Background
The link between socioeconomic status (SES) and health has long been recognized, with lower SES associated with poorer health [
1,
2]. With more health needs, low SES populations would be expected to consume more health services, yet accessing certain types of services is often impeded by financial and organizational barriers. Inequitable use of health services has been described even in countries with universal coverage. In In Israel, all residents are covered by mandatory health insurance, financed mainly by a progressive health tax, and provided by one of four health funds, operating as insurers and providers. Clalit Health Services is the largest (non-for-profit) health fund in Israel, with over 3.9 million enrolees (53% market share), operating services distributed throughout Israel, including 1500 primary and secondary care clinics, 14 hospitals, labs and diagnostic imaging facilities. Clalit members receive, at the point of care, free primary care and hospitalization services. Specialty care and imaging services incur a copayment. Persons of low SES (who receive social security entitlements) receive a complete or partial waiver for these copayments, depending on their social security entitlement status. Despite universal coverage and copayment waivers, previous Israeli studies have shown that disadvantaged groups face more barriers to specialty care than the rest of the population [
3,
4].
Studies from other developed countries have demonstrated that persons of low economic status utilize less specialist services than their more affluent counterparts [
5‐
8], yet findings on pro-rich specialty care use also exist [
9]. Primary care is generally shown to be equitably distributed in universal coverage health care systems [
6,
10], however inequitable primary care use is also reported [
11]. These inconsistent findings may reflect actual variations in patterns of SES and health care use, yet they may be a result of differences in measurement of health needs [
12].
Various measures of morbidity are used for case-mix adjustments in the health care inequity literature. The most commonly used measures include survey-based health status measures [
6,
7], comorbidity indices such as the Charlson comorbidity index [
13], simple morbidity counts [
14], or diagnoses-based morbidity measures [
15]. Studies show that the use of a comprehensive survey based health-needs adjustment measure affects the degree to which equitable resource use can be assessed [
6,
7]. A Dutch study on SES utilization differences has shown that by controlling for health status (using a survey-based measure), differences by SES changed markedly for all health services analyzed, as compared to a basic age-gender adjustment method [
16]. Yet, survey-derived measures are not easily collectable. Moreover, patient self-reported health status has been shown to differ between population sub-groups [
17,
18]. The relative value of different adjustment measures that are based on readily available morbidity data (from electronic medical records or administrative data bases), such as the Charlson index, or comprehensive diagnoses-based morbidity measures, has not been previously assessed.
The aim of this study was to examine the degree to which adjustment for morbidity using a diagnoses-based morbidity measure-based on tools of the Johns Hopkins University Adjusted Clinical Groups
® (ACGs) [
19‐
22], explains differences in health care use by socioeconomic status, better than other commonly used health needs measures. We compared models of SES utilization differences, adjusting for overall morbidity burden (using the ACG system), with models that used age and gender only, or comorbidity (using the Charlson index).
Results
During 2009 a total of over 6 million diagnoses from hospitalizations or community physician visits were assigned to all persons in the Clalit sample (average of 6.3 distinct diagnoses per person). On average each person was assigned 4.2 morbidity groups (ADGs) (SD 3.2, range: 0-25, of a possible 32 groups) (Table
1). Social security waiver holders (adults) had on average of about 1.5 times more morbidity groups assigned than non waiver holders (6.5 ADGs, SD: 3.8 and 4.3 ADGs, SD: 3.1 respectively). The average CCI score for the total sample and for the adults sub-sample was 1.2 and 1.6, respectively (range in each sample 0-22). About 60% of all enrolees (and about 50% of all adults) had a CCI score of zero.
Table 1
Sample characteristics
Age (mean, SD) | 35.3 (23.1) | 45.8 (19.4) |
Gender, female (N, %) | 203,652 (51.0) | 145,205 (52.0) |
Social Security Waiver (N, %) | 49,241 (12.4) | 40,947 (14.6) |
Charlson Comorbidity Index (mean, SD) | 1.19 (2.1) | 1.62 (2.3) |
Number of ADGs (mean, SD) | 4.2 (3.2) | 4.7 (3.5) |
Tests of the correlation of the overall distribution of ADGs in Clalit showed that ADGs are distributed similarly in different populations. The Pearson correlation was very similar to that of the US and the Spanish samples (r = 0.89, and 0.95 respectively).
