Impacts of the COVID-19 pandemic on health insurance in southeastern Brazil

an interrupted time series analysis

Authors

Keywords:

quasi-experimental models, causal inference, coronavirus

Abstract

Interrupted time series analysis is the quasi-experimental approach to evaluating the effects of discrete interventions on longitudinal data. Thus, a time series of a given outcome of interest is used to establish an underlying trend, which is "interrupted" by an intervention at a known point in time. The hypothetical scenario in which the intervention did not occur and the trend continues unchanged is referred to as the 'counterfactual'. This counterfactual scenario provides a comparison for evaluating the impact of the intervention by examining any changes that occur in the post-intervention period. In this sense, the aim of this work is to analyze the effects of the COVID-19 pandemic on health insurance contracting in the Southeast region of Brazil by means of interrupted series analysis. The data used refers to the number of health insurance beneficiaries in the southeast region from the first quarter of 2000 to the first quarter of 2023 (93 observations), from the National Supplementary Health Agency. Based on the time series analysis, a quadratic model was fitted for the period before the treatment (COVID-19) and after the treatment. Based on the fits of these models, it was possible to observe that the number of health insurance beneficiaries in the southeast was falling, but the intervention may have caused a more pronounced reduction, with an average of 10,000 fewer beneficiaries than predicted without the intervention.

Author Biographies

Leonardo Biazoli, Federal University of Alfenas

Lecturer of Actuarial Sciences of course at the Federal University of Alfenas (UNIFAL) 
Doctorate student in Statistics and Experimentation Agricultural (UFLA)
leonardo.biazoli@unifal-mg.edu.br

Izabela Regina Cardoso de Oliveira, Federal University of Lavras

Lecturer at the Federal University of Lavras (UFLA)
PhD in Statistics and Experimentation Agricultural from the "Luiz de Queiroz"
School of Agriculture, ESALQ/USP, in a double degree whit Universiteit Hasselt, Belgium
Master's degree in Statistics and Experimentation Agricultural (2010)
Bachelor's and Administration (2008) from Federal University of Lavras (UFLA)

References

BRASIL. Lei no 8.080, de 19 de setembro de 1990. Dispõe sobre as condições para a promoção, proteção e recuperação da saúde, a organização e o funcionamento dos serviços correspondentes e dá outras providências. Disponível em: https://www.planalto.gov.br/ccivil_03/leis/l8080.htm. Acesso em: 30 de Agosto de 2023.

BRASIL. Lei no 9.656, de 03 de junho de 1998. Dispõe sobre os planos e seguros privados de assistência à saúde. Disponível em: https://www.planalto.gov.br/ccivil_03/leis/l9656.htm. Acesso em: 16 de Junho de 2023.

BRASIL. Lei no 9.961, de 28 de janeiro de 2000. Cria a Agência Nacional de Saúde Suplementar – ANS e dá outras providências. Disponível em: https://www.planalto.gov.br/ccivil_03/leis/l9961.htm. Acesso em: 16 de Junho de 2023.

FIGUEIREDO, D. C. M. M. et al. Efeitos da recessão econômica na mortalidade por suicídio no Brasil: análise com séries temporais interrompidas. Revista Brasileira de Enfermagem, v. 75, p. e20210778, 2022.

IRVINE, M. A. et al. An interrupted time-series analysis of pediatric emergency department visits during the coronavirus disease 2019 pandemic. Pediatric Emergency Care, v. 37, n. 6, p. 325-328, 2021.

NASCIMENTO, M. I. et al. Mortalidade prematura por câncer de colo uterino: estudo de séries temporais interrompidas. Revista de Saúde Pública, v. 54, 2020.

NUNES, H. R. C et al. Impacto da Lei Seca sobre a mortalidade no trânsito nas Unidades Federativas do Brasil: uma análise de série temporal interrompida. Revista Brasileira de Epidemiologia, v. 24, p. e210045, 2021.

OLIVEIRA, C. C. M. et al. Efetividade do serviço móvel de urgência (Samu): uso de séries temporais interrompidas. Revista de Saúde Pública, v. 53, p. 99, 2019.

PEARL, J. Causal inference. Causality: Objectives and Assessment, PMLR, p. 39–58, 2010.

PENFOLD, R. B.; ZHANG, F. Use of interrupted time series analysis in evaluating health care quality improvements. Academic pediatrics, v. 13, n. 6, p. S38-S44, 2013.

RUBIN, D. B. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of educational Psychology, American Psychological Association, v. 66, n. 5, p. 688, 1974.

ROSENBAUM, P. R.; RUBIN, D. B. The central role of the propensity score in observational studies for causal effects. Biometrika, Oxford University Press, v. 70, n. 1, p. 41–55, 1983.

VINCI, A. et al. Emergency medical services calls analysis for trend prediction during epidemic outbreaks: interrupted time series analysis on 2020–2021 COVID-19 epidemic in Lazio, Italy. International Journal of Environmental Research and Public Health, v. 19, n. 10, p. 5951, 2022.

Downloads

Published

15-03-2024 — Updated on 11-04-2024

Versions

How to Cite

Biazoli, L., & Oliveira, I. R. C. de. (2024). Impacts of the COVID-19 pandemic on health insurance in southeastern Brazil: an interrupted time series analysis. Sigmae, 13(1), 45–50. Retrieved from https://publicacoes.unifal-mg.edu.br/revistas/index.php/sigmae/article/view/2263 (Original work published March 15, 2024)