Volume 11, Issue 4 (Autumn - In Press 2023)                   Iran J Health Sci 2023, 11(4): 0-0 | Back to browse issues page

Ethics code: IR.GOUMS.REC.1401.185
Clinical trials code: IR.GOUMS.REC.1401.185

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Khorsha H, Babanezhad M, Behnampour N. Evaluation of the Causal Association of Risk Factors for Death from COVID-19 Patients Admitted to Golestan Hospitals by Propensity Score Estimation Method. Iran J Health Sci 2023; 11 (4)
URL: http://jhs.mazums.ac.ir/article-1-870-en.html
Department of Statistics, Faculty of Sciences, Golestan University, Gorgan, Golestan, Iran. , mbabab22@yahoo.com
Abstract:   (120 Views)

Background and purpose: Evaluating the causal association effect of risk factors is inevitable when one aims to estimate   mortality rate from COVID-19 patients. Plenty of research has estimated the impact of COVID-19 on death in various countries worldwide, they have rarely addressed the effect of causal association of risk factors. This study aims to fill this gap by estimating the impact of COVID-19 on death by evaluating the effect of the causal association of the risk factors.
Materials and Methods: The research population included all inpatients in the hospitals under the supervision of Golestan University of Medical Science, Golestan, Iran, in 2020 with initial COVID-19 symptoms based on their PCR test results. The method that we used is called propensity score which is an effective statistical technique for evaluating the causal association effect of risk factors in observational studies. We also used Student's t-tests and Chi-squared test to compare differences between two groups.
Results: We used propensity score and propensity score matching estimation approaches and logistic regression analysis for comparison. Of 6,379 inpatients, 5,581 (87.5%) patients were discharged/recovered, and 798 (12.5%) patients died, respectively. The causal association between treatment results (discharged vs. died) and PCR test, SPO2, gender, age, and hospitalization duration in ICU were statistically significant.
Conclusion: Using the propensity score matching estimation method showed the high risk of death in patients with PCR+ test diagnosis. Specifically, the above measured risk factors increased the risk of death in patients with PCR+ to 72% by using the propensity score matching estimation approach; in contrast by using the traditional multiple logistic regression model, the risk of death was 46%. This might be due to better controlling the effect of the above measured risk factors. Therefore, the former estimating approach is more effective in estimating the impact of COVID-19 on death.

Type of Study: Original Article | Subject: Biostatistics

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