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Statistical actuarial estimation of the Capitation Payment Unit from copula functions and deep learning: historical comparability analysis for the Colombian health system, 2015–2021

Author

Listed:
  • Oscar Espinosa

    (Universidad Nacional de Colombia)

  • Valeria Bejarano

    (Universidad Nacional de Colombia)

  • Jeferson Ramos

    (Universidad Nacional de Colombia)

  • Boris Martínez

    (Universidad Nacional de Colombia)

Abstract

The Capitation Payment Unit (CPU) financing mechanism constitutes more than 70% of health spending in Colombia, with a budget allocation of close to 60 trillion Colombian pesos for the year 2022 (approximately 15.7 billion US dollars). This article estimates actuarially, using modern techniques, the CPU for the contributory regime of the General System of Social Security in Health in Colombia, and compares it with what is estimated by the Ministry of Health and Social Protection. Using freely available information systems, by means of statistical copulas functions and artificial neural networks, pure risk premiums are calculated between 2015 and 2021. The study concludes that the weights by risk category are systematically different, showing historical pure premiums surpluses in the group of 0–1 years and deficits (for the regions normal and cities) in the groups over 54 years of age.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:hecrev:v:13:y:2023:i:1:d:10.1186_s13561-022-00416-5
    DOI: 10.1186/s13561-022-00416-5
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    References listed on IDEAS

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    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. 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.
    3. Zhao, Xiaobing & Zhou, Xian, 2012. "Estimation of medical costs by copula models with dynamic change of health status," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 480-491.
    4. Peng Shi & Gee Y. Lee, 2022. "Copula Regression for Compound Distributions with Endogenous Covariates with Applications in Insurance Deductible Pricing," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1094-1109, September.
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