Relationship Between Avoidable Hospitalizations for Diabetes Mellitus and Income Level FREE

THERE HAS BEEN increasing attention devoted to understanding social inequalities in access to health care. In the United States, individuals living in low-income areas have considerably higher rates of avoidable hospitalizations (AHs) for diabetes mellitus (DM), asthma, hypertension, and several other chronic diseases.^1- 3 Diabetes mellitus is the prototype of an ambulatory care–sensitive condition because its management relies heavily on outpatient services, and hospital admissions for hyperglycemia or hypoglycemia are generally preventable in patients receiving good ambulatory care.

Differences in income can explain up to 50% of the variation in AH rates across neighborhoods in the United States,¹ but other factors are clearly important. For example, insurance status is a major confounder because inadequate health care coverage predisposes to poor access to ambulatory care and higher AH rates, independent of socioeconomic indicators.^3- 5

The Canadian health care system provides insurance coverage for all medically necessary physician, laboratory, and hospital services. However, social disparities in health persist despite universal access to care. As in other countries, there is a strong inverse gradient between income level and mortality from various causes,^6- 7 in part owing to a higher prevalence of cardiovascular disease, DM, and other conditions in low-income populations. Commensurate with greater health needs, Canadians of lower socioeconomic status (SES) have more frequent primary care visits and hospitalizations^8- 9 but also reduced access to specialists and specialized services compared with wealthier individuals.^6,10 A comparative study² reported that poor neighborhoods in Canadian cities have hospital admission rates for ambulatory care–sensitive conditions that are 1.4 times greater than more affluent areas; however, this gradient is less pronounced than in the United States, where rates may vary by more than 6-fold.

In Canada, the relationship between SES and acute hospital admissions for DM is unknown. Previous studies combined admissions for DM and other conditions as an aggregate outcome and may have been confounded by an excess of disease in lower SES quintiles and other factors known to affect AHs.^2- 3,11 The purpose of this study is to evaluate the effect of SES on hospital admissions for acute diabetic complications (hyperglycemia and hypoglycemia) using a large database of patients with DM derived from hospital and physician claims data in Ontario. This setting controls for access to care by eliminating the role of health insurance status in the development of these events.

We used the Ontario Diabetes Database (ODD), which contains electronic records from administrative data sources, to identify all patients with DM in the province. Creation of the database is described in full elsewhere.¹² Briefly, hospital discharge abstracts prepared by the Canadian Institute for Health Information were used to identify patients admitted to the hospital with a diagnosis of DM based on the presence of an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), code of 250.x on any 1 of 16 diagnostic fields. The Ontario Health Insurance Plan database was used to identify physician service claims for visits coded with a diagnosis of "250," a modified version of the ICD-9-CM code for DM. Individuals having at least 1 hospitalization or 2 physician service claims for DM within 2 years were included in the ODD. All records for individuals were linked using a reproducibly scrambled unique health care identifier, retaining patient anonymity. The ODD has been demonstrated¹² to have a sensitivity of 86% and a specificity of more than 95% for detecting patients in whom DM was reported in primary care charts.

This study included prevalent cases of DM in the ODD between April 1, 1992, and March 31, 1999. We included individuals who had a valid Ontario postal code and who lived in regions for which census data on household income were available.

The main outcome in this study was the occurrence of at least 1 hospitalization or emergency department (ED) visit for an acute complication of DM. Admissions for hyperglycemic emergencies were identified from hospitalization records that listed diabetic ketoacidosis, hyperosmolar nonketotic coma, or mixed ketoacidosis and hyperglycemic coma (ICD-9-CM codes 250.1-250.3) as the most responsible or primary diagnosis. Admissions for hypoglycemic or insulin coma (ICD-9-CM code 251.0) were identified from hospitalization records using the same criteria. Emergency department visits were derived from physician service claims in which the diagnostic code was 250 or 251 and the visit occurred in an ED. Unlike hospital discharge abstracts, these claims only allow the reporting of a single diagnostic code.

