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Bayesian Spatial Modelling of HIV Prevalence in Jimma Zone, Ethiopia
Legesse Kassa Debusho1, Nemso Geda Bedaso2
1Department of Statistics, College of Science, Engineering and Technology, University of South Africa, Private Bag X6, Florida 1710, South Africa.
Diseases (Basel, Switzerland)
|March 28, 2023
Summary
This study mapped human immunodeficiency virus (HIV) prevalence in Ethiopia's Jimma Zone, identifying hotspots and coldspots. Patient characteristics explained the spatial clustering, enabling targeted prevention strategies.
Area of Science:
- Epidemiology
- Spatial Analysis
- Public Health
Background:
- Human immunodeficiency virus (HIV) prevalence in Ethiopia exhibits spatial heterogeneity, often masked by regional estimates.
- District-level analysis is crucial for understanding and addressing localized HIV epidemic variations.
- Targeted HIV prevention strategies require a granular understanding of disease distribution.
Purpose of the Study:
- To examine the spatial clustering of HIV prevalence at the district level within Jimma Zone.
- To assess the influence of patient characteristics on HIV prevalence.
- To inform geographically specific HIV prevention efforts.
Main Methods:
- Utilized patient data from 8440 individuals tested for HIV in 22 districts of Jimma Zone (September 2018 - August 2019).
- Employed global Moran's index, Getis-Ord Gi* local statistic, and Bayesian hierarchical spatial modeling.
- Analyzed spatial autocorrelation and identified significant hotspots and coldspots of HIV prevalence.
Main Results:
- Observed significant positive spatial autocorrelation in HIV prevalence across districts.
- Identified Agaro, Gomma, and Nono Benja as HIV prevalence hotspots (95% confidence).
- Identified Mancho and Omo Beyam as HIV prevalence coldspots (90% confidence).
- Eight patient characteristics were associated with HIV prevalence.
- After accounting for patient characteristics, spatial clustering of HIV prevalence was no longer significant, indicating these factors explained the heterogeneity.
Conclusions:
- District-level spatial analysis reveals critical insights into HIV distribution in Jimma Zone.
- Identification of hotspots and coldspots facilitates the development of tailored, geographically specific HIV prevention strategies.
- Patient characteristics play a significant role in explaining spatial variations in HIV prevalence, guiding resource allocation and intervention design.
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