![]() Our results findings may have important suggestions for the clinical management and future research of obesity and COVID-19. Our study suggests a significant association between obesity and COVID-19 severity and poor outcomes. In meta-analysis, COVID-19 patient with obesity had higher odds of poor outcomes compared to better outcomes with a pooled OR of 1.88 (95%CI:1.25-2.80 p=0.002), with 86% heterogeneity between studies (p<0.00001). The overall prevalence of obesity in our study was 33.9% (3473/10,233). The odds ratio (OR) and 95% confidence interval (95%CI) were obtained and forest plots were created using random-effects models.Ī total of 10 studies with 10,233 confirmed COVID-19 patients were included. Adverse outcomes defined as intensive care units (ICU), oxygen saturation <90%, invasive mechanical ventilation (IMV), severe disease and in-hospital mortality. In this meta-analysis, we assessed the association of obesity and outcomes in COVID-19 hospitalized patients.ĭata from observational studies describing the obesity or body mass index (BMI) and outcomes of COVID-19 hospitalized patients from December 1, 2019, to August 15, 2020, was extracted following PRISMA guidelines with a consensus of two independent reviewers. Very few studies have reported association between obesity and severity of COVID-19. Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain.ĬOVID-19 pandemic is a global health crisis. This potentially captures the spread of the disease during the first wave of the epidemic. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This corresponds to a posterior probability of a positive effect equal to 0.93 and 0.78 respectively. We find a 0.5% (95% credible interval: −0.2%, 1.2%) and 1.4% (95% CrI: −2.1%, 5.1%) increase in COVID-19 mortality risk for every 1 μg/m³ increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. We retrieved averaged NO2 and PM2.5 concentration during 2014–2018 from the Pollution Climate Mapping. ![]() In this nationwide cross-sectional study in England, we included 38,573 COVID-19 deaths up to Jat the Lower Layer Super Output Area level (n = 32,844 small areas). We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 mortality in England using high geographical resolution. ![]() However, due to their ecological design based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. ![]()
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