Table
2 shows that morbidity groups (ADGs) explained the largest percent of variance or in health care resource use: 23% to 54% of the variation in primary care physician visits, specialist visits, performance of diagnostic tests, and hospitalizations. Models that adjusted for comorbidity (using the CCI) explained only 11-18% of all types of resource use. Age and gender alone explained only 5-13% of variation in use of the above health care services.
Table 2
Coefficients of determination (r2) of the multiple linear regression models explaining resource use
Age and gender | 0.13 | 0.12 | 0.13 | 0.05 |
Charlson Comorbidity Index, age, gender | 0.18 | 0.13 | 0.15 | 0.11 |
ADGs, age, gender | 0.54 | 0.45 | 0.37 | 0.24 |
Differences in resource use by socioeconomic status
Table
3 reports the differences in high resource use between persons holding a social-security waiver and all other adult enrolees. Adults holding a social-security waiver were about twice as likely to have an above average number of primary care visits, 30% more likely to have an above average number of specialist visits or diagnostic tests performed, and more than twice as likely to be hospitalized at least one, compared with all other adults.
Table 3
Percent with high service use by socioeconomic status*
Above average number of primary care visits | 63% | 34% |
Above average number of specialist visits | 42% | 31% |
Above average number of diagnostic tests | 38% | 28% |
One or more hospitalizations | 16% | 7% |
Table
4 reports the Odds Ratios (OR) and confidence intervals (CI) for having above average resource use in persons with a social security waiver compared to all other adults. Controlling for age and gender only or for comorbidity (using the CCI), persons of low socioeconomic status (i.e., those holding a waiver) were significantly more likely to have above average number of primary care visits, specialist visits, diagnostic tests performed, and one or more hospitalization. Controlling for morbidity burden (using ADGs), compared to all other adults, waiver holders were less likely to have an above average number of specialist visits or an above average number of diagnostic tests performed. In all morbidity burden models (Models C), the likelihood of having an above average resource use for each type of service examined was reduced by 17-35% relative to the age and gender only models (Models A).
Table 4
Socioeconomic class and high service use: odds-ratios for alternative morbidity adjustment models
Model A: Adjusting for age and gender |
Odds ratio (95% CI) | 1.92 (1.87-1.97) | 1.13 (1.10-1.57) | 1.11 (1.08-1.37) | 1.67 (1.62-1.73) |
Model B: Adjusting for age, gender and the Charlson Comorbidity Index |
Odds ratio (95% CI) | 1.62 (1.58-1.66) | 1.04 (1.01-1.06) | 1.05 (1.03-1.08) | 1.38 (1.33-1.42) |
Model C: Adjusting for age, gender, and morbidity using ADG categories |
Odds ratio (95% CI) | 1.64 (1.60-1.69) | 0.95 (0.94-0.99) | 0.91 (0.86-0.96) | 1.24 (1.20-1.29) |
Tests of model fit (area under the ROC curve or AIC tests) indicated a better fit of all morbidity burden models (Models C; area under the ROC curve: between 0.72-0.86, indicating good-very good fit) than models controlling for age and gender only (Models A; area under the ROC curve: between 0.66-0.74, indicating a poor-good fit) or the comorbidity (CCI) models (Models B; area under the ROC curve: between 0.67-0.77, indicating a poor-good fit) [
23] (Table
5).
