Evidence map›Paper›PMID 38778134›Full record

ArticleScientific reports2024

Evaluating social protection mitigation effects on HIV/AIDS and Tuberculosis through a mathematical modelling study.

Felipe Alves Rubio, Alan Alves Santana Amad, Temidayo James Aransiola, Robson Bruniera de Oliveira, Megan Naidoo, Erick Manuel Delgado Moya, Rodrigo Volmir Anderle, Alberto Pietro Sironi, José Alejandro Ordoñez, Mauro Niskier Sanchez and 5 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Felipe Alves RubioInstitute of Collective Health (ISC), Federal University of Bahia (UFBA), Bahia, Brazil. felipearubio@gmail.com.
Alan Alves Santana AmadCenter for Data and Knowledge Integration for Health, Salvador, Brazil.
Temidayo James AransiolaInstitute of Collective Health (ISC), Federal University of Bahia (UFBA), Bahia, Brazil.
Robson Bruniera de OliveiraInstitute of Collective Health (ISC), Federal University of Bahia (UFBA), Bahia, Brazil.
Megan NaidooISGLOBAL, Hospital Clínic-Universitat de Barcelona, Barcelona, Spain.
Erick Manuel Delgado MoyaInstitute of Collective Health (ISC), Federal University of Bahia (UFBA), Bahia, Brazil.
Rodrigo Volmir AnderleInstitute of Collective Health (ISC), Federal University of Bahia (UFBA), Bahia, Brazil.
Alberto Pietro SironiInstitute of Collective Health (ISC), Federal University of Bahia (UFBA), Bahia, Brazil.
José Alejandro OrdoñezInstitute of Collective Health (ISC), Federal University of Bahia (UFBA), Bahia, Brazil.
Mauro Niskier SanchezDepartment of Public Health, University of Brasilia, Brasilia, Brazil.
Juliane Fonseca de OliveiraCenter for Data and Knowledge Integration for Health, Salvador, Brazil.
Luis Eugenio de SouzaInstitute of Collective Health (ISC), Federal University of Bahia (UFBA), Bahia, Brazil.
Inês DouradoInstitute of Collective Health (ISC), Federal University of Bahia (UFBA), Bahia, Brazil.
James MacinkoDepartment of Health Policy and Management, University of California, Los Angeles, USA.
Davide RasellaInstitute of Collective Health (ISC), Federal University of Bahia (UFBA), Bahia, Brazil.

Funding

The impact of social determinants, conditional cash transfers and primary health care on HIV/AIDS: an integrated retrospective and forecasting approach based on a cohort of 100 million BraziliansR01AI152938 · NIAID · FEDERAL UNIVERSITY OF BAHIA · PI RASELLA, DAVIDE · 2020 to 2024
$2.0M
Ministério da Saúde VPGDI-003-FIO-19National Institute of Allergy and Infectious Diseases 1R01AI152938NIAID NIH HHS R01 AI152938
6 · The paper itself

Abstract

The global economic downturn due to the COVID-19 pandemic, war in Ukraine, and worldwide inflation surge may have a profound impact on poverty-related infectious diseases, especially in low-and middle-income countries (LMICs). In this work, we developed mathematical models for HIV/AIDS and Tuberculosis (TB) in Brazil, one of the largest and most unequal LMICs, incorporating poverty rates and temporal dynamics to evaluate and forecast the impact of the increase in poverty due to the economic crisis, and estimate the mitigation effects of alternative poverty-reduction policies on the incidence and mortality from AIDS and TB up to 2030. Three main intervention scenarios were simulated-an economic crisis followed by the implementation of social protection policies with none, moderate, or strong coverage-evaluating the incidence and mortality from AIDS and TB. Without social protection policies to mitigate the impact of the economic crisis, the burden of HIV/AIDS and TB would be significantly larger over the next decade, being responsible in 2030 for an incidence 13% (95% CI 4-31%) and mortality 21% (95% CI 12-34%) higher for HIV/AIDS, and an incidence 16% (95% CI 10-25%) and mortality 22% (95% CI 15-31%) higher for TB, if compared with a scenario of moderate social protection. These differences would be significantly larger if compared with a scenario of strong social protection, resulting in more than 230,000 cases and 34,000 deaths from AIDS and TB averted over the next decade in Brazil. Using a comprehensive approach, that integrated economic forecasting with mathematical and epidemiological models, we were able to show the importance of implementing robust social protection policies to avert a significant increase in incidence and mortality from AIDS and TB during the current global economic downturn.

Indexed as

Acquired Immunodeficiency SyndromeHIV InfectionsModels, TheoreticalTuberculosisBrazilCOVID-19HumansIncidencePoverty

Identifiers

PMID38778134
PMCPMC11111786

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.