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埃塞俄比亚吉姆马地区艾滋病毒流行率的贝叶斯空间建模

Bayesian Spatial Modelling of HIV Prevalence in Jimma Zone, Ethiopia.

作者信息

Debusho Legesse Kassa, Bedaso Nemso Geda

机构信息

Department of Statistics, College of Science, Engineering and Technology, University of South Africa, Private Bag X6, Florida 1710, South Africa.

Department of Statistics, College of Natural and Computational Science, Madda Walabu University, Bale Robe P.O. Box 247, Ethiopia.

出版信息

Diseases. 2023 Mar 8;11(1):46. doi: 10.3390/diseases11010046.

Abstract

: Although the human immunodeficiency virus (HIV) is spatially heterogeneous in Ethiopia, current regional estimates of HIV prevalence hide the epidemic's heterogeneity. A thorough examination of the prevalence of HIV infection using district-level data could assist to develop HIV prevention strategies. The aims of this study were to examine the spatial clustering of HIV prevalence in Jimma Zone at district level and assess the effects of patient characteristics on the prevalence of HIV infection. : The 8440 files of patients who underwent HIV testing in the 22 Districts of Jimma Zone between September 2018 and August 2019 were the source of data for this study. The global Moran's index, Getis-Ord Gi* local statistic, and Bayesian hierarchical spatial modelling approach were applied to address the research objectives. : Positive spatial autocorrelation was observed in the districts and the local indicators of spatial analysis using the Getis-Ord statistic also identified three districts, namely Agaro, Gomma and Nono Benja, as hotspots, and two districts, namely Mancho and Omo Beyam, as coldspots with 95% and 90% confidence levels, respectively, for HIV prevalence. The results also showed eight patient-related characteristics that were considered in the study were associated with HIV prevalence in the study area. Furthermore, after accounting for these characteristics in the fitted model, there was no spatial clustering of HIV prevalence suggesting the patient characteristics had explained most of the heterogeneity in HIV prevalence in Jimma Zone for the study data. : The identification of hotspot districts and the spatial dynamic of HIV infection in Jimma Zone at district level may allow health policymakers in the zone or Oromiya region or at national level to develop geographically specific strategies to prevent HIV transmission. Because clinic register data were used in the study, it is important to use caution when interpreting the results. The results are restricted to Jimma Zone districts and may not be generalizable to Ethiopia or the Oromiya region.

摘要

尽管埃塞俄比亚的人类免疫缺陷病毒(HIV)在空间上存在异质性,但目前对HIV流行率的区域估计掩盖了该流行病的异质性。使用地区层面的数据对HIV感染流行率进行全面检查有助于制定HIV预防策略。本研究的目的是在地区层面检查吉姆马地区HIV流行率的空间聚集情况,并评估患者特征对HIV感染流行率的影响。:2018年9月至2019年8月期间在吉姆马地区22个区接受HIV检测的8440份患者档案是本研究的数据来源。应用全局莫兰指数、Getis-Ord Gi*局部统计量和贝叶斯层次空间建模方法来实现研究目标。:在各地区观察到了正空间自相关,使用Getis-Ord统计量进行的空间分析局部指标也确定了三个区,即阿加罗、戈马和诺诺·本贾,为热点地区,以及两个区,即曼乔和奥莫·贝亚姆,为冷点地区,HIV流行率的置信水平分别为95%和90%。结果还表明,本研究中考虑的八个与患者相关的特征与研究区域内的HIV流行率相关。此外,在拟合模型中考虑这些特征后,HIV流行率没有空间聚集,这表明患者特征解释了吉姆马地区研究数据中HIV流行率的大部分异质性。:确定热点地区以及吉姆马地区地区层面HIV感染的空间动态,可能使该地区、奥罗米亚地区或国家层面的卫生政策制定者制定针对特定地理区域的策略来预防HIV传播。由于本研究使用了诊所登记数据,在解释结果时务必谨慎。结果仅限于吉姆马地区的区,可能无法推广到埃塞俄比亚或奥罗米亚地区。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e62/10047877/1e6e8c60292c/diseases-11-00046-g001.jpg

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