Selected article for: "binary logistic regression model and logistic regression"

Author: Zandkarimi, E.
Title: Factors affecting the recovery of Kurdistan province COVID-19 patients: A cross-sectional study from March to June 2020
  • Cord-id: oc3g65l7
  • Document date: 2021_1_1
  • ID: oc3g65l7
    Snippet: 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 20
    Document: 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. © 2021 Walter de Gruyter GmbH, Berlin/Boston.

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