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Background: Throughout the Covid-19 pandemic, researchers have made use of electronic health records to research this disease in a rapidly evolving environment of questions and discoveries. These studies are prone to collider bias as they restrict the population of Covid-19 patients to only those with severe disease. Inverse probability weighting is typically used to correct for this bias but requires information from the unrestricted population. Using electronic health records from a South London NHS trust, this work demonstrates a method to correct for collider bias using externally sourced data while examining the relationship between minority ethnicities and poor Covid-19 outcomes., Methods: The probability of inclusion within the observed hospitalised cohort was modelled based on estimates from published national data. The model described the relationship between patient ethnicity, hospitalisation, and death due to Covid-19 - a relationship suggested to be susceptible to collider bias. The obtained probabilities (as applied to the observed patient cohort) were used as inverse probability weights in survival analysis examining ethnicity (and covariates) as a risk factor for death due to Covid-19., Results: Within the observed cohort, unweighted analysis of survival suggested a reduced risk of death in those of Black ethnicity - differing from the published literature. Applying inverse probability weights to this analysis amended this aberrant result to one more compatible with the literature. This effect was consistent when the analysis was applied to patients within only the first wave of Covid-19 and across two waves of Covid-19 and was robust against adjustments to the modelled relationship between hospitalisation, patient ethnicity, and death due to Covid-19 made as part of a sensitivity analysis., Conclusions: In conclusion, this analysis demonstrates the feasibility of using external publications to correct for collider bias (or other forms of selection bias) induced by the restriction of a population to a hospitalised cohort using an example from the recent Covid-19 pandemic., (© 2024. The Author(s).)

Background: Alpha-1 adrenergic receptor antagonists prevent cytokine storm in mouse sepsis models. This led to the hypothesis that alpha-1 blockers may prevent severe coronavirus disease 2019 (COVID-19), which is characterized by hypercytokinemia and progressive respiratory failure., Methods: We performed an observational case-control study in male Medicare beneficiaries aged 65 years or older, with or without benign prostatic hyperplasia (BPH), and treated with alpha-1 receptor blockers or 5-alpha reductase inhibitors. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were estimated for outcomes of uncomplicated and severe COVID-19 hospitalization (intensive care unit admission, invasive mechanical ventilation, or death)., Results: There were 20,963 cases of hospitalized COVID-19 matched to 101,161 controls on calendar date and neighborhood of residence. In the primary analysis (males with BPH), there was no difference in risk of uncomplicated COVID-19 hospitalization (aOR 1.08, 95% CI 0.996-1.17) or hospitalization with severe complications (aOR 0.97, 95% CI 0.88-1.08). In the secondary analysis (males with or without BPH), the corresponding aORs were 1.02 (95% CI, 0.96-1.09) (uncomplicated) and 0.99 (95% CI, 0.91-1.07) (complicated), respectively. Subgroup and sensitivity analyses yielded similar results. Of note, there was no difference in risk of severe COVID-19 hospitalization when comparing non-selective vs selective alpha-1 blocker use (aOR 0.98, 95% CI 0.86-1.10), higher- vs lower-dose alpha-1 blocker use (aOR 0.96, 95% CI 0.86-1.08), or current vs remote alpha-1 blocker use (aOR 1.04, 95% CI 0.91-1.18)., Conclusions: Prevalent use of alpha-1 receptor blockers was not associated with a protective or harmful effect on risk of uncomplicated or severe hospitalized COVID-19., (Published by Elsevier Inc.)

