Dataset Information

The effects of "Fangcang, Huoshenshan, and Leishenshan" hospitals and environmental factors on the mortality of COVID-19.

ABSTRACT:

Background

In December 2019, a novel coronavirus disease (COVID-19) broke out in Wuhan, China; however, the factors affecting the mortality of COVID-19 remain unclear.

Methods

Thirty-two days of data (the growth rate/mortality of COVID-19 cases) that were shared by Chinese National Health Commission and Chinese Weather Net were collected by two authors independently. Student's t-test or Mann-Whitney U test was used to test the difference in the mortality of confirmed/severe cases before and after the use of "Fangcang, Huoshenshan, and Leishenshan" makeshift hospitals (MSHs). We also studied whether the above outcomes of COVID-19 cases were related to air temperature (AT), relative humidity (RH), or air quality index (AQI) by performing Pearson's analysis or Spearman's analysis.

Results

Eight days after the use of MSHs, the mortality of confirmed cases was significantly decreased both in Wuhan (t = 4.5, P < 0.001) and Hubei (U = 0, P < 0.001), (t and U are the test statistic used to test the significance of the difference). In contrast, the mortality of confirmed cases remained unchanged in non-Hubei regions (U = 76, P = 0.106). While on day 12 and day 16 after the use of MSHs, the reduce in mortality was still significant both in Wuhan and Hubei; but in non-Hubei regions, the reduce also became significant this time (U = 123, P = 0.036; U = 171, P = 0.015, respectively). Mortality of confirmed cases was found to be negatively correlated with AT both in Wuhan (r = - 0.441, P = 0.012) and Hubei (r = - 0.440, P = 0.012). Also, both the growth rate and the mortality of COVID-19 cases were found to be significantly correlated with AQI in Wuhan and Hubei. However, no significant correlation between RH and the growth rate/mortality of COVID-19 cases was found in our study.

Conclusions

Our findings indicated that both the use of MSHs, the rise of AT, and the improvement of air quality were beneficial to the survival of COVID-19 patients.

SUBMITTER: Cai Y

PROVIDER: S-EPMC7380280 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The effects of "Fangcang, Huoshenshan, and Leishenshan" hospitals and environmental factors on the mortality of COVID-19.

Cai Yuwen Y Huang Tianlun T Liu Xin X Xu Gaosi G

PeerJ 20200721

<h4>Background</h4>In December 2019, a novel coronavirus disease (COVID-19) broke out in Wuhan, China; however, the factors affecting the mortality of COVID-19 remain unclear.<h4>Methods</h4>Thirty-two days of data (the growth rate/mortality of COVID-19 cases) that were shared by Chinese National Health Commission and Chinese Weather Net were collected by two authors independently. Student's t-test or Mann-Whitney U test was used to test the difference in the mortality of confirmed/severe cases ...[more]

PMID: 32742816

Similar Datasets

Project description:BackgroundPeru was one of the countries with the highest COVID-19 mortality worldwide during the first stage of the pandemic. It is then relevant to evaluate the risk factors for mortality in patients hospitalized for COVID-19 in three hospitals in Peru in 2020, from March to May, 2020. Methods: We carried out a retrospective cohort study. The population consisted of patients from three Peruvian hospitals hospitalized for a diagnosis of COVID-19 during the March-May 2020 period. Independent sociodemographic variables, medical history, symptoms, vital functions, laboratory parameters and medical treatment were evaluated. In-hospital mortality was assessed as the outcome. We performed Cox regression models (crude and adjusted) to evaluate risk factors for in-hospital mortality. Hazard ratios (HR) with their respective 95% confidence intervals (95% CI) were calculated. Results: We analyzed 493 hospitalized adults; 72.8% (n=359) were male and the mean age was 63.3 ± 14.4 years. COVID-19 symptoms appeared on average 7.9 ± 4.0 days before admission to the hospital, and the mean oxygen saturation on admission was 82.6 ± 13.8. While 67.6% (n=333) required intensive care unit admission, only 3.3% (n=16) were admitted to this unit, and 60.2% (n=297) of the sample died. In the adjusted regression analysis, it was found that being 60 years old or older (HR=1.57; 95% CI: 1.14-2.15), having two or more comorbidities (HR=1.53; 95% CI: 1.10-2.14), oxygen saturation between 85-80% (HR=2.52; 95% CI: 1.58-4.02), less than 80% (HR=4.59; 95% CI: 3.01-7.00), and being in the middle (HR=1.65; 95% CI: 1.15-2.39) and higher tertile (HR=2.18; 95% CI: 1.51-3.15) of the neutrophil-to-lymphocyte ratio, increased the risk of mortality. Conclusions: The risk factors found agree with what has been described in the literature and allow the identification of vulnerable groups in whom monitoring and early identification of symptoms should be prioritized in order to reduce mortality.

Project description:BackgroundIn response to the COVID-19 pandemic, two new temporary hospitals were constructed in record time in Wuhan, China, to help combat the fast-spreading virus in February 2020. Using the experience of one of the hospitals as a case study, we discuss the health and economic implications of this response strategy and its potential application in other countries.MethodsThis retrospective observational study analyzed health resource utilization and clinical outcomes data for 2011 inpatients diagnosed with COVID-19 and admitted to Leishenshan Hospital during its 67 days of operation from February 8th to April 14th, 2020. We used a top-down costing approach to estimate the total cost of treating patients at the Leishenshan Hospital, including capital cost for hospital construction, health personnel costs, and direct health care costs. We used a multivariate generalized linear model to examine risk factors associated with in-hospital deaths.ResultsDuring the 67 days of hospital operation, 19 medical teams comprising of 933 doctors and 2312 nurses were gradually transferred to Leishenshan Hospital from across China. Of the 2011 admissions, 4.5% used intensive care and 2.0% used ventilators. Overall median length of stay was 19 days, and 21 days for patients in the intensive care unit (ICU). The case fatality rate (CFR) was 2.3% overall, 41.8% in the ICU, and 0.4% in general ward (GW). CFRs were 55% and 50% among patients using non-invasive and invasive ventilators, respectively. The mean total cost and direct health care cost were CNY806 997 (US$114 793) and CNY16 087 (US$2288), respectively. Patients admitted to the ICU had much higher direct health care costs, on average, compared to those in the GW (CNY150 415 vs CNY9720, or US$21 396 vs US$1383). The mean direct health care cost per patient with severe or critical diseases was more than five times higher than those with mild or moderate diseases (CNY45 191 vs CNY8838, or US$6428 vs US$1257). Older age, having comorbidities, and critical disease were associated with higher risks of death from COVID-19. Lower health worker to patient ratio (<2.6) was not associated with in-hospital death.ConclusionAn adequate health workforce were mobilized and deployed to a new temporary hospital. The Leishenshan Hospital increased access to care during the surge in COVID-19 infections, facilitated timely treatment, and transferred COVID-19 patients between GWs and ICUs within the hospital, all of which are potential contributors to lowering the CFR. Patients in the ICU experienced a much higher CFR and a greater burden of health care cost than those in GW. Our results have important implications for other countries interested in constructing temporary emergency hospitals, such as the need for adequate infrastructure capacities and financial support, centralized strategies to mobilize health workforce and to provide respiratory protective devices, and improvement in access to health care.

Dataset Information

The effects of "Fangcang, Huoshenshan, and Leishenshan" hospitals and environmental factors on the mortality of COVID-19.

Background

Methods

Results

Conclusions

Publications

The effects of "Fangcang, Huoshenshan, and Leishenshan" hospitals and environmental factors on the mortality of COVID-19.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure