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

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.

A Mathematical Model for the Impact of 3HP and Social Programme Implementation on the Incidence and Mortality of Tuberculosis: Study in Brazil.

In this paper, we presented a mathematical model for tuberculosis with treatment for latent tuberculosis cases and incorporated social implementations based on the impact they will have on tuberculosis incidence, cure, and recovery. We incorporated two variables containing the accumulated deaths and active cases into the model in order to study the incidence and mortality rate per year with the data reported by the model. Our objective is to study the impact of social program implementations and therapies on latent tuberculosis in particular the use of once-weekly isoniazid-rifapentine for 12 weeks (3HP). The computational experimentation was performed with data from Brazil and for model calibration, we used the Markov Chain Monte Carlo method (MCMC) with a Bayesian approach. We studied the effect of increasing the coverage of social programs, the Bolsa Familia Programme (BFP) and the Family Health Strategy (FHS) and the implementation of the 3HP as a substitution therapy for two rates of diagnosis and treatment of latent at 1% and 5%. Based of the data obtained by the model in the period 2023-2035, the FHS reported better results than BFP in the case of social implementations and 3HP with a higher rate of diagnosis and treatment of latent in the reduction of incidence and mortality rate and in cases and deaths avoided. With the objective of linking the social and biomedical implementations, we constructed two different scenarios with the rate of diagnosis and treatment. We verified with results reported by the model that with the social implementations studied and the 3HP with the highest rate of diagnosis and treatment of latent, the best results were obtained in comparison with the other independent and joint implementations. A reduction of the incidence by 36.54% with respect to the model with the current strategies and coverage was achieved, and a greater number of cases and deaths from tuberculosis was avoided.

Income determines the impact of cash transfers on HIV/AIDS: cohort study of 22.7 million Brazilians.

Living with extremely low-income is an important risk factor for HIV/AIDS and can be mitigated by conditional cash transfers. Using a cohort of 22.7 million low-income individuals during 9 years, we evaluated the effects of the world's largest conditional cash transfer, the Programa Bolsa Família, on HIV/AIDS-related outcomes. Exposure to Programa Bolsa Família was associated with reduced AIDS incidence by 41% (RR:0.59; 95%CI:0.57-0.61), mortality by 39% (RR:0.61; 95%CI:0.57-0.64), and case fatality rates by 25% (RR:0.75; 95%CI:0.66-0.85) in the cohort, and Programa Bolsa Família effects were considerably stronger among individuals of extremely low-income [reduction of 55% for incidence (RR:0.45, 95% CI:0.42-0.47), 54% mortality (RR:0.46, 95% CI:0.42-0.49), and 37% case-fatality (RR:0.63, 95% CI:0.51 -0.76)], decreasing gradually until having no effect in individuals with higher incomes. Similar effects were observed on HIV notification. Programa Bolsa Família impact was also stronger among women and adolescents. Several sensitivity and triangulation analyses demonstrated the robustness of the results. Conditional cash transfers can significantly reduce AIDS morbidity and mortality in extremely vulnerable populations and should be considered an essential intervention to achieve AIDS-related sustainable development goals by 2030.

Modelling HIV/AIDS epidemiological complexity: A scoping review of Agent-Based Models and their application.

OBJECTIVE: To end the AIDS epidemic by 2030, despite the increasing poverty and inequalities, policies should be designed to deal with population heterogeneity and environmental changes. Bottom-up designs, such as the Agent-Based Model (ABM), can model these features, dealing with such complexity. HIV/AIDS has a complex dynamic of structural factors, risk behaviors, biomedical characteristics and interventions. All embedded in unequal, stigmatized and heterogeneous social structure. To understand how ABMs can model this complexity, we performed a scoping review of HIV applications, highlighting their potentialities. METHODS: We searched on PubMed, Web of Science, and Scopus repositories following the PRISMA extension for scoping reviews. Our inclusion criteria were HIV/AIDS studies with an ABM application. We identified the main articles using a local co-citation analysis and categorized the overall literature aims, (sub)populations, regions, and if the papers declared the use of ODD protocol and limitations. RESULTS: We found 154 articles. We identified eleven main papers, and discussed them using the overall category results. Most studies model Transmission Dynamics (37/154), about Men who have sex with Men (MSM) (41/154), or individuals living in the US or South Africa (84/154). Recent studies applied ABM to model PrEP interventions (17/154) and Racial Disparities (12/154). Only six papers declared the use of ODD Protocol (6/154), and 34/154 didn't mention the study limitations. CONCLUSIONS: While ABM is among the most sophisticated techniques available to model HIV/AIDS complexity. Their applications are still restricted to some realities. However, researchers are challenged to think about social structure due model characteristics, the inclusion of these features is still restricted to case-specific. Data and computational power availability can enhance this feature providing insightful results.

Model Choice for Quantitative Health Impact Assessment and Modelling: An Expert Consultation and Narrative Literature Review.

BACKGROUND: Health impact assessment (HIA) is a widely used process that aims to identify the health impacts, positive or negative, of a policy or intervention that is not necessarily placed in the health sector. Most HIAs are done prospectively and aim to forecast expected health impacts under assumed policy implementation. HIAs may quantitatively and/ or qualitatively assess health impacts, with this study focusing on the former. A variety of quantitative modelling methods exist that are used for forecasting health impacts, however, they differ in application area, data requirements, assumptions, risk modelling, complexities, limitations, strengths, and comprehensibility. We reviewed relevant models, so as to provide public health researchers with considerations for HIA model choice. METHODS: Based on an HIA expert consultation, combined with a narrative literature review, we identified the most relevant models that can be used for health impact forecasting. We narratively and comparatively reviewed the models, according to their fields of application, their configuration and purposes, counterfactual scenarios, underlying assumptions, health risk modelling, limitations and strengths. RESULTS: Seven relevant models for health impacts forecasting were identified, consisting of (i) comparative risk assessment (CRA), (ii) time series analysis (TSA), (iii) compartmental models (CMs), (iv) structural models (SMs), (v) agent-based models (ABMs), (vi) microsimulations (MS), and (vii) artificial intelligence (AI)/machine learning (ML). These models represent a variety in approaches and vary in the fields of HIA application, complexity and comprehensibility. We provide a set of criteria for HIA model choice. Researchers must consider that model input assumptions match the available data and parameter structures, the available resources, and that model outputs match the research question, meet expectations and are comprehensible to end-users. CONCLUSION: The reviewed models have specific characteristics, related to available data and parameter structures, computational implementation, interpretation and comprehensibility, which the researcher should critically consider before HIA model choice.

The effects of social determinants of health on acquired immune deficiency syndrome in a low-income population of Brazil: a retrospective cohort study of 28.3 million individuals.

Background: Social determinants of health (SDH) include factors such as income, education, and race, that could significantly affect the human immunodeficiency virus and acquired immunodeficiency syndrome (HIV/AIDS). Studies on the effects of SDH on HIV/AIDS are limited, and do not yet provide a systematic understanding of how the various SDH act on important indicators of HIV/AIDS progression. We aimed to evaluate the effects of SDH on AIDS morbidity and mortality. Methods: A retrospective cohort of 28.3 million individuals was evaluated over a 9-year period (from 2007 to 2015). Multivariable Poisson regression, with a hierarchical approach, was used to estimate the effects of SDH-at the individual and familial level-on AIDS incidence, mortality, and case-fatality rates. Findings: A total of 28,318,532 individuals, representing the low-income Brazilian population, were assessed, who had a mean age of 36.18 (SD: 16.96) years, 52.69% (14,920,049) were female, 57.52% (15,360,569) were pardos, 34.13% (9,113,222) were white/Asian, 7.77% (2,075,977) were black, and 0.58% (154,146) were indigenous. Specific socioeconomic, household, and geographic factors were significantly associated with AIDS-related outcomes. Less wealth was strongly associated with a higher AIDS incidence (rate ratios-RR: 1.55; 95% confidence interval-CI: 1.43-1.68) and mortality (RR: 1.99; 95% CI: 1.70-2.34). Lower educational attainment was also greatly associated with higher AIDS incidence (RR: 1.46; 95% CI: 1.26-1.68), mortality (RR: 2.76; 95% CI: 1.99-3.82) and case-fatality rates (RR: 2.30; 95% CI: 1.31-4.01). Being black was associated with a higher AIDS incidence (RR: 1.53; 95% CI: 1.45-1.61), mortality (RR: 1.69; 95% CI: 1.57-1.83) and case-fatality rates (RR: 1.16; 95% CI: 1.03-1.32). Overall, also considering the other SDH, individuals experiencing greater levels of socioeconomic deprivation were, by far, more likely to acquire AIDS, and to die from it. Interpretation: In the population studied, SDH related to poverty and social vulnerability are strongly associated with a higher burden of HIV/AIDS, most notably less wealth, illiteracy, and being black. In the absence of relevant social protection policies, the current worldwide increase in poverty and inequalities-due to the consequences of the COVID-19 pandemic, and the effects of war in the Ukraine-could reverse progress made in the fight against HIV/AIDS in low- and middle-income countries (LMIC). Funding: National Institute of Allergy and Infectious Diseases (NAIDS), National Institutes of Health (NIH), US Grant Number: 1R01AI152938.

