Malaria survey data and geospatial suitability mapping for understanding spatial and temporal variations of risk across Kenya
Malaria remains a public health concern in Kenya where children and pregnant women are vulnerable groups. The common interventions in place to fight malaria include using insecticide-treated bed nets (ITNs), knowledge and awareness about malaria, and intake of malaria anti-malaria drugs. Despite the...
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Elsevier
2025-02-01
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| Series: | Parasite Epidemiology and Control |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405673124000631 |
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| author | Caroline Kioko Justine Blanford |
| author_facet | Caroline Kioko Justine Blanford |
| author_sort | Caroline Kioko |
| collection | DOAJ |
| description | Malaria remains a public health concern in Kenya where children and pregnant women are vulnerable groups. The common interventions in place to fight malaria include using insecticide-treated bed nets (ITNs), knowledge and awareness about malaria, and intake of malaria anti-malaria drugs. Despite the availability of these interventions, Kenya still records more than 10,000 clinical cases annually. In this study, we examined how malaria and interventions varied across Kenya for 2015 and 2020. We analyzed the Kenya Malaria Indicator Survey (N = 10,072) for 2015 and, (N = 11,549) for 2020, and climate data with Fuzzy overlay method to examine how malaria and its interventions relate to environmental conditions required for malaria. The study found that 79 % of malaria cases were distributed in lake endemic, 11 % in coastal endemic, 7 % in highland epidemic, and 3 % in seasonal zone. Use of Insecticide-treated bed nets (ITNs) was 77 % in lake endemic, 13 % in coastal endemic, 9 % in highland epidemic, and 1 % in seasonal zone. Knowledge about malaria was 82 % in lake endemic, 9 % in highland epidemic, 6 % in coastal endemic, and 3 % in seasonal zone. Additionally, based on climate data, lake endemic zone was 94 % suitable for malaria transmission compared to other zones. Despite the use of ITNs and awareness about malaria, malaria transmission continues to be a threat especially in counties in the lake endemic zone. Furthermore, place of residence, climate factors, ownership of ITNs may be associated with malaria in the region. |
| format | Article |
| id | doaj-art-8d9a65cb0d194e5ba81d83e04cd94a93 |
| institution | Kabale University |
| issn | 2405-6731 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Parasite Epidemiology and Control |
| spelling | doaj-art-8d9a65cb0d194e5ba81d83e04cd94a932025-02-06T05:12:28ZengElsevierParasite Epidemiology and Control2405-67312025-02-0128e00399Malaria survey data and geospatial suitability mapping for understanding spatial and temporal variations of risk across KenyaCaroline Kioko0Justine Blanford1Corresponding author.; ITC Faculty Geo-Information Science and Earth Observation, University of Twente, Enschede, the NetherlandsITC Faculty Geo-Information Science and Earth Observation, University of Twente, Enschede, the NetherlandsMalaria remains a public health concern in Kenya where children and pregnant women are vulnerable groups. The common interventions in place to fight malaria include using insecticide-treated bed nets (ITNs), knowledge and awareness about malaria, and intake of malaria anti-malaria drugs. Despite the availability of these interventions, Kenya still records more than 10,000 clinical cases annually. In this study, we examined how malaria and interventions varied across Kenya for 2015 and 2020. We analyzed the Kenya Malaria Indicator Survey (N = 10,072) for 2015 and, (N = 11,549) for 2020, and climate data with Fuzzy overlay method to examine how malaria and its interventions relate to environmental conditions required for malaria. The study found that 79 % of malaria cases were distributed in lake endemic, 11 % in coastal endemic, 7 % in highland epidemic, and 3 % in seasonal zone. Use of Insecticide-treated bed nets (ITNs) was 77 % in lake endemic, 13 % in coastal endemic, 9 % in highland epidemic, and 1 % in seasonal zone. Knowledge about malaria was 82 % in lake endemic, 9 % in highland epidemic, 6 % in coastal endemic, and 3 % in seasonal zone. Additionally, based on climate data, lake endemic zone was 94 % suitable for malaria transmission compared to other zones. Despite the use of ITNs and awareness about malaria, malaria transmission continues to be a threat especially in counties in the lake endemic zone. Furthermore, place of residence, climate factors, ownership of ITNs may be associated with malaria in the region.http://www.sciencedirect.com/science/article/pii/S2405673124000631Malaria survey dataGeospatial suitability mappingSpatialTemporalVariationsRisk |
| spellingShingle | Caroline Kioko Justine Blanford Malaria survey data and geospatial suitability mapping for understanding spatial and temporal variations of risk across Kenya Parasite Epidemiology and Control Malaria survey data Geospatial suitability mapping Spatial Temporal Variations Risk |
| title | Malaria survey data and geospatial suitability mapping for understanding spatial and temporal variations of risk across Kenya |
| title_full | Malaria survey data and geospatial suitability mapping for understanding spatial and temporal variations of risk across Kenya |
| title_fullStr | Malaria survey data and geospatial suitability mapping for understanding spatial and temporal variations of risk across Kenya |
| title_full_unstemmed | Malaria survey data and geospatial suitability mapping for understanding spatial and temporal variations of risk across Kenya |
| title_short | Malaria survey data and geospatial suitability mapping for understanding spatial and temporal variations of risk across Kenya |
| title_sort | malaria survey data and geospatial suitability mapping for understanding spatial and temporal variations of risk across kenya |
| topic | Malaria survey data Geospatial suitability mapping Spatial Temporal Variations Risk |
| url | http://www.sciencedirect.com/science/article/pii/S2405673124000631 |
| work_keys_str_mv | AT carolinekioko malariasurveydataandgeospatialsuitabilitymappingforunderstandingspatialandtemporalvariationsofriskacrosskenya AT justineblanford malariasurveydataandgeospatialsuitabilitymappingforunderstandingspatialandtemporalvariationsofriskacrosskenya |