Matern Child Health J
DOI 10.1007/s10995-011-0940-4
Environments for Healthy Living (EFHL) Griffith Birth Cohort
Study: Background and Methods
Cate M. Cameron • Paul A. Scuffham • Anneliese Spinks • Rani Scott •
Neil Sipe • ShuKay Ng • Andrew Wilson • Judy Searle • Ronan A. Lyons
Elizabeth Kendall • Kim Halford • Lyn R. Griffiths • Ross Homel •
Roderick J. McClure
•
Ó Springer Science+Business Media, LLC 2012
Abstract The health of an individual is determined by
the interaction of genetic and individual factors with wider
social and environmental elements. Public health approaches to improving the health of disadvantaged populations
will be most effective if they optimise influences at each of
these levels, particularly in the early part of the life course.
In order to better ascertain the relative contribution of these
multi-level determinants there is a need for robust studies,
longitudinal and prospective in nature, that examine individual, familial, social and environmental exposures. This
paper describes the study background and methods, as it
has been implemented in an Australian birth cohort study,
Environments for Healthy Living (EFHL): The Griffith
Study of Population Health. EFHL is a prospective, multilevel, multi-year longitudinal birth cohort study, designed
to collect information from before birth through to adulthood across a spectrum of eco-epidemiological factors,
including genetic material from cord-blood samples at
birth, individual and familial factors, to spatial data on the
living environment. EFHL commenced the pilot phase of
recruitment in 2006 and open recruitment in 2007, with a
target sample size of 4000 mother/infant dyads. Detailed
Trial Registration Number: ACTRN12610000931077.
C. M. Cameron (&) P. A. Scuffham A. Spinks R. Scott S. Ng J. Searle R. J. McClure
School of Medicine, Griffith University, Logan Campus,
L03 2.45, University Drive, Meadowbrook, QLD 4131, Australia
e-mail: [email protected]
P. A. Scuffham
e-mail: [email protected]
A. Spinks
e-mail: [email protected]
R. Scott
e-mail: [email protected]
S. Ng
e-mail: [email protected]
J. Searle
e-mail: [email protected]
R. J. McClure
e-mail: [email protected]
C. M. Cameron P. A. Scuffham N. Sipe S. Ng E. Kendall
Griffith Health Institute, Griffith University, Southport,
QLD 4222, Australia
N. Sipe
e-mail: [email protected]
E. Kendall
e-mail: [email protected]
A. Spinks
Commonwealth Scientific and Industrial Research Organisation
(CSIRO) Ecosystem Sciences, Dutton Park, QLD 4102,
Australia
N. Sipe
School of Environment, Griffith University, Nathan, QLD 4111,
Australia
A. Wilson
Faculty of Health, Queensland University of Technology,
Kelvin Grove, QLD 4059, Australia
e-mail: [email protected]
R. A. Lyons
School of Medicine, Swansea University, Singleton Park,
Swansea SA2 8PP, Wales UK
e-mail: [email protected]
E. Kendall
School of Human Services and Social Work, Griffith University,
Meadowbrook, QLD 4131, Australia
123
Matern Child Health J
information on each participant is obtained at birth,
12-months, 3-years, 5-years and subsequent three to five
yearly intervals. The findings of this research will provide
detailed evidence on the relative contribution of multi-level
determinants of health, which can be used to inform social
policy and intervention strategies that will facilitate healthy
behaviours and choices across sub-populations.
Keywords Child Cohort studies Longitudinal studies Epidemiology Methods
Introduction
The health of an individual is determined by the interaction
of a number of factors operating at multiple levels [1, 2],
including the environment, economic and social context,
family interactions, individual behaviours and genetic
factors. Figure 1 presents an eco-epidemiological model of
health demonstrating the multi-level nature of factors
which may influence various health outcomes [3, 4]. These
multi-level influences operate throughout the life course,
with prenatal and early years of life having a crucial role in
later outcomes [5–8]. Public health approaches to
improving the health of disadvantaged populations will be
most effective if they optimise influences at each of the
subordinate levels, particularly in the early part of the life
course.
