COVID-19 OUTCOMES IN KOREAN PATIENTS WITH GOUT

BackgroundPatients with autoimmune inflammatory rheumatic diseases are at higher risk for coronavirus disease (COVID)-19 hospitalization and worse clinical outcomes compared with the general population. However, data on the association between COVID-19 outcomes and gout, or gout-related medications are still lacking.ObjectivesWe aimed to compare COVID-19 related clinical outcomes in gout vs. non-gout patients.MethodsWe conducted a retrospective cohort study using the electronic health record-based databases of Seoul National University hospital (SNUH) from January 2021 to April 2022 mapped to a common data model. Patients with gout and without gout were matched using a large-scale propensity score (PS) algorithm. The clinical outcomes of interest were COVID-19 infection, severe COVID-19 outcomes defined as the use of mechanical ventilation, tracheostomy or extracorporeal membrane oxygenation, and death within 30 days of COVID-19 diagnosis. The hazard ratio (HR) for gout vs. non-gout patients derived by Cox proportional hazard models were estimated utilizing a 1:5 PS-matched cohort.Results2,683 patients with gout and 417,035 patients without gout were identified among the patients who visited SNUH. After 1:5 PS matching, 1,363 gout patients and 4,030 non-gout patients remained for the analysis. The risk of COVID-19 infection was not significantly different between patients with gout and those without gout (HR 1.07 [95% CI 0.59-1.84]). Within the first month after the COVID-19 diagnosis, there was also no significant difference in the risk of hospitalization (HR 0.57 [95% CI 0.03-3.90], severe COVID-19 outcomes (HR 2.90 [95% CI 0.54-13.71]), or death (HR 1.35 [95% CI 0.06-16.24]).ConclusionPatients with gout did not have an increased risk of COVID-19 infection or worse clinical outcomes. Updates of temporal trends of COVID-19 outcomes in gout patients are yet warranted as new SARS-CoV-2 variants emerge.References[1]Shin YH, et al. Autoimmune inflammatory rheumatic diseases and COVID-19 outcomes in South Korea: a nationwide cohort study. Lancet Rheumatol. 2021 Oct;3(10):e698-e706.[2]Topless RK, et al. Gout and the risk of COVID-19 diagnosis and death in the UK Biobank: a population-based study. Lancet Rheumatol. 2022 Apr;4(4):e274-e281.[3]Xie D, et al. Gout and Excess Risk of Severe SARS-CoV-2 Infection Among Vaccinated Individuals: A General Population Study. Arthritis Rheumatol.2023 Jan;75(1):122-132.Table 1.Clinical outcomes of COVID-19 infection in patients with goutOutcomesUnmatched populationPopulation with PS stratification using 10 strata1:5 PS matched populationHazard ratio (95% CI)p-valueHazard ratio (95% CI)p-valueHazard ratio (95% CI)p-valueCOVID-19 infection1.68 (1.03-2.57)0.031.20 (0.72-1.87)0.461.07 (0.59-1.84)0.82Hospitalization due to COVID-191.92 (0.32-6.05)0.391.63 (0.26-5.77)0.540.57 (0.03-3.90)0.66Severe COVID-19 infection4.72 (1.44-11.28)<0.014.22 (1.17-12.21)0.022.90 (0.54-13.71)0.20Death due to COVID-191.15 (0.07-5.18)0.900.77 (0.04-3.81)0.821.35 (0.06-16.24)0.84Acknowledgements:NIL.Disclosure of InterestsNone Declared.

Disaggregating Asian Race Reveals COVID-19 Disparities among Asian Americans at New York City's Public Hospital System (preprint)

There is growing recognition of the burden of COVID-19 among Asian Americans, but data on outcomes among Asian ethnic subgroups remain extremely limited. We conducted a retrospective analysis of 85,328 patients tested for COVID-19 at New York City's public hospital system between March 1 and May 31, 2020, to describe characteristics and COVID-19 outcomes of Asian ethnic subgroups compared to Asians overall and other racial/ethnic groups. South Asians had the highest rates of positivity and hospitalization among Asians, second only to Hispanics for positivity and Blacks for hospitalization. Chinese patients had the highest mortality rate of all groups and were nearly 1.5 times more likely to die than Whites. The high burden of COVID-19 among South Asian and Chinese Americans underscores the urgent needs for improved data collection and reporting as well as public health program and policy efforts to mitigate the disparate impact of COVID-19 among these communities.

Effects of New York's Executive Order on Face Mask Use on COVID-19 Infections and Mortality: A Modeling Study (preprint)

Background: New York City (NYC) was the epicenter of the COVID-19 pandemic in the United States. On April 17, 2020, the State of New York implemented an Executive Order that requires all people in New York to wear a face mask or covering in public settings where social distancing cannot be maintained. It is unclear how this Executive Order has affected the spread of COVID-19 in NYC. Methods: A dynamic compartmental model of COVID-19 transmission among NYC residents was developed to assess the effect of the Executive Order on face mask use on infections and deaths due to COVID-19 in NYC. Data on daily and cumulative COVID-19 infections and deaths were obtained from the NYC Department of Health and Mental Hygiene. Results: The Executive Order on face mask use is estimated to avert 99,517 (95% CIs: 72,723-126,312) COVID-19 infections and 7,978 (5,692-10,265) deaths in NYC. If the Executive Order was implemented one week earlier (on April 10), the averted infections and deaths would be 111,475 (81,593-141,356) and 9,017 (6,446-11,589), respectively. If the Executive Order was implemented two weeks earlier (on April 3 when the Centers for Disease Control and Prevention recommended face mask use), the averted infections and deaths would be 128,598 (94,373-162,824) and 10,515 (7,540-13,489), respectively. Conclusions: New York's Executive Order on face mask use is projected to have significantly reduced the spread of COVID-19 in NYC. Implementing the Executive Order at an earlier date would avert even more COVID-19 infections and deaths.

Answering Questions on COVID-19 in Real-Time (preprint)

The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it. One reason why the fight is difficult is due to the lack of information and knowledge. In this work, we outline our effort to contribute to shrinking this knowledge vacuum by creating covidAsk, a question answering (QA) system that combines biomedical text mining and QA techniques to provide answers to questions in real-time. Our system also leverages information retrieval (IR) approaches to provide entity-level answers that are complementary to QA models. Evaluation of covidAsk is carried out by using a manually created dataset called COVID-19 Questions which is based on information from various sources, including the CDC and the WHO. We hope our system will be able to aid researchers in their search for knowledge and information not only for COVID-19, but for future pandemics as well.