{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T07:01:42Z","timestamp":1790146902911,"version":"4.0.1"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Depression is a leading global cause of disability, that disproportionately affects populations in low-resource settings in sub-Saharan Africa, where poverty, limited healthcare, and environmental stressors such as climate variability, poor water and sanitation, and household air pollution compound vulnerability. In Kilifi County, Kenya, recurrent droughts and economic insecurity amplify these challenges, yet the associations between environmental exposures and mental health impacts remain poorly characterized. This study applies machine learning models to examine how climate perceptions, water and sanitation access, and household air pollution proxies are associated with depressive symptoms in Kilifi County.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methodology<\/jats:title>\n                    <jats:p>This cross-sectional study analyzed primary household survey data collected from July to December 2021 within the Kaloleni\/Rabai Community Health and Demographic Surveillance System. Structured surveys captured demographics, mental health status via the Patient Health Questionnaire-9 (PHQ-9), and environmental exposures, including climate perceptions, agricultural stressors, water and sanitation access, and air pollution proxies. Depression was defined as PHQ-9\u202f\u2265\u202f10. Thirty-one variables were grouped into environmental perceptions, climate impacts, air pollution proxies, and water\/sanitation categories. Random Forest and XGBoost models were trained in Python using 80\/20 household-grouped train-test split (GroupShuffleSplit) to prevent data leakage, with 10-fold stratified group cross-validation. Class imbalance was addressed using balanced class weights (Random Forest) and scale_pos_weight (XGBoost). Model performance was assessed using accuracy, balanced accuracy, ROC-AUC, PR-AUC, sensitivity, specificity, precision, recall, F1-scores, and Brier scores with bootstrap 95% confidence intervals. Feature contributions were evaluated using Gini importance, gain and SHAP values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 17,738 participants (median age 41.0 [IQR 31.0\u201354.0], 77.8% female), 14.9% (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f2,635) screened positive for depression (PHQ-9\u202f\u2265\u202f10). XGBoost outperformed Random Forest across all metrics: 0.802 (95% CI: 0.788\u20130.815) versus 0.796 (95% CI: 0.782\u20130.808), ROC-AUC 0.845 (95% CI, 0.826\u20130.862) versus 0.823 (95% CI, 0.804\u20130.841), balanced accuracy 0.753 versus 0.738, and sensitivity 0.684 versus 0.657. Both models showed moderate precision for depression-positive cases (XGBoost 0.40, Random Forest 0.38) reflecting the precision-sensitivity trade-off inherent in class-imbalance handling. Key predictors across Gini importance, gain and SHAP analyses included drinking water source, heat-related health risks, drought exposure, home thermal discomfort and cooking with open fire highlighting the convergence of water\/sanitation, climate, and household air quality factors.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Environmental and household exposures are consistently associated with depression in rural Kilifi County. Machine learning identified water and sanitation conditions, climate variability perceptions, and household energy practices as leading predictors. Integrating environmental and climate-sensitive indicators into community health workflows, alongside interventions such as clean cookstoves, water access and climate-resilient agriculture, could reduce depression burden and enhance precision public health for climate-vulnerable communities. Longitudinal studies with refined exposure measurement are needed to clarify the temporal and causal nature of these associations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1826112","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T06:19:35Z","timestamp":1790144375000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Environmental and household factors associated with depressive symptoms in rural Kilifi County, Kenya: a machine learning analysis"],"prefix":"10.3389","volume":"14","author":[{"given":"John","family":"Andrew","sequence":"first","affiliation":[{"name":"Brain and Mind Institute, Aga Khan University","place":["Nairobi, Kenya"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashley","family":"Njoroge","sequence":"additional","affiliation":[{"name":"Brain and Mind Institute, Aga Khan University","place":["Nairobi, Kenya"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Andai","sequence":"additional","affiliation":[{"name":"Brain and Mind Institute, Aga Khan University","place":["Nairobi, Kenya"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edna","family":"Bosire","sequence":"additional","affiliation":[{"name":"Brain and Mind Institute, Aga Khan University","place":["Nairobi, Kenya"]},{"name":"Department of Population Health, Aga Khan University","place":["Nairobi, Kenya"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasmit","family":"Shah","sequence":"additional","affiliation":[{"name":"Brain and Mind Institute, Aga Khan University","place":["Nairobi, Kenya"]},{"name":"Department of Medicine, Aga Khan University","place":["Nairobi, Kenya"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e162","DOI":"10.1016\/S2542-5196(18)30051-2","article-title":"Residential greenness and prevalence of major depressive disorders: a cross-sectional, observational, associational study of 94\u2008879 adult UK biobank participants","volume":"2","author":"Sarkar","year":"2018","journal-title":"Lancet Planet Health"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e128","DOI":"10.1016\/S2542-5196(20)30081-4","article-title":"Mental