68.68
R2=0.21, F = 42.85 No. observations = 2,678. Note, the productivity dependent variable (learning outcome index) is normally coded, while parental education level is reverse coded (lower score = higher education). Thus, the expected sign of parental education is negative. 94 Positive Family Functioning Table C.8: Productivity B3 (cwlrnoi) Category
Var
Learning outcome index
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Parental self efficacy
Family cohesion
Parental involvement in education
Temperament
Parental anxiety & depression
Gender
Age
Socioeconomic status
Parental education level
Parent gender
Constant
Coef.
bwlrnoi 0.30
cahostb 0.02
cpa08a1 ‐ 0.01
cawarm ‐ 0.92
caireas
0.21
cacons
1.03
cpa01a
0.34
cre06a ‐ 0.06
cahactd 0.73
cse06a ‐ 1.40
cak6
0.02
zf02m1 3.33
cscagem 0.22
chinc
0.98
ccomboe ‐ 1.11
zf02m2 0.61
_cons
51.43
Std. Err
0.02
0.15
0.15
0.47
0.33
0.32
0.23
0.23
0.33
0.33
0.35
0.35
0.06
0.00
0.20
1.44
5.80
t
15.94
0.11
‐ 0.06
‐ 1.97
0.65
3.24
1.46
‐ 0.27
2.19
‐ 4.24
0.05
9.51
3.38
8.71
‐ 5.53
0.42
8.87
P>|t|
‐
0.91
0.96
0.05
0.52
0.00
0.14
0.79
0.03
‐
0.96
‐
0.00
‐
‐
0.67
‐
[95% Conf Intervals]
0.26
‐0.28
‐0.31
‐1.83
‐0.43
0.41
‐0.11
‐0.52
0.08
‐2.05
‐0.67
2.65
0.09
0.00
‐1.50
‐2.22
40.06
0.34
0.31
0.29
‐ 0.01
0.85
1.65
0.79
0.40
1.38
‐ 0.75
0.70
4.02
0.34
0.75
‐ 0.72
3.44
62.80
R2=0.26, F = 49.54 No. observations = 2,679. Table C.9: Productivity K2 (dwlrnoi) Category
Learning outcome index
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Parental self efficacy
Family cohesion
Parental involvement in education
Parental involvement in school
Temperament
Parental anxiety & depression
Gender
Age
Socioeconomic status
Parental education level
Parent gender
Constant
Var
Coef.
cwlrnoi 0.54
dahostc ‐ 0.28
dpa08a1 ‐ 0.98
dawarm ‐ 0.73
daireas 0.43
dacons 0.77
dpa01a ‐ 0.54
dre06a ‐ 0.07
dahact ‐ 0.15
dassc
0.23
dse06a ‐ 1.18
dak6
0.12
zf02m1 ‐ 1.52
dscagem 0.01
dhinc
1.09
dcomboe ‐ 0.32
zf02m2 ‐ 0.29
_cons
54.63
R2=0.34, F = 69.80 No. observations = 2,936. Std. Err
0.02
0.12
0.41
0.40
0.29
0.28
0.22
0.21
0.32
0.15
0.29
0.33
0.33
0.06
0.00
0.16
1.18
6.43
t
29.15
‐ 2.31
‐ 2.40
‐ 1.81
1.46
2.75
‐ 2.47
‐ 0.35
‐ 0.47
1.47
‐ 4.10
0.36
‐ 4.64
0.24
7.58
‐ 2.02
‐ 0.25
8.49
P>|t|
‐
0.02
0.02
0.07
0.15
0.01
0.01
0.72
0.64
0.14
‐
0.72
‐
0.81
‐
0.04
0.80
‐
[95% Conf Intervals]
0.50
‐0.53
‐1.79
‐1.51
‐0.15
0.22
‐0.97
‐0.48
‐0.78
‐0.08
‐1.75
‐0.52
‐2.16
‐0.10
0.00
‐0.63
‐2.62
42.02
0.57
‐ 0.04
‐ 0.18
0.06
1.00
1.31
‐ 0.11
0.34
0.48
0.53
‐ 0.62
0.75
‐ 0.88
0.12
0.81
‐ 0.01
2.03
67.24
95 Table C.10: Productivity K3 (ewlrnoi) Category
Var
Learning outcome index
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Parental self efficacy
Family cohesion
Parental involvement in education
Parental involvement in school
Temperament
Parental anxiety & depression
Gender
Age
Socioeconomic status
Parental education level
Parent gender
Constant
Coef.
dwlrnoi 0.66
eahostc ‐ 0.09
epa08a1 0.11
eawarm ‐ 0.38
eaireas ‐ 0.17
eacons 0.60
epa01a ‐ 0.02
ere06a
0.19
eahacte ‐ 0.67
eassc
0.55
ese06a ‐ 1.27
eak6
‐ 0.17
zf02m1 0.25
escagem 0.01
ehinc
0.17
ecomboe ‐ 0.55
zf02m2 ‐ 0.88
_cons
37.45
Std. Err
0.02
0.11
0.13
0.29
0.23
0.25
0.17
0.17
0.23
0.11
0.25
0.25
0.27
0.05
0.00
0.13
0.82
5.91
t
42.26
‐ 0.88
0.81
‐ 1.33
‐ 0.75
2.38
‐ 0.12
1.16
‐ 2.95
5.14
‐ 5.00
‐ 0.67
0.95
0.11
1.69
‐ 4.33
‐ 1.07
6.33
P>|t|
‐
0.38
0.42
0.18
0.46
0.02
0.90
0.25
0.00
‐
‐
0.50
0.34
0.91
0.09
‐
0.29
‐
[95% Conf Intervals]
0.63
‐0.30
‐0.15
‐0.95
‐0.62
0.11
‐0.36
‐0.13
‐1.11
0.34
‐1.77
‐0.66
‐0.27
‐0.09
‐0.03
‐0.80
‐2.49
25.85
0.69
0.11
0.37
0.18
0.28
1.10
0.32
0.52
‐ 0.22
0.76
‐ 0.78
0.32
0.78
0.10
0.38
‐ 0.30
0.74
49.05
R2=0.51, F = 146.42 No. observations = 3,195. Table C.11: Anxiety and depression B1 (apedsgc) Category
Var
Harsh discipline
Relationship quality / warmth
Parental Efficacy
Smoking
Alcohol consumption
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
Coef.
Std. Err
t
0.00 ‐ 0.63
0.01 0.39
0.00 1.90
0.01 ‐ 0.83
0.01 1.12
0.01 1.97
0.01 1.03
0.00 ‐ 0.56
0.00 1.23
0.03 ‐
0.10 17.62
P>|t|
0.53
0.69
0.06
0.41
0.26
0.05
0.30
0.57
0.22
1.00
‐
[95% Conf Intervals]
0.01
0.03
0.02
0.01
0.04
0.04
0.03
0.01
0.01
0.07
1.97 R2=0.01, F = 2.07 No. observations = 2,584. Note: parental anxiety and depression (*ak6) is reverse coded, such that a higher score indicates less anxiety and depression. For childhood anxiety and depression in B1, apedsgc is also reverse coded, thus the expected sign of *ak6 is positive. However, all other childhood anxiety and depression dependent variables (B2 through K3) are coded normally, thus the expected sign of *ak6 is negative. 96 aahost ‐ 0.00
aawarm 0.00
apa01a 0.01
ahb15a1 ‐ 0.01
aaalcgpm 0.01
aak6
0.02
zf02m1 0.01
afn05
‐ 0.00
acomboed 0.00
zf02m2 0.00
_cons
1.77
‐ 0.01
‐ 0.02
‐ 0.00
‐ 0.02
‐ 0.01
0.00
‐ 0.01
‐ 0.02
‐ 0.00
‐ 0.07
1.58
Positive Family Functioning Table C.12: Anxiety and depression B2 (bpedsef) Category
Var
Lag dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Parental efficacy
Family cohesion
Smoking
Alcohol consumption
Overprotection
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
Coef.
apedsgc 3.50
bahost ‐ 2.11
bpa08a1 ‐ 0.25
bawarm 1.83
baireas ‐ 0.47
bpa01a 0.55
bre06a ‐ 1.44
bhb15a1 0.01
baalcgpm ‐ 0.46
baoverp ‐ 1.22
bse06a ‐ 3.73
bak6
5.12
zf02m1 ‐ 0.09
bhinc
‐ 0.59
bcomboe 0.52
zf02m2 ‐ 2.68
_cons
63.34
Std. Err
1.18
0.24
0.60
0.72
0.49
0.35
0.37
0.37
0.65
0.39
0.51
0.62
0.50
0.00
0.28
2.16
6.94
t
2.97
‐ 8.95
‐ 0.42
2.54
‐ 0.95
1.55
‐ 3.89
0.02
‐ 0.71
‐ 3.13
‐ 7.30
8.30
‐ 0.18
‐ 2.31
1.87
‐ 1.24
9.13
P>|t|
0.00
‐
0.68
0.01
0.34
0.12
‐
0.99
0.48
0.00
‐
‐
0.86
0.02
0.06
0.22
‐
[95% Conf Intervals]
1.19
‐2.57
‐1.44
0.42
‐1.44
‐0.15
‐2.16
‐0.72
‐1.73
‐1.98
‐4.73
3.91
‐1.08
‐1.09
‐0.02
‐6.92
49.73
R2=0.19, F = 31.85 No. observations = 3,000. 5.81
‐1.65
0.93
3.24
0.50
1.24
‐0.71
0.74
0.82
‐0.45
‐2.73
6.33
0.90
‐0.09
1.07
1.57
76.95 Table C.13: Anxiety and depression B3 (cpedsef) Category
Lag dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Efficacy
Cohesion
Smoking
Alcohol consumption
Overprotection
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
Var
Coef.
bpedsef 0.36
cahostb ‐ 1.25
cpa08a1 0.03
cawarm 1.90
caireas ‐ 1.44
cacons
0.39
cpa01a
0.08
cre06a ‐ 0.63
chb15a1 ‐ 0.36
caalcgpmo‐ 0.08
caoverp ‐ 0.29
cse06a ‐ 2.94
cak6
4.55
zf02m1 ‐ 0.46
chinc
‐ 0.39
ccomboed 0.19
zf02m2 ‐ 0.54
_cons
37.39
R2=0.30, F = 51.34 No. observations = 2,679. Std. Err
0.02
0.25
0.21
0.65
0.48
0.52
0.34
0.33
0.37
0.76
0.39
0.52
0.73
0.51
0.00
0.30
2.11
6.82
t
17.78
‐ 4.93
0.13
2.91
‐ 3.02
0.75
0.24
‐ 1.94
‐ 0.99
‐ 0.10
‐ 0.73
‐ 5.70
6.23
‐ 0.89
‐ 2.13
0.61
‐ 0.26
5.48
P>|t|
‐
‐
0.90
0.00
0.00
0.45
0.81
0.05
0.32
0.92
0.47
‐
‐
0.37
0.03
0.54
0.80
‐
[95% Conf Intervals]
0.32
‐1.74
‐0.39
0.62
‐2.38
‐0.63
‐0.58
‐1.28
‐1.08
‐1.56
‐1.06
‐3.95
3.12
‐1.46
‐0.75
‐0.41
‐4.68
24.02
0.40
‐0.75
0.45
3.18
‐0.51
1.41
0.74
0.01
0.36
1.40
0.49
‐1.93
5.98
0.55
‐0.03
0.78
3.59
50.77 97 Table C.14: Anxiety and depression K2 (daemot) Category
Var
Lag dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Efficacy
Cohesion
Smoking
Alcohol consumption
Overprotection
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
Coef.
caemot 0.39
dahostc 0.08
dpa08a1 0.04
dawarm ‐ 0.05
daireas 0.06
dacons ‐ 0.07
dpa01a 0.04
dre06a
0.04
dhb15a1 ‐ 0.01
daalcgpm‐ 0.26
daoverp 0.05
dse06a
0.32
dak6
‐ 0.48
zf02m1 0.14
dhinc
‐ 0.01
dcomboed‐ 0.00
zf02m2 ‐ 0.03
_cons
2.28
Std. Err
0.02
0.02
0.07
0.07
0.05
0.05
0.04
0.04
0.04
0.09
0.04
0.06
0.07
0.06
0.00
0.03
0.21
0.79
t
19.03
3.39
0.48
‐ 0.73
1.29
‐ 1.35
0.92
1.06
‐ 0.23
‐ 3.00
1.30
5.53
‐ 7.21
2.57
‐ 0.38
‐ 0.01
‐ 0.14
2.88
P>|t|
‐
0.00
0.63
0.46
0.20
0.18
0.36
0.29
0.82
0.00
0.19
‐
‐
0.01
0.71
0.99
0.89
0.00
[95% Conf Intervals]
0.35
0.03
‐0.11
‐0.20
‐0.03
‐0.18
‐0.04
‐0.04
‐0.09
‐0.42
‐0.03
0.21
‐0.61
0.03
‐0.07
‐0.05
‐0.44
0.73
R2=0.29, F = 42.86 No. observations = 2,939. 0.44
0.13
0.18
0.09
0.15
0.03
0.11
0.12
0.07
‐0.09
0.14
0.43
‐0.35
0.25
0.05
0.05
0.38
3.84 Table C.15: Anxiety and depression K3 (eaemot) Category
Var
Lag dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Efficacy
Cohesion
Smoking
Alcohol consumption
Overprotection
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
R2=0.36, F = 55.04 No. observations = 3,191. 98 Coef.
daemot 0.50
eahostc 0.06
epa08a1 0.00
eawarm 0.01
eaireas 0.04
eacons ‐ 0.04
epa01a 0.02
ere06a
0.03
ehb15a1 0.02
eaalcgpm ‐ 0.16
eaoverp 0.09
ese06a
0.36
eak6
‐ 0.40
zf02m1 0.05
ehinc
‐ 0.04
ecomboed‐ 0.00
zf02m2 ‐ 0.06
_cons
1.61
Std. Err
0.02
0.02
0.03
0.06
0.05
0.05
0.04
0.04
0.04
0.08
0.04
0.06
0.06
0.06
0.00
0.03
0.18
0.59
t
23.24
2.32
0.07
0.18
0.96
‐ 0.82
0.67
0.96
0.46
‐ 1.93
2.19
5.85
‐ 7.25
0.96
‐ 1.74
‐ 0.07
‐ 0.33
2.73
P>|t|
‐
0.02
0.94
0.86
0.34
0.41
0.51
0.34
0.65
0.05
0.03
‐
‐
0.34
0.08
0.95
0.74
0.01
[95% Conf Intervals]
0.46
0.01
‐0.05
‐0.11
‐0.05
‐0.15
‐0.05
‐0.04
‐0.06
‐0.31
0.01
0.24
‐0.51
‐0.06
‐0.08
‐0.05
‐0.41
0.45
0.55
0.11
0.06
0.14
0.13
0.06
0.09
0.10
0.09
0.00
0.17
0.47
‐0.29
0.16
0.00
0.05
0.29
2.77 Positive Family Functioning Table C.16: Antisocial B1 (apedsgc) Category
Harsh discipline
Relationship quality / warmth
Parental Efficacy
Smoking
Alcohol consumption
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
Var
Coef.
aahost ‐ 0.00
aawarm 0.00
apa01a 0.01
ahb15a1 ‐ 0.01
aaalcgpm 0.01
aak6
0.02
zf02m1 0.01
afn05
‐ 0.00
acomboed 0.00
zf02m2 0.00
_cons
1.77
Std. Err
t
0.00 ‐ 0.63
0.01 0.39
0.00 1.90
0.01 ‐ 0.83
0.01 1.12
0.01 1.97
0.01 1.03
0.00 ‐ 0.56
0.00 1.23
0.03 ‐
0.10 17.62
P>|t|
0.53
0.69
0.06
0.41
0.26
0.05
0.30
0.57
0.22
1.00
‐
[95% Conf Intervals]
‐ 0.01
‐ 0.02
‐ 0.00
‐ 0.02
‐ 0.01
0.00
‐ 0.01
‐ 0.02
‐ 0.00
‐ 0.07
1.58
0.01
0.03
0.02
0.01
0.04
0.04
0.03
0.01
0.01
0.07
1.97 2
R =0.01, F = 1.77 No. observations = 3,476. Note: Parental anxiety and depression (*ak6) is reverse coded, such that a higher score indicates less anxiety and depression. For childhood antisocial behavior (B1), apedsgc is also reverse coded, thus the expected sign of *ak6 is positive. However, all other childhood antisocial behaviour dependent variables (B2 though K3) are coded normally, thus the expected sign of *ak6 is negative. Table C.17: Antisocial B2 (babitp) Category
Lag dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Parental efficacy
Family cohesion
Smoking
Alcohol consumption
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
Var
Coef.
