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Socioeconomic inequalities in the diffusion of health technology: Uptake of coronary procedures as an example
Social Science & Medicine 72 (2011) 224e229
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Social Science & Medicine journal homepage: www.elsevier.com/locate/socscimed
Short report
Socioeconomic inequalities in the diffusion of health technology: Uptake of coronary procedures as an example Rosemary J. Korda a, b, *, Mark S. Clements b, Jane Dixon b a b
Australian Centre for Economic Research on Health, The Australian National University, Australia National Centre for Epidemiology and Population Health, The Australian National University, Australia
a r t i c l e i n f o
a b s t r a c t
Article history: Available online 23 November 2010
This paper examines socioeconomic lags in the diffusion of high technology health care, focusing on the diffusion of coronary procedures in people with ischaemic heart disease. Using linked hospital and mortality data, we studied patients admitted to Western Australian hospitals with a first admission for acute myocardial infarction between 1989 and 2003 (n ¼ 27,209). An outcome event was the receipt, within a year, of a coronary proceduredangiography, angioplasty and/or coronary artery bypass surgery (CABG). Socioeconomic status (SES) was assigned to each individual using the SEIFA Index of Disadvantage. Cox regression was used to model the association between SES and procedure rates in five consecutive three-year time periods. Angiography and CABG showed socioeconomic lags in diffusion, with rates peaking earlier in higher SES patients, such that the inequality patterns were consistent with the inverse equity hypothesis. The evidence for a lag in diffusion for angioplasty was weaker. Overall, that there is some evidence for a lag in diffusion of health technology indicates that it is essential to consider trends over time when examining the equity impact of health technologies. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Inequalities Socioeconomic status Diffusion Technology Health care Coronary procedures Australia Heart disease Time trends
Introduction The final report of the Commission on the Social Determinants of Health concluded that while the evidence on health inequalities is well established, there is considerable urgency to expand the knowledge on contributory factors, including those associated with health systems (CSDH, 2008). This paper attends to this challenge through studying socioeconomic inequalities in the diffusion of high technology health care innovations, arguing that such innovations are part of a socio-technical system which may perpetuate health inequalities. Diffusion of an innovation is defined as the process by which a novel development is communicated over time among the members of a social system. The process can be illustrated by graphing the cumulative uptake of the innovation over time. The diffusion curve of a successful innovation typically shows an S-shaped distributiondearly in the diffusion process relatively few individuals adopt or receive the innovation in each time period, the rate of uptake then accelerates, and finally it increases at a slower rate as fewer and fewer remaining potential individuals adopt or receive it. For some innovations, the curve may actually show * Corresponding author. Australian Centre for Economic Research on Health, The Australian National University, Australia. Tel.: þ61 2 61255583; fax: þ61 2 61259123. E-mail address: [email protected] (R.J. Korda). 0277-9536/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2010.11.002
a downward trend after reaching a ceiling as the new innovation is superseded or falls out of favour (Rogers, 2003). Innovations often diffuse at different rates among subgroups in the population, with ‘early adopters’ more likely to be of advantaged status while lower status is associated with later adoption (Rogers, 2003). Because of this lag in diffusion, the ‘inverse equity hypothesis’ predicts that new health interventions will tend to increase inequities because the intervention will initially reach those of higher SES, but that the early increase in inequity ratios will then be followed by a reduction when those of higher SES have reached threshold levels for the intervention and those of lower SES gain greater access to the interventions (Victora, Vaughan, Barros, Silva, & Tomasi, 2000). In the health field, diffusion or similar models have been broadly applied in the area of public health (Haider & Kreps, 2004) but there is little direct evidence on inequalities in the diffusion of medical technology. The aim of this study is to investigate inequalities in the uptake of health care technology over time by socioeconomic status (SES) in order to examine whether or not inequality patterns are consistent with the lag in diffusion/inverse equity hypothesis. We specifically focus on the diffusion of coronary procedures between 1989 and 2003 in people with ischaemic heart disease, the leading cause of avoidable mortality in Australia (Korda & Butler, 2006) and the main contributor to the socioeconomic mortality gap (Page et al., 2006). The examination of coronary proceduresdangiography, angioplasty and coronary artery bypass surgery (CABG)dis
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suited to this study because these procedures are high-volume procedures, which are well recorded in hospital datasets; furthermore, although the diffusion process for each procedure is not fully captured during the study period, it is still evident for each procedure, and with the different procedures at different stages of diffusion this allows examination of inequality in diffusion both within and across innovations.
225
Data System, which records all deaths. An established populationbased record linkage system allows linking of records relating to individual patients both within and between these datasets. We restricted cases to patients admitted to hospital with acute myocardial infarction (AMI). The criteria used to select cases for the study were adapted from established methods (Korda, Clements, & Kelman, 2009). Briefly, an AMI case was a patient aged 35 years or older with an AMI index admission, defined as an admission to hospital between 1989 and 2003, with a principal or co-diagnosis of AMI, and with no previous admissions for AMI recorded. There were a total of 27,209 cases. The main outcome event in this study was the receipt of a coronary proceduredangiography, angioplasty and/or CABGdidentified using ICD procedure codes (introduced in mid-1988, hence the index case definition restricting cases to after this time). Socioeconomic status was assigned to each individual using a census-based
Methods We used administrative hospital and death data from Western Australia, a state comprising a tenth of the Australian population (Australian Bureau of Statistics, 2006). Data were extracted from the Hospital Morbidity Data System, which contains information on each hospital admission in the state, including sociodemographic characteristics, clinical diagnoses and procedures, and from the Mortality
Angiography Males
Females 60
90 80
**
50
*
**
60
40
Percentage
Percentage
70
50 40 30
30 20
20 10
10 0
0 1989-1991
1992-1994
1995-1997
1998-2000
2001-2003
1989-1991
1992-1994
Period
1995-1997
1998-2000
2001-2003
1998-2000
2001-2003
1998-2000
2001-2003
Period
Angioplasty Males
Females
50
*
45
*
40
20
35
Percentage
Percentage
25
30
*
25 20
15 10
*
15 10
5
5 0
0 1989-1991
1992-1994
1995-1997
1998-2000
2001-2003
1989-1991
1992-1994
Period
1995-1997 Period
Coronary artery bypass surgery Females
30
30
25
25
20
Percentage
Percentage
Males
*
15 10 5
20 15 ** 10
**
5
0
0 1989-1991
1992-1994
1995-1997
1998-2000
Period
2001-2003
1989-1991
1992-1994
1995-1997 Period
Notes. 1. SES Quintile 1 is most disadvantaged. 2. P-value for test for SES trend < .05 in age-adjusted models* and in age-adjusted and fully-adjusted models** Fig. 1. Age-adjusted diffusion of coronary procedures, 1989e2003, by socioeconomic status (SES).
