深度健康:基于地理空间和ml的方法来确定健康差异和决定因素,以改善大流行卫生保健

Jinwei Liu, Rui Gong, Long Cheng, Richard A. Aló
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引用次数: 0

摘要

2019冠状病毒病大流行加剧了现有的卫生差距,对处境不利和脆弱社区的影响尤为严重。种族和少数民族,如美国黑人,处于特别不利的地位,更有可能成为COVID-19感染的潜在目标,并且死亡率高得惊人。尽管COVID-19疫苗接种的有希望解决方案带来了希望,但公平获得COVID-19疫苗在美国仍然是一项挑战,这加剧了种族和少数民族群体在病例、住院和死亡方面的现有差异。美国根深蒂固的医学种族主义历史导致了少数族裔对疫苗的犹豫,从而造成了差异。虽然一些研究考察了健康差异的决定因素(例如社会健康决定因素),但单独或与其他决定因素一起考察社会、结构和结构性健康决定因素的研究还很缺乏。很少有研究注意到利用地理信息来追踪社会、结构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DeepHealth,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(包括社会、结构和结构决定因素),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,并检查了社会、结构和结构性健康决定因素,以帮助确定为什么COVID-19大流行导致的发病率和死亡率出现差异(在特别弱势的种族和少数民族中)。大量的实验结果表明了该方法的有效性。本研究为健康差异识别和决定因素跟踪提供了新的策略,旨在减轻健康差异和改善大流行卫生保健。研究表明,政策制定者应重视将保护人口健康的举措(即减少健康差距的上游办法),而不是仅仅侧重于提供保健和社会服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DeepHealth: Geospatial and ML-Based Approach to Identify Health Disparities and Determinants for Improving Pandemic Health Care
The COVID-19 pandemic has exacerbated existing health disparities, and its impact has fallen disproportionately on disadvantaged and vulnerable communities. Racial and ethnic minorities such as Black Americans who are at a particular disadvantage are more likely to be the potential target of COVID-19 infection and are dying at alarmingly high rates. Despite a promising solution of the COVID-19 vaccination offers hope, equitable access to COVID-19 vaccines remains a challenge in the US, which has compounded the existing disparities in cases, hospitalizations, and deaths among racial and ethnic minority groups. The deep and pervasive history of medical racism in the US has led to the vaccine hesitancy in racial and ethnic minorities, and thereby caused the disparities. Although some studies examine determinants of health disparities (e.g., social health determinants), there is a shortage of studies examining the social, structural and constructural health determinants, either alone or in tandem with other determinants. Little research paid attention to leveraging geographic information to trace the social, structural and constructural health determinants, which can provide a lower level of granularity. In this paper, we propose DeepHealth, a geospatial and ML-based (machine learning based) approach to identify diverse determinants (including the social, structural, 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 social, structural, and constructural health determinants to assist in ascertaining why disparities (in racial and ethnic minorities who are particularly disadvantaged) occur in incidence and mortality rates due to COVID-19 pandemic. Extensive experimental results show the effectiveness of our approach. This research provides new strategies for health disparity identification and determinant tracking with a goal of mitigating health disparities and improving pandemic health care. The research suggests that policymakers should give attention to initiatives that will protect the health of populations (i.e., an upstream approach to reducing health disparities) rather than solely focusing only on providing health and social services.
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