International Journal for Quality in Health Care 2000; Volume 12, Number 5: pp. 425–431
The relationship between physician cost
and functional status in the elderly
CHRISTINE A. POLLICINO AND DEBORAH C. SALTMAN
Department of General Practice, University of Sydney
Abstract
Objective. To explore the relationship between functional status and physician cost (general practitioner/specialist) in an
elderly population.
Design, setting and participants. A longitudinal study involving 328 patients aged 65 years or over admitted to medical and
surgical wards of a Sydney metropolitan hospital over a 10-month period.
Main outcome measures. Two predictive cost models were developed using multiple linear regression analyses. Nine
predictors were modelled including functional status (Short Form 36; SF-36) and major diagnostic categories. These models
were then applied to the Australian SF-36 norms to produce a profile of cost by level of functioning.
Results. After adjusting for potential confounders, five variables were found to be predictive of general practitioner cost at
a 5% significance level. Females and age were positively associated, whereas case note mention of post-discharge services
and high SF-36 vitality and role emotional scores were negatively predictive. For specialist cost, five variables were statistically
significant. The SF-36 domains of physical functioning and mental health were positively associated. Higher vitality, role
emotional scores and case note mention of post-discharge services were negatively associated.
Conclusions. Cost models can be used to highlight the differences between general practitioner and specialist attendances,
guide future physician care of the aged, and facilitate informed decision making.
Keywords: cost, elderly, functional status, general practitioner, SF-36, specialist
Interest in predicting the cost of health services associated
with the elderly has grown in recent years [1,2]. This growth
has coincided with the ageing of the population and progressive increase in total health services expenditure [3,4].
Knowing the cost of health services has been shown to be
valuable in the planning and funding of health services, and
prompting changes in practitioners’ behaviour to produce
cost savings [5,6].
Traditionally, analysts have sought to predict the cost
incurred by the elderly in hospital or in the interval prior to
death [1,2,7]. Little is known about how much physician
attendances cost the elderly. In Australia, general practitioners
(GPs) and specialists are reimbursed differently [8]. However,
total physician attendances account for 48% of the total
benefit paid by the government [9] and of this, approximately
24% relates to elderly physician attendances [9]. Understanding the determinants of these physician costs can assist
in explaining the components of the total benefit paid and
thus assist in future health care planning.
Previous literature has shown functional status to be
important in explaining health services utilization and outcomes, such as prognosis and in-hospital mortality [10–12].
Only recently have researchers sought to explain cost variation
with functional status. Several published studies have used
some measure of functional status to estimate elderly health
care cost: Culler et al. used self-reported health status [1] and
Mor et al. [2] and McNamee et al. [13] used activities of daily
living. However, these measures were either non-standardized
approaches or limited in scope, for example, restricted to
physical activities without consideration of mental health. To
our knowledge, this is the first study to use Short Form 36
(SF-36) to determine physician costs incurred by the elderly
in the period immediately following hospitalization.
This paper aims to answer the following research questions
with respect to an elderly population (aged 65 years and
over):
• Is functional status statistically related to post-hospital
physician cost after adjusting for potential confounders?
Address reprint requests to D. Saltman, Department of General Practice, University of Sydney, Manly Hospital, Darley Road,
Manly NSW 2095, Australia. E-mail: [email protected]
 2000 International Society for Quality in Health Care and Oxford University Press
425
C. A. Pollicino and D. C. Saltman
• Are different functional status domains statistically
associated with different physician attendances, specifically GP and specialist?
• Do low functioners incur greater cost than average or
high functioners?
Answers to these questions will provide guidance to clinicians as to the future care of the aged, and valuable information to health planners and consumers to assist in the
decision making process.
Method
Sample
Data for this study came from the Manly Hospital Cohort
Study [14]. This longitudinal study was designed to explore
the relationship between functional status and continuity of
care with scope to analyse Medicare data. Ethical clearance
for the study was obtained from two authorities: the Manly
Hospital Ethics Committee and the University of Sydney
Ethics Committee.