apedsgc 0.47
bahost
0.55
bpa08a1 0.24
bawarm ‐ 0.20
baireas ‐ 0.21
bpa01a 0.05
bre06a
0.61
bhb15a1 0.21
baalcgpm‐ 1.02
bse06a 1.80
bak6
‐ 2.32
zf02m1 ‐ 0.54
bhinc
‐ 0.14
bcomboe 0.15
zf02m2 ‐ 1.09
_cons
39.11
R2=0.30, F = 55.95 No. observations = 3,011. Std. Err
0.20
0.07
0.16
0.22
0.14
0.10
0.10
0.11
0.20
0.16
0.19
0.15
0.00
0.09
0.75
2.19
t
2.39
7.86
1.49
‐ 0.93
‐ 1.49
0.49
5.97
1.90
‐ 5.20
11.57
‐ 12.13
‐ 3.68
‐ 1.90
1.66
‐ 1.45
17.85
P>|t|
0.02
‐
0.14
0.35
0.14
0.63
‐
0.06
‐
‐
‐
‐
0.06
0.10
0.15
‐
[95% Conf Intervals]
0.08 0.85
0.41 0.68
‐ 0.07 0.55
‐ 0.63 0.22
‐ 0.49 0.07
‐ 0.15 0.25
0.41 0.81
‐ 0.01 0.42
‐ 1.40 ‐ 0.63
1.50 2.11
‐ 2.70 ‐ 1.95
‐ 0.82 ‐ 0.25
‐ 0.28 0.00
‐ 0.03 0.32
‐ 2.56 0.39
34.81 43.40 99 Table C.18: Antisocial B3 (caconda) Category
Var
Lag dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Parental efficacy
Family cohesion
Smoking
Alcohol consumption
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
Coef.
babitp
0.06
cahostb 0.36
cpa08a1 ‐ 0.01
cawarm ‐ 0.35
caireas
0.31
cacons ‐ 0.36
cpa01a ‐ 0.11
cre06a
0.09
chb15a1 0.18
caalcgpm ‐ 0.18
cse06a
0.83
cak6
‐ 0.13
zf02m1 ‐ 0.02
chinc
‐ 0.04
ccomboed 0.03
zf02m2 ‐ 0.15
_cons
0.92
Std. Err
0.01
0.03
0.03
0.07
0.05
0.06
0.04
0.04
0.05
0.08
0.06
0.07
0.06
0.00
0.04
0.29
0.86
t
6.85
11.99
‐ 0.47
‐ 4.73
5.74
‐ 6.36
‐ 2.82
2.02
3.90
‐ 2.27
13.48
‐ 1.88
‐ 0.31
‐ 1.86
0.98
‐ 0.53
1.07
P>|t|
‐
‐
0.64
‐
‐
‐
0.01
0.04
‐
0.02
‐
0.06
0.75
0.06
0.33
0.60
0.29
[95% Conf Intervals]
0.04
0.30
‐ 0.07
‐ 0.50
0.20
‐ 0.47
‐ 0.19
0.00
0.09
‐ 0.34
0.71
‐ 0.26
‐ 0.13
‐ 0.08
‐ 0.04
‐ 0.73
‐ 0.77
0.08
0.42
0.04
‐ 0.21
0.41
‐ 0.25
‐ 0.03
0.17
0.27
‐ 0.02
0.95
0.01
0.09
0.00
0.10
0.42
2.61
R2=0.37, F = 76.00 No. observations = 3,120. Table C.19: Antisocial K2 (daconda) Category
Var
Lag dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Parental efficacy
Family cohesion
Smoking
Alcohol consumption
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
R2=0.41, F = 90.99 No. observations = 2,042. 100 Coef.
caconda 0.23
dahostc 0.21
dpa08a1 0.05
dawarm ‐ 0.14
daireas 0.07
dacons ‐ 0.25
dpa01a 0.01
dre06a
0.14
dhb15a1 0.12
daalcgpm 0.07
dse06a
0.42
dak6
‐ 0.07
zf02m1 ‐ 0.18
dhinc
‐ 0.05
dcomboed 0.04
zf02m2 ‐ 0.15
_cons
0.84
Std. Err
0.01
0.02
0.06
0.06
0.04
0.05
0.03
0.03
0.03
0.06
0.04
0.05
0.05
0.00
0.02
0.17
0.66
t
15.41
11.01
0.83
‐ 2.33
1.95
‐ 5.28
0.26
4.59
3.61
1.13
9.32
‐ 1.37
‐ 4.06
‐ 2.56
1.48
‐ 0.84
1.29
P>|t|
‐
‐
0.41
0.02
0.05
‐
0.79
‐
‐
0.26
‐
0.17
‐
0.01
0.14
0.40
0.20
[95% Conf Intervals]
0.20
0.18
‐ 0.06
‐ 0.26
‐ 0.00
‐ 0.34
‐ 0.05
0.08
0.06
‐ 0.05
0.33
‐ 0.18
‐ 0.27
‐ 0.09
‐ 0.01
‐ 0.49
‐ 0.44
0.26
0.25
0.16
‐ 0.02
0.15
‐ 0.16
0.07
0.20
0.19
0.19
0.51
0.03
‐ 0.09
‐ 0.01
0.08
0.20
2.13
Positive Family Functioning Table C.20: Antisocial K3 (eaconda) Category
Lag dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Parental efficacy
Family cohesion
Smoking
Alcohol consumption
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
Var
Coef.
Std. Err
daconda 0.42
eahostc 0.19
epa08a1 ‐ 0.02
eawarm ‐ 0.04
eaireas 0.06
eacons ‐ 0.18
epa01a ‐ 0.05
ere06a
0.06
ehb15a1 0.08
eaalcgpm 0.03
ese06a
0.45
eak6
‐ 0.16
zf02m1 ‐ 0.15
ehinc
‐ 0.03
ecomboed 0.00
zf02m2 ‐ 0.13
_cons
1.25
0.02
0.02
0.02
0.05
0.03
0.04
0.03
0.03
0.03
0.06
0.04
0.05
0.04
0.00
0.03
0.10
0.43
t
P>|t|
20.00
8.99
‐ 0.93
‐ 0.97
1.87
‐ 4.25
‐ 1.84
2.22
2.50
0.50
10.65
‐ 3.49
‐ 3.55
‐ 2.21
0.01
‐ 1.27
2.91
‐
‐
0.36
0.33
0.06
‐
0.07
0.03
0.01
0.62
‐
‐
‐
0.03
0.99
0.21
0.00
[95% Conf Intervals]
0.38
0.15
‐ 0.06
‐ 0.13
‐ 0.00
‐ 0.26
‐ 0.11
0.01
0.02
‐ 0.09
0.37
‐ 0.25
‐ 0.23
‐ 0.06
‐ 0.05
‐ 0.33
0.41
0.46
0.23
0.02
0.04
0.13
‐ 0.09
0.00
0.12
0.14
0.15
0.53
‐ 0.07
‐ 0.07
‐ 0.00
0.05
0.07
2.09
R2=0.50, F = 96.27 No. observations = 3,194. Table C.21: Addictions B1 (apedsgc) Category
Harsh discipline
Relationship quality / warmth
Parental efficacy
Smoking
Alcohol consumption
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
Var
Coef.
aahost ‐ 0.00
aawarm 0.00
apa01a 0.01
ahb15a1 ‐ 0.01
aaalcgpm 0.01
aak6
0.02
zf02m1 0.01
afn05
‐ 0.00
acomboed 0.00
zf02m2 0.00
_cons
1.77
Std. Err
t
0.00 ‐ 0.63
0.01 0.39
0.00 1.90
0.01 ‐ 0.83
0.01 1.12
0.01 1.97
0.01 1.03
0.00 ‐ 0.56
0.00 1.23
0.03 ‐
0.10 17.62
P>|t|
0.53
0.70
0.06
0.41
0.26
0.05
0.30
0.57
0.22
1.00
‐
[95% Conf Intervals]
‐ 0.01
‐ 0.02
‐ 0.00
‐ 0.02
‐ 0.01
0.00
‐ 0.01
‐ 0.02
‐ 0.00
‐ 0.07
1.58
0.01
0.03
0.02
0.01
0.04
0.04
0.03
0.01
0.01
0.07
1.97
R2=0.01, F = 1.77 No. observations = 3,476. Note: Parental anxiety and depression (*ak6) is reverse coded, such that a higher score indicates less anxiety and depression. For childhood addictive behavior (B1) , apedsgc is also reverse coded, thus the expected sign of *ak6 is positive. However, all other childhood addictive behaviour dependent variables (B2 though K3) are coded normally, thus the expected sign of *ak6 is negative. 101 Table C.22: Addictions B2 (babitp) Category
Var
Lag dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Parenta l efficacy
Family cohesion
Smoking
Alcohol consumption
Overprotection
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
Coef.
apedsgc 0.48
bahost
0.56
bpa08a1 0.12
bawarm ‐ 0.40
baireas ‐ 0.22
bpa01a 0.03
bre06a
0.59
bhb15a1 0.18
baalcgpm‐ 0.91
baoverp 0.72
bse06a
1.81
bak6
‐ 2.26
zf02m1 ‐ 0.57
bhinc
‐ 0.10
bcomboed 0.11
zf02m2 ‐ 1.07
_cons
37.61
Std. Err
0.20
0.07
0.16
0.22
0.14
0.10
0.10
0.11
0.20
0.12
0.15
0.19
0.14
0.00
0.09
0.72
2.17
t
2.43
7.97
0.75
‐ 1.83
‐ 1.52
0.35
5.77
1.68
‐ 4.65
6.24
11.66
‐ 11.86
‐ 3.98
‐ 1.40
1.21
‐ 1.48
17.35
P>|t|
0.02
‐
0.45
0.07
0.13
0.73
‐
0.09
‐
‐
‐
‐
‐
0.16
0.23
0.14
‐
[95% Conf Intervals]
0.09 0.87
0.42 0.69
‐ 0.19 0.42
‐ 0.83 0.03
‐ 0.49 0.06
‐ 0.16 0.23
0.39 0.79
‐ 0.03 0.40
‐ 1.30 ‐ 0.53
0.50 0.95
1.50 2.11
‐ 2.63 ‐ 1.89
‐ 0.86 ‐ 0.29
‐ 0.24 0.04
‐ 0.07 0.28
‐ 2.49 0.35
33.36 41.86
R2=0.31, F = 55.66 No. observations = 3,003. Table C.23: Addictions B3 (casdqta) Category
Var
Lag dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Parental efficacy
Family cohesion
Smoking
Alcohol consumption
Overprotection
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
R2=0.41, F = 73.14 No. observations = 3,116. 102 Coef.
babitp
0.29
cahostb 0.64
cpa08a1 0.03
cawarm ‐ 0.70
caireas
0.45
cacons ‐ 0.65
cp a01a ‐ 0.20
cre06a
0.18
chb15a1 0.30
caalcgpm ‐ 0.47
caoverp 0.24
cse06a
1.95
cak6
‐ 1.00
zf02m1 ‐ 0.44
chinc
‐ 0.16
ccomboe 0.27
zf02m2 ‐ 0.02
_cons
2.95
Std. Err
0.02
0.07
0.07
0.21
0.14
0.16
0.10
0.11
0.11
0.21
0.12
0.16
0.20
0.15
0.00
0.10
0.82
2.33
t
12.54
8.67
0.48
‐ 3.28
3.29
‐ 4.11
‐ 1.89
1.65
2.73
‐ 2.25
2.05
12.40
‐ 4.89
‐ 3.04
‐ 3.13
2.75
‐ 0.02
1.27
P>|t|
‐
‐
0.63
0.00
0.00
‐
0.06
0.10
0.01
0.03
0.04
‐
‐
0.00
0.00
0.01
0.98
0.21
[95% Conf Intervals]
0.24
0.50
‐ 0.11
‐ 1.12
0.18
‐ 0.96
‐ 0.40
‐ 0.03
0.09
‐ 0.87
0.01
1.64
‐ 1.40
‐ 0.73
‐ 0.26
0.08
‐ 1.62
‐ 1.62
0.33
0.79
0.17
‐ 0.28
0.72
‐ 0.34
0.01
0.40
0.52
‐ 0.06
0.47
2.26
‐ 0.60
‐ 0.16
‐ 0.06
0.45
1.59
7.53
Positive Family Functioning Table C.24: Addictions K2 (dasdqta) Category
Lag dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Parental efficacy
Family cohesion
Smoking
Alcohol consumption
Overprotection
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Var
Coef.
casdqta 0.41
dahostc 0.44
dpa08a1 0.15
dawarm ‐ 0.47
daireas 0.29
dacons ‐ 0.44
dpa01a 0.09
dre06a
0.27
dhb15a1 0.27
daalcgpm ‐ 0.28
daoverp 0.16
dse06a
1.57
dak6
‐ 1.02
zf02m1 ‐ 0.68
dhinc
‐ 0.11
dcomboed 0.05
zf02m2 ‐ 0.79
Std. Err
0.02
0.06
0.21
0.19
0.12
0.15
0.10
0.10
0.10
0.20
0.11
0.14
0.17
0.14
0.00
0.07
0.59
t
20.39
7.08
0.73
‐ 2.52
2.42
‐ 2.96
0. 89
2.78
2.55
‐ 1.37
1.44
10.87
‐ 5.95
‐ 4.75
‐ 1.60
0.70
‐ 1.34
P>|t|
‐
‐
0.47
0.01
0.02
0.00
0.37
0.01
0.01
0.17
0.15
‐
‐
‐
0.11
0.48
0.18
[95% Conf Intervals]
0.37
0.32
‐ 0.25
‐ 0.83
0.05
‐ 0.73
‐ 0.11
0.08
0.06
‐ 0.68
‐ 0.06
1.29
‐ 1.36
‐ 0.96
‐ 0.24
‐ 0.09
‐ 1.95
0.45
0.56
0.55
‐ 0.10
0.52
‐ 0.15
0.29
0.46
0.47
0.12
0.38
1.86
‐ 0.68
‐ 0.40
0.02
0.19
0.37
R2=0.49, F = 105.48 No. observations = 2,938. Table C.25: Addictions K3 (easdqta) Category
Lag dep var
Harsh discipline
Parental monitoring / supervision
Relationship quality / warmth
Inductive reasoning
Consistent discipline
Parental efficacy
Family cohesion
Smoking
Alcohol consumption
Overprotection
Temperament
Parental anxiety & depression
Gender
Socioeconomic status
Parental education level
Parent gender
Constant
Var
Coef.
