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

Author

Listed:
  • Sherri Rose

    (Harvard Medical School)

  • Julie Shi

    (Peking University, Haidian District)

  • Thomas G. McGuire

    (Harvard Medical School)

  • Sharon-Lise T. Normand

    (Harvard Medical School
    Harvard School of Public Health)

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.

Suggested Citation

  • Sherri Rose & Julie Shi & Thomas G. McGuire & Sharon-Lise T. Normand, 2017. "Matching and Imputation Methods for Risk Adjustment in the Health Insurance Marketplaces," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 525-542, December.
    • Handle: RePEc:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-015-9135-7
    DOI: 10.1007/s12561-015-9135-7
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    References listed on IDEAS

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    1. Rose Sherri & van der Laan Mark J., 2008. "Simple Optimal Weighting of Cases and Controls in Case-Control Studies," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-24, September.
    2. Porter Kristin E. & Gruber Susan & van der Laan Mark J. & Sekhon Jasjeet S., 2011. "The Relative Performance of Targeted Maximum Likelihood Estimators," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-34, August.
    3. Rose Sherri & van der Laan Mark J., 2009. "Why Match? Investigating Matched Case-Control Study Designs with Causal Effect Estimation," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-26, January.
    4. Rose Sherri & van der Laan Mark J., 2011. "A Targeted Maximum Likelihood Estimator for Two-Stage Designs," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-21, March.
    5. King, Gary & Honaker, James & Joseph, Anne & Scheve, Kenneth, 2001. "Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation," American Political Science Review, Cambridge University Press, vol. 95(1), pages 49-69, March.
    6. McGuire, Thomas G. & Newhouse, Joseph P. & Normand, Sharon-Lise & Shi, Julie & Zuvekas, Samuel, 2014. "Assessing incentives for service-level selection in private health insurance exchanges," Journal of Health Economics, Elsevier, vol. 35(C), pages 47-63.
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    Cited by:

    1. Timothy J. Layton & Thomas G. McGuire, 2017. "Marketplace Plan Payment Options for Dealing with High-Cost Enrollees," American Journal of Health Economics, MIT Press, vol. 3(2), pages 165-191, Spring.

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