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Background <p>Achieving universal and equitable access to water, sanitation, and hygiene (WASH) services is central to Sustainable Development Goal 6 (SDG 6). However, progress monitoring in sub-Saharan Africa remains constrained by the coarse spatial resolution of existing frameworks, such as WASHNORM, which provide only state-level estimates and are therefore insufficient for targeted local-level planning and intervention. To address this gap, we present the first high-resolution, local government area (LGA)-level spatial modelling of WASH conditions across Ogun State, integrating environmental and socioeconomic predictors within a machine learning framework.</p> Methods <p>We conducted a cross-sectional household survey involving 3,069 households across 88 communities spanning all 20 LGAs in Ogun State, Nigeria. Water, sanitation, and hygiene conditions were assessed using WHO/UNICEF Joint Monitoring Programme standards. A Random Forest classification model was developed using environmental predictors (rainfall and elevation) and socioeconomic predictors (population density, economic index, and degree of urbanization) to generate spatially continuous predictions of WASH conditions across the state.</p> Results <p>Although 68.4% of households had access to improved water sources, open defecation remained common (40.1%), and fewer than half (42.3%) had functional handwashing facilities, indicating substantial deficits in sanitation and hygiene relative to water access. The model demonstrated modest to moderate discriminatory performance across all WASH indicators and classes (AUC: 0.627–0.764). Spatial predictions identified 11 of the 20 LGAs as having critical or poor WASH conditions. Population density and economic index emerged as stronger predictors of WASH access than degree of urbanization, with middle- and upper-income areas consistently demonstrating better WASH outcomes across all components.</p> Conclusion <p>This study provides the first sub-district spatial baseline of WASH conditions across Ogun State and presents a replicable geospatial modelling framework for sub-national SDG 6 monitoring in Nigeria. The findings highlight the need to prioritize densely populated and economically disadvantaged communities, particularly for sanitation and hygiene interventions, rather than relying solely on rural–urban classifications as the primary basis for WASH planning and resource allocation<b>.</b></p>

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Spatial modelling of household and environmental correlates of inequitable WASH access in Ogun State, Nigeria

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月3日

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  • A. S. Babalola,
  • L. O. Busari,
  • B. Adewale,
  • O. O. Omitola,
  • A. O. Gbadewole,
  • J. K. Fawole,
  • O. F. Olaonipekun,
  • T. A. Adekunle,
  • O. A. Babalola,
  • F. Oyelude,
  • A. B. Adigun,
  • C. J. Idemili,
  • O. Adeniran,
  • H. O. Mogaji,
  • O. A. Ajayi,
  • V. P. Gyang,
  • D. Bayegun,
  • K. Ajisafe,
  • A. M. Adedeji,
  • T. P. Babatunde,
  • O. A. Surakat,
  • A. O. Adeogun,
  • M. A. Adeleke,
  • O. A. Idowu

摘要

Background

Achieving universal and equitable access to water, sanitation, and hygiene (WASH) services is central to Sustainable Development Goal 6 (SDG 6). However, progress monitoring in sub-Saharan Africa remains constrained by the coarse spatial resolution of existing frameworks, such as WASHNORM, which provide only state-level estimates and are therefore insufficient for targeted local-level planning and intervention. To address this gap, we present the first high-resolution, local government area (LGA)-level spatial modelling of WASH conditions across Ogun State, integrating environmental and socioeconomic predictors within a machine learning framework.

Methods

We conducted a cross-sectional household survey involving 3,069 households across 88 communities spanning all 20 LGAs in Ogun State, Nigeria. Water, sanitation, and hygiene conditions were assessed using WHO/UNICEF Joint Monitoring Programme standards. A Random Forest classification model was developed using environmental predictors (rainfall and elevation) and socioeconomic predictors (population density, economic index, and degree of urbanization) to generate spatially continuous predictions of WASH conditions across the state.

Results

Although 68.4% of households had access to improved water sources, open defecation remained common (40.1%), and fewer than half (42.3%) had functional handwashing facilities, indicating substantial deficits in sanitation and hygiene relative to water access. The model demonstrated modest to moderate discriminatory performance across all WASH indicators and classes (AUC: 0.627–0.764). Spatial predictions identified 11 of the 20 LGAs as having critical or poor WASH conditions. Population density and economic index emerged as stronger predictors of WASH access than degree of urbanization, with middle- and upper-income areas consistently demonstrating better WASH outcomes across all components.

Conclusion

This study provides the first sub-district spatial baseline of WASH conditions across Ogun State and presents a replicable geospatial modelling framework for sub-national SDG 6 monitoring in Nigeria. The findings highlight the need to prioritize densely populated and economically disadvantaged communities, particularly for sanitation and hygiene interventions, rather than relying solely on rural–urban classifications as the primary basis for WASH planning and resource allocation.