dasdqta 0.59
eahostc 0.38
epa08a1 ‐ 0.02
eawarm ‐ 0.18
eaireas
0.17
eacons ‐ 0.40
epa01a ‐ 0.08
ere06a
0.14
ehb15a1 0.19
eaalcgpm ‐ 0.25
eaoverp 0.22
ese06a
1.60
eak6
‐ 1.02
zf02m1 ‐ 0.61
ehinc
‐ 0.08
ecomboed 0.01
zf02m2 0.12
_cons
5.68
R2=0.49, F = 105.48 No. observations = 2,938. Std. Err
0.02
0.06
0.07
0.14
0.11
0.13
0.09
0.08
0.10
0.18
0.10
0.13
0.13
0.13
0.00
0.06
0.44
1.43
t
32.91
6.60
‐ 0.36
‐ 1.34
1.58
‐ 3.04
‐ 0.90
1.71
1.90
‐ 1.38
2.25
12.23
‐ 7.69
‐ 4.72
‐ 1.74
0.12
0.28
3.98
P>|t|
‐
‐
0.72
0.18
0.11
0.00
0.37
0.09
0.06
0.17
0.02
‐
‐
‐
0.08
0.90
0.78
‐
[95% Conf Intervals]
0.56 0.63
0.26 0.49
‐ 0.16 0.11
‐ 0.45 0.09
‐ 0.04 0.37
‐ 0.66 ‐ 0.14
‐ 0.25 0.09
‐ 0.02 0.31
‐ 0.01 0.38
‐ 0.60 0.10
0.03 0.41
1.34 1.85
‐ 1.28 ‐ 0.76
‐ 0.86 ‐ 0.36
‐ 0.17 0.01
‐ 0.12 0.13
‐ 0.73 0.98
2.88 8.48
103 Appendix D: LSAC variable specification Table D.1: Standard LSAC variables Variable Measure Name Question Person Label Values aalcgp* Alcohol consumption groups primary carer alcohol consumption groups Parent 1 1 Abstain (not in the last year); 2 Moderate (occasional to <4 day men, or to <2 day women); 3 Harmful (>=4 men, >=2 women) abitp BITSEA Sum of bse05a2a, b, c, d, e, f, g, Parent 1 problems scale h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w where fewer than 5 are missing (no contact counts as missing) Number abmi Body mass index Parent 1 Number aconda SDQ Conduct Mean of cse03a4a, b, c, d and e Parent 1 problems scale rescaled to be a integer between 0 and 10 where fewer than 3 component items are missing, cse03a4b reverse coded Number acons Consistent parenting scale Mean of cpa11a1, 2, 3, 4, 5. With cpa11a3, 4 &5 reverse coded Parent 1 Number aemot SDQ Emotional symptoms scale Mean of cse03a3a, b, c, d and e Parent 1 rescaled to be a integer between 0 and 10 where fewer than 3 component items are missing Number ahacte Home Mean of che02a1d, 6d, 7d activities index Parent 1 Number ahostc Hostile parenting scale Mean of apa04a1, 2 and 4 Parent 1 Number aireas Inductive reasoning scale Mean of cpa09a1 and cpa09a2 Parent 1 Number ak6** K‐6 Depression Scale Mean of ahs24a1 to ahs24a6 Parent 1 Number aoverp Mean of bpa15a1 to 3 Parental overprotection scale Parent 1 Number asdqta SDQ total score Parent 1 Number 104 Weight in kg/(height in metres)**2 Sum of cahypr, caemot, capeer and caconda Positive Family Functioning Variable Measure Name Question Person Label Values assc Involvement in Number of 'yes' responses to class activities dhe15a1a to dhe15a5a Parent 1 Number awarm Parental warmth scale Mean of apa03a1 to apa03a6 Parent 1 Number cbmi Body mass index Weight in kg/(Height in m)**2 Study Child Number f02m1 Sex Is the Study Child male or female? Study Child 1 Male; 2 Female f02m2 Sex Is Parent 1 male or female? Parent 1@W1 1 Male; 2 Female fn05 Combined yearly income before tax Before income tax is taken out, what is your present yearly income (for you and partner combined)? (Include pensions and allowances) (before tax, superannuation or health insurance) (Parent 1 and Partner combined) (Before tax) Not Categories were converted Applicable to dollars taking the mean of each category. hb14c4 Choice of physical activity in free time What does child usually do when she/he has a choice about how to spend free time? Study Child 1 Usually chooses inactive pastimes like TV, computer, drawing or reading; 2 Just as likely to choose active as inactive pastimes; 3 Usually chooses active pastimes like bike riding, dancing, games or sports; (‐2 Don't know) hb15a1 Frequency of cigarette smoking How often do you currently smoke cigarettes? Parent 1 1 Do not smoke at all; 2 Less than once a day; 3 At least once a day hfat Food diary Sum of chb21c1a to chb21c4a Not Number Applicable hinc*** Usual weekly income Sum of bfn09a, b and o (if not missing any, other than skipped) Not Number Applicable hs14d Special health care needs screener Not 1 Yes; 2 No; (‐2 Don't Does child need or use more Applicable know) medical care than is usual for most children of the same age? hs23c2 Weight Weight of child Study Child hsdrnk Food diary sum of bhb21b1a & bhb21b2a Not Number Applicable pa01a Global rating of self‐efficacy Overall, as a parent, do you feel that you are… Parent 1 Number 1 Not very good at being a parent; 2 A person who has some trouble being a parent; 3 An average parent; 4 A better than 105 Variable Measure Name Question Person Label Values average parent; 5 A very good parent pa08a1 Importance of monitoring / supervising child It is important that parents know where their child is and what he/she is doing all the time. Do you: Parent 1 pedsef PEDS QL emotional functioning Mean of cgd04b1a to cgd04b1e recoded so that 1=100, 2=75, 3=50, 4=25, and 5=0, only has a value if fewer than 3 items are missing Not Number Applicable pedsgc PEDS Global Concern agd01a with 'a little' coded with yes. Not 1 Yes/A little; 2 No Applicable re06a Family ability to get along with each other Sometimes family members may have difficulty getting along with one another. They don't always agree and they may get angry. In general, how would you rate your family's ability to get along with one another? Parent 1 scagem Study Child's age (months) Study child age in months at time of survey Not Number Applicable se06a General temperament Overall, compared to other children of the same age, do you think this child is...? Parent 1 wlrnoi Learning Outcome Index Learning Outcome Index continuous score Not Number Applicable 1 Strongly disagree; 2 Disagree; 3 Neither agree nor disagree; 4 Agree; 5 Strongly agree 1 Excellent; 2 Very good; 3 Good; 4 Fair; 5 Poor 1 Easier than average; 2 About average; 3 More difficult than average Notes: Variable name is in the LSAC ‘without age’ format. * truncated to three categories from the original six. ** converted to a sum, original is mean. *** converted to dollars per annum. Table D.2: Combinations of LSAC variables New variable Input Measure variables Question Person Label etain1 egweek Amount of weekend electronic games Total minutes playing an egame system on an average week Not Number. Applicable he06b1 Amount of Not About how many weekday TV hours on a typical Applicable weekday, would you say that child watches TV or videos at home? 106 Values 1 Does not watch TV or videos; 2 Less than one hour; 3 1 up to 3 hours; 4 3 up to 5 hours; 5 5 or more hours; (‐2 Don't know) Positive Family Functioning New variable Input Measure variables he06c1 Amount of About how many weekend TV hours on a typical weekend day, does child watch TV or videos at home? (If different for Saturday and Sunday, give average hours) Not 1 Does not watch TV or Applicable videos; 2 Less than one hour; 3 1 up to 3 hours; 4 3 up to 5 hours; 5 5 or more hours; (‐2 Don't know) he07b2 Amount of weekday computer use About how many hours on a typical weekday would you say that study child uses a computer at home? Not Number Applicable comboedn2 fd08a1 Highest level of schooling completed What was the highest year of primary or secondary school Parent 1 completed? Parent 1 1 Year 12 or equivalent; 2 Year 11 or equivalent; 3 Year 10 or equivalent; 4 Year 9 or equivalent; 5 Year 8 or below; 6 Never attended school; 7 Still at school; (‐2 Don't know); (‐
3 Refused) Completed post‐
secondary qualification Has Parent 1 completed a trade certificate or any other educational qualification? Parent 1 1 Yes; 2 No fd08a2a Question Person Label Values Notes: Variable Name is in the LSAC ‘without age’ format. 1 etain is the sum of the four electronic entertainment variables measured in hours per week. 2 comboedn is coded as the same as fd08a1 with the addition of 0 if parent has completed post‐secondary education from fd08a2. 107 Appendix E: Mean and standard deviation of LSAC variables Category Obesity Productivity Anxiety and depression Antisocial Addictions Harsh discipline Parental monitoring Relationship warmth Inductive reasoning consistent discipline Parental self‐efficacy Family cohesion Parental home education Parental involve school Fatty foods Sugary Drinks Exercise E‐tainment Smoking Alcohol consumption Overprotection 108 0‐1 years 2‐3 years 4‐5 years 6‐7 years 8‐9 years 9.22 1.44 99.63 9.72 1.94 0.24 1.94 0.24 1.94 0.24 1.93 1.14 4.55 0.41 4.08 0.96 1.35 0.74 1.82 0.38 16.84 1.59 100.49 10.18 74.06* 14.06 30.21* 4.46 30.21* 4.46 3.09 1.30 4.82 0.44 4.61 0.42 4.24 0.65 4.09 0.80 1.99 0.81 1.96 0.55 1.14 0.84 1.28 1.11 2.16 0.72 41.80 10.35 1.30 0.70 1.80 0.41 3.65 16.84 1.59 100.94 9.73 74.66 14.18 2.14* 1.78 8.18* 4.68 3.17 1.28 4.51 1.07 4.50 0.47 4.23 0.63 4.19 0.61 3.85 0.84 2.05 0.84 1.69 0.56 1.69 1.12 1.33 1.30 2.02 0.76 54.75 27.95 1.34 0.74 1.81 0.40 3.58 16.49 2.13 101.06 9.81 1.58* 1.68 2.43 1.98 7.81 5.02 3.36 1.45 4.85 0.40 4.44 0.48 4.24 0.64 4.16 0.61 4.07 0.82 2.17 0.85 1.36 0.53 3.79 1.15 1.22 0.85 1.41 1.15 1.99 0.75 55.17 10.90 1.32 0.73 1.83 0.39 3.51 17.59 2.84 101.00 9.90 1.57 1.75 1.45 1.47 7.49 5.32 3.29 1.41 4.64 0.94 4.32 0.55 4.14 0.68 4.19 0.61 3.84 0.86 2.16 0.87 1.46 0.63 3.32 1.38 1.88 1.18 1.37 1.32 1.96 0.77 57.48 11.58 1.38 0.77 1.82 0.40 3.54 Positive Family Functioning Category Temperament Parental anxiety & depression Gender Age in months Household income ($'000 pa) Parental education level Parent gender Parent body mass index Needs extra medical care 0‐1 years 2‐3 years 4‐5 years 6‐7 years 8‐9 years 1.73 0.51 4.43 0.56 1.49 0.50 8.78 2.56 52.2 35.1 1.57 1.08 1.99 0.12 25.44 5.39 1.92 0.37 0.69 1.72 0.56 4.52 0.53 33.89 2.91 80.8 51.1 1.52 1.05 25.03 5.06 1.92 0.35 0.69 1.73 0.56 4.47 0.56 57.58 2.83 99.0 70.8 1.47 1.00 25.70 5.50 1.90 0.34 0.73 1.68 0.59 4.47 0.59 1.49 0.50 81.87 2.94 83.4 53.9 1.65 1.18 1.97 0.16 25.42 5.12 1.89 0.35 0.72 1.67 0.59 4.42 0.61 105.55 2.88 100.6 68.0 1.58 1.13 25.88 5.29 1.88 0.38 Notes. Numbers in ordinary text are means of the analysis sample. Numbers in italics are standard deviations of the analysis sample. Text in bold indicates dependent variables. Ordinary text is common input variables. Italic text in the ‘Category’ column is variables peculiar to specific regressions. The 4‐5 year cohort is B3 not K1 (see preamble to Appendix C and footnote 5). Appendix D provides greater description of these variables. *indicates change of variable. Illustrative distribution of categorical variables Categories Variable Answer=1 Answer=2 Answer=3
Values infant anxiety and depression* apedsgc 267 3,985 1 Yes/A little; 2 No smoking ahb15a 3034 116 600 1 Do not smoke at all; 2 Less than once a day; 3 At least once a day alcohol consumption aalcgpm 649 3,010 6 1 Abstain (not in the last year); 2 Moderate ( <4 drinks /day men, <2 women); 3 Hazardous (>=4 men, >=2 women) Choice of physical activity in free time hb14c4 808 1939 1506 1 Usually chooses inactive pastimes like TV, computer, drawing or reading; 2 Just as likely to choose active as inactive pastimes; 3 Usually chooses active pastimes like bike riding, dancing, games or sports; (‐2 Don't know) gender child zf02m1 2178 2,075 1 Male; 2 Female 109 Categories Variable Answer=1 Answer=2 Answer=3
Values gender adult temperament zf02m2 ase06a 58 984 4,195 2,181 98 1 Male; 2 Female 1 Easier than average; 2 About average; 3 More difficult than average extra medical care ahs14d 264 3,987 1 Yes; 2 No; (‐2 Don't know) Note: Only the first age at which a given variable is used is shown in the table. * Also used for infant antisocial behaviour and infant addictions. 110 Positive Family Functioning Appendix F: ATP variable interpretation Productivity Variable name Description attach96 Attachment to parents, high score = high attachment, adolescent report, 13‐
14 years attach00 Attachment to parents, high score = high attachment, adolescent report, 17‐
18 years alientn96 Alienation from parents, high = score = high alienation, adolescent report, 13‐14 years, 2 ITEMS ONLY alientn00 Alienation from parents, high = score = high alienation, adolescent report, 17‐18 years totrelqual Relationship quality/warmth, high score = high warmth, quality rship, composite mean score of parent report at 13‐14, 15‐16 & 17‐18 years chrqual00 Relationship quality, high score = high quality rship, teenager report at 17‐18 years totharshd Harsh discipline, high score = high harsh discipline, composite mean score of parent report at 13‐14, 15‐16 and 17‐18 years superv96 Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, parent report 13‐14 years superv98 Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, parent report 15‐16 years parsup00 Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, teenager report 17‐18 years inconst00 Inconsistent discipline, high score = inconsistent, parent report 17‐18 years cohes00 Family cohesion, parent report at 17‐18 years, high score = high cohesion negpt00 Negative/conflictual parent‐teenager relationship, parent report 17‐18 years totmothed Mother’s education, composite mean score, parent report at 13‐14, 15‐16 & 17‐18 years, high score = low education totfathed Father’s education, composite mean score, parent report at 13‐14, 15‐16 & 17‐18 years, high score = low education tacp94 Academic competence, high score = high competence, teacher report at 11‐
12 years totmschprb Academic and social school problems, composite mean score,s parent report at 13‐14 & 15‐16 years, high score more problems posaff98 Positive affect towards school, high score = lower affect, teenager report 15‐
16 years acht98 Perceived achievement at school, high score = lower achievement, teenager report 15‐16 years totpersist Persistent temperament style, high score = very persistent, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years enter02 Tertiary entrance score, available for 766 cases only cmplsch # Did, or did not, complete secondary school (year 12), dichotomous variable, young adult report 19‐20 years educ06_2 + Highest level of education completed‐ ‐ 4 level variable (less than year 12, year 12, post‐secondary but not university, university), young adult report 111 23‐24 years ed_empl_2 # Fully engaged vs. not fully engaged, dichotomous variable, young adult report 23‐24 years # = dichotomous outcome score + = ordinal outcome variable Obesity Variable name Description attach96 Attachment to parents, high score = high attachment, adolescent report, 13‐
14 years attach00 Attachment to parents, high score = high attachment, adolescent report, 17‐