Table 5
Socioeconomic class and high service use: predictive accuracy of alternative morbidity models
Model A: Adjusting for age and gender |
Area under the ROC curve | 0.74 | 0.66 | 0.66 | 0.67 |
AIC | 315900 | 331209 | 262971 | 150883 |
Model B: Adjusting for age, gender and the Charlson Comorbidity Index |
Area under the ROC curve | 0.77 | 0.67 | 0.67 | 0.72 |
AIC | 307183 | 329009 | 262267 | 142501 |
Model C: Adjusting for age, gender, and morbidity using ADG categories |
Area under the ROC curve | 0.85 | 0.77 | 0.72 | 0.86 |
AIC | 254608 | 290254 | 247195 | 117215 |
Analysis conducted with the two other SES measures (having supplemental insurance or area level SES) showed similar results-i.e., controlling for age, gender, and morbidity burden, the more socially deprived were significantly more likely to have an above average number of primary care visits or one or more hospitalizations, and significantly less likely to have an above average number of specialist visits or diagnostic tests performed, than those with supplemental private insurance or higher SES (data not shown). All models that accounted for overall morbidity burden resulted in a better fit than adjustments using the comorbidity measure or age and gender only.
Discussion
Our findings show that accounting for overall morbidity levels with a comprehensive measure based on the combination of all types of diagnoses, inequity in various types of health care resource use could be identified. Persons of low economic status were more likely than those of higher economic status to be high users of primary care services, more likely to be hospitalized at least once during the 12-month study period, and were less likely to be high users of specialty care and diagnostic imaging services.
Comparisons between models that used different adjustment measures showed that morbidity burden provides a better explanation of differential resource use than other commonly used measures. Adjustments using age and gender or the Charlson index overestimated the difference in utilization between persons from low and high SES. Moreover, adjusting for age and gender or the Charlson index misleadingly showed that persons of low SES are more likely to have higher utilization of specialists and diagnostic services. Adjustments using the morbidity burden measure (ADGs) revealed a reverse relationship, more accurately explaining differences in resource use (as shown by the tests of model fit).
Other countries and health systems with universal coverage face similar challenges-differences in health care use by SES are reported, with reports on pro-rich inequity in specialty care use [
5‐
8] and in performance of diagnostic imaging tests [
15,
34]. Studies on primary care use by SES are mixed, with some showing an equitable (i.e., needs-based) distribution across SES groups [
6,
10], while others support the findings reported here, showing that person of poorer economic status use more primary care services than their richer counterparts [
35]. Higher rates of hospitalizations of those from lower SES were also found by others [
14]. Our study supports these findings, and adds to current knowledge by showing that the choice of health-need adjustment measure can affect the magnitude and accuracy of identification of gaps in service use.
Countries and health care systems differ in benefit design, patient-cost-sharing, and the role of private insurance, and the reasons and magnitude of inequalities vary. Yet, common findings on utilization inequity in universal coverage systems suggest that implementation of universal coverage principles deserves further consideration. A possible explanation for the inequitable use of health services reported here is that, as reported also by others, persons of low SES face non-financial barriers to health service use [
3,
4]. These barriers may include poorer availability of services, cultural and language gaps that may affect minorities, who constitute large percentages of low SES populations, or differences in preferences. Future research is required to test whether differences in use reflect the level of needed care by persons from diverse SES groups (i.e., whether underutilization or overutilization exists) and to assess the degree to which inequity reduction programs succeed in minimizing unwarranted gaps.
A potential criticism on the use of diagnoses-based measures, such as ADGs, is that they may be biased due to their reliance on data registered during patient visits, and thus non-clinically measured aspect of health and underutilization may affect the completeness of data. A recent Canadian study addressed this potential shortcoming by examining the contribution of survey-derived indicators of health status to explanatory models of physician service use based on morbidity adjustment using the ACG system [
36]. Adjustments for health status in that study did not contribute significantly to models on the basis of the diagnoses-based ACG measures. As availability of survey data, in comparison to routinely collected administrative and clinical data, may be limited, it is important to acknowledge the benefits of using diagnoses-based measures for planning, reimbursement, and research.