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measure, the SEIFA Index of Disadvantage (Australian Bureau of Statistics, 2004). The Index was assigned at the postcode level (3% missing), after which, patients were classified into populationbased quintiles of SES. There were between 243 and 257 postcode areas in each time period (mean number of cases per postcode ¼ 21, mean population size ¼ 5414). Potential confounders included in the analyses were: Age Group (ten-year age bands), Sex, Marital Status (single or married/defacto), Aboriginal (yes or no), Country of Birth (Australia/New Zealand or other), Area of Residence (urban or rural), Hospital Area of index admission (metropolitan or rural) and Comorbidity (measured using a modified Charlson Index (Sundararajan et al., 2004)). Cases were grouped into one of five three-year periods, based on the year of their index admission: 1989e1991, 1992e1994, 1995e1997, 1998e2000, and 2001e2003. Survival analysis methods were used to analyse the data, where survival is the time between the first day of the index admission and the date of a subsequent coronary procedure. Data were right-censored when the patient had a subsequent AMI, he/she died, or 12 months had elapsed since the index admission. For analysis of PTCA and CABG, censoring also occurred when the patient had the alternative procedure. To provide an initial summary of the data, the crude probability of receiving a procedure by 12 months after admission was estimated for each time period using the KaplaneMeier method. Cox proportional hazards models were used to calculate hazard ratios (HRs) for each quintile of SES (using Q1, the most disadvantaged, as the reference group) for each period. Hazard
ratios were adjusted for age alone and then for all covariates. Inequality estimates are presented for separate time periods and the changes over time are described, but not formally modelled. Analyses were carried out in STATA, release 9 (StataCorp, 2005). The project was approved by The Australian National University Human Research Ethics Committee. Results Angiography Diffusion curves of the age-adjusted probabilities of having angiography, by socioeconomic quintile and period, are shown in Fig. 1. To simplify the presentation of results, diffusion curves are only shown for Q1 and Q5 for each procedure; however, these should be interpreted in conjunction with the Cox regression results shown in Table 1 (males) and Table 2 (females). The probability of having angiography rose steadily between 1989e1991 and 2001e2003 in all SES quintiles. The use of this procedure in AMI patients was still relatively low at the start of the series (1989e1991). At this time and up until the mid 1990s, there was no evidence of systematic SES inequality in procedure rates. After this time there was a relatively rapid rise in the use of the procedure, during which time socioeconomic inequalities appeared. In male patients, there were significant SES gradients in procedure rates in both 1995e1997 and 1998e2000, after adjusting for age and all confounding factors. Procedure rates then began to
Table 1 Fully-adjusted hazard ratios for coronary procedures by socioeconomic status quintile (SES Q) for each time period, males. Angiography
Angioplasty
CABG
n
HR
95% CI
p
HR
95% CI
p
HR
95% CI
p
1989e1991 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
843 747 725 627 565
1.00 1.23 1.05 1.08 .97
e 1.01e1.49 .85e1.29 .88e1.33 .78e1.21 SES trend
e .040 .642 .432 .817 .596
1.00 .99 1.17 1.09 .97
e .70e1.39 .83e1.64 .77e1.54 .68e1.40 SES trend
e .934 .376 .621 .888 .826
1.00 1.11 1.32 1.18 1.24
e .80e1.53 .96e1.83 .84e1.64 .89e1.75 SES trend
e .546 .087 .338 .208 .168
1992e1994 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
778 760 741 615 536
1.00 1.35 1.10 .90 .99
e 1.13e1.62 .92e1.33 .74e1.00 .81e1.21 SES trend
e .001a .295 .280 .928 .081
1.00 1.20 1.03 1.17 .91
e .90e1.59 .77e1.39 .88e1.57 .66e1.25 SES trend
e .220 .819 .286 .566 .671
1.00 1.11 .93 1.18 1.33
e .80e1.53 .67e1.31 .85e1.64 .96e1.84 SES trend
e .542 .690 .335 .087 .103
1995e1997 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
722 702 651 531 584
1.00 1.09 1.16 1.14 1.22
e .94e1.27 .99e1.35 .97e1.34 1.05e1.43 SES trend
e .260 .065 .102 .012 .011
1.00 1.04 .96 1.16 1.04
e .82e1.32 .75e1.23 .91e1.48 .81e1.33 SES trend
e .744 .739 .231 .757 .180
1.00 .98 1.26 .97 1.04
e .72e1.34 .94e1.69 .70e1.35 .76e1.42 SES trend
e .920 .120 .852 .812 .770
1998e2000 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
801 737 669 654 581
1.00 1.18 1.11 1.11 1.24
e 1.03e1.34 .97e1.28 .96e1.28 1.07e1.43 SES trend
e .018 .121 .151 .004 .022
1.00 1.19 1.02 1.03 1.16
e .99e1.43 .84e1.24 .85e1.26 .95e1.41 SES trend
e .068 .843 .759 .154 .517
1.00 1.35 1.10 1.13 1.33
e 1.00e1.85 .80e1.50 .82e1.55 .96e1.84 SES trend
e .048 .568 .473 .090 .305
2001e2003 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
751 722 687 711 637
1.00 1.24 1.02 1.02 1.03
e 1.10e1.41 .89e1.16 .90e1.17 .90e1.18 SES trend
e .001 .802 .726 .647 .452
1.00 1.13 1.08 1.05 1.05
e .96e1.33 .91e1.27 .89e1.25 .88e1.24 SES trend
e .135 .395 .568 .607 .901
1.00 1.12 .93 .73 .86
e .83e1.52 .68e1.28 .51e1.04 .60e1.22 SES trend
e .460a .654 .079 .390a .072
Notes: CABG ¼ coronary artery bypass grafting; SES Q1 is most disadvantaged quintile; Adjusted for age group, country of birth, Aboriginal/Torres Strait Islander status, marital status, comorbidities, area of residence and hospital area. a Proportional hazards assumption violated.
