Precision Assessment Algorithm for Reducing Disaster-related Respiratory Health Disparities

减少灾害相关呼吸健康差异的精确评估算法

基本信息

批准号：
10764033
负责人：
Jessica Castner
金额：
$ 5.5万
依托单位：
CASTNER INCORPORATED
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-21 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10764033
关键词：
Address Algorithms Assessment tool Black race COVID-19 COVID-19 pandemic Cessation of life Chronic Obstructive Airway Disease Communicable Diseases Communities Complex Computer software Computerized Medical Record Crime Data Data Analyses Dehydration Device or Instrument Development Disasters Disease Disparity Elderly Electronic Health Record Emergency Situation Ensure Exposure to Floods Frequencies Goals Health Health Personnel Health Services Healthcare Systems Hospitals Household Housing Injury Intervention Interview Knowledge Learning Length of Stay Marketing Measures Mental Health Methods Modeling Molds Morbidity - disease rate Motivation National Institute on Minority Health and Health Disparities Outcome Participant Particulate Pathway interactions Patient Outcomes Assessments Patient Self-Report Patients Plug-in Politics Population Predisposition Preparation Process Quality of life Quarantine Readiness Research Resources Respiratory Signs and Symptoms Risk Sampling Savings Self Assessment Self Management Severities Software Tools Surveys System Testing Translating Trauma Victimization Visit Work algorithmic bias behavior change behavioral response chronic respiratory disease climate disaster cost disparities in morbidity emergency preparedness environmental change experience hazard health care service utilization health disparity high risk hospital readmission improved instrument interoperability long-standing disparities machine learning algorithm machine learning model mathematical model member mortality disparity next generation novel pandemic disease personalized intervention prognostic prototype racial bias racial disparity racial identity recruit respiratory respiratory health respiratory morbidity segregation skill acquisition stay-at-home order telehealth weather-related disaster

项目摘要

ABSTRACT Weather and climate disasters are responsible for over 13,000 deaths and $1.7 trillion additional costs over the last 40 years in the USA. Older adults are particularly susceptible to respiratory symptoms, disease exacerbation, unscheduled health care utilization, and decreased quality of life after disaster exposure to particulates, mold, and flooding. Our preliminary data reveal those with Black racial identities possess fewer resources to prepare for disaster. Profound racial disparities observed in the COVID-19 pandemic illustrate the devastating sequelae of long-standing macro-level disparities of segregated housing and sociopolitical networks. The long-term goal of this work is to eliminate racial disparities in large scale disaster health outcomes. The short-term goal of this research is to identify pathways of equal opportunity and disaster affirmative action interventions. The objective here is to create a software prototype of a machine learning algorithm with a novel, valid and reliable assessment tool of disaster vulnerability for older adults with chronic obstructive respiratory disease, prioritizing equality of opportunity to reduce racial bias and disparity. Our specific aims are to 1) Empirically validate a novel assessment tool of disaster vulnerability using self-reported items and scoring system, 2) Refine the validated instrument with a machine learning based algorithm for precision prediction of household emergency preparedness for disaster, 3) Assess racial disparities, data and algorithm bias for Black participants in household hazard vulnerabilities and our instrument development process, and 4) Test interoperability with existing customer software platforms as a plug-in software add-on. We will accomplish these aims using a mixed-methods approach, recruiting 20 expert panel members and up to 600 potential end-user participants, working to over-sample those who reside in predominantly Black communities and Black racial identities. The knowledge gained from this study will provide foundational work to develop precision interventions to reduce post-disaster respiratory symptoms, disease exacerbation, unscheduled health care utilization, and decrements in respiratory quality of life. The results of this study will inform the next generation of electronic health record and patient reported outcomes applications, ensuring the validity, prognostic accuracy, and machine learning models are most relevant to those at highest risk for racial disparities: those with Black racial identities. Health care providers can use our software tool to target and optimize disaster telehealth service lines and increase intervention precision to reduce disaster morbidity and mortality disparities.

抽象的天气和气候灾难造成13,000多人的死亡和1.7万亿美元的额外费用在美国过去40年。老年人特别容易患呼吸道症状，疾病灾难暴露于颗粒物，霉菌和洪水。我们的初步数据揭示了黑人种族身份的人拥有更少的为灾难做准备的资源。在COVID-19大流行中观察到的严重的种族差异说明了这一点长期存在的宏观宏观差异的毁灭性后遗症和社会政治差异网络。这项工作的长期目标是消除大规模灾难健康中的种族差异结果。这项研究的短期目标是确定机会平等和灾难的途径平权行动干预措施。这里的目的是创建机器学习的软件原型具有新颖，有效且可靠的评估工具的算法，用于慢性老年人的灾难脆弱性阻塞性呼吸道疾病，优先考虑减少种族偏见和差异的机会。我们的具体目的是1）凭经验验证一种新颖的评估灾难脆弱性工具项目和评分系统，2）使用基于机器学习的算法来完善经过验证的仪器对灾难的家庭紧急准备的精确预测，3）评估种族差异，数据和黑人参与者的家庭危害漏洞和我们的仪器开发的算法偏见过程，以及4）与现有客户软件平台的互操作性作为插件软件插件。我们将使用混合方法方法来实现这些目标，招募20位专家小组成员及对于600名潜在的最终用户参与者，努力过度样本的人主要是黑色的社区和黑人种族身份。这项研究所获得的知识将为制定精确干预措施以减少灾后呼吸道症状，疾病加剧，未定义的医疗保健利用以及呼吸质量的降低。这项研究的结果将告知下一代电子健康记录和患者报告的结果申请，以确保有效性，预后准确性和机器学习模型与种族风险最高的人最相关差异：具有黑色种族身份的人。医疗保健提供者可以使用我们的软件工具来针对目标和优化灾难远程医疗服务线并提高干预精度以减少灾难发病率和死亡率差异。