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

Matching and Imputation Methods for Risk Adjustment in the Health Insurance Marketplaces 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics in Biosciences Springer Journals

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

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Publisher
Springer Journals
Copyright
Copyright © 2015 by International Chinese Statistical Association
Subject
Statistics; Statistics for Life Sciences, Medicine, Health Sciences; Biostatistics; Theoretical Ecology/Statistics
ISSN
1867-1764
eISSN
1867-1772
D.O.I.
10.1007/s12561-015-9135-7
Publisher site
See Article on Publisher Site

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.

Journal

Statistics in BiosciencesSpringer Journals

Published: Aug 5, 2015

References

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