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.

[1]  N. Kakwani,et al.  Socioeconomic inequalities in health: Measurement, computation, and statistical inference , 1997 .

[2]  John Van Hoewyk,et al.  A multivariate technique for multiply imputing missing values using a sequence of regression models , 2001 .

[3]  U. Gerdtham,et al.  A note on the decomposition of the health concentration index. , 2003, Health economics.

[4]  G. Kalton,et al.  Handling missing data in survey research , 1996, Statistical methods in medical research.

[5]  Sourushe Zandvakili,et al.  Statistical Inference Via Bootstrapping for Measures of Inequality , 1995 .

[6]  A. Wagstaff,et al.  Equity in the delivery of health care: some international comparisons. , 1992, Journal of health economics.

[7]  Xiao-Li Meng,et al.  Multiple-Imputation Inferences with Uncongenial Sources of Input , 1994 .

[8]  Buhong Zheng,et al.  Statistical inference for testing inequality indices with dependent samples , 2001 .

[9]  C. Propper,et al.  Equity in the finance of health care: some further international comparisons. , 1999, Journal of health economics.

[10]  D. Lam,et al.  The sensitivity to key data imputations of recent estimates of income poverty and inequality in South Africa. , 2006, Economic modelling.

[11]  T. Raghunathan,et al.  Multiple Imputation of Missing Income Data in the National Health Interview Survey , 2006 .

[12]  P. Lambert,et al.  Redistribution effect and unequal income tax treatment , 1993 .

[13]  Sohan Kumar Karna,et al.  Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and Their Implementation , 2009 .

[14]  Pierella Paci,et al.  Equity in the finance and delivery of health care: some tentative cross-country comparisons , 1989 .

[15]  E. Doorslaer,et al.  EQUITY IN THE USE OF PHYSICIAN VISITS IN OECD COUNTRIES: HAS EQUAL TREATMENT FOR EQUAL NEED BEEN ACHIEVED? , 2003 .

[16]  Roger A. Sugden,et al.  Multiple Imputation for Nonresponse in Surveys , 1988 .

[17]  D. Rubin,et al.  Statistical Analysis with Missing Data , 1988 .

[18]  Cheti Nicoletti,et al.  The effects of income imputation on microanalyses: evidence from the European Community Household Panel , 2006 .

[19]  Andrew Briggs,et al.  Missing... presumed at random: cost-analysis of incomplete data. , 2003, Health economics.

[20]  E. van Doorslaer,et al.  Equity in health and health care in a decentralised context: evidence from Canada. , 2008, Health economics.

[21]  Regina T. Riphahn,et al.  Item non-response on income and wealth questions , 2002, SSRN Electronic Journal.

[22]  E. van Doorslaer,et al.  Inequalities in access to medical care by income in developed countries , 2006, Canadian Medical Association Journal.

[23]  L Gross,et al.  Equity in the delivery of health care in Europe and the US. , 2000, Journal of health economics.

[24]  D. Rubin,et al.  Handling “Don't Know” Survey Responses: The Case of the Slovenian Plebiscite , 1995 .

[25]  Peter J. Lambert,et al.  Inequality Decomposition Analysis and the Gini Coefficient Revisited , 1993 .

[26]  D. Rubin.  Multiple Imputation After 18+ Years , 1996 .