Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA.
Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA.
复制标题
了解与Covid-19有关的合并症和健康差异：在美国印第安纳州对776 936病例和1 362 545个对照的全面研究。
作者：
描述美国印第安纳州COVID-19患者的特征，并评价其人口统计学和合并症作为COVID-19严重程度的风险因素。从印第安纳州的COVID-19研究数据共享（CoRDaCo）收集了776 936例COVID-19病例和1 362 545例对照的EHR数据。有关县人口和人均收入的数据来自美国人口普查局。进行了统计分析，以确定人口统计学和临床变量与COVID-19严重程度的关联。进行了预测分析，以评估CoRDaCo EHR数据在确定COVID-19严重程度方面的预测能力。慢性阻塞性肺疾病、心血管疾病和2型糖尿病分别占COVID-19患者的3.49%、2.59%和4.76%。与整个COVID-19患者人群（1.94%）相比，此类COVID-19患者的ICU入院率分别为10.23%、14.33%和11.11%。此外，与整个COVID-19患者人群相比，患有这些合并症的患者的死亡率显著更高。健康差异分析表明，印第安纳州各县之间存在潜在的健康差异。预测分析实现的F1评分分别为0.8011和0.7072，用于分类COVID-19病例与对照组以及ICU与非ICU病例。印第安纳州的黑人人口受到COVID-19的不利影响大于白色人口。这与现有研究的结果一致。我们的研究结果还表明了人口和经济因素方面的其他健康差异。本研究描述了印第安纳州大量COVID-19患者人群中合并症与COVID-19结局之间在ICU入院方面的关系。
To characterize COVID-19 patients in Indiana, United States, and to evaluate their demographics and comorbidities as risk factors to COVID-19 severity. EHR data of 776 936 COVID-19 cases and 1 362 545 controls were collected from the COVID-19 Research Data Commons (CoRDaCo) in Indiana. Data regarding county population and per capita income were obtained from the US Census Bureau. Statistical analysis was conducted to determine the association of demographic and clinical variables with COVID-19 severity. Predictive analysis was conducted to evaluate the predictive power of CoRDaCo EHR data in determining COVID-19 severity. Chronic obstructive pulmonary disease, cardiovascular disease, and type 2 diabetes were found in 3.49%, 2.59%, and 4.76% of the COVID-19 patients, respectively. Such COVID-19 patients have significantly higher ICU admission rates of 10.23%, 14.33%, and 11.11%, respectively, compared to the entire COVID-19 patient population (1.94%). Furthermore, patients with these comorbidities have significantly higher mortality rates compared to the entire COVID-19 patient population. Health disparity analysis suggests potential health disparities among counties in Indiana. Predictive analysis achieved F1-scores of 0.8011 and 0.7072 for classifying COVID-19 cases versus controls and ICU versus non-ICU cases, respectively. Black population in Indiana was more adversely affected by COVID-19 than the White population. This is consistent to findings from existing studies. Our findings also indicate other health disparities in terms of demographic and economic factors. This study characterizes the relationship between comorbidities and COVID-19 outcomes with respect to ICU admission across a large COVID-19 patient population in Indiana.
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影响因子：
5.2
作者：
Hatef E;Chang HY;Kitchen C;Weiner JP;Kharrazi H
通讯作者：
Kharrazi H
影响因子：
39
作者：
Gupta, Shruti;Hayek, Salim S.;Leaf, David E.
通讯作者：
Leaf, David E.
影响因子：
4.6
作者：
Cho SI;Yoon S;Lee HJ
通讯作者：
Lee HJ
影响因子：
3.7
作者：
Dixon BE;Grannis SJ;Lembcke LR;Valvi N;Roberts AR;Embi PJ
通讯作者：
Embi PJ
DOI：
10.1093/jamia/ocaa196
发表时间：
2021-03-01
影响因子：
6.4
作者：
Haendel, Melissa A.;Chute, Christopher G.;Gersing, Ken R.
通讯作者：
Gersing, Ken R.