Optimal use of available claims to identify a Medicare population free of coronary heart disease

Am J Epidemiol. 2015 Nov 1;182(9):808-19. doi: 10.1093/aje/kwv116. Epub 2015 Oct 5.

Abstract

We examined claims-based approaches for identifying a study population free of coronary heart disease (CHD) using data from 8,937 US blacks and whites enrolled during 2003-2007 in a prospective cohort study linked to Medicare claims. Our goal was to minimize the percentage of persons at study entry with self-reported CHD (previous myocardial infarction or coronary revascularization). We assembled 6 cohorts without CHD claims by requiring 6 months, 1 year, or 2 years of continuous Medicare fee-for-service insurance coverage prior to study entry and using either a fixed-window or all-available look-back period. We examined adding CHD-related claims to our "base algorithm," which included claims for myocardial infarction and coronary revascularization. Using a 6-month fixed-window look-back period, 17.8% of participants without claims in the base algorithm reported having CHD. This was reduced to 3.6% using an all-available look-back period and adding other CHD claims to the base algorithm. Among cohorts using all-available look-back periods, increasing the length of continuous coverage from 6 months to 1 or 2 years reduced the sample size available without lowering the percentage of persons with self-reported CHD. This analysis demonstrates approaches for developing a CHD-free cohort using Medicare claims.

Keywords: Medicare; algorithms; bias (epidemiology); coronary disease; epidemiologic methods.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Algorithms
  • Bias
  • Black People / statistics & numerical data
  • Black or African American
  • Coronary Artery Disease / epidemiology*
  • Female
  • Humans
  • Male
  • Medicare*
  • Middle Aged
  • Population Surveillance
  • Prospective Studies
  • United States / epidemiology
  • White People / statistics & numerical data