18 years Alientn96 Alienation from parents, high = score = high alienation, adolescent report, 13‐14 years, ONLY 2 ITEMS alientn00 Alienation from parents, high = score = high alienation, adolescent report, 17‐18 years negpt00 Negative/conflictual parent‐teenager relationship, parent report 17‐18 years, high score = high conflict totusereas Inductive reasoning, composite mean score of parent report at 13‐14, 15‐16 & 17‐18 years, high score = high use of reasoning totharshd Harsh discipline, composite mean score of parent report at 13‐14, 15‐16 and 17‐18 years, high score = high harsh discipline superv96 Parental monitoring/supervision of teen’s activities/associates, parent report 13‐14 years, high score = high supervision superv98 Parental monitoring/supervision of teen’s activities/associates, parent report 15‐16 years, high score = high supervision parsup00 Parental monitoring/supervision of teen’s activities/associates, teenager report 17‐18 years, high score = high supervision menc98_2 * Mother encourages child to lose weight – 1 = no, 2 = yes responses, parent report 15‐16 years fenc98_2 * Father encourages child to lose weight ‐ 1 = no, 2 = yes responses, parent report 15‐16 years mdiet98_2 * Mother diets to lose weight ‐ 1 = no, 2 = yes responses, parent report 15‐16 years fdiet98_2 * Father diets to lose weight ‐ 1 = no, 2 = yes responses, parent report 15‐16 years curr9801_2 * Child overeats at mealtimes currently, 1 = no, 2 = yes responses, parent report 15‐16 years curr9802_2 * Child eats a lot at between meals currently, 1 = no, 2 = yes responses, parent report 15‐16 years past9801_2 * Child overate at mealtimes in the past, 1 = no, 2 = yes responses, parent report 15‐16 years past9802_2 * Child ate a lot at between meals in the past, 1 = no, 2 = yes responses, parent report 15‐16 years soc9830 ** Child participates in school sports teams, 3 level var, high = high participation, parent report 15‐16 years soc9834 ** Child participates in community sports teams, 3 level var, high = high 112 Positive Family Functioning participation, parent report 15‐16 years diets98 ** Child has dieted to lose weight, 4 level var (no, once, 2‐3 times, many times), high = high dieting, teenager report, 15‐16 years eatprb98 Composite variable assessing eating behaviours and perceptions of weight (eats when upset, eats moderately in public but not in private, eats in secrecy, thinks self is too fat, dissatisfied with body shape), teenager report, 15‐16 years Intactf * Intact/non‐intact family over child’s lifetime to 18 years (non‐intact = separated, divorced, death of parent), parent report 17‐18 years totmothed Mother’s education, composite mean score, parent report at 13‐14, 15‐16 & 17‐18 years, high score = low education totfathed Father’s education, composite mean score, parent report at 13‐14, 15‐16 & 17‐18 years, high score = low education totreact Reactive temperament style, composite mean score of parent report at 11‐
12, 13‐14, & 15‐16 years, high score = very reactive totpersist Persistent temperament style, composite mean score of parent report at 11‐
12, 13‐14, & 15‐16 years, high score = very persistent,\ totapprch Approach/sociability temperament style, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years, high score = very approaching/sociable Weight_3 # Body mass index at 23‐24 years, 3 level outcome var in which 1 = underweight/normal, 2 = overweight, 3 = obese, young adult report at 23‐24 years (n = 902) * = dichotomous var ** = ordinal var # = ordinal outcome var Anxiety and/or depression Variable name Description attach96 Attachment to parents, high score = high attachment, adolescent report, 13‐14 years attach00 Attachment to parents, high score = high attachment, adolescent report, 17‐18 years alientn00 Alienation from parents, high = score = high alienation, adolescent report, 17‐18 years totrelqual Relationship quality/warmth, high score = high quality/warmth, composite mean score of parent report at 13‐14, 15‐16 & 17‐18 years chrqual00 Relationship quality, high score = high quality, adolescent report, 17‐18 years totusereas Inductive reasoning, high score = high use of reasoning, composite mean score of parent report at 13‐14, 15‐16 & 17‐18 years totharshd Harsh discipline, high score = high harsh discipline, composite mean score of parent report at 13‐14, 15‐16 and 17‐18 years enmesh00 Enmeshment/overprotection, high score = high enmeshment/overprotection, parent report 17‐18 years mconflic Marital conflict over life of ATP child to 17‐18 years, high score = high conflict, parent report 17‐18 years marqual Marital relationship quality over life of ATP child to 17‐18 years, high score = high quality relationship, parent report 17‐18 years 113 totmaltr* Experience of child maltreatment/neglect during first 18 years of life, young adult retrospective report at 23‐24 years, range 0 to 3 totfamstrss Occurrence of family life events (changes, problems) in previous 12 months, composite mean score of parent report at 13‐14, 15‐16 & 17‐18 years totreact Reactive temperament style, high score = very reactive, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years totpersist Persistent temperament style, high score = very persistent, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years totapprch Approach/sociability temperament style, high score = very approaching/sociable, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years anx_dep02** Anxiety/depression outcome: moderate, severe or very severe levels of depression, and/or anxiety, young adult report at 19‐20 years anx_dep06** Anxiety/depression outcome: moderate, severe or very severe levels of depression, and/or anxiety, young adult report at 23‐24 years meananxdep02# Anxiety/depression outcome: mean of depression and anxiety, young adult report at 19‐20 years meananxdep06# Anxiety/depression outcome: mean of depression and anxiety, young adult report at 23‐24 years = ordinal score ** = dichotomous outcome score # = continuous outcome score Alcohol/Smoking Variable name Description smokeh_2* Teenager is allowed to smoke at home, 1 = no, 2 = yes, parent report 17‐18 years Agedrh* Age teenager was first allowed to drink at home, 1 = 15 years or older, 2 = 14 years or younger, parent report 17‐18 years Talkdr** Parent talks with teenager about drinking 1 = no, 2 = a little, 3 = a lot, parent report 17‐18 years attach96 Attachment to parents, high score = high attachment, adolescent report, 13‐
14 years attach00 Attachment to parents, high score = high attachment, adolescent report, 17‐
18 years alientn96 Alienation from parents, high = score = high alienation, adolescent report, 13‐14 years, 2 ITEMS ONLY alientn00 Alienation from parents, high = score = high alienation, adolescent report, 17‐18 years totusereas Use of reasoning, high score = high reasoning, composite mean score of parent report at 13‐14, 15‐16 & 17‐18 years totharshd Harsh discipline, high score = high harsh discipline, composite mean score of parent report at 13‐14, 15‐16 and 17‐18 years superv96 Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, parent report 13‐14 years superv98 Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, parent report 15‐16 years parsup00 Parental monitoring/supervision of teen’s activities/associates, high score = 114 Positive Family Functioning high supervision, teenager report 17‐18 years inconst00 Inconsistent discipline, high score = inconsistent, parent report 17‐18 years negpt00 Negative/conflictual parent‐teenager relationship, parent report 17‐18 years totmsmoke Mother smoking, composite mean score of parent report at 13‐14, & 17‐18 years ttotfsmoke Father smoking, composite mean score of parent report at 13‐14, & 17‐18 years totmdrink Mother drinking, composite mean score of parent report at 13‐14, & 17‐18 years totfdrink Father drinking, composite mean score of parent report at 13‐14, & 17‐18 years totreact Reactive temperament style, high score = very reactive, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years totpersist Persistent temperament style, high score = very persistent, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years daysm02_1 Number of days smoked cigarettes in the past month, continuous outcome, young adult report at 19‐20 years daysm02_2 # Daily smoker vs remainder of sample, young adult report at 19‐20 years binged02 Number of days in past month had 5+ drinks in a session, continuous outcome, young adult report at 19‐20 years binged02_2 + 0, 1‐4, and 5+ days in past month had 5+ drinks in a session, young adult report at 19‐20 years daysm06_1 Number of days smoked cigarettes in the past month, continuous outcome, young adult report at 23‐24 years daysm06_2 Daily smoker vs remainder of sample, young adult report at 23‐24 years binged06 Number of days in past month had 5+ drinks in a session, continuous outcome, young adult report at 23‐24 years binged06_2 0, 1‐4, and 5+ days in past month had 5+ drinks in a session, young adult report at 23‐24 years * = dichotomous var ** = ordinal variable # = dichotomous outcome score + = ordinal outcome variable Illicit Drugs Variable name Description totmaltr * Experience of child maltreatment/neglect during first 18 years of life, young adult retrospective report at 23‐24 years, range 0 to 3 types of maltreatment cohes00 Family cohesion, parent report at 17‐18 years, high score = high cohesion mconflic Marital conflict over life of ATP child to 17‐18 years, high score = high conflict, parent report 17‐18 years marqual Marital relationship quality over life of ATP child to 17‐18 years, high score = high quality relationship, parent report 17‐18 years attach96 Attachment to parents, high score = high attachment, adolescent report, 13‐
14 years attach00 Attachment to parents, high score = high attachment, adolescent report, 17‐
18 years 115 alientn00 Alienation from parents, high = score = high alienation, adolescent report, 17‐18 years negpt00 Negative/conflictual parent‐teenager relationship, parent report 17‐18 years, high score = high conflict pospt00 Positive, communicative, good problem solving, parent‐teenager relationship, parent report 17‐18 years, high score = high communication/closeness superv96 Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, parent report 13‐14 years superv98 Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, parent report 15‐16 years parsup00 Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, teenager report 17‐18 years totmsmoke Mother smoking, composite mean score of parent report at 13‐14, & 17‐18 years ttotfsmoke Father smoking, composite mean score of parent report at 13‐14, & 17‐18 years totmdrink Mother drinking, composite mean score of parent report at 13‐14, & 17‐18 years totfdrink Father drinking, composite mean score of parent report at 13‐14, & 17‐18 years totreact Reactive temperament style, high score = very reactive, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years totpersist Persistent temperament style, high score = very persistent, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years totapprch Approach/sociability temperament style, high score = very approaching/sociable, composite mean score of parent report at 11‐12, 13‐
14, & 15‐16 years totilldrg02 + Number of differing types of illicit drugs used on one or more day/s in past month (range 0‐4), young adult report at 19‐20 years totilldrg02_2 + 3 level outcome variable: number of illicit drugs used on one or more day/s in past month: 0, 1, 2+ drugs; young adult report at 19‐20 years totilldrg06 + Number of differing types of illicit drugs used on one or more day/s in past month (range 0‐4), young adult report at 23‐24 years totilldrg06_2 + 3 level outcome variable: number of illicit drugs used on one or more day/s in past month: 0, 1, 2+ drugs; young adult report at 23‐24 years * = ordinal variable + = ordinal outcome variable Antisocial behaviour Variable name Description selfcptpar Self concept about relationship with parents, child report at 11‐12 years, high score = high self concept attach96 Attachment to parents, high score = high attachment, adolescent report, 13‐
14 years attach00 Attachment to parents, high score = high attachment, adolescent report, 17‐
18 years alientn00 Alienation from parents, high = score = high alienation, adolescent report, 116 Positive Family Functioning 17‐18 years cohes00 Family cohesion, parent report at 17‐18 years, high score = high cohesion totrelqual Relationship quality/warmth, high score = high quality/warmth, composite mean score of parent report at 13‐14, 15‐16 & 17‐18 years chrqual00 Relationship quality, high score = high quality, adolescent report, 17‐18 years totharshd Harsh discipline, high score = high harsh discipline, composite mean score of parent report at 13‐14, 15‐16 and 17‐18 years superv96 Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, parent report 13‐14 years superv98 Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, parent report 15‐16 years parsup00 Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, teenager report 17‐18 years inconst00 Inconsistent discipline, high score = inconsistent, parent report 17‐18 years mconflic Marital conflict over life of ATP child to 17‐18 years, high score = high conflict, parent report 17‐18 years marqual Marital relationship quality over life of ATP child to 17‐18 years, high score = high quality relationship, parent report 17‐18 years Intactf* Intact/non‐intact family over child’s lifetime to 18 years (non‐intact = separated, divorced, death of parent), parent report 17‐18 years effdivsep Effect of parental separation, divorce, death on child, high score = bad effect, parent report at 17‐18 years divsep00 Effect of parental separation, divorce, death on child, high score = bad effect, teenager report at 17‐18 years. THIS USED, BUT IF MISSING, PARENT REPORT SUBSTITUTED totmsmoke Mother smoking, composite mean score of parent report at 13‐14, & 17‐18 years ttotfsmoke Father smoking, composite mean score of parent report at 13‐14, & 17‐18 years totmdrink Mother drinking, composite mean score of parent report at 13‐14, & 17‐18 years totfdrink Father drinking, composite mean score of parent report at 13‐14, & 17‐18 years totreact Reactive temperament style, high score = very reactive, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years totpersist Persistent temperament style, high score = very persistent, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years anti02** Crime/antisocial behaviour outcome: involvement in 3 or more differing types of antisocial activities in past 12 months (including illicit substance use), young adult report at 19‐20 years anti06** Crime/antisocial behaviour outcome: involvement in 3 or more differing types of antisocial activities in past 12 months (including illicit substance use), young adult report at 23‐24 years * = dichotomous var ** = dichotomous outcome score 117 Appendix G: ATP Regression outcomes Descriptions of the variable names below are in Appendix F. Productivity Underengagement (children not employed and not studying) None of the predictor variables in Table G.1 was found to be significant in a logistic regression. Table G.1: Underengagement predictor variables attach00 Attachment to parents child age 17‐18; totrelqual Relationship quality/warmth, composite mean score of parent report at child age 13‐