Environments and environmental exposures can have
profound effects on child development and behaviour.
Environmental injury hazards are related to child safety,
and spatial relationships between building structures affect
opportunities children have for health advancement [9].
There is evidence that certain environments discourage
physical activity and encourage excess consumption of
food [10]. Characteristics of environments that appear to
discourage walking behavior include lack of public
K. Halford
School of Psychology, University of Queensland, Brisbane,
QLD 4072, Australia
e-mail: [email protected]
L. R. Griffiths
Genomics Research Centre, Griffith Health Institute,
Griffith University, Southport, QLD 4222, Australia
e-mail: [email protected]
R. Homel
School of Criminology and Criminal Justice, Griffith University,
Mt Gravatt, QLD 4122, Australia
e-mail: [email protected]
R. J. McClure
Monash University Accident Research Centre,
Monash University, Clayton, VIC 3800, Australia
123
Fig. 1 Ecological model of health across the life course (Nicholson
and Sanson 2003 modified with permission from Lynch 2000)
reproduced with permission from the Medical Journal of Australia
transport and poor ‘walkability/safety’ of urban streets
[11]. Proximity to fast food outlets and poor access to fresh
fruit and vegetables may contribute to unhealthy eating
patterns [11, 12]. Children are considered a specific risk
group from environmental night noise exposures with
consequences such as skill learning problems, poor memory as well as depression and obesity [13–15].
A lack of social and economic resources has the
potential to place children and young people at risk of a
number of issues such as developmental problems, [16]
school failure, [17] conduct problems [18] and poor
physical and mental health [19]. Factors such as maternal
depression, family structure and exposure to violence have
been found to have a negative impact on child health and
development [20]. The mechanisms of the health promoting effects of family, and how they interact within social
contexts, are only partially understood, but include promotion of healthier lifestyles, [21] modelling of effective
interpersonal skills [22] and affect regulation [22].
Gene-environment interactions are central in any multilevel explanation of health determinants [23, 24]. Studies
of attention deficit hyperactivity disorder (ADHD) have
suggested a causal interaction between genetic and environmental factors, such as toxins in utero and pregnancy
and delivery complications [25]. The foetal environment is
considered a key factor in the aetiology of some diseases
later in life, including cardiovascular disease, [26, 27]
kidney disease, [28] asthma and respiratory functioning
[29]. Intrauterine growth is an independent predictor of
Matern Child Health J
health outcomes both in childhood and throughout life [30]
with health behaviours (e.g., nutrition, smoking, alcohol)
adopted by parents, important determinants of intrauterine
growth [31].
The majority of health behaviours (e.g. health screening,
nutrition, alcohol, smoking, physical activity) which have
been related to poor health outcomes are differentially
distributed by socioeconomic status [32]. Prevention of
health inequities therefore requires strategies that not only
address individual risk factors but also the fundamental
structural and environmental characteristics inherent to
poverty [33–35]. It is the structural and environmental
characteristics of the community in which an individual
lives, that will ultimately influence whether protective
health and lifestyle choices are possible.
In order to better ascertain the relative contribution of
these multi-level determinants of health, there is a need for
robust studies, longitudinal and prospective in nature, that
examine individual, familial, social and environmental
exposures. Empirical studies must quantify the respective
causal, and confounding effects of the many covariates
involved and account simultaneously for the broad range of
biological, social, and behavioural measures operating at the
individual and group level. This paper describes the study
methods, as it has been implemented in an Australian birth
cohort study, Environments for Healthy Living (EFHL): The
Griffith Study of Population Health. Australian and New
Zealand Clinical Trials Registry ACTRN12610000931077.
Methods
Study Aim
By using a multi-level approach, EFHL aims to investigate
social and environmental factors, neighbourhood and family functioning, maternal lifestyle and in utero exposures on
child development and health outcomes. A number of
health outcomes will be investigated including obstetric
complications and birth weight, obesity, child development
and quality of life, child injury, oral and dental health,
utilisation of health services and all cause morbidity from
birth to adulthood. Findings will be used to identify where
improvements can be made within the community to make
healthy lifestyle choices easier for families.
