{ "background": "Public health surveillance systems are critical for disease control, yet their methodological reliability in longitudinal application within complex, resource-variable settings is inadequately characterised. This is particularly pertinent in sub-Saharan Africa, where system performance directly impacts health outcomes.", "purpose and objectives": "This study aimed to conduct a longitudinal methodological evaluation of a national public health surveillance system. Its primary objective was to quantify system reliability over time using a multilevel modelling framework, identifying facility- and district-level determinants of reporting consistency.", "methodology": "We employed a longitudinal cohort design, tracking reporting entities over a multi-decade period. System reliability was operationalised as a composite score of timeliness, completeness, and accuracy. A three-level random intercepts model was fitted: $Y{ijt} = \\beta0 + \\beta X{ijt} + uj + vk + \\epsilon{ijt}$, where $i$, $j$, and $k$ index reports, facilities, and districts, respectively. Inference was based on robust standard errors clustered at the district level.", "findings": "Analysis indicates a significant positive longitudinal trend in overall reliability, with a 22% improvement in the mean composite score. However, substantial heterogeneity persisted; facility-level resource allocation was a stronger predictor (β = 0.31, 95% CI: 0.24, 0.38) than district-level governance indicators. The intra-class correlation suggested 40% of the variance in reliability scores was attributable to district-level factors.", "conclusion": "The surveillance system demonstrated measurable improvement in methodological reliability, yet inequities rooted in structural and resource determinants remain entrenched. System strengthening must address multilevel influences to ensure consistent performance.", "recommendations": "Resource allocation should target facility-level capacity as a priority. Monitoring frameworks must integrate multilevel reliability metrics. Future evaluations should employ similar longitudinal, hierarchical models to disentangle systemic drivers of performance.", "key words": "public health surveillance, system reliability, longitudinal evaluation, multilevel