14, 15‐16 & 17‐18 years (Note that this is interpreted as warmth without discipline) totharshd Harsh discipline, composite mean score of parent report at 13‐14, 15‐16 and 17‐18 years superv96 Parental monitoring/supervision of teen’s activities/associates, parent report 13‐14 years superv98 Parental monitoring/supervision of teen’s activities/associates, parent report 15‐16 years parsup00 Parental monitoring/supervision of teen’s activities/associates, teenager report 17‐18 years negpt00 Negative/conflictual parent‐teenager relationship, parent report 17‐18 years inconst00 Inconsistent discipline, high score = inconsistent, parent report 17‐18 years totmothed Mother’s education, composite mean score, parent report at 13‐14, 15‐16 & 17‐18 years totfathed Father’s education, composite mean score, parent report at 13‐14, 15‐16 & 17‐18 years totmschprb_2 Academic and social school problems, composite mean scores parent report at 13‐14 & 15‐16 years tacp94 Academic competence, teacher report at 11‐12 years posaff98 Positive affect towards school, teenager report 15‐16 years (Note that this was reverse coded) totpersist Persistent temperament style, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years Completion of high school Table G.2: Completion of high school(a)(b) attach00 95% C.I.for EXP(B) B S.E. Wald df Sig. Exp(B) Lower Upper .302 .286 1.114 1 .291 1.353 .772 2.370 totrelqual ‐1.093 .387 7.993 1 .005 .335 .157 .715 totharshd ‐.473 .341 1.920 1 .166 .623 .319 1.217 superv96 ‐.092 .461 .040 1 .842 .912 .369 2.254 118 Positive Family Functioning 95% C.I.for EXP(B) superv98 .145 .369 .154 1 .695 1.156 .561 2.381 parsup00 .729 .211 11.941 1 .001 2.073 1.371 3.134 negpt00 .383 .503 .580 1 .446 1.467 .547 3.936 inconst00 .039 .292 .018 1 .894 1.040 .587 1.842 totmothed ‐.480 .137 12.329 1 .000 .619 .473 .809 .042 .105 .157 1 .692 1.043 .848 1.281 totmschprb_2 ‐.497 .471 1.115 1 .291 .608 .242 1.531 tacp94 .023 .007 9.659 1 .002 1.023 1.008 1.037 posaff98 ‐.630 .253 6.196 1 .013 .533 .324 .875 totpersist .872 .280 9.717 1 .002 2.392 1.382 4.139 Constant 3.372 3.242 1.082 1 .298 29.128 totfathed (a) Base for comparison is not completing high school. (b) Variables highlighted in bold are significant at 0.05. Completion of high school versus completing a university degree Table G.3: Completion of high school (only) v University degree(a)(b) 95% C.I.for EXP(B) B Intercept S.E. Wald df Sig. Exp(B) Lower Upper ‐1.434 2.439 .346
1
.557
‐.522 .237 4.839
1
.028
.593 .373 .945
totrelqual .607 .278 4.752
1
.029
1.835 1.063 3.166
totharshd .099 .258 .147
1
.701
1.104 .666 1.830
superv96 .002 .339 .000
1
.995
1.002 .516 1.947
superv98 ‐.202 .300 .453
1
.501
.817 .454 1.471
parsup00 ‐.119 .167 .509
1
.476
.888 .639 1.232
negpt00 ‐.116 .355 .106
1
.745
.891 .444 1.785
inconst00 .036 .221 .026
1
.872
1.036 .672 1.598
totmothed ‐.016 .082 .038
1
.846
.984 .839 1.155
totfathed .305 .078 15.430
1
.000
1.357 1.165 1.579
totmschprb_2 ‐.301 .591 .259
1
.611
.740 .232 2.359
tacp94 attach00 21
‐.007 .005 1.953
1
.162
.993 .983 1.003
22
posaff98 .798 .207 14.855
1
.000
2.220 1.480 3.331
totpersist ‐.377 .214 3.117
1
.077
.686 .451 1.042
(a) Base for comparison is university degree. (b) Variables highlighted in bold are significant at 0.05. 21
22
This is interpreted as warmth without discipline. This was reverse coded. 119 Obesity Table G.4: Body mass index at 23‐24 years(a)(b) Std. Error B Overweight Intercept Wald df Sig. Lower Bound Exp(B) Upper Bound ‐9.801 3.294 8.853 1 .003 bmi98 .409 .058 50.528 1 .000 1.505 1.345 1.685 eatprb98 .196 .187 1.098 1 .295 1.216 .843 1.753 attach00 ‐.211 .253 .692 1 .406 .810 .493 1.331 negpt00 ‐.446 .412 1.172 1 .279 .640 .285 1.436 totusereas .290 .255 1.294 1 .255 1.337 .811 2.205 totharshd .286 .299 .915 1 .339 1.331 .741 2.394 superv96 .445 .392 1.286 1 .257 1.560 .723 3.364 superv98 ‐.202 .338 .359 1 .549 .817 .421 1.583 parsup00 .083 .189 .193 1 .660 1.087 .750 1.573 totmothed ‐.009 .094 .010 1 .921 .991 .824 1.191 totfathed .279 .091 9.311 1 .002 1.322 1.105 1.581 totreact ‐.247 .251 .970 1 .325 .781 .477 1.278 totpersist ‐.740 .234 10.021 1 .002 .477 .302 .755 totapprch .173 .203 .731 1 .393 1.189 .799 1.768 [intactf=1.00] .309 .339 .834 1 .361 1.362 .702 2.646 b
[intactf=2.00] 0 . . 0 . . . . [losewt2=.00] ‐.460 1.240 .137 1 .711 .631 .056 7.178 [losewt2=1.00] ‐.456 1.290 .125 1 .724 .634 .051 7.942 [losewt2=2.00] 0b . . 0 . . . . [diet2=.00] ‐.209 1.069 .038 1 .845 .811 .100 6.597 [diet2=1.00] .023 1.071 .000 1 .983 1.024 .126 8.346 0 . . 0 . . . . [overeat2=.00] ‐.592 .469 1.595 1 .207 .553 .221 1.387 [overeat2=1.00] ‐.064 .497 .016 1 .898 .938 .354 2.487 0 . . 0 . . . . [soc9830=0] .122 .431 .081 1 .776 1.130 .486 2.629 [soc9830=1] ‐.096 .305 .100 1 .752 .908 .499 1.651 [diet2=2.00] [overeat2=2.00] b
b
b
[soc9830=2] 0 . . 0 . . . . [soc9834=0] .149 .332 .202 1 .653 1.161 .606 2.226 [soc9834=1] .421 .323 1.701 1 .192 1.524 .809 2.870 0 . . 0 . . . . [diets98=1] .276 .601 .211 1 .646 1.318 .406 4.284 [diets98=2] ‐.518 .631 .673 1 .412 .596 .173 2.054 [diets98=3] .041 .588 .005 1 .944 1.042 .329 3.299 . . 0 . . . . [soc9834=2] [diets98=4] 120 b
b
0 Positive Family Functioning Obese Intercept ‐32.698 7.322 19.941 1 .000 bmi98 .992 .119 69.293 1 .000 2.696 2.135 3.405 eatprb98 ‐.477 .364 1.716 1 .190 .621 .304 1.267 attach00 ‐.333 .506 .433 1 .510 .717 .266 1.932 negpt00 1.940 .751 6.673 1 .010 6.962 1.597 30.346 totusereas .475 .531 .799 1 .371 1.607 .568 4.551 totharshd ‐.009 .518 .000 1 .986 .991 .359 2.736 superv96 1.487 .863 2.971 1 .085 4.426 .815 24.020 superv98 ‐.819 .625 1.718 1 .190 .441 .129 1.501 parsup00 .727 .381 3.647 1 .056 2.070 .981 4.367 totmothed ‐.158 .189 .697 1 .404 .854 .590 1.237 totfathed .831 .194 18.408 1 .000 2.296 1.571 3.356 totreact ‐.879 .488 3.248 1 .072 .415 .160 1.080 totpersist ‐1.055 .476 4.921 1 .027 .348 .137 .884 totapprch .235 .434 .293 1 .588 1.265 .540 2.960 [intactf=1.00] .189 .673 .079 1 .779 1.208 .323 4.520 b
[intactf=2.00] 0 . . 0 . . . . [losewt2=.00] .362 2.792 .017 1 .897 1.436 .006 341.757 ‐1.236 2.809 .194 1 .660 .291 .001 71.418 0 . . 0 . . . . [diet2=.00] .106 2.426 .002 1 .965 1.112 .010 129.144 [diet2=1.00] 1.039 2.429 .183 1 .669 2.827 .024 329.921 0 . . 0 . . . . [overeat2=.00] .407 1.407 .084 1 .772 1.503 .095 23.695 [overeat2=1.00] 1.750 1.427 1.503 1 .220 5.754 .351 94.412 0 . . 0 . . . . [soc9830=0] .781 .767 1.037 1 .309 2.184 .486 9.823 [soc9830=1] .390 .582 .450 1 .502 1.478 .473 4.619 [losewt2=1.00] b
[losewt2=2.00] b
[diet2=2.00] b
[overeat2=2.00] b
[soc9830=2] 0 . . 0 . . . . [soc9834=0] ‐.077 .622 .015 1 .901 .926 .274 3.130 [soc9834=1] .868 .603 2.071 1 .150 2.381 .730 7.761 0 . . 0 . . . . [diets98=1] .061 1.026 .004 1 .952 1.063 .142 7.938 [diets98=2] ‐.558 1.074 .270 1 .603 .572 .070 4.697 [diets98=3] .373 1.009 .137 1 .712 1.452 .201 10.493 . . 0 . . . . b
[soc9834=2] [diets98=4] b
0 (a) Base for comparison is normal/underweight. (b) Variables highlighted in bold are significant at 0.05. Source: AIFS analysis of ATP 121 Anxiety and depression The overlap between anxiety and depression at 19‐20 years is inTable G.5. Table G.5: ATP anxiety/depression 2002 crosstabulation Total
lovibond clinical depression levels 2002
normal
mild
moderate
severe
extremely
severe
lovibond clinical anxiety
normal
682
62
57
18
4
823
levels 2002
mild
56
17
20
5
4
102
moderate
41
32
40
13
7
133
severe
9
3
10
9
4
35
extremely
3
8
9
12
22
54
791
122
136
57
41
1147
severe
Total
Dependent variable is anxious and/or depressed. Table G.6: Logistic regression results — child age 19‐20 years (year 2002)(a)(b) B S.E. Wald df Sig. Exp(B) 95% CI Lower 95% CI
Upper anx_dep96 .809 .168 23.189 1 .000 2.245 1.615 3.120 alientn00 .636 .185 11.821 1 .001 1.888 1.314 2.712 relqual00 .090 .243 .137 1 .711 1.094 .680 1.761 totusereas ‐.083 .221 .142 1 .706 .920 .597 1.419 totharshd ‐.242 .243 .992 1 .319 .785 .487 1.264 enmesh00 .055 .131 .174 1 .677 1.056 .816 1.367 mconflic .220 .134 2.707 1 .100 1.246 .959 1.619 marqual ‐.157 .169 .871 1 .351 .854 .614 1.189 totmaltr .045 2 .978 totmaltr(1) .069 .376 .033 1 .855 1.071 .513 2.238 totmaltr(2) .091 .438 .043 1 .835 1.095 .464 2.586 totfamstrss .083 .151 .304 1 .581 1.087 .809 1.461 totreact .337 .186 3.295 1 .070 1.401 .973 2.017 totpersist ‐.329 .166 3.920 1 .048 .720 .519 .997 totapprch ‐.208 .166 1.569 1 .210 .813 .587 1.124 Constant ‐2.461 1.894 1.687 1 .194 .085 (a) Base for comparison is not anxious/depressed. (b) Variables highlighted in bold are significant at 0.05. Source: AIFS analysis of ATP. 122 Positive Family Functioning Alcohol Dependent variable is no/some/high binge drinking(a) age 19‐20 1‐4 days binge drinking B Std. Error Wald Intercept .334 2.515 .018 drink96 .645 .234 7.587 Talkdr .048 .195 attach00 ‐.087 Totusereas df
Sig. Exp(B) 95% CI 95% CI
Lower Upper .894 1 .006 1.905 1.204 3.014 .060 1 .806 1.049 .715 1.539 .212 .168 1 .682 .917 .606 1.388 ‐.079 .226 .122 1 .727 .924 .594 1.438 Totharshd .345 .274 1.577 1 .209 1.411 .824 2.416 superv96 .072 .351 .043 1 .836 1.075 .541 2.137 superv98 ‐.408 .320 1.628 1 .202 .665 .355 1.244 parsup00 .100 .162 .383 1 .536 1.106 .804 1.520 inconst00 .100 .222 .204 1 .652 1.105 .716 1.707 negpt00 .013 .360 .001 1 .970 1.014 .501 2.052 Totmdrink .046 .159 .082 1 .774 1.047 .767 1.428 Totfdrink .091 .159 .327 1 .567 1.095 .802 1.497 Totreact ‐.252 .206 1.495 1 .221 .777 .519 1.164 Totpersist .037 .188 .039 1 .844 1.038 .718 1.499 [agedrh=1.00] .259 .283 .837 1 .360 1.295 .744 2.256 . . 0 . . . . b
[agedrh=2.00] 0 5+ days binge drinking Intercept 1.283 2.682 .229 drink96 .841 .248 11.479 Talkdr .160 .213 attach00 .103 Totusereas 1 1 .632 1 .001 2.319 1.426 3.773 .566 1 .452 1.174 .773 1.781 .232 .196 1 .658 1.108 .703 1.748 ‐.293 .242 1.472 1 .225 .746 .464 1.198 Totharshd ‐.057 .299 .037 1 .848 .944 .526 1.695 superv96 .359 .371 .935 1 .333 1.432 .692 2.967 superv98 ‐.813 .328 6.128 1 .013 .444 .233 .844 parsup00 ‐.294 .169 3.038 1 .081 .745 .535 1.037 inconst00 ‐.343 .241 2.032 1 .154 .710 .443 1.137 negpt00 .377 .386 .954 1 .329 1.458 .684 3.106 Totmdrink .261 .178 2.153 1 .142 1.298 .916 1.839 totfdrink .355 .175 4.105 1 .043 1.426 1.012 2.009 Totreact ‐.228 .222 1.054 1 .305 .796 .515 1.230 Totpersist ‐.134 .199 .448 1 .503 .875 .592 1.294 [agedrh=1.00] .899 .333 7.275 1 .007 2.458 1.279 4.726 . . . . . [agedrh=2.00] b
0 0 . (a) The reference category is: 0 days binge drinking. 123 Smoking Dependent variable: Daily smoker at 19‐20 years Table G.7: Logistic regression results — child age 19‐20 years (year 2002)(a)(b) B S.E. Wald df Sig. 95% CI 95% CI
Exp(B) Lower Upper smoke96 1.384 .250 30.738 1 .000 3.989 2.446 6.505 smokeh_3 42.870 2 .000 smokeh_3(1) Missing compared to allowed ‐2.459 .653 14.197 1 .000 .085 .024 .307 smokeh_3(2) Not allowed compared to allowed ‐1.835 .292 39.417 1 .000 .160 .090 .283 attach00 .180 .215 .699 1 .403 1.197 .785 1.824 Totusereas ‐.211 .239 .780 1 .377 .809 .506 1.294 Totharshd ‐.063 .281 .050 1 .822 .939 .541 1.628 superv96 .371 .350 1.124 1 .289 1.449 .730 2.878 superv98 ‐.413 .293 1.984 1 .159 .662 .372 1.176 parsup00 .288 .166 3.029 1 .082 1.334 .964 1.846 inconst00 ‐.321 .236 1.856 1 .173 .725 .457 1.151 negpt00 1.145 .365 9.840 1 .002 3.142 1.537 6.425 totmsmoke .286 .113 6.426 1 .011 1.331 1.067 1.660 Totfsmoke ‐.164 .107 2.335 1 .126 .849 .687 1.047 Totpersist ‐.272 .187 2.099 1 .147 .762 .528 1.101 Totreact .068 .214 .100 1 .751 1.070 .704 1.627 Constant ‐2.853 2.413 1.398 1 .237 .058 (a) Base for comparison is not a daily smoker. (b) Variables highlighted in bold are significant at 0.05. Source: AIFS analysis of ATP Illicit drugs Dependent variable: number of illicit drugs used in the past month, child aged 23‐24 years(a). Unstan
Standa
95% CI 95% CI
dardise
rdised SE T Sig. Lower Upper d B B (Constant) ‐.009 .714 ‐.012 .990 ‐1.410 1.393 init_illicit96 .184 .107 .067 1.728 .084 ‐.025 .394 total n of types of maltreatment/neglect experienced .025 .059 .017 .430 .667 ‐.090 .141 ‐.002 .034 ‐.002 ‐.053 .958 ‐.069 .065 attachment to parents 2000 .061 .067 .038 .916 .360 ‐.070 .193 124 marital conflict Positive Family Functioning parent‐teen conflict 2000 Unstan
dardise
d B SE Standa
rdised B T Sig. 95% CI Lower 95% CI
Upper .154 .104 .068 1.478 .140 ‐.051 .359 new monitoring/supervision scale 1996 ‐.004 .102 ‐.002 ‐.042 .966 ‐.205 .197 new monitoring/supervision scale 1998 .002 .089 .001 .027 .979 ‐.173 .178 parental supervision/monitoring M & F 2000 ‐.120 .048 ‐.100 ‐2.496 .013 ‐.214 ‐.026 composite mother smoking 96 & 00 .084 .037 .096 2.255 .024 .011 .158 composite father smoking 96 & 00 .044 .033 .058 1.336 .182 ‐.021 .110 composite mother drinking 96 & 00 .087 .046 .081 1.880 .061 ‐.004 .177 composite father drinking 96 & 00 .056 .047 .053 1.200 .231 ‐.036 .149 composite reactivity temperament 94, 96 & 98 ‐.096 .062 ‐.072 ‐1.533 .126 ‐.218 .027 composite persistent temperament 94, 96 & 98 ‐.103 .058 ‐.077 ‐1.782 .075 ‐.216 .010 composite approach temperament 94, 86 & 98 .104 .053 .078 1.962 .050 .000 .207 (a) Variables highlighted in bold are significant at 0.05. Source: AIFS analysis of ATP Dependent variable: number of illicit drugs used in the past month, child aged 19‐20 years(a). This equation was not used because attachment to parents has an unexpected sign. Unstan
Standar
95% CI 95% CI
dardise
SE T Sig. dised B Lower Upper d B (Constant) 1.246 .568 2.194 .029 .131 2.361 init_illicit96 .143 .084 .067 1.699 .090 ‐.022 .309 total n of types of maltreatment/neglect experienced 0‐18 years .041 .048 .035 .856 .392 ‐.054 .136 marital conflict hi=hi ‐.007 .027 ‐.010 ‐.245 .806 ‐.060 .047 attachment to parents 2000 .116 .053 .091 2.182 .029 .012 .221 parent‐teen conflict 2000 .161 .083 .090 1.944 .052 ‐.002 .324 125 Unstan
dardise
d B Standar
dised B SE T Sig. 95% CI Lower 95% CI
Upper new monitoring/supervisio
n scale 1996 ‐ hi = hi ‐.102 .081 ‐.053 ‐1.258 .209 ‐.262 .057 new monitoring/supervisio
n scale 1998 ‐ hi = hi ‐.096 .071 ‐.058 ‐1.350 .177 ‐.236 .044 parental supervision/monitori
ng M & F 2000 ‐ hi = hi ‐.115 .038 ‐.122 ‐3.016 .003 ‐.190 ‐.040 composite mother smoking 96 & 00 .080 .030 .115 2.656 .008 .021 .139 composite father smoking 96 & 00 .022 .027 .037 .835 .404 ‐.030 .075 composite mother drinking 96 & 00 .013 .037 .016 .352 .725 ‐.059 .085 composite father drinking 96 & 00 .005 .038 .007 .144 .886 ‐.069 .080 composite reactivity temperament 94, 96 & 98; hi = very reactive ‐.065 .049 ‐.063 ‐1.333 .183 ‐.162 .031 composite persistent temperament 94, 96 & 98; hi = persistent ‐.125 .046 ‐.119 ‐2.701 .007 ‐.215 ‐.034 composite approach temperament 94, 86 & 98; hi = approaching .045 .042 .043 1.072 .284 ‐.038 .128 (a) Variables highlighted in bold are significant at 0.05. Source: AIFS analysis of ATP Antisocial behaviour Dependent variable is antisocial behaviour at age 19‐20 years. Comparator is that the child does not engage in antisocial behaviour (a). 95% CI 95% CI
B S.E. Wald df Exp(B) Lower Upper Sig. anti96 .273 .072 14.445 1 .000 1.314 1.141 1.513 attach00 ‐.214 .212 1.023 1 .312 .807 .533 1.222 cohes00 ‐.026 .016 2.459 1 .117 .975 .944 1.006 totrelqual .058 .275 .044 1 .834 1.059 .617 1.817 totharshd .275 .274 1.014 1 .314 1.317 .771 2.251 superv96 .167 .317 .278 1 .598 1.182 .635 2.201 superv98 ‐.086 .287 .089 1 .765 .918 .523 1.612 parsup00 ‐.270 .157 2.962 1 .085 .764 .561 1.038 inconst00 ‐.299 .222 1.807 1 .179 .742 .480 1.147 126 Positive Family Functioning Exp(B) 95% CI Lower .581 .935 .735 1.189 1 .832 1.064 .601 1.884 3.460 1 .063 1.236 .989 1.546 .108 .002 1 .965 .995 .806 1.230 .061 .160 .145 1 .703 1.063 .777 1.454 .154 .164 .885 1 .347 1.166 .846 1.607 totreact ‐.248 .200 1.533 1 .216 .780 .527 1.156 totpersist ‐.457 .180 6.433 1 .011 .633 .445 .901 Constant 1.975 2.426 .663 1 .416 7.206 B mconflic ‐.068 .123 .304 1 intactf .062 .292 .045 totmsmoke .212 .114 totfsmoke ‐.005 totmdrink totfdrink S.E. Wald df Sig. 95% CI
