Mapping the spatial disparities of HIV prevalence in Ethiopian zones using the generalized additive model
Abstract HIV is a worldwide social and health pandemic that poses a significant problem. This study contributes to the 2030 global agenda of reducing HIV prevalence. The study analyzed HIV prevalence using the 2016 Ethiopian Demographic and Health Survey data. The study included men aged 15–54 years...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-55850-8 |
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| author | Seyifemickael Amare Yilema Yegnanew A. Shiferaw Alebachew Taye Belay Denekew Bitew Belay |
| author_facet | Seyifemickael Amare Yilema Yegnanew A. Shiferaw Alebachew Taye Belay Denekew Bitew Belay |
| author_sort | Seyifemickael Amare Yilema |
| collection | DOAJ |
| description | Abstract HIV is a worldwide social and health pandemic that poses a significant problem. This study contributes to the 2030 global agenda of reducing HIV prevalence. The study analyzed HIV prevalence using the 2016 Ethiopian Demographic and Health Survey data. The study included men aged 15–54 years and women aged 15–49 years who responded to questions about HIV tests. A generalized geo-additive model (GAM) was fitted to HIV data using nonparametric smooth terms for geolocations. Two smoothing techniques were used in GAMs to evaluate spatial disparities and the probable effects of variables on HIV risk. There were certain areas in Ethiopia that were identified as hot spot zones for HIV, including Nuer and Agnuak in Gambella, West Wollega and Illubabor in Oromia, Benchi Maji and Shaka in SNNPR, Awsi, Fantana, Kilbet, and Gabi in the Afar region, Shinilie of the Somalia region, North and South Wollo, Oromia special zones of the Amhara region, Central Ethiopia, and Addis Ababa city. On the other hand, the eastern parts of Ethiopia, particularly most zones in the Somalia region, were identified as cold spot zones with the lowest HIV odds ratio. The odds of HIV+ were higher for those who reside in rural areas than in urban areas. Furthermore, people who have STIs, who used contraceptive methods, and who learned at the secondary level of education were more likely to be infected with HIV. After adjusting for confounding variables, the results indicated that there are substantially significant spatial variations in HIV prevalence across Ethiopian zones. These results provide essential information to strategically target geographic areas to allocate resources and policy interventions at zonal level administrations. |
| first_indexed | 2024-04-24T23:08:06Z |
| format | Article |
| id | doaj.art-62fb9b90401545598bd7598296463f00 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T23:08:06Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-62fb9b90401545598bd7598296463f002024-03-17T12:21:53ZengNature PortfolioScientific Reports2045-23222024-03-0114111010.1038/s41598-024-55850-8Mapping the spatial disparities of HIV prevalence in Ethiopian zones using the generalized additive modelSeyifemickael Amare Yilema0Yegnanew A. Shiferaw1Alebachew Taye Belay2Denekew Bitew Belay3Department of Statistics, College of Natural and Computational Science, Debre Tabor UniversityDepartment of Statistics, University of JohannesburgDepartment of Statistics, College of Natural and Computational Science, Debre Tabor UniversityDepartment of Statistics, College of Science, Bahir Dar UniversityAbstract HIV is a worldwide social and health pandemic that poses a significant problem. This study contributes to the 2030 global agenda of reducing HIV prevalence. The study analyzed HIV prevalence using the 2016 Ethiopian Demographic and Health Survey data. The study included men aged 15–54 years and women aged 15–49 years who responded to questions about HIV tests. A generalized geo-additive model (GAM) was fitted to HIV data using nonparametric smooth terms for geolocations. Two smoothing techniques were used in GAMs to evaluate spatial disparities and the probable effects of variables on HIV risk. There were certain areas in Ethiopia that were identified as hot spot zones for HIV, including Nuer and Agnuak in Gambella, West Wollega and Illubabor in Oromia, Benchi Maji and Shaka in SNNPR, Awsi, Fantana, Kilbet, and Gabi in the Afar region, Shinilie of the Somalia region, North and South Wollo, Oromia special zones of the Amhara region, Central Ethiopia, and Addis Ababa city. On the other hand, the eastern parts of Ethiopia, particularly most zones in the Somalia region, were identified as cold spot zones with the lowest HIV odds ratio. The odds of HIV+ were higher for those who reside in rural areas than in urban areas. Furthermore, people who have STIs, who used contraceptive methods, and who learned at the secondary level of education were more likely to be infected with HIV. After adjusting for confounding variables, the results indicated that there are substantially significant spatial variations in HIV prevalence across Ethiopian zones. These results provide essential information to strategically target geographic areas to allocate resources and policy interventions at zonal level administrations.https://doi.org/10.1038/s41598-024-55850-8Generalized additive modelSpatial heterogeneityHIVOdds ratioZones |
| spellingShingle | Seyifemickael Amare Yilema Yegnanew A. Shiferaw Alebachew Taye Belay Denekew Bitew Belay Mapping the spatial disparities of HIV prevalence in Ethiopian zones using the generalized additive model Scientific Reports Generalized additive model Spatial heterogeneity HIV Odds ratio Zones |
| title | Mapping the spatial disparities of HIV prevalence in Ethiopian zones using the generalized additive model |
| title_full | Mapping the spatial disparities of HIV prevalence in Ethiopian zones using the generalized additive model |
| title_fullStr | Mapping the spatial disparities of HIV prevalence in Ethiopian zones using the generalized additive model |
| title_full_unstemmed | Mapping the spatial disparities of HIV prevalence in Ethiopian zones using the generalized additive model |
| title_short | Mapping the spatial disparities of HIV prevalence in Ethiopian zones using the generalized additive model |
| title_sort | mapping the spatial disparities of hiv prevalence in ethiopian zones using the generalized additive model |
| topic | Generalized additive model Spatial heterogeneity HIV Odds ratio Zones |
| url | https://doi.org/10.1038/s41598-024-55850-8 |
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