AIMC Topic: Socioeconomic Factors
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Identifying biological markers and sociodemographic factors that influence the gap between phenotypic and chronological ages.
Informatics for health & social care
Sep 24, 2024
INTRODUCTION: The world's population is aging rapidly, leading to increased public health and economic burdens due to age-related cardiovascular and neurodegenerative diseases. Early risk detection is essential for prevention and to improve the quali...
What explains adolescents' physical activity and sports participation during the COVID-19 pandemic? - an interpretable machine learning approach.
Journal of sports sciences
Sep 19, 2024
Adolescents' physical activity (PA) and sports participation declined due to the COVID-19 pandemic. This study aimed to determine the critical socio-ecological factors for PA and sports participation using a machine learning approach. We did a cross-...
Geographic inequities in neonatal survival in Nigeria: a cross-sectional evidence from spatial and artificial neural network analyses.
Journal of biosocial science
Sep 19, 2024
This study was conducted to provide empirical evidence of geographical variations of neonatal mortality and its associated social determinants with a view to improving neonatal survival at the subnational level in Nigeria. With a combination of spati...
Explainable artificial intelligence models for predicting pregnancy termination among reproductive-aged women in six east African countries: machine learning approach.
BMC pregnancy and childbirth
Sep 16, 2024
Pregnancy termination remains a complex and sensitive issue with approximately 45% of abortions worldwide being unsafe, and 97% of abortions occurring in developing countries. Unsafe pregnancy terminations have implications for women's reproductive h...
Prediction of delayed breastfeeding initiation among mothers having children less than 2 months of age in East Africa: application of machine learning algorithms.
Frontiers in public health
Sep 2, 2024
BACKGROUND: Delayed breastfeeding initiation is a significant public health concern, and reducing the proportion of delayed breastfeeding initiation in East Africa is a key strategy for lowering the Child Mortality rate. However, there is limited evi...
Exploring the determinants of under-five mortality and morbidity from infectious diseases in Cambodia-a traditional and machine learning approach.
Scientific reports
Aug 27, 2024
Cambodia has made progress in reducing the under-five mortality rate and burden of infectious diseases among children over the last decades. However the determinants of child mortality and morbidity in Cambodia is not well understood, and no recent a...
Determinants of adoption of household water treatment in Haiti using two analysis methods: logistic regression and machine learning.
Journal of water and health
Aug 21, 2024
Household water treatment (HWT) is recommended when safe drinking water is limited. To understand determinants of HWT adoption, we conducted a cross-sectional survey with 650 households across different regions in Haiti. Data were collected on 71 dem...
Coping with the tale of natural resources and environmental inequality: an application of the machine learning tools.
Environmental science and pollution research international
Aug 20, 2024
With the rising momentum according to the environmentalist voices seeking climate justice for more equity and the importance of encouraging environmental justice mechanisms and tools, in this perspective, the objective of this study is to analyze in ...
Predictive modeling and socioeconomic determinants of diarrhea in children under five in the Amhara Region, Ethiopia.
Frontiers in public health
Aug 1, 2024
BACKGROUND: Diarrheal disease, characterized by high morbidity and mortality rates, continues to be a serious public health concern, especially in developing nations such as Ethiopia. The significant burden it imposes on these countries underscores t...
Fuzzy and spatial analysis of cutaneous leishmaniasis in Pará State, Brazilian Amazon: an ecological and exploratory study.
Journal of infection in developing countries
Jul 29, 2024
INTRODUCTION: This study sought to analyze the relationships between cutaneous leishmaniasis and its epidemiological, environmental and socioeconomic conditions, in the 22 microregions of Pará state, Brazil, for the period from 2017 to 2022.
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