Collider bias undermines our understanding of COVID-19 disease risk and severity

The limitations of large-scale volunteer databases to address inequalities and global challenges in health and aging

Large-scale volunteer databanks (LSVD) have emerged from the recognized value of cohorts, attracting substantial funding and promising great scientific value. A major focus is their size, with the implicit and sometimes explicit assumption that large size (thus power) creates generalizability. We contend that this is open to challenge. In the context of aging and age-related disease research, LSVD typically have limitations such as healthy volunteer, white ethnicity and high-education biases, and they omit early and late life stages critical for understanding aging. Their outputs are heavily focused on biomedical pathways of single chronic diseases. LSVD outputs increasingly dominate the funding and the publication landscapes. This Perspective discusses LSVD limitations and calls for more transparent reporting in LSVD research, as well as a greater reflection on the value of LSVD in relation to resources consumed. We invite funders and researchers to examine whether LSVD do actually contribute knowledge needed for our acute global health challenges including inequalities.

Risk factors for developing COVID-19: a population-based longitudinal study (COVIDENCE UK)

BACKGROUND

Risk factors for severe COVID-19 include older age, male sex, obesity, black or Asian ethnicity and underlying medical conditions. Whether these factors also influence susceptibility to developing COVID-19 is uncertain.

METHODS

We undertook a prospective, population-based cohort study (COVIDENCE UK) from 1 May 2020 to 5 February 2021. Baseline information on potential risk factors was captured by an online questionnaire. Monthly follow-up questionnaires captured incident COVID-19. We used logistic regression models to estimate multivariable-adjusted ORs (aORs) for associations between potential risk factors and odds of COVID-19.

RESULTS

We recorded 446 incident cases of COVID-19 in 15 227 participants (2.9%). Increased odds of developing COVID-19 were independently associated with Asian/Asian British versus white ethnicity (aOR 2.28, 95% CI 1.33 to 3.91), household overcrowding (aOR per additional 0.5 people/bedroom 1.26, 1.11 to 1.43), any versus no visits to/from other households in previous week (aOR 1.31, 1.06 to 1.62), number of visits to indoor public places (aOR per extra visit per week 1.05, 1.02 to 1.09), frontline occupation excluding health/social care versus no frontline occupation (aOR 1.49, 1.12 to 1.98) and raised body mass index (BMI) (aOR 1.50 (1.19 to 1.89) for BMI 25.0-30.0 kg/m2 and 1.39 (1.06 to 1.84) for BMI >30.0 kg/m2 versus BMI <25.0 kg/m2). Atopic disease was independently associated with decreased odds (aOR 0.75, 0.59 to 0.97). No independent associations were seen for age, sex, other medical conditions, diet or micronutrient supplement use.

CONCLUSIONS

After rigorous adjustment for factors influencing exposure to SARS-CoV-2, Asian/Asian British ethnicity and raised BMI were associated with increased odds of developing COVID-19, while atopic disease was associated with decreased odds.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT04330599).

Diabetes and COVID-19 testing, positivity, and mortality: A population-wide study in Northern Italy

AIMS

To assess if patients with type 2 diabetes mellitus (DM2) are: a) at excess risk of undergoing testing, contracting, and dying from SARS-CoV-2 infection compared to the general population; b) whether cardiovascular diseases (CAVDs) contribute to COVID-19-related death; and c) what is the effect of DM2 duration and control on COVID-19-related death.

METHODS

This population-based study involved all 449,440 adult residents of the Reggio Emilia province, Italy. DM2 patients were divided in groups by COVID testing, presence of CAVDs and COVID death. Several mediation analyses were performed.

RESULTS

Patients with DM2 had an increased likelihood of being tested (Odds ratio, OR 1.27 95 %CI 1.23-1.30), testing positive (OR 1.21 95 %CI 1.16-1.26) and dying from COVID-19 (OR 1.75 95 %CI 1.54-2.00). COVID-19-related death was almost three times higher among obese vs non-obese patients with DM2 (OR 4.3 vs 1.6, respectively). For COVID-19 death, CAVDs mediated a) just 5.1 % of the total effect of DM2, b) 40 % of the effect of DM2 duration, and c) did not mediate the effect of glycemic control.

CONCLUSIONS

For COVID-19-related deaths in DM2 patients, the effect is mostly direct, obesity amplifies it, DM2 control and duration are important predictors, while CAVDs only slightly mediates it.