The effects of cash transfer programmes on HIV/AIDS prevention and care outcomes: a systematic review and meta-analysis of intervention studies.

BACKGROUND: Poverty and social inequality are risk factors for poor health outcomes in patients with HIV/AIDS. In addition to eligibility, cash transfer programmes can be divided into two categories: those with specific requirements (conditional cash transfers [CCTs]) and those without specific requirements (unconditional cash transfers). Common CCT requirements include health care (eg, undergoing an HIV test) and education (eg, children attending school). Trials assessing the effect of cash transfer programmes on HIV/AIDS outcomes have yielded divergent findings. This review aimed to summarise evidence to evaluate the effects of cash transfer programmes on HIV/AIDS prevention and care outcomes. METHODS: For this systematic review and meta-analysis, we searched PubMed, EMBASE, Cochrane Library, LILACS, WHO IRIS, PAHO-IRIS, BDENF, Secretaria Estadual de Saúde SP, Localizador de Informação em Saúde, Coleciona SUS, BINACIS, IBECS, CUMED, SciELO, and Web of Science up to Nov 28, 2022. We included randomised controlled trials (RCTs) that evaluated the effects of cash transfer programmes on HIV incidence, HIV testing, retention in HIV care, and antiretroviral therapy adherence, and conducted risk of bias and quality of evidence assessments using the Cochrane Risk of Bias tool and the Grading of Recommendations, Assessment, Development, and Evaluations approach. A random-effects meta-analysis model was used to combine studies and calculate risk ratios (RRs). Subgroup analyses were performed using conditionality types (ie, school attendance or health care). The protocol was registered with PROSPERO, CRD42021274452. FINDINGS: 16 RCTs, which included 5241 individuals, fulfilled the inclusion criteria. Of these, 13 studies included conditionalities for receiving cash transfer programmes. The results showed that receiving a cash transfer was associated with lowered HIV incidence among individuals who had to meet health-care conditionalities (RR 0·74, 95% CI 0·56-0·98) and with increased retention in HIV care for pregnant women (1·14, 95% CI 1·03-1·27). No significant effect was observed for HIV testing (RR 0·45, 95% CI 0·18-1·12) or antiretroviral therapy adherence (1·13, 0·73-1·75). Lower risk of bias was observed for HIV incidence and having an HIV test. The strength of available evidence can be classified as moderate. INTERPRETATION: Cash transfer programmes have a positive effect on mitigating HIV incidence for individuals who have to meet health-care conditionalities and on increasing retention in HIV care for pregnant women. These results show the potential of cash transfer programmes for HIV prevention and care, especially among people in extreme poverty, and highlight that cash transfer programmes must be considered when developing policies for HIV/AIDS control, as indicated by the UNAIDS 95-95-95 target of the HIV care continuum. FUNDING: National Institute of Allergy and Infectious Diseases, National Institutes of Health, USA.

Incorporating social determinants of health into the mathematical modeling of HIV/AIDS.