Study Design
EFHL is a prospective, multi-level, multi-year longitudinal
birth cohort study, designed to collect information from
before birth through to adulthood across a spectrum of ecoepidemiological factors, including genetic material from
cord-blood samples at birth, individual and familial factors,
to spatial data on the living environment. It is anticipated
that future sub-studies will be incorporated into the overall
design that may include qualitative and interventional
components.
The EHL Wales study has been designed in collaboration with EFHL to be complementary and is currently in its
pilot phase of recruitment [36]. The underlying eco-epidemiological premise for the study design, some outcomes
of interest, and branding features of the EFHL are being
utilised by EHL Wales. The collaborative nature of the two
cohorts will enable international comparisons of several
outcomes of interest.
Study Setting and Population
The study population is the offspring of women residing in
the geographically defined Logan/Beaudesert, Gold Coast
and Tweed Districts. These districts cover an area of almost
6,000 square kilometres in South East Queensland and
Northern New South Wales, Australia and contain an estimated population of over 1,300,000 people or approximately
30% of Queensland’s population. It has one of the fastest
rates of population growth in Australia and illustrates the
problem of providing effective health promotion in outer
metropolitan areas. The region is markedly heterogeneous
with respect to age and socioeconomic distribution [37].
Sample Size
The target sample size for EFHL is 4000 mother/infant
dyads. Sample size calculations have been derived for three
key paediatric outcomes of interest—obesity, quality of life
and injury, demonstrating that the target sample size will be
sufficient for detecting statistically significant and practically important differences in the primary outcomes, if true
differences exist. All the sample size calculations are based
on significance level of 5% and a two-sided test (Table 1).
The sample size calculations for obesity and quality of
life are based on an initial cohort of N = 4000 and attrition
of 15% per wave of follow-up, resulting in N = 2457 at
5-years. Sample sizes are calculated using GPower 3.1
software [38]. The estimated proportions of obesity and
overweight are obtained from a recent study of 1688
children in Australia [39]. Body Mass Index BMI (kg/m2)
categories were defined using the International Obesity
Task Force age-specific cut-off points for children aged
5-years, BMI [ 19.2 and BMI [ 17.3 for obesity and
overweight respectively [40]. The sample size calculations
indicate that EFHL has 80% power to detect a difference of
0.015 in the proportion of children who are obese and
0.024 in the proportion of children who are overweight.
The estimated mean and standard deviation of child
quality of life using the Health Utilities Index Mark 2
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Matern Child Health J
Table 1 Minimum detectable difference in primary outcomes of interest for power of 70, 80 and 90%
Outcome of interest
Obesitya
Overweight
a
Child quality of lifea
Hospitalised injury
b
Medically treated injuryb
a
Value
SD
Minimum detectable difference
Power = 70%
Power = 80%
Power = 90%
0.069c
NA
0.014
0.015
0.018
0.219c
NA
0.021
0.024
0.028
0.950d
0.072
0.0036
0.0041
0.0047
e
0.066
0.281
0.011
0.012
0.014
0.204e
0.495
0.019
0.021
0.024
Cross-sectional data, at 5 years of age through anthropomorphic assessment and proxy report
b
Longitudinal data, from birth to 5 years of age through linkage to administrative data
c
Proportion
d
Mean
e
Rate
(HUI2) scores are obtained based on the validated Canadian
population norms for male and female children [41]. The
minimally important difference in HUI2 score is 0.03 [42].
With a sample of 2457 followed up at 5 years, EFHL will be
able to detect a difference of 0.004 in mean HUI2 score at
80% power; thus, this study has a sufficient sample size to
detect differences in child health-related quality of life.
The sample size calculation for paediatric injury is
based on linkage to administrative databases to identify
medically treated injury and admissions to hospital, with an
estimated 95% data obtainment at the 5-year follow-up (i.e.
N = 3800). Injury rates were estimated from pilot data
from within the study region, [43] which are 0.204 per year
for injuries requiring medical treatment and 0.066 per year
for hospital treated injuries. A sample of 3800 with complete data at 5 years provides the study with 80% power to
detect a difference of 0.021 in medically treated injury
rates and 0.012 in hospital treated injury rates.
