{ "background": "Public health surveillance is critical for disease control, yet the methodological rigour and yield of such systems in sub-Saharan Africa require systematic evaluation. In Tanzania, diverse surveillance modalities have been implemented, but a consolidated quantitative assessment of their performance and determinants of yield is lacking. ", "purpose and objectives": "This meta-analysis aims to methodologically evaluate the performance of public health surveillance systems in Tanzania and to identify key factors optimising case detection yield, using a Bayesian hierarchical framework. ", "methodology": "We conducted a systematic review and meta-analysis of studies reporting surveillance system outputs. A Bayesian hierarchical model was fitted to estimate pooled yield and study heterogeneity. The core model was yi \ (\, \²i), \ \ (\, \²), where yi are log-transformed yield measures, \ are study-specific means, \ is the overall mean log-yield, and \² represents study variance. Prior distributions were weakly informative. Inference was based on posterior distributions with 95% credible intervals (CrI). ", "findings": "The pooled analysis indicated that integrated community-based surveillance significantly outperformed facility-based reporting, with a median yield ratio of 1. 82 (95% CrI: 1. 45 to 2. 24). System yield was positively associated with the use of digital reporting tools and regular supervisory feedback, accounting for approximately 40% of the study variance. ", "conclusion": "Surveillance yield in Tanzania is substantially enhanced by community integration and technological support. Methodological heterogeneity across studies remains a challenge for comparative evaluation. ", "recommendations": "National policy should prioritise investment in integrated, community-centred surveillance architectures with robust digital infrastructure. Future research must adopt standardised reporting metrics to facilitate cross-system comparison. ", "key words": "disease surveillance, health systems, Bayesian meta-analysis, yield optimisation
Mwambene et al. (Wed,) studied this question.
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