Can primary health care mitigate the effects of economic crises on child health in Latin America? An integrated multicountry evaluation and forecasting analysis.

Background: Latin American and Caribbean countries are dealing with the combined challenges of pandemic-induced socicoeconomic stress and increasing public debt, potentially leading to reductions in welfare and health-care services, including primary care. We aimed to evaluate the impact of primary health-care coverage on child mortality in Latin America over the past two decades and to forecast the potential effects of primary health-care mitigation during the current economic crisis.

Methods: This multicountry study integrated retrospective impact evaluations in Brazil, Colombia, Ecuador, and Mexico from 2000 to 2019 with forecasting models covering up to 2030.

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.

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.

Understanding the social determinants of child mortality in Latin America over the last two decades: a machine learning approach.

The reduction of child mortality rates remains a significant global public health challenge, particularly in regions with high levels of inequality such as Latin America. We used machine learning (ML) algorithms to explore the relationship between social determinants and child under-5 mortality rates (U5MR) in Brazil, Ecuador, and Mexico over two decades. We created a municipal-level cohort from 2000 to 2019 and trained a random forest model (RF) to estimate the relative importance of social determinants in predicting U5MR.

Evaluation and Forecasting Analysis of the Association of Conditional Cash Transfer With Child Mortality in Latin America, 2000-2030.

Importance: Latin America has implemented the world's largest and most consolidated conditional cash transfer (CCT) programs during the last 2 decades. As a consequence of the COVID-19 pandemic, poverty rates have markedly increased, and a large number of newly low-income individuals, especially children, have been left unprotected.

Objective: To evaluate the association of CCT programs with child health in Latin American countries during the last 2 decades and forecast child mortality trends up to 2030 according to CCT alternative implementation options.