Ganjkhanloo Fardin, Ahmadi Farzin, Dong Ensheng, Parker Felix, Gardner Lauren, Ghobadi Kimia
Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.
Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.
PLOS Glob Public Health. 2024 Sep 10;4(9):e0003590. doi: 10.1371/journal.pgph.0003590. eCollection 2024.
The COVID-19 pandemic emphasized the need for pandemic preparedness strategies to mitigate its impacts, particularly in the United States, which experienced multiple waves with varying policies, population response, and vaccination effects. This study explores the relationships between county-level factors and COVID-19 mortality outcomes in the U.S. from 2020 to 2023, focusing on disparities in healthcare access, vaccination coverage, and socioeconomic characteristics. We conduct multi-variable rolling regression analyses to reveal associations between various factors and COVID-19 mortality outcomes, defined as Case Fatality Rate (CFR) and Overall Mortality to Hospitalization Rate (OMHR), at the U.S. county level. Each analysis examines the association between mortality outcomes and one of the three hierarchical levels of the Social Vulnerability Index (SVI), along with other factors such as access to hospital beds, vaccination coverage, and demographic characteristics. Our results reveal persistent and dynamic correlations between various factors and COVID-19 mortality measures. Access to hospital beds and higher vaccination coverage showed persistent protective effects, while higher Social Vulnerability Index was associated with worse outcomes persistently. Socioeconomic status and vulnerable household characteristics within the SVI consistently associated with elevated mortality. Poverty, lower education, unemployment, housing cost burden, single-parent households, and disability population showed significant associations with Case Fatality Rates during different stages of the pandemic. Vulnerable age groups demonstrated varying associations with mortality measures, with worse outcomes predominantly during the Original strain. Rural-Urban Continuum Code exhibited predominantly positive associations with CFR and OMHR, while it starts with a positive OMHR association during the Original strain. This study reveals longitudinal persistent and dynamic factors associated with two mortality rate measures throughout the pandemic, disproportionately affecting marginalized communities. The findings emphasize the urgency of implementing targeted policies and interventions to address disparities in the fight against future pandemics and the pursuit of improved public health outcomes.
新冠疫情凸显了制定大流行防范策略以减轻其影响的必要性,尤其是在美国,该国经历了多波疫情,政策、民众反应和疫苗接种效果各不相同。本研究探讨了2020年至2023年美国县级因素与新冠死亡率结果之间的关系,重点关注医疗保健可及性、疫苗接种覆盖率和社会经济特征方面的差异。我们进行多变量滚动回归分析,以揭示美国县级层面各种因素与新冠死亡率结果之间的关联,新冠死亡率结果定义为病死率(CFR)和总体死亡率与住院率(OMHR)。每项分析都考察了死亡率结果与社会脆弱性指数(SVI)三个层次之一以及其他因素(如医院床位可及性、疫苗接种覆盖率和人口特征)之间的关联。我们的结果揭示了各种因素与新冠死亡率指标之间持续且动态的相关性。医院床位可及性和更高的疫苗接种覆盖率显示出持续的保护作用,而更高的社会脆弱性指数则始终与更差的结果相关。SVI中的社会经济地位和弱势家庭特征始终与死亡率升高相关。贫困、低教育水平、失业、住房成本负担、单亲家庭和残疾人口在疫情不同阶段与病死率显示出显著关联。弱势年龄组与死亡率指标的关联各不相同,在原始毒株流行期间结果主要更差。农村-城市连续体代码与CFR和OMHR主要呈现正相关,而在原始毒株流行期间与OMHR开始呈现正相关。本研究揭示了整个疫情期间与两种死亡率指标相关的纵向持续且动态的因素,这些因素对边缘化社区的影响尤为严重。研究结果强调了实施有针对性的政策和干预措施以解决差异的紧迫性,以应对未来的大流行并追求改善公共卫生结果。