Factors influencing the technical efficiency of diabetes care at primary care level in Mexico.

Diabetes prevalence is rising globally, especially in low- and middle-income countries like Mexico, posing challenges for healthcare systems that require efficient primary care to manage the disease. However, healthcare efficiency is influenced by factors beyond decision-makers, including socioeconomic and political conditions. This study aims to evaluate the technical efficiency of primary healthcare for diabetes patients in Mexico over a 12-year period and explore the impact of contextual variables on efficiency. A longitudinal analysis was conducted using administrative and socio-demographic data from 242 health jurisdictions between 2009 and 2020. Data envelopment analysis with bootstrapping and output orientation was used to measure the technical efficiency; health resources in infrastructure and human resources were used as inputs. As outcome, the number of patients receiving treatment for diabetes and the number of patients with controlled diabetes were considered. Machine learning algorithms were employed to analyse multiple factors affecting the provision of diabetes health services and assess heterogeneity and trends in efficiency across different health jurisdictions. The average technical efficiency in primary healthcare for diabetes patients was 0.44 (CI: 0.41-0.46) in 2009, reaching a peak of 0.71 (CI: 0.69-0.72) in 2016, and moderately declining to 0.60 (CI: 0.57-0.62) in 2020; these differences were statistically significant. The random forest analysis identified the marginalization index, primary healthcare coverage, proportion of indigenous population and demand for health services as the most influential variables in predicting efficiency levels. This research underscores the crucial need for the formulation of targeted public policies aimed at extending the scope of primary healthcare services, with a particular focus on addressing the unique challenges faced by marginalized and indigenous populations. According to our results, it is necessary that medical care management adjust to the specific demands and needs of these populations to guarantee equitable care in Mexico.

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. Design, Setting, and Participants: This cohort study used a multicountry, longitudinal, ecological design with multivariable negative binomial regression models, which were adjusted for all relevant demographic, socioeconomic, and health care variables, integrating the retrospective impact evaluations from January 1, 2000, to December 31, 2019, with dynamic microsimulation models to forecast potential child mortality scenarios up to 2030. The study cohort included 4882 municipalities from Brazil, Ecuador, and Mexico with adequate quality of civil registration and vital statistics according to a validated multidimensional criterion. Data analysis was performed from September 2022 to February 2023. Exposure: Conditional cash transfer coverage of the target (lowest-income) population categorized into 4 levels: low (0%-29.9%), intermediate (30.0%-69.9%), high (70.0%-99.9%), and consolidated (≥100%). Main Outcomes and Measures: The main outcomes were mortality rates for those younger than 5 years and hospitalization rates (per 1000 live births), overall and by poverty-related causes (diarrheal, malnutrition, tuberculosis, malaria, lower respiratory tract infections, and HIV/AIDS), and the mortality rates for those younger than 5 years by age groups, namely, neonatal (0-28 days), postneonatal (28 days to 1 year), infant (<1 year), and toddler (1-4 years). Results: The retrospective analysis included 4882 municipalities. During the study period of January 1, 2000, to December 31, 2019, mortality in Brazil, Ecuador, and Mexico decreased by 7.8% in children and 6.5% in infants, and an increase in coverage of CCT programs of 76.8% was observed in these Latin American countries. Conditional cash transfer programs were associated with significant reductions of mortality rates in those younger than 5 years (rate ratio [RR], 0.76; 95% CI, 0.75-0.76), having prevented 738 919 (95% CI, 695 641-782 104) child deaths during this period. The association of highest coverage of CCT programs was stronger with poverty-related diseases, such as malnutrition (RR, 0.33; 95% CI, 0.31-0.35), diarrhea (RR, 0.41; 95% CI, 0.40-0.43), lower respiratory tract infections (RR, 0.66, 95% CI, 0.65-0.68), malaria (RR, 0.76; 95% CI, 0.63-0.93), tuberculosis (RR, 0.62; 95% CI, 0.48-0.79), and HIV/AIDS (RR, 0.32; 95% CI, 0.28-0.37). Several sensitivity and triangulation analyses confirmed the robustness of the results. Considering a scenario of moderate economic crisis, a mitigation strategy that will increase the coverage of CCTs to protect those newly in poverty could reduce the mortality rate for those younger than 5 years by up to 17% (RR, 0.83; 95% CI, 0.80-0.85) and prevent 153 601 (95% CI, 127 441-180 600) child deaths by 2030 in Brazil, Ecuador, and Mexico. Conclusions and Relevance: The results of this cohort study suggest that the expansion of CCT programs could strongly reduce childhood hospitalization and mortality in Latin America and should be considered an effective strategy to mitigate the health impact of the current global economic crisis in low- and middle-income countries.

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. We conducted a sensitivity analysis training two more ML models and presenting the mean square error, root mean square error, and median absolute deviation. Our findings indicate that poverty, illiteracy, and the Gini index were the most important variables for predicting U5MR according to the RF. Furthermore, non-linear relationships were found mainly for Gini index and U5MR. Our study suggests that long-term public policies to reduce U5MR in Latin America should focus on reducing poverty, illiteracy, and socioeconomic inequalities. This research provides important insights into the relationships between social determinants and child mortality rates in Latin America. The use of ML algorithms, combined with large longitudinal data, allowed us to evaluate the effects of social determinants on health more carefully than traditional models.