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RESEARCH AND PRACTICE |
Jessica M. Robbins is with the Philadelphia Department of Public Health, Philadelphia, Pa. At the time this work was completed, David A. Webb was with both the Philadelphia Department of Public Health and the Drexel University School of Public Health, Philadelphia.
Correspondence: Requests for reprints should be sent to Jessica M. Robbins, Philadelphia Department of Public Health, Ambulatory Health Services, 500 South Broad St, Philadelphia, PA 19146 (e-mail: jessica.robbins{at}phila.gov).
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ABSTRACT |
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Objective. We sought to determine the frequency and costs of hospitalization and to assess possible racial/ethnic disparities in a large cohort of low-income patients with diabetes who had received primary care at municipal health clinics.
Methods. Administrative data from Philadelphia Health Care Centers were linked with discharge data from Pennsylvania hospitals for March 1993 through December 2001. We tested differences in hospitalization rates and mean hospital charges by age, gender, and race/ethnicity.
Results. A total of 18 800 patients with diabetes experienced 30 528 hospital admissions, for a hospitalization rate of 0.35 per person-year. Rates rose with age and with the interaction of male gender and age. Rates for non-Hispanic Whites were higher than those for African Americans, whereas those for Hispanics, Asian Americans, and "others" were lower. Patients who were hospitalized at least 5 times made up 10.5% of the study population and accounted for 64% of hospital admissions and hospital charges in this cohort.
Conclusions. Hospitalization rates for this low-income cohort with access to primary care and pharmacy services were comparable to those of other diabetic patient populations, suggesting that reducing financial barriers to care may have benefited these patients. A subgroup of patients with multiple hospitalizations accounted for the majority of hospital admissions.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Diabetes is a treatable disease, and the benefits of appropriate treatment have been demonstrated in major clinical trials.6–8 Nonetheless, surveys in a variety of populations and health care systems have found that most diabetic patients are not receiving optimal care or achieving recommended levels of glycemic control.9–12
There is abundant evidence that African Americans and other minority patients and patients of lower socioeconomic status receive less intensive and poorer quality care across a number of major conditions13–15 and experience poorer health status and health outcomes. However, we have limited information on possible disparities in outcomes among individuals with diabetes.16–21
Hospitalization is both an adverse health event and a marker for serious health complications, and is often predictive of disability.22 Persons with diabetes are admitted to hospitals substantially more frequently, and experience longer hospital stays, than nondiabetic individuals.12,16 Diabetes is considered an ambulatory care–sensitive condition, and many hospitalizations are potentially preventable.23 We sought to determine the frequency and costs of hospital admission in a large, unselected multiracial cohort of predominantly low-income patients with diabetes who had received primary care at municipal health clinics in Philadelphia over a period of 106 months, and to examine possible racial/ethnic disparities within this cohort.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Data on outpatient care were extracted from the management information system maintained by the Philadelphia Department of Public Health for the 9 Health Care Centers it operates (8 primary care clinics and 1 central sexually transmitted disease clinic). The data included basic demographic information obtained by clerical staff when patients registered with the Health Care Centers and data from encounter forms filled out by clinical staff at each patient visit. The encounter forms record the date of the visit and up to 4 diagnostic codes. This information system has data on all patient encounters since March 1, 1993. A list of all patients who received a diagnosis of diabetes (International Classification of Disease, Ninth Revision24 code 250) at any time between March 1, 1993, and December 31, 2001, was obtained from the system, with the date of the first visit for which a diabetes diagnosis was recorded. We did not attempt to distinguish between type 1 and type 2 diabetes, as use of the 2 codes was not consistent throughout this period.
Hospital discharge data for these patients were obtained from the Pennsylvania Health Care Cost Containment Council, a state agency mandated to collect data on all admissions to hospitals in Pennsylvania. The data collected included demographic information, the reason for admission, up to 8 diagnostic codes, dates of admission and discharge, and total charges. Because hospitals are required by state law to submit the data and the Pennsylvania Health Care Cost Containment Council undertakes extensive data quality checks, the data were essentially complete for all Pennsylvania hospitals for the period covered in this study.25
Patients identified from the outpatient database were linked to records in the hospital discharge data on the basis of social security number (SSN), gender, and date of birth (DOB). Patients with no SSN recorded were excluded. Records were linked on the basis of 1 of several criteria: SSN, gender, and DOB; SSN, gender, and month and year of birth; SSN, gender, and month and day of birth, with year of birth within 2 years; or gender, DOB, and 8 of 9 digits matched for the SSN. More than 99% of all matches were exact matches on all 3 identifiers.
