Public health surveillance systems are critical for disease control, yet their methodological evaluation in resource-limited settings remains underdeveloped. In Nigeria, systemic inefficiencies have historically hampered timely data collection and response. This study aimed to methodologically evaluate the efficiency of the national public health surveillance system and to quantify the determinants of efficiency gains using a multilevel modelling framework. We conducted a longitudinal analysis of surveillance performance metrics from national and state-level databases. Efficiency was measured via a composite score of timeliness, completeness, and data quality. A three-level linear mixed model was fitted: y₈₉ₓ = ₀ + X₈₉ₓ + uⱼ + vₜ + ₈₉ₓ, where i, j, and t index facilities, states, and years, respectively. Robust standard errors were clustered at the state level. Investment in electronic reporting infrastructure was the strongest predictor of efficiency gains, associated with a 34. 2% improvement in the composite score (95% CI: 28. 1 to 40. 3). State-level coordination capacity and facility staffing levels were also significant positive predictors. The intra-class correlation indicated that 31% of the variance in efficiency was attributable to state-level factors. The methodological evaluation demonstrates that surveillance efficiency is influenced by factors operating at multiple levels of the health system, with technological infrastructure being paramount. Policy should prioritise sustained investment in digital health infrastructure and strengthen state-level technical capacity for data management and coordination. health surveillance, efficiency, multilevel regression, health systems, Nigeria This study provides a novel methodological framework for the multilevel evaluation of surveillance system efficiency and generates the first longitudinal, national-level evidence on its determinants in the country.
Suleiman et al. (Thu,) studied this question.
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