Study participants, aged 65 and over, were recruited from
all medical and surgical wards of the Manly Hospital over a
10-month period in 1996. Patients were excluded if they were
unable to comprehend the questions asked, were too sick,
or not in hospital long enough for our instruments to
measure change. Therefore patients were excluded if they
were admitted to the intensive care unit for more than 48
hours, were discharged in less than 48 hours, had severe
speech or hearing difficulties, or scored 23 or less on the
Folstein Mini Mental State Examination [15].
Eligible patients were asked to consent to participate in
the study and to have their medical records and Medicare
data retrieved. The SF-36 was administered by the research
team to consenting patients at three points in time: admission,
discharge and 3 months post-discharge. At the completion
of the study, Medicare data on health services utilization was
extracted and a medical record audit conducted.
Model specification
Physician cost data (proxied by the total benefit paid for
physician attendances by Medicare) were supplied by the
Health Insurance Commission. The Medicare data obtained
covered the cost of private physician services provided out
of hospital, based on the Government recommended schedule
of fees, as well as the medical costs for privately admitted
patients in public and private hospitals [16]. Excluded were
the cost of services provided by hospital physicians to public
patients in public hospitals, and patients who qualified for a
Department of Veteran Affairs benefit. Physician costs were
identified and examined for the 5-month post-hospital period.
The distinction between GP and specialist cost was based
on the Broad Type of Service classification [16].
Functional status was measured by SF-36 (Australian English Version) [17]. Its reliability and validity has been described
in detail elsewhere [17,18]. Briefly, SF-36 is a generic measure
426
of functional status comprised of 36 items covering eight
domains: physical functioning, role physical, bodily pain,
general health, vitality, social functioning, role emotional and
mental health. For the purposes of this paper, the SF-36
scores obtained on discharge were used. For each domain,
the SF-36 scores were coded appropriately, summed and
transformed to a scale from 0 to 100, with higher scores
indicating better functional status [17].
A further eight variables thought to be potential confounders were included. Classification of hospital patients
with similar clinical conditions and resource usage was
achieved by the Major Diagnostic Categories (MDCs) grouping [19]. MDCs are mutually exclusive principal diagnosis
categories based on the Australian system of Diagnosis Related Groups (AN-DRGs) casemix classification [19]. ANDRGs information from the patients’ extract summaries was
grouped into six MDCs. Diseases and disorders of the
musculoskeletal system and connective tissue was used as
the reference category because of its high estimated total
hospital cost and clinical relevance [3].
Deyo’s International Classification of Diseases 9th Revision
Clinical Modification (ICD-9-CM) adaptation of Charlson’s
index was used to formulate a measure of comorbidity severity
[20,21]. Information on coexisting conditions was extracted
from the patients’ discharge summaries, Charlson’s weights
applied and scores totalled [21]. Higher scores indicated
greater severity of illness.
Length of stay, case note mention of post-discharge services
(dichotomized), number of pre-admissions to Manly hospital
6 months prior to the episode and the number of readmissions to Manly hospital 6 months after discharge were
ascertained from the medical record audit.
Statistical methods
A total of 507 patients were approached and assessed for
eligibility. This sample size calculation was based on Cohen’s
estimates to detect a 5-point difference in average SF-36
scores (=0.05, power=80%) with a 40% allowance for
non-respondents and drop outs [22].
Data were analysed using Stata 6.0 and MINITAB software
[23,24]. Data were checked for recording errors, logic, influential outliers and multicollinearity using techniques such
as boxplots, Cook’s distance, DFFITS and Variance Inflation
Factor [25]. Bivariate and multivariate analysis of GP and
specialist cost was completed. Specifically, two separate multiple regression analyses were conducted to estimate the
linear association between GP and specialist cost and the
hypothesized predictor variables. The all-possible-regressions
procedure was used to identify several ‘good’ variable subsets
[25]. The model selected was based on the largest adjusted
R2 and checked for departures from the standard assumptions
of ordinary least squares estimation [25].The interpretation
of the assumptions was facilitated by descriptive and graphical
analysis. Where non-normality occurred, transformation as
indicated by the Box Cox procedure was performed [25].
Predictors were deemed statistically significant if their respective P-value was <0.05 (5% significance level).
Physician cost and functional status
For the purposes of exploring levels of functioning rather
than unit changes, and to estimate individual patient cost,
the two generated cost models were applied to the Australian
SF-36 norms to produce a profile of cost by level of functioning [26]. Level of functioning was categorized into low,
average and high. ‘Low’ refers to functional status domains
one standard deviation (SD) below the Australian norm,
‘average’ to the population norms and ‘high’ to functional
status domains 1 SD above the Australian norm.
