Related Experiment Video
Updated: Sep 14, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Analysis of Influencing Factors of Death in the Elderly With Coronavirus Disease 2019 Based on Propensity Score
Ying Chen1, Hai-Ping Huang1, Xin Li1
1Department of Epidemiology and Health Statistics,West China School of Public Health/West China Fourth Hospital,Sichuan University,Chengdu 610041,China.
Insights
Elderly individuals with COVID-19 face increased death risks from smoking, stroke, tumors, heart disease, breathing difficulties, and vomiting. Fever, however, showed a protective effect in this vulnerable population.
Area of Science:
- Gerontology
- Infectious Diseases
- Epidemiology
Background:
- Coronavirus disease 2019 (COVID-19) poses a significant threat to the elderly population.
- Understanding mortality risk factors in elderly COVID-19 patients is crucial for targeted interventions.
Purpose of the Study:
- To identify and analyze the key factors influencing mortality among elderly patients diagnosed with COVID-19.
- To develop a predictive model for COVID-19 death risk in the elderly population.
Main Methods:
- Retrospective case-control study analyzing data from West China Fourth Hospital (January 1 to July 8, 2023).
- Propensity score matching was used to compare deceased COVID-19 patients with survivors from the West China Elderly Health Cohort.
- LASSO-Logistic regression analysis was employed to identify significant risk factors, with model validity assessed by ROC curve.
Main Results:
- The study included 3,239 COVID-19 survivors and 142 deaths.
- Significant risk factors for mortality included smoking (OR=3.33), stroke (OR=3.55), malignant tumors (OR=19.93), coronary heart disease (OR=7.68), difficulty breathing/asthma (OR=21.48), and vomiting (OR=8.19).
- Fever (OR=0.51) was associated with a reduced risk of death. The predictive model achieved an AUC of 0.889.
Conclusions:
- Smoking, stroke, malignant tumors, coronary heart disease, breathing difficulties, and vomiting are critical determinants of mortality in elderly COVID-19 patients.
- Fever may indicate a different disease trajectory or host response in some elderly COVID-19 cases.
- These findings underscore the need for proactive management of comorbidities and symptoms in elderly COVID-19 patients to mitigate mortality risk.
Abstract:
Objective To analyze the influencing factors of death in the elderly with coronavirus disease 2019(COVID-19).Methods The case data of death caused by COVID-19 in West China Fourth Hospital from January 1 to July 8,2023 were collected,and surviving cases from the West China Elderly Health Cohort infected with COVID-19 during the same period were selected as the control.LASSO-Logistic regression was adopted to analyze the data after propensity score matching and the validity of the model was verified by drawing the receiver operating characteristic curve.Results A total of 3 239 COVID-19 survivors and 142 deaths with COVID-19 were included.The results of LASSO-Logistic regression showed that smoking(OR=3.33,95%CI=1.46-7.59,P=0.004),stroke(OR=3.55,95%CI=1.15-10.30,P=0.022),malignant tumors(OR=19.93, 95%CI=8.52-49.23, P<0.001),coronary heart disease(OR=7.68, 95%CI=3.52-17.07, P<0.001),fever(OR=0.51, 95%CI=0.26-0.96, P=0.042),difficulty breathing or asthma symptoms(OR=21.48, 95%CI=9.44-51.95, P<0.001),and vomiting(OR=8.19,95%CI=2.87-23.58, P<0.001)increased the risk of death with COVID-19.The prediction model constructed based on the influencing factors achieved an area under the curve of 0.889 in the test set.Conclusions Smoking,stroke,malignant tumors,coronary heart disease,fever,breathing difficulty or asthma symptoms,and vomiting were identified as key factors influencing the death risk in COVID-19.
More Related Videos
Related Concept Videos
Comparing the Survival Analysis of Two or More Groups
Cancer Survival Analysis
Factors Affecting Illness
For instance, risk factors are connected to illness,...
Statistical Methods for Analyzing Epidemiological Data
Assumptions of Survival Analysis
Bias in Epidemiological Studies

