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Sample Selection for Medicare Risk Adjustment Due to Systematically Missing Data
Author(s) -
Bergquist Savannah L.,
McGuire Thomas G.,
Layton Timothy J.,
Rose Sherri
Publication year - 2018
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/1475-6773.13046
Subject(s) - sample (material) , statistics , propensity score matching , selection bias , sample size determination , medicine , matching (statistics) , actuarial science , missing data , econometrics , mathematics , economics , chemistry , chromatography
Objective To assess the issue of nonrepresentative sampling in Medicare Advantage (MA) risk adjustment. Data Sources Medicare enrollment and claims data from 2008 to 2011. Data Extraction Risk adjustment predictor variables were created from 2008 to 2010 Part A and B claims and the Medicare Beneficiary Summary File. Spending is based on 2009–2011 Part A and B, Durable Medical Equipment, and Home Health Agency claims files. Study Design A propensity‐score matched sample of Traditional Medicare (TM) beneficiaries who resembled MA enrollees was created. Risk adjustment formulas were estimated using multiple techniques, and performance was evaluated based on R 2 , predictive ratios, and formula coefficients in the matched sample and a random sample of TM beneficiaries. Principal Findings Matching improved balance on observables, but performance metrics were similar when comparing risk adjustment formula results fit on and evaluated in the matched sample versus fit on the random sample and evaluated in the matched sample. Conclusions Fitting MA risk adjustment formulas on a random sample versus a matched sample yields little difference in MA plan payments. This does not rule out potential improvements via the matching method should reliable MA encounter data and additional variables become available for risk adjustment.