Address correspondence to Thomas G. McGuire, Ph.D., Department of Health Care Policy, Harvard Medical School, Professor of Health Economics, 180 Longwood Ave., Boston, MA 02115. Ana I. Balsa, Ph.D., is with the Department of Sociology, University of Miami, Miami, FL. Lisa S. Meredith, Ph.D., is with RAND Health, Los Angeles.
Testing for Statistical Discrimination in Health Care
Version of Record online: 21 JAN 2005
Health Services Research
Volume 40, Issue 1, pages 227–252, February 2005
How to Cite
Balsa, A. I., McGuire, T. G. and Meredith, L. S. (2005), Testing for Statistical Discrimination in Health Care. Health Services Research, 40: 227–252. doi: 10.1111/j.1475-6773.2005.00351.x
This paper was presented at the International Conference on Health Policy Research (American Statistical Association), the Boston University-Harvard-MIT Health Economics Seminar, the Latino Research Program Project Working Conference, and the International Health Economics Association Conference. We thank seminar and conference participants for their useful comments.
- Issue online: 21 JAN 2005
- Version of Record online: 21 JAN 2005
- Health-care disparities;
- statistical discrimination;
- clinical decision making;
- clinical uncertainty
Objective. To examine the extent to which doctors' rational reactions to clinical uncertainty (“statistical discrimination”) can explain racial differences in the diagnosis of depression, hypertension, and diabetes.
Data Sources. Main data are from the Medical Outcomes Study (MOS), a 1986 study conducted by RAND Corporation in three U.S. cities. The study compares the processes and outcomes of care for patients in different health care systems. Complementary data from National Health And Examination Survey III (NHANES III) and National Comorbidity Survey (NCS) are also used.
Study design. Across three systems of care (staff health maintenance organizations, multispecialty groups, and solo practices), the MOS selected 523 health care clinicians. A representative cross-section (21,480) of patients was then chosen from a pool of adults who visited any of these providers during a 9-day period.
Data Collection. We analyzed a subsample of the MOS data consisting of patients of white family physicians or internists (11,664 patients). We obtain variables reflecting patients' health conditions and severity, demographics, socioeconomic status, and insurance from the patients' screener interview (administered by MOS staff prior to the patient's encounter with the clinician). We used the reports made by the clinician after the visit to construct indicators of doctors' diagnoses. We obtained prevalence rates from NHANES III and NCS.
Findings. We find evidence consistent with statistical discrimination for diagnoses of hypertension, diabetes, and depression. In particular, we find that if clinicians act like Bayesians, plausible priors held by the physician about the prevalence of the disease across racial groups could account for racial differences in the diagnosis of hypertension and diabetes. In the case of depression, we find evidence that race affects decisions through differences in communication patterns between doctors and white and minority patients.
Conclusions. To contend effectively with inequities in health care, it is necessary to understand the mechanisms behind the problem. Discrimination stemming from prejudice is of a very different character than discrimination stemming from the application of rules of conditional probability as a response to clinical uncertainty. While in the former case, doctors are not acting in the best interests of their patients, in the latter, they are doing the best they can, given the information available. If miscommunication is the culprit, then efforts should be aimed at reducing disparities in the ways in which doctors communicate with patients.