The human genetic epidemiology of COVID-19

Human genetics can inform the biology and epidemiology of coronavirus disease 2019 (COVID-19) by pinpointing causal mechanisms that explain why some individuals become more severely affected by the disease upon infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Large-scale genetic association studies, encompassing both rare and common genetic variants, have used different study designs and multiple disease phenotype definitions to identify several genomic regions associated with COVID-19. Along with a multitude of follow-up studies, these findings have increased our understanding of disease aetiology and provided routes for management of COVID-19. Important emergent opportunities include the clinical translatability of genetic risk prediction, the repurposing of existing drugs, exploration of variable host effects of different viral strains, study of inter-individual variability in vaccination response and understanding the long-term consequences of SARS-CoV-2 infection. Beyond the current pandemic, these transferrable opportunities are likely to affect the study of many infectious diseases.

Associations of air pollution with COVID-19 positivity, hospitalisations, and mortality: Observational evidence from UK Biobank

Individual-level studies with adjustment for important COVID-19 risk factors suggest positive associations of long-term air pollution exposure (particulate matter and nitrogen dioxide) with COVID-19 infection, hospitalisations and mortality. The evidence, however, remains limited and mechanisms unclear. We aimed to investigate these associations within UK Biobank, and to examine the role of underlying chronic disease as a potential mechanism. UK Biobank COVID-19 positive laboratory test results were ascertained via Public Health England and general practitioner record linkage, COVID-19 hospitalisations via Hospital Episode Statistics, and COVID-19 mortality via Office for National Statistics mortality records from March-December 2020. We used annual average outdoor air pollution modelled at 2010 residential addresses of UK Biobank participants who resided in England (n = 424,721). We obtained important COVID-19 risk factors from baseline UK Biobank questionnaire responses (2006-2010) and general practitioner record linkage. We used logistic regression models to assess associations of air pollution with COVID-19 outcomes, adjusted for relevant confounders, and conducted sensitivity analyses. We found positive associations of fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) with COVID-19 positive test result after adjustment for confounders and COVID-19 risk factors, with odds ratios of 1.05 (95% confidence intervals (CI) = 1.02, 1.08), and 1.05 (95% CI = 1.01, 1.08), respectively. PM 2.5 and NO 2 were positively associated with COVID-19 hospitalisations and deaths in minimally adjusted models, but not in fully adjusted models. No associations for PM₁₀ were found. In analyses with additional adjustment for pre-existing chronic disease, effect estimates were not substantially attenuated, indicating that underlying chronic disease may not fully explain associations. We found some evidence that long-term exposure to PM_2.5 and NO₂ was associated with a COVID-19 positive test result in UK Biobank, though not with COVID-19 hospitalisations or deaths.

A prospective observational study of post-COVID-19 chronic fatigue syndrome following the first pandemic wave in Germany and biomarkers associated with symptom severity

A subset of patients has long-lasting symptoms after mild to moderate Coronavirus disease 2019 (COVID-19). In a prospective observational cohort study, we analyze clinical and laboratory parameters in 42 post-COVID-19 syndrome patients (29 female/13 male, median age 36.5 years) with persistent moderate to severe fatigue and exertion intolerance six months following COVID-19. Further we evaluate an age- and sex-matched postinfectious non-COVID-19 myalgic encephalomyelitis/chronic fatigue syndrome cohort comparatively. Most post-COVID-19 syndrome patients are moderately to severely impaired in daily live. 19 post-COVID-19 syndrome patients fulfill the 2003 Canadian Consensus Criteria for myalgic encephalomyelitis/chronic fatigue syndrome. Disease severity and symptom burden is similar in post-COVID-19 syndrome/myalgic encephalomyelitis/chronic fatigue syndrome and non-COVID-19/myalgic encephalomyelitis/chronic fatigue syndrome patients. Hand grip strength is diminished in most patients compared to normal values in healthy. Association of hand grip strength with hemoglobin, interleukin 8 and C-reactive protein in post-COVID-19 syndrome/non-myalgic encephalomyelitis/chronic fatigue syndrome and with hemoglobin, N-terminal prohormone of brain natriuretic peptide, bilirubin, and ferritin in post-COVID-19 syndrome/myalgic encephalomyelitis/chronic fatigue syndrome may indicate low level inflammation and hypoperfusion as potential pathomechanisms.