Patients were also linked to death records maintained by the Philadelphia Department of Public Health for all Philadelphia residents in order to determine length of follow-up time. Records were linked on the basis of either first and last name, gender, and DOB, or first or last name, gender, SSN, and partial DOB match. Race/ethnicity was classified on the basis of the outpatient records because there were extensive missing data on this variable in the hospitalization records. For this study, follow-up time for each patient began with the first outpatient visit with a diagnosis of diabetes within the study period, and ended at December 31, 2001, or date of death, if a death was identified.
Differences in hospitalization rates and mean hospital charges were tested by linear regression in SAS version 9.1 (SAS Institute Inc, Cary, NC), with gender, age, and racial/ethnic groups entered as independent variables and number of hospital admissions and hospital charges per year as the dependent variables. Individual patients were used as the unit of analysis, and results were weighted by follow-up time.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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The characteristics of the patient population are shown in Table 1
. Females made up 57% of the group. A majority of patients (73%) were African American, with substantial numbers of non-Hispanic Whites (n=2284 [12%]), Hispanics (n=1828 [10%]), and Asian Americans (n=579 [3%]). The median age at beginning of follow-up was 52.0 years; the age range was from younger than 1 year to older than 100 years. Patients had an average follow-up time of 4.6 years. Income and insurance information were retained in the administrative data only for the 9379 patients with recent visits to the Health Care Centers. Among these patients, the mean income was $8671. Forty-one percent had no insurance, 16% had Medicaid, 27% had Medicare, 8% had private insurance, and 8% were recorded as having some other type of insurance. Deaths during the study period were ascertained for 1794 patients, 9.5% of the cohort.
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). Males were more frequently hospitalized than females (0.39 vs 0.32 admissions per year), and hospital admissions and charges generally increased with age except in the oldest age group (
75 years). Non-Hispanic Whites were the most frequently hospitalized group (0.41 admissions per year), compared with African Americans (0.36), Hispanics (0.27), Asian Americans (0.13), and other racial/ethnic groups (0.16). Total hospital charges were $818749563, or $9414 per person-year of follow-up. The mean charges per hospital admission were $26820. At least 1 hospital admission was identified for 41.8% of the patients. Patients who were hospitalized at least once had an average of 3.9 hospitalizations. Patients who were hospitalized at least 5 times made up 10.5% of the study population and accounted for 64% of all hospital admissions and hospital charges and 36% of the deaths in this patient cohort.
In multivariate models adjusted for race/ethnicity, age, and gender, the associations of hospital admission rates and charges with race/ethnicity were the same as in the crude rates (Table 2), but a significant interaction between gender and age was detected for both outcomes. This interaction on hospitalization rates is illustrated in Figure 1, which shows unadjusted hospital rates by age group and gender. Among patients aged younger than 35 years, hospital admissions were higher in female patients than in male patients. Crossover occurred in early adulthood, with men aged 55 to 74 years having a substantial excess rate of hospitalization relative to women of the same age group. Other interactions were not statistically significant and were not included in the final models.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Racial/Ethnic Differences
Hispanics and Asian Americans in this low-income population were hospitalized substantially less frequently than either non-Hispanic Whites or African Americans. The rates for Hispanics were comparable to those reported in other studies.19,26,27 We are unaware of any published data on hospitalization rates among Asian patients with diabetes in the United States. The poorest outcomes in this patient population were experienced by non-Hispanic Whites, who were hospitalized significantly more frequently than any other group. It is possible that this reflects underutilization of appropriate and needed health services by minority patients, as has been documented in other contexts.21,28,29 However, in this patient cohort, non-Hispanic Whites also had higher rates of myocardial infarction and all heart disease than other groups, in spite of lower prevalence of hypertension,30 which would be consistent with more frequent hospitalizations reflecting poorer health status rather than more aggressive treatment.
Gender–Age Interaction
Gender differences in hospitalization rates and charges were age-dependent, reflected in a significant interaction between gender and age. Boys and men aged younger than 35 years had lower rates than girls and women of the same age, whereas men aged older than 45 years had more than 30% higher rates of hospital admissions and charges than women in the same age group. We speculated that reproductive events, which lead to a significant number of hospital admissions in early female adulthood, might explain this pattern. However, excluding hospitalizations associated with reproduction from the calculations of hospitalization rates did not eliminate the interaction between age and gender. Because few studies of hospitalization among diabetic patients have been published, we are not aware of any other research either confirming or contradicting the interaction we detected.
