Prosperi Mattia, Xu Jie, Guo Jingchuan Serena, Bian Jiang, Chen Wei-Han William, Canidate Shantrel, Marini Simone, Wang Mo
Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2022 Dec;2022:2934-2939. doi: 10.1109/bibm55620.2022.9995662. Epub 2023 Jan 2.
Florida -the 3 most populous state in the USA-has the highest rates of Human Immunodeficiency Virus (HIV) infections and of unfavorable HIV outcomes, with marked social and racial disparities. In this work, we leveraged large-scale, real-world data, i.e., statewide surveillance records and publicly available data resources encoding social determinants of health (SDoH), to identify social and racial disparities contributing to individuals' risk of HIV infection. We used the Florida Department of Health's Syndromic Tracking and Reporting System (STARS) database (including 100,000+ individuals screened for HIV infection and their partners), and a novel algorithmic fairness assessment method -the Fairness-Aware Causal paThs decompoSition (FACTS)- merging causal inference and artificial intelligence. FACTS deconstructs disparities based on SDoH and individuals' characteristics, and can discover novel mechanisms of inequity, quantifying to what extent they could be reduced by interventions. We paired the deidentified demographic information (age, gender, drug use) of 44,350 individuals in STARS -with non-missing data on interview year, county of residence, and infection status- to eight SDoH, including access to healthcare facilities, % uninsured, median household income, and violent crime rate. Using an expert-reviewed causal graph, we found that the risk of HIV infection for African Americans was higher than for non- African Americans (both in terms of direct and total effect), although a null effect could not be ruled out. FACTS identified several paths leading to racial disparity in HIV risk, including multiple SDoH: education, income, violent crime, drinking, smoking, and rurality.
佛罗里达州是美国人口第三多的州，其人类免疫缺陷病毒（HIV）感染率和不良HIV结局发生率最高，存在明显的社会和种族差异。在这项研究中，我们利用大规模的真实世界数据，即全州范围的监测记录和编码健康社会决定因素（SDoH）的公开可用数据资源，来确定导致个体HIV感染风险的社会和种族差异。我们使用了佛罗里达州卫生部的症状跟踪和报告系统（STARS）数据库（包括10万多名接受HIV感染筛查的个体及其伴侣），以及一种新颖的算法公平性评估方法——公平感知因果路径分解（FACTS），该方法融合了因果推断和人工智能。FACTS根据SDoH和个体特征解构差异，并能发现新的不平等机制，量化通过干预可以在多大程度上减少这些差异。我们将STARS数据库中44350名个体的去识别化人口统计学信息（年龄、性别、药物使用情况）与访谈年份、居住县和感染状况的非缺失数据，与八个SDoH进行配对，包括获得医疗保健设施的机会、未参保率、家庭收入中位数和暴力犯罪率。使用经过专家评审的因果图，我们发现非裔美国人的HIV感染风险高于非非裔美国人（在直接效应和总效应方面），尽管不能排除零效应。FACTS确定了几条导致HIV风险种族差异的路径，包括多个SDoH：教育、收入、暴力犯罪、饮酒、吸烟和农村地区。