Currently, it is estimated that 37.6 million people are living with the HIV/AIDS virus worldwide, placing HIV/AIDS among the ten leading causes of death, mostly among low- and lower-middle-income countries. Despite the effective intervention in the prevention and treatment, this reduction did not occur equally among populations, subpopulations and geographic regions. This difference in the occurrence of the disease is associated with the social determinants of health (SDH), which could affect the transmission and maintenance of HIV. With the recognition of the importance of SDH in HIV transmission, the development of mathematical models that incorporate these determinants could increase the accuracy and robustness of the modeling. This article aims to propose a theoretical and conceptual way of including SDH in the mathematical modeling of HIV/AIDS. The theoretical mathematical model with the Social Determinants of Health has been developed in stages. For the selection of SDH that were incorporated into the model, a narrative literature review was conducted. Secondly, we proposed an extended model in which the population (N) is divided into Susceptible (S), HIV-positive (I), Individual with AIDS (A) and individual under treatment (T). Each SDH had a different approach to embedding in the model. We performed a calibration and validation of the model. A total of 31 SDH were obtained in the review, divided into four groups: Individual Factors, Socioeconomic Factors, Social Participation, and Health Services. In the end, four determinants were selected for incorporation into the model: Education, Poverty, Use of Drugs and Alcohol abuse, and Condoms Use. the section "Numerical simulation" to simulate the influence of the poverty rate on the AIDS incidence and mortality rates. We used a Brazilian dataset of new AIDS cases and deaths, which is publicly available. We calibrated the model using a multiobjective genetic algorithm for the years 2003 to 2019. To forecast from 2020 to 2035, we assumed two lines of poverty rate representing (i) a scenario of increasing and (ii) a scenario of decreasing. To avoid overfitting, we fixed some parameters and estimated the remaining. The equations presented with the chosen SDH exemplify some approaches that we can adopt when thinking about modeling social effects on the occurrence of HIV. The model was able to capture the influence of the employment/poverty on the HIV/AIDS incidence and mortality rates, evidencing the importance of SDOH in the occurrence of diseases. The recognition of the importance of including the SDH in the modeling and studies on HIV/AIDS is evident, due to its complexity and multicausality. Models that do not take into account in their structure, will probably miss a great part of the real trends, especially in periods, as the current on, of economic crisis and strong socioeconomic changes.

Evaluating the impact of social determinants, conditional cash transfers and primary health care on HIV/AIDS: Study protocol of a retrospective and forecasting approach based on the data integration with a cohort of 100 million Brazilians.

BACKGROUND: Despite the great progress made over the last decades, stronger structural interventions are needed to end the HIV/AIDS pandemic in Low and Middle-Income Countries (LMIC). Brazil is one of the largest and data-richest LMIC, with rapidly changing socioeconomic characteristics and an important HIV/AIDS burden. Over the last two decades Brazil has also implemented the world's largest Conditional Cash Transfer programs, the Bolsa Familia Program (BFP), and one of the most consolidated Primary Health Care (PHC) interventions, the Family Health Strategy (FHS). OBJECTIVE: We will evaluate the effects of socioeconomic determinants, BFP exposure and FHS coverage on HIV/AIDS incidence, treatment adherence, hospitalizations, case fatality, and mortality using unprecedently large aggregate and individual-level longitudinal data. Moreover, we will integrate the retrospective datasets and estimated parameters with comprehensive forecasting models to project HIV/AIDS incidence, prevalence and mortality scenarios up to 2030 according to future socioeconomic conditions and alternative policy implementations. METHODS AND ANALYSIS: We will combine individual-level data from all national HIV/AIDS registries with large-scale databases, including the "100 Million Brazilian Cohort", over a 19-year period (2000-2018). Several approaches will be used for the retrospective quasi-experimental impact evaluations, such as Regression Discontinuity Design (RDD), Random Administrative Delays (RAD) and Propensity Score Matching (PSM), combined with multivariable Poisson regressions for cohort analyses. Moreover, we will explore in depth lagged and long-term effects of changes in living conditions and in exposures to BFP and FHS. We will also investigate the effects of the interventions in a wide range of subpopulations. Finally, we will integrate such retrospective analyses with microsimulation, compartmental and agent-based models to forecast future HIV/AIDS scenarios. CONCLUSION: The unprecedented datasets, analyzed through state-of-the-art quasi-experimental methods and innovative mathematical models will provide essential evidences to the understanding and control of HIV/AIDS epidemic in LMICs such as Brazil.