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Table 2 Fully-adjusted hazard ratios for coronary procedures by socioeconomic status quintile (SES Q) for each time period, females. Angiography
Angioplasty
CABG
n
HR
95% CI
p
HR
95% CI
p
1989e1991 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
496 406 453 347 310
1.00 1.01 1.01 1.10 1.33
e .71e1.45 .70e1.44 .76e1.58 .92e1.94 SES trend
e .949 .977 .613 .130 .159
1.00 .83 1.48 1.29 1.42
e .43e1.61 .83e2.62 .72e2.33 .77e2.61 SES trend
e
1992e1994 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
455 427 395 321 334
1.00 1.33 1.35 1.28 1.06
e .96e1.85 .96e1.89 .91e1.81 .73e1.54 SES trend
e .087 .083 .154 .750 .573
1.00 .91 1.16 1.08 1.03
e .53e1.55 .69e1.97 .63e1.84 .59e1.80 SES trend
e
1995e1997 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
372 357 347 292 327
1.00 1.06 1.33 1.02 1.03
e .82e1.38 1.02e1.73 .78e1.35 .78e1.36 SES trend
e .667 .034 .862 .856 .797
1.00 .96 1.42 .81 .95
e .62e1.49 .93e2.18 .50e1.30 .60e1.48 SES trend
e
1998e2000 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
369 408 283 310 365
1.00 1.30 1.33 1.19 1.18
e 1.03e1.64 1.03e1.72 .92e1.54 .92e1.52 SES trend
e .026 .029 .189 .196 .362
1.00 1.49 1.77 1.26 1.46
e 1.05e2.13 1.21e2.60 .84e1.88 1.00e2.14 SES trend
e
2001e2003 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
383 409 335 398 326
1.00 1.14 1.09 1.06 .90
e .93e1.41 .87e1.38 .84e1.33 .71e1.15 SES trend
e .211 .441 .639 .396 .385
1.00 1.25 1.13 .95 1.06
e .93e1.67 .82e1.56 .68e1.33 .76e1.48 SES trend
e
.590 .180 .395 .257 .116
.722 .578 .783 .916 .696
.863 .105 .375 .807 .690
.027 .003 .258 .052 .169
.136 .449 .760 .740 .718
HR
95% CI
p
1.00 .71 1.34 1.20 1.61
e .37e1.37 .76e2.36 .66e2.20 .89e2.92 SES trend
e .304 .313 .550 .113 .046
1.00 .86 1.09 1.74 1.81
e .48e1.54 .62e1.91 1.04e2.93 1.06e3.10 SES trend
e .617 .777 .036 .030 .004
1.00 .95 1.25 1.27 1.08
e .54e1.70 .71e2.22 .74e2.19 .61e1.91 SES trend
e .874 .441 .384 .780 .481
1.00 1.57 1.69 1.24 1.24
e .91e2.72 .93e3.08 .66e2.32 .67e2.33 SES trend
e .108 .085 .500 .494 .740
1.00 1.00 .88 1.03 .35
e .57e1.75 .46e1.67 .57e1.87 .14e.87 SES trend
e .990 .694 .911 .023 .092
Notes: CABG ¼ coronary artery bypass grafting; SES Q1 is most disadvantaged quintile; Adjusted for age group, country of birth, Aboriginal/Torres Strait Islander status, marital status, comorbidities, area of residence and hospital area.
level off in the higher SES patient population (a possible ceiling), while rates continued to rise in the lower SES patients such that by 2001e2003 SES trends in procedure rates were no longer significant. This diffusion pattern, of increasing then stabilising of procedure rates with rising then falling inequality, is consistent with the lag in the diffusion/inverse equity hypothesis. A broadly similar pattern was evident in female patients, although the SES trend was only significant in 1998e2000 after adjusting for age (p ¼ .002) but not after adjusting for all covariates.
time. In contrast to the patterns for men however, rates initially rose more steeply in the lower SES women (between 1992e1994 and 1995e1997), then from 1995 to 1997 they rose more steeply in the higher SES women. Consequently there was no clear pattern of SES inequality, although, similar to male patients, significant inequalities were apparent in 1998e2000, with Q2, Q3 and Q5 having higher procedure rates than Q1 after adjusting for all covariates.
Angioplasty
The diffusion of CABG over the period 1989e1991 to 2001e2003 differed by SES, with the proportion of patients having CABG peaking earlier in the higher SES patients than the lower SES patients. Amongst male patients, the proportion in the lowest SES groups (Q1) having CABG continued to rise over the period; in Q2 and Q3 this proportion rose until 1998e2000 and 1995e1997, respectively, before possibly stabilising; and in the highest SES groups (Q4 and Q5) the proportion having CABG rose until 1998e2000, then fell (see figure for Q1 and Q5). Consequently, inequality models show a significant SES trend favouring advantaged patients early in the period (trend significant in 1992e1994 in the age-adjusted model, although this did not reach significance in the fully-adjusted model). Later, as rates continued to increase in the lower SES patients but fall in the higher SES patients, inequality trends disappeared and by 2001e2003 a reverse trend started to appear with rates higher in the lower SES patients. In female patients, the pattern was not dissimilar to that of males with the probability of CABG peaking earlier in higher SES
The probability of angioplasty also rose steadily over the period in all SES quintiles, in both males and females, with no clear evidence of rates reaching a ceiling in any of the SES quintiles. For males, the diffusion patterns by SES are similar to that for angiography in that the probability of having a procedure was low at the start of the period, with little difference across SES groups in the first two time periods (1989e1991 and 1992e1994). After this time, the probability of angioplasty continued to rise in all SES quintiles, but it rose more steeply in the higher SES patients. (The rises were more apparent in Q4 than Q5 between 1992e1994 and 1995e1997 so SES trends, while significant, are not apparent in 1995e1997 on the graph in the figure where only trends for Q1 and Q5 are shown.) Age-adjusted SES trends were significant in each period from 1995 to 1997 onward; however, none of these were significant after adjusting for all covariates. In female patients, the probability of angioplasty also remained low until 1992e1994 and then increased relatively rapidly after this
Coronary artery bypass grafting
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patients. The proportion of female patients having CABG in lower SES groups (Q1 and Q2) rose until the mid to late 1990s, and in the higher SES groups this proportion rose then gradually fell, reaching a peak progressively earlier moving from Q3 to Q5 (peaking in 1998e2000 for Q3, 1995e1997 for Q4 and 1994e1996 for Q5, see figure for Q1 and Q5). Fully-adjusted inequality models show significant SES trends favouring advantaged patients early in the period (1989e1991 and 1992e1994); however, as rates fell in the higher SES groups after this time, inequality trends disappeared and by 2001e2003 the trend started to reverse with rates higher in the lower SES patients. Overall, this pattern of CABG rates peaking (and falling) earlier in high than low SES patients is consistent with the inverse inequity hypothesis. Discussion This study provides some evidence of inequality in the diffusion of high technology health care in Australia. The SES inequalities in diffusion observed for angiography and CABG are consistent with the lag in diffusion/inverse inequity