{ "background": "The adoption of integrated health information systems in district hospitals is critical for improving healthcare delivery and policy planning. However, robust methodological frameworks for quantifying and analysing the determinants of adoption rates across different administrative levels are lacking, particularly in sub-Saharan African contexts.", "purpose and objectives": "This case study aims to demonstrate the application of a multilevel regression model to measure and explain the adoption rates of a standardised health management information system across district hospitals, using a longitudinal dataset from Senegal.", "methodology": "We employed a three-level hierarchical linear model with repeated measures (Level 1) nested within hospitals (Level 2) nested within regions (Level 3). The core model is specified as $y{tij} = \\beta{0ij} + \\beta{1}X{tij} + \\epsilon{tij}$, where $\\beta{0ij} = \\gamma{00} + u{0j} + v_{0i}$. Inference was based on restricted maximum likelihood estimation with robust standard errors.", "findings": "The analysis indicates that hospital-level infrastructural readiness was the strongest predictor of adoption, with a one-unit increase in the readiness score associated with a 0.42 increase in the adoption rate (95% CI: 0.38 to 0.46). Regional-level policy interventions showed negligible fixed effects, but significant random intercept variance, suggesting heterogeneous implementation efficacy.", "conclusion": "Multilevel regression provides a powerful, nuanced framework for analysing health systems adoption, effectively partitioning variance across temporal, institutional, and geographical scales. The approach reveals that key drivers are situated at the hospital level, not the regional policy level.", "recommendations": "Future evaluations of health system interventions should employ multilevel modelling to account for clustered data structures. Investment and support should prioritise direct hospital-level capacity building over broad regional policy directives.", "key words": "health information systems, multilevel modelling, hierarchical linear model, adoption rates, health systems research, Senegal", "cont