Spatial–temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast

Abstract

Background The over-distributed pattern of malaria transmission has led to attempts to define malaria “hotspots” that could be targeted for purposes of malaria control in Africa. However, few studies have investigated the use of routine health facility data in the more stable, endemic areas of Africa as a low-cost strategy to identify hotspots. Here the objective was to explore the spatial and temporal dynamics of fever positive rapid diagnostic test (RDT) malaria cases routinely collected along the Kenyan Coast. Methods Data on fever positive RDT cases between March 2018 and February 2019 were obtained from patients presenting to six out-patients health-facilities in a rural area of Kilifi County on the Kenyan Coast. To quantify spatial clustering, homestead level geocoded addresses were used as well as aggregated homesteads level data at enumeration zone. Data were sub-divided into quarterly intervals. Kulldorff’s spatial scan statistics using Bernoulli probability model was used to detect hotspots of fever positive RDTs across all ages, where cases were febrile individuals with a positive test and controls were individuals with a negative test. Results Across 12 months of surveillance, there were nine significant clusters that were identified using the spatial scan statistics among RDT positive fevers. These clusters included 52% of all fever positive RDT cases detected in 29% of the geocoded homesteads in the study area. When the resolution of the data was aggregated at enumeration zone (village) level the hotspots identified were located in the same areas. Only two of the nine hotspots were temporally stable accounting for 2.7% of the homesteads and included 10.8% of all fever positive RDT cases detected. Conclusion Taking together the temporal instability of spatial hotspots and the relatively modest fraction of the malaria cases that they account for; it would seem inadvisable to re-design the sub-county control strategies around targeting hotspots.

Spatiotemporal dynamics of malaria in Zanzibar, 2015–2020

“…32 In contrast, in Kilifi county, Kenya, only two temporally stable hotspots were identified over the 1-year study period, comprising 2.7% of all study households and contributing to 10.8% of all malaria cases confirmed by RDT. 35 Using passive case detection, our findings support the hypothesis that-in an elimination setting such as Zanzibar-malaria tends to significantly cluster within certain hotspot geographical units. 25 28 29 36-38 Across…”

“… 32 In contrast, in Kilifi county, Kenya, only two temporally stable hotspots were identified over the 1-year study period, comprising 2.7% of all study households and contributing to 10.8% of all malaria cases confirmed by RDT. 35 …”

Spatio-temporal dynamics of malaria in Zanzibar, 2015–2020

“…In contrast, in Kilifi county, Kenya, only two temporally stable hotspots were identified over the 1-year study period, comprising 2.7% of all study households and contributing to 10.8% of all malaria cases confirmed by RDT. [27] Using PCD, our findings support the hypothesis that-in an elimination setting such as Zanzibar-malaria tends to significantly cluster within certain hotspot geographic units. [17,20,21,28,29,30] Across Zanzibar's shehias, 79 (20.4%) were identified as a hotspot in any given year, with malaria observed to significantly cluster spatially and temporally.…”

“…In contrast, in Kilifi county, Kenya, only two temporally stable hotspots were identified over the 1-year study period, comprising 2.7% of all study households and contributing to 10.8% of all malaria cases confirmed by RDT. [27]…”

Mapping the stability of febrile illness hotspots in Punjab from 2012 to 2019- a spatial clustering and regression analysis

“…The availability of water bodies (natural in rural and artificial in urban areas) significantly affected the clustering of FI. Our study reinforces the hypothesis developed by previous studies that Malaria and Dengue tend to cluster with specific geographic units significantly [ 61 ]. Our study calls for augmented actions in such districts, and stricter implementation of public health interventions can impact the overall indicators of the state.…”