We analyzed disparities in health care use among Blacks and Whites in two data sets: the EHDIC sample (Baltimore), and the 2002 MEPS adult sample. We used the 2002 MEPS for comparison because the EHDIC survey was fielded in the summer of 2003 and asked about health care utilization during the prior year. EHDIC survey is a cross-sectional face-to-face survey of the adult population (age 18 and older) of two contiguous, census tracts in Baltimore, Maryland. This study site was selected because it is racially and economically balanced. We conducted a nationwide assessment of census tracts that met the following three criteria: (a) racially balanced, that is, at least 35% Blacks adults and 35% Whites adults; (b) economically balanced, that is, ratio of Black/White median income between 0.85 and 1.18; and (c) educationally balanced, that is, ratio of Black/White high school graduation rate ratios between 0.85 and 1.18. Nationally out of 66,438 census tracts only 435 met these criteria. Among these racially integrated communities, we selected census tracts representative of low- and high-income areas in urban and rural environments. The present study was based on results of the first EHDIC data collection, which were two contiguous low-income urban census tracts. Of the 3,555 adult residents, approximately 40% were enrolled into the study (N = 1,489) between June and August 2003. The sample had a higher proportion of Blacks but otherwise was representative of the residents of the two census blocks. The EHDIC sample was 59.3% Black and 44.9% male compared with 51% Black and 49.7% male from the 2000 census. The median incomes in EDHIC were $23,400 for Blacks and $24,900 for Whites. This was comparable with $23,500 for Black and $24,100 for Whites in the 2000 census. The age and educational attainment distributions were also similar to the census data, and the survey had similar coverage across the seven census blocks within two tracts.
The MEPS is a longitudinal survey that covers the U.S. civilian noninstitutionalized population. It is fielded by the AHRQ based on a sampling frame of the National Health Interview Survey. The MEPS is widely used as authoritative source of information on the nation’s health care use. AHRQ uses it to monitor the nation’s progress on health care disparities (AHRQ, 2006
). More information about the MEPS is available on their Web site, www.meps.ahrq.gov/mepsweb
. The 2002 MEPS sample consist of 23,264 noninstitutionalized adults between the ages of 18 and 64. Because the EHDIC consisted of only Blacks and Whites, we included only Black and White respondents reducing the sample to 16,546.
Our dependent variables were (a) whether the individual had visited to a health care professional in the past 12 months and (b) the total number of medical care visits in the past 12 months. We controlled for predisposing, enabling, and health need factors in our analysis. The independent variables included race, gender, marital status, age, education, health insurance status, income, general health, and presence of chronic conditions. In the MEPS sample, we also controlled for region of the country. Race, gender, and marital status were dichotomous variables with White, female, and married persons as the respective reference groups. To control for differences in health care utilization related to age, we used age minus 18 and the square of age minus 18. Educational attainment was measured using a set of categorical variables: 8 years or less, 9 to 12 years, some college, and college degree. (The reference group was persons with a high school diploma or GED.) Income was measured as a continuous variable in units of $10,000. Health insurance status was categorized as private, Medicare, and Medicaid/other public insurance coverage. The uninsured was the reference category. We used the respondents’ self-reported general health and the presence of chronic conditions to measure health need. We created a dichotomous variable that indicated whether the respondents reported they were in fair or poor health. The presence of a chronic condition was ascertained by asking respondents if a “doctor or health care professional” had informed them they had hypertension, a heart condition, stroke, cancer asthma, or diabetes. The total number of “yes” responses was summed to create a continuous variable. In the MEPS sample, individuals were categorized by census region and urban–rural location. The reference categories were northeast and urban.
We estimated racial differences in having at least one health care visit and in the total number of health care visits on the MEPS and EHDIC samples. Data analysis was conducted using Stata 9 statistical analysis software. We estimated separate regression models for each outcome. Because the dependent variable in our first model was dichotomous, we used logistic regression analysis. Because our second dependent variable was a count variable, we used negative binomial analysis. Negative binominal regression controls for overdispersion in the dependent variable. To produce accurate national estimates, we used the sampling weights and accounted for the complex survey design of the MEPS. We used survey regressions procedures in Stata to adjust the estimates for clustering and stratification. We compared the coefficients on race between the MEPS and EHDIC regressions. To determine if the coefficient on race differed across samples, we pooled the data and estimated models, including a variable indicating the observation was from the EHDIC sample and an interaction term with this variable and race. We estimated this final model without the weighting the data.