Results
Of the 400 eligible and consenting patients (response rate of
78.9%), 14 people could not be matched with the Medicare
data, five agreed to participate in the study but refused
consent to have their Medicare data retrieved, and 41 were
recipients of Veterans’ benefits which are excluded from
Medicare records. Therefore, 340 patients were successfully
matched with the Health Insurance Commission. Of these,
12 died within 3 months of hospital discharge and were
excluded from subsequent analysis because literature has
shown that the last months of life are the most expensive in
terms of health services utilization [7]. The final sample
consisted of 328 subjects. Significant differences in length of
stay, and vitality and mental health scores were found between
the included and excluded patients.
Preliminary descriptive analysis identified a number of
influential outliers that had dramatic effects on the fitted
regression function. Prior to analysis it was decided that these
observations would be deleted for statistical reasons. For the
GP model, this involved the exclusion of six patients (revised
sample size of 322) and for the specialist model four patients
were excluded (revised sample size of 324). Multicollinearity
problems were undetected.
The skewed distribution of the cost data required transformation. The Box Cox procedure identified a square root
transformation of the dependent variable as the most appropriate [25].Visual inspection of the transformed residuals
confirmed no departures from the ordinary least squares
assumptions.
Table 1 shows the characteristics of the study sample. The
mean age of males and females was 75.09 years (SD =7.06
years) and 76.81 years (SD=6.33 years) respectively. A
statistically significant difference was found between the mean
age of males and females.
Over the 5-month period, 72% of the sampled population
incurred both GP and specialist costs amounting to an average
total physician cost of $403.98 (SD =229.40; minimum,
$47.60; maximum, $1200.40) (Australian dollars). Nineteen
per cent of the sample incurred only GP cost totalling an
average of $191.92 (SD=161.35; minimum, $20.85; maximum, $848.35) and 3% incurred only specialist cost totalling
an average of $77.04 (SD=58.22; minimum, $26.75; maximum, $180.9). The remaining 6% of the sampled population
incurred neither cost. At the time of the study, one Australian
dollar was equal to 0.769 $US and 0.495 UK pounds sterling
[27].
Table 1 Characteristics of Manly Hospital Cohort Study (n =
328)
Characteristic
Mean (SD)
............................................................................................................
Age (years)
76.10 (6.69)
Female sex (%)
59
Length of stay (days)
8.46 (5.28)
Case note mention of post-discharge
services (%)
58
Severity of illness
1.25 (2.44)
Pre-admissions
0.34 (0.98)
Re-admissions
0.73 (1.80)
Major diagnostic categories (%)
Musculoskeletal system and
connective tissue
20
Circulatory system
26
Respiratory system
15
Digestive system
8
Nervous system
8
Other diseases and disorders
23
MOS SF-36 domains
Physical functioning
46.10 (25.40)
Role physical
31.40 (36.17)
Bodily pain
54.62 (33.75)
General health
59.35 (19.17)
Vitality
49.70 (24.51)
Social functioning
54.65 (31.80)
Role emotional
80.69 (33.59)
Mental health
76.06 (20.77)
Results of the bivariate and multivariate analysis are summarized in Tables 2 and 3 respectively. The differences
observed between the bivariate and multivariate regression
coefficients suggest that confounding is present. Interpretation will focus on the results of the multivariate
analysis.
Five variables were found to be statistically predictive of
post-hospital GP cost at the 5% significance level. After the
adjustment for other variables, higher post-hospital GP cost
was associated with increasing age and being female. Case
note mention of post-discharge services, and high vitality and
role emotional SF-36 scores lowered post-hospital cost. All
other variables (length of stay, severity of illness, number of
pre-admissions and re-admissions and MDCs) were found to
have little or no influence on GP cost. The GP cost model
explained 9% of the cost variation for GP attendances (F=
4.50; P<0.000).
