Multilevel regression analysis reveals infrastructure and education impact adoption rates of water treatment facilities in Uganda, indicating areas for improvement.
Water treatment facilities play a crucial role in improving water quality and public health outcomes in Uganda. However, adoption rates have been inconsistent across different regions. A multilevel logistic regression model was employed to analyse data from a survey conducted in three districts, incorporating both individual and district-level variables. The multilevel model revealed that infrastructure availability (20% increase) and community education programmes (15% increase) were significant predictors of adoption rates. Multilevel regression analysis provided a robust framework for understanding the complex factors affecting water treatment facility adoption in Uganda. Future studies should consider longitudinal data to better track changes over time and incorporate additional socioeconomic variables. The maintenance outcome was modelled as Yᵢₜ=β₀+β₁Xᵢₜ+uᵢ+εᵢₜ, with robustness checked using heteroskedasticity-consistent errors.
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Mutesi et al. (2007) studied this question.
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