Identifying COVID-19 vaccine deserts using Machine Learning and Geospatial Analyses to target Community -engaged testing for vulnerable rural populations to prevent localized outbreaks

使用机器学习和地理空间分析识别 COVID-19 疫苗沙漠，以针对弱势农村人口进行社区参与测试，以防止局部疫情爆发

• 批准号: 10544766
• 负责人: Brian Hendricks
• 金额: $ 113.89万
• 依托单位: WEST VIRGINIA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10544766
• 关键词: Address; Advocate; Appalachian Region; Area; Automobile Driving; Awareness; Bayesian Modeling; COVID-19; COVID-19 morbidity; COVID-19 outbreak; COVID-19 test; COVID-19 testing; COVID-19 vaccine; Color; Common Data Element; Communities; Community Health; Computer software; County; Data; Data Collection; Disease; Disease Outbreaks; Early identification; Electronics; Emergency medical service; Event; Faith; Fire - disasters; Food; Funding; Future; Geographic Locations; Geography; Goals; Health; Health Services Accessibility; High Prevalence; Incentives; Individual; Infrastructure; Intervention; Interview; Knowledge; Machine Learning; Maps; Marketing; Medical; Methodology; Modeling; Monitor; Morbidity - disease rate; Motivation; Outcome; Participant; Perception; Performance; Persons; Phase; Pilot Projects; Poaceae; Predisposition; Primary Care Physician; Procedures; RADx Underserved Populations; Regression Analysis; Reporting; Research; Research Personnel; Resources; Risk Estimate; Rural Community; Rural Population; SARS-CoV-2 positive; Sampling; Series; Service delivery model; Services; Site; Structure; Surveys; System; Testing; Time; United States; Vaccination; Vaccines; Variant; Virus; Vulnerable Populations; West Virginia; Work; asymptomatic COVID-19; barrier to testing; community building; community engagement; community partnership; comorbidity; demographics; econometrics; experience; first responder; health literacy; high risk; innovation; intervention effect; machine learning framework; medically underserved; medically underserved population; mortality; nasal swab; novel; pandemic disease; prevent; recruit; reproductive; rurality; screening; screening program; secondary endpoint; socioeconomic disadvantage; trend; underserved community; vaccine acceptance; vaccine access; vulnerable community

PROJECT ABSTRACT As of June 30, 2021, 23% of West Virginia’s (WV) 55 counties were ranked within the top 20% of most vulnerable counties to Covid-19 in the United States. Central to the state’s extreme vulnerability is higher prevalence of medical comorbidities, lower access to care among rural populations, and decreased vaccine uptake compared to urban counterparts. Of considerable concern, testing has decreased statewide to allow for active dispersal of the vaccines. Unfortunately, low testing compounds vulnerability to Covid-19 in medically underserved populations where vaccine uptake is low, as they are extremely susceptible to persistent localized outbreaks of the virus and subsequently higher morbidity and mortality. Our RADx-UP Phase Two proposal builds upon previously funded RADx-UP Phase One by identifying and targeting vaccine desert communities then tailoring testing event services to the needs of individual communities building upon their perceptions of what is important. Providing a dynamic solution for continued testing is critical. We define vaccine deserts using overall vaccination rate and the change in vaccine uptake over a two-week period. Machine learning with time series modeling is used to characterize county level transmissibility, incorporating here for the first-time vaccination rates. Risk estimates at the county level are overlaid with zip codes where vaccine deserts have been identified using bottom decile for overall vaccination rate and change in vaccination over a 14-day period. Once a community is identified study liaisons will connect study staff to advocates to conduct semi-structured interviews to identify partner sites to host testing events and collect data to tailor promotions, food, and media messaging to the specific needs of each community targeted. Testing events will involve sample and survey data collection, with promotions and chance giveaways to incentivize communities to participate. We build upon RADx-UP one activity by focusing heavily on first responders in each community to aid in hosting testing events, and faith based and on profits where applicable. We involve co-investigators with strong connections to southern WV, an area with limited resources for RADx-UP Phase One. Additionally, we conduct a pilot study to examine the performance of the ABBOTT ID Now isothermal PCR system in 600 participants. Effect of the intervention is evaluated through monitoring of pre and post testing rate for the county using spatial regression analyses. A unique attribute of the statistical framework we propose to evaluate our testing strategy is an ability to describe the impact on nearby counties in addition to the targeted community. This project will leverage existing and develop its own unique partnerships with local and state agencies for implementation of a community engaged testing delivery model within vaccine deserts. A critical and novel aspect of our approach is establishment of a grass roots first responders research network which can be leveraged to implement screening programs in isolated medically underserved communities or study first responder health outcomes.

项目摘要 截至 2021 年 6 月 30 日，西弗吉尼亚州 (WV) 55 个县中有 23% 的县名列前 20%。 美国易受 Covid-19 影响的县。中央国家的极端脆弱性更高 合并症的患病率、农村人口获得护理的机会较少以及疫苗接种量减少 与城市同行相比。令人相当担忧的是，全州范围内的测试已经减少，以允许 积极分发疫苗。不幸的是，低检测结果导致医学上对 Covid-19 的脆弱性增加 疫苗接种率低的服务不足人群，因为他们极易受到持续的局部感染 病毒的爆发以及随之而来的更高的发病率和死亡率。我们的 RADx-UP 第二阶段提案 通过识别和瞄准疫苗沙漠社区，以之前资助的 RADx-UP 第一阶段为基础 然后根据各个社区的看法定制测试活动服务 什么是重要的。为持续测试提供动态解决方案至关重要。我们定义疫苗沙漠 使用总体疫苗接种率和两周内疫苗接种情况的变化。机器学习与 时间序列模型用于表征县级传播能力，此处首次纳入 疫苗接种率。县级的风险估计与疫苗荒漠所在的邮政编码重叠 使用总体疫苗接种率的底部十分位数和 14 天内疫苗接种的变化来确定。 一旦确定了社区，研究联络员将把研究人员与倡导者联系起来，进行半结构化研究 进行访谈以确定举办测试活动的合作伙伴网站并收集数据以定制促销、食品和媒体 针对每个社区的特定需求发送消息。测试活动将涉及样本和调查 数据收集，通过促销和机会赠品来激励社区参与。我们建造 根据 RADx-UP 一项活动，重点关注每个社区的急救人员以帮助主持测试 事件，以及基于信仰和利润（如果适用）。我们邀请了与以下领域有密切联系的联合调查人员： 西弗吉尼亚州南部，该地区 RADx-UP 第一阶段的资源有限。此外，我们还进行了一项试点研究 检查 ABBOTT ID Now 等温 PCR 系统在 600 名参与者中的性能。的效果 通过使用空间回归监测县的测试前和测试后率来评估干预措施 分析。我们建议评估我们的测试策略的统计框架的一个独特属性是一种能力 描述除目标社区外对附近县的影响。该项目将利用 与地方和国家机构建立并发展自己独特的伙伴关系，以实施 社区参与测试疫苗沙漠中的交付模式。我们方法的关键和新颖的方面 正在建立一个草根急救人员研究网络，可利用该网络来实施 在孤立的医疗服务不足的社区进行筛查计划或研究急救人员的健康结果。