The Social, Demographic, and Clinical Predictors of COVID-19 Severity: a Model-based Analysis of United States Veterans.

IF 3.2 3区医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Journal of Racial and Ethnic Health Disparities Pub Date : 2024-10-01 Epub Date: 2023-09-01 DOI:10.1007/s40615-023-01773-5

Alyssa R Greenhouse, Danielle Richard, Anjali Khakharia, Michael Goodman, Lawrence S Phillips, Julie A Gazmararian

{"title":"The Social, Demographic, and Clinical Predictors of COVID-19 Severity: a Model-based Analysis of United States Veterans.","authors":"Alyssa R Greenhouse, Danielle Richard, Anjali Khakharia, Michael Goodman, Lawrence S Phillips, Julie A Gazmararian","doi":"10.1007/s40615-023-01773-5","DOIUrl":null,"url":null,"abstract":"Purpose: This study aims to identify the contributions of individual and community social determinants of health (SDOH), demographic, and clinical factors in COVID-19 disease severity through a model-based analysis.Methods: This national cross-sectional study focused on hospitalization among those tested for COVID-19 and use of intensive care, analyzing data on 220,848 Veterans tested between February 20, 2020 and October 20, 2021. Multiple logistic regression models were constructed using backwards elimination. The predictive value of each model was assessed with a c-statistic.Results: Those hospitalized were older, more likely to be male, of Black or Asian race, have an income less than $39,999, live in an urban residence, and have medical comorbidities. The strongest predictors for hospitalization included Gini inequality index, race, income, heart failure, chronic kidney disease (CKD), and chronic obstructive pulmonary disease (COPD). For intensive care, Asian race, rural residence, COPD, and CKD were the strongest predictors. C-statistics were c = 0.749 for hospitalization and c = 0.582 for ICU admission.Conclusions: A combination of clinical, demographic, individual and community SDOH factors predict COVID-19 hospitalization with good predictive ability and can inform risk stratification, discharge planning, and public health interventions. Racial disparities were not explained by social or clinical factors. Intensive care models had low discriminative power and may be better explained by other characteristics.","PeriodicalId":16921,"journal":{"name":"Journal of Racial and Ethnic Health Disparities","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Racial and Ethnic Health Disparities","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s40615-023-01773-5","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/9/1 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}

引用次数: 0

Abstract

Purpose: This study aims to identify the contributions of individual and community social determinants of health (SDOH), demographic, and clinical factors in COVID-19 disease severity through a model-based analysis.

Methods: This national cross-sectional study focused on hospitalization among those tested for COVID-19 and use of intensive care, analyzing data on 220,848 Veterans tested between February 20, 2020 and October 20, 2021. Multiple logistic regression models were constructed using backwards elimination. The predictive value of each model was assessed with a c-statistic.

Results: Those hospitalized were older, more likely to be male, of Black or Asian race, have an income less than $39,999, live in an urban residence, and have medical comorbidities. The strongest predictors for hospitalization included Gini inequality index, race, income, heart failure, chronic kidney disease (CKD), and chronic obstructive pulmonary disease (COPD). For intensive care, Asian race, rural residence, COPD, and CKD were the strongest predictors. C-statistics were c = 0.749 for hospitalization and c = 0.582 for ICU admission.

Conclusions: A combination of clinical, demographic, individual and community SDOH factors predict COVID-19 hospitalization with good predictive ability and can inform risk stratification, discharge planning, and public health interventions. Racial disparities were not explained by social or clinical factors. Intensive care models had low discriminative power and may be better explained by other characteristics.

查看原文本刊更多论文

COVID-19 严重程度的社会、人口和临床预测因素：基于模型的美国退伍军人分析》（The Social, Demographic, and Clinical Predictors of COVID-19 Severity: a Model-based Analysis of United States Veterans）。

目的：本研究旨在通过基于模型的分析，确定个人和社区健康社会决定因素（SDOH）、人口统计学和临床因素对 COVID-19 疾病严重程度的影响：这项全国性横断面研究重点关注 COVID-19 检测者的住院情况和重症监护的使用情况，分析了 2020 年 2 月 20 日至 2021 年 10 月 20 日期间检测的 220,848 名退伍军人的数据。采用反向排除法构建了多元逻辑回归模型。用c统计量评估了每个模型的预测价值：住院患者年龄较大，更有可能是男性、黑人或亚裔、收入低于 39,999 美元、居住在城市、患有合并症。吉尼不平等指数、种族、收入、心力衰竭、慢性肾病（CKD）和慢性阻塞性肺病（COPD）是住院的最强预测因素。在重症监护方面，亚裔、农村居民、慢性阻塞性肺病和慢性阻塞性肺病是最强的预测因素。住院治疗的 C 统计量为 c = 0.749，入住重症监护室的 C 统计量为 c = 0.582：结论：综合临床、人口、个人和社区 SDOH 因素可预测 COVID-19 的住院情况，具有良好的预测能力，可为风险分层、出院规划和公共卫生干预提供依据。社会或临床因素无法解释种族差异。重症监护模型的判别能力较低，其他特征可能可以更好地解释这一现象。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Racial and Ethnic Health Disparities PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-

CiteScore

7.30

自引率

5.10%

发文量

263

期刊介绍： Journal of Racial and Ethnic Health Disparities reports on the scholarly progress of work to understand, address, and ultimately eliminate health disparities based on race and ethnicity. Efforts to explore underlying causes of health disparities and to describe interventions that have been undertaken to address racial and ethnic health disparities are featured. Promising studies that are ongoing or studies that have longer term data are welcome, as are studies that serve as lessons for best practices in eliminating health disparities. Original research, systematic reviews, and commentaries presenting the state-of-the-art thinking on problems centered on health disparities will be considered for publication. We particularly encourage review articles that generate innovative and testable ideas, and constructive discussions and/or critiques of health disparities.Because the Journal of Racial and Ethnic Health Disparities receives a large number of submissions, about 30% of submissions to the Journal are sent out for full peer review.