Diarrheal disease remains a major cause of under-five mortality in low- and middle-income countries (LMICs). This study investigates the diarrhea determinants, employing a novel dual approach comparing classical epidemiology and machine learning (ML) to determine the most important predictors and to optimize intervention targeting in Nigeria. A cross-sectional analysis of 33,924 children aged < 5 years using 2018 Nigeria Demographic and Health Survey (NDHS) was used. Traditional logistic regression was contrasted to ML models (Random Forest, Gradient Boosting Machines, Decision Trees) to consider non-linear associations and variable importance, while using model parameters to assess model performance. The prevalence of diarrhea was 11.98%, with significant disparities between regions. Child’s age was the strongest predictor across all models, with a significant odd seen among children aged 6–23 months (AOR = 2.48–2.54). Increased maternal education was protective (AOR = 0.77–0.79), while exposure to the media had multicomponent associations. Urban-rural wealth index was a robust strong socioeconomic predictor. Logistic regression had the best predictive performance (AUC = 0.727), closely followed by that of Gradient Boosting (AUC = 0.718). Sensitivity analysis showed that MICE generated more accurate estimates than complete case analysis because missingness was not random. The study stresses the significance of core modifiable determinants such as maternal education and contextual wealth. Methodological triangulation illustrates the complementarity of classic regression for inference and machine learning for feature discovery. These findings justify the imposition of hyper-localized, multi-sectoral interventions on high-risk age groups and areas based on sound data analysis to optimize public health resources. This mixed approach provides a scalable model for disease burden measurement in LMICs.

Malaria remains a serious public health challenge in sub-Saharan Africa, and Nigeria accounts for almost 30% of global child malaria deaths. This study employs machine learning (ML) to improve prediction efficiency and identify the most significant risk factors associated with childhood malaria in high-burden populations. A cross-sectional study was conducted among 693 under-5 children from Nigerian Internally Displaced Persons (IDP) camps. Sociodemographic data, household living conditions, and malaria knowledge were collected in addition to Rapid Diagnostic Test (RDT) outcomes. The dataset was split 70:30 to train and evaluate four ML models: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosting Machine (GBM). The performance of the models was measured by AUC, precision, recall, F1-score, and variable importance. Malaria prevalence was 68.5%. Key risk factors were a caregiver with no education (aOR = 3.23, p = 0.026), while female caregivers were significantly associated (aOR = 0.53, p = 0.024). The Random Forest model performed best (AUC = 0.892), where caregiver occupation and residential camp were the most significant predictors. A vast knowledge-practice gap was observed, where 60.3% of caregivers had knowledge of prevention but low bed net usage (2%). Random Forest machine learning greatly improves the precision of malaria risk prediction. The results underscore the importance of extensive, modifiable factors such as caregiver occupation and education. Integration of these ML models into surveillance can enable precision public health interventions, including enhanced vector control and focused health education, to combat malaria effectively in high-burden populations. The online version contains supplementary material available at 10.1186/s12879-025-12116-6.

The burden of health issues surrounding the children and adolescents (ChAds) population is of great concern. From metabolic disorders such as obesity, non-alcoholic fatty liver disease, diabetes, multiple sclerosis including mental and several other psychological disorders like attention deficit hyperactivity, internet addiction, autism spectrum, subthreshold mood symptoms and depression, and stress disorders, is a nexus of the gut microbiota (GM) profile and physical activity (PA), as they both play a crucial role in ameliorating these disorders. The aim of this review is to explore the disorders with regard to their association with ChAds’ GM and its health. We started with brief descriptions of PA and its connections with GM profile cum health in ChAds. In addition, the disorders in ChAds were broadly classified into metabolic and psychological. Observations from the discussed sections show that exercise and physical activity are efficient methods for enhancing physical, metabolic, and mental health, which are often GM-mediated, and thus might require certain levels of personalization. Furthermore, age, gender, genetics, baseline BMI, baseline exercise level, and diet are key parameters to be explored when proffering intervention of combative mechanisms against these disorders.

Internally displaced persons (IDPs) in conflict-affected populations are disproportionately affected by non-communicable diseases (NCDs), while system-leve

To investigate the determinants of childhood diarrhea in Nigeria, examining socio-economic, geographic, nutritional, and WASH factors, while systematically comparing Multiple Imputation by Chained Equations (MICE) versus Complete Case Analysis (CCA) to demonstrate how missing data methods bias conclusions. A secondary cross-sectional analysis was conducted using the latest Nigerian Demographic and Health Survey (2018 NDHS) data on 33,924 under-5 children. The primary outcome was a caregiver-reported diarrhea incidence. Independent predictors were identified using multivariable logistic regression. Robustness was tested by sensitivity analyses through comparison between MICE, CCA and testing principal results under Missing Not at Random (MNAR) assumptions. Multi-dimensional risk heatmaps and correlation networks illustrated interactive risks. Diarrhea prevalence was 12.0%. Profound geo-economic and geographical disparities were uncovered; residing in the North-East was the strongest risk factor (aOR = 2.61), while wealth and maternal education demonstrated protective associations. There was a very high synergistic interaction between poor nutrition and WASH, with combined high-risk categories having the greatest prevalence of diarrhea (15.4%). Intake of eggs had a strong protective association (aOR = 0.87) that persisted under MNAR models. Sensitivity analyses revealed CCA-generated biased estimates, masking protective effects of education and inducing spurious risks for vitamin A and fruit consumption. Childhood diarrhea in Nigeria is driven by a complex interplay of interacting factors. Effective control requires a mix of integrated, multi-sectoral interventions involving WASH, nutrition, and poverty concurrently, focusing on high-burden zones. Methodologically, advanced management of missing data is critical to produce valid evidence.

Diarrhea remains one of the leading causes of under-5 mortality in Nigeria. Traditional logistic regression has been used to assess risk factors; however,