Temporal Dynamics of Socioeconomic Inequalities in COVID-19 Outcomes Over the Course of the Pandemic—A Scoping Review

Objectives: International evidence of socioeconomic inequalities in COVID-19 outcomes is extensive and growing, but less is known about the temporal dynamics of these inequalities over the course of the pandemic.

Methods: We systematically searched the Embase and Scopus databases. Additionally, several relevant journals and the reference lists of all included articles were hand-searched. This study follows the PRISMA guidelines for scoping reviews.

Results: Forty-six studies were included. Of all analyses, 91.4% showed stable or increasing socioeconomic inequalities in COVID-19 outcomes over the course of the pandemic, with socioeconomically disadvantaged populations being most affected. Furthermore, the study results showed temporal dynamics in socioeconomic inequalities in COVID-19, frequently initiated through higher COVID-19 incidence and mortality rates in better-off populations and subsequent crossover dynamics to higher rates in socioeconomically disadvantaged populations (41.9% of all analyses).

Conclusion: The identified temporal dynamics of socioeconomic inequalities in COVID-19 outcomes have relevant public health implications. Socioeconomic inequalities should be monitored over time to enable the adaption of prevention and interventions according to the social particularities of specific pandemic phases.

Clinical manifestations and disease severity of SARS-CoV-2 infection among infants in Canada

Background

There are limited data on outcomes of SARS-CoV-2 infection among infants (<1 year of age). In the absence of approved vaccines for infants, understanding characteristics associated with hospitalization and severe disease from COVID-19 in this age group will help inform clinical management and public health interventions. The objective of this study was to describe the clinical manifestations, disease severity, and characteristics associated with hospitalization among infants infected with the initial strains of SARS-CoV-2.

Methods

This is a national, prospective study of infants with SARS-CoV-2 from April 8^th 2020 to May 31^st 2021 using the infrastructure of the Canadian Paediatric Surveillance Program. Infants <1 year of age with microbiologically confirmed SARS-CoV-2 infection from both inpatients and outpatients seen in clinics and emergency departments were included. Cases were classified as either: 1) Non-hospitalized patient with SARS-CoV-2 infection; 2) COVID-19-related hospitalization; or 3) non-COVID-19-related hospitalization (e.g., incidentally detected SARS-CoV-2). Case severity was defined as asymptomatic, outpatient care, mild (inpatient care), moderate or severe disease. Multivariable logistic regression was performed to identify characteristics associated with hospitalization.

Results

A total of 531 cases were reported, including 332 (62.5%) non-hospitalized and 199 (37.5%) hospitalized infants. Among hospitalized infants, 141 of 199 infants (70.9%) were admitted because of COVID-19-related illness, and 58 (29.1%) were admitted for reasons other than acute COVID-19. Amongst all cases with SARS-CoV-2 infection, the most common presenting symptoms included fever (66.5%), coryza (47.1%), cough (37.3%) and decreased oral intake (25.0%). In our main analysis, infants with a comorbid condition had higher odds of hospitalization compared to infants with no comorbid conditions (aOR = 4.53, 2.06–9.97), and infants <1 month had higher odds of hospitalization then infants aged 1–3 months (aOR = 3.78, 1.97–7.26). In total, 20 infants (3.8%) met criteria for severe disease.

Conclusions

We describe one of the largest cohorts of infants with SARS-CoV-2 infection. Overall, severe COVID-19 in this age group was found to be uncommon. Comorbid conditions and younger age were associated with COVID-19-related hospitalization amongst infants.

Peptic Ulcer Disease and Risk of Hip Fracture: A General Population-based Cohort Study

AIMS

Previous studies reported proton pump inhibitor (PPI) use may increase the risk of fracture; however, the findings may be susceptible to indication bias because peptic ulcer disease (PUD), 1 major indication for PPIs, may affect skeletal health. Determining whether PUD would increase hip fracture risk may help identify high-risk populations and explore risk factors.

METHODS

We conducted a cohort study using data from The Health Improvement Network (THIN) in the United Kingdom. THIN contains patient information such as disease diagnosis and medicine prescriptions. Up to 5 non-PUD individuals (n = 138 265) were matched to each case of incident PUD (n = 27 653) by age, sex, and body mass index. We examined the association between PUD and hip fracture by a multivariable Cox proportional hazard model. We repeated the same analysis among individuals with incident PUD and gastroesophageal reflux disease (GERD) (n = 27 160), another disease with similar indication for PPIs, as a positive control exposure.

