To quantify the role of age, gender, exposure and rate of infection as predictor of mortality in COVID-19, a cross sectional study was conducted covering 219 patients who were tested positive out of 554 suspects in Qazi Hussain Ahmed Medical Complex Nowshera, Pakistan. Linear regression analysis was used as statistical tool. We observed that the positive contact history/exposure has been shown to have strong predictive value for mortality due to Covid-19 (β=0.394**,ΔR²=0.011, p=0.001). Similarly age>60 years and rate of infection were also a strong predictor of mortality with interaction values of (β=0.389**,ΔR²=0.046, p=0.001) and (β=0.431**; ΔR²=0.005, p =0.03) respectively. Gender as such is not a statistically significant predictor in mortality due to COVID-19. We concluded that using this regression analysis: the age of patients and positive exposure irrespective of gender statistically predicts the mortality in COVID-19.
khan, H., & Rehman, K. (2020). Linear regression analysis showing the predictors of mortality in COVID-19.. Microbes and Infectious Diseases, 1(3), 136-139. doi: 10.21608/mid.2020.37850.1046
MLA
hamzullah khan; Khalida Rehman. "Linear regression analysis showing the predictors of mortality in COVID-19.", Microbes and Infectious Diseases, 1, 3, 2020, 136-139. doi: 10.21608/mid.2020.37850.1046
HARVARD
khan, H., Rehman, K. (2020). 'Linear regression analysis showing the predictors of mortality in COVID-19.', Microbes and Infectious Diseases, 1(3), pp. 136-139. doi: 10.21608/mid.2020.37850.1046
VANCOUVER
khan, H., Rehman, K. Linear regression analysis showing the predictors of mortality in COVID-19.. Microbes and Infectious Diseases, 2020; 1(3): 136-139. doi: 10.21608/mid.2020.37850.1046
)
View on SCiNiTO