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Causal Inference During a Pandemic: Evidence on the Effectiveness of Nebulized Ibuprofen as an Unproven Treatment for COVID-19 in Argentina

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  • Sebastian Calonico
  • Rafael Di Tella
  • Juan Cruz Lopez Del Valle

Abstract

Many medical decisions during the pandemic were made without the support of causal evidence obtained in clinical trials. We study the case of nebulized ibuprofen (NaIHS), a drug that was extensively used on COVID-19 patients in Argentina amidst wild claims about its effectiveness and without regulatory approval. We study data on 5,146 patients hospitalized in 11 health centers spread over 4 provinces, of which a total of 1,019 (19.8%) received the treatment. We find a large, negative and statistically significant correlation between NaIHS treatment and mortality using inverse probability weighting estimators. We consider several threats to identification, including the selection of “low” risks into NaIHS, spillovers affecting patients in the control group, and differences in the quality of care in centers that use NaIHS. While the negative correlation appears to be, broadly, robust, our results are best interpreted as emphasizing the benefits of running a randomized controlled trial and the challenges of incorporating information produced in other, less rigorous circumstances.

Suggested Citation

  • Sebastian Calonico & Rafael Di Tella & Juan Cruz Lopez Del Valle, 2022. "Causal Inference During a Pandemic: Evidence on the Effectiveness of Nebulized Ibuprofen as an Unproven Treatment for COVID-19 in Argentina," NBER Working Papers 30084, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:30084
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    1. Cattaneo, Matias D., 2010. "Efficient semiparametric estimation of multi-valued treatment effects under ignorability," Journal of Econometrics, Elsevier, vol. 155(2), pages 138-154, April.
    2. Pierre Azoulay & Joshua S Graff Zivin & Danielle Li & Bhaven N Sampat, 2019. "Public R&D Investments and Private-sector Patenting: Evidence from NIH Funding Rules," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(1), pages 117-152.
    3. Matias Busso & John DiNardo & Justin McCrary, 2014. "New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 885-897, December.
    4. Joseph J. Doyle Jr. & John A. Graves & Jonathan Gruber & Samuel A. Kleiner, 2015. "Measuring Returns to Hospital Care: Evidence from Ambulance Referral Patterns," Journal of Political Economy, University of Chicago Press, vol. 123(1), pages 170-214.
    5. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    6. Matias D. Cattaneo, 2010. "multi-valued treatment effects," The New Palgrave Dictionary of Economics,, Palgrave Macmillan.
    7. Jason Abrevaya & Yu-Chin Hsu & Robert P. Lieli, 2015. "Estimating Conditional Average Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 485-505, October.
    8. Słoczyński, Tymon & Wooldridge, Jeffrey M., 2018. "A General Double Robustness Result For Estimating Average Treatment Effects," Econometric Theory, Cambridge University Press, vol. 34(1), pages 112-133, February.
    9. Joseph Doyle & John Graves & Jonathan Gruber, 2019. "Evaluating Measures of Hospital Quality: Evidence from Ambulance Referral Patterns," The Review of Economics and Statistics, MIT Press, vol. 101(5), pages 841-852, December.
    10. Eric Budish & Benjamin N. Roin & Heidi Williams, 2015. "Do Firms Underinvest in Long-Term Research? Evidence from Cancer Clinical Trials," American Economic Review, American Economic Association, vol. 105(7), pages 2044-2085, July.
    11. Mark B. McClellan & Douglas O. Staiger, 2000. "Comparing Hospital Quality at For-Profit and Not- for-Profit Hospitals," NBER Chapters, in: The Changing Hospital Industry: Comparing Not-for-Profit and For-Profit Institutions, pages 93-112, National Bureau of Economic Research, Inc.
    12. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    13. Amitabh Chandra & Douglas O Staiger, 2020. "Identifying Sources of Inefficiency in Healthcare [“The Determinants of Productivity in Medical Testing: Intensity and Allocation of Care,”]," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(2), pages 785-843.
    14. Jonathan Skinner & Douglas Staiger, 2015. "Technology Diffusion and Productivity Growth in Health Care," The Review of Economics and Statistics, MIT Press, vol. 97(5), pages 951-964, December.
    15. Skinner, Jonathan, 2011. "Causes and Consequences of Regional Variations in Health Care," Handbook of Health Economics, in: Mark V. Pauly & Thomas G. Mcguire & Pedro P. Barros (ed.), Handbook of Health Economics, volume 2, chapter 0, pages 45-93, Elsevier.
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    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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