Five statistically significant variables were identified in
the specialist cost model. The SF-36 domains of physical
functioning and mental health showed a positive predictive
relationship with specialist cost. That is, the better the person
rated these domains the more likely they were to incur higher
specialist cost. Cost decreased for higher vitality and role
emotional scores, and case note mention of post-discharge
services. Age, sex, length of stay, severity of illness, number
of pre-admissions and re-admissions and MDCs were not
427
C. A. Pollicino and D. C. Saltman
Table 2 Bivariate analysis of each predictor against GP and specialist cost
GP Cost (n=322)
Specialist cost (n=324)
.................................................................... ....................................................................
Parameter
Parameter
estimate
P
estimate
P
.............................................................................................................................................................................................................................
Age
0.154
0.006∗∗
−0.171
0.001∗∗
Sex
1.503
0.045∗
−0.879
0.221
Length of stay
−0.029
0.678
−0.127
0.057
Severity
−0.112
0.479
−0.098
0.499
Continuity of care
−0.704
0.349
−1.852
0.010∗∗
Pre-admissions
−0.529
0.222
−0.543
0.188
Re-admissions
−0.123
0.588
−0.376
0.060
MDC1
0.368
0.665
1.950
0.016∗
MDC2
0.830
0.417
0.400
0.686
MDC3
−0.181
0.898
1.763
0.192
MDC4
−1.025
0.459
−1.221
0.357
MDC5
−0.593
0.501
−1.221
0.143
MOS SF-36 domains
Physical functioning
−0.006
0.704
0.044
0.002∗∗
Role physical
−0.010
0.329
−0.008
0.396
Bodily pain
0.005
0.670
−0.005
0.661
General health
0.014
0.463
0.016
0.396
Vitality
−0.040
0.009∗∗
−0.002
0.872
Social functioning
−0.006
0.605
0.002
0.832
Role emotional
−0.036
0.001∗∗
−0.019
0.069
Mental health
−0.025
0.165
0.013
0.435
P value corresponding to the null hypothesis that the coefficient is equal to zero. ∗P<0.05; ∗∗P<0.01. Sex (1: female). Continuity of
care (1: case note mention of post-discharge services); MDC1 (1: circulatory system; 0: musculoskeletal system and connective tissue);
MDC2 (1: respiratory system; 0: musculoskeletal system and connective tissue); MDC3 (1: digestive system; 0: musculoskeletal system
and connective tissue); MDC4 (1: nervous system; 0: musculoskeletal system and connective tissue); MDC5 (1: other; 0: musculoskeletal
system and connective tissue).
found to be statistically significantly associated with specialist
cost. The specialist cost model yields an R2 of 9% (F=3.59;
P<0.000).
Figure 1 portrays the relationship between level of functioning and significant predictors. Hypothesized patient characteristics (contained in the footnote of each graph) and
functional status scores were substituted into the selected
cost model to estimate the total physician cost paid by
an individual exhibiting those defined characteristics. For
simplification reasons, only those profiles associated with the
greatest cost were presented. The profile related to the highest
GP cost of $266 over 5 months was a low functioning 75year-old female with no case note mention of post-discharge
services or previous hospital admission. For specialist attendances, the largest cost combination was $196 for a 65year-old with a disease of the digestive system and no case
note mention of post-discharge services or re-admission.
Overall, low functioners incurred the largest cost for both
types of physician attendances. Average and high functioners
incurred comparatively lower cost, yet similar levels to each
other.
428
Discussion
Five findings of this study warrant discussion. Firstly, our
study shows that certain SF-36 domains are predictive of posthospital cost. These statistically significant domains differ by
type of physician attendance and in the nature of their
relationship with cost. Secondly, in the prediction of specialist
cost two functional status domains (physical functioning and
mental health) were found to be unique. Interestingly, these
two distinct and positively associated domains suggest that
higher scores in physical functioning and mental health are
associated with the patient’s decision to attend a specialist
thereby incurring specialist costs. These same domains were
found not to impact on GP cost. This suggests that access
issues may impact on which physician the elderly choose for
a follow-up visit. In Australia, specialists do not conduct
house calls therefore it is more likely that patients who are
able to transport themselves will consult a specialist. Further,
outcomes may impact on which physician the elderly choose
for a follow-up visit. Patients who are more likely to have a
resolution of their health problem and therefore have better
Physician cost and functional status
Table 3 Multivariate analysis of GP and specialist cost
Parameter
Variables
estimate
SE
P
95% CI
.............................................................................................................................................................................................................................
