Water, sanitation and hygiene (WASH) are critical determinants of public health, particularly in the developing Global South. The interdependence of these three factors suggests that improvements in one area alone may not fully realize public health benefits without concurrent advancements in the others. Therefore, understanding the complex relationships between WASH indicators is essential for informing public health strategies and interventions. Traditional modelling approaches, such as fully parametric or semiparametric frameworks, often fail to accurately capture these interdependencies due to challenges related to unknown or less tractable joint distributions of the variables of interest. In response to these challenges, this study employs a copula-based generalized joint regression model (GJRM) to simultaneously analyse WASH indicators, aiming to capture the underlying dependencies and account for spatial variations in the data. Results from the joint model fitted with spatial effects, fixed effects and non-linear effects showed that residence was significantly associated with improved drinking water and hygiene, while female-headed households show a negative association with improved drinking water. Occupation and working status are significant to improved hygiene, while the level of skills is negatively associated. The results also showed that marital status is negatively associated with improved hygiene and widowed was negatively associated with improved drinking water. In addition, household income showed a positive association with improved drinking water, sanitation and hygiene.