Assessing healthcare utilization patterns among low SES groups is a key step in planning health inequity-reduction strategies in healthcare organizations. To reduce inequities in health and in the delivery of health care, Clalit has laid out in 2008 an organization-wide strategic plan that addresses health care workforce, quality of care and utilization differences between low and high SES groups [
37]. Examination of differences in use of health care services, based on robust measures of need, as reported here, can direct organizational efforts by suggesting areas of potential inequitable access to care.
Diagnoses-based morbidity measures, which classify the population according to diagnoses from all medical encounters, are increasingly being used by health care organizations worldwide for various applications, including equitable allocation of resources, assessments of providers' performance, care management, and for research and evaluation [
28,
38,
39]. Clalit Health Services, mainly due to historical reasons [
40], has a significant overrepresentation of underprivileged populations-low socioeconomic groups, minorities, new immigrants, and persons with disabilities [
41]. Clalit has developed in recent years various case-mix tools for medical, economic and administrative purposes. A recent study has demonstrated the feasibility and validity of using ACGs in Clalit [
42]. Our cross-national comparisons, as well as studies from other countries [
43‐
45], show that ACGs provide robust classification of morbidity across different countries with markedly different health care systems. Moreover, our results show that ACGs explain a large percent of resource use at Clalit, similar to reports from other countries; ACGs have been shown to explain 53-59% of the variation in primary care visits in Spain [
22], 41-58% of variation in ambulatory visits in Taiwan [
46], and 32-59% of ambulatory visits in the US [
19].
A potential limitation of any diagnoses-based system is the quality of the diagnostic coding. The accuracy of the diagnoses has not been systematically estimated here, however, the similarity between the ACGs' distributions in different countries suggests that coding is not a major limitation in Clalit. Additionally, the JHU-ACG system is relatively robust and minor differences in coding do not necessarily affect the system's groupings, which are based on types of diagnoses and health states and not on specific diagnoses and diseases.
Another limitation is that our measure of low SES is only a proxy measure and does not incorporate important information on education, income and wealth components [
47]. To address this we have examined different proxies for individual SES, all leading to the same results regarding the direction, magnitude and significance of difference between those with lower versus higher SES.
Additionally, our results may not be representative of differences in utilization between socioeconomic groups in other countries. Health care systems world-wide differ in benefit design, patient-cost-sharing, and the role of private insurance. Yet, our main finding, i.e., that adjustment for morbidity using a robust diagnoses-based measure allows for a more accurate assessment of inequity in resource use is of relevance to other countries and health systems. Finally, this study only examines how well morbidity, as measured by the ACG system, explains actual service use, rather than what ought to have been provided based on patients' needs. Future studies are needed to test whether adjustment for additional factors that define health care need (e.g., proximity to healthcare facilities or patient care seeking behaviours) provide additional insight into the reasons for differences in healthcare use.
Conclusions
Relative to all other enrolees, persons of low socioeconomic status were less likely to be high users of specialty care and imaging services, a phenomenon identified only with the use of a robust health-needs measure. Using routinely recorded data from electronic medical records, the JHU-ACG System is a feasible and valid method to account for the morbidity level of an Israeli population, strongly associated with different types of healthcare services utilization. Accurate adjustment to morbidity level afforded by this system, allows new insights on differences in healthcare services utilization between population sub-groups.
With the launching of a new inequity reduction strategy within Clalit, accurate assessment of these differences is of considerable importance, as assessment of inequalities in health and health care, care management and resource allocation can be performed more precisely and fairly. Further research is needed to test these differences in other Israeli populations as well as in other countries and to better understand the possible contribution of differences in utilization by SES to health outcomes.
Competing interests
The ACG Systems is commercially distributed under license with The Johns Hopkins University, which holds the copyright to the ACG System. Johns Hopkins University benefits from the sale of this software. The majority of these royalties is used toward supporting ongoing development work of the ACG System, including the research presented in this article.
Authors' contributions
ES and RDB designed the study, performed the statistical analysis and drafted the manuscript. CA, KK, and JW provided insight into the concept and design of the study and revised the manuscript critically for important intellectual content. All authors read and approved the final version of the manuscript.