Updated subnational estimates of Water, Sanitation and Hygiene access in Low- and Middle-Income countries: a spatially referenced hierarchical ordinal multinomial modeling analysis using R template model builder.

Access to safe drinking water, improved sanitation, and basic hygiene is a critical factor of infectious disease risk and child health, particularly in low- and middle-income countries (LMICs). Spatially detailed information on household-level water, sanitation, and hygiene (WASH) conditions is critical for characterizing pathways of infectious disease transmission and exposure; however, such information is not directly available for most locations. We produced a harmonized global dataset of WASH conditions derived from 376 nationally representative household surveys, including the Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and national surveys, covering more than six million households and approximately 291,000 georeferenced clusters across LMICs. Drinking water source, sanitation facility type, and hygiene status were classified as ordered categorical variables reflecting service levels. Household survey data were integrated with 24 environmental and socioeconomic covariates from multiple data sources. Spatial ordinal regression models were fit using R Template Model Builder (RTMB), incorporating cluster-level random effects and spatial random fields represented by the stochastic partial differential equation (SPDE) formulation. The resulting dataset provides high-resolution gridded estimates of WASH service levels and associated probabilities, suitable for geographic distribution pattern analyses, environmental health research, and public health planning.