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© 2003 American Public Health Association
RESEARCH AND PRACTICE |
Leonard E. Egede is with the Department of Medicine and Deyi Zheng is with the Department of Biometry and Epidemiology, Medical University of South Carolina, Charleston, SC.
Correspondence: Requests for reprints should be sent to Leonard E. Egede, MD, MS, Medical University of South Carolina, Division of General Internal Medicine and Geriatrics, 326 Calhoun St, PO Box 250100, Charleston, SC 29401 (e-mail: egedel{at}musc.edu).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Objectives. This study examined whether differences in access to health care, health coverage, and socioeconomic status (SES) explained racial differences in influenza and pneumococcal vaccination rates in individuals with diabetes.
Methods. We analyzed data on 1906 individuals from the 1998 National Health Interview Survey. We used multiple logistic regression to adjust for race/ethnicity, age, access to care, health insurance, and SES, and used SUDAAN for statistical analyses to yield national estimates.
Results. Whites had higher vaccination rates than did African Americans or Hispanics. After adjustment for covariates, race/ethnicity predicted receipt of both vaccines independent of age, access to care, health care coverage, and SES.
Conclusions. Racial disparity in vaccination rates for adults with diabetes is independent of access to care, health care coverage, and SES. (Am J Public Health. 2003;93:324–329)
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Diabetes mellitus is a chronic debilitating illness that affects about 16 million people in the United States.1 Diabetes is more prevalent in minority populations, and complication and death rates from diabetes are higher in African Americans and Hispanics than in Whites.1 Individuals with diabetes are susceptible to influenza and pneumonia,2,3 and they have higher death rates during episodes of influenza epidemics compared with people without diabetes.4,5
Influenza and pneumococcal vaccines are efficacious6,7 and cost-effective,8–10 and current guidelines recommend influenza and pneumococcal vaccination for people with diabetes.11–13 Also, Healthy People 2010 has set target vaccination rates for adults with diabetes at 65% for individuals younger than 65 years and 90% for those 65 years and older.14 Regrettably, vaccination rates for people with diabetes are a long way from this objective, notably in African Americans and Hispanic.15,16 Socioeconomic status (SES) and unequal access to health care15 are thought to contribute to disparities in vaccination rates. Debate on this topic parallels the debate in the larger field of racial/ethnic disparity research about the contribution of differential access to care and SES to racial inequities in health outcomes in the United States.17–19
We examined data from the 1998 National Health Interview Survey to determine whether differences in access to care, health care coverage, and SES explained racial/ethnic differences in immunization rates. Our hypothesis was that differences in access to care, health care coverage, and SES would explain racial disparities in influenza and pneumococcal vaccination rates in adults with diabetes.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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We performed our analysis on data from 1906 individuals aged 18 years or older with diabetes, excluding women with a diagnosis of gestational diabetes. We used the National Health Interview Survey, which is a national household survey of nonmilitary and noninstitutionalized adults aged 18 years or older in the United States sponsored by the National Center for Health Statistics of the Centers for Disease Control and Prevention.20 A complex sampling design that involved stratification, clustering, and multistage sampling was used to select a representative sample of US adults. The final survey response was 73.9%, and final survey weights were designed to allow generalization to nonmilitary and noninstitutionalized adults with diabetes. Details about the survey methodology are available on-line.20,21
Clinical Variables
A label of diabetes was based on self-report, and influenza vaccination was based on receipt of an influenza shot in the previous 12 months. A label of pneumococcal vaccination was based on ever having had a pneumococcal vaccination. Health status was based on a comparison of a person’s present health with what it was the year before and classified as worse than last year or better/same as last year.
Adjustment for Comorbidity
We adjusted for 10 other conditions because we assumed that guideline recommendations12,13 were likely to cause primary care providers to preferentially vaccinate individuals with these comorbid conditions. Selected conditions were cardiac conditions (coronary heart disease, angina, heart attack, other heart condition), chronic pulmonary condition (emphysema, chronic bronchitis), asthma, chronic renal failure, chronic liver disease, and cancer. We created 3 categories: people with diabetes, people with diabetes and 1 other condition, and people with diabetes and 2 or more other conditions.
