Abstract
BackgroundThe 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.MethodsData 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.ResultsAcross 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.ConclusionTaking 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.
Highlights
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
The overall smoothed 1 km test positivity rate (TPR) ranged between 0% and 89.3% in 5,323 geocoded homesteads located in 36 enumeration zone (EZ) (Additional file 1)
Conclusion and programme implications In this study, approximately a third of the homesteads in the study area fell within identified hotspots and accounted for half of all health facility fever positive rapid diagnostic test (RDT) cases
Summary
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. 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. The concept that malaria is over-distributed in space has led to attempts to define “hotspots” in more stable, endemic areas of Africa [13]. Under stable endemic settings that remain under the control phases, passive case detection (PCD) data from routine health information systems is all that is typically available to define households or local areas with the highest burdens. Far fewer studies have investigated the potential of passive case detection in health facilities to identify hotspots [10, 15,16,17,18,19,20,21]
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