Exploring mobility data for enhancing HIV care engagement in Black/African American and Hispanic/Latinx individuals: a longitudinal observational study protocol.

Exploring mobility data for enhancing HIV care engagement in Black/African American and Hispanic/Latinx individuals: a longitudinal observational study protocol.
复制标题

DOI:
10.1136/bmjopen-2023-079900
复制
发表时间:
2023-12-14
期刊:
影响因子:
2.9
通讯作者:
--
中科院分区:
医学3区
文献类型:
--
作者:

文献摘要

参考文献

相似文献

迫切需要增加艾滋病毒感染者,特别是来自黑人/非裔美国人和西班牙裔/拉丁裔社区的艾滋病毒感染者对艾滋病毒护理的参与。流动性数据,衡量个人的运动随着时间的推移,结合社会结构数据(如犯罪,人口普查)可以潜在地确定艾滋病毒护理参与的障碍和促进因素,并可以加强公共卫生监督和知情干预。拟议的工作是一项纵向观察性队列研究,旨在招募400名黑人/非洲裔美国人和西班牙裔/拉丁裔艾滋病毒感染者,他们生活在美国艾滋病毒高流行率地区。每名参与者将被要求每月通过研究应用程序共享至少连续14天的移动数据,持续1年,并在5个时间点(基线、3、6、9和12个月)完成调查。研究应用程序将收集全球定位系统(GPS)数据。这些GPS数据将与其他数据集合并,其中包含与艾滋病毒护理设施,其他医疗保健,商业和服务地点以及社会结构数据相关的信息。机器学习和深度学习模型将用于数据分析,以确定艾滋病毒护理参与的背景预测因素。该研究包括与利益相关者的访谈,以评估使用流动数据增加艾滋病毒护理参与的实施和道德问题。我们试图研究流动模式和艾滋病毒护理参与之间的关系。已获得加州大学欧文分校机构审查委员会的伦理批准(#20205923)。收集的数据将被去识别并安全存储。将与其他研究小组合作,通过介绍、海报和研究论文传播研究结果。
Increasing engagement in HIV care among people living with HIV, especially those from Black/African American and Hispanic/Latinx communities, is an urgent need. Mobility data that measure individuals’ movements over time in combination with sociostructural data (eg, crime, census) can potentially identify barriers and facilitators to HIV care engagement and can enhance public health surveillance and inform interventions. The proposed work is a longitudinal observational cohort study aiming to enrol 400 Black/African American and Hispanic/Latinx individuals living with HIV in areas of the USA with high prevalence rates of HIV. Each participant will be asked to share at least 14 consecutive days of mobility data per month through the study app for 1 year and complete surveys at five time points (baseline, 3, 6, 9 and 12 months). The study app will collect Global Positioning System (GPS) data. These GPS data will be merged with other data sets containing information related to HIV care facilities, other healthcare, business and service locations, and sociostructural data. Machine learning and deep learning models will be used for data analysis to identify contextual predictors of HIV care engagement. The study includes interviews with stakeholders to evaluate the implementation and ethical concerns of using mobility data to increase engagement in HIV care. We seek to study the relationship between mobility patterns and HIV care engagement. Ethical approval has been obtained from the Institutional Review Board of the University of California, Irvine (#20205923). Collected data will be deidentified and securely stored. Dissemination of findings will be done through presentations, posters and research papers while collaborating with other research teams.
DOI: 10.1007/s10461-018-2163-9
发表时间: 2018-09-01
期刊: AIDS AND BEHAVIOR
影响因子: 4.4
作者:
Duncan, Dustin T.;Chaix, Basile;Hickson, DeMarc A.
通讯作者: Hickson, DeMarc A.
DOI: 10.1073/pnas.0906910106
发表时间: 2009-12-22
影响因子: 11.1
作者:
Balcan, Duygu;Colizza, Vittoria;Vespignani, Alessandro
通讯作者: Vespignani, Alessandro
DOI: 10.1038/s41467-021-22160-w
发表时间: 2021-03-25
影响因子: 16.6
作者:
Hong B;Bonczak BJ;Gupta A;Kontokosta CE
通讯作者: Kontokosta CE
DOI: 10.1038/s41598-021-92892-8
发表时间: 2021-06-29
期刊: Scientific reports
影响因子: 4.6
作者:
Ilin C;Annan-Phan S;Tai XH;Mehra S;Hsiang S;Blumenstock JE
通讯作者: Blumenstock JE
DOI: 10.3390/s23187917
发表时间: 2023-09-15
期刊: Sensors (Basel, Switzerland)
影响因子: --
作者:
Emish M;Kelani Z;Hassani M;Young SD
通讯作者: Young SD