Health Disparities

Investigator Thomas LaVeist was always left with a half empty feeling by the traditional approach to health disparities. On one hand, he was grateful to have any data at all. In the 1980s, the National Health Interview Survey (NHIS) gave researchers like LaVeist their first comprehensive glimpse of how different populations manifested illness.

But the NHIS survey also provided LaVeist with a dilemma. It created reams of data pointing out racial differences in health disparities, especially when comparing minorities to whites. But that’s as far as the data went—in essence delivering a lot of “who” and little “why?” Having spent his whole career trying to unravel “why,” LaVeist felt that the data was leading policymakers and scientists to seek medical explanations and solutions where none existed. “It’s perfectly accurate to look at the national statistics and say that African Americans have three times the death rate from a certain condition as do whites. But then you have to ask why that’s so.”

To that end, LaVeist created  the Exploring Health Disparities in Integrated Communities (EHDIC) study, a survey that mimics NHIS’s data collection. In Baltimore, the area bordering on Washington Village turned out to be fertile territory for data mining, with nearly equal levels of blacks and whites, similar median incomes and similar high school graduation rates. In the summer of 2003, surveyors asked each of the 1,489 respondents identical health behavior and status questions, mimicking the 2003 NHIS questionnaire, along with other psychosocial queries designed to measure potential stressors. Environmental factors were noted, as were physical factors such as blood pressure, body mass index, cigarette smoking and alcohol consumption.

In 2007 LaVeist and his colleagues published their first findings. The results turned some of NHIS’s data inside out. When it came to rates of obesity, inactivity and drinking, blacks and whites in the survey area were the same. These findings contradicted the national data that adjusted for socioeconomic status; it showed blacks to have significantly higher odds of being obese and significantly lower odds of drinking.

LaVeist sees these studies as moving toward a critical mass that could tilt possible policy solutions away from a medicalizing trend that wants to explain health disparities as genetic.

“But what is that gene that produces these outcomes?” he asks. “The solution isn’t isolating some gene that’s somehow producing diabetes and heart disease and obesity and stroke and homicide … we need to understand the social and perhaps the behavioral factors that are accounting for these differences.”

Ultimately, LaVeist hopes to use his data to influence decision makers. “This is all about policy,” he says. “Eventually there will be a book or a series of policy papers that say, when it comes to health disparities, we have to look at social factors.”