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Regression by clustering using metropolis-hastings

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  • Simón Ramírez Amaya

    ()

  • Adolfo J. Quiroz

    ()

  • Álvaro José Riascos Villegas

    ()

Abstract

High quality risk adjustment in health insurance markets weakens insurer incentives to engage in inefficient behavior to attract lower-cost enrollees. We propose a novel methodology based on Markov Chain Monte Carlo methods to improve risk adjustment by clustering diagnostic codes into risk groups optimal for health expenditure prediction. We test the performance of our methodology against common alternatives using panel data from 500 thousand enrollees of the Colombian Healthcare System. Results show that our methodology outperforms common alternatives and suggest that it has potential to improve access to quality healthcare for the chronically ill.

Suggested Citation

  • Simón Ramírez Amaya & Adolfo J. Quiroz & Álvaro José Riascos Villegas, 2019. "Regression by clustering using metropolis-hastings," Documentos de Trabajo Quantil 018180, Quantil.
  • Handle: RePEc:col:000508:018180
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    References listed on IDEAS

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    5. Ã lvaro Riascos & Eduardo Alfonso & Mauricio Romero, 2014. "The Performance of Risk Adjustment Models in Colombian Competitive Health Insurance Market," Documentos CEDE 012062, Universidad de los Andes - CEDE.
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    More about this item

    Keywords

    Risk adjustmenthealth insurance clusteringMarkov chain Monte Carlohealth expenditure;

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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