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Healing the past, reimagining the present, investing in the future: What should be the role of race as a proxy covariate in health economics informed health care policy?

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  • Andrew H. Briggs

Abstract

In this perspective, the assertion that race‐free risk assessment would harm patients of all races is critiqued from the viewpoint that race is not just another covariate in our arsenal. Although race may be associated with outcome, it is nevertheless a proxy for a myriad of other potential explanatory variables that could be genetic/biological but in many circumstances are more likely to be sociological/socioeconomic. It is argued that the pursuit of health maximization through the use of socially constructed variables like race must be done sensitively, recognizing that racial covariates in the medical arena can be subject to structural, institutional or personal biases. Even when such biases are thought to be minimized, the appearance of such bias may be sufficient to justify the removal of its use, particularly where employing a racial covariate could further increase existing disparities. While racial covariates may have descriptive value in helping to understand such disparities, it is beholden on the scientific community to explore alternatives to racial covariates that may provide the same or perhaps even better prognostic value in our analyses.

Suggested Citation

  • Andrew H. Briggs, 2022. "Healing the past, reimagining the present, investing in the future: What should be the role of race as a proxy covariate in health economics informed health care policy?," Health Economics, John Wiley & Sons, Ltd., vol. 31(10), pages 2115-2119, October.
  • Handle: RePEc:wly:hlthec:v:31:y:2022:i:10:p:2115-2119
    DOI: 10.1002/hec.4577
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    References listed on IDEAS

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    1. Jeffrey S. Hoch & Andrew H. Briggs & Andrew R. Willan, 2002. "Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 11(5), pages 415-430, July.
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    1. Nhung Nghiem & June Atkinson & Binh P. Nguyen & An Tran-Duy & Nick Wilson, 2023. "Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets," Health Economics Review, Springer, vol. 13(1), pages 1-13, December.

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