Measuring health inequality using qualitative data

Abstract

Many questions in health policy require an understanding of the distribution of health status across a given population and how it changes as a result of policy interventions. Since objective data on individual health status are often unavailable or incomplete, especially for populations with very low mortality, increasing use has been made of self-reported health status (SRHS) data, which record people's own perceptions of their health status. SRHS has been shown to be a strong predictor of objective health outcomes and indications, including mortality. Nevertheless, the qualitative or categorical nature of SRHS data prevents the straightforward use of traditional tools of distributional analysis, such as the Lorenz curve, in evaluating inequality. This paper presents a methodology for evaluating inequality when the data are qualitative rather than quantitative in nature. A partial inequality ordering is defined to indicate when a distribution is more "spread out" than another; a second partial ordering (first order dominance) is used to indicate when the overall health level rises. Both are applicable to qualitative data, such as SRHS, in that results do not depend on the numerical scaling assigned to the categories. The approach is illustrated using SRHS data from the National Health Interview Survey (NHIS) State Data Files for 1994, focusing on the distribution of SRHS within states.