hypothesisdfor both these procedures, rates peaked earlier in the higher SES patients than the lower SES patients resulting in inequalities, which then disappeared over time as rates peaked in the higher SES patients but continued to increase in the lower SES patients. However, the evidence for angioplasty is not strong, with age-adjusted but not fully-adjusted inequality patterns in males consistent with the hypothesis and no clear patterns in females. This may or may not be attributable to the fact that the diffusion of this procedure had possibly not yet reached a ceiling even in higher SES patients in the time frame under study. The use of linked administrative data enabled individuals to be followed through time and data to be censored. Nevertheless there are limitations in using these data, which may have biased the results. These include (a) the difficulty in adjusting for clinical ‘need’dwhile we limited the study population to only those patients admitted with AMI, appropriateness of care is complex and it is not possible to capture this complexity; (b) beyond metropolitan and rural hospital location, inability to examine supply characteristics, such as surgeon availability; (c) the availability of only area-level SES measures, which may have resulted in an underestimate of inequality; (d) the changes in definitions of AMI and ICD coding practices over timedthese changes may affect the size of inequality estimates, however unless they systematically relate to SES (and there is little reason to think they would), conclusions regarding trends should essentially remain valid; and (e) the entire diffusion process is not captured in these datasets. In addition, the study is exploratory rather than analytic with respect to changes over time; thus, findings are suggestive only. To our knowledge, there are no previous studies that have directly examined SES inequality in diffusion of coronary procedures with which the findings from the present study can be directly compared. Only a small number of international studies have reported inequality in rates across areas or time periods and, while inequality in diffusion was not analysed directly, their results are broadly consistent with the diffusion/inverse equity hypothesis (Haglund, Koster, Nilsson, & Rosen, 2004; Hetemaa, Keskimaki, Manderbacka, Leyland, & Koskinen, 2003; Manson-Siddle & Robinson, 1999). Incidentally, that procedure rates were notably lower in women than men is consistent with previous studies in Australia (AIHW: Davies J, 2003) and elsewhere (Fang & Alderman, 2006). While this study described inequalities in the diffusion process, it did not attempt to explain what factors underlie the diffusion process per se, including the complex interplay between demand and supply. It is likely that certain factors underlying the diffusion process are unequally distributed with regard to SES, which, either singly or in
combination, may explain the inequality in diffusion. Alongside individual patient characteristics such as the greater propensity of higher SES patients to seek new innovations, these might include expansion of the private health care sector, the increased trend toward treating more severely diseased patients as the innovation becomes more established, and the role of health practitioners in adopting an intervention and offering it to their patients. Regarding the latter, Rogers outlined the importance of ‘change agents’, including clinicians, to the diffusion process, observing that these agents vary in their technological competence, their beliefs, and their capacity to communicate to ‘clients’ of differing socioeconomic and cultural backgrounds (Rogers, 2003). Moreover, inequality in uptake especially of newer procedures might be partly explained physicians’ perceptions of a patient’s SES status, with doctors less likely to offer coronary procedures to disadvantaged patients (Barnhart, Cohen, Wright, & Wylie-Rosett, 2006). Conclusions/policy implications The evidence for a SES lag in diffusion of health technology confirms the importance of examining time trends when interpreting the equity impact of new technologies. Such consideration, in the context of diffusion theory, may help to shape policy. In particular, a decrease in inequality in health care over time, instead of being viewed as an explicit increase in equality, might for some technologies be interpreted as inequality in diffusion. This inequality is also consistent with the theory of SES as a fundamental cause of ill health, the theory predicting that the benefits of new technologies will be distributed according to SES-related resources such as money and power (Phelan & Link, 2005). Medical technology poses a particular challenge to the threat of rising relative inequalities in health. Cutler, Deaton, and LlerasMuney (2006) predict there will be acceleration in the production of new technologies in the coming years. Thus, for those conditions where the new interventions are effective, this technological progress may result in rising health inequalities (Chang & Lauderdale, 2009; Glied & Lleras-Muney, 2008) While such inequalities in diffusion might be seen as inevitable, it may be difficult to agree with Cutler and colleagues’ view that the consequent increases in the gradient “have a silver lining [in that they] indicate that help is on the way, not only for those who receive it first, but eventually for everyone” (p.117) (Cutler et al., 2006). Instead, it should be important for organisations and policymakers to know whether or not technologies can be developed and diffused in ways that leads to greater (or at least not less) health equality. The highly plausible, but under-researched, intermediary role of the medical workforce in the diffusion of technologies and mediation of health inequalities is an important consideration. We acknowledge the contributions of the following people who provided valuable advice on this project: Prof. Jim Butler, Director, Australian Centre for Economic Research on Health, Australian National University; Dr Darryl McGill, Cardiologist, The Canberra Hospital; and Dr Rachel Moorin, Associate Professor, University of Western Australia. We would also like to acknowledge the Data Linkage Branch of the Western Australia Department of Health who provided the data for this project. References AIHW: Davies J. (2003). Coronary revascularisation in Australia, 2000. Canberra: Australian Institute of Health and Welfare. Bulletin no. 7. AIHW cat. no. AUS 35. Australian Bureau of Statistics. (2004). Technical paper. Census of population and housing: socio-economic indexes for areas (SEIFA), Australia, 2001. Canberra: ABS. ABS cat. no. 2039.0.55.001. Australian Bureau of Statistics. (2006). Australian demographic statistics, June 2006. Canberra: ABS. ABS cat. no. 3101.0.