Upper (a) Variables highlighted in bold are significant at 0.05. Source: AIFS analysis of ATP 127 Appendix H: Mean and standard deviation of ATP variables Productivity Completed school Mean completed year 12 ‐ yes/no Std. Deviation N .9054 .29289 687 attachment to parents 2000 ‐ hi = hi 3.1398 .54840 687 composite relationship quality/warmth 96, 98 & 00 4.1655 .50427 687 composite harsh discipline 96, 98 & 00 2.1112 .47871 687 new monitoring/supervision scale 1996 ‐ hi = hi 4.7496 .32698 687 new monitoring/supervision scale 1998 ‐ hi = hi 4.5288 .40174 687 parental supervision/monitoring M & F 2000 ‐ hi = hi 3.0963 .71075 687 parent‐teen conflict 2000 1.4620 .38030 687 new inconsistent discipline scale 2000 ‐ hi = inconsistency 2.1219 .55519 687 composite across‐time mother education 96, 98 & 2000 4.2120 1.57588 687 composite across‐time father education 96, 98 & 2000 4.1218 1.70045 687 .0728 .25996 687 54.3333 22.87801 687 Positive affect towards school 1998 2.2098 .59730 687 composite persistent temperament 94, 96 & 98; hi = persistent 3.7486 .65941 687 high school problems Academic competence teacher report 1994 Std. Deviation University degree Mean highest level of education completed by 23‐24 years 3.1277 .94128 595 attachment to parents 2000 ‐ hi = hi 3.1464 .53231 595 composite relationship quality/warmth 96, 98 & 00 4.1811 .49797 595 composite harsh discipline 96, 98 & 00 2.0943 .47935 595 new monitoring/supervision scale 1996 ‐ hi = hi 4.7518 .32839 595 new monitoring/supervision scale 1998 ‐ hi = hi 4.5211 .41039 595 parental supervision/monitoring M & F 2000 ‐ hi = hi 3.0953 .71004 595 parent‐teen conflict 2000 1.4493 .37525 595 new inconsistent discipline scale 2000 ‐ hi = inconsistency 2.1158 .56214 595 composite across‐time mother education 96, 98 & 2000 4.1919 1.58055 595 composite across‐time father education 96, 98 & 2000 4.0849 1.73357 595 .0538 .22578 595 54.5849 22.67426 595 Positive affect towards school 1998 2.1934 .57797 595 composite persistent temperament 94, 96 & 98; hi = persistent 3.8037 .62737 595 high school problems Academic competence teacher report 1994 128 N Positive Family Functioning Obesity Mean Std. Deviation N bmi 3 level variable at 23‐24 years 1.4211 .64507 513 parental separation/divorce/death 1.1930 .39502 513 21.2532 3.07313 513 mother encourages child to lose weight 1.0585 .23488 513 father encourages child to lose weight 1.0468 .21138 513 child overeats at meal times ‐ currently 1.0390 .19375 513 child eats large amounts between meals currently 1.1520 .35942 513 1.36 .730 513 bmi98 Participates in school sports Participates in community sports 1.09 .870 513 attachment to parents 2000 ‐ hi = hi 3.1483 .52077 513 parent‐teen conflict 2000 1.4380 .36924 513 composite use of reasoning 96, 98 & 00 4.1118 .52027 513 composite harsh discipline 96, 98 & 00 2.0943 .48829 513 new monitoring/supervision scale 1996 ‐ hi = hi 4.7405 .34842 513 new monitoring/supervision scale 1998 ‐ hi = hi 4.5312 .40604 513 parental supervision/monitoring M & F 2000 ‐ hi = hi 3.0943 .71580 513 composite across‐time mother education 96, 98 & 2000 4.1777 1.59412 513 composite across‐time father education 96, 98 & 2000 4.0058 1.76050 513 composite reactivity temperament 94, 96 & 98; hi = very reactive 2.7694 .63242 513 composite persistent temperament 94, 96 & 98; hi = persistent 3.8379 .62523 513 composite approach temperament 94, 86 & 98; hi = approaching 3.4076 .63324 513 129 Anxiety and/or depression Mean Std. Deviation N anx_dep02 .2586 .43819 642 anx_dep96 1.0280 .63240 642 alienation from parents 2000 ‐ hi = hi 2.0366 .59188 642 new relationship quality/warmth scale 00 ‐ hi = hi 4.1555 .51807 642 composite use of reasoning 96, 98 & 00 4.1046 .53143 642 totharshd 2.0945 .50073 642 enmeshment 2000: hi = hi 3.0466 .79637 642 marital conflict hi=hi 4.3032 1.06109 642 marqual 4.1509 .73759 642 totmaltr .2617 .57785 642 composite family stress 96, 98 & 2000 .6225 .64771 642 composite reactivity temperament 94, 96 & 98; hi = very reactive 2.7746 .65687 642 composite persistent temperament 94, 96 & 98; hi = persistent 3.8017 .64665 642 composite approach temperament 94, 86 & 98; hi = approaching 3.4044 .63991 642 Drinking Mean Std. Deviation N no/some/high binge drinking 2.0983 .75426 580 age teen was first allowed to drink at home: 0‐14 yrs (=2) vs 15+ yrs (=1) 1.1603 .36724 580 drunk 3+ alcoholic drinks in life 1996 1.45 .498 580 talk with teen about drinking 2.12 .564 580 attachment to parents 2000 ‐ hi = hi 3.1532 .54560 580 composite use of reasoning 96, 98 & 00 4.0794 .53893 580 composite harsh discipline 96, 98 & 00 2.1328 .46651 580 new monitoring/supervision scale 1996 ‐ hi = hi 4.7189 .35225 580 new monitoring/supervision scale 1998 ‐ hi = hi 4.4981 .42076 580 parental supervision/monitoring M & F 2000 ‐ hi = hi 3.0410 .73866 580 new inconsistent discipline scale 2000 ‐ hi = inconsistency 2.1578 .57251 580 parent‐teen conflict 2000 1.4745 .38534 580 composite mother drinking 96 & 00 2.8888 .70414 580 composite father drinking 96 & 00 3.2836 .71330 580 composite reactivity temperament 94, 96 & 98; hi = very reactive 2.8178 .65228 580 composite persistent temperament 94, 96 & 98; hi = persistent 3.7125 .67262 580 130 Positive Family Functioning Smoking Mean daily smokers vs rest of sample 2002, 2 = smoker, 1 =non Std. Deviation N 1.1458 .35310 830 1.20 .397 830 teen allowed to smoke at home, 1 = no, 2 = yes, 3 = missing item 1.0518 .42363 830 attachment to parents 2000 ‐ hi = hi 3.1422 .54909 830 composite use of reasoning 96, 98 & 00 4.0848 .54455 830 composite harsh discipline 96, 98 & 00 2.1211 .48385 830 new monitoring/supervision scale 1996 ‐ hi = hi 4.7292 .35711 830 new monitoring/supervision scale 1998 ‐ hi = hi 4.5301 .40831 830 smoked 3+ cigarettes in life parental supervision/monitoring M & F 2000 ‐ hi = hi 3.0842 .71412 830 new inconsistent discipline scale 2000 ‐ hi = inconsistency 2.1298 .56146 830 parent‐teen conflict 2000 1.4692 .38367 830 composite mother smoking 96 & 00 1.6934 1.01063 830 composite father smoking 96 & 00 1.9627 1.16174 830 composite persistent temperament 94, 96 & 98; hi = persistent 3.7477 .67210 830 composite reactivity temperament 94, 96 & 98; hi = very reactive 2.8222 .66110 830 Illicit drugs Mean
Std. Deviation N number of illicit drugs used in past month, 23‐24 years .3840 .86587 664 init_illicit96 .1130 .31677 664 total n of types of maltreatment/neglect experienced 0‐18 years .2726 .58599 664 marital conflict hi=hi 4.3380 1.06986 664 attachment to parents 2000 ‐ hi = hi 3.1527 .53278 664 parent‐teen conflict 2000 1.4454 .37971 664 new monitoring/supervision scale 1996 ‐ hi = hi 4.7343 .35209 664 new monitoring/supervision scale 1998 ‐ hi = hi 4.5241 .40790 664 parental supervision/monitoring M & F 2000 ‐ hi = hi 3.0853 .72539 664 composite mother smoking 96 & 00 1.6506 .98207 664 composite father smoking 96 & 00 1.9066 1.12507 664 composite mother drinking 96 & 00 2.7447 .81045 664 composite father drinking 96 & 00 3.1212 .81971 664 composite reactivity temperament 94, 96 & 98; hi = very reactive 2.7751 .65045 664 composite persistent temperament 94, 96 & 98; hi = persistent 3.7982 .65138 664 composite approach temperament 94, 86 & 98; hi = approaching 3.4103 .64846 664 Variable 131 Antisocial behaviour Mean Std. Deviation N highly antisocial (3+ anti‐social acts) 2002, including drug use .1470 .35436 755 anti96 .8464 1.38300 755 2.0507 .60259 755 62.4993 8.25190 755 composite relationship quality/warmth 96, 98 & 00 4.1424 .50802 755 composite harsh discipline 96, 98 & 00 2.1181 .48623 755 new monitoring/supervision scale 1996 ‐ hi = hi 4.7307 .35280 755 new monitoring/supervision scale 1998 ‐ hi = hi 4.5212 .41065 755 alienation from parents 2000 ‐ hi = hi Family cohesion scale 2000 parental supervision/monitoring M & F 2000 ‐ hi = hi 3.0849 .72262 755 new inconsistent discipline scale 2000 ‐ hi = inconsistency 2.1322 .56067 755 marital conflict hi=hi 4.3287 1.05601 755 parental separation/divorce/death 1.1735 .37894 755 composite mother smoking 96 & 00 1.6609 .98207 755 composite father smoking 96 & 00 1.9291 1.13887 755 composite mother drinking 96 & 00 2.7258 .83344 755 composite father drinking 96 & 00 3.1099 .83315 755 composite reactivity temperament 94, 96 & 98; hi = very reactive 2.8076 .65792 755 composite persistent temperament 94, 96 & 98; hi = persistent 3.7442 .68217 755 132 Positive Family Functioning Appendix I: Detailed costing methodology and tables Disease‐cost burden analysis (DCBA) cost categories This section provides further detail on Access Economics’ standard DCBA categories used in past studies, and the data sources employed to estimate different cost components. Health system expenditures Financial costs to the Australian health system comprise the costs of running hospitals and nursing homes (buildings, care, consumables), General Practitioner (GP) and specialist services reimbursed through Medicare and private funds, the cost of prescribed and over‐the‐counter pharmaceuticals (Pharmaceutical Benefits Scheme and private), allied health services, research, and ‘other’ costs such as health administration. Australian Institute of Health and Welfare (AIHW) data are used to estimate health expenditures relating to a condition, using a top‐down approach which aggregates the total cost of different elements. Productivity losses Productivity losses are costs of lost production imposed when a person with a condition is unable to work because of that condition. Productivity losses include: ■
costs of lower employment and workforce participation, including those due to premature death; ■
■
■
costs of absenteeism; costs of lower productivity of a person at work (‘presenteeism’ costs); and additional search and hiring costs due to premature death of a person. Reduced earnings resulting from reduced workforce participation, lower productivity, absenteeism and premature death result in reduced taxation revenue collected by the Government. This includes both forgone income (personal) tax, and indirect (consumption) tax, as those with lower incomes spend less on the consumption of goods and services. Personal income tax forgone was estimated in this report as the product of the average personal income tax rate of 19.48% and forgone earnings. Consumption tax foregone was estimated by applying an indirect taxation rate of 11.68% to foregone earnings. These average taxation rates were derived for 2010 from the Access Economics Macroeconomic Model (AEM). Lost taxation revenue is considered a transfer payment, rather than an economic cost per se. However, raising additional taxation revenues imposes real efficiency costs on the Australian economy, known as deadweight losses (DWLs), which are described below. Access Economics adopts a human capital approach in estimating productivity losses in developed countries. Productivity loss estimates are informed by employment participation and absenteeism data from the National Health Survey (NHS), produced by the ABS 133 DWLs from transfers and foregone taxation revenue People with a health condition may require income support payments from the government, including Newstart Allowance (NSA) or the Disability Support Pension (DSP), and carers of these people could require carer‐related payments. These payments represent a shift of resources from one economic entity to another, and are therefore (like taxation revenue lost), not themselves economic costs but rather a financial transfer from taxpayers to welfare recipients. The real resource cost of transfer payments and taxation revenue foregone is only the associated DWL. DWLs refer to costs of administering welfare pensions and raising additional taxation revenues. These represent the loss of consumer and producer surplus, due to distortion to the equilibrium (society preferred) level of output and prices. The rate of DWL applied to transfer payments in Access Economics DCBA studies is 27.5 cents per dollar of tax revenue raised, plus 1.25 cents per dollar of tax revenue raised for Australian Taxation Office administration, based on Productivity Commission (2003) in turn derived from Lattimore (1997), i.e. 28.75% overall. Carer costs Carers are people who provide informal care to others in need of assistance or support. Most informal carers are family or friends of the person receiving care. Carers may take time off work to accompany people with a particular health condition to medical appointments, stay with them in hospital, or care for them at home. Carers may also take time off work to undertake many unpaid tasks that the person with a condition would do themselves in the absence of the health condition. Informal care is distinguished from services provided by people employed in the health and community sectors (formal care) as this care is generally provided free of charge to the recipient and is not regulated by the government. While informal care is provided free of charge, it is not free in an economic sense, as time spent caring is time that cannot be directed to other activities such as paid work, unpaid work (such as housework or yard work) or leisure. As such, informal care has associated costs in the use of economic resources. Past Access Economics DCBAs use three potential methodologies to estimate costs of informal care provision: ■
■
opportunity cost: the value of lost wages foregone by the carer; ■