Study Sample, Recruitment and Data Collection
Process
Eligible participants are the offspring of women who plan
to give birth at one of three participating public maternity
hospitals (Logan, Gold Coast and The Tweed Hospitals) in
South East Queensland and Tweed area. All women waiting for third trimester antenatal clinic appointments (on or
after their routine 24 week antenatal visit) at each of the
locations, are approached by research trained midwives,
provided with a detailed explanation of the study aims and
invited to participate in the study. Pregnant women aged
less than 16 years or unable to provide informed consent
are excluded. Information packages, including the study
explanation and aims are provided to participants.
Written informed consent is obtained for the release of
hospital perinatal data related to the birth of their child,
123
completion of a participant maternal baseline survey and
for individual follow-up. Additional and separate consents
are sought from each participant for the collection of
umbilical cord blood at the time of delivery and for the
release of Medicare, Pharmaceutical Benefits (PBS) claims
information, and immunisation history related to their
child, from Medicare Australia. Consent for release of
Medicare Australia data is sought from the calendar month
of the child’s birth for a 5-year period inclusively. Women
who decline consent to cord blood collection or Medicare
Australia data release are not restricted from participation
in the study.
Figure 2 illustrates the recruitment, consent and data
collection processes for EFHL. Following recruitment,
expectant mothers complete a baseline questionnaire either
at the antenatal clinic or returned by post in a reply-paid
envelope. At the time of the birth of the child (Time 00)
umbilical cord blood is collected (where consent has been
provided). Perinatal data is extracted from the medical
records following discharge from hospital. Follow-up
questionnaires are sent to the primary carer when the child
is 12 months of age (Time 01), 3 years of age (Time 03)
and 5 years of age (Time 05). Subsequent follow-ups are
planned to occur every 3–5 years until the participant
reaches 25 years of age.
Multiple strategies have been established to minimise
attrition. Routine telephone reminder processes are utilised
to prompt questionnaire return rates, however primary carers are also provided with an alternate ‘not this time’
option to decline completing a questionnaire without
withdrawing from the entire study. This option enables
time poor participants or those with particular limiting
circumstances to continue to contribute long term to the
study. Concerted efforts are made by the project team to
maintain up to date contact records for all participants
through the use of the reminder processes for questionnaire
Matern Child Health J
Fig. 2 Flow diagram of the
recruitment and data collection
process for EFHL
returns; alternate contacts provided by participants to follow up ‘return to sender’ items; and newsletters which are
sent to all participants three times a year.
EFHL commenced the pilot phase of recruitment in
November 2006 and open recruitment in August 2007. The
study has a multi-year recruitment approach which has
utilised a four-month recruitment period every year since
2006 and is anticipated to continue enrolling new participants until 2013 (Fig. 3). Each of these sub-cohorts is
defined by the calendar year of recruitment. One-year
(Time 01) and three-year (Time 03) follow-ups have
commenced as planned. Medicare Australia data extraction
commenced in 2011 when the first cohort of children
approached 5 years of age.
Ethics
Ethical approval for EFHL participant recruitment and follow-up of the birth cohort has been obtained from the Griffith
University Human Research Ethics Committee (HREC) (Ref:
MED/16/06/HREC, renewed MED/23/11/HREC). Ethical
approval for EFHL participant recruitment was also obtained
from each of the individual HRECs associated with the three
participating public maternity hospitals (Refs: Logan Hospital
HREC/06/QPAH/96; Gold Coast Hospital HREC/06/GCH/
52 and The Tweed Hospital NCAHS HREC 358 N). University and participating hospital ethics approvals have been
granted for a maximum 5 and 3 year periods respectively,
with renewal applications required at the end of each period.
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Matern Child Health J
Fig. 3 Multi-year recruitment
and longitudinal follow-up
design of EFHL
Data Sources and Instruments
ups are planned to occur every 3–5 years until the participant reaches 25 years of age.
