Patel Chirag J, Claypool Kajal T, Chow Eric, Chung Ming-Kei, Mai Don, Chen Jessie, Bendavid Eran
Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA.
Lincoln Laboratory, MIT, Lexington, MA USA.
Commun Med (Lond). 2022 Aug 18;2:104. doi: 10.1038/s43856-022-00170-z. eCollection 2022.
Predisposition to become HIV positive (HIV + ) is influenced by a wide range of correlated economic, environmental, demographic, social, and behavioral factors. While evidence among a candidate handful have strong evidence, there is lack of a consensus among the vast array of variables measured in large surveys.
We performed a comprehensive data-driven search for correlates of HIV positivity in >600,000 participants of the Demographic and Health Survey across 29 sub-Saharan African countries from 2003 to 2017. We associated a total of 7251 and of 6,288 unique variables with HIV positivity in females and males respectively in each of the 50 surveys. We performed a meta-analysis within countries to attain 29 country-specific associations.
Here we identify 344 (5.4% out possible) and 373 (5.1%) associations with HIV + in males and females, respectively, with robust statistical support. The associations are consistent in directionality across countries and sexes. The association sizes among individual correlates and their predictive capability were low to modest, but comparable to established factors. Among the identified associations, variables identifying being head of household among females was identified in 17 countries with a mean odds ratio (OR) of 2.5 (OR range: 1.1-3.5, R = 0.01). Other common associations were identified, including marital status, education, age, and ownership of land or livestock.
Our continent-wide search for variables has identified under-recognized variables associated with being HIV + that are consistent across the continent and sex. Many of the association sizes are as high as established risk factors for HIV positivity, including male circumcision.
感染艾滋病毒呈阳性(HIV+)的易感性受到一系列相关的经济、环境、人口、社会和行为因素的影响。虽然少数候选因素有强有力的证据,但在大型调查中测量的大量变量之间缺乏共识。
我们对2003年至2017年撒哈拉以南非洲29个国家的60多万名人口与健康调查参与者进行了全面的数据驱动搜索,以寻找与HIV阳性相关的因素。在50项调查中的每一项中,我们分别将总共7251个和6288个独特变量与女性和男性的HIV阳性相关联。我们在各国范围内进行了荟萃分析,以获得29个国家特定的关联。
在此,我们分别在男性和女性中确定了344个(占可能因素的5.4%)和373个(占5.1%)与HIV+相关的因素,并得到了有力的统计支持。这些关联在不同国家和性别之间的方向性是一致的。各个相关因素之间的关联大小及其预测能力较低至中等,但与既定因素相当。在确定的关联中,有17个国家发现女性中作为户主的变量,平均优势比(OR)为2.5(OR范围:1.1 - 3.5,R = 0.01)。还发现了其他常见的关联,包括婚姻状况、教育程度、年龄以及土地或牲畜所有权。
我们在整个非洲大陆范围内对变量的搜索确定了一些未被充分认识的与HIV+相关的变量,这些变量在整个大陆和不同性别中是一致的。许多关联大小与HIV阳性的既定风险因素一样高,包括男性包皮环切术。