Design, Technical Architecture, and Empirical Field Evaluation of a Digital Environmental Health Premises Inspection Model in Kwara State, Nigeria

Abstract

This study designed, architected, deployed, and empirically evaluated the Digital Environmental Health Inspection System (DEHIS) a context-tailored, four-tier mobile-to-cloud electronic inspection framework engineered for active field operations across Kwara State, Nigeria. A paired crossover method-comparison design was conducted across Ilorin West, Ilorin East, and Ilorin South Local Government Areas (LGAs) involving a census sample of 35 active field Environmental Health Officers (EHOs). DEHIS integrates mobile field data capture via KoboToolbox client software on Android mobile devices, a server-side Express/Node.js middleware synchronization pipeline utilizing Bearer token authentication, and a Supabase PostgreSQL relational database featuring Row-Level Security (RLS) and stored generated columns to automate domain sub-scoring and sanitary classification. Field performance was evaluated through non-parametric Wilcoxon signed-rank tests for paired score agreement, Bland-Altman method comparison limits of agreement, Technology Acceptance Model (TAM) construct evaluations, and Multiple Imputation by Chained Equations (MICE) sensitivity analyses across missing data profiles. DEHIS successfully digitized the 23-item ES1 evaluation tool, establishing a 115-point total compliance scale and an automated dual-condition Sanitary/Not Sanitary classification algorithm. Among 28 complete paired inspections in the primary EHO cohort, overall mean compliance scores demonstrated no statistically significant difference between legacy paper () and digital DEHIS () modes (mean score difference =  points, 95% CI:  to , ). Bland-Altman analysis revealed a mean bias of  points with 95% limits of agreement spanning  to  points. Multiple imputation sensitivity testing across all 50 paired records confirmed score parity (pooled mean difference =  points, 95% CI:  to , ). Field evaluation demonstrated dramatic operational efficiency gains: field data capture time decreased by 36.1% (from  to  minutes per premise, ), while central data synchronization latency dropped from  hours to under  seconds. User acceptance evaluation () indicated strong overall system acceptance ( out of , ) and high perceived usefulness (, 84.5% agreement). However, facilitating conditions received lower ratings (, 54.9% agreement), driven by cellular network instability and device accessibility constraints. DEHIS establishes a scientifically validated, automated digital inspection architecture that preserves aggregate scoring parity with paper baselines while eliminating reporting latency, enforcing data completeness, and establishing real-time spatial surveillance. Sustainable scaling across urban and rural wards requires institutionalized device provisioning, dedicated cellular data allowances, and resilient offline synchronization protocols.