GP cost model (n=322)
Age
0.176
0.056
0.002∗∗
(0.07 to 0.29)
Sex
1.555
0.762
0.042∗
(0.06 to 3.05)
Continuity of care
−1.910
0.766
0.013∗
(−3.42 to −0.40)
Pre-admissions
−0.560
0.417
0.180
(−1.38 to 0.26)
MOS SF-36 domains
Physical functioning
0.024
0.017
0.154
(−0.01 to 0.06)
Vitality
−0.053
0.020
0.007∗∗
(−0.09 to −0.01)
General health
0.023
0.020
0.253
(−0.02 to 0.06)
Role emotional
−0.045
0.012
<0.00∗∗
(−0.07 to −0.02)
Mental health
0.032
0.023
0.164
(−0.01 to 0.08)
Constant
1.270
4.729
0.788
(−8.04 to 10.58)
Adjusted R2
9.0%
Specialist cost model (n=324)
Age
Continuity of care
Re-admissions
MDC1
MDC2
MDC3
MDC4
MDC5
MOS SF-36 domains
Physical functioning
Role physical
Vitality
Role emotional
Mental health
Constant
Adjusted R2
−0.102
−1.571
−0.294
1.952
0.136
2.220
−0.607
−0.379
0.055
0.732
0.194
1.022
1.174
1.481
1.445
1.036
0.064
0.033∗
0.132
0.057
0.908
0.135
0.675
0.715
0.043
−0.014
−0.043
−0.029
0.046
16.651
9.4 %
0.016
0.010
0.019
0.012
0.022
4.703
0.008∗∗
0.173
0.022∗
0.014∗
0.036∗
<0.00
(−0.21
(−3.01
(−0.68
(−0.06
(−2.17
(−0.69
(−3.45
(−2.42
to
to
to
to
to
to
to
to
0.01)
−0.13)
0.09)
3.96)
2.45)
5.13)
2.24)
1.66)
(0.01 to 0.08)
(−0.03 to 0.01)
(−0.08 to −0.01)
(−0.05 to −0.01)
(0.00 to 0.09)
(7.40 to 25.90)
P value corresponding to the null hypothesis that the coefficient is equal to zero. ∗P<0.05; ∗∗P<0.01. Sex (1: female). Continuity of
care (1: case note mention of post-discharge services); MDC1 (1: circulatory system; 0: musculoskeletal system and connective tissue);
MDC2 (1: respiratory system; 0: musculoskeletal system and connective tissue); MDC3 (1: digestive system; 0: musculoskeletal system
and connective tissue); MDC4 (1: nervous system; 0: musculoskeletal system and connective tissue); MDC5 (1: other; 0: musculoskeletal
system and connective tissue).
mental functioning maybe more likely to visit a specialist to
complete their health episode. Once again these findings have
been previously unexplored.
Thirdly, the functional status domains of role emotional
and vitality were found to be common predictors of GP and
specialist cost. Whilst negatively predictive of cost, these
domains imply that cost is related to the mental rather than
physical component of functional status. This result extends
the findings of previous studies that restricted their analysis
to the physical component [2,13].
Fourthly, at all levels of functioning, GP cost was greater
than specialist cost over the 5-month period. Whilst no
figures have been produced on post-hospital fee-for-service
cost, this result is consistent with previous total hospital and
non-hospital Medicare figures reporting a greater benefit paid
for GP attendances compared with specialist attendances in
the 65 years and over age group [9].
Finally, irrespective of the type of cost, low functioners
incurred higher cost over the 5-month period. The difference
in costs between low and high functioners varied between
$38 and $68 for GPs and between $13 and $31 for specialists.
This result agrees with the UK findings that more favourable
health states were associated with lower cost in an elderly
population [13].