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Socioeconomic inequalities in the diffusion of health technology: Uptake of coronary procedures as an example Rosemary J. Korda a, b, *, Mark S. Clements b, Jane Dixon b a b
Australian Centre for Economic Research on Health, The Australian National University, Australia National Centre for Epidemiology and Population Health, The Australian National University, Australia
a r t i c l e i n f o
a b s t r a c t
Article history: Available online 23 November 2010
This paper examines socioeconomic lags in the diffusion of high technology health care, focusing on the diffusion of coronary procedures in people with ischaemic heart disease. Using linked hospital and mortality data, we studied patients admitted to Western Australian hospitals with a first admission for acute myocardial infarction between 1989 and 2003 (n ¼ 27,209). An outcome event was the receipt, within a year, of a coronary proceduredangiography, angioplasty and/or coronary artery bypass surgery (CABG). Socioeconomic status (SES) was assigned to each individual using the SEIFA Index of Disadvantage. Cox regression was used to model the association between SES and procedure rates in five consecutive three-year time periods. Angiography and CABG showed socioeconomic lags in diffusion, with rates peaking earlier in higher SES patients, such that the inequality patterns were consistent with the inverse equity hypothesis. The evidence for a lag in diffusion for angioplasty was weaker. Overall, that there is some evidence for a lag in diffusion of health technology indicates that it is essential to consider trends over time when examining the equity impact of health technologies. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Inequalities Socioeconomic status Diffusion Technology Health care Coronary procedures Australia Heart disease Time trends
Introduction The final report of the Commission on the Social Determinants of Health concluded that while the evidence on health inequalities is well established, there is considerable urgency to expand the knowledge on contributory factors, including those associated with health systems (CSDH, 2008). This paper attends to this challenge through studying socioeconomic inequalities in the diffusion of high technology health care innovations, arguing that such innovations are part of a socio-technical system which may perpetuate health inequalities. Diffusion of an innovation is defined as the process by which a novel development is communicated over time among the members of a social system. The process can be illustrated by graphing the cumulative uptake of the innovation over time. The diffusion curve of a successful innovation typically shows an S-shaped distributiondearly in the diffusion process relatively few individuals adopt or receive the innovation in each time period, the rate of uptake then accelerates, and finally it increases at a slower rate as fewer and fewer remaining potential individuals adopt or receive it. For some innovations, the curve may actually show * Corresponding author. Australian Centre for Economic Research on Health, The Australian National University, Australia. Tel.: þ61 2 61255583; fax: þ61 2 61259123. E-mail address: [email protected] (R.J. Korda). 0277-9536/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2010.11.002
a downward trend after reaching a ceiling as the new innovation is superseded or falls out of favour (Rogers, 2003). Innovations often diffuse at different rates among subgroups in the population, with ‘early adopters’ more likely to be of advantaged status while lower status is associated with later adoption (Rogers, 2003). Because of this lag in diffusion, the ‘inverse equity hypothesis’ predicts that new health interventions will tend to increase inequities because the intervention will initially reach those of higher SES, but that the early increase in inequity ratios will then be followed by a reduction when those of higher SES have reached threshold levels for the intervention and those of lower SES gain greater access to the interventions (Victora, Vaughan, Barros, Silva, & Tomasi, 2000). In the health field, diffusion or similar models have been broadly applied in the area of public health (Haider & Kreps, 2004) but there is little direct evidence on inequalities in the diffusion of medical technology. The aim of this study is to investigate inequalities in the uptake of health care technology over time by socioeconomic status (SES) in order to examine whether or not inequality patterns are consistent with the lag in diffusion/inverse equity hypothesis. We specifically focus on the diffusion of coronary procedures between 1989 and 2003 in people with ischaemic heart disease, the leading cause of avoidable mortality in Australia (Korda & Butler, 2006) and the main contributor to the socioeconomic mortality gap (Page et al., 2006). The examination of coronary proceduresdangiography, angioplasty and coronary artery bypass surgery (CABG)dis
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suited to this study because these procedures are high-volume procedures, which are well recorded in hospital datasets; furthermore, although the diffusion process for each procedure is not fully captured during the study period, it is still evident for each procedure, and with the different procedures at different stages of diffusion this allows examination of inequality in diffusion both within and across innovations.
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Data System, which records all deaths. An established populationbased record linkage system allows linking of records relating to individual patients both within and between these datasets. We restricted cases to patients admitted to hospital with acute myocardial infarction (AMI). The criteria used to select cases for the study were adapted from established methods (Korda, Clements, & Kelman, 2009). Briefly, an AMI case was a patient aged 35 years or older with an AMI index admission, defined as an admission to hospital between 1989 and 2003, with a principal or co-diagnosis of AMI, and with no previous admissions for AMI recorded. There were a total of 27,209 cases. The main outcome event in this study was the receipt of a coronary proceduredangiography, angioplasty and/or CABGdidentified using ICD procedure codes (introduced in mid-1988, hence the index case definition restricting cases to after this time). Socioeconomic status was assigned to each individual using a census-based
Methods We used administrative hospital and death data from Western Australia, a state comprising a tenth of the Australian population (Australian Bureau of Statistics, 2006). Data were extracted from the Hospital Morbidity Data System, which contains information on each hospital admission in the state, including sociodemographic characteristics, clinical diagnoses and procedures, and from the Mortality
Angiography Males
Females 60
90 80
**
50
*
**
60
40
Percentage
Percentage
70
50 40 30
30 20
20 10
10 0
0 1989-1991
1992-1994
1995-1997
1998-2000
2001-2003
1989-1991
1992-1994
Period
1995-1997
1998-2000
2001-2003
1998-2000
2001-2003
1998-2000
2001-2003
Period
Angioplasty Males
Females
50
*
45
*
40
20
35
Percentage
Percentage
25
30
*
25 20
15 10
*
15 10
5
5 0
0 1989-1991
1992-1994
1995-1997
1998-2000
2001-2003
1989-1991
1992-1994
Period
1995-1997 Period
Coronary artery bypass surgery Females
30
30
25
25
20
Percentage
Percentage
Males
*
15 10 5
20 15 ** 10
**
5
0
0 1989-1991
1992-1994
1995-1997
1998-2000
Period
2001-2003
1989-1991
1992-1994
1995-1997 Period
Notes. 1. SES Quintile 1 is most disadvantaged. 2. P-value for test for SES trend < .05 in age-adjusted models* and in age-adjusted and fully-adjusted models** Fig. 1. Age-adjusted diffusion of coronary procedures, 1989e2003, by socioeconomic status (SES).
