Precision Assessment Algorithm for Reducing Disaster-related Respiratory Health Disparities

摘要 天气和气候灾害造成13，000多人死亡，1.7万亿美元的额外成本。 过去40年在美国。老年人特别容易出现呼吸道症状， 急性加重，计划外的医疗保健利用，以及灾难暴露后生活质量下降， 微粒，霉菌，还有洪水我们的初步数据显示，黑人种族身份的人拥有的 为灾难做好准备。在COVID-19大流行中观察到的深刻种族差异表明， 隔离住房和社会政治生活长期存在的宏观不平等造成的毁灭性后果 网络.这项工作的长期目标是消除大规模灾难卫生方面的种族差异 结果。这项研究的短期目标是确定机会平等和灾难的途径 积极行动干预。这里的目标是创建机器学习的软件原型 算法与一种新的，有效的和可靠的评估工具的灾害脆弱性的老年人慢性 阻塞性呼吸道疾病，优先考虑机会平等，以减少种族偏见和差距。我们 具体目标是：1）利用自我报告的数据， 项目和评分系统，2）使用基于机器学习的算法优化经验证的工具， 准确预测家庭对灾害的应急准备，3）评估种族差异，数据和 算法偏见的黑人参与者在家庭危险的脆弱性和我们的仪器开发 过程，以及4）测试与现有客户软件平台的互操作性，作为插件软件附件。 我们将采用混合方法实现这些目标，招募20名专家小组成员， 600名潜在的最终用户参与者，努力对那些居住在黑人占主导地位的人进行过度抽样 黑人的种族认同和种族认同。从这项研究中获得的知识将提供基础工作， 制定精确的干预措施，以减少灾后呼吸道症状，疾病恶化， 不定期的医疗保健利用和呼吸系统生活质量下降。这项研究的结果将 告知下一代电子健康记录和患者报告结果应用程序，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：那些有黑人种族身份的人。医疗保健提供者可以使用我们的软件工具， 优化灾害远程保健服务线路，提高干预精确度，以减少灾害发病率， 死亡率差距。

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.