These research findings have implications for future physician care of the elderly. Care of the aged should include
attention to those specific (predominantly mental) functional
status domains associated with cost reductions. Concentration
on a few domains offers guidance to physicians treating
elderly patients with an array of coexisting conditions. Also,
429
C. A. Pollicino and D. C. Saltman
Figure 1 Level of functioning and significant predictors. Costs are reported in Australian dollars (equal to 0.769 US dollars)
and 0.495 UK pounds sterling. Level of functioning: low, functional status domains 1 SD below the Australian norm;
average, Australian norm; high, functional status domains 1 SD above the Australian norm. Contact: case notes mention of
post-discharge services. Pre-adm: number of pre-admissions within the last 6 months. Re-adm: number of re-admissions
with the last 6 months. MDC4: disease and disorder of the nervous system relative to a disease and disorder of the
musculoskeletal system and connective tissue.
management which includes functional assessment has been
shown to be what patients seek and need [28].
Given the large disparity in cost levels between low and
average functioners (ranging between $31 and $50 for GP
and between $19 and $28 for specialist cost), it may be
opportune for physicians to seek to maintain average functioning levels. In fact, physicians need not be overly concerned
with a patient’s regression from high to average functioning
as the associated cost savings are small, ranging between $8
and $12 for GPs and $1 and $2 for specialists. Clearly, further
research is necessary to ascertain patient views on average
or higher levels of functioning and the strategies that physicians can use to achieve these levels of functioning.
There were several limitations to this study. Patients were
not selected randomly into the study, introducing the possibility of selection bias. However, previous research has
shown the representativeness of our sample with respect to
the Australian SF-36 norms [14]. The study’s exclusion criteria
restricted the generalizability of the results: those patients
excluded tended to be the chronically ill. The exclusion of
such people means that the cost estimates are conservative
given that the chronically ill are more likely to consume a
greater volume of services thereby incurring greater physician
costs. Patients who were not included in the study had
statistically significantly longer lengths of stay and poorer
430
vitality and mental health scores compared with patients with
matched Medicare records. To our knowledge there has been
no published literature on whether these differences translate
into cost.
This study examined the total benefit paid by Medicare
which is distinct from the total cost incurred by the patient.
Inadequacies of Australia’s information systems and the noted
complexity of estimating elderly heath care expenditure introduce difficulties with estimating total out-of-pocket expenses [29]. The pre-admission and re-admission data were
obtained only for Manly Hospital. Data on other hospital
admissions were not available. The model selection strategy
may not have identified the best model in a global sense.
However, the strategy used was chosen in relation to the
goal of the analysis and is that preferred by Kleinbaum et al.
[30]. Non-statistically significant variables may have been
clinically significant. The merits of presenting parsimonious
models has been noted by Neter et al. and Kleinbaum et al.
[25,30]. The cost models generated explained a small proportion of the cost variation suggesting that other unaccounted factors affect physician cost. Whilst a low R2 is
consistent with McNamee et al.’s paper, the aim of our study
was to build a model to promote better physician practices
rather than explain total cost variance [13]. Caution should
be exercised when using such information for cost projections.
Physician cost and functional status
The generated cost models developed in this study could
be used to facilitate informed decision making. This cost
information could be used by policy makers to plan and fund
future health services and by consumers to plan their own
future costs.
13. McNamee P, Gregson BA, Wright K et al. Estimation of a
multiproduct cost function for physically frail older people.
Health Econ 1998; 7: 701–710.
Acknowledgements
15. Folstein MF, Folstein SE, McHugh PR. ‘Mini-Mental State’: A
practical method for grading the cognitive state of patients for
the clinician. J Psychiatr Res 1975; 12: 189–198.
The authors thank the General Practice Evaluation Program
and Manly Hospital Centenary Research Fund. Thanks also
to Catherine D’Este and Maureen Ahern whose contributions
to the project made this paper possible.
14. Snow L, O’Brien E, Saltman DC, Ahern M. When should we
measure functioning? A comparison of serial measurement of
the MOS SF-36 in an Australian hospital sample with Australian
norms. Australas J Ageing 1999; 18: 40–43.
16. Health Insurance Commission. Annual Report 1995–96. Canberra: Health Insurance Commission, 1996.
17. Ware JE, Snow KK, Kosinski M, Gandek B. SF-36 Health Survey
Manual and Interpretation Guide. Boston: Nimrod Press, 1993.
References
1. Culler SD, Callahan CM, Wolinsky FD. Predicting hospital costs
among older decedents over time. Med Care 1995; 33: 1089–1105.
2. Mor V, Wilcox V, Rakowski W, Hiris J. Functional transitions
among the elderly: patterns, predictors and related hospital use.
