Causal relationship between COVID-19 and the risk of asthma: A Mendelian Randomisation study (preprint)

Background Existing research has focused on new-onset asthma and viral infections, particularly respiratory syncytial virus (RSV). However, studies on whether COVID-19 can induce asthma are limited.Methods We performed bidirectional two-sample Mendelian Randomization (MR) to assess the potential causal relationship between COVID-19 and asthma using genome-wide association study (GWAS) summary data obtained from the COVID-19 Host Genetic Initiative GWAS Meta-analysis Round 5 (release date: 18 January 2021). Several methods (random-effects inverse variance weighted, weighted median, MR-Egger regression, and MR-PRESSO) were used to ensure the robustness of the causal effects. Heterogeneity was measured using Cochran's Q value. Horizontal pleiotropy was evaluated using MR-Egger regression and leave-one-out analyses.Results We observed a significant causal association between COVID-19 hospitalisation and asthma (odds ratio (OR) = 1.042, 95% Confidence Interval (CI) = 1.004–1.081, p = 0.031), indicating a significantly increased risk of COVID-19 hospitalisation associated with asthma. However, no statistically significant causal relationships were observed for COVID-19 susceptibility (OR = 1.023, 95% CI = 0.931–1.124, p = 0.637), COVID-19 severity (OR = 1.006, 95% CI = 0.978–1.035, p = 0.669), and asthma.Conclusions COVID-19 can trigger the onset of asthma. Individuals experiencing prolonged coughing, chest tightness, or difficulty in breathing long after recovery from COVID-19 should remain vigilant about the possibility of developing asthma.

The impact of the COVID-19 pandemic on the emergency department visits: a retrospective analysis in Shanghai, China (preprint)

Background The novel coronavirus diseases (COVID-19) has led to a pandemic and affected people's lives greatly, including their health seeking behavior. We aimed to evaluate the impact of the current COVID-19 pandemic on characteristics and trends of emergency department (ED) visits in Shanghai, China.Methods This was a retrospective observational study using medical record databases from the Shanghai Sixth People's Hospital (East Campus) for years 2016 through 2020. All the patients referred to the ED between January 2016 and June 2020 were retrospectively reviewed. January 1, 2020, was chosen as the cutoff date for the statistical analysis and data of January and February in 2020 was compared with the same period of 2019.Results During the first two months of 2020, there was a 60.9% decline in ED visits when compared with the same period of 2019 (9,904 vs. 25,316, respectively), and the waiting time in ED has been greatly reduced correspondingly (12±4 vs. 66±19 min, p < 0.001); ED visits for acute ischemic stroke (AIS) and acute coronary syndrome(ACS) decreased by 53.9% and 41.2% respectively; proportion of intravenous thrombolysis for AIS has dropped(42.1% vs. 11.4%, p = 0.003), and percutaneous coronary intervention for ACS was similar (70.6% vs. 63.3%, p = 0.668); and onset-to-door time (ODT) of these patients increased significantly (AIS: 217(136-374) vs. 378(260-510)min, ACS: 135(85-195) vs. 226(155-368)min, all p < 0.001).Conclusion The outbreak of COVID-19 pandemic was correlated with a significant decline in the number of ED visits including AIS and ACS patients when compared to the pre-COVID-19 period. ODT of AIS and ACS patients increased significantly. Raising public awareness is necessary to avoid serious healthcare and economic consequences of undiagnosed and untreated stroke and myocardial infarction attack.

Understand Research Hotspots Surrounding COVID-19 and Other Coronavirus Infections Using Topic Modeling (preprint)

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a virus that causes severe respiratory illness in humans, which eventually results in the current outbreak of novel coronavirus disease (COVID-19) around the world. The research community is interested to know what are the hotspots in coronavirus (CoV) research and how much is known about COVID-19. This study aimed to evaluate the characteristics of publications involving coronaviruses as well as COVID-19 by using a topic modeling analysis. Methods: We extracted all abstracts and retained the most informative words from the COVID-19 Open Research Dataset, which contains all the 35,092 pieces of coronavirus related literature published up to March 20, 2020. Using Latent Dirichlet Allocation modeling, we trained an eight-topic model from the corpus. We then analyzed the semantic relationships between topics and compared the topic distribution between COVID-19 and other CoV infections. Results: Eight topics emerged overall: clinical characterization, pathogenesis research, therapeutics research, epidemiological study, virus transmission, vaccines research, virus diagnostics, and viral genomics. It was observed that COVID-19 research puts more emphasis on clinical characterization, epidemiological study, and virus transmission at present. In contrast, topics about diagnostics, therapeutics, vaccines, genomics and pathogenesis only accounted for less than 10% or even 4% of all the COVID-19 publications, much lower than those of other CoV infections. Conclusions: These results identified knowledge gaps in the area of COVID-19 and offered directions for future research. Keywords: COVID-19, coronavirus, topic modeling, hotspots, text mining