Published in PLOS One on 2026-06-10, this study is presented as an attributed evidence briefing. The excerpts below are the original researchers’ words, reproduced under their Creative Commons Attribution licence. Health Signal has selected the abstract and, where needed, opening background paragraphs; the complete methods, results, limitations and references remain in the linked paper.
Original authors: Andualem Addisu Birlie, Nega Abebe Meshesha, Sefefe Birhanu Tizie, Smegnew Gichew Wondie, Selamawit Gashaw Teshome, Tesfaye Deribe Bedada, Ayana Alebachew Muluneh, Biruktawit Lelisa Eticha, Geleta Nenko Dube, Gelgelo Wodessa, Muluken Belachew Mengistie, Muammar Qadafi
Source: PLOS One • DOI 10.1371/journal.pone.0350923
Read the complete primary study ↗ · Creative Commons Attribution licence
From the researchers
Abstract
Open defecation is the disposal of human feces in fields, bushes, forests, open waterways, beaches, and other open areas. It worsens the environment, contaminates drinking water sources, causes malnutrition and low school attendance in children, and aids in the spread of diseases like cholera, diarrhea, dysentery, typhoid, polio, and hepatitis A. The purpose of this study was geospatial variation and machine learning approaches to predict open defecation in Zambia.
This study used secondary data analysis from the cross-sectional Zambia Demographic and Health Survey (ZMDHS) 2024. Spatial distribution, spatial autocorrelation, incremental autocorrelation, spatial interpolation, and hot spot area detection were all examined using ArcGIS 10.7. Python was used to identify the features of open defecation practice using machine-learning algorithms. We carried out an 80/20% data split, one-hot data encoding, data transformation and integration, data cleaning, and 10-fold stratified cross-validation. This study employed seven machine-learning algorithms, including adaptive boosting, cat boosting, random forest, light boosting, extreme gradient boosting, decision tree and logistic regression.
Among 12,808 households in Zambia, 12.1% were practiced open defecation. Spatial analysis revealed significant clustering, with hot spots concentrated in Southern, Western and Eastern regions, highlighting areas in urgent need of intervention. Machine learning models were applied to predict open defecation practices, with light gradient boost performing the best model with AUC of 83.83%. From this study, 198 true positives were generated by the model for the classification of open defecation practice, accurately identifying those who reported engaging in this behavior.
Wealth index, access to treated water, access to electricity, educational level, and age of household head, access to media, and region were the most significant features of open defecation practice. Governments, NGOs, policy makers, and researchers can use these data to create targeted interventions for improving health and environmental sanitation based on the gaps and disparities discovered.
Background
Open defecation is the term used to describe the disposal of human feces in fields, bushes, forests, open waterways, beaches, and other open areas [1]. Developing countries, particularly those in Asia and Sub-Saharan Africa, are disproportionately affected by the dangerous and damaging practice of open defecation, which also contributes to pollution [2]. It poses environmental and social problems and aids in the spread of diseases, making it a serious public health concern [3]. Poor sanitation causes the deaths of 775,000 people worldwide each year [4]. 2.4 billion people worldwide, the majority of whom reside in Sub-Saharan Africa (SSA), lacked access to improved sanitation in 2015 [5]. In 2020, World Health Organization (WHO) and United Nations Children’s Fund (UNICEF) reported that over 673 million people worldwide engage in open defecation, and approximately 3.6 billion people lack access to safely managed sanitation services [4]. 494 million people engage in open defecation, according to WHO and UNICEF Joint Monitoring Program (JMP) 2021 reports [6]. Nearly half of them were from Sub-Saharan Africa (SSA), and the majority (92%) of them lived in rural areas. The environment, the economy, and societal health are all negatively impacted by inadequate sanitation facilities and poor sanitation [4,7]. It worsens the environment, contaminates drinking water sources, causes malnutrition and low school attendance in children, and aids in the spread of diseases like cholera, diarrhea, dysentery, typhoid, polio, and hepatitis A [8,9]. Inadequate sanitation, tainted drinking water, and poor hygiene are the main causes of diarrheal illnesses worldwide, accounting for over 297,000 deaths among children under five in 2019 [6,10]. According to a study, communities that practice open defecation had a fourfold higher prevalence of diarrhea than communities that do not [11]. Additionally, there is a risk that open defecation will expose hundreds of millions of women and girls worldwide to increased sexual exploitation and a lack of privacy during their periods [12].
Reading this evidence
Use the full paper to check the study design, participant selection, comparator and outcome definitions before drawing a practical conclusion. An association does not by itself show cause and effect. A reported performance measure needs its denominator and uncertainty. A finding from a particular facility, sample or time period should retain that scope when it is discussed elsewhere.
This briefing makes the source accessible within the wider technology & industry desk. It does not assess an individual patient or certify a product. When considering relevance to another health system, examine the original authors’ account of context and limitations, then seek local implementation evidence. The related reporting below provides other studies to read alongside this one; it is a topic connection, rather than a claim that their results are directly comparable.
© 2026 Andualem Addisu Birlie, Nega Abebe Meshesha, Sefefe Birhanu Tizie, Smegnew Gichew Wondie, Selamawit Gashaw Teshome, Tesfaye Deribe Bedada, Ayana Alebachew Muluneh, Biruktawit Lelisa Eticha, Geleta Nenko Dube, Gelgelo Wodessa, Muluken Belachew Mengistie, Muammar Qadafi. Excerpts reproduced under CC BY. Selection, formatting and contextual notes by Health Signal. The researchers are the authors of the source study, not members of Health Signal’s staff.
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