Younger women may have especially high rates of psychosocial stressors impacting their health behaviors and outcomes.31 Depression, which is more common among women than men and among younger rather than older diabetic patients,32 is associated with poorer outcomes for diabetic patients, including increased hospital utilization.33
Interactions of gender and age have been reported in outcomes for patients with coronary heart disease, with younger, but not older, women having poorer outcomes than men. Several possible explanations have been proposed, including gender differentials in referrals for treatment or intensity of treatment.34,35 Younger women with diagnosed diabetes may similarly be a select group with unusually severe disease, which could lead to the interaction we observed. Additional investigations are required to confirm this interaction and to determine its causes and significance for patient care.
Safety-Net Clinics
Patients treated in safety-net clinics such as the Philadelphia Health Care Centers typically have a range of risk factors that put them at increased risk of poor health outcomes. They are far more likely than the general population to be low-income, lack medical insurance, have low educational status, and be members of minority racial/ethnic groups. Other researchers have found low socioeconomic status indicators to be associated with increased rates of hospitalization among diabetic patients.36,37
In spite of these factors, this large cohort of diabetic patients did not have unusually high rates of hospital admissions compared with other patient populations.10,11,19,38–42 It is notable that this was true even though most previous studies have reported either on nationally representative samples or on predominantly White populations, not on low-income patient groups. The reasons for the relatively low rates of hospitalization in this cohort deserve further examination. Use of pharmaceuticals by patients with diabetes has been found to be particularly sensitive to out-of-pocket costs, so it is possible that access to prescription drugs and supplies for self-monitoring blood glucose without financial barriers helped these patients to avoid hospitalizations.43,44
Limitations
There are several limitations to this study. The completeness and accuracy of data collected for administrative purposes are uncertain, and may have varied over time and among centers. It is possible that some patients with diabetes treated in the Health Care Centers for other conditions were not ascertained, as a majority of encounter forms list only 1 or 2 diagnoses.
Admissions to hospitals outside Pennsylvania would not be included in the hospitalizations we tabulated. Similarly, follow-up time was censored for patients who died during the study period, based on death records maintained by the Philadelphia Department of Public Health. Although all deaths to Philadelphia residents should be reported to Philadelphia Department of Public Health and included in these records regardless of place of death, patients who moved out of the city and subsequently died would not be included. Missed hospital admissions and missed death records would both tend to lower reported hospitalization rates. However, the hospitals outside Philadelphia most frequently used by Philadelphia residents are in surrounding counties in Pennsylvania and are included here. Out-of-state hospitalizations are particularly infrequent for low-income and uninsured patients. Hospitalizations for patients who moved out of state and were subsequently hospitalized there would be missed in these data. On the basis of census data on out-of-county and out-of-state moves,45 we estimate that fewer than 5% of this low-income patient cohort would have moved out of the city during the study period, fewer than 2% of them out of state (details available from corresponding author). The missed data were therefore unlikely to substantially affect the reported results.
Conclusions
The patient population described here resembles other populations in that a small proportion of patients experienced a majority of the negative outcomes assessed and incurred a majority of the total costs.46 Although some interventions designed to reduce repeat hospital admissions among diabetic patients have not been successful,47 further efforts must be made to define this high-risk patient population and develop methods to reduce morbidity and repeat hospitalizations. In addition, the possibility that access to primary care and pharmacy services without out-of-pocket costs enabled this predominantly minority, low-income patient population to avoid excess hospitalizations suggests that efforts to improve outcomes for diabetic patients should include efforts to expand such access and to resist policy changes that would create additional financial barriers to care.
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Acknowledgments |
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The authors wish to acknowledge the advice and support of Neil I. Goldfarb, Barry J. Goldstein, David B. Nash, Etienne Phipps, Warner S. Tillack, Jr., and Viola Vaccarino. Data were collected by the Pennsylvania Health Care Cost Containment Council and the Philadelphia Department of Public Health Division of Ambulatory Health Services.
Human Participant Protection
The study protocol was approved by the Philadelphia Department of Public Health institutional review board.
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Footnotes |
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Contributors
The authors jointly originated the study. J.M. Robbins obtained the data, designed the study, performed the analyses, and drafted the article. D.A. Webb helped to conceptualize ideas and interpret findings, and revised the article.
Accepted for publication July 17, 2005.
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References |
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