The principal predictor variable was SES. In Ontario, personal income is not available in administrative data sources. Therefore, income level for individuals in the ODD was estimated from neighborhood-level data collected in the 1996 Canadian Census, using a validated algorithm.⁶ Each neighborhood in Ontario, representing a census enumeration area (average population ≈700), was divided into 5 categories based on median household income, which ranged from Can $7680 to Can $39 852 in the lowest quintile (Q1) up to Can $70 512 to Can $304 454 in the highest quintile (Q5). Income quintiles were then assigned to individual patients in the ODD by linking the 2 databases using the patient's postal code as a common identifier. Income data for 17% of the 15 401 enumeration areas in Ontario were suppressed by Statistics Canada because of small sample size. As a conservative measure, individuals living in enumeration areas that lacked income data (n = 3101 or 0.5% of the sample) were assigned to the middle quintile (Q3). This approach would tend to minimize income-related differences, particularly since individuals with DM are overrepresented in the lower SES categories. In contrast, income data were missing for only 2% of forward sortation areas, larger regions corresponding to the first 3 digits of the postal code. Therefore, the analysis was repeated using quintiles based on median household income for each forward sortation area.

The main analysis was a comparison of the proportion of individuals who had an acute event (≥1 hospitalization or ED visit for hyperglycemia or hypoglycemia) across income quintiles using a χ² analysis. Logistic regression was used to adjust for the following factors: age, sex, comorbidity, type of residential area (urban vs rural, based on definitions used by Canada Post), geographic region of the province, and ambulatory care use. Ambulatory measures were derived from physician service claims for visits occurring during the year before the index date, regardless of the date of entry into the ODD. The following variables were included: number of primary care office visits, presence of a usual care provider (≥50% of ambulatory visits to a single primary care physician), and provision of DM care by a specialist, defined as at least 1 physician service claim submitted by an endocrinologist, general internist, or pediatrician in the preceding year for which the diagnostic code was 250. To derive ambulatory measures for individuals who were event free, the index date was defined as the midpoint of their duration in the DM database. A sensitivity analysis revealed that choosing an earlier index date (such as in the first year of entry into the database) did not alter the results of this analysis. Case-mix adjustment was performed using the Johns Hopkins Ambulatory Care Groups assignment software (Sparc/Solaris version 4.52; Sun Microsystems Inc, Santa Clara, Calif).^13- 14 Clinical details, such as case severity and type of therapy, are not available in administrative data sources and therefore were not included in the model.

As a control, we evaluated the effect of SES on nonavoidable hospitalizations in people with DM by comparing the proportion of individuals in the ODD 65 years and older who were admitted for hip fracture (ICD-9-CM codes 820.0-820.9) with the proportion of those younger than 65 years who were admitted for appendicitis (ICD-9-CM codes 540.0-541.0) in each quintile during the same period.

There were 611 404 prevalent cases of DM identified in the ODD between April 1, 1992, and March 30, 1999. Of these, 605 825 individuals had an Ontario postal code for which census data were available. Characteristics of individuals in the highest and lowest SES groups are compared in Table 1. There were considerably more people with DM in the lowest than in the highest income category. Patients in the highest quintile were somewhat younger and less likely to live in rural or remote areas of the province than those in the lowest quintile. However, measures of comorbidity and ambulatory care use were comparable across all economic strata. Annual outpatient visit rates were high across all groups, yet more than one tenth of the people with DM in the province did not see a primary care physician in the preceding year.

Overall, 14% of individuals (n = 87 425) had at least 1 hospitalization (total number of admissions = 43 440) or ED visit (total number of episodes = 184 646 [157 550 ED visits only and 27 096 that led to hospitalization]) for hyperglycemia or hypoglycemia during the 7 study years. In most cases, the reason for hospital admission was hyperglycemia (94%), with most of these due to diabetic ketoacidosis (36%) or mixed cases of hyperglycemic coma with acidosis (56%). Emergency department visits followed a similar pattern. From the main analysis, there was a clear inverse gradient between income level and event rates (Table 2). Individuals in the lowest income quintile were 44% more likely to have an event than individuals in the highest quintile (16.4% vs 11.4%; P<.001). For each drop in quintile level (roughly equivalent to a Can $12 000 decrease in median household income), the risk of having an AH or ED visit was 10% higher (odds ratio, 1.10; 95% confidence interval [CI], 1.09-1.11). Using a larger region (forward sortation area) to assign income resulted in a lower but still significant odds ratio (1.30 [95% CI, 1.26-1.33] for lowest vs highest quintile). The SES gradient was also apparent when AHs and ED visits were examined separately (Table 2) and after excluding events that occurred in the first year of diagnosis (data not shown).