Objectives: Studies from the first waves of the coronavirus disease 2019 (COVID-19) pandemic suggest that individuals from minority ethnicities are at an increased risk of worse outcomes. Concerns exist that this relationship is potentially driven by bias from analyzing hospitalized patients only. We investigate this relationship and the possible presence of bias., Study Design and Setting: Using data from South London hospitals across two COVID-19 waves (February 2020 - May 2021), the relationship between ethnicity and COVID-19 outcomes was examined using regression models. Three iterations of each model were completed: 1) an unadjusted analysis, 2) adjusting for covariates (medical history and deprivation), and 3) adjusting for covariates and bias induced by conditioning on hospitalization., Results: Among 3,133 patients, those who were Asian had a two-fold increased risk of death during the hospital stay that was consistent across the two COVID-19 waves and was not affected by correcting for conditioning on hospitalization. However, wave-specific effects demonstrate significant differences between ethnic groups until bias from using a hospitalized cohort was corrected for., Conclusion: Worsened COVID-19 outcomes in minority ethnicities may be minimized by correcting for bias induced by conditioning on hospitalization. Consideration of this bias should be a key component of study design., Competing Interests: Declaration of competing interest There are no conflicts of interest to declare., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

COVID-19 research has relied heavily on convenience-based samples, which-though often necessary-are susceptible to important sampling biases. We begin with a theoretical overview and introduction to the dynamics that underlie sampling bias. We then empirically examine sampling bias in online COVID-19 surveys and evaluate the degree to which common statistical adjustments for demographic covariates successfully attenuate such bias. This registered study analysed responses to identical questions from three convenience and three largely representative samples (total N = 13,731) collected online in Canada within the International COVID-19 Awareness and Responses Evaluation Study ( www.icarestudy.com ). We compared samples on 11 behavioural and psychological outcomes (e.g., adherence to COVID-19 prevention measures, vaccine intentions) across three time points and employed multiverse-style analyses to examine how 512 combinations of demographic covariates (e.g., sex, age, education, income, ethnicity) impacted sampling discrepancies on these outcomes. Significant discrepancies emerged between samples on 73% of outcomes. Participants in the convenience samples held more positive thoughts towards and engaged in more COVID-19 prevention behaviours. Covariates attenuated sampling differences in only 55% of cases and increased differences in 45%. No covariate performed reliably well. Our results suggest that online convenience samples may display more positive dispositions towards COVID-19 prevention behaviours being studied than would samples drawn using more representative means. Adjusting results for demographic covariates frequently increased rather than decreased bias, suggesting that researchers should be cautious when interpreting adjusted findings. Using multiverse-style analyses as extended sensitivity analyses is recommended., (© 2022. Springer Nature B.V.)

Abstract Background Throughout the Covid-19 pandemic, researchers have made use of electronic health records to research this disease in a rapidly evolving environment of questions and discoveries. These studies are prone to collider bias as they restrict the population of Covid-19 patients to only those with severe disease. Inverse probability weighting is typically used to correct for this bias but requires information from the unrestricted population. Using electronic health records from a South London NHS trust, this work demonstrates a method to correct for collider bias using externally sourced data while examining the relationship between minority ethnicities and poor Covid-19 outcomes. Methods The probability of inclusion within the observed hospitalised cohort was modelled based on estimates from published national data. The model described the relationship between patient ethnicity, hospitalisation, and death due to Covid-19 – a relationship suggested to be susceptible to collider bias. The obtained probabilities (as applied to the observed patient cohort) were used as inverse probability weights in survival analysis examining ethnicity (and covariates) as a risk factor for death due to Covid-19. Results Within the observed cohort, unweighted analysis of survival suggested a reduced risk of death in those of Black ethnicity – differing from the published literature. Applying inverse probability weights to this analysis amended this aberrant result to one more compatible with the literature. This effect was consistent when the analysis was applied to patients within only the first wave of Covid-19 and across two waves of Covid-19 and was robust against adjustments to the modelled relationship between hospitalisation, patient ethnicity, and death due to Covid-19 made as part of a sensitivity analysis. Conclusions In conclusion, this analysis demonstrates the feasibility of using external publications to correct for collider bias (or other forms of selection bias) induced by the restriction of a population to a hospitalised cohort using an example from the recent Covid-19 pandemic.