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Published on: May 11, 2020
Spatial heterogeneity of Rift Valley fever burden in Kenya: a 10-year One Health zDALY evaluation
Lilian Owino1,2, Bryan O Nyawanda1,2, Samuel Kahariri3,4
1Swiss Tropical and Public Health Institute, Kreuzstrasse 2, 4123 Allschwil, Switzerland.
Background:
Rift Valley fever (RVF) is an endemic zoonotic disease in Kenya, with recurring outbreaks that affect both humans and livestock. Despite its impact, the full burden of RVF remains poorly quantified. This study presents the first integrated and spatially disaggregated estimates of RVF burden in Kenya using a One Health approach.
Methods:
We quantified the national and subnational burden of RVF in Kenya from 2014 to 2024 using the zoonotic disability-adjusted life years (zDALYs) framework, which combines human health loss (disability-adjusted life years [DALYs]) and livestock-related losses (measured as animal loss equivalents [ALE]). Estimates were based on reported RVF cases, livestock mortality, abortions, and treatment costs. Monte Carlo simulations were used to generate 95% uncertainty intervals (UIs).
Results:
The burden of RVF (zDALY per 1000 persons) varied across the counties, with the highest burden observed in northern Kenya. The highest burden was recorded in Marsabit (24.67, 95% UI: 20.22-29.17), Baringo (12.23, 95% UI: 8.05-17.22), Wajir (9.31, 95% UI: 6.08-12.4), Embu (9.07, 95% UI: 6.58-11.63), and Tana River (6.23, 95% UI: 4.23-8.48). The counties with the lowest burden of RVF per 1000 inhabitants were observed in Kiambu (0.02; 95% UI: 0.00-0.07), Kisii (0.01; 95% UI: 0.00-0.07), Meru (0.00; 95% UI: 0.00-0.04), Samburu (0.00; 95% UI: 0.00-0.34), Tharaka Nithi (0.00; 95% UI: 0.00-0.00), and Uasin Gishu counties (0.00; 95% UI: 0.00-0.00). DALY and ALE differed across the counties with Marsabit found to have the highest animal burden (ALE) and Nairobi to have the highest human burden (DALYs).
Conclusion:
This study quantitatively demonstrates the substantial burden of RVF in Kenya across human and livestock populations. The findings can guide targeted risk assessments, preparedness planning, and cost-effective One Health strategies for future RVF outbreaks.
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