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The Performance of Risk Adjustment Models in Colombian Competitive Health Insurance Market

Author

Listed:
  • Álvaro Riascos
  • Eduardo Alfonso
  • Mauricio Romero

Abstract

We introduce new risk groups to a standard capitation formula and evaluate risk selection incentives of insurers. The study uses a unique data set of almost 24 million affiliates to Government’s mandatory health insurance system. This data set is very rich in the sense of reporting all claims during year 2010, basic demographic variables, initial diagnostic, health services, pharmaceuticals used, etc. It compromises more than 300 million claims. We construct two diagnostic related groups: an adaptation of the 3M algorithm, and a ad hoc diagnostic related group constructed by the authors. Using standard linear capitations formulas we evaluate incentives for cream skimming using several measures. In general, results show a notable improvement in the explanatory power of health expenditures by introducing the ad hoc diagnostic related groups to the standard Colombian risk adjustment formula. With the new risk groups the R2 of the model is 13.53% as opposed to 1.45% of the current formula. Furthermore, for users in the highest expenditure quintile, expected expenditure is 71% of actual expenditure, as opposed to 27% under the current formula. This suggest there is much space for improving the current Colombian capitation formula using information that is currently available.

Suggested Citation

  • Álvaro Riascos & Eduardo Alfonso & Mauricio Romero, 2014. "The Performance of Risk Adjustment Models in Colombian Competitive Health Insurance Market," Documentos CEDE 12062, Universidad de los Andes, Facultad de Economía, CEDE.
    • Handle: RePEc:col:000089:012062
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    File URL: https://repositorio.uniandes.edu.co/bitstream/handle/1992/8506/dcede2014-33.pdf
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    References listed on IDEAS

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    1. Schokkaert, Erik & Van de Voorde, Carine, 2004. "Risk selection and the specification of the conventional risk adjustment formula," Journal of Health Economics, Elsevier, vol. 23(6), pages 1237-1259, November.
    2. Castano, Ramon & Zambrano, Andres, 2006. "Biased selection within the social health insurance market in Colombia," Health Policy, Elsevier, vol. 79(2-3), pages 313-324, December.
    3. Lamers, Leida M., 1998. "Risk-adjusted capitation payments: Developing a diagnostic cost groups classification for the Dutch situation," Health Policy, Elsevier, vol. 45(1), pages 15-32, July.
    4. Erik Schokkaert & Carine Van de Voorde, 2000. "Risk adjustment and the fear of markets: The case of Belgium," Health Care Management Science, Springer, vol. 3(2), pages 121-130, February.
    5. van de Ven, Wynand P. M. M. & van Vliet, Rene C. J. A. & Schut, Frederik T. & van Barneveld, Erik M., 2000. "Access to coverage for high-risks in a competitive individual health insurance market: via premium rate restrictions or risk-adjusted premium subsidies?," Journal of Health Economics, Elsevier, vol. 19(3), pages 311-339, May.
    6. Yujing Shen & Randall P. Ellis, 2002. "How profitable is risk selection? A comparison of four risk adjustment models," Health Economics, John Wiley & Sons, Ltd., vol. 11(2), pages 165-174, March.
    7. Konstantin Beck, 2000. "Growing importance of capitation in Switzerland," Health Care Management Science, Springer, vol. 3(2), pages 111-119, February.
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    Citations

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    Cited by:

    1. Juan Esteban Carranza & Álvaro J. Riascos & Natalia Serna, 2015. "Poder de mercado, contratos y resultados de salud en el sistema de salud colombiano entre 2009 y 2011," Borradores de Economia 918, Banco de la Republica de Colombia.
    2. Alvaro J. Riascos & Mauricio Romero & Natalia Serna, 2017. "Risk Adjustment Revisited using Machine Learning Techniques," Documentos CEDE 15601, Universidad de los Andes, Facultad de Economía, CEDE.
    3. Álvaro Riascos & Natalia Serna & Ramiro Guerrero, 2017. "Capital requirements of health insurers under different risk-adjusted capitation payments," Documentos CEDE 15292, Universidad de los Andes, Facultad de Economía, CEDE.
    4. Simón Ramírez Amaya & Adolfo J. Quiroz & Álvaro José Riascos Villegas, 2019. "Regression by clustering using metropolis-hastings," Documentos de Trabajo 18180, Quantil.
    5. Juan Esteban Carranza & Álvaro J. Riascos & Natalia Serna, 2017. "Market Power, Contracts and Outcomes: The Case of Patients with Long-Term Diseases in the Colombian Health Care System," Documentos de Trabajo 15283, Quantil.
    6. Oscar Espinosa & Valeria Bejarano & Jeferson Ramos & Boris Martínez, 2023. "Statistical actuarial estimation of the Capitation Payment Unit from copula functions and deep learning: historical comparability analysis for the Colombian health system, 2015–2021," Health Economics Review, Springer, vol. 13(1), pages 1-20, December.

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    More about this item

    Keywords

    Risk adjustment; Diagnostic Related Groups; Risk Selection.;
    All these keywords.

    JEL classification:

    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • 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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