Clinical characteristics and risk factors for mortality in patients with coronavirus disease 2019 in intensive care unit: a single- center, retrospective, observational study in China.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a potentially life-threatening contagious disease which has spread all over the world. Risk factors associated with the clinical outcomes of COVID-19 pneumonia in intensive care unit (ICU) have not yet been well determined. METHODS: This was a retrospective, single-centered, observational study, in which 47 patients with confirmed COVID-19 were consecutively enrolled from February 24 to April 5, 2020. The patients were registered from the ICU of Leishenshan Hospital in Wuhan, China. Clinical characteristics and outcomes were collected and compared between survivors and non-survivors. Multivariable logistic regression was performed to analyze the risk factors of death in patients with COVID-19. RESULTS: The study cohort included 47 adult patients with an average age of 70.55±12.52 years, and 30 (63.8%) patients were men. Totally 15 (31.9%) patients died. When compared to survivors, nonsurvivors showed a higher proportion of septic shock [6 (40%) patients vs. 3 (9.4%) patients], disseminated intravascular coagulation [3 (21.4%) vs. 0], and had higher score of APACHE II (25.07±8.03 vs. 15.56±5.95), CURB-65 {3 [2-4] vs. 2 [1-3]}, Sequential Organ Failure Assessment (SOFA) {7 [5-9] vs. 3 [1-6]}, higher level of D-dimer {5.74 [2.32-18] vs 2.05 [1.09-4.00]} and neutrophil count {9.4 [7.68-14.54] vs. 5.32 [3.85-9.34]}. SOFA score (OR 1.47; 95% CI: 1.01-2.13; P=0.0042) and the lymphocyte count (OR 0.02; 95% CI: 0.00-0.86; P=0.042) on admission were independently risk factors for mortality. Patients with higher lymphocyte count (>0.63×109 /L) and lower SOFA score (≤4) on admission had a significantly better prognosis than those with lower lymphocyte count (≤0.63×109 /L) and higher SOFA score (>4) in overall survival. CONCLUSIONS: Higher SOFA score and lower lymphocyte count at admission were connected with poor prognosis of patients with COVID-19 in ICU. Lymphocyte count may serve as a promising prognostic biomarker.

Meteorological factors, governmental responses and COVID-19: Evidence from four European countries.

With the global lockdown, meteorological factors are highly discussed for COVID-19 transmission. In this study, national-specific and region-specific data sets from Germany, Italy, Spain and the United Kingdom were used to explore the effect of temperature, absolute humidity and diurnal temperature range (DTR) on COVID-19 transmission. From February 1st to November 1st, a 7-day COVID-19 case doubling time (Td), meteorological factors with cumulative 14-day-lagged, government response index and other factors were fitted in the distributed lag nonlinear models. The overall relative risk (RR) of the 10th and the 25th percentiles temperature compared to the median were 0.0074 (95% CI: 0.0023, 0.0237) and 0.1220 (95% CI: 0.0667, 0.2232), respectively. The pooled RR of lower (10th, 25th) and extremely high (90th) absolute humidity were 0.3266 (95% CI: 0.1379, 0.7734), 0.6018 (95% CI: 0.4693, 0.7718) and 0.3438 (95% CI: 0.2254, 0.5242), respectively. While the DTR did not have a significant effect on Td. The total cumulative effect of temperature (10th) and absolute humidity (10th, 90th) on Td increased with the change of lag days. Similarly, a decline in temperature and absolute humidity at cumulative 14-day-lagged corresponded to the lower RR on Td in pooled region-specific effects. In summary, the government responses are important factors in alleviating the spread of COVID-19. After controlling that, our results indicate that both the cold and the dry environment also likely facilitate the COVID-19 transmission.

Airborne particulate matter, population mobility and COVID-19: a multi-city study in China.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease, which has caused numerous deaths and health problems worldwide. This study aims to examine the effects of airborne particulate matter (PM) pollution and population mobility on COVID-19 across China. METHODS: We obtained daily confirmed cases of COVID-19, air particulate matter (PM2.5, PM10), weather parameters such as ambient temperature (AT) and absolute humidity (AH), and population mobility scale index (MSI) in 63 cities of China on a daily basis (excluding Wuhan) from January 01 to March 02, 2020. Then, the Generalized additive models (GAM) with a quasi-Poisson distribution were fitted to estimate the effects of PM10, PM2.5 and MSI on daily confirmed COVID-19 cases. RESULTS: We found each 1 unit increase in daily MSI was significantly positively associated with daily confirmed cases of COVID-19 in all lag days and the strongest estimated RR (1.21, 95% CIs:1.14 ~ 1.28) was observed at lag 014. In PM analysis, we found each 10 µg/m3 increase in the concentration of PM10 and PM2.5 was positively associated with the confirmed cases of COVID-19, and the estimated strongest RRs (both at lag 7) were 1.05 (95% CIs: 1.04, 1.07) and 1.06 (95% CIs: 1.04, 1.07), respectively. A similar trend was also found in all cumulative lag periods (from lag 01 to lag 014). The strongest effects for both PM10 and PM2.5 were at lag 014, and the RRs of each 10 µg/m3 increase were 1.18 (95% CIs:1.14, 1.22) and 1.23 (95% CIs:1.18, 1.29), respectively. CONCLUSIONS: Population mobility and airborne particulate matter may be associated with an increased risk of COVID-19 transmission.

Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China.

The purpose of the present study is to explore the associations between novel coronavirus disease 2019 (COVID-19) case counts and meteorological factors in 30 provincial capital cities of China. We compiled a daily dataset including confirmed case counts, ambient temperature (AT), diurnal temperature range (DTR), absolute humidity (AH) and migration scale index (MSI) for each city during the period of January 20th to March 2nd, 2020. First, we explored the associations between COVID-19 confirmed case counts, meteorological factors, and MSI using non-linear regression. Then, we conducted a two-stage analysis for 17 cities with more than 50 confirmed cases. In the first stage, generalized linear models with negative binomial distribution were fitted to estimate city-specific effects of meteorological factors on confirmed case counts. In the second stage, the meta-analysis was conducted to estimate the pooled effects. Our results showed that among 13 cities that have less than 50 confirmed cases, 9 cities locate in the Northern China with average AT below 0 °C, 12 cities had average AH below 4 g/m3, and one city (Haikou) had the highest AH (14.05 g/m3). Those 17 cities with 50 and more cases accounted for 90.6% of all cases in our study. Each 1 °C increase in AT and DTR was related to the decline of daily confirmed case counts, and the corresponding pooled RRs were 0.80 (95% CI: 0.75, 0.85) and 0.90 (95% CI: 0.86, 0.95), respectively. For AH, the association with COVID-19 case counts were statistically significant in lag 07 and lag 014. In addition, we found the all these associations increased with accumulated time duration up to 14 days. In conclusions, meteorological factors play an independent role in the COVID-19 transmission after controlling population migration. Local weather condition with low temperature, mild diurnal temperature range and low humidity likely favor the transmission.