Public health surveillance systems are critical for disease control, yet their methodological rigour and operational yield in resource-limited settings are often inadequately assessed. In Nigeria, systematic evaluations quantifying the determinants of surveillance performance are lacking. This study aimed to methodologically evaluate the yield of surveillance systems and identify modifiable factors for optimisation using a multilevel analytical framework. We conducted a secondary analysis of national surveillance data, employing a three-level mixed-effects negative binomial regression model. The model was specified as (₈₉₊) = ₀ + X₈₉₊ + uⱼ + vₖ, where is the expected case reporting yield, i, j, k index facilities, districts, and states, and uⱼ, vₖ are random intercepts. Robust standard errors were used for inference. Surveillance yield was significantly associated with timeliness of reporting and laboratory confirmation capacity. A one-day improvement in median reporting time increased yield by an estimated 8. 2% (95% CI: 5. 1 to 11. 4). Substantial variation was attributable to the district level, indicating the importance of intermediate administrative coordination. The methodological approach demonstrates that surveillance yield is optimised through enhanced timeliness and diagnostic capacity, with district-level management being a key leverage point. Investment should prioritise reducing reporting delays and strengthening laboratory networks. Programme managers should empower district health authorities with targeted resources and data feedback mechanisms. health surveillance, health systems, multilevel modelling, health metrics, health policy This paper provides a novel application of multilevel regression to decompose variance in surveillance yield across health system tiers, offering a replicable method for identifying specific intervention points.
Bello et al. (Mon,) studied this question.