Evaluation of the Kenya Livestock and Wildlife Syndromic Surveillance System for Rift Valley Fever, Narok County, Kenya, 2018-2019

Abstract Background Rift Valley Fever (RVF) outbreaks in livestock have had a detrimental impact on livestock trade, animal breeding, and productivity. Routine evaluation and data analysis of surveillance systems ensure that health events are efficiently and effectively monitored. This study evaluated Kenya Livestock and Wildlife Surveillance System (KLWSS) and characterized RVF cases reported for Narok County. Methods We evaluated KLWSS from January 2018 to December 2019 using CDC guidelines for evaluating surveillance systems. Attributes of simplicity, flexibility, data quality, acceptability, representativeness, timeliness, stability, sensitivity, and predictive value positive were examined. A retrospective review of RVF surveillance data for Narok County was performed. Demographic and clinical variables were assessed. Data were cleaned in Ms. Excel and descriptive analysis was done using Epi Info 7. Categorical variables were summarized using frequencies and proportions while continuous variables were summarized using measures of central tendency and dispersion. Study authorization was granted by the Directorate of Veterinary Services. Results System was simple in structure and operation, accommodated upgrading of its application, data quality performance was 69.8%, stakeholder’s participation rate was 80% with 842 reports coming from six sub-counties and 30 wards. The median time between event occurrence and event reporting was two days (range one to six days). The system had been operational since 2018 with no reports of any unscheduled outages and downtimes. Suspected cases of RVF reported were 11% (95/842) of the reported cases. The livestock species affected were cattle 56% (53/95) and Sheep 44% (42/95). About 96% (91/95) of the suspected cases were in mixed livestock production systems. The common syndrome was abortions 74% (95/129) with Loita ward recording 97% (92/95) suspected RVF cases. All suspected cases were reported in March 2018. Conclusions The KLWSS system was found to be stable but with below-par performance in data quality. Improvement in data quality is required to ensure that the surveillance system is efficient and effective.