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...
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ftdoajarticles:oai:doaj.org/article:7fcb3b72c38046579dda2f2140c74162 2023-05-15T15:18:21+02:00 Spatial–temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast Alice Kamau Grace Mtanje Christine Mataza Philip Bejon Robert W. Snow 2021-05-01T00:00:00Z https://doi.org/10.1186/s12936-021-03758-3 https://doaj.org/article/7fcb3b72c38046579dda2f2140c74162 EN eng BMC https://doi.org/10.1186/s12936-021-03758-3 https://doaj.org/toc/1475-2875 doi:10.1186/s12936-021-03758-3 1475-2875 https://doaj.org/article/7fcb3b72c38046579dda2f2140c74162 Malaria Journal, Vol 20, Iss 1, Pp 1-13 (2021) Malaria Hotspots Spatial clusters Spatial–temporal dynamics Heterogeneity Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2021 ftdoajarticles https://doi.org/10.1186/s12936-021-03758-3 2022-12-31T10:27:49Z 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. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Homestead ENVELOPE(-119.369,-119.369,55.517,55.517) Malaria Journal 20 1 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Malaria Hotspots Spatial clusters Spatial–temporal dynamics Heterogeneity Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
spellingShingle |
Malaria Hotspots Spatial clusters Spatial–temporal dynamics Heterogeneity Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Alice Kamau Grace Mtanje Christine Mataza Philip Bejon Robert W. Snow Spatial–temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast |
topic_facet |
Malaria Hotspots Spatial clusters Spatial–temporal dynamics Heterogeneity Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Alice Kamau Grace Mtanje Christine Mataza Philip Bejon Robert W. Snow |
author_facet |
Alice Kamau Grace Mtanje Christine Mataza Philip Bejon Robert W. Snow |
author_sort |
Alice Kamau |
title |
Spatial–temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast |
title_short |
Spatial–temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast |
title_full |
Spatial–temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast |
title_fullStr |
Spatial–temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast |
title_full_unstemmed |
Spatial–temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast |
title_sort |
spatial–temporal clustering of malaria using routinely collected health facility data on the kenyan coast |
publisher |
BMC |
publishDate |
2021 |
url |
https://doi.org/10.1186/s12936-021-03758-3 https://doaj.org/article/7fcb3b72c38046579dda2f2140c74162 |
long_lat |
ENVELOPE(-119.369,-119.369,55.517,55.517) |
geographic |
Arctic Homestead |
geographic_facet |
Arctic Homestead |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 20, Iss 1, Pp 1-13 (2021) |
op_relation |
https://doi.org/10.1186/s12936-021-03758-3 https://doaj.org/toc/1475-2875 doi:10.1186/s12936-021-03758-3 1475-2875 https://doaj.org/article/7fcb3b72c38046579dda2f2140c74162 |
op_doi |
https://doi.org/10.1186/s12936-021-03758-3 |
container_title |
Malaria Journal |
container_volume |
20 |
container_issue |
1 |
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1766348546068447232 |