Testing for statistical discrimination by race/ethnicity in panel data for depression treatment in primary care

Thomas G. McGuire, John Z. Ayanian, Daniel E. Ford, Rachel E.M. Henke, Kathryn M. Rost, Alan M. Zaslavsky

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Objective. To test for discrimination by race/ethnicity arising from clinical uncertainty in treatment for depression, also known as "statistical discrimination." Data Sources. We used survey data from 1,321 African-American, Hispanic, and white adults identified with depression in primary care. Surveys were administered every six months for two years in the Quality Improvement for Depression (QID) studies. Study Design. To examine whether and how change in depression severity affects change in treatment intensity by race/ethnicity, we used multivariate cross-sectional and change models that difference out unobserved time-invariant patient characteristics potentially correlated with race/ethnicity. Data Collection/Extraction Methods. Treatment intensity was operationalized as expenditures on drugs, primary care, and specialty services, weighted by national prices from the Medical Expenditure Panel Survey. Patient race/ethnicity was collected at baseline by self-report. Principal Findings. Change in depression severity is less associated with change in treatment intensity in minority patients than in whites, consistent with the hypothesis of statistical discrimination. The differential effect by racial/ethnic group was accounted for by use of mental health specialists. Conclusions. Enhanced physician-patient communication and use of standardized depression instruments may reduce statistical discrimination arising from clinical uncertainty and be useful in reducing racial/ethnic inequities in depression treatment.

Original languageEnglish (US)
Pages (from-to)531-551
Number of pages21
JournalHealth services research
Volume43
Issue number2
DOIs
StatePublished - Apr 2008

Keywords

  • Depression
  • Racial/ethnic disparities
  • Statistical discrimination

ASJC Scopus subject areas

  • Health Policy

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