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[Preprint]. 2025 Apr 23:2025.04.23.25326250.
doi: 10.1101/2025.04.23.25326250.

Advance Warning and Response Systems in Kenya: A Scoping Review

Affiliations

Advance Warning and Response Systems in Kenya: A Scoping Review

Lisa M Were et al. medRxiv. .

Abstract

Introduction: Infectious diseases (IDs) cause approximately 13.7 million deaths globally. The Kenyan Advance Warning and Response Systems (AW&RS) against ID outbreaks is a core capacity of the 2005 International Health Regulations and a key indicator of health security. We mapped evidence on Kenya's AW&RS and their enablers, and barriers for successfully detecting IDs, including climate-sensitive IDs.

Methods: We searched Cochrane Library, MEDLINE, EMBASE, Web of Science, Africa Index Medicus, and SCOPUS before August 26th, 2024. We also searched for grey literature on the Google Scholar search engine alongside the main repositories of Kenyan Universities. Two independent reviewers conducted study selection, while one reviewer extracted data. Discrepancies were resolved through discussion. Results were synthesised narratively and thematically.

Results: The search yielded 4,379 records from databases and 1,363 articles from websites, university repositories, and citations; we included 166 articles in the analysis. Integrated Disease Surveillance and Response (IDSR) and cohort surveillance systems were the most common (37.2%). Most studies were concentrated in Nairobi County (25.7%) and reported on malaria (23.6%). Most systems (82.4%) monitored the disease burden and outbreaks using hospital-based data (35.1%) and automated alert mechanisms (27.7%). National bulletins report a temporal association between environmental factors and disease prevalence. Malaria, Rift Valley Fever (RVF), and cholera cases increased with higher precipitation, lower temperatures and increased vegetative index. AW&RS used the accuracy and reliability of the model prediction to measure the system's performance. Effectiveness was evaluated based on system acceptability and timeliness. Health system factors were predominant, with 121 enablers and 127 barriers. Key enablers included skilled personnel (13 studies), whereas inadequate finances were a major barrier (21 studies).

Conclusion: Most AW&RS were IDSR and cohort-based surveillance. Climate changes have resulted in observed trends in diseases such as malaria and RVF, but further studies are needed to determine causal links. Insufficient funding hinders the effective implementation of AW&RS. Future research should assess the cost drivers influencing system effectiveness.

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Conflict of interest statement

Conflict of Interest The authors declare no conflict of interest.

Figures

Figure 1:
Figure 1:
PRISMA 2020 flow diagram of included studies and reports
Figure 2:
Figure 2:
Enablers and barriers to implementation of AW&RS using the SURE framework

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