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      Impact of emergency financial support program on testing demand, SARS-CoV-2 prevalence, and social isolation during COVID-19 outbreak in Brazil: a quasi-experimental study

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          Abstract

          Background

          Programs offering financial support have been established to mitigate the economic fallout and vulnerabilities arising from the COVID-19 pandemic. Moreover, financial incentives were introduced to promote compliance with social isolation measures, thereby preventing SARS-CoV-2 infections and transmission. However, few low- and middle-income countries have implemented financial aid to alleviate the socio-economic impact of the COVID-19 pandemic. We estimated the association between the Brazilian Emergency Aid (EA) Program and SARS-CoV-2 testing demand, SARS-CoV-2 prevalence, and social isolation levels.

          Methods

          In this quasi-experimental study, we assessed microdata information from 380,000 individuals surveyed in the COVID-19 National Household Sample Survey (COVID-19 NHSS) between July 1, 2020, and Nov 30, 2020. Individuals meeting the EA eligibility criteria, those who have taken the SARS-CoV-2 test, those diagnosed with COVID-19, and/or those complying with social isolation measures were included. Beneficiaries and non-beneficiaries were matched (1:1) using propensity score matching, combined with the entropy balance technique and inverse probability treatment weighting, considering all relevant socioeconomic and health covariates. We performed logistic regression to compute the monthly average impact of EA benefits on the outcomes above.

          Results

          We included 171,183 performed tests, 19,218 COVID-19 cases, and individuals’ self-response in complying with mild ( N = 660,297), moderate ( N = 597,159), strict social isolation ( N = 149,125), and those who did not constrain social gatherings ( N = 1,406,581). EA recipients showed greater testing demand (November; odds ratio [OR] = 1.014, 95% confidence interval [CI]: 1.001–1.026); however, this did not significantly reduce SARS-CoV-2 prevalence. Beneficiaries adhered better to mild isolation from July–September (September; OR: 1.017, 95% CI: 1.007–1.028), with no significant improvement from October–November. EA beneficiaries did not adhere to moderate (July, OR: 0.95, 95% CI: 0.94–0.97; November, OR: 0.83, 95% CI: 0.80–0.86) or strict (July, OR: 0.79, 95% CI: 0.75–0.84; November, OR: 0.77, 95% CI: 0.73–0.81) isolation compared to non-beneficiaries.

          Conclusions

          Despite the severe COVID-19 situation in Brazil owing to the lack of effective public health policies and government resistance to scientific guidance, being an EA beneficiary was associated with increased testing demand and better adherence to mild social isolation measures.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12889-024-20877-z.

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          Most cited references28

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          The central role of the propensity score in observational studies for causal effects

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            Statistical aspects of the analysis of data from retrospective studies of disease.

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              Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies

              This paper proposes entropy balancing, a data preprocessing method to achieve covariate balance in observational studies with binary treatments. Entropy balancing relies on a maximum entropy reweighting scheme that calibrates unit weights so that the reweighted treatment and control group satisfy a potentially large set of prespecified balance conditions that incorporate information about known sample moments. Entropy balancing thereby exactly adjusts inequalities in representation with respect to the first, second, and possibly higher moments of the covariate distributions. These balance improvements can reduce model dependence for the subsequent estimation of treatment effects. The method assures that balance improves on all covariate moments included in the reweighting. It also obviates the need for continual balance checking and iterative searching over propensity score models that may stochastically balance the covariate moments. We demonstrate the use of entropy balancing with Monte Carlo simulations and empirical applications.
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                Author and article information

                Contributors
                gabriel.morais@ufba.br
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                18 December 2024
                18 December 2024
                2024
                : 24
                : 3435
                Affiliations
                [1 ]Collective Health Institute, Federal University of Bahia, ( https://ror.org/03k3p7647) Salvador, Brazil
                [2 ]Latin American Institute of Economics, Society and Politics, Federal University of Latin American Integration, ( https://ror.org/02gp35s66) Foz do Iguaçu, Brazil
                [3 ]Life Science Department, State University of Bahia, ( https://ror.org/015n1m812) Salvador, Brazil
                [4 ]Polytechnic School, Federal University of Bahia, ( https://ror.org/03k3p7647) Salvador, Brazil
                Article
                20877
                10.1186/s12889-024-20877-z
                11654062
                39695481
                c9895b61-14ca-4bd5-b3c4-281fc86cae13
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 8 October 2023
                : 27 November 2024
                Funding
                Funded by: UNITAID, World Health Organization
                Award ID: Grant 2017-15-FIOTECPrEP
                Award ID: Grant 2017-15-FIOTECPrEP
                Award ID: Grant 2017-15-FIOTECPrEP
                Award ID: Grant 2017-15-FIOTECPrEP
                Award ID: Grant 2017-15-FIOTECPrEP
                Award Recipient :
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Public health
                economic incentive,sars-cov-2 testing demand,sars-cov-2 prevalence,social isolation

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