Questionnaires
Umbilical Cord Blood
All questionnaires are self-administered. The maternal
baseline questionnaire consists of 48 questions and takes
approximately 30 min to complete. Items include maternal
and family demographics, socioeconomic status, household
income, family structure, Kessler 6 psychological distress
scale [44], neighbourhood and community connectedness
[45], short form of the Family Environment Scale [46],
maternal smoking and drinking behaviour, gestational
medical conditions and injuries, the usage of health supplements as well as recreational substances during pregnancy.
The first follow-up contact with participants occurs when
the child reaches 12 months of age (Time 01) and again at
3 years of age (Time 03). The primary carer, who is predominantly the mother, is mailed a questionnaire eliciting
details on familial, social and environmental exposures as
well as child health and developmental outcomes. The
questionnaires consist of a range of self-report and proxy
child measures and take approximately 40 min to complete.
Items include infant feeding practices and child nutrition
[47], sun exposure, sleep patterns [48], developmental
milestones, [49] child temperament [50], Spence Children’s
Anxiety Scale (SCAS-P) Parent Form [51] and Modified
Checklist for Autism in Toddlers (M-CHAT) at 3 years, [52]
child height and weight measurements, child injury and child
medical conditions. Primary carer items include the Kessler
6 psychological distress scale, [44] adult attachment style,
[53] EuroQOL five dimensions (EQ-5D) health status measure, [54] as well as household income, structure and social
support, neighbourhood and community connectedness, [45]
short form of the Family Environment Scale [46] and Life
Events Stress Scale [55].
The questionnaire for 5-year follow-up (Time 05) is
currently under construction. Web development is also
underway to enable an online option for participants to
complete and submit questionnaires. Subsequent follow-
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Umbilical cord blood is collected by the midwife present at
the time of delivery. Approximately 10–12 ml of cord
blood is collected in two labelled vacutainers which are
then couriered to the Griffith University Genomics
Research Centre to be stored at -80°C until required for
analysis. This comprehensive birth cohort study will provide a valuable resource to study the development of disorders in this population enabling an assessment of genetic
and environmental contributors to disease. Future plans
include conducting analyses to investigate genetic risk
factors relating to the causes of common chronic diseases
such as cardiovascular disease, obesity, diabetes, hypertension, mental health and various types of cancer. The
collection of cord blood samples and the assessment of
longitudinal medical information will allow us to investigate the interplay of genetic and environmental factors in
disease susceptibility. For these studies we will undertake
DNA extraction in-house using established kit based
methods (e.g. Qiagen) and investigate genetic variants in
relation to disease development. Because the technology
for genetic analysis is developing at a rapid pace, with for
example, whole genome sequencing becoming much less
expensive, the method finally used for genetic analysis of
cohort samples will be selected at the time of analysis. In
addition, biochemical analyses can also be undertaken to
investigate exposure to or levels of various chemicals or
toxins, such as heavy metals and selenium, as well as to
investigate disease biomarkers.
Perinatal Data Collection
Perinatal data is extracted from the medical records following discharge from hospital. The data is obtained from
the Queensland and New South Wales Perinatal Data
Matern Child Health J
Collection completed by the hospital midwives. Data items
include previous pregnancies, maternal conditions, obstetric care and complications, delivery information and baby
information such as gender, plurality, gestational age, birth
weight, APGAR (Appearance, Pulse, Grimace, Activity,
Respiration) scores at birth and any complications. Comparisons will be made to Queensland and New South Wales
Health local area population perinatal data.
of participants, home safety assessments, ongoing participant/proxy completed questionnaires and linkage to
administrative databases to access health and education
records. It is anticipated that future sub-studies will be
incorporated into the overall design that may include focus
studies, qualitative or interventional components.
