The impact of missing data in the estimation of concentration index: a potential source of bias.

The purpose of this paper is to raise awareness of missing data when concentration indices are used to evaluate health-related inequality. Concentration indices are most commonly calculated using individual-level survey data. Incomplete data is a pervasive problem faced by most applied researchers who use survey data. The default analysis method in most statistical software packages is complete-case analysis. This excludes any cases where any variables are missing. If the missing variables in question are not completely random, the calculated concentration indices are likely to be biased, which may lead to inappropriate policy recommendations. In this paper, I use both a case study and a simulation study to show how complete-case analysis may lead to biases in the estimation of concentration indices. A possible solution to correct such biases is proposed.