基于地理空间和ml的健康差异识别和决定因素追踪方法,以改善大流行卫生保健

Jinwei Liu, R. Aló, Yohn Jairo Parra Bautista, C. Yedjou, Carlos Theran
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引用次数: 1

摘要

2019年冠状病毒病(COVID-19)大流行严重影响了世界各国,造成了前所未有的死亡率和经济破坏,并对不同社区产生了不成比例的负面影响,特别是种族和少数民族,他们尤其处于不利地位,因为他们更有可能成为COVID-19感染的潜在目标。美国黑人长期处于不利地位(例如,在健康结果方面长期存在差距),并且处于易受这种流行病影响的弱势地位。一些研究表明,老年人和潜在合并症患者具有高风险和脆弱性,然而,很少有研究关注利用地理信息来追踪社会和结构性健康决定因素,这可以提供较低的粒度水平。在本文中,我们提出了GMLTrace,这是一种基于地理空间和ml(基于机器学习)的方法,用于识别COVID-19大流行中健康差异的各种决定因素(包括结构、社会和结构决定因素),它提供了更低的粒度水平。我们基于多个COVID-19数据集对健康差异进行了全面分析,并检查了结构性、社会和结构性健康决定因素,以帮助确定COVID-19大流行导致的感染和死亡率差异(在特别弱势的种族和少数民族中)的原因。大量的实验结果表明了该方法的有效性。该研究为健康差异识别和决定因素追踪提供了新的策略,旨在改善大流行卫生保健。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Geospatial and ML-based Approach to Health Disparity Identification and Determinant Tracing for Improving Pandemic Health Care
The Coronavirus disease 2019 (COVID-19) pan-demic has severely impacted countries around the world with unprecedented mortality and economic devastation and has dis-proportionately and negatively impacted different communities-especially racial and ethnic minorities who are at a particular disadvantage as they are more likely to be the potential target of COVID-19 infection. Black Americans have a long-standing history of disadvantage (e.g., long-standing disparities in health outcomes) and are in a vulnerable position to experience the impact of this pandemic. Some studies indicate high-risk and vulnerability of the elderly and patients with underlying co-morbidities, however, little research paid attention to leveraging geographic information to trace the social and structural health determinants, which can provide a lower level of granularity. In this paper, we propose GMLTrace, a geospatial and ML-based (machine learning based) approach to identify diverse determinants (including the structural, social, and constructural determinants) of health disparities in COVID-19 pandemic, which provides a lower level of granularity. We provide a thorough analysis of health disparities based on multiple COVID-19 datasets and examine the structural, social, and constructural health determinants to assist in ascertaining why disparities (in racial and ethnic minorities who are particularly disadvantaged) occur in infection and death rates due to COVID-19 pandemic. Extensive experimental results show the effectiveness of our approach. The research provides new strategies for health disparity identification and determinant tracing with a goal to improve pandemic health care.
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