Data Linkage
The analytic strategy for this study incorporates two
dimensions. The first of these relates to the level at which
the exposure factors are situated within the conceptual
model (Fig. 1). A multi-level modelling (MLM) approach
will be used to determine the effects of the various factors
on child health and development including obesity, injury
and quality of life. MLM recognises the existence of
hierarchies in the data by allowing for residual components
for each level analysed. Levels in the analyses include
individual child characteristics (e.g. child temperament
[50]), proximal social environments (e.g. family environment scale [46]), and distal social environments (e.g. the
purpose built neighbourhood characteristics database). In
addition, longitudinal data, where individual’s responses
over time are correlated with each other, can be accounted
for in MLM. Thus, the multi-level regression models can
be used to quantify the relationships between determinants
(risk factors) and outcomes whilst controlling for confounders and covariates. This approach overcomes common methodological barriers associated with conventional
regression analysis.
The second dimension underpinning the analytic strategy is the use of the multi-year recruitment design to
facilitate the identification of possible ‘cohort effects’.
Cohort effects are defined as the ‘‘variation in health status
that arises from the different causal factors to which each
birth cohort in the population is exposed as the environment and society change’’ [58]. Cohort effects will be
analysed by comparing changes in exposures and outcomes
between each of the sub-cohorts for any given age. These
differences in risk will be interpreted in the context of
changes in the population exposure to area level risk factors or macro environmental factors, over the time period.
Medicare Australia data is retrospectively sought from the
calendar month of the child’s birth for a 5 year period
inclusively. Medicare data includes claims for listed items
on the Medicare benefits schedule (MBS), Pharmaceutical
benefits schedule (PBS) and the childhood immunisation
register. The Medicare data will be matched to the EFHL
cohort on date of birth, names and Medicare number.
Those data contain item numbers and dates for General
Practitioner (GP) consultations, fees paid by the MBS to
the GP, cost to the patient, pharmaceutical records
(including medications prescribed, date of prescription and
cost to the PBS), and full immunisation records. Hospital
inpatient data will be obtained through linkage with
Queensland Health and New South Wales Health Admitted
Patients Data Collection and Morbidity and Mortality
System databases.
Environmental Level Data
All participant residential information is geo-coded using
ArcGIS software [56] and will be linked to community
profile information at the appropriate scale. Much of these
data have been collected as part of the Australian Bureau of
Statistics [57] census which is conducted every 5 years
(2001, 2006, 2011). These socio-demographic data are
available at various scales ranging from the census collection district CD (approximately 200 households) to
statistical local areas SLAs (suburbs) to local government
areas. Within the EFHL study region there are 64 SLAs and
1,257 CDs based on the 2006 ABS census [57]. These data
will be supplemented with land use and transport data
(available from public and private databases) to build the
neighbourhood characteristics database. The land use and
transport data is an important part of the database as it will
provide information on a household’s accessibility to
essential services including medical facilities, shopping,
schools and other key social services.
Future Data Collection
Future data collection methods are projected to include
face to face interviews and anthropometric measurements
Analytic Strategy
Discussion
EFHL is a multi-level, multi-year longitudinal birth cohort
recruiting pregnant mothers in South East Queensland,
Australia, with the intention of following their children for
up to 25 years. The main outcome measures, obesity,
injury and quality of life were selected as a diverse range of
outcomes to cover a spectrum of common health states in
children. The long-term consequences of obesity are a
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Matern Child Health J
serious public health concern, since they are associated
with psychological morbidity [59] and the development of
chronic disease in later life [60]. Injury is one of the most
prevalent serious problems in childhood [61] and is an
outcome of behavioural and environmental factors; and
quality of life captures general health and wellbeing across
all health conditions.
While EFHL has features similar to existing birth cohort
studies, such as the pre-natal recruitment of participants in
order to collect in utero exposures and cord blood samples
at birth for genetic analyses [62, 63], it differs from
existing birth cohorts in a number of ways. This study will
measure changes in individuals as well as their environments over time. Based on an eco-epidemiological framework, EFHL offers a robust study design to understand the
various micro to macro factors that contribute to overall
health and wellbeing of populations. In addition to genetic
samples and individual survey data, EFHL includes the
collection of geographic and area level data to enable
spatial analysis of the effects of neighbourhood characteristics such as safety, transport and services availability on
lifestyle choices, physical activity and health service use.
