PDF(Pubmed)
Abstract：
The COVID-19 pandemic provided an additional spotlight on the longstanding socioeconomic/health impacts of redlining and has added to the myriad of environmental justice issues, which has caused significant loss of life, health, and productive work. The Centers for Disease Control and Prevention (CDC) reports that a person with any selected underlying health conditions is more likely to experience severe COVID-19 symptoms, with more than 81% of COVID-19-related deaths among people aged 65 years and older. The effects of COVID-19 are not homogeneous across populations, varying by socioeconomic status, PM2.5 exposure, and geographic location. This variability is supported by analysis of existing data as a function of the number of cases and deaths per capita/1,00,000 persons. We investigate the degree of correlation between these parameters, excluding health conditions and age. We found that socioeconomic variables alone contribute to ~40% of COVID-19 variability, while socioeconomic parameters, combined with political affiliation, geographic location, and PM2.5 exposure levels, can explain ~60% of COVID-19 variability per capita when using an OLS regression model; socioeconomic factors contribute ~28% to COVID-19-related deaths. Using spatial coordinates in a Random Forest (RF) regressor model significantly improves prediction accuracy by ~120%. Data visualization products reinforce the fact that the number of COVID-19 deaths represents 1% of COVID-19 cases in the US and globally. A larger number of democratic voters, larger per-capita income, and age >65 years is negatively correlated (associated with a decrease) with the number of COVID cases per capita. Several distinct regions of negative and positive correlations are apparent, which are dominated by two major regions of anticorrelation: (1) the West Coast, which exhibits high PM2.5 concentrations and fewer COVID-19 cases; and (2) the middle portion of the US, showing mostly high number of COVID-19 cases and low PM2.5 concentrations. This paper underscores the importance of exercising caution and prudence when making definitive causal statements about the contribution of air quality constituents (such as PM2.5) and socioeconomic factors to COVID-19 mortality rates. It also highlights the importance of implementing better health/lifestyle practices and examines the impact of COVID-19 on vulnerable populations, particularly regarding preexisting health conditions and age. Although PM2.5 contributes comparable deaths (~7M) per year, globally as smoking cigarettes (~8.5M), quantifying any causal contribution toward COVID-19 is non-trivial, given the primary causes of COVID-19 death and confounding factors. This becomes more complicated as air pollution was reduced significantly during the lockdowns, especially during 2020. This statistical analysis provides a modular framework, that can be further expanded with the context of multilevel analysis (MLA). This study highlights the need to address socioeconomic and environmental disparities to better prepare for future pandemics. By understanding how factors such as socioeconomic status, political affiliation, geographic location, and PM2.5 exposure contribute to the variability in COVID-19 outcomes, policymakers and public health officials can develop targeted strategies to protect vulnerable populations. Implementing improved health and lifestyle practices and mitigating environmental hazards will be essential in reducing the impact of future public health crises on marginalized communities. These insights can guide the development of more resilient and equitable health systems capable of responding effectively to similar future scenarios.
摘要：
COVID-19大流行使人们更加关注重新修订对社会经济/健康的长期影响，并增加了无数的环境正义问题，造成了巨大的生命损失，健康,和富有成效的工作。疾病控制和预防中心（CDC）报告说，患有任何选定的潜在健康状况的人更有可能出现严重的COVID-19症状，超过81%的与COVID-19相关的死亡发生在65岁及以上的人群中。COVID-19的影响在人群中并不均匀，根据社会经济地位的不同，PM2.5暴露，和地理位置。对现有数据的分析支持了这种差异，这些数据是人均病例数和死亡人数/10万人的函数。我们调查这些参数之间的相关程度，不包括健康状况和年龄。我们发现，仅社会经济变量就贡献了约40%的COVID-19变异性，而社会经济参数，结合政治派别，地理位置,和PM2.5暴露水平，使用OLS回归模型可以解释约60%的人均COVID-19变异性；社会经济因素对COVID-19相关死亡的贡献约为28%。在随机森林（RF）回归模型中使用空间坐标可将预测精度显着提高〜120％。数据可视化产品强化了这样一个事实，即在美国和全球，COVID-19死亡人数占COVID-19病例的1%。大量的民主选民，人均收入更高，年龄>65岁与人均COVID病例数呈负相关(与下降相关)。几个明显的负相关和正相关区域很明显，它们由两个主要的反相关区域主导：(1)西海岸，PM2.5浓度较高，COVID-19病例较少；(2)美国中部，主要表现为较高的COVID-19病例数和较低的PM2.5浓度。本文强调了在就空气质量成分(如PM2.5)和社会经济因素对COVID-19死亡率的贡献做出明确的因果陈述时，谨慎和谨慎的重要性。它还强调了实施更好的健康/生活方式的重要性，并研究了COVID-19对弱势群体的影响，特别是关于先前存在的健康状况和年龄。尽管PM2.5每年造成的死亡人数相当（约7M），全球吸烟（约8.5M），量化对COVID-19的任何因果贡献是不平凡的，考虑到COVID-19死亡的主要原因和混杂因素。随着封锁期间空气污染大大减少，情况变得更加复杂，尤其是在2020年。这种统计分析提供了一个模块化的框架，这可以在多水平分析(MLA)的背景下进一步扩展。这项研究强调了解决社会经济和环境差异的必要性，以更好地为未来的大流行做好准备。通过了解社会经济地位等因素，政治派别,地理位置,PM2.5暴露导致COVID-19结果的变异性，政策制定者和公共卫生官员可以制定有针对性的战略来保护弱势群体。实施改善的健康和生活方式以及减轻环境危害对于减少未来公共卫生危机对边缘化社区的影响至关重要。这些见解可以指导开发更有弹性和公平的卫生系统，能够有效应对类似的未来情景。