RESULTS

Over a mean of 5.6 years of follow-up, hip fracture occurred in 589 individuals with PUD and 2015 individuals without PUD (3.8 vs 2.6/1000 person-years), with a multivariable-adjusted hazard ratio (HR) being 1.44 (95% confidence interval [CI], 1.31-1.58). The association persisted among subgroups stratified by sex and age. In positive control exposure analysis, the hip fracture risk was also higher in PUD than GERD (3.8 vs 2.4/1000 person-years; multivariable-adjusted HR = 1.65; 95% CI, 1.45-1.7).

CONCLUSIONS

This general population-based cohort study suggests, after controlling for acid-lowering medication and other potential risk factors, PUD is independently associated with an increased risk of hip fracture.

Racial Disparities in COVID-19 Case Positivity and Social Context: The Role of Housing, Neighborhood, and Health Insurance

Research on racial-ethnic COVID-19 disparities has yet to employ housing variables measured at the individual level, limiting our understanding of housing's role in determining early exposure to the virus. To address this gap, we linked data from SARS-CoV-2 polymerase chain reaction (PCR) tests within the Duke University Health System between March 12, 2020, and July 31, 2020 (N = 23,057 individuals), with housing parcel data. We then analyzed how housing, neighborhood, and health insurance explain disparities in case positivity between and within racial-ethnic groups in Durham County, North Carolina. We find that 55% of the Black-White disparity and 25% of the Hispanic-White disparity in positive cases are explained by these social-contextual variables. Neighborhood-fixed effects explained the largest portion (27%) of the Black-White disparity, whereas health insurance type explained the largest portion (14%) of the Hispanic-White disparity. We conclude that housing, neighborhood, and health insurance had a significant role in producing racial-ethnic disparities in COVID-19 case positivity.

Association of COVID-19 vs Influenza With Risk of Arterial and Venous Thrombotic Events Among Hospitalized Patients

IMPORTANCE

The incidence of arterial thromboembolism and venous thromboembolism in persons with COVID-19 remains unclear.

OBJECTIVE

To measure the 90-day risk of arterial thromboembolism and venous thromboembolism in patients hospitalized with COVID-19 before or during COVID-19 vaccine availability vs patients hospitalized with influenza.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective cohort study of 41 443 patients hospitalized with COVID-19 before vaccine availability (April-November 2020), 44 194 patients hospitalized with COVID-19 during vaccine availability (December 2020-May 2021), and 8269 patients hospitalized with influenza (October 2018-April 2019) in the US Food and Drug Administration Sentinel System (data from 2 national health insurers and 4 regional integrated health systems).

EXPOSURES

COVID-19 or influenza (identified by hospital diagnosis or nucleic acid test).

MAIN OUTCOMES AND MEASURES

Hospital diagnosis of arterial thromboembolism (acute myocardial infarction or ischemic stroke) and venous thromboembolism (deep vein thrombosis or pulmonary embolism) within 90 days. Outcomes were ascertained through July 2019 for patients with influenza and through August 2021 for patients with COVID-19. Propensity scores with fine stratification were developed to account for differences between the influenza and COVID-19 cohorts. Weighted Cox regression was used to estimate the adjusted hazard ratios (HRs) for outcomes during each COVID-19 vaccine availability period vs the influenza period.

RESULTS

A total of 85 637 patients with COVID-19 (mean age, 72 [SD, 13.0] years; 50.5% were male) and 8269 with influenza (mean age, 72 [SD, 13.3] years; 45.0% were male) were included. The 90-day absolute risk of arterial thromboembolism was 14.4% (95% CI, 13.6%-15.2%) in patients with influenza vs 15.8% (95% CI, 15.5%-16.2%) in patients with COVID-19 before vaccine availability (risk difference, 1.4% [95% CI, 1.0%-2.3%]) and 16.3% (95% CI, 16.0%-16.6%) in patients with COVID-19 during vaccine availability (risk difference, 1.9% [95% CI, 1.1%-2.7%]). Compared with patients with influenza, the risk of arterial thromboembolism was not significantly higher among patients with COVID-19 before vaccine availability (adjusted HR, 1.04 [95% CI, 0.97-1.11]) or during vaccine availability (adjusted HR, 1.07 [95% CI, 1.00-1.14]). The 90-day absolute risk of venous thromboembolism was 5.3% (95% CI, 4.9%-5.8%) in patients with influenza vs 9.5% (95% CI, 9.2%-9.7%) in patients with COVID-19 before vaccine availability (risk difference, 4.1% [95% CI, 3.6%-4.7%]) and 10.9% (95% CI, 10.6%-11.1%) in patients with COVID-19 during vaccine availability (risk difference, 5.5% [95% CI, 5.0%-6.1%]). Compared with patients with influenza, the risk of venous thromboembolism was significantly higher among patients with COVID-19 before vaccine availability (adjusted HR, 1.60 [95% CI, 1.43-1.79]) and during vaccine availability (adjusted HR, 1.89 [95% CI, 1.68-2.12]).

