Modelling the impact of social protection on tuberculosis: the S-PROTECT project.

BACKGROUND: Tackling the social determinants of Tuberculosis (TB) through social protection is a key element of the post-2015 End TB Strategy. However, evidence informing policies are still scarce. Mathematical modelling has the potential to contribute to fill this knowledge gap, but existing models are inadequate. The S-PROTECT consortium aimed to develop an innovative mathematical modelling approach to better understand the role of social protection to improve TB care, prevention and control. METHODS: S-PROTECT used a three-steps approach: 1) the development of a conceptual framework; 2) the extraction from this framework of three high-priority mechanistic pathways amenable for modelling; 3) the development of a revised version of a standard TB transmission model able to capture the structure of these pathways. As a test case we used the Bolsa Familia Programme (BFP), the Brazilian conditional cash transfer scheme. RESULTS: Assessing one of these pathways, we estimated that BFP can reduce TB prevalence by 4% by improving households income and thus their nutritional status. When looking at the direct impact via malnutrition (not income mediated) the impact was 33%. This variation was due to limited data availability, uncertainties on data transformation and the pathway approach taken. These results are preliminary and only aim to serve as illustrative example of the methodological challenges encountered in this first modelling attempt, nonetheless they suggest the potential added value of integrating TB standard of care with social protection strategies. CONCLUSIONS: Results are to be confirmed with further analysis. However, by developing a generalizable modelling framework, S-PROTECT proved that the modelling of social protection is complex, but doable and allowed to draw the research road map for the future in this field

Effect of economic recession and impact of health and social protection expenditures on adult mortality: a longitudinal analysis of 5565 Brazilian municipalities

Background Economic recession might worsen health in low-income and middle-income countries with precarious job markets and weak social protection systems. Between 2014–16, a major economic crisis occurred in Brazil. We aimed to assess the association between economic recession and adult mortality in Brazil and to ascertain whether health and social welfare programmes in the country had a protective effect against the negative impact of this recession. Methods In this longitudinal analysis, we obtained data from the Brazilian Ministry of Health, the Brazilian Institute for Geography and Statistics, the Ministry of Social Development and Fight Against Hunger, and the Information System for the Public Budget in Health to assess changes in state unemployment level and mortality among adults (aged ≥15 years) in Brazil between 2012 and 2017. Outcomes were municipal all-cause and cause-specific mortality rates for all adults and across population subgroups stratified by age, sex, and race. We used fixed-effect panel regression models with quarterly timepoints to assess the association between recession and changes in mortality. Mortality and unemployment rates were detrended using Hodrick–Prescott filters to assess cyclical variation and control for underlying trends. We tested interactions between unemployment and terciles of municipal social protection and health-care expenditure to assess whether the relationship between unemployment and mortality varied. Findings Between 2012 and 2017, 7 069 242 deaths were recorded among adults (aged ≥15 years) in 5565 municipalities in Brazil. During this time period, the mean crude municipal adult mortality rate increased by 8·0% from 143·1 deaths per 100 000 in 2012 to 154·5 deaths per 100 000 in 2017. An increase in unemployment rate of 1 percentage-point was associated with a 0·50 increase per 100 000 population per rter (95% CI 0·09–0·91) in all-cause mortality, mainly due to cancer and cardiovascular disease. Between 2012 and 2017, higher unemployment accounted for 31 415 excess deaths (95% CI 29 698–33 132). All-cause mortality increased among black or mixed race (pardo) Brazilians (a 0·46 increase [95% CI 0·15–0·80]), men (0·67 [0·22–1·13]), and individuals aged 30–59 years (0·43 [0·16–0·69] per 1 percentage-point increase in the unemployment rate. No significant association was identified between unemployment and all-cause mortality for white Brazilian, women, adolescents (aged 15–29 years), or older and retired individuals (aged ≥60 years). In municipalities with high expenditure on health and social protection programmes, no significant increases in recession-related mortality were observed. Interpretation The Brazilian recession contributed to increases in mortality. However, health and social protection expenditure seemed to mitigate detrimental health effects, especially among vulnerable populations. This evidence provides support for stronger health and social protection systems globally

Projecting the impact of air pollution on child stunting in India – synergies and trade-offs between climate change mitigation, ambient air quality control, and clean cooking access

Many children in India face the double burden of high exposure to ambient (AAP) and household air pollution (HAP), both of which can affect their linear growth. Although climate change mitigation is expected to decrease AAP, climate policies could increase the cost of clean cooking fuels. Here, we develop a static microsimulation model to project the air pollution-related burden of child stunting in India up to 2050 under four scenarios combining climate change mitigation (2°C target) with national policies for AAP control and subsidised access to clean cooking. We link data from a nationally representative household survey, satellite-based estimates of fine particulate matter (PM2.5), multi-dimensional demographic projection and PM2.5 and clean cooking access projections from an integrated assessment model. We find that the positive effects on child linear growth from reductions in AAP under the 2°C Paris Agreement target could be fully offset by the negative effects of climate change mitigation through reduced clean cooking access. Targeted AAP control or subsidised access to clean cooking could shift this trade-off to result in net benefits of 2.8 (95% uncertainty interval [UI]: 1.4, 4.2) or 6.5 (UI: 6.3, 6.9) million cumulative prevented cases of child stunting between 2020-50 compared to business-as-usual. Implementation of integrated climate, air quality, and energy access interventions has a synergistic impact, reducing cumulative number of stunted children by 12.1 (UI: 10.7, 13.7) million compared to business-as-usual, with the largest health benefits experienced by the most disadvantaged children and geographic regions. Findings underscore the importance of complementing climate change mitigation efforts with targeted air quality and energy access policies to concurrently deliver on carbon mitigation, health and air pollution and energy poverty reduction goals in India

Development of a composite outcome score for a complex intervention - measuring the impact of Community Health Workers.

BACKGROUND: In health services research, composite scores to measure changes in health-seeking behaviour and uptake of services do not exist. We describe the rationale and analytical considerations for a composite primary outcome for primary care research. We simulate its use in a large hypothetical population and use it to calculate sample sizes. We apply it within the context of a proposed cluster randomised controlled trial (RCT) of a Community Health Worker (CHW) intervention. METHODS: We define the outcome as the proportion of the services (immunizations, screening tests, stop-smoking clinics) received by household members, of those that they were eligible to receive. First, we simulated a population household structure (by age and sex), based on household composition data from the 2011 England and Wales census. The ratio of eligible to received services was calculated for each simulated household based on published eligibility criteria and service uptake rates, and was used to calculate sample size scenarios for a cluster RCT of a CHW intervention. We assume varying intervention percentage effects and varying levels of clustering. RESULTS: Assuming no disease risk factor clustering at the household level, 11.7% of households in the hypothetical population of 20,000 households were eligible for no services, 26.4% for 1, 20.7% for 2, 15.3% for 3 and 25.8% for 4 or more. To demonstrate a small CHW intervention percentage effect (10% improvement in uptake of services out of those who would not otherwise have taken them up, and additionally assuming intra-class correlation of 0.01 between households served by different CHWs), around 4,000 households would be needed in each of the intervention and control arms. This equates to 40 CHWs (each servicing 100 households) needed in the intervention arm. If the CHWs were more effective (20%), then only 170 households would be needed in each of the intervention and control arms. CONCLUSIONS: This is a useful first step towards a process-centred composite score of practical value in complex community-based interventions. Firstly, it is likely to result in increased statistical power compared with multiple outcomes. Second, it avoids over-emphasis of any single outcome from a complex intervention

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