Spatial, temporal, and spatiotemporal cluster detection of malaria incidence in Southwest Ethiopia
BackgroundMalaria is a major global health hazard, particularly in developing countries such as Ethiopia, where it contributes to high morbidity and mortality rates. According to reports from the South Omo Zone Health Bureau, despite various interventions such as insecticide-treated bed nets and ind...
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Frontiers Media S.A.
2025-01-01
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| author | Lidetu Demoze Fetlework Gubena Eyob Akalewold Helen Brhan Tigist Kifle Gelila Yitageasu |
| author_facet | Lidetu Demoze Fetlework Gubena Eyob Akalewold Helen Brhan Tigist Kifle Gelila Yitageasu |
| author_sort | Lidetu Demoze |
| collection | DOAJ |
| description | BackgroundMalaria is a major global health hazard, particularly in developing countries such as Ethiopia, where it contributes to high morbidity and mortality rates. According to reports from the South Omo Zone Health Bureau, despite various interventions such as insecticide-treated bed nets and indoor residual spraying, the incidence of malaria has increased in recent years. Therefore, this study aimed to assess the spatial, temporal, and spatiotemporal variation in malaria incidence in the South Omo Zone, Southwest Ethiopia.MethodsA retrospective study was conducted using 4 years of malaria data from the South Omo Zone District Health Information Software (DHIS). The incidence rate of malaria per 1,000 people was calculated using Microsoft Excel software. Kulldorff SaTScan software with a discrete Poisson model was used to identify statistically significant spatial, temporal, and spatiotemporal malaria clusters. Graduated color maps depicting the incidence of malaria were generated using ArcGIS 10.7 software.ResultsSpatial clusters were identified in the districts of Dasenech (RR = 2.06, p < 0.0001), Hamer (RR = 1.90, p < 0.0001), Salamago (RR = 2.00, p < 0.0001), Bena Tsemay (RR = 1.71, p < 0.0001), Malie (RR = 1.50, p < 0.0001), Nyngatom (RR = 1.91, p < 0.0001) and North Ari (RR = 1.05, p < 0.0001) during the period from 08th July 2019 to 07th July 2023. A temporal cluster was identified as the risk period across all districts between 08th July 2022 and 07th July 2023 (RR = 1.59, p = 0.001). Spatiotemporal clusters were detected in Dasenech (RR = 2.26, p < 0.001) Salamago, (RR = 2.97, p < 0.001) Hamer (RR = 1.95, p < 0.001), Malie (RR = 2.03, p < 0.001), Bena Tsemay (RR = 1.80, p < 0.001), Nyngatom (RR = 2.65, p < 0.001), North Ari (RR = 1.50, p < 0.001), and Jinka town (RR = 1.19, p < 0.001).ConclusionSignificant spatial, temporal, and spatiotemporal clusters in malaria incidence were identified in the South Omo Zone. To better understand the factors contributing to these high-risk areas, further research is needed to explore individual, household, geographical, and climatic factors. Targeted interventions based on these findings could help reduce malaria incidence and associated risks in the region. |
| format | Article |
| id | doaj-art-9d6f91a0b5b54e189f39339a24af1a10 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-9d6f91a0b5b54e189f39339a24af1a102025-01-13T06:10:19ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-01-011210.3389/fpubh.2024.14666101466610Spatial, temporal, and spatiotemporal cluster detection of malaria incidence in Southwest EthiopiaLidetu Demoze0Fetlework Gubena1Eyob Akalewold2Helen Brhan3Tigist Kifle4Gelila Yitageasu5Department of Environmental and Occupational Health and Safety, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, EthiopiaDepartment of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, EthiopiaDepartment of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, EthiopiaDepartment of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, EthiopiaDepartment of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, EthiopiaDepartment of Environmental and Occupational Health and Safety, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, EthiopiaBackgroundMalaria is a major global health hazard, particularly in developing countries such as Ethiopia, where it contributes to high morbidity and mortality rates. According to reports from the South Omo Zone Health Bureau, despite various interventions such as insecticide-treated bed nets and indoor residual spraying, the incidence of malaria has increased in recent years. Therefore, this study aimed to assess the spatial, temporal, and spatiotemporal variation in malaria incidence in the South Omo Zone, Southwest Ethiopia.MethodsA retrospective study was conducted using 4 years of malaria data from the South Omo Zone District Health Information Software (DHIS). The incidence rate of malaria per 1,000 people was calculated using Microsoft Excel software. Kulldorff SaTScan software with a discrete Poisson model was used to identify statistically significant spatial, temporal, and spatiotemporal malaria clusters. Graduated color maps depicting the incidence of malaria were generated using ArcGIS 10.7 software.ResultsSpatial clusters were identified in the districts of Dasenech (RR = 2.06, p < 0.0001), Hamer (RR = 1.90, p < 0.0001), Salamago (RR = 2.00, p < 0.0001), Bena Tsemay (RR = 1.71, p < 0.0001), Malie (RR = 1.50, p < 0.0001), Nyngatom (RR = 1.91, p < 0.0001) and North Ari (RR = 1.05, p < 0.0001) during the period from 08th July 2019 to 07th July 2023. A temporal cluster was identified as the risk period across all districts between 08th July 2022 and 07th July 2023 (RR = 1.59, p = 0.001). Spatiotemporal clusters were detected in Dasenech (RR = 2.26, p < 0.001) Salamago, (RR = 2.97, p < 0.001) Hamer (RR = 1.95, p < 0.001), Malie (RR = 2.03, p < 0.001), Bena Tsemay (RR = 1.80, p < 0.001), Nyngatom (RR = 2.65, p < 0.001), North Ari (RR = 1.50, p < 0.001), and Jinka town (RR = 1.19, p < 0.001).ConclusionSignificant spatial, temporal, and spatiotemporal clusters in malaria incidence were identified in the South Omo Zone. To better understand the factors contributing to these high-risk areas, further research is needed to explore individual, household, geographical, and climatic factors. Targeted interventions based on these findings could help reduce malaria incidence and associated risks in the region.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1466610/fullmalariaincidencespatialtemporalspatiotemporalcluster |
| spellingShingle | Lidetu Demoze Fetlework Gubena Eyob Akalewold Helen Brhan Tigist Kifle Gelila Yitageasu Spatial, temporal, and spatiotemporal cluster detection of malaria incidence in Southwest Ethiopia Frontiers in Public Health malaria incidence spatial temporal spatiotemporal cluster |
| title | Spatial, temporal, and spatiotemporal cluster detection of malaria incidence in Southwest Ethiopia |
| title_full | Spatial, temporal, and spatiotemporal cluster detection of malaria incidence in Southwest Ethiopia |
| title_fullStr | Spatial, temporal, and spatiotemporal cluster detection of malaria incidence in Southwest Ethiopia |
| title_full_unstemmed | Spatial, temporal, and spatiotemporal cluster detection of malaria incidence in Southwest Ethiopia |
| title_short | Spatial, temporal, and spatiotemporal cluster detection of malaria incidence in Southwest Ethiopia |
| title_sort | spatial temporal and spatiotemporal cluster detection of malaria incidence in southwest ethiopia |
| topic | malaria incidence spatial temporal spatiotemporal cluster |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1466610/full |
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