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      The combined effect of social pensions and cash transfers on child mortality: evaluating the last two decades in Brazil and projecting their mitigating effect during the global economic crisis

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          Summary

          Background

          The world is currently experiencing multiple economic crises due to the COVID-19 pandemic, war in Ukraine, and inflation surge, which disproportionately affect children, especially in low- and middle-income countries (LMICs). We evaluated if the expansion of Social Assistance, represented by Social Pensions (SP) and Conditional Cash Transfers (CCT), could reduce infant and child mortality, and mitigate excess deaths among children in Brazil, one of the LMICs most affected by these economic crises.

          Methods

          We conducted a retrospective impact evaluation in a cohort of Brazilian municipalities from 2004 to 2019 using multivariable fixed-effects negative binomial models, adjusted for relevant demographic, social, and economic factors, to estimate the effects of the SP and CCT on infant and child mortality. To verify the robustness of the results, we conducted several sensitivity and triangulation analyses, including difference-in-difference with propensity-score matching. These results were incorporated into dynamic microsimulation models to generate projections to 2030 of various economic crises and Social Assistance scenarios.

          Findings

          Consolidated coverage of SP was associated with significant reductions in infant and child mortality rates, with a rate ratio (RR) of 0.843 (95% CI: 0.826–0.861) and 0.840 (95% CI: 0.824–0.856), respectively. Similarly, CCT consolidated coverages showed RRs of 0.868 (95% CI: 0.842–0.849) and 0.874 (95% CI: 0.850–0.899) for infant and child mortality, respectively. The higher the degree of poverty in the municipalities, the stronger the impact of CCT on reducing child mortality. Given the current economic crisis, a mitigation strategy that will increase the coverage of SP and CCT could avert 148,736 (95% CI: 127,148–170,706) child deaths up to 2030, compared with fiscal austerity measures.

          Interpretation

          SP and CCT programs could strongly reduce child mortality in LMICs, and their expansion should be considered as an effective strategy to mitigate the impact of the current multiple global economic crises.

          Funding

          doi 10.13039/100000865, Bill & Melinda Gates Foundation; , Grant_Number:INV-027961. doi 10.13039/501100000265, Medical Research Council; (MRC-UKRI),Grant_Number:MC_PC_MR/T023678/1.

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          Most cited references25

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          Specification Tests in Econometrics

          J. Hausman (1978)
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            Triangulation in aetiological epidemiology

            Abstract Triangulation is the practice of obtaining more reliable answers to research questions through integrating results from several different approaches, where each approach has different key sources of potential bias that are unrelated to each other. With respect to causal questions in aetiological epidemiology, if the results of different approaches all point to the same conclusion, this strengthens confidence in the finding. This is particularly the case when the key sources of bias of some of the approaches would predict that findings would point in opposite directions if they were due to such biases. Where there are inconsistencies, understanding the key sources of bias of each approach can help to identify what further research is required to address the causal question. The aim of this paper is to illustrate how triangulation might be used to improve causal inference in aetiological epidemiology. We propose a minimum set of criteria for use in triangulation in aetiological epidemiology, summarize the key sources of bias of several approaches and describe how these might be integrated within a triangulation framework. We emphasize the importance of being explicit about the expected direction of bias within each approach, whenever this is possible, and seeking to identify approaches that would be expected to bias the true causal effect in different directions. We also note the importance, when comparing results, of taking account of differences in the duration and timing of exposures. We provide three examples to illustrate these points.
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              Modeling good research practices--overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-1.

              Models-mathematical frameworks that facilitate estimation of the consequences of health care decisions-have become essential tools for health technology assessment. Evolution of the methods since the first ISPOR modeling task force reported in 2003 has led to a new task force, jointly convened with the Society for Medical Decision Making, and this series of seven papers presents the updated recommendations for best practices in conceptualizing models; implementing state-transition approaches, discrete event simulations, or dynamic transmission models; dealing with uncertainty; and validating and reporting models transparently. This overview introduces the work of the task force, provides all the recommendations, and discusses some quandaries that require further elucidation. The audience for these papers includes those who build models, stakeholders who utilize their results, and, indeed, anyone concerned with the use of models to support decision making.
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                Author and article information

                Contributors
                Journal
                Lancet Reg Health Am
                Lancet Reg Health Am
                Lancet Regional Health - Americas
                Elsevier
                2667-193X
                03 November 2023
                November 2023
                03 November 2023
                : 27
                : 100618
                Affiliations
                [a ]Institute of Collective Health (ISC) at the Federal University of Bahia (UFBA), Bahia, Brazil
                [b ]Institute of Global Health (ISGlobal), Barcelona, Spain
                Author notes
                []Corresponding author. Institute of Collective Health (ISC) at the Federal University of Bahia (UFBA), Bahia, Brazil. davide.rasella@ 123456isglobal.org davide.rasella@ 123456gmail.com
                [c]

                These authors contributed equally to the study.

                [d]

                These authors contributed equally to the study.

                Article
                S2667-193X(23)00192-8 100618
                10.1016/j.lana.2023.100618
                10661114
                a0f7ee5b-0921-4e7c-af9a-1520b4a8aa82
                © 2023 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 31 March 2023
                : 18 September 2023
                : 9 October 2023
                Categories
                Articles

                social pension,cash transfer programs,social protection,child mortality,global economic crisis,multiple crisis,fiscal austerity,poverty,impact evaluation,microsimulation models,forecasting

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