Health Services and Outcomes Research Methodology

, 9:1

First online:

Adjusting for health status in non-linear models of health care disparities

  • Benjamin L. CookAffiliated withCenter for Multicultural Mental Health Research, Cambridge Health Alliance—Harvard Medical School Email author 
  • , Thomas G. McGuireAffiliated withDepartment of Health Care Policy, Harvard Medical School
  • , Ellen MearaAffiliated withDepartment of Health Care Policy, Harvard Medical SchoolNational Bureau of Economic Research
  • , Alan M. ZaslavskyAffiliated withDepartment of Health Care Policy, Harvard Medical School

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This article compared conceptual and empirical strengths of alternative methods for estimating racial disparities using non-linear models of health care access. Three methods were presented (propensity score, rank and replace, and a combined method) that adjust for health status while allowing SES variables to mediate the relationship between race and access to care. Applying these methods to a nationally representative sample of blacks and non-Hispanic whites surveyed in the 2003 and 2004 Medical Expenditure Panel Surveys (MEPS), we assessed the concordance of each of these methods with the Institute of Medicine (IOM) definition of racial disparities, and empirically compared the methods’ predicted disparity estimates, the variance of the estimates, and the sensitivity of the estimates to limitations of available data. The rank and replace and combined methods (but not the propensity score method) are concordant with the IOM definition of racial disparities in that each creates a comparison group with the appropriate marginal distributions of health status and SES variables. Predicted disparities and prediction variances were similar for the rank and replace and combined methods, but the rank and replace method was sensitive to limitations on SES information. For all methods, limiting health status information significantly reduced estimates of disparities compared to a more comprehensive dataset. We conclude that the two IOM-concordant methods were similar enough that either could be considered in disparity predictions. In datasets with limited SES information, the combined method is the better choice.


Racial disparities Statistical adjustment for health status Propensity score Rank and replace