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March 13, 20260 citationsOpen Access

Methodological Evaluation of Public Health Surveillance Systems in Rwanda: A Randomized Field Trial for Clinical Outcomes Assessment

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KMKizito MukasoHUHelen UmuhireVNVictor Niyonzima

Key Points

  • The aim is to evaluate the effectiveness of public health surveillance systems in Rwanda by comparing two different methods of disease detection.
  • Conducted a randomized controlled trial in two districts of Rwanda
  • Compared passive surveillance methods with enhanced active surveillance
  • Collected clinical data over six months using standardized protocols
  • Detected 75% of clinical cases in the intervention district compared to 60% in the control area
  • Improved sensitivity and specificity in identifying infections with the enhanced approach
  • No increase in false positives, indicating reliable detection

Abstract

Public health surveillance systems in Rwanda are crucial for monitoring disease outbreaks efficiently. However, their effectiveness varies widely across different regions and requires methodological refinement. A randomized controlled trial was conducted in two districts of Rwanda to compare traditional passive surveillance methods with an enhanced active surveillance approach. Clinical data from patients suspected of having infectious diseases were collected using standardised protocols over six months. In the intervention district, a higher proportion (75%) of clinical cases were detected compared to the control area (60%), demonstrating improved system sensitivity and specificity in identifying infections. The enhanced active surveillance approach significantly improved case detection rates without an increase in false positives, suggesting its potential for broader implementation within Rwanda's public health infrastructure. Immediate replication of this study across all districts to ensure consistent performance and further refinement based on findings is recommended. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.

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Cite This Study

Mukaso et al. (2012) studied this question.

synapsesocial.com/papers/69b3abf602a1e69014ccd52fhttps://doi.org/10.5281/zenodo.18949225
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