Spatial and temporal analysis of cholera cases in high-risk areas of Kenya

Abstract

AbstractCholera outbreaks remain an important public health challenge in Kenya, especially in areas with poor sanitation and hygiene including urban informal settlements, refugee camps and rural areas bordering large water bodies. Even in endemic settings, the distribution of cases exhibits spatial and temporal variations. Utilizing a Poisson discrete space-time scan statistic (SaTScan), this study investigated the temporal trends and the nature of spatial spread of cholera within selected high-risk areas in Kenya. The study was conducted in an urban informal settlement in Nairobi (Mukuru), a refugee camp in Northern Kenya (Dadaab) and the four counties bordering Lake Victoria region. Retrospective cholera line list data from January 2012 to December 2022 in the selected high-risk areas was used. SaTScan v10.1.2 was used to carry out Spatiotemporal analysis and generate spatial clusters. Throughout the study period, a total of 7,372 cholera cases were reported, corresponding to a lower annual incidence rate of 12.2 per 100,000 people compared to a mean annual incidence of 25 cases per 100,000 population previously reported in Kenya. The highest number of cases (n=5934) were reported between 2015 and 2018 with an annual incidence rate of 27.0 per 100,000 people, indicating a relative risk (RR) of 7.22 and a log-likelihood ratio (LLR) of 3015.17 (p< 0.001). The risky clusters (RR>1) were in Dadaab, Fafi, Suna West, Nyatike, Ugunja, Ndhiwa and Suna East sub counties with annual cases of 111.6, 164.1, 28.4, 19.9, 19.4, 14.0 and 13.9 per 100,000, respectively. The sub-counties of Nyakach, Nyando, Rachuonyo East, Kisumu East and Kisumu Central were reported as low-risk clusters, with a relative risk of 0.055 and an annual incidence rate of 1.1 cases per 100,000 individuals. Out of the thirty-two sub-counties included in the study, ten of them did not report any cases of cholera during the study period. Cholera cases waxed every three years in the selected high-risk areas. This data on hotspots specific to endemic settings forms a basis for prompt public health response and resource allocation by prioritizing the significantly high-risk clusters to control and eventually eliminate the disease.