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Health Services and Outcomes Research Methodology

Published in:

01-03-2009

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

Authors: Benjamin L. Cook, Thomas G. McGuire, Ellen Meara, Alan M. Zaslavsky

Published in: Health Services and Outcomes Research Methodology | Issue 1/2009

Abstract

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.

Peterson et al. (1997) state as a limitation of their methods that “race may only be a surrogate marker for other socioeconomic factors… that may affect decisions about care to an equal or greater extent.” (p. 485). This is actually not a limitation according the IOM definition of disparities.

In some studies using population-based surveys, researchers use a clinical diagnosis or a set of diagnostic items to establish need. A study typical of this group (Wells et al. 2001) identifies individuals with a depression diagnosis using an SF-12 mental health score, and then assesses the utilization of care by this subpopulation.

Rubin argues that race is not a treatment at the individual level in the sense of his causal model since it cannot be manipulated by any conceivable experiment; however since our purpose is to compare group rather than individual outcomes we do not consider this an obstacle to our analysis.

An alternative (not used in this paper) is to compare a counterfactual black population that has the same health status distribution of whites, with an actual white population. Our choice is based on the presumption that remediation of disparities would entail providing blacks with the (generally superior) level of health services received by whites, rather than the reverse.

Though we apply the disparities definition to detect disparities between means for two groups (one counterfactual and one factual), our methods can easily be applied to measure disparities within subgroups (e.g., racial disparities among the critically ill), either by fitting a separate model within that subgroup or by applying the overall model (calibrated to balance in the subgroup) to the appropriate subsample.

In a model with interactions, the expected value of the interaction (product) term enters into the prediction. Specifically, in a model with HS X SES interactions, adjustment of health status means will alter the effects of SES variables.

In our analysis, propensity score weights were capped at 0.9 to prevent single individuals from having undue influence on the predicted outcome. As a result, two of the 22,209 white individuals had their final weights (propensity score weight times MEPS survey weights) reduced from 960,797 to 40,380 and 8,700,800 to 131,648, respectively. This adjustment greatly improved the match between black and white health status variable distributions.

The propensity score’s discordance from the IOM definition is similar to a regression of expenditure on health status variables, ignoring adjustment of SES variables (e.g., see preliminary models in Saha et al. (2003). Leaving SES out of the model will load racial differences in SES on to the race coefficient allowing for an approximation of the IOM-defined disparity (Balsa et al. 2007), but will not identify the contribution of racial differences in SES that is correlated with health status.

Insurance companies with limited information on individuals’ health status often use age and sex alone as risk adjusters for determining premiums. In addition, recent studies on disparities among Medicare beneficiaries have used age and sex to adjust for health status (e.g., Escarce and McGuire 2004).

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Title: Adjusting for health status in non-linear models of health care disparities
Authors: Benjamin L. Cook
Thomas G. McGuire
Ellen Meara
Alan M. Zaslavsky
Publication date: 01-03-2009
Publisher: Springer US
Published in: Health Services and Outcomes Research Methodology / Issue 1/2009
Print ISSN: 1387-3741
Electronic ISSN: 1572-9400
DOI: https://doi.org/10.1007/s10742-008-0039-6

At a glance: The STEP trials

Springer Medicine

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

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

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