使用广义相加模型研究高危女性与艾滋病毒感染的空间差异：来自一组南非女性的结果。
Investigating spatial disparities in high-risk women and HIV infections using generalized additive models: Results from a cohort of South African women.
作者信息
Wand Handan, Reddy Tarylee, Ramjee Gita
机构信息
Kirby Institute, University of New South Wales, Kensington 2052, New South Wales, Australia.
Biostatistics Unit, South African Medical Research Council, Durban, Kwazulu-Natal, South Africa.
出版信息
Spat Spatiotemporal Epidemiol. 2019 Aug;30:100283. doi: 10.1016/j.sste.2019.100283. Epub 2019 May 29.
OBJECTIVE
We identified the geographical clustering of HIV as well as those at highest risk of infection using a decade long data (2002-2012) from KwaZulu-Natal, South Africa.
METHODS
A total of 5,776 women who enrolled in several HIV prevention trials were included in the study. Geo-coded individual-level data were linked to the community-level characteristics using the South African Census. High-risk women were identified using a risk scoring algorithm. Generalized additive models were used to identify the significant geographical clustering of high-risk women and HIV.
RESULTS
Overall, 60% of the women were classified as high risk of HIV. HIV infection rates were estimated as high as 10 to 15 per 100 person year. Areas with high rates of HIV infections were spatially clustered and overlapped particularly in the Northern part of Durban.
CONCLUSION
Targeting multifactorial and complex nature of the epidemic is urgently needed to identify the "high transmission" areas.
目的
我们利用来自南非夸祖鲁 - 纳塔尔省长达十年（2002 - 2012年）的数据，确定了艾滋病毒的地理聚集情况以及感染风险最高的人群。
方法
本研究纳入了参与多项艾滋病毒预防试验的5776名女性。利用南非人口普查将地理编码的个体层面数据与社区层面特征相联系。使用风险评分算法确定高风险女性。采用广义相加模型来确定高风险女性和艾滋病毒的显著地理聚集情况。
结果
总体而言，60%的女性被归类为艾滋病毒高风险人群。艾滋病毒感染率估计高达每100人年10至15例。艾滋病毒感染率高的地区在空间上聚集且重叠，尤其在德班北部。
结论
迫切需要针对该流行病的多因素和复杂性质来确定“高传播”地区。
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