Am J Public Health 1994; 84: 1274–1280.
3. Australian Institute of Health and Welfare. Australia’s Health
1998: the Sixth Biennial Health Report of the Australian Institute of
Health and Welfare. Canberra: Australian Institute of Health and
Welfare, 1998.
4. Australian Institute of Health and Welfare. Health Expenditure
Bulletin no.15: Australia’s Health Services Expenditure to 1997–98.
Canberra: Australian Institute of Health and Welfare (Health
and welfare expenditure series), 1999.
5. Drummond MF, O’Brien B, Stoddart GL, Torrance GW. Methods
for the Economic Evaluation of Health Care Programmes. New York:
Oxford University Press, 1997.
6. Beilby J, Silagy C. Providing costing information to general
practitioners – will this intervention change behaviour and
create cost savings? A systematic review. Med J Austral [serial
online] 1997 28 April. Available from: URL: http://www.mja.
com.au/public/papers/beilby/beilby.html [visited 6.9.99].
7. Malmgren JA, Koepsell TD, Martin DP et al. Mortality, health
services use, and health behaviour in a cohort of well older
adults. J Am Geriatr Soc 1999; 47: 51–59.
8. Commonwealth Department of Health and Aged Care. Medicare
Benefits Schedule Book. Canberra: Commonwealth Department of
Health and Aged Care, 1996.
9. Health Insurance Commission. Medicare Benefits Schedule Item
Statistics. Canberra: Health Insurance Commission, 1999.
10. Korten AE, Jacomb Z, Jiao H et al. Predictors of GP service
use: a community survey of an elderly Australian sample. Aust
NZ J Public Health 1998; 22: 609–615.
11. Covinsky KE, Justice AC, Rosenthal GE et al. Measuring
prognosis and case mix in hospitalized elders: The importance
of functional status. J Gen Intern Med 1997; 12: 203–208.
12. Davis RB, Iezzoni LI, Phillips RS et al. Predicting in-hospital
mortality: The importance of functional status information. Med
Care 1995; 33: 906–921.
18. Shadbolt B, McCallum J, Singh M. Health outcomes by selfreport: validity of the SF-36 among Australian hospital patients.
Qual Life Res 1997; 6: 343–352.
19. 3M Health Information Systems. Australian National Diagnosis
Related Groups (AN-DRGS) Version 3.0 Definitions Manual.
Wallingford, CT: 3M Health Information Systems, 1993.
20. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity
index for use with ICD-9-CM administrative databases. J Clin
Epidemiol 1992; 45: 613–619.
21. Charlson ME, Pompei P, Ales Kl, MacKenzie CR. A new
method of classifying prognostic comorbidity in longitudinal
studies: development and validation. J Chronic Dis 1987; 40:
373–383.
22. Cohen J. Statistical Power for the Behavioural Sciences. Hillsdale, NJ:
Lawrence Erlbaum Associates, 1988.
23. StataCorp. Stata Statistical Software: Release 6.0. College Station,
TX: Stata Corporation, 1999.
24. MINITAB. Minitab Statistical Software: Release 12. State College,
PA: Minitab Inc., 1998.
25. Neter J, Kutner MH, Nachtsheim CJ, Wasserman W. Applied
Linear Statistical Models, 4th edn. Chicago: Irwin, 1996.
26. Australian Bureau of Statistics. National Health Survey: SF-36
Population Norms Australia. Catalogue No. 4399.0. Canberra:
Australian Bureau of Statistics, 1995.
27. Organization for Economic Co-operation and Development.
Main Economic Indicators. Paris: Organization for Economic Cooperation and Development, 1999.
28. Cluff LE. Chronic disease, function and the quality of care. J
Chron Dis 1981; 34: 299–304.
29. Pearlman DN, Branch LG, Ozminkowski RJ et al. Transitions
in health care use and expenditures among frail older adults by
payor/provider type. J Am Geriatr Soc 1997; 45: 550–557.
30. Kleinbaum DG, Kupper LL, Muller KE, Nizam A. Applied
Regression Analysis and Other Mutlivariable Methods, 3rd edn. Pacific
Grove, CA: Duxbury Press, 1998.
Accepted for publication 19 July 2000
431