CONCLUSIONS AND RELEVANCE

Based on data from a US public health surveillance system, hospitalization with COVID-19 before and during vaccine availability, vs hospitalization with influenza in 2018-2019, was significantly associated with a higher risk of venous thromboembolism within 90 days, but there was no significant difference in the risk of arterial thromboembolism within 90 days.

Identifying factors contributing to increased susceptibility to COVID-19 risk: a systematic review of Mendelian randomization studies

BACKGROUND

To summarize modifiable factors for coronavirus disease 2019 (COVID-19) suggested by Mendelian randomization studies.

METHODS

In this systematic review, we searched PubMed, EMBASE and MEDLINE, from inception to 15 November 2021, for Mendelian randomization studies in English. We selected studies that assessed associations of genetically predicted exposures with COVID-19-related outcomes (severity, hospitalization and susceptibility). Risk of bias of the included studies was evaluated based on the consideration of the three main assumptions for instrumental variable analyses.

RESULTS

We identified 700 studies through systematic search, of which 50 Mendelian randomization studies were included. Included studies have explored a wide range of socio-demographic factors, lifestyle attributes, anthropometrics and biomarkers, predisposition to diseases and druggable targets in COVID-19 risk. Mendelian randomization studies suggested that increases in smoking, obesity and inflammatory factors were associated with higher risk of COVID-19. Predisposition to ischaemic stroke, combined bipolar disorder and schizophrenia, attention-deficit and hyperactivity disorder, chronic kidney disease and idiopathic pulmonary fibrosis was potentially associated with higher COVID-19 risk. Druggable targets, such as higher protein expression of histo-blood group ABO system transferase (ABO), interleukin (IL)-6 and lower protein expression of 2'-5' oligoadenylate synthetase 1 (OAS1) were associated with higher risk of COVID-19. There was no strong genetic evidence supporting the role of vitamin D, glycaemic traits and predisposition to cardiometabolic diseases in COVID-19 risk.

CONCLUSION

This review summarizes modifiable factors for intervention (e.g. smoking, obesity and inflammatory factors) and proteomic signatures (e.g. OAS1 and IL-6) that could help identify drugs for treating COVID-19.

Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification

Simple Summary

Artificial Intelligence (AI) algorithms can assist clinicians in their daily tasks by automatically detecting and/or classifying nodules in chest CT scans. Bias of such algorithms is one of the reasons why implementation of them in clinical practice is still not widely adopted. There is no published review on the bias that these algorithms may contain. This review aims to present different types of bias in such algorithms and present possible ways to mitigate them. Only then it would be possible to ensure that these algorithms work as intended under many different clinical settings.

Abstract

Artificial Intelligence (AI) algorithms for automatic lung nodule detection and classification can assist radiologists in their daily routine of chest CT evaluation. Even though many AI algorithms for these tasks have already been developed, their implementation in the clinical workflow is still largely lacking. Apart from the significant number of false-positive findings, one of the reasons for that is the bias that these algorithms may contain. In this review, different types of biases that may exist in chest CT AI nodule detection and classification algorithms are listed and discussed. Examples from the literature in which each type of bias occurs are presented, along with ways to mitigate these biases. Different types of biases can occur in chest CT AI algorithms for lung nodule detection and classification. Mitigation of them can be very difficult, if not impossible to achieve completely.

Breastfeeding, pregnancy, medicines, neurodevelopment, and population databases: the information desert

BACKGROUND

The pharmacoepidemiology of the long-term benefits and harms of medicines in pregnancy and breastfeeding has received little attention. The impact of maternal medicines on children is increasingly recognised as a source of avoidable harm. The focus of attention has expanded from congenital anomalies to include less visible, but equally important, outcomes, including cognition, neurodevelopmental disorders, educational performance, and childhood ill-health. Breastfeeding, whether as a source of medicine exposure, a mitigator of adverse effects or as an outcome, has been all but ignored in pharmacoepidemiology and pharmacovigilance: a significant 'blind spot'.

WHOLE-POPULATION DATA ON BREASTFEEDING

WHY WE NEED THEM: Optimal child development and maternal health necessitate breastfeeding, yet little information exists to guide families regarding the safety of medicine use during lactation. Breastfeeding initiation or success may be altered by medicine use, and breastfeeding may obscure the true relationship between medicine exposure during pregnancy and developmental outcomes. Absent or poorly standardised recording of breastfeeding in most population databases hampers analysis and understanding of the complex relationships between medicine, pregnancy, breastfeeding and infant and maternal health. The purpose of this paper is to present the arguments for breastfeeding to be included alongside medicine use and neurodevelopmental outcomes in whole-population database investigations of the harms and benefits of medicines during pregnancy, the puerperium and postnatal period. We review: 1) the current situation, 2) how these complexities might be accommodated in pharmacoepidemiological models, using antidepressants and antiepileptics as examples; 3) the challenges in obtaining comprehensive data.

CONCLUSIONS

The scarcity of whole-population data and the complexities of the inter-relationships between breastfeeding, medicines, co-exposures and infant outcomes are significant barriers to full characterisation of the benefits and harms of medicines during pregnancy and breastfeeding. This makes it difficult to answer the questions: 'is it safe to breastfeed whilst taking this medicine', and 'will this medicine interfere with breastfeeding and/ or infants' development'?

SARS-CoV-2 tests, confirmed infections and COVID-19-related hospital admissions in children and young people: birth cohort study

BACKGROUND

There have been no population-based studies of SARS-CoV-2 testing, PCR-confirmed infections and COVID-19-related hospital admissions across the full paediatric age range. We examine the epidemiology of SARS-CoV-2 in children and young people (CYP) aged <23 years.

METHODS

We used a birth cohort of all children born in Scotland since 1997, constructed via linkage between vital statistics, hospital records and SARS-CoV-2 surveillance data. We calculated risks of tests and PCR-confirmed infections per 1000 CYP-years between August and December 2020, and COVID-19-related hospital admissions per 100 000 CYP-years between February and December 2020. We used Poisson and Cox proportional hazards regression models to determine risk factors.

RESULTS

Among the 1 226 855 CYP in the cohort, there were 378 402 tests (a rate of 770.8/1000 CYP-years (95% CI 768.4 to 773.3)), 19 005 PCR-confirmed infections (179.4/1000 CYP-years (176.9 to 182.0)) and 346 admissions (29.4/100 000 CYP-years (26.3 to 32.8)). Infants had the highest COVID-19-related admission rates. The presence of chronic conditions, particularly multiple types of conditions, was strongly associated with COVID-19-related admissions across all ages. Overall, 49% of admitted CYP had at least one chronic condition recorded.

CONCLUSIONS

Infants and CYP with chronic conditions are at highest risk of admission with COVID-19. Half of admitted CYP had chronic conditions. Studies examining COVID-19 vaccine effectiveness among children with chronic conditions and whether maternal vaccine during pregnancy prevents COVID-19 admissions in infants are urgently needed.

Characteristics associated with the residual risk of severe COVID-19 after a complete vaccination schedule: A cohort study of 28 million people in France

Background

Methods

Findings

Interpretation

Funding

Venous or arterial thrombosis and deaths among COVID-19 cases: a European network cohort study

Background

There are few data on the incidence of thrombosis among COVID-19 cases, with most research concentrated on hospitalised patients. We aimed to estimate the incidence of venous thromboembolism, arterial thromboembolism, and death among COVID-19 cases and to assess the impact of these events on the risks of hospitalisation and death.

Methods

We conducted a distributed network cohort study using primary care records from the Netherlands, Italy, Spain, and the UK, and outpatient specialist records from Germany. The Spanish database was linked to hospital admissions. Participants were followed up from the date of a diagnosis of COVID-19 or positive RT-PCR test for SARS-CoV-2 (index date) for 90 days. The primary study outcomes were venous thromboembolic events, arterial thromboembolic events, and death, all over the 90 days from the index date. We estimated cumulative incidences for the study outcomes. Multistate models were used to calculate adjusted hazard ratios (HRs) for the association between venous thromboembolism or arterial thromboembolism occurrence and risks of hospitalisation or COVID-19 fatality.