This is a contemporary study, addressing current issues
such as obesity, an emerging disease of the modern era,
which has not been typically addressed in older cohort
studies. There are few studies that have examined risk
factors for child health outcomes across multiple levels
[64]. For example, obesity studies tend to focus either on
physical activity or nutritional behaviours and less frequently consider both determinants within one analysis
[65]. There are no longitudinal analyses relating obesogenic social and physical environments to obesity outcomes [66, 67]. Developed in collaboration with EHL
Wales [36], the planned multi-level analysis is a design
element that sets these studies apart from the traditional
risk-outcome analyses that characterise epidemiological
cohort studies.
This study has a major advantage compared with other
cohort studies by including linkage of the socio-demographic survey data with administrative healthcare databases, including hospital inpatient, emergency department,
GP, immunisation and pharmaceutical data. Historically,
data linkage was not available and only few recent cohort
studies have incorporated individual consents for record
linkage [68]. The use of administrative data circumvents
issues arising from attrition and provides a robust data source
that is not limited by self or proxy recall biases.
The ‘multi-year’ longitudinal study design of EFHL
enables potential analysis of cohort effects by comparing
data between each of the sub-cohorts from different
recruitment years. This design will provide a rare opportunity to assess and quantify the impact of structural or
environment changes and health policy implementations
123
during the cohort period. In addition, the research design
allows for a series of nested studies to be developed,
with the opportunity to examine complex exposures and
outcomes.
The study is not without its limitations. The EFHL
sample is likely to include fewer low birth-weight, low
gestational age babies or multiple births than in the general
population. This is mostly because the women are recruited
from only public maternity units and are not approached
for inclusion until approximately 24 weeks gestation at
routine clinics, with a significant proportion of women
approached closer to 36 weeks, by which time most pregnancies have ended for babies at risk for low birth weight.
However, this is unlikely to have a significant effect on
outcomes at 5 years of age and beyond.
Further limitations common to most cohort studies, are
related to the amount of information that is feasible to collect,
bias from self or proxy administered surveys, and attrition.
The length of the self-administered surveys is kept to a
minimum and additional data obtained from linkage with
administrative sources at the individual and local area level,
will provide externally validated data with no participant
burden and as previously stated, will minimise self-report
bias. A number of other strategies are also implemented that
aim to minimise participant loss to follow-up, including
regular mailed newsletters–often be returned by post if the
participant has moved house, the use of alternative phone
contacts supplied at enrolment, and telephone options for
questionnaire completion provided at reminder follow-ups.
Limitations and constraints also pertain to the use of
linked administrative data [69, 70]. Administrative errors
exist with regards to data entry and name discrepancies
increasing matching errors. Recording of any non-mandatory data fields may not be complete or the quality may be
questionable. Diagnostic information is reliant on physicians who code the clinical visits and a lag time also exists
between the events occurrence and the information
becoming accessible in the different data sources. However, despite these inherent problems with the reliability of
data from large databases these techniques are cost efficient
and allow for a large amount of data to be collected prospectively on a large sample size with minimal attrition,
which is a clear strength of the study.
The findings of this research will provide detailed evidence on the relative contribution of multi-level determinants of health, which can be used to inform social policy
and intervention strategies that will facilitate healthy
behaviours and choices across sub-populations.
Acknowledgments The research reported in this publication is part
of the Griffith Study of Population Health: Environments for Healthy
Living (EFHL) (Australian and New Zealand Clinical Trials Registry:
ACTRN12610000931077). Core funding to support EFHL is provided by Griffith University. We are thankful for the crucial
Matern Child Health J
contributions of the Project Manager Rani Scott and the current and
past Database Managers. We gratefully acknowledge the administrative staff, research staff, hospital antenatal and birth suite midwives
of the participating hospitals for their valuable contributions to the
study. Expert advice has been provided by Associate Investigators
throughout the project. Dr Cameron was supported by a Public Health
Fellowship (ID 428254) from the National Health and Medical
Research Council (NHMRC) Australia and Professor Scuffham was
supported by a Career Development Award (ID 401742) also from the
NHMRC.
Conflict of interest
interests.
The authors declare there are no competing
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