Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts.
Center for Health Policy and the Center for Primary Care and Outcomes Research, Stanford University, Stanford, California.
AIDS. 2018 Apr 24;32(7):933-943. doi: 10.1097/QAD.0000000000001767.
Better identification of at-risk groups could benefit HIV-1 care programmes. We systematically identified HIV-1 risk factors in two nationally representative cohorts of women in the Demographic and Health Surveys.
We identified and replicated the association of 1415 social, economic, environmental, and behavioral factors with HIV-1 status. We used the 2007 and 2013-2014 surveys conducted among 5715 and 15 433 Zambian women, respectively (688 shared factors). We used false discovery rate criteria to identify factors that are strongly associated with HIV-1 in univariate and multivariate models of the entire population, as well as in subgroups stratified by wealth, residence, age, and past HIV-1 testing.
In the univariate analysis, we identified 102 and 182 variables that are associated with HIV-1 in the two surveys, respectively (79 factors were associated in both). Factors that were associated with HIV-1 status in full-sample analyses and in subgroups include being formerly married (adjusted OR 2007, 2.8, P < 10(-16); 2013-2014 2.8, P < 10(-29)), widowhood (aOR 3.7, P < 10(-12); and 4.2, P < 10(-30)), genital ulcers within 12 months (aOR 2.4, P < 10(-5); and 2.2, P < 10(-6)), and having a woman head of the household (aOR 1.7, P < 10(-7); and 2.1, P < 10(-26), while owning a bicycle (aOR 0.6, P < 10(-6); and 0.6, P < 10(-8)) and currently breastfeeding (aOR 0.5, P < 10(-9); and 0.4, P < 10(-26)) were associated with decreased risk. Area under the curve for HIV-1 positivity was 0.76-0.82.
Our X-wide association study identifies under-recognized factors related to HIV-1 infection, including widowhood, breastfeeding, and gender of head of the household. These features could be used to improve HIV-1 identification programs.
更好地识别高危人群将使 HIV-1 护理项目受益。我们系统地在两个具有全国代表性的妇女人口动态调查队列中识别了与 HIV-1 相关的 1415 个社会、经济、环境和行为因素。
我们鉴定并复制了 1415 个社会、经济、环境和行为因素与 HIV-1 状态之间的关联。我们分别使用了 2007 年和 2013-2014 年对 5715 名和 15433 名赞比亚妇女进行的调查（688 个共享因素）。我们使用错误发现率标准来鉴定在整个人群的单变量和多变量模型中以及在按财富、居住地、年龄和过去 HIV-1 检测分层的亚组中与 HIV-1 强相关的因素。
在单变量分析中，我们分别在两个调查中鉴定了 102 个和 182 个与 HIV-1 相关的变量（有 79 个变量在两个调查中都相关）。在全样本分析和亚组中与 HIV-1 状态相关的因素包括曾经结婚（调整后的 OR 2007，2.8，P<10(-16)；2013-2014 年 2.8，P<10(-29)）、丧偶（aOR 3.7，P<10(-12)；和 4.2，P<10(-30)）、12 个月内有生殖器溃疡（aOR 2.4，P<10(-5)；和 2.2，P<10(-6)）和女性为家庭户主（aOR 1.7，P<10(-7)；和 2.1，P<10(-26)），而拥有自行车（aOR 0.6，P<10(-6)；和 0.6，P<10(-8)）和当前母乳喂养（aOR 0.5，P<10(-9)；和 0.4，P<10(-26)）与风险降低相关。HIV-1 阳性的曲线下面积为 0.76-0.82。
我们的广泛关联研究确定了与 HIV-1 感染相关的一些被低估的因素，包括丧偶、母乳喂养和家庭户主的性别。这些特征可用于改进 HIV-1 识别方案。
