Public health surveillance systems in sub-Saharan Africa are critical for monitoring clinical outcomes, yet their methodological robustness for longitudinal analysis is often unassessed. This creates uncertainty in trend estimation and policy evaluation. This study aimed to methodologically evaluate the surveillance architecture and to estimate longitudinal trends in key clinical outcomes using a panel-data framework. We conducted a multi-level assessment of surveillance data quality, completeness, and timeliness. A balanced panel dataset was constructed from national surveillance records. The core estimation used a two-way fixed effects model: Y₈ₓ = + X₈ₓ + ᵢ + ₜ + ₈ₓ, where ᵢ and ₜ are unit and time fixed effects. Inference was based on cluster-robust standard errors. The methodological evaluation revealed substantial heterogeneity in reporting completeness across regions, averaging 78% (95% CI: 72–84). Panel-data estimation showed a statistically significant negative trend in malaria-related admissions, with an average annual decrease of 3. 2% (p<0. 01). The surveillance system provides a viable, though imperfect, foundation for longitudinal analysis. The panel-data approach successfully isolated temporal trends in clinical outcomes, controlling for unobserved heterogeneity. Invest in standardising digital reporting protocols to improve data homogeneity. Integrate the panel estimation methodology into routine surveillance analytics for more robust monitoring of health system performance. public health surveillance, panel data, fixed effects, clinical outcomes, data quality, health systems, longitudinal analysis This paper provides a novel dual methodological framework, combining a structural evaluation of surveillance systems with advanced panel-data econometrics, applied for the first time to this national context.
Kondo et al. (Mon,) studied this question.
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