Factors affecting the recovery of Kurdistan province COVID-19 patients: a cross-sectional study from March to June 2020

Q3 Mathematics Epidemiologic Methods Pub Date : 2021-02-01 DOI:10.1515/em-2020-0041
Eghbal Zandkarimi
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引用次数: 8

Abstract

Abstract Objectives The Coronavirus disease 2019 (COVID-19) is a new viral disease of the coronavirus family that has a close relationship with SARS species. This study aims to identify factors affecting the recovery of COVID-19 patients in a population with a majority of Kurdish residents. Methods For this purpose, all clinical and demographic parameters were collected from patients with COVID-19 who were outpatients or hospitalized in Kurdistan province (located in western Iran) from March to June 2020. We used the binary logistic regression model to recognition affecting factors to recovery in the COVID-19. Results According to the results of this study, age, sex, coronary heart disease (CHD), cancer, and using antiviral drugs were associated with the chance of recovery. Conclusions Based on the findings of this study, it can be concluded that the chances of recovery of COVID-19 patients who are elderly or have underlying diseases such as CHD or cancer are low. On the other hand, viral drugs are effective in increasing the chances of recovery.
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库尔德斯坦省新冠肺炎患者康复影响因素:2020年3 - 6月横断面研究
摘要目的2019冠状病毒病(COVID-19)是冠状病毒科的一种新型病毒性疾病,与SARS有密切的关系。本研究旨在确定影响以库尔德居民为主的人群中COVID-19患者康复的因素。方法收集2020年3月至6月在伊朗西部库尔德斯坦省(Kurdistan province)门诊或住院的COVID-19患者的所有临床和人口统计学参数。我们使用二元logistic回归模型识别影响COVID-19康复的因素。结果年龄、性别、冠心病(CHD)、癌症、使用抗病毒药物与康复机会相关。根据本研究结果,可以得出结论,老年或有冠心病、癌症等基础疾病的COVID-19患者康复的机会较低。另一方面,抗病毒药物在增加康复机会方面是有效的。
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来源期刊
Epidemiologic Methods
Epidemiologic Methods Mathematics-Applied Mathematics
CiteScore
2.10
自引率
0.00%
发文量
7
期刊介绍: Epidemiologic Methods (EM) seeks contributions comparable to those of the leading epidemiologic journals, but also invites papers that may be more technical or of greater length than what has traditionally been allowed by journals in epidemiology. Applications and examples with real data to illustrate methodology are strongly encouraged but not required. Topics. genetic epidemiology, infectious disease, pharmaco-epidemiology, ecologic studies, environmental exposures, screening, surveillance, social networks, comparative effectiveness, statistical modeling, causal inference, measurement error, study design, meta-analysis
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