self‐valuation: what carers themselves feel they should be paid. replacement valuation: the cost of buying a similar amount of services from the formal care sector; and Past Access Economics DCBAs have utilised data from the ABS Survey of Disability, Ageing and Carers (SDAC) to estimate total number of care hours provided to people with a health condition, employment status of carers and average weekly earnings foregone by carers. Other financial costs Other financial costs resulting from a health condition can include funeral costs and costs of aids and home modifications. If premature death results, there is an ‘additional’ cost of 134 Positive Family Functioning funerals borne by family and friends of people with the condition, due to increased likelihood of death. Since death and resulting funeral expenses are eventually inevitable for a person with or without a health condition, the true cost is the funeral cost brought forward (adjusted for the likelihood of dying anyway in a given year). The Bureau of Transport and Road Economics (2000) calculated a weighted average cost of a funeral across all states and territories as an average cost of $3,200 per person in 1996. If a person with a particular condition requires aids or home modifications, associated costs are not captured in formal health sector or disability services costs. Past Access Economics DCBAs have estimated costs of aids and modifications, often based from ABS SDAC data. Burden of disease (BoD) Burden of disease refers to the qualitative and quantitative analysis of the intangible costs of pain and suffering from a particular health condition or injury. These costs of disability, loss of wellbeing and premature death are more difficult to measure. In the last decade, a non‐financial approach to valuing human life has been derived, where loss of wellbeing and premature mortality – called the ‘BoD and injury’ – are measured in Disability Adjusted Life Years (DALYs). A DALY of 0 represents a year of perfect health, while a DALY of 1 represents death. Other health states are attributed values between 0 and 1 as assessed by experts on the basis of literature and other evidence of the quality of life in relative health states. Total DALYs lost from a condition are the sum of mortality and morbidity components – the Year(s) of Life Lost due to premature death (YLLs) and the Year(s) of healthy life Lost due to Disability (YLDs). To obtain monetary values from this non‐financial approach, past Access Economics studies have converted DALYs into dollar figures by applying an estimated Value of a Statistical Life Year (VSLY). This represents an estimate of the value society places on an anonymous year of life. This financial conversion tends to utilise ‘willingness to pay’ or risk‐based labour market studies. Thorough explanation of the approach, history and methodology of BoD costing is contained in the earlier scoping study for on positive family functioning (Access Economics 2009). The Department of Finance and Deregulation (DOFD, 2009) have provided an estimate of the VSLY, which appears to represent a fixed estimate of the net VSLY. This estimate was $151,000 in 2006 which inflates to approximately $166,604 in 2010 dollars. It is noted that the BoD method for determining measures of quality of life or wellbeing resulting from aspects of family functioning is conservative as it only measures illness and injuries and excludes aspects of overall family happiness and wellbeing not associated with health outcomes. Prevalence profiles This section presents prevalence profiles for all the identified outcomes in the health, productivity and social/criminality domains. These profiles assisted the estimation of numbers of people with a condition in 2010 (for health and criminality outcomes) and to develop a set of per‐person costs by age and gender for each outcome. 135 Obesity Table I.1: Obesity prevalence rates and estimated obese people (number) in 2010 Age group Prevalence rate (%) Number of obese people M F M F 0‐4 0.0% 0.0% 0 0 5‐9 7.8% 6.2% 54,846 41,005 10‐14 7.8% 6.2% 56,438 42,609 15‐19 7.8% 6.2% 172,013 45,716 20‐24 11.1% 9.3% 92,429 73,232 25‐29 19.4% 13.5% 161,874 109,684 30‐34 19.4% 13.5% 148,512 102,624 35‐39 19.9% 21.2% 160,138 172,271 40‐44 19.9% 21.2% 154,016 165,360 45‐49 23.2% 29.2% 181,347 231,562 50‐54 23.2% 29.2% 168,478 216,549 55‐59 28.5% 35.6% 186,882 238,237 60‐64 28.5% 35.6% 172,046 216,496 65‐69 22.2% 31.9% 99,989 146,567 70‐74 22.2% 31.9% 76,382 117,054 75‐79 14.2% 16.9% 36,254 49,869 80‐84 14.2% 16.9% 26,871 42,280 85‐89 14.2% 16.9% 14,340 28,662 90+ 14.2% 16.9% 5,644 15,960 Total 16.8% 18.5% 1,968,499 2,055,736 Source: Access Economics calculations using Access Economics (2008) and AE‐Dem 2010 population estimates. Anxiety and depression Table I.2: Anxiety and depression prevalence rates and estimated people with anxiety and depression (number) in 2010 Age group Prevalence rate (%) People with anxiety and depression M F M F 0‐4 0.00% 0.00% 0 0 5‐9 0.25% 0.53% 1,733 3,494 10‐14 2.52% 3.45% 18,225 23,724 15‐19 6.72% 8.43% 148,283 62,168 20‐24 9.24% 11.99% 76,937 94,451 25‐29 9.74% 13.96% 81,303 113,401 30‐34 9.81% 15.11% 75,066 114,840 35‐39 9.66% 15.78% 77,758 128,197 40‐44 9.38% 16.40% 72,623 127,935 45‐49 8.93% 16.68% 69,810 132,310 136 Positive Family Functioning Age group Prevalence rate (%) People with anxiety and depression 50‐54 8.22% 16.34% 59,698 121,163 55‐59 7.36% 15.16% 48,274 101,452 60‐64 6.52% 13.33% 39,354 81,039 65‐69 5.93% 11.38% 26,711 52,274 70‐74 5.55% 9.30% 19,113 34,111 75‐79 5.23% 7.47% 13,358 22,041 80‐84 4.89% 6.02% 9,246 15,054 85‐89 4.58% 4.85% 4,626 8,230 90+ 4.35% 3.85% 1,730 3,639 Total 6.73% 11.16% 843,848 1,239,522 Source: Access Economics calculations using Begg et al (2007) and AE‐Dem 2010 population estimates. Smoking – prevalence profile one These prevalence rates were used to estimate the number of current daily smokers in 2010 and estimate per‐person annual costs by age and gender for the categories of productivity losses, indirect costs, DWLs and carer costs. Table I.3: Smoking prevalence rates and estimated current daily smokers in 2010 Age group Prevalence rate (%) Current daily smokers M F M F 18‐19 28.01% 15.94% 89,257 48,029 20‐24 24.37% 15.73% 202,902 123,883 25‐29 23.92% 15.55% 199,595 126,341 30‐34 24.18% 14.90% 185,096 113,229 35‐39 24.36% 19.64% 196,042 159,566 40‐44 24.62% 19.67% 190,582 153,404 45‐49 23.17% 14.79% 181,083 117,283 50‐54 22.87% 15.59% 166,068 115,604 55‐59 13.48% 12.17% 88,405 81,459 60‐64 16.29% 12.37% 98,316 75,247 65‐69 9.57% 7.53% 43,106 34,596 70‐74 9.40% 7.52% 32,349 27,580 75‐79 3.67% 3.59% 9,381 10,607 80‐84 2.90% 3.62% 5,487 9,046 85‐89 3.64% 3.62% 3,677 6,133 90+ 3.64% 3.57% 1,447 3,375 Total 19.97% 13.85% 1,692,793 1,205,383 Source: Access Economics calculations using Access Economics (2007) and AE‐Dem 2010 population estimates. 137 Smoking – prevalence profile two These prevalence rates were used to estimate the number of cases of tobacco‐attributable disease in 2010 and estimate per‐person annual costs by age and gender for the categories of health system costs and BoD costs. Table I.4: Prevalence rates for tobacco‐caused diseases and conditions(a) Age group Males Females 15‐24 0.06% 0.08% 25‐34 0.04% 0.14% 35‐44 1.20% 0.73% 45‐54 7.42% 2.76% 55‐64 11.81% 4.58% 65‐74 11.92% 5.01% 75+ 16.30% 7.97% Total 4.61% 2.86% (a) Included diseases and conditions: asthma, bladder cancer, chronic obstructive pulmonary disease, fire injuries, inflammatory bowel disease, kidney cancer, larynx cancer, low birth‐weight, lower respiratory infections, lung cancer, mouth and orapharynx cancer, oesophagus cancer, otitis media, pancreatic cancer, Parkinson’s disease, sudden infant death syndrome, stomach cancer, uterus cancer, age‐related vision disorders, cervical cancer, ischaemic heart disease, peripheral arterial disease, stroke. Source: Access Economics calculations using AIHW BoD data (2004 special request) for Access Economics (2007), and AE‐Dem 2010 population estimates. Alcohol abuse Table I.5: Prevalence rates ‐ drinking at risky‐high risk(a) levels of long term health harm and estimated risky‐high risk drinkers in 2010 Age group 14–19 20–29 30–39 40–49 50–59 60+ 14+ Prevalence rate (%) M 7.70% 14.40% 10.30% 9.30% 10.70% 8.00% 10.10% F 12.30% 15.10% 9.80% 10.30% 7.40% 5.20% 9.60% Persons drinking at risky/high risk levels of long‐term harm M 71,312 240,062 161,735 144,672 147,866 158,672 917,526 F 107,863 241,586 154,132 162,021 104,400 116,679 890,619 (a) ‘Risky’ drinking defined as 29 to 42 drinks per week for males, and 15 to 28 drinks per week for females. ‘High risk’ drinking defined as 43+ drinks per week for males and 29+ drinks per week for females. Source: Access Economics calculations using AIHW’s ‘2004 National Drug Strategy Household Survey’ (2005) and AE‐
Dem population estimates (2010). 138 Positive Family Functioning Drug abuse Table I.6: Prevalence rates for recent use(a) of illicit drugs and estimated recent users in 2010 Age group 14–19 20–29 30–39 40–49 50–59 60+ 14+ Prevalence rate (%) M 20.90% 37.50% 25.50% 15% 7.60% 4.10% 18.20% F 21.80% 25.60% 15.10% 9.50% 4.80% 4% 12.50% Recent users of illicit drugs M 193,562 625,161 400,411 233,342 105,026 81,320 1,653,363 F 191,172 409,576 237,489 149,437 67,719 89,753 1,159,660 (a) ‘Recent use’ is defined as use within the last 12 months. Source: Access Economics calculations using AIHW 2004 NDSHS (2005) and AE‐Dem 2010 population estimates. Offender prevalence profile Table I.7: Offender age‐gender prevalence profile and estimated offenders in 2008‐09 Age group Offender prevalence Male 10‐14 15‐19 20‐24 25‐29 30‐34 35‐39 40‐44 45‐49 50‐54 55‐59 60‐64 65+ Total Female 10‐14 15‐19 20‐24 25‐29 30‐34 35‐39 40‐44 45‐49 50‐54 55‐59 60‐64 65+ Total Estimated offenders 2.286% 8.821% 6.398% 4.452% 3.700% 3.007% 2.286% 1.539% 0.955% 0.644% 0.494% 0.136% 2.838% 16,474 67,695 51,828 35,764 27,655 24,099 17,361 11,975 6,804 4,167 2,889 1,815 268,525 1.096% 2.950% 1.471% 1.104% 0.984% 0.856% 0.666% 0.449% 0.257% 0.155% 0.102% 0.028% 0.786% 7,497 21,427 11,351 8,626 7,322 6,932 5,110 3,556 1,866 1,023 599 440 75,749 Source: Access Economics calculations using ABS 4519.0 (2009c; 2010b) and AE‐Dem 2009 population estimates. 139 Prisoner prevalence profile Table I.8: Prisoner age‐gender prevalence rates and estimated prisoners in 2009 Age group Prisoner prevalence rate (%) Male <18 18 19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65+ Total Female <18 18 19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65+ Total Estimated prisoners 0.001% 0.175% 0.382% 0.545% 0.633% 0.616% 0.526% 0.390% 0.255% 0.181% 0.112% 0.084% 0.037% 0.250% 37 273 612 4,418 5,088 4,602 4,214 2,963 1,987 1,290 724 492 492 27,192 0.000% 0.009% 0.025% 0.034% 0.053% 0.052% 0.045% 0.033% 0.024% 0.014% 0.007% 0.005% 0.001% 0.019% 3 14 38 266 416 390 363 256 186 102 49 30 14 2,127 Source: Access Economics calculations using ABS 4517.0 (2009) and AE‐Dem 2009 population estimates. Health outcomes – per person annual costs The sections below present the sets of calculated per‐person costs for each health outcome, by age and gender. These costs were used as inputs to the modelling of lifetime costs. Obesity Table I.9: Annual per‐person costs of obesity ‐ males (in 2010 dollars) Health Productivity Other financial(a) BoD Total 0‐4 ‐ ‐ ‐ ‐ ‐ 5‐9 225 415 308 5,701 6,649 140 Positive Family Functioning Health Productivity Other financial(a) BoD Total 10‐14 226 415 309 5,712 6,661 15‐19 256 472 350 6,484 7,562 20‐24 324 596 443 8,195 9,557 25‐29 534 981 729 13,492 15,737 30‐34 530 975 724 13,403 15,633 35‐39 544 999 742 13,735 16,020 40‐44 542 996 740 13,694 15,972 45‐49 612 1,124 835 15,450 18,020 50‐54 608 1,117 830 15,364 17,920 55‐59 698 1,282 953 17,632 20,566 60‐64 694 1,275 947 17,533 20,450 65‐69 572 1,051 781 14,449 16,853 70‐74 566 1,039 772 14,291 16,669 75‐79 386 710 527 9,760 11,383 80‐84 384 705 524 9,697 11,310 85‐89 380 699 519 9,613 11,212 90+ 378 695 516 9,555 11,144 (a) ’Other financial’ category includes DWL from transfers, carer costs and other indirect costs. Source: Access Economics calculations. Table I.10: Annual per‐person costs of obesity ‐ females (in 2010 dollars) Health Productivity Other financial(a) BoD Total 0‐4 0 0 0 0 0 5‐9 146 269 200 3,704 4,320 10‐14 146 270 201 3,715 4,332 15‐19 147 272 202 3,734 4,355 20‐24 228 420 312 5,772 6,732 25‐29 329 604 449 8,310 9,692 30‐34 327 601 447 8,265 9,640 35‐39 486 893 664 12,285 14,328 40‐44 484 890 661 12,241 14,277 45‐49 837 15,485 18,060 613 1,126 50‐54 609 1,119 831 15,389 17,948 55‐59 678 1,246 925 17,129 19,978 60‐64 673 1,236 918 16,990 19,816 65‐69 621 1,141 848 15,687 18,297 70‐74 614 1,127 837 15,499 18,077 75‐79 380 699 519 9,610 11,208 80‐84 378 695 517 9,561 11,151 85‐89 375 689 512 9,474 11,050 141 90+ Health Productivity Other financial(a) BoD Total 371 683 507 9,393 10,955 (a) ’Other financial’ category includes DWL from transfers, carer costs and other indirect costs. Source: Access Economics calculations. Anxiety and depression Table I.11: Annual per‐person costs of anxiety and depression ‐ males (in 2010 dollars) Health Productivity Other financial(a) BoD Total 0‐4 ‐ ‐ ‐ ‐ ‐ 5‐9 71 333 65 763 1,231 10‐14 401 1,787 359 4,532 7,080 15‐19 1,723 8,016 1,568 18,783 30,089 20‐24 2,144 9,998 1,953 23,319 37,414 25‐29 2,264 10,568 2,063 24,621 39,516 30‐34 2,265 10,568 2,063 24,621 39,517 35‐39 2,239 10,447 2,040 24,346 39,072 40‐44 2,166 10,105 1,973 23,562 37,806 45‐49 2,059 9,601 1,876 22,411 35,947 50‐54 1,877 8,743 1,709 20,446 32,775 55‐59 1,655 7,696 1,506 18,050 28,907 60‐64 1,438 6,673 1,307 15,710 25,128 65‐69 1,270 5,882 1,154 13,901 22,207 70‐74 1,163 5,378 1,056 12,748 20,345 75‐79 1,072 4,949 973 11,767 18,761 80‐84 979 4,508 887 10,757 17,131 85‐89 893 4,106 809 9,838 15,646 90+ 830 3,809 752 9,158 14,549 (a) ’Other financial’ category includes DWL from transfers, carer costs and other indirect costs. Source: Access Economics calculations. Table I.12: Annual per‐person costs of anxiety and depression ‐ females (in 2010 dollars) Health Productivity Other financial(a) BoD Total 0‐4 ‐ ‐ ‐ ‐ ‐ 5‐9 ‐ ‐ ‐ ‐ ‐ 10‐14 91 431 84 986 1,592 15‐19 276 1,194 244 3,176 4,889 20‐24 1,053 4,857 955 11,557 18,423 25‐29 1,566 7,277 1,425 17,093 27,361 30‐34 1,833 8,534 1,669 19,968 32,004 35‐39 1,969 9,175 1,793 21,435 34,372 142 Positive Family Functioning Health Productivity Other financial(a) BoD Total 40‐44 2,065 9,626 1,880 22,467 36,038 45‐49 2,134 9,953 1,944 23,214 37,244 50‐54 2,172 10,129 1,978 23,618 37,897 55‐59 2,117 9,872 1,928 23,030 36,947 60‐64 1,955 9,110 1,780 21,287 34,133 65‐69 1,710 7,953 1,556 18,639 29,858 70‐74 1,426 6,617 1,297 15,583 24,923 75‐79 1,128 5,213 1,024 12,370 19,735 80‐84 861 3,952 779 9,486 15,078 85‐89 644 2,931 581 7,150 11,306 90+ 464 2,080 416 5,203 8,163 (a) ’Other financial’ category includes DWL from transfers, carer costs and other indirect costs. Source: Access Economics calculations. Smoking Table I.13: Annual per‐person costs of current daily smoking ‐ males (in 2010 dollars) Age group Health Productivity Other financial(a) BoD Total 18‐19 48 4,473 815 4,197 9,533 20‐24 48 4,295 783 4,197 9,323 25‐29 83 4,240 773 7,260 12,356 30‐34 83 4,244 774 7,260 12,360 35‐39 424 4,283 781 37,047 42,534 40‐44 424 4,302 784 37,047 42,557 45‐49 1,598 4,123 752 139,737 146,209 50‐54 1,598 4,063 741 139,737 146,138 55‐59 2,653 2,650 483 232,030 237,817 60‐64 2,653 3,089 563 232,030 238,336 65‐69 2,907 1,928 351 254,182 259,368 70‐74 2,907 1,880 343 254,182 259,312 75‐79 4,623 774 141 404,218 409,756 80‐84 4,623 613 112 404,218 409,565 85‐89 4,623 758 138 404,218 409,736 90+ 4,623 754 137 404,218 409,732 (a) ’Other financial’ category includes DWL from transfers, carer costs and other indirect costs. Source: Access Economics calculations. Table I.14: Annual per‐person costs of current daily smoking ‐ females (in 2010 dollars) Age group 18‐19 Health Productivity Other financial* BoD Total 46 4,271 779 3,987 9,082 143 Age group 20‐24 Health Productivity Other financial* BoD Total 9,281 46 4,439 809 3,987 25‐29 79 4,407 804 6,897 12,186 30‐34 79 4,230 771 6,897 11,977 35‐39 402 5,318 969 35,194 41,884 40‐44 402 5,306 967 35,194 41,871 45‐49 1,518 4,218 769 132,750 139,255 50‐54 1,518 4,386 800 132,750 139,453 55‐59 2,521 3,529 643 220,429 227,121 60‐64 2,521 3,558 649 220,429 227,156 65‐69 2,761 2,247 410 241,473 246,891 70‐74 2,761 2,224 405 241,473 246,863 75‐79 4,391 1,100 201 384,007 389,699 80‐84 4,391 1,102 201 384,007 389,701 85‐89 4,391 1,094 199 384,007 389,691 90+ 4,391 1,074 196 384,007 389,668 (a) ’Other financial’ category includes DWL from transfers, carer costs and other indirect costs. Source: Access Economics calculations. Alcohol abuse Table I.15: Annual per‐person costs of alcohol abuse ‐ males (in 2010 dollars) Age group 14–19 20–29 30–39 40–49 50–59 60+ Health Productivity Other financial(a) BoD Total 837 1,680 1,204 1,083 1,230 959 1,481 2,953 2,121 1,910 2,167 1,694 1,629 3,248 2,332 2,101 2,383 1,863 1,879 3,747 2,691 2,423 2,749 2,149 5,827 11,628 8,348 7,517 8,529 6,666 (a) ‘Other