Understanding the social determinants of child mortality in Latin America over the last two decades: a machine learning approach
Por:
Chivardi C., Zamudio Sosa A., Cavalcanti D.M., Ordoñez J.A., Diaz J.F., Zuluaga D., Almeida C., Serván-Mori E., Hessel P., Moncayo A.L., Rasella D.
Publicada:
1 ene 2023
Categoría:
Multidisciplinary
Resumen:
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. © 2023, The Author(s).
Filiaciones:
Chivardi C.:
Centre for Health Economics (CHE), University of York, York, United Kingdom
Zamudio Sosa A.:
School of Psychology, National Autonomous University of Mexico (UNAM), Mexico, Mexico
Cavalcanti D.M.:
Institute of Collective Health (ISC), Federal University of Bahia (UFBA), Bahia, Salvador, Brazil
Ordoñez J.A.:
Institute of Collective Health (ISC), Federal University of Bahia (UFBA), Bahia, Salvador, Brazil
Diaz J.F.:
Alberto Lleras Camargo School of Government, Universidad de los Andes, Bogota, Colombia
Department of Public Health and Epidemiology, Swiss Tropical and Public Health Institute, Basel, Switzerland
Zuluaga D.:
Alberto Lleras Camargo School of Government, Universidad de los Andes, Bogota, Colombia
Department of Public Health and Epidemiology, Swiss Tropical and Public Health Institute, Basel, Switzerland
Almeida C.:
Centro de Investigación para la Salud en América Latina (CISeAL), Pontificia Universidad Católica del Ecuador, Quito, Ecuador
Serván-Mori E.:
National Institute of Public Health (INSP), Cuernavaca, Mexico
Hessel P.:
Alberto Lleras Camargo School of Government, Universidad de los Andes, Bogota, Colombia
Department of Public Health and Epidemiology, Swiss Tropical and Public Health Institute, Basel, Switzerland
Moncayo A.L.:
Centro de Investigación para la Salud en América Latina (CISeAL), Pontificia Universidad Católica del Ecuador, Quito, Ecuador
Rasella D.:
Institute of Collective Health (ISC), Federal University of Bahia (UFBA), Bahia, Salvador, Brazil
Institute of Global Health (ISGlobal), Barcelona, Spain
Green Submitted, gold, All Open Access, Gold
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