Investigation of selection bias in the association of race with prevalent atrial fibrillation in a national cohort study: REasons for Geographic And Racial Differences in Stroke (REGARDS).

Academic Article

Abstract

  • PURPOSE: Atrial fibrillation (AF) is diagnosed more commonly in whites than blacks in the United States. In epidemiologic studies, selection bias could induce a noncausal positive association of white race with prevalent AF if voluntary enrollment was influenced by both race and AF status. We investigated whether nonrandom enrollment biased the association of race with prevalent self-reported AF in the US-based REasons for Geographic And Racial Differences in Stroke Study (REGARDS). METHODS: REGARDS had a two-stage enrollment process, allowing us to compare 30,183 fully enrolled REGARDS participants with 12,828 people who completed the first-stage telephone survey but did not complete the second-stage in-home visit to finalize their REGARDS enrollment (telephone-only participants). RESULTS: REGARDS enrollment was higher among whites (77.1%) than among blacks (62.3%) but did not differ by self-reported AF status. The prevalence of AF was 8.45% in whites and 5.86% in blacks adjusted for age, sex, income, education, and perceived general health. The adjusted white/black prevalence ratio of self-reported AF was 1.43 (95% CI, 1.32-1.56) among REGARDS participants and 1.38 (1.22-1.55) among telephone-only participants. CONCLUSIONS: These findings suggest that selection bias is not a viable explanation for the higher prevalence of self-reported AF among whites in population studies such as REGARDS.
  • Published In

    Keywords

  • Atrial fibrillation, Cohort study, Race, Selection bias, African Americans, Age Distribution, Aged, Atrial Fibrillation, Cross-Sectional Studies, Databases, Factual, Electrocardiography, European Continental Ancestry Group, Female, Humans, Male, Middle Aged, Prevalence, Risk Assessment, Selection Bias, Self Report, Severity of Illness Index, Sex Distribution, Stroke, Survival Analysis, United States
  • Digital Object Identifier (doi)

    Author List

  • Thacker EL; Soliman EZ; Pulley L; Safford MM; Howard G; Howard VJ
  • Start Page

  • 534
  • End Page

  • 539
  • Volume

  • 26
  • Issue

  • 8