health and climate change: tackling invisible injustice","volume":"4","author":"Ingle","year":"2020","journal-title":"Lancet Planet Heath"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s13033-018-0210-6","article-title":"Climate change and mental health: risks, impacts and priority actions","volume":"12","author":"Hayes","year":"2018","journal-title":"Int J Ment Heal Syst"},{"key":"ref4","author":"Chen","year":"2024"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"355","DOI":"10.2478\/v10001-009-0031-6","article-title":"Air pollution and daily emergency department visits for depression","volume":"22","author":"Szyszkowicz","year":"2009","journal-title":"Int J Occup Med Environ Health"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"126002","DOI":"10.1289\/EHP4595","article-title":"Air pollution (particulate matter) exposure and associations with depression, anxiety, bipolar, psychosis and suicide risk: a systematic review and meta-analysis","volume":"127","author":"Braithwaite","year":"2019","journal-title":"Environ Health Perspect"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"e0198283","DOI":"10.1371\/journal.pone.0198283","article-title":"Association between air pollution from residential wood burning and dementia incidence in a longitudinal study in northern Sweden","volume":"13","author":"Oudin","year":"2018","journal-title":"PLoS One"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"e202","DOI":"10.1016\/s2542-5196(18)30067-6","article-title":"The lancet countdown on health benefits from the UK climate change act: a modelling study for Great Britain","volume":"2","author":"Williams","year":"2018","journal-title":"Lancet Planet Health"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"e333","DOI":"10.1016\/S2542-5196(19)30114-7","article-title":"Urgent action is needed to protect human health from the increasing effects of climate change","volume":"3","author":"Hobbhahn","year":"2019","journal-title":"Lancet Planet Heath"},{"key":"ref10","volume-title":"Stress, Appraisal, and Coping","author":"Lazarus","year":"1984"},{"key":"ref11","volume-title":"Environmental Psychology: Principles and Practice","author":"Gifford","year":"2007"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1146\/annurev.clinpsy.1.102803.143916","article-title":"Cognitive vulnerability to emotional disorders","volume":"1","author":"Mathews","year":"2005","journal-title":"Annu Rev Clin Psychol"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"1847","DOI":"10.1158\/1055-9965.EPI-05-0456","article-title":"Complementing the genome with an \u201cexposome\u201d: the outstanding challenge of environmental exposure measurement in molecular epidemiology","volume":"14","author":"Wild","year":"2005","journal-title":"Cancer Epidemiol Biomarkers Prev"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/S0140-6736(20)32290-X","article-title":"The 2020 report of the lancet countdown on health and climate change: responding to converging 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Estimations","author":"Maino","year":"2022"},{"key":"ref19","first-page":"1","volume-title":"Accessing and Using Climate Data and Information in Fragile, Data-Poor States","author":"Mason","year":"2015"},{"key":"ref20","volume-title":"Mental Health and Climate change: Policy Brief","year":"2022"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"113880","DOI":"10.1016\/j.envres.2022.113880","article-title":"Household air pollution and cognitive health among Indian older adults: evidence from LASI","volume":"214","author":"Jana","year":"2022","journal-title":"Environ Res"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"155394","DOI":"10.1016\/j.scitotenv.2022.155394","article-title":"In-utero exposure to indoor air pollution or tobacco smoke and cognitive development in a south African birth cohort study","volume":"834","author":"Christensen","year":"2022","journal-title":"Sci Total Environ"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"17","DOI":"10.2166\/wh.2016.158","article-title":"Psychosocial impacts of the lack of access to water and sanitation in low-and middle-income countries: a scoping review","volume":"15","author":"Bisung","year":"2017","journal-title":"J Water Health"},{"key":"ref24","volume-title":"Climate Change in Kenya: Focus on Children","author":"Thornton","year":"2010"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"37","DOI":"10.47941\/jps.751","article-title":"Vulnerability of communities to climate change induced disaster risks and potential mental health outcomes in Isiolo county, Kenya","volume":"3","author":"Mwenda","year":"2021","journal-title":"J Phys Sci"},{"key":"ref26","first-page":"34","article-title":"The impact of climate change-induced disasters on mental health in Isiolo county, Kenya","volume":"22","author":"Mwenda","year":"2019","journal-title":"Afr J Psychol Study Soc 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implications","volume":"13","author":"Crane","year":"2022","journal-title":"Atmos"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"e075105","DOI":"10.1136\/bmjopen-2023-075105","article-title":"Association between indoor air pollution and depression: a systematic review and meta-analysis of cohort studies","volume":"14","author":"Zhang","year":"2024","journal-title":"BMJ Open"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/s10113-015-0761-x","article-title":"Climate change, food security, and livelihoods in sub-Saharan Africa","volume":"16","author":"Connolly-Boutin","year":"2016","journal-title":"Reg Environ Chang"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1038\/s41558-018-0092-2","article-title":"Ecological grief as a mental health response to climate change-related loss","volume":"8","author":"Cunsolo","year":"2018","journal-title":"Nat Clim 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