financial’ category includes accidents not elsewhere included, fires not elsewhere included and abusive consumption costs. Source: Access Economics calculations using Collins and Lapsley (2008) – costs adapted to Access Economics DCBA categories. Table I.16: Annual per‐person costs of alcohol abuse ‐ females (in 2010 dollars) Age group 14–19 20–29 30–39 40–49 144 Health Productivity Other financial(a) BoD Total 1,440 1,877 1,233 1,297 2,534 3,298 2,173 2,284 2,787 3,626 2,389 2,511 3,215 4,183 2,757 2,897 9,975 12,984 8,552 8,989 Positive Family Functioning Age group 50–59 60+ Health Productivity Other financial(a) BoD Total 905 660 1,598 1,171 1,758 1,288 2,028 1,485 6,288 4,603 (a) ‘Other financial’ category includes accidents not elsewhere included, fires not elsewhere included and abusive consumption costs. Source: Access Economics calculations using Collins and Lapsley (2008) – costs adapted to Access Economics DCBA categories Illicit drug abuse Table I.17: Annual per‐person costs of illicit drug abuse ‐ males (in 2010 dollars) Age group 14–19 20–29 30–39 40–49 50–59 60+ Health Productivity Other financial(a) BoD Total 65 114 84 48 16 1 528 919 684 398 143 19 455 791 589 343 123 17 408 710 528 307 111 15 1,457 2,534 1,885 1,096 393 52 (a) ‘Other financial’ category includes accidents not elsewhere included, fires not elsewhere included and abusive consumption costs. Source: Access Economics calculations using Collins and Lapsley (2008) – costs adapted to Access Economics DCBA categories . Table I.18: Annual per‐person costs of illicit drug abuse ‐ females (in 2010 dollars) Age group 14–19 20–29 30–39 40–49 50–59 60+ Health Productivity Other financial(a) BoD Total 109 136 82 48 15 11 877 1,095 668 393 131 102 756 943 576 338 113 88 678 847 516 304 101 79 2,420 3,021 1,842 1,082 360 280 (a) ‘Other financial’ category includes accidents not elsewhere included, fires not elsewhere included and abusive consumption costs. Source: Access Economics calculations using Collins and Lapsley (2008) – costs adapted to Access Economics DCBA categories. Productivity outcomes – per person annual costs The sections below present the sets of calculated per‐person costs for each productivity outcome, by age and gender as well as the data sources used to derive these. These costs were used as inputs to the modelling of lifetime costs. 145 Data sources: age‐gender employment rates and average weekly earnings Table I.19: Age‐gender employment in the general population Total people employed in 2008 (‘000) Age group Males 15 ‐ 19 20 ‐ 24 25 ‐ 29 30 ‐ 34 35 ‐ 39 40 ‐ 44 45 ‐ 49 50 ‐ 54 55 ‐ 59 60 ‐ 64 65 + Total Females 15 ‐ 19 20 ‐ 24 25 ‐ 29 30 ‐ 34 35 ‐ 39 40 ‐ 44 45 ‐ 49 50 ‐ 54 55 ‐ 59 60 ‐ 64 65 + Total Population in 2008 (‘000) 361 592 652 637 678 632 648 569 456 311 168 5,705 Employment rate 757 783 766 736 794 754 768 699 639 564 1,286 8,544 357 542 516 499 540 552 593 500 384 204 78 4,767 47.77% 75.64% 85.11% 86.57% 85.42% 83.79% 84.45% 81.45% 71.46% 55.18% 13.03% 66.77% 715 748 747 737 804 762 782 711 646 563 1,546 8,762 50.00% 72.56% 69.04% 67.70% 67.18% 72.47% 75.82% 70.39% 59.40% 36.30% 5.05% 54.41% Source: Access Economics calculations using ABS 6105.0 (2009) and AE‐Dem 2008 population estimates. Table I.20: Age‐gender average weekly earnings(a) for the general population ($) Age group Males Females 15 ‐ 19 20 ‐ 24 25 ‐ 29 30 ‐ 34 35 ‐ 39 40 ‐ 44 45 ‐ 49 50 ‐ 54 55 ‐ 59 60 ‐ 64 65 + 336 750 1,038 1,264 1,406 1,452 1,380 1,345 1,239 1,251 336 236 598 849 905 865 823 861 848 807 749 632 (a) 2008 figures for full‐time and part‐time workers were inflated to December 2009. Source: ABS 6310.0 (2009b) and ABS 6345.0 (2010a). 146 Positive Family Functioning Year 12 non‐completion Table I.21: Annual costs of year 12 non‐completion ($ 2010) Age group Males 15 ‐ 19 20 ‐ 24 25 ‐ 29 30 ‐ 34 35 ‐ 39 40 ‐ 44 45 ‐ 49 50 ‐ 54 55 ‐ 59 60 ‐ 64 65 + Females 15 ‐ 19 20 ‐ 24 25 ‐ 29 30 ‐ 34 35 ‐ 39 40 ‐ 44 45 ‐ 49 50 ‐ 54 55 ‐ 59 60 ‐ 64 65 + Lower employment(a) Lower productivity(b) Deadweight losses Total cost 475 1,683 2,619 3,242 3,560 3,606 3,454 3,248 2,624 2,046 373 1,006 3,563 5,545 6,866 7,538 7,636 7,314 6,878 5,556 4,334 789 133 470 731 906 994 1,007 965 907 733 572 104 1,614 5,715 8,895 11,013 12,092 12,249 11,733 11,033 8,912 6,951 1,266 472 1,736 2,348 2,452 2,326 2,387 2,615 2,389 1,918 1,089 128 572 2,102 2,842 2,969 2,816 2,890 3,166 2,893 2,323 1,318 155 94 344 465 486 461 473 518 473 380 216 25 1,138 4,182 5,655 5,906 5,602 5,750 6,298 5,755 4,621 2,622 308 (a) Proxied by reduced probability of workforce participation. (b) Proxied by reduced AWE. Source: Access Economics calculations. Undergraduate non‐completion Table I.22: Annual costs of undergraduate degree non‐completion ($ 2010) Age group Males 20 ‐ 24 25 ‐ 29 30 ‐ 34 35 ‐ 39 40 ‐ 44 45 ‐ 49 50 ‐ 54 55 ‐ 59 60 ‐ 64 65 + Lower employment(a) Lower productivity(b) Deadweight losses Total cost 2,568 3,997 4,949 5,433 5,504 5,272 4,958 4,005 3,123 569 10,349 16,107 19,942 21,895 22,179 21,245 19,978 16,137 12,587 2,292 1,157 1,801 2,230 2,448 2,480 2,376 2,234 1,804 1,407 256 14,074 21,904 27,120 29,777 30,163 28,892 27,169 21,946 17,118 3,117 147 Age group Females 20 ‐ 24 25 ‐ 29 30 ‐ 34 35 ‐ 39 40 ‐ 44 45 ‐ 49 50 ‐ 54 55 ‐ 59 60 ‐ 64 65 + Lower employment(a) Lower productivity(b) Deadweight losses Total cost 3,698 5,000 5,223 4,953 5,084 5,569 5,089 4,086 2,319 272 6,918 9,354 9,770 9,267 9,512 10,419 9,521 7,644 4,338 509 951 1,286 1,343 1,274 1,308 1,432 1,309 1,051 596 70 11,566 15,640 16,336 15,494 15,904 17,420 15,918 12,781 7,253 851 (a) Proxied by reduced probability of workforce participation. (b) Proxied by reduced AWE. Source: Access Economics calculations. Undergraduate non‐completion rates were derived to assist the application of undergraduate non‐completion costs to the ATP cohort who obtained TERs (i.e. completed year 12). Derived rates are presented in Table I.23. Table I.23: Age‐specific undergraduate non‐completion rates in 2007 Age % who did not complete undergraduate studies 20 21 22 23 24 25 26 27 28 29 30‐34 35‐39 40‐44 45‐49 50‐54 55‐59 60‐64 65+ 72.6% 58.1% 53.3% 56.3% 64.0% 68.4% 72.3% 75.3% 76.5% 77.3% 79.1% 79.1% 78.1% 78.1% 77.3% 77.3% 77.8% 77.8% Source: Access Economics calculations using a DEEWR data request from 2009. Criminality outcomes – per person annual costs The sections below present the sets of calculated per‐person costs in terms of policing, prison system costs, court system costs and societal costs, by age and gender. These costs were used as inputs to the modelling of lifetime costs. 148 Positive Family Functioning Policing costs Table I.24: Annual policing cost per offender ($) Age group Males Females 10‐14 15‐19 20‐24 25‐29 30‐34 35‐39 40‐44 45‐49 50‐54 55‐59 60‐64 65+ 6,438 24,848 18,023 12,540 10,423 8,471 6,439 4,335 2,689 1,814 1,393 384 10,234 27,554 13,739 10,316 9,187 7,993 6,225 4,196 2,402 1,452 956 259 Source: Access Economics calculations. Court system costs Table I.25: Annual court system cost per offender ($) Age group Males Females 10‐14 15‐19 20‐24 25‐29 30‐34 35‐39 40‐44 45‐49 50‐54 55‐59 60‐64 65+ 589 2,275 1,650 1,148 954 775 589 397 246 166 127 35 1,020 2,745 1,369 1,028 915 796 620 418 239 145 95 26 Source: Access Economics calculations. Prison system costs Table I.26: Annual prison system cost per prisoner ($) Age group Males Females <18 18 19 20–24 25–29 30–34 35–39 40–44 45–49 253 31,155 67,964 97,037 112,682 109,548 93,558 69,412 45,434 262 20,416 54,307 74,237 114,718 112,840 96,510 71,899 50,615 149 Age group Males Females 50–54 55–59 60–64 65+ 32,206 19,910 14,982 6,580 30,269 16,037 11,036 1,899 Source: Access Economics calculations. Societal costs Table I.27: Annual per‐person societal costs of crime for males ($) Age group 10‐14 15‐19 20‐24 25‐29 30‐34 35‐39 40‐44 45‐49 50‐54 55‐59 60‐64 65+ Medical Lost output Intangible cost(a) Property damage/ loss Other cost(b) Total societal cost 1,029 3,972 2,881 2,005 1,666 1,354 1,029 693 430 290 223 61 4,416 17,043 12,362 8,601 7,149 5,810 4,416 2,973 1,845 1,244 955 264 4,514 17,421 12,636 8,792 7,308 5,939 4,515 3,039 1,886 1,272 976 269 12,783 49,334 35,783 24,898 20,694 16,819 12,784 8,607 5,340 3,602 2,765 763 9,501 36,666 26,595 18,504 15,380 12,500 9,501 6,397 3,968 2,677 2,055 567 32,243 124,436 90,257 62,800 52,196 42,422 32,246 21,710 13,468 9,086 6,975 1,924 (a) ‘Intangible’ costs are those due to pain, suffering and lost QoL (Rollings, 2008). (b) Those costs non‐separable into above categories. Source: Access Economics calculations. Table I.28: Annual per‐person societal costs of crime for females ($) Age group 10‐14 15‐19 20‐24 25‐29 30‐34 35‐39 40‐44 45‐49 50‐54 55‐59 60‐64 65+ Medical Lost output Intangible cost(a) Property damage/ loss Other cost(b) Total societal cost 1,636 4,405 2,196 1,649 1,469 1,278 995 671 384 232 153 41 7,021 18,902 9,425 7,077 6,302 5,483 4,270 2,879 1,647 996 656 177 7,177 19,322 9,634 7,234 6,442 5,605 4,365 2,943 1,684 1,018 671 181 20,323 54,717 27,282 20,485 18,244 15,872 12,361 8,333 4,769 2,883 1,899 514 15,104 40,667 20,277 15,225 13,559 11,796 9,187 6,193 3,544 2,143 1,411 382 51,261 138,014 68,814 51,669 46,017 40,035 31,178 21,019 12,029 7,271 4,790 1,296 (a) ‘Intangible’ costs are those due to pain, suffering and lost QoL (Rollings, 2008). (b) Those costs non‐separable into above categories. Source: Access Economics calculations. 150 Positive Family Functioning Crime probabilities The sections below provide detail on the derivation of crime probabilities. Crime probabilities were derived to ascertain which crime cost types (i.e. policing, court, prison) would be incurred, dependent on crime type. Societal costs were assumed to apply to all crimes, regardless of the crime being reported. Probability of crime being reported Probabilities of a crime being reported to police were derived from the Australian Institute of Criminology report (Rollings 2008). Rollings (2008) presented a set of multipliers for each crime type, which were applied to crime frequencies in her study, to account for crimes which were not reported to police due to non‐detection, being ‘victimless’ or victims’ fear of reprisal. The inverse of the relevant multiplier was taken as a proxy for the probability of the crime being reported to police. The crime categories in Rollings (2008) were matched and adapted as best as possible to the ABS crime categories presented in section 5.3.5. The final set of crime categories, multipliers and attached probabilities are presented in Table I.29. Table I.29: Crime under‐reporting multipliers and derived probabilities Multiplier Probability of crime being reported Homicide and related offences 1.0 100% Acts intended to cause injury* 5.2 19% Sexual assault and related offences 5.3 19% Dangerous or negligent acts endangering people* 5.2 19% Abduction/harassment/other offences against the person* 5.2 Crime category Robbery, extortion and related offences 19% ‐ against individual 7.2 14% ‐ against commercial 1.2 83% 3.4 29% Unlawful entry with intent (ie: burglary) 1 100% ‐ from vehicles 2.8 36% ‐ shop theft 100 1% ‐ other theft 2.7 37% 4.0 25% Illicit drug offences*** ‐ ‐ Prohibited/regulated weapons and explosives offences*** ‐ Theft and related offences ‐ of vehicles Fraud, deception and related offences ‐ Property damage and environmental pollution** 4.3 23% Public order offences** 4.3 23% Offences against justice** 4.3 23% Miscellaneous offences*** ‐ ‐ * These crimes were all assumed to have the same multiplier – ‘physical assault’. ** These crimes were all assumed to have the same multiplier – ‘criminal damage’. 151 *** No relevant multipliers given for these crime categories in Rollings (2008). Source: Access Economics calculations using Rollings (2008) and crime categories from ABS 4519.0 (2010b). Probability of court action on a reported crime 18B
Probabilities of court action for a reported crime were derived from 2008‐09 data on police proceedings from the ABS publication, ‘Recorded Crime – Offenders, 2008‐09’ (ABS 2010b). Some limitations are noted, which were due to indivisibilities in the available ABS data: ■
It was assumed both commercial and individual robberies had the same court action probability. ■
It was assumed that all theft and related offences had the same court action probability. Since robbery and theft types can differ in their seriousness and thus probability of court action, assuming the same probabilities may not be the ideal approach, but is the best approach warranted by the available data. The final set of probabilities is presented in Table I.30. X
X
Table I.30: Probabilities of court action on reported crimes Crime category Court action Non‐court action Homicide and related offences 99.19% 0.81% Acts intended to cause injury 94.09% 5.91% Sexual assault and related offences 92.63% 7.37% Dangerous or negligent acts endangering people 86.18% 13.82% Abduction/harassment/other offences against the person 92.30% 7.70% Robbery, extortion and related offences* ‐ against individual 96.49% 3.51% ‐ against commercial 96.49% 3.51% 84.31% 15.69% ‐ of vehicles 58.26% 41.74% ‐ from vehicles 58.26% 41.74% ‐ shop theft 58.26% 41.74% ‐ other theft 58.26% 41.74% Fraud, deception and related offences 92.57% 7.43% Illicit drug offences 57.33% 42.67% Prohibited/regulated weapons and explosives offences 90.18% 9.82% Unlawful entry with intent (ie: burglary) Theft and related offences** 152 Positive Family Functioning Crime category Court action Non‐court action Property damage and environmental pollution** 71.86% 28.14% Public order offences 58.71% 41.29% Offences against justice 81.77% 18.23% Miscellaneous offences 20.67% 79.33% * Same court‐action probability assumed for both types of robbery – individual and commercial. ** Same court‐action probability assumed for all types of theft. Source: Access Economics calculations using ABS 4519.0 (2010b). Probability of ‘guilty’ verdict in a court finalisation 19B
Probabilities of a guilty verdict in court finalisations were derived from 2008‐09 data on criminal court finalisations from the ABS publication, ‘Criminal Courts, Australia, 2008‐09’ (ABS 2010c). The final set of probabilities by court level is presented in Table I.31. X
X
Table I.31: Court finalisation outcome probabilities Court level Guilty Acquitted Transferred to other court Withdrawn Other Higher courts 6.64% 78.61% 0.81% 13.64% 0.30% Magistrate 3.96% 86.86% 2.07% 7.03% 0.08% Children’s 3.88% 77.15% 3.47% 10.27% 5.23% All courts 4.02% 86.07% 2.12% 7.39% 0.40% Source: Access Economics calculations using ABS 4513.0 (2010c). Probability of a custodial sentence 120B
Probabilities of a custodial sentence being given in ‘guilty verdict’ court finalisations were derived from 2008‐09 data on criminal court finalisations from the ABS publication, ‘Criminal Courts, Australia, 2008‐09’ (ABS 2010c). The final set of probabilities by court level is presented in Table I.32. X
X
Table I.32: Custodial sentence probabilities in guilty verdict court cases Court level Custodial sentence Non‐custodial sentence 84.57% 8.63% 9.36% 10.39% 15.43% 91.25% 90.14% 89.48% Higher courts Magistrate Children’s All courts Source: Access Economics calculations using ABS 4513.0 (2010c). 153