ABSTRACT
An increasing number of patients are being killed by coronavirus disease 2019 (COVID-19), however, risk factors for the fatality of COVID-19 remain unclear. A total of 21,392 COVID-19 cases were recruited in the Hubei Province of China between December 2019 and February 2020, and followed up until March 18, 2020. We adopted Cox regression models to investigate the risk factors for case fatality and predicted the death probability under specific combinations of key predictors. Among the 21,392 patients, 1,020 (4.77%) died of COVID-19. Multivariable analyses showed that factors, including age (≥60 versus <45 years, hazard ratio [HR] = 7.32; 95% confidence interval [CI], 5.42, 9.89), sex (male versus female, HR = 1.31; 95% CI, 1.15, 1.50), severity of the disease (critical versus mild, HR = 39.98; 95% CI, 29.52, 48.86), comorbidity (HR = 1.40; 95% CI, 1.23, 1.60), highest body temperature (>39°C versus <39°C, HR = 1.28; 95% CI, 1.09, 1.49), white blood cell counts (>10 × 109/L versus (4-10) × 109/L, HR = 1.69; 95% CI, 1.35, 2.13), and lymphocyte counts (<0.8 × 109/L versus (0.8-4) × 109/L, HR = 1.26; 95% CI, 1.06, 1.50) were significantly associated with case fatality of COVID-19 patients. Individuals of an older age, who were male, with comorbidities, and had a critical illness had the highest death probability, with 21%, 36%, 46%, and 54% within 1-4 weeks after the symptom onset. Risk factors, including demographic characteristics, clinical symptoms, and laboratory factors were confirmed to be important determinants of fatality of COVID-19. Our predictive model can provide scientific evidence for a more rational, evidence-driven allocation of scarce medical resources to reduce the fatality of COVID-19.
ABSTRACT
BACKGROUND: Ambient PM1 (particulate matter with aerodynamic diameter ≤1⯵m) is an important contribution of PM2.5 mass. However, little is known worldwide regarding the PM1-associated health effects due to a wide lack of ground-based PM1 measurements from air monitoring stations. METHODS: We collected daily records of hospital admission for respiratory diseases and station-based measurements of air pollution and weather conditions in Shenzhen, China, 2015-2016. Time-stratified case-crossover design and conditional logistic regression models were adopted to estimate hospitalization risks associated with short-term exposures to PM1 and PM2.5. RESULTS: PM1 and PM2.5 showed significant adverse effects on respiratory disease hospitalizations, while no evident associations with PM1-2.5 were identified. Admission risks for total respiratory diseases were 1.09 (95% confidence interval: 1.04 to 1.14) and 1.06 (1.02 to 1.10), corresponding to per 10⯵g/m3 rise in exposure to PM1 and PM2.5 at lag 0-2 days, respectively. Both PM1 and PM2.5 were strongly associated with increased admission for pneumonia and chronic obstructive pulmonary diseases, but exhibited no effects on asthma and upper respiratory tract infection. Largely comparable risk estimates were observed between male and female patients. Groups aged 0-14 years and 45-74 years were significantly affected by PM1- and PM2.5-associated risks. PM-hospitalization associations exhibited a clear seasonal pattern, with significantly larger risks in cold season than those in warm season among some subgroups. CONCLUSIONS: Our study suggested that PM1 rather than PM1-2.5 contributed to PM2.5-induced risks of hospitalization for respiratory diseases and effects of PM1 and PM2.5 mainly occurred in cold season.