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Using machine learning to estimate health spillover effects

Author

Listed:
  • Bruno Wichmann

    (College of Natural and Applied Sciences, University of Alberta)

  • Roberta Moreira Wichmann

    (World Bank
    Brazilian Institute of Education, Development and Research IDP, Economics Graduate Program)

Abstract

We develop a nonparametric model to study health spillover effects of policy interventions. We use double/debiased machine learning to estimate the model using data from 74 hospitals in Rio de Janeiro, Brazil, and examine cross-patient spillover effects during the COVID-19 pandemic. The pandemic forced hospitals to develop new protocols to offer intensive care to both COVID and non-COVID patients. Our results show that the need to care for COVID patients affects health outcomes of non-COVID patients. Controlling for a number of confounders, we find that mortality rates and length of stay of non-COVID ICU patients increase when hospitals simultaneously offer intensive care to both types of patients. Policy simulations suggest that an increase in the number of ICU beds can counter morbidity spillover, but it is unlikely to be a feasible approach to counter mortality spillover.

Suggested Citation

  • Bruno Wichmann & Roberta Moreira Wichmann, 2024. "Using machine learning to estimate health spillover effects," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(4), pages 717-730, June.
    • Handle: RePEc:spr:eujhec:v:25:y:2024:i:4:d:10.1007_s10198-023-01621-7
    DOI: 10.1007/s10198-023-01621-7
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Rossi, Pauline & Villar, Paola, 2020. "Private health investments under competing risks: Evidence from malaria control in Senegal," Journal of Health Economics, Elsevier, vol. 73(C).
    3. Nicholas Wilson, 2015. "Erratum to: Can Disease Specific Funding Harm Health? Child Mortality in the Shadow of HIV/AIDS Service Expansion," Demography, Springer;Population Association of America (PAA), vol. 52(5), pages 1701-1701, October.
    4. Edward Miguel & Michael Kremer, 2004. "Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities," Econometrica, Econometric Society, vol. 72(1), pages 159-217, January.
    5. Nicholas Wilson, 2015. "Can Disease-Specific Funding Harm Health? in the Shadow of HIV/AIDS Service Expansion," Demography, Springer;Population Association of America (PAA), vol. 52(5), pages 1671-1700, October.
    6. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    7. Tomas J. Philipson & William H. Dow & Xavier Sala-i-Martin, 1999. "Longevity Complementarities under Competing Risks," American Economic Review, American Economic Association, vol. 89(5), pages 1358-1371, December.
    8. Avitabile, Ciro, 2021. "Spillovers and Social Interaction Effects in the Demand for Preventive Healthcare: Evidence from the PROGRESA program," Journal of Health Economics, Elsevier, vol. 79(C).
    9. Grossman, Michael, 1972. "On the Concept of Health Capital and the Demand for Health," Journal of Political Economy, University of Chicago Press, vol. 80(2), pages 223-255, March-Apr.
    10. Cho, Hyunkuk & Kwon, Jihyeon, 2021. "Pandemic and hospital avoidance: Evidence from the 2015 Middle East respiratory syndrome outbreak in South Korea," Economics Letters, Elsevier, vol. 203(C).
    11. Tamás Krisztin & Philipp Piribauer & Michael Wögerer, 2020. "The spatial econometrics of the coronavirus pandemic," Letters in Spatial and Resource Sciences, Springer, vol. 13(3), pages 209-218, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Machine learning; Intensive care units; Spillover effects; Non-COVID-19 patients; Brazil; COVID-19 pandemic;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • D62 - Microeconomics - - Welfare Economics - - - Externalities

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