When a graphical presentation shows a difference in health outcomes across segments of the population, a logical next step is to ask if they represent disparities for those populations,
or if they are explained by other factors. This graphic shows differences in outcomes by race, but allows you to see if those difference remain if you adjust for other factors; for example ,
do those differences remain even if we assume that for each race, people have similar ages – if they do, then the observed differences are more likely to represent a true disparity.
This “disparities explorer” allows you to quickly adjust for single or multiple variables to come to your own conclusions.
Disparities explorer: Health disparities are differences in health outcomes that are seen across segments of the population.
In some cases, apparent disparities may be explained by one or more factors.
The disparities explorer allows us to explore evidence of disparity in CKD outcomes.
The explorer uses a regression model to associate the prevalence of CKD with multiple factors, and identify the most important associations.
We use National Health and Nutrition Examination Survey (NHANES) data from 1988-1994 through 2011-2012 to predict prevalence of CKD.
Multiple models are run, for all combinations of covariates. The regression coefficients from each model are then used to calculate
expected values of the prevalence of CKD, with adjustment for that (combination of) factors.
1 CDC health disparities and inequalities report—United States, 2011. MMWR Suppl 2011;60(Suppl; January 24, 2011).