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measure, the SEIFA Index of Disadvantage (Australian Bureau of Statistics, 2004). The Index was assigned at the postcode level (3% missing), after which, patients were classified into populationbased quintiles of SES. There were between 243 and 257 postcode areas in each time period (mean number of cases per postcode ¼ 21, mean population size ¼ 5414). Potential confounders included in the analyses were: Age Group (ten-year age bands), Sex, Marital Status (single or married/defacto), Aboriginal (yes or no), Country of Birth (Australia/New Zealand or other), Area of Residence (urban or rural), Hospital Area of index admission (metropolitan or rural) and Comorbidity (measured using a modified Charlson Index (Sundararajan et al., 2004)). Cases were grouped into one of five three-year periods, based on the year of their index admission: 1989e1991, 1992e1994, 1995e1997, 1998e2000, and 2001e2003. Survival analysis methods were used to analyse the data, where survival is the time between the first day of the index admission and the date of a subsequent coronary procedure. Data were right-censored when the patient had a subsequent AMI, he/she died, or 12 months had elapsed since the index admission. For analysis of PTCA and CABG, censoring also occurred when the patient had the alternative procedure. To provide an initial summary of the data, the crude probability of receiving a procedure by 12 months after admission was estimated for each time period using the KaplaneMeier method. Cox proportional hazards models were used to calculate hazard ratios (HRs) for each quintile of SES (using Q1, the most disadvantaged, as the reference group) for each period. Hazard
ratios were adjusted for age alone and then for all covariates. Inequality estimates are presented for separate time periods and the changes over time are described, but not formally modelled. Analyses were carried out in STATA, release 9 (StataCorp, 2005). The project was approved by The Australian National University Human Research Ethics Committee. Results Angiography Diffusion curves of the age-adjusted probabilities of having angiography, by socioeconomic quintile and period, are shown in Fig. 1. To simplify the presentation of results, diffusion curves are only shown for Q1 and Q5 for each procedure; however, these should be interpreted in conjunction with the Cox regression results shown in Table 1 (males) and Table 2 (females). The probability of having angiography rose steadily between 1989e1991 and 2001e2003 in all SES quintiles. The use of this procedure in AMI patients was still relatively low at the start of the series (1989e1991). At this time and up until the mid 1990s, there was no evidence of systematic SES inequality in procedure rates. After this time there was a relatively rapid rise in the use of the procedure, during which time socioeconomic inequalities appeared. In male patients, there were significant SES gradients in procedure rates in both 1995e1997 and 1998e2000, after adjusting for age and all confounding factors. Procedure rates then began to
Table 1 Fully-adjusted hazard ratios for coronary procedures by socioeconomic status quintile (SES Q) for each time period, males. Angiography
Angioplasty
CABG
n
HR
95% CI
p
HR
95% CI
p
HR
95% CI
p
1989e1991 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
843 747 725 627 565
1.00 1.23 1.05 1.08 .97
e 1.01e1.49 .85e1.29 .88e1.33 .78e1.21 SES trend
e .040 .642 .432 .817 .596
1.00 .99 1.17 1.09 .97
e .70e1.39 .83e1.64 .77e1.54 .68e1.40 SES trend
e .934 .376 .621 .888 .826
1.00 1.11 1.32 1.18 1.24
e .80e1.53 .96e1.83 .84e1.64 .89e1.75 SES trend
e .546 .087 .338 .208 .168
1992e1994 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
778 760 741 615 536
1.00 1.35 1.10 .90 .99
e 1.13e1.62 .92e1.33 .74e1.00 .81e1.21 SES trend
e .001a .295 .280 .928 .081
1.00 1.20 1.03 1.17 .91
e .90e1.59 .77e1.39 .88e1.57 .66e1.25 SES trend
e .220 .819 .286 .566 .671
1.00 1.11 .93 1.18 1.33
e .80e1.53 .67e1.31 .85e1.64 .96e1.84 SES trend
e .542 .690 .335 .087 .103
1995e1997 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
722 702 651 531 584
1.00 1.09 1.16 1.14 1.22
e .94e1.27 .99e1.35 .97e1.34 1.05e1.43 SES trend
e .260 .065 .102 .012 .011
1.00 1.04 .96 1.16 1.04
e .82e1.32 .75e1.23 .91e1.48 .81e1.33 SES trend
e .744 .739 .231 .757 .180
1.00 .98 1.26 .97 1.04
e .72e1.34 .94e1.69 .70e1.35 .76e1.42 SES trend
e .920 .120 .852 .812 .770
1998e2000 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
801 737 669 654 581
1.00 1.18 1.11 1.11 1.24
e 1.03e1.34 .97e1.28 .96e1.28 1.07e1.43 SES trend
e .018 .121 .151 .004 .022
1.00 1.19 1.02 1.03 1.16
e .99e1.43 .84e1.24 .85e1.26 .95e1.41 SES trend
e .068 .843 .759 .154 .517
1.00 1.35 1.10 1.13 1.33
e 1.00e1.85 .80e1.50 .82e1.55 .96e1.84 SES trend
e .048 .568 .473 .090 .305
2001e2003 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
751 722 687 711 637
1.00 1.24 1.02 1.02 1.03
e 1.10e1.41 .89e1.16 .90e1.17 .90e1.18 SES trend
e .001 .802 .726 .647 .452
1.00 1.13 1.08 1.05 1.05
e .96e1.33 .91e1.27 .89e1.25 .88e1.24 SES trend
e .135 .395 .568 .607 .901
1.00 1.12 .93 .73 .86
e .83e1.52 .68e1.28 .51e1.04 .60e1.22 SES trend
e .460a .654 .079 .390a .072
Notes: CABG ¼ coronary artery bypass grafting; SES Q1 is most disadvantaged quintile; Adjusted for age group, country of birth, Aboriginal/Torres Strait Islander status, marital status, comorbidities, area of residence and hospital area. a Proportional hazards assumption violated.