Many characteristics were associated with higher event rates on univariate analysis. The strongest predictor of having an acute event was failure to see a primary care physician in the previous year (25% vs 13%; P<.001). Patients who had an event were also younger (56.2 vs 59.2 years) and more likely to live in a rural area (18% vs 14%) or a remote region of the province (21% vs 14%) than those who did not (P<.001 for all). Fewer events occurred in patients with more primary care visits (2.7% [95% CI, 2.6%-2.8%] lower for each additional visit per year), a usual primary care provider (22% vs 13%; P<.001), and provision of DM care by a specialist in the preceding year (15% vs 11%; P<.001).

The gradient in event rates was most marked in the 45- to 64-year age group and was less apparent in children (Table 3). Low income was also associated with a relatively greater risk in rural than in urban settings (odds ratio, 1.60 [95% CI, 1.50-1.70] vs 1.37 [95% CI, 1.34-1.40]). Interactions between income and age and between income and rural vs urban residence were significant (P<.001 for each analysis); however, SES continued to exert an independent effect on event rates after adjusting for both.

On multivariate analysis, all of these factors remained strongly associated with the likelihood of requiring either admission to the hospital or an ED visit for an acute complication of DM. Similarly, the relationship between SES and event rates persisted after adjusting for age, sex, urban vs rural residence, comorbidity, frequency of physician visits, continuity of care, physician specialty, and geographic region (adjusted odds ratio, 1.09 [95% CI, 1.08-1.10] per decline in income quintile) (Table 4).

Individuals in the lowest income quintile also had a greater propensity toward recurrent ED visits than those in the highest quintile (1.9 vs 1.6 episodes per patient; P<.001) and were more likely to be admitted to the hospital at each encounter (15.2% vs 14.4%; P = .001). Recurrent ED visits and hospitalizations were more common among those younger than 18 years than older people (2.6 vs 1.6 ED visits; P<.001 and 2.1 vs 1.5 hospitalizations; P<.001). Although individuals living in a rural setting had an increased rate of AHs, urban dwellers had somewhat higher recurrence rates (2.0 vs 1.8; P<.001 for ED visits and 1.9 vs 1.8; P = .004 for hospitalizations).

Hospital admission rates for non–ambulatory care–sensitive conditions (appendicitis and hip fracture) were unaffected by SES. The proportion of individuals younger than 65 years who were admitted to the hospital with appendicitis was equally low in the upper and lower income groups (0.45% vs 0.42%; P = .30). Similarly, admissions for hip fracture among individuals older than 65 years were comparable across economic strata (1.9% vs 1.7%; P = .20 for the highest vs lowest income groups).

Individuals who reside in low-income neighborhoods were more than 40% more likely to have a hospitalization or ED visit for an acute complication of DM, even after adjustment for other important predictors. Moreover, a clear inverse gradient was evident across all income quintiles, implying that an excess risk of AH extends also to individuals residing in areas of higher-than-average affluence. The magnitude of these differences is comparable to previous data comparing AH rates for ambulatory care–sensitive conditions across urban communities in Ontario but substantially lower than that observed in US cities. However, the disparity in event rates was greater among individuals living in rural or remote regions of the province, where regular access to ambulatory care may be more limited. Our data suggest that universal health care reduces but does not eliminate the tendency for more frequent admissions for DM in low-income groups.

Most research on the topic of AH from the United States defined the lowest income group as being below the poverty line; therefore, income-related differences in this study may be blunted relative to those shown in the literature. However, even if we had used a lower threshold, our results would likely have differed from the United States data owing to reduced financial barriers to accessing care in Canada. The distribution in wealth is narrower and the degree of social inequality less in Canada than in the United States, factors that may also contribute to reduced variation in rates of complications across income groups in Ontario.

Economic status may affect the propensity to receive care for a variety of reasons. Individuals from lower-SES groups may have more difficulties keeping scheduled appointments because of transportation costs and inability to take time off work or to obtain help with child care.¹⁵ For those who seek medical care, the costs of medication and DM monitoring supplies, which are not universally covered, may limit the ability of low-SES patients to benefit from the care they receive. In our analysis, elderly patients from low-income neighborhoods, whose drugs are funded by Ontario's Ministry of Health and Long Term Care, were also more likely to experience an acute complication, although the effect size was less than in younger adults. These findings suggest that costs of therapy, although considerable, are not the sole explanation for this relationship.

