Bayesian Covariance Analysis of Geographical Variation in Medicare Service Use

• Activity Number: 323
• Type: Contributed
• Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
• Sponsor: Health Policy Statistics Section
• Author(s): Alan M. Zaslavsky* and James O'Malley and Bruce E. Landon
• Companies: Harvard Medical School and Geisel School of Medicine at Dartmouth and Harvard Medical School
• Keywords: Bayesian covariance analysis ; factor analysis ; multilevel model ; health care utilization ; Medicare

Abstract

Abstract: Geographical variation in patterns of service use has been well studied in Traditional Medicare (TM), using data from claims. However, less is known about the extent of such variation in Medicare Advantage (MA) contracts, which do not submit claims to CMS. In particular, it is not known to what extent the MA contracts follow local patterns of utilization in TM, except at a very aggregated "per member per month" level. We investigated this question using a relatively new data source for MA, the HEDIS utilization measures, and similarly defined measures constructed from TM claims. We used a Bayesian approach to covariance matrix estimation in multilevel data (O'Malley and Zaslavsky, JASA 2008) to yield posterior probabilities for descriptive statements about correlations of measures, summarized through factor analysis and regressions. In general MA utilization patterns are well aligned with those in TM and show similar levels of variation across Healthcare Referral Regions (HRRs).