Population Health Commons^™

Key Predictors Of Postpartum Depression And Anxiety Symptoms Among Mothers In Kilifi, Kenya: A Machine Learning Approach, Faith Benson, Rachel Odhiambo, Willie Brink, Anthony Ngugi, Akbar Waljee, Eileen Weinheimer-Haus, Cheryl Moyer, Ji Zhu, Amina Abubakar

Institute for Human Development, East Africa

Background:

The burden of maternal postpartum depression and anxiety is disproportionately high in sub-Saharan Africa (SSA), yet the use of advanced analytical methods to capture the complex interplay of variables influencing these conditions remains underexplored.

Objective:

To apply machine learning (ML) methods to predict depressive and anxiety symptoms in postpartum mothers and to identify key and actionable predictors.

Methods:

This cross-sectional study included 1,995 biological mothers of singleton infants aged 0–6 months, using survey data collected between March 2023 and March 2024 in Kaloleni and Rabai sub-counties, Kilifi County, Kenya, within the Kaloleni–Rabai Health and Demographic Surveillance …

Social, Environmental And Policy Contexts Affecting The Feasibility And Acceptability Of Improving Household Flooring For Better Health In Rural Kenya, Ulrike Fillinger, Victoria Ochwal, Hugo Legge, Sharon Muli, Karisa Kazungu, Carlos Mcharo, Jacinta Mwongeli, Charles Mwandawiro, Katherine Halliday, Doris Njomo

Institute for Human Development, East Africa

Background Homes with unimproved floors (earth, sand, or clay) are associated with increased risks of soil-transmitted parasites and enteric infections, leading to higher morbidity and reduced quality of life. This study explored the enablers and barriers to adopting improved flooring (sealed, washable, and durable) in three culturally diverse regions of Kenya - Narok, Bungoma, and Kwale - to mitigate disease burdens.

Methods Between August 2021 and July 2022, we conducted 24 focus group discussions with homeowners, stratified by age, gender, and floor type. Additionally, 28 key informant interviews with local government officials, microfinance representatives, and masonry trainers in Kwale and …

Prediction Of First And Multiple Antiretroviral Therapy Interruptions In People Living With Hiv: Comparative Survival Analysis Using Cox And Explainable Machine Learning Models, Donald Salami, Emily Koech, Janet Turan, Kristen Stafford, Lilly Nyagah, Stephen Ohakanu, Anthony Ngugi, Manhattan Charurat

Institute for Human Development, East Africa

Background: The Cox proportional hazards (CPH) model is a common choice for analyzing time-to-treatment interruptions in patients on antiretroviral therapy (ART), valued for its straightforward interpretability and flexibility in handling time-dependent covariates. Machine learning (ML) models have increasingly been adapted for handling temporal data, with added advantages of handling complex, nonlinear relationships and large datasets, and providing clear practical interpretations.

Objective: This study aims to compare the predictive performance of the traditional CPH model and ML models in predicting treatment interruptions among patients on ART, while also providing both global and individual-level explanations to support personalized, data-driven interventions …