Wealthier individuals may have stronger skills in negotiating the health care system. In Ontario, people who have higher income levels have healthier lifestyles¹⁶ and are more likely to receive preventive screening examinations¹⁷ and other services that depend on physician referral. For example, Alter et al¹⁰ found large income-related differences in access to cardiovascular procedures after myocardial infarction, favoring individuals from more affluent neighborhoods. We were unable to discern whether rates of referral to other health professionals, such as educators and dietitians, differed based on SES status, but patients from low-income areas may be less inclined to enroll in DM education programs,^18- 19 which can reduce hospital admissions for diabetic ketoacidosis.²⁰ One cross-sectional study²¹ noted that patients with DM undergoing intensive insulin therapy belonged to a higher SES status than those receiving more conservative regimens, although it is unclear whether this choice was made by patients or their providers.

In general, low-income groups use more outpatient services, in keeping with the greater burden of illness in this population.^8,22- 23 The fact that diabetic patients from poor neighborhoods have equivalent visit rates as their wealthier counterparts may represent a shortfall in care delivery. Alternatively, the presence of DM may be partly responsible for the income-related differences in service use observed in whole populations. Even if practice patterns of physicians caring for individuals from different SES strata are comparable, there may be subtle qualitative differences in care. For example, coexisting medical and social problems in low-income groups might warrant more attention, leaving glycemic control relatively neglected despite equal exposure to primary care providers.²⁴

There are several limitations to our analysis that merit discussion. First, we did not have access to other patient-related factors that can contribute to the development of acute complications, including level of education, compliance, glycemic control, or the presence of alcohol or substance abuse.^25- 28 Furthermore, inadequate social and financial support leads to additional stress that, in itself, can negatively impact DM self-management and metabolic control.^29- 30

The term "ecologic fallacy" questions the appropriateness of using area-level data as a substitute for individual data. However, there are many reasons why this approach may be valid. As a surrogate, community-level data seem to be as strong a predictor of poor outcomes as income from individual sources and in some cases exert an independent effect.^31- 32 Controversy exists over potential size restrictions of using large areas to estimate personal income. Block group data are composed of a smaller and theoretically more homogeneous sample; however, in some studies, information from census tracts performed equally well.³² Despite a greater risk of misclassification, income based on forward sortation area was an independent predictor of hospital admissions and ED visits, lending further credence to our findings.

Last, by using administrative data to identify patients with DM, we selected only individuals who have interacted with the health care system. It is unlikely that our findings have overestimated the relative difference in AHs across economic groups. Failure to see a primary care physician in the previous year was associated with event rates that were twice as high; thus, patients who escape detection from the health care system are even likelier to develop complications. Moreover, the ODD, which has high sensitivity and specificity for identifying individuals with DM, is designed so that patients remain in the database regardless of subsequent health care use.¹²

In summary, despite universal access to health care in Ontario, we found that patients with DM from low-SES neighborhoods experience an excess of complications that should be prevented by optimal ambulatory care. Several measures could reduce the gradient in AH rates based on income. Although out-of-pocket expenses are not the only contributing factor, development of programs to subsidize the cost of expensive medications and monitoring supplies would likely diminish the variation in rates across socioeconomic strata. Furthermore, widened exposure to DM education programs that are accessible to people from a variety of cultural and educational backgrounds may also reduce admissions by improving motivation and adherence.

Even if access to care was equivalent across SES groups, income-related disparities in health might persist. Careful study is needed to elicit the underlying causes of this gradient, with attention devoted to individual and regional factors. Physicians should be aware that vulnerable groups carry a greater risk of AH and should obtain additional support from DM educators, social workers, and other health professionals and consider early referral to a DM specialist for such patients. If event rates in the entire diabetic population in Ontario were equivalent to those in the highest SES group, then as many as 40 000 episodes might have been avoided during the 7-year observation period. Thus, strategies to prevent acute complications may not only reduce the burden of illness among vulnerable groups but may lead to an appreciable cost savings to the health care system.

Corresponding author and reprints: Gillian L. Booth, MD, Division of Endocrinology and Metabolism, St Michael's Hospital, 61 Queen St E, Sixth Floor, Toronto, Ontario, Canada M5C 2T2 (e-mail: gillian.booth@utoronto.ca).

Accepted for publication May 8, 2002.

This work was funded by a Banting and Best Diabetes Centre (Toronto) Pilot Grant for Innovative Activities Related to Diabetes Education, Management, and Care; by a St Michael's Hospital/University of Toronto/Glaxo SmithKline Junior Faculty Scholarship in Endocrinology (Dr Booth); and by an Ontario Ministry of Health Career Scientist Award (Toronto) (Dr Hux).

The opinions, results, and conclusions are those of the authors, and no endorsement by Ontario's Ministry of Health and Long Term Care (Toronto) or by the Institute for Clinical Evaluative Sciences is intended or should be inferred.