Statistical Approaches to Assessing Health and Healthcare Disparities

Abstract

Determining whether racial and ethnic disparities exist for a health-related outcome requires first specifying how outcomes will be measured and disparities calculated. We explain and contrast two common approaches for quantifying racial/ethnic disparities in health, with an applied example from nursing research. Data from a national for-profit chain of nursing homes in the US were analyzed to estimate racial/ethnic disparities in incidence of pressure ulcer within 90 days of nursing home admission. Two approaches were used and then compared: logistic regression and Peters-Belson. Advantages and disadvantages of each approach are given. Logistic regression can be used to quantify disparities as the odds of the outcome for one group relative to another. Peters-Belson can be used to quantify an overall disparity between groups as a risk difference and also provides the proportion of that disparity that is explained by available risk factors. Extensions to continuous outcomes, to survival outcomes, and to clustered data are outlined. Both logistic regression and Peters-Belson are easily implementable and interpretable and provide information on the predictors associated with the outcome. These disparity estimation methods have different interpretations, assumptions, strengths, and weaknesses, of which the researcher should be aware when planning an analytic approach.

Keywords: health disparities; healthcare disparities; pressure ulcers; racial/ethnic disparities; statistical methods.