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Table 2 Fully-adjusted hazard ratios for coronary procedures by socioeconomic status quintile (SES Q) for each time period, females. Angiography
Angioplasty
CABG
n
HR
95% CI
p
HR
95% CI
p
1989e1991 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
496 406 453 347 310
1.00 1.01 1.01 1.10 1.33
e .71e1.45 .70e1.44 .76e1.58 .92e1.94 SES trend
e .949 .977 .613 .130 .159
1.00 .83 1.48 1.29 1.42
e .43e1.61 .83e2.62 .72e2.33 .77e2.61 SES trend
e
1992e1994 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
455 427 395 321 334
1.00 1.33 1.35 1.28 1.06
e .96e1.85 .96e1.89 .91e1.81 .73e1.54 SES trend
e .087 .083 .154 .750 .573
1.00 .91 1.16 1.08 1.03
e .53e1.55 .69e1.97 .63e1.84 .59e1.80 SES trend
e
1995e1997 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
372 357 347 292 327
1.00 1.06 1.33 1.02 1.03
e .82e1.38 1.02e1.73 .78e1.35 .78e1.36 SES trend
e .667 .034 .862 .856 .797
1.00 .96 1.42 .81 .95
e .62e1.49 .93e2.18 .50e1.30 .60e1.48 SES trend
e
1998e2000 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
369 408 283 310 365
1.00 1.30 1.33 1.19 1.18
e 1.03e1.64 1.03e1.72 .92e1.54 .92e1.52 SES trend
e .026 .029 .189 .196 .362
1.00 1.49 1.77 1.26 1.46
e 1.05e2.13 1.21e2.60 .84e1.88 1.00e2.14 SES trend
e
2001e2003 SES Q1 SES Q2 SES Q3 SES Q4 SES Q5
383 409 335 398 326
1.00 1.14 1.09 1.06 .90
e .93e1.41 .87e1.38 .84e1.33 .71e1.15 SES trend
e .211 .441 .639 .396 .385
1.00 1.25 1.13 .95 1.06
e .93e1.67 .82e1.56 .68e1.33 .76e1.48 SES trend
e
.590 .180 .395 .257 .116
.722 .578 .783 .916 .696
.863 .105 .375 .807 .690
.027 .003 .258 .052 .169
.136 .449 .760 .740 .718
HR
95% CI
p
1.00 .71 1.34 1.20 1.61
e .37e1.37 .76e2.36 .66e2.20 .89e2.92 SES trend
e .304 .313 .550 .113 .046
1.00 .86 1.09 1.74 1.81
e .48e1.54 .62e1.91 1.04e2.93 1.06e3.10 SES trend
e .617 .777 .036 .030 .004
1.00 .95 1.25 1.27 1.08
e .54e1.70 .71e2.22 .74e2.19 .61e1.91 SES trend
e .874 .441 .384 .780 .481
1.00 1.57 1.69 1.24 1.24
e .91e2.72 .93e3.08 .66e2.32 .67e2.33 SES trend
e .108 .085 .500 .494 .740
1.00 1.00 .88 1.03 .35
e .57e1.75 .46e1.67 .57e1.87 .14e.87 SES trend
e .990 .694 .911 .023 .092
Notes: CABG ¼ coronary artery bypass grafting; SES Q1 is most disadvantaged quintile; Adjusted for age group, country of birth, Aboriginal/Torres Strait Islander status, marital status, comorbidities, area of residence and hospital area.
level off in the higher SES patient population (a possible ceiling), while rates continued to rise in the lower SES patients such that by 2001e2003 SES trends in procedure rates were no longer significant. This diffusion pattern, of increasing then stabilising of procedure rates with rising then falling inequality, is consistent with the lag in the diffusion/inverse equity hypothesis. A broadly similar pattern was evident in female patients, although the SES trend was only significant in 1998e2000 after adjusting for age (p ¼ .002) but not after adjusting for all covariates.
time. In contrast to the patterns for men however, rates initially rose more steeply in the lower SES women (between 1992e1994 and 1995e1997), then from 1995 to 1997 they rose more steeply in the higher SES women. Consequently there was no clear pattern of SES inequality, although, similar to male patients, significant inequalities were apparent in 1998e2000, with Q2, Q3 and Q5 having higher procedure rates than Q1 after adjusting for all covariates.
Angioplasty
The diffusion of CABG over the period 1989e1991 to 2001e2003 differed by SES, with the proportion of patients having CABG peaking earlier in the higher SES patients than the lower SES patients. Amongst male patients, the proportion in the lowest SES groups (Q1) having CABG continued to rise over the period; in Q2 and Q3 this proportion rose until 1998e2000 and 1995e1997, respectively, before possibly stabilising; and in the highest SES groups (Q4 and Q5) the proportion having CABG rose until 1998e2000, then fell (see figure for Q1 and Q5). Consequently, inequality models show a significant SES trend favouring advantaged patients early in the period (trend significant in 1992e1994 in the age-adjusted model, although this did not reach significance in the fully-adjusted model). Later, as rates continued to increase in the lower SES patients but fall in the higher SES patients, inequality trends disappeared and by 2001e2003 a reverse trend started to appear with rates higher in the lower SES patients. In female patients, the pattern was not dissimilar to that of males with the probability of CABG peaking earlier in higher SES
The probability of angioplasty also rose steadily over the period in all SES quintiles, in both males and females, with no clear evidence of rates reaching a ceiling in any of the SES quintiles. For males, the diffusion patterns by SES are similar to that for angiography in that the probability of having a procedure was low at the start of the period, with little difference across SES groups in the first two time periods (1989e1991 and 1992e1994). After this time, the probability of angioplasty continued to rise in all SES quintiles, but it rose more steeply in the higher SES patients. (The rises were more apparent in Q4 than Q5 between 1992e1994 and 1995e1997 so SES trends, while significant, are not apparent in 1995e1997 on the graph in the figure where only trends for Q1 and Q5 are shown.) Age-adjusted SES trends were significant in each period from 1995 to 1997 onward; however, none of these were significant after adjusting for all covariates. In female patients, the probability of angioplasty also remained low until 1992e1994 and then increased relatively rapidly after this
Coronary artery bypass grafting
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patients. The proportion of female patients having CABG in lower SES groups (Q1 and Q2) rose until the mid to late 1990s, and in the higher SES groups this proportion rose then gradually fell, reaching a peak progressively earlier moving from Q3 to Q5 (peaking in 1998e2000 for Q3, 1995e1997 for Q4 and 1994e1996 for Q5, see figure for Q1 and Q5). Fully-adjusted inequality models show significant SES trends favouring advantaged patients early in the period (1989e1991 and 1992e1994); however, as rates fell in the higher SES groups after this time, inequality trends disappeared and by 2001e2003 the trend started to reverse with rates higher in the lower SES patients. Overall, this pattern of CABG rates peaking (and falling) earlier in high than low SES patients is consistent with the inverse inequity hypothesis. Discussion This study provides some evidence of inequality in the diffusion of high technology health care in Australia. The SES inequalities in diffusion observed for angiography and CABG are consistent with the lag in diffusion/inverse inequity hypothesisdfor both these procedures, rates