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 rom 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 subcounty control strategies around targeting hotspots