Matching and Imputation Methods for Risk Adjustment in the Health Insurance Marketplaces

Sherri Rose (), Julie Shi (), Thomas G. McGuire and Sharon-Lise T. Normand
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Sherri Rose: Harvard Medical School
Thomas G. McGuire: Harvard Medical School
Sharon-Lise T. Normand: Harvard Medical School

Statistics in Biosciences, 2017, vol. 9, issue 2, No 12, 525-542

Abstract: Abstract New state-level health insurance markets, denoted as Marketplaces, created under the Affordable Care Act, use risk-adjusted plan payment formulas derived from a population ineligible to participate in the Marketplaces. We develop methodology to derive a sample from the target population and to assemble information to generate improved risk-adjusted payment formulas using data from the Medical Expenditure Panel Survey and Truven MarketScan databases. Our approach requires multi-stage data selection and imputation procedures because both data sources have systemic missing data on crucial variables and arise from different populations. We present matching and imputation methods adapted to this setting. The long-term goal is to improve risk adjustment estimation utilizing information found in Truven MarketScan data supplemented with imputed Medical Expenditure Panel Survey values.

Keywords: Matching; Imputation; Prediction; Risk adjustment (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s12561-015-9135-7

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