peaked earlier in the higher SES patients than the lower SES patients resulting in inequalities, which then disappeared over time as rates peaked in the higher SES patients but continued to increase in the lower SES patients. However, the evidence for angioplasty is not strong, with age-adjusted but not fully-adjusted inequality patterns in males consistent with the hypothesis and no clear patterns in females. This may or may not be attributable to the fact that the diffusion of this procedure had possibly not yet reached a ceiling even in higher SES patients in the time frame under study. The use of linked administrative data enabled individuals to be followed through time and data to be censored. Nevertheless there are limitations in using these data, which may have biased the results. These include (a) the difficulty in adjusting for clinical ‘need’dwhile we limited the study population to only those patients admitted with AMI, appropriateness of care is complex and it is not possible to capture this complexity; (b) beyond metropolitan and rural hospital location, inability to examine supply characteristics, such as surgeon availability; (c) the availability of only area-level SES measures, which may have resulted in an underestimate of inequality; (d) the changes in definitions of AMI and ICD coding practices over timedthese changes may affect the size of inequality estimates, however unless they systematically relate to SES (and there is little reason to think they would), conclusions regarding trends should essentially remain valid; and (e) the entire diffusion process is not captured in these datasets. In addition, the study is exploratory rather than analytic with respect to changes over time; thus, findings are suggestive only. To our knowledge, there are no previous studies that have directly examined SES inequality in diffusion of coronary procedures with which the findings from the present study can be directly compared. Only a small number of international studies have reported inequality in rates across areas or time periods and, while inequality in diffusion was not analysed directly, their results are broadly consistent with the diffusion/inverse equity hypothesis (Haglund, Koster, Nilsson, & Rosen, 2004; Hetemaa, Keskimaki, Manderbacka, Leyland, & Koskinen, 2003; Manson-Siddle & Robinson, 1999). Incidentally, that procedure rates were notably lower in women than men is consistent with previous studies in Australia (AIHW: Davies J, 2003) and elsewhere (Fang & Alderman, 2006). While this study described inequalities in the diffusion process, it did not attempt to explain what factors underlie the diffusion process per se, including the complex interplay between demand and supply. It is likely that certain factors underlying the diffusion process are unequally distributed with regard to SES, which, either singly or in
combination, may explain the inequality in diffusion. Alongside individual patient characteristics such as the greater propensity of higher SES patients to seek new innovations, these might include expansion of the private health care sector, the increased trend toward treating more severely diseased patients as the innovation becomes more established, and the role of health practitioners in adopting an intervention and offering it to their patients. Regarding the latter, Rogers outlined the importance of ‘change agents’, including clinicians, to the diffusion process, observing that these agents vary in their technological competence, their beliefs, and their capacity to communicate to ‘clients’ of differing socioeconomic and cultural backgrounds (Rogers, 2003). Moreover, inequality in uptake especially of newer procedures might be partly explained physicians’ perceptions of a patient’s SES status, with doctors less likely to offer coronary procedures to disadvantaged patients (Barnhart, Cohen, Wright, & Wylie-Rosett, 2006). Conclusions/policy implications The evidence for a SES lag in diffusion of health technology confirms the importance of examining time trends when interpreting the equity impact of new technologies. Such consideration, in the context of diffusion theory, may help to shape policy. In particular, a decrease in inequality in health care over time, instead of being viewed as an explicit increase in equality, might for some technologies be interpreted as inequality in diffusion. This inequality is also consistent with the theory of SES as a fundamental cause of ill health, the theory predicting that the benefits of new technologies will be distributed according to SES-related resources such as money and power (Phelan & Link, 2005). Medical technology poses a particular challenge to the threat of rising relative inequalities in health. Cutler, Deaton, and LlerasMuney (2006) predict there will be acceleration in the production of new technologies in the coming years. Thus, for those conditions where the new interventions are effective, this technological progress may result in rising health inequalities (Chang & Lauderdale, 2009; Glied & Lleras-Muney, 2008) While such inequalities in diffusion might be seen as inevitable, it may be difficult to agree with Cutler and colleagues’ view that the consequent increases in the gradient “have a silver lining [in that they] indicate that help is on the way, not only for those who receive it first, but eventually for everyone” (p.117) (Cutler et al., 2006). Instead, it should be important for organisations and policymakers to know whether or not technologies can be developed and diffused in ways that leads to greater (or at least not less) health equality. The highly plausible, but under-researched, intermediary role of the medical workforce in the diffusion of technologies and mediation of health inequalities is an important consideration. We acknowledge the contributions of the following people who provided valuable advice on this project: Prof. Jim Butler, Director, Australian Centre for Economic Research on Health, Australian National University; Dr Darryl McGill, Cardiologist, The Canberra Hospital; and Dr Rachel Moorin, Associate Professor, University of Western Australia. We would also like to acknowledge the Data Linkage Branch of the Western Australia Department of Health who provided the data for this project. References AIHW: Davies J. (2003). Coronary revascularisation in Australia, 2000. Canberra: Australian Institute of Health and Welfare. Bulletin no. 7. AIHW cat. no. AUS 35. Australian Bureau of Statistics. (2004). Technical paper. Census of population and housing: socio-economic indexes for areas (SEIFA), Australia, 2001. Canberra: ABS. ABS cat. no. 2039.0.55.001. Australian Bureau of Statistics. (2006). Australian demographic statistics, June 2006. Canberra: ABS. ABS cat. no. 3101.0.
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