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: 10446844
• 负责人: Brian Hendricks
• 金额: $ 101.53万
• 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/10446844
• 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; Code; Color; Common Data Element; Communities; Community Health; Computer software; County; Data; Data Collection; Disease; Disease Outbreaks; Early identification; Emergency medical service; Event; Fire - disasters; Food; Funding; Future; Geographic Locations; Geography; Goals; Health; Health Services Accessibility; High Prevalence; Incentives; Individual; Infrastructure; Intervention; Interview; Knowledge; Machine Learning; Marketing; Medical; Methodology; Modeling; Monitor; Morbidity - disease rate; Motivation; Outcome; Participant; Perception; Performance; Persons; Phase; Pilot Projects; Plant Roots; Poaceae; Population Decreases; Pre-Post Tests; 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; base; community building; 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; rate of change; 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个县的23％被排名最高的20％ 美国脆弱的县在美国的Covid-19。该州极端脆弱性的核心是更高的 医疗合并症的患病率，农村人口中较低的护理和疫苗的扩大 与城市同行相比，吸收。令人担忧的是，测试已在全州范围内减少以允许 疫苗的积极分散。不幸的是，低测试化合物化合物在医学上与covid-19的脆弱性 疫苗摄取量低的人口低迷，因为它们非常容易持续局部 病毒爆发，随后发病率更高。我们的RADX-UP阶段第二建议 通过识别和针对疫苗沙漠社区来建立以前资助的Radx-Up阶段 然后根据对各个社区的需求，根据对各个社区的需求进行调整测试服务服务 什么很重要。为继续测试提供动态解决方案至关重要。我们定义疫苗沙漠 使用总体疫苗率和两周内疫苗摄取的变化。与机器学习 时间序列建模用于表征县级传输，首次合并此处 疫苗率。县一级的风险估计与疫苗销毁的邮政编码覆盖 使用底部十分位数确定了14天的整体液泡率和疫苗变化。 一旦确定了一个社区的研究，联络人将研究人员与倡导者联系，以进行半结构化 采访以确定合作伙伴网站以主持测试活动并收集数据以量身定制促销，食品和媒体 向每个社区的特定需求发送消息。测试事件将涉及样本和调查 数据收集，促销和机会赠品激励社区参与。我们建造 在Radx-Up One活动中，通过大量专注于每个社区的急救人员，以帮助进行测试 事件和信仰基于适用的利润。我们涉及共同研究人员，与 南部WV，该地区的RADX-UP第一阶段资源有限。此外，我们进行了一项试点研究 现在检查600名参与者的Abbott ID等温度PCR系统的性能。效果 通过使用空间回归监测县的测试率和后测试率来评估干预措施 我们建议评估测试策略的统计框架的独特属性是一种能力 描述除目标社区外，对附近县的影响。这个项目将利用 现有并与地方和州机构建立自己的独特合作伙伴关系，以实施 疫苗沙漠中的社区订婚测试交付模型。我们方法的批判性和新颖方面 是建立基层第一响应者研究网络，可以利用该网络来实施 在孤立的医学服务不足的社区中筛选计划或研究第一响应者健康成果。