Findings

Overall, 909 473 COVID-19 cases and 32 329 patients hospitalised with COVID-19 on or after Sept 1, 2020, were studied. The latest index dates across the databases ranged from Jan 30, 2021, to July 31, 2021. Cumulative 90-day incidence of venous thromboembolism ranged from 0·2% to 0·8% among COVID-19 cases, and up to 4·5% for those hospitalised. For arterial thromboembolism, estimates ranged from 0·1% to 0·8% among COVID-19 cases, increasing to 3·1% among those hospitalised. Case fatality ranged from 1·1% to 2·0% among patients with COVID-19, rising to 14·6% for hospitalised patients. The occurrence of venous thromboembolism in patients with COVID-19 was associated with an increased risk of death (adjusted HRs 4·42 [3·07–6·36] for those not hospitalised and 1·63 [1·39–1·90] for those hospitalised), as was the occurrence of arterial thromboembolism (3·16 [2·65–3·75] and 1·93 [1·57–2·37]).

Interpretation

Risks of venous thromboembolism and arterial thromboembolism were up to 1% among COVID-19 cases, and increased with age, among males, and in those who were hospitalised. Their occurrence was associated with excess mortality, underlying the importance of developing effective treatment strategies that reduce their frequency.

Funding

European Medicines Agency.

Inpatient Care Costs of COVID-19 in South Africa's Public Healthcare System

BACKGROUND

Coronavirus disease 2019 (COVID-19) has had a devastating impact globally, with severe health and economic consequences. To prepare health systems to deal with the pandemic, epidemiological and cost projection models are required to inform budgets and efficient allocation of resources. This study estimates daily inpatient care costs of COVID-19 in South Africa, an important input into cost projection and economic evaluation models.

METHODS

We adopted a micro-costing approach, which involved the identification, measurement and valuation of resources used in the clinical management of COVID-19. We considered only direct medical costs for an episode of hospitalisation from the South African public health system perspective. Resource quantities and unit costs were obtained from various sources. Inpatient costs per patient day was estimated for consumables, capital equipment and human resources for three levels of inpatient care - general wards, high care wards and intensive care units (ICUs).

RESULTS

Average daily costs per patient increased with the level of care. The highest average daily cost was estimated for ICU admissions - 271 USD to 306 USD (financial costs) and ~800 USD to 830 USD (economic costs, excluding facility fee) depending on the need for invasive vs. non-invasive ventilation (NIV). Conversely, the lowest cost was estimated for general ward-based care - 62 USD to 79 USD (financial costs) and 119 USD to 278 USD (economic costs, excluding facility fees) depending on the need for supplemental oxygen. In high care wards, total cost was estimated at 156 USD, financial costs and 277 USD, economic costs (excluding facility fees). Probabilistic sensitivity analyses suggest our costs estimates are robust to uncertainty in cost inputs.

CONCLUSION

Our estimates of inpatient costs are useful for informing budgeting and planning processes and cost-effectiveness analysis in the South African context. However, these estimates can be adapted to inform policy decisions in other context.

COVID-19 and kidney disease: insights from epidemiology to inform clinical practice

Over the course of the COVID-19 pandemic, numerous studies have aimed to address the challenges faced by patients with kidney disease and their caregivers. These studies addressed areas of concern such as the high infection and mortality risk of patients on in-centre haemodialysis and transplant recipients. However, the ability to draw meaningful conclusions from these studies has in some instances been challenging, owing to barriers in aspects of usual care, data limitations and problematic methodological practices. In many settings, access to SARS-CoV-2 testing differed substantially between patient groups, whereas the incidence of SARS-CoV-2 infection varied over time and place because of differences in viral prevalence, targeted public health policies and vaccination rates. The absence of baseline kidney function data posed problems in the classification of chronic kidney disease and acute kidney injury in some studies, potentially compromising the generalizability of findings. Study findings also require attentive appraisal in terms of the effects of confounding, collider bias and chance. As this pandemic continues and in the future, the implementation of sustainable and integrated research infrastructure is needed in settings across the world to minimize infection transmission and both prevent and plan for the short-term and long-term complications of infectious diseases. Registries can support the real-world evaluation of vaccines and therapies in patients with advanced kidney disease while enabling monitoring of rare complications.

Racial and Ethnic Disparities in the Observed COVID-19 Case Fatality Rate Among the U.S. Population

Purpose

During the initial 12 months of the pandemic, racial and ethnic disparities in COVID-19 death rates received considerable attention but it has been unclear whether disparities in death rates were due to disparities in case fatality rates (CFRs), incidence rates or both. We examined differences in observed COVID-19 CFRs between U.S. White, Black/African American, and Latinx individuals during this period.

Methods

Using data from the COVID Tracking Project and the Centers for Disease Control and Prevention COVID-19 Case Surveillance Public Use dataset, we calculated CFR ratios comparing Black and Latinx to White individuals, both overall and separately by age group. We also used a model of monthly COVID-19 deaths to estimate CFR ratios, adjusting for age, gender, and differences across states and time.

Results

Overall Black and Latinx individuals had lower CFRs than their White counterparts. However, when adjusting for age, Black and Latinx had higher CFRs than White individuals among those younger than 65. CFRs varied substantially across states and time.

Conclusions

Disparities in COVID-19 case fatality among U.S. Black and Latinx individuals under age 65 were evident during the first year of the pandemic. Understanding racial and ethnic differences in COVID-19 CFRs is challenging due to limitations in available data.

Income differences in COVID-19 incidence and severity in Finland among people with foreign and native background: A population-based cohort study of individuals nested within households

BACKGROUND

Although intrahousehold transmission is a key source of Coronavirus Disease 2019 (COVID-19) infections, studies to date have not analysed socioeconomic risk factors on the household level or household clustering of severe COVID-19. We quantify household income differences and household clustering of COVID-19 incidence and severity.

METHODS AND FINDINGS

We used register-based cohort data with individual-level linkage across various administrative registers for the total Finnish population living in working-age private households (N = 4,315,342). Incident COVID-19 cases (N = 38,467) were identified from the National Infectious Diseases Register from 1 July 2020 to 22 February 2021. Severe cases (N = 625) were defined as having at least 3 consecutive days of inpatient care with a COVID-19 diagnosis and identified from the Care Register for Health Care between 1 July 2020 and 31 December 2020. We used 2-level logistic regression with individuals nested within households to estimate COVID-19 incidence and case severity among those infected. Adjusted for age, sex, and regional characteristics, the incidence of COVID-19 was higher (odds ratio [OR] 1.67, 95% CI 1.58 to 1.77, p < 0.001, 28.4% of infections) among individuals in the lowest household income quintile than among those in the highest quintile (18.9%). The difference attenuated (OR 1.23, 1.16 to 1.30, p < 0.001) when controlling for foreign background but not when controlling for other household-level risk factors. In fact, we found a clear income gradient in incidence only among people with foreign background but none among those with native background. The odds of severe illness among those infected were also higher in the lowest income quintile (OR 1.97, 1.52 to 2.56, p < 0.001, 28.0% versus 21.6% in the highest quintile), but this difference was fully attenuated (OR 1.08, 0.77 to 1.52, p = 0.64) when controlling for other individual-level risk factors-comorbidities, occupational status, and foreign background. Both incidence and severity were strongly clustered within households: Around 77% of the variation in incidence and 20% in severity were attributable to differences between households. The main limitation of our study was that the test uptake for COVID-19 may have differed between population subgroups.

CONCLUSIONS

Low household income appears to be a strong risk factor for both COVID-19 incidence and case severity, but the income differences are largely driven by having foreign background. The strong household clustering of incidence and severity highlights the importance of household context in the prevention and mitigation of COVID-19 outcomes.

The consequences of a year of the COVID-19 pandemic for the mental health of young adult twins in England and Wales

BACKGROUND

The COVID-19 pandemic has affected all our lives, not only through the infection itself but also through the measures taken to control the spread of the virus (e.g. lockdown).

AIMS

Here, we investigated how the COVID-19 pandemic and unprecedented lockdown affected the mental health of young adults in England and Wales.

METHOD

We compared the mental health symptoms of up to 4773 twins in their mid-20s in 2018 prior to the COVID-19 pandemic (T1) and during four-wave longitudinal data collection during the pandemic in April, July and October 2020, and in March 2021 (T2-T5) using phenotypic and genetic longitudinal designs.

RESULTS

The average changes in mental health were small to medium and mainly occurred from T1 to T2 (average Cohen d = 0.14). Despite the expectation of catastrophic effects of the pandemic on mental health, we did not observe trends in worsening mental health during the pandemic (T3-T5). Young people with pre-existing mental health problems were disproportionately affected at the beginning