Relationship of socio-demographics, comorbidities, symptoms and healthcare access with early COVID-19 presentation and disease severity

社会人口统计学特征、合并症、症状和医疗保健服务获取与 COVID-19 早期表现和疾病严重程度的关系

阅读:2

Abstract

BACKGROUND: COVID-19 studies are primarily from the inpatient setting, skewing towards severe disease. Race and comorbidities predict hospitalization, however, ambulatory presentation of milder COVID-19 disease and characteristics associated with progression to severe disease is not well-understood. METHODS: We conducted a retrospective chart review including all COVID-19 positive cases from Stanford Health Care (SHC) in March 2020 to assess demographics, comorbidities and symptoms in relationship to: 1) their access point of testing (outpatient, inpatient, and emergency room (ER)) and 2) development of severe disease. RESULTS: Two hundred fifty-seven patients tested positive: 127 (49%), 96 (37%), and 34 (13%) at outpatient, ER and inpatient, respectively. Overall, 61% were age < 55; age > 75 was rarer in outpatient setting (11%) than ER (14%) or inpatient (24%). Most patients presented with cough (86%), fever/chills (76%), or fatigue (63%). 65% of inpatients reported shortness of breath compared to 30-32% of outpatients and ER patients. Ethnic/minority patients had a significantly higher risk of developing severe disease (Asian OR = 4.8 [1.6-14.2], Hispanic OR = 3.6 [1.1-11.9]). Medicare-insured patients were marginally more likely (OR = 4.0 [0.9-17.8]). Other factors associated with developing severe disease included kidney disease (OR = 6.1 [1.0-38.1]), cardiovascular disease (OR = 4.7 [1.0-22.1], shortness of breath (OR = 5.4 [2.3-12.6]) and GI symptoms (OR = 3.3 [1.4-7.7]; hypertension without concomitant CVD or kidney disease was marginally significant (OR = 2.3 [0.8-6.5]). CONCLUSIONS: Early widespread symptomatic testing for COVID-19 in Silicon Valley included many less severely ill patients. Thorough manual review of symptomatology reconfirms the heterogeneity of COVID-19 symptoms, and challenges in using clinical characteristics to predict decline. We re-demonstrate that socio-demographics are consistently associated with severity.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。