Dawit Rahel, Goedel William C, Chang Hsien-Yen, Nunn Amy S, Chan Philip A, Doshi Jalpa A, Dean Lorraine T
Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Department of Epidemiology, Brown University, Providence, RI, USA.
AIDS Behav. 2025 Apr;29(4):1089-1095. doi: 10.1007/s10461-024-04585-8. Epub 2024 Dec 30.
Identifying county-level factors that influence pre-exposure prophylaxis (PrEP) adherence is critical for ending the HIV epidemic in the United States (US). PrEP primary reversal is a term used to describe patients who do not obtain their prescribed medication from the pharmacy. This study sought to identify factors associated with PrEP reversal at the county level in 2018. Data were collected from Symphony Health Analytics, AIDS Vu, the US Census Bureau, and the Centers for Disease Control and Prevention National Prevention Information Network. Bivariate Choropleth maps were created to identify counties with high and low levels of PrEP reversal and HIV incidence. This was followed by bivariate analysis to determine the association between predictor variables and percent PrEP reversal. Finally multivariable logistic regressions were used to assess the association between percent PrEP reversal and variables that were significant from the bivariate analysis. A total of 308 counties were included in this analysis, where the mean number of PrEP prescriptions for counties was 44, with a median of 14 (Interquartile range 7-34). In the multivariable analysis, counties with higher level of unemployment (aOR: 1.10, 95% CI: 1.05-1.16) and rural counties (1.10: 1.04-1.17) had higher odds of PrEP reversal; while counties with higher household crowding (0.97: 0.95-0.99) had lower odds of PrEP reversal. Findings show the need for expanding and implementing programs as well as policies to improve PrEP services that are tailored to local socioeconomic circumstances.
识别影响暴露前预防（PrEP）依从性的县级因素对于在美国终结艾滋病流行至关重要。PrEP初次停药是一个用于描述未从药房获取其处方药物的患者的术语。本研究旨在确定2018年县级与PrEP停药相关的因素。数据收集自Symphony Health Analytics、AIDS Vu、美国人口普查局以及疾病控制与预防中心国家预防信息网络。创建了双变量分级统计图以识别PrEP停药水平和艾滋病毒发病率高和低的县。随后进行双变量分析以确定预测变量与PrEP停药百分比之间的关联。最后，使用多变量逻辑回归来评估PrEP停药百分比与双变量分析中具有显著性的变量之间的关联。本分析共纳入308个县，各县PrEP处方的平均数为44，中位数为14（四分位间距7 - 34）。在多变量分析中，失业率较高的县（调整后比值比：1.10，95%置信区间：1.05 - 1.16）和农村县（1.10：1.04 - 1.17）PrEP停药的几率较高；而家庭拥挤程度较高的县（0.97：0.95 - 0.99）PrEP停药的几率较低。研究结果表明需要扩大和实施相关项目及政策，以改善针对当地社会经济情况量身定制的PrEP服务。