Utilization Variable
Four commonly used measures of access to primary care are having a usual source of care, having a regular health care provider, probability of a visit to a physician, and level of emergency room use.22 We used having a primary care visit in the past 12 months as a measure of access because having a visit to a physician is a predictor of immunization.23 We created 3 health insurance categories: private, public, and uninsured.
Demographic Variables
We created 3 categories for age (65 years or older, 50 to 64 years, 18 to 49 years). There were 4 racial/ethnic categories (non-Hispanic White, non-Hispanic Black, Hispanic, Other), 3 categories for education (less than a high school education, high school graduate, more than a high school education), 2 categories for household income ($20,000 or more, less than $20,000), and 2 categories for marital status (married, not married). We defined employment as working for pay in the previous year, and we included the 4 census regions (Northeast, Midwest, South, West). In addition, we included a variable that indicated whether the respondent was born within or outside the 50 United States.
Statistical Analyses
We used SAS24 for statistical analyses and SUDAAN25 to generate population estimates and to account for the complex sampling design of the National Health Interview Survey. We compared demographic, clinical, and utilization variables in persons with diabetes by influenza and pneumococcal vaccination status. We then used multiple logistic regression to develop models for influenza and pneumococcal vaccination in people with diabetes by race/ethnicity, access to a primary care physician, and SES. In the base model (model 1), we entered age, gender, marital status, health care coverage, general health status, census region, place of birth (US-born or not), and number of comorbid conditions as independent variables and influenza and pneumococcal vaccination status as dependent variables. In subsequent models, we kept influenza and pneumococcal vaccination status as dependent variables, and we entered race/ethnicity (model 2), access to primary care (model 3), and SES—education, household income, and employment—(model 4) sequentially as independent variables to the base model.
Overall, we created 4 models: the base model, base model plus race/ethnicity, base model plus race/ethnicity and access to care, and base model plus race/ethnicity, access to care, and SES. All tests were 2-tailed with the significance level set at 
< .05.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Sample Characteristics
Of the 32 440 adults surveyed in 1998, 1906 persons (5.8%) had diabetes. Table 1
shows the baseline characteristics of individuals with diabetes in 1998.
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Baseline Differences in Vaccination Rates
Table 2
shows baseline differences in receipt of influenza and pneumococcal vaccines. Of the adults with diabetes, 51% and 33% received influenza and pneumococcal vaccines, respectively. Influenza and pneumococcal vaccination rates were highest in Whites, individuals older than 65 years, those with a household income under $20,000, and those who were employed. US-born persons and persons with health care coverage, access to a primary care physician, or comorbid conditions also had higher influenza and pneumococcal vaccination rates. There were no significant differences by gender, education, marital status, general health status, or census region.
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Multiple Logistic Regression Analyses
Table 3
shows the odds ratios for the 4 logistic regression models for influenza vaccination. In the base model (model 1), age, having health coverage, and having comorbid conditions were independent predictors of vaccination. With the addition of race/ethnicity (model 2), age, health coverage, comorbidity, and race/ethnicity were independent predictors of vaccination. With the addition of access to care (model 3), race/ethnicity remained an independent predictor. Finally, the addition of SES variables (model 4) indicated that age, health care coverage, and health status were independently associated with influenza vaccination along with race/ethnicity, access to care, education, and employment.
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Similar results were obtained for pneumococcal vaccination (Table 4
). In the base model, age, health coverage, birth in the United States, and comorbidity independently predicted pneumococcal vaccination. The addition of race/ethnicity, access to care, and SES showed a pattern similar to that for influenza vaccination. In the final model, age, birth in the United States, comorbidity, race/ethnicity, education, and employment were independent predictors of pneumococcal vaccination.
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These results suggest that race/ethnicity is an important predictor of influenza and pneumococcal vaccination independent of access to care, health coverage, and SES.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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This study documents the persistence of racial/ethnic disparities in influenza and pneumococcal vaccination rates in adults with diabetes despite adjustment for access to care, health care coverage, and SES.
Our results are similar to those of previous studies15,16 with a few notable differences. First, we used 4 racial/ethnic categories instead of the 2 categories that were used in previous studies. This approach showed that the disparities in vaccination rates between Whites and non-Whites with diabetes were due largely to differences between Whites and Blacks. Second, we adjusted for access to care, health insurance coverage, and SES. Lack of adjustment for these variables has been identified as a flaw in previous studies on racial/ethnic disparities in health care.17,26,27 Finally, we included the respondent’s place of birth in our analysis because of data that suggest that beliefs, behavior, and physical functioning differ significantly between individuals born and raised in the United States and those born and raised outside this country.28
The results of this study have 3 major implications. First, the findings suggest that there are "missed opportunities" for vaccination of individuals with diabetes in primary care settings, particularly minority individuals. Only 54% of people with diabetes who had contact with a primary care physician received the influenza vaccine despite evidence that when a physician recommends vaccination, the likelihood of a patient’s accepting it increases significantly.23 Because primary care providers treat the majority of people with diabetes29 and because pneumonia is one of the 10 leading causes of death in the United States,30 there is a need to improve vaccination coverage in primary care settings. Several effective vaccination strategies that can be easily implemented in primary care settings have been documented.12,13 Therefore, collective efforts at all levels need to be directed at ensuring vaccination of all individuals with diabetes at primary care encounters.
The second implication of this study is that race/ethnicity appears to be a proxy for a variable that is either currently unmeasured or unidentified, because the current thinking is that race/ethnicity is a social construct that has little or no biological significance.31 Therefore, although we have shown that race/ethnicity is an independent predictor of influenza and pneumococcal vaccination in adults with diabetes, there is still a need to tease out what race/ethnicity truly represents. To accomplish this, there is continuing need to collect data on race/ethnicity, but more importantly, additional data are needed on social and cultural factors that may influence health outcomes across racial/ethnic groups.
The third implication is the need to devote research efforts toward identifying the true reasons for the observed racial/ethnic disparities in vaccination rates for people with diabetes. An editorial in Morbidity and Mortality Weekly Report suggested that unequal access to preventive care, social or cultural values that result in differential acceptance of vaccination by people with diabetes, and differential recommendation of vaccination by physicians were responsible for disparities in vaccination coverage.15 This study has shown that disparities in vaccination rates in people with diabetes appear to be independent of access to care, health insurance coverage, and SES. This independence leaves the alternative explanation that cultural values that result in differential acceptance of vaccination by patients and differential recommendation of vaccination by physicians may be responsible.
The concept of culture as distinct from race/ethnicity has been proposed as a better explanation for differences in health behavior and health outcomes.32 Culture in this context has been defined as "unique shared values, beliefs, and practices that are directly associated with a health-related behavior, indirectly associated with a behavior, or influence acceptance and adoption of the health education message."33 Although the concept of culture seems plausible, more research is needed to provide support for this hypothesis. Future studies on the relationship between culture and health outcomes need to do more than merely demonstrate an association between cultural differences and health outcomes; they must provide a causal pathway for any such association. Of particular importance will be a better understanding of how differences in the cultural beliefs and values of health care providers and patients influence recommendation of services by providers and acceptance of health services by patients.
There are limitations to observe in interpreting the results of this study. Recall of diabetes and vaccination status may be problematic; however, previous studies have established the reliability of diabetes and influenza vaccination information collected by self-report.34,35 On the other hand, self-report of pneumococcal vaccination may be less reliable; therefore, conclusions about pneumococcal vaccination rates in people with diabetes should be interpreted with caution. Another limitation is the small sample size of individuals of "Other" race/ethnicity. Estimates of vaccination coverage in these individuals may be unstable and should be interpreted with caution. Finally, because our sample was limited to nonpregnant and noninstitutionalized civilian adults, generalization should not be made beyond this population.
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Acknowledgments |
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Leonard E. Egede was supported in part by grant 1K08HS11418 from the Agency for Health Care Research and Quality, Rockville, MD.
Leonard E. Egede and Deyi Zheng were supported in part by grant U50/CCU417281-02 from the Centers for Disease Control and Prevention, Atlanta, Ga.
We gratefully acknowledge John Colwell, MD, PhD, for reviewing the manuscript and for his helpful comments.
Human Participant Protection
The institutional review board of the Medical University of South Carolina approved this study.
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Footnotes |
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Note. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the Agency for Health Care Research and Quality or the Centers for Disease Control and Prevention.
Both authors planned the study, performed the statistical analyses, and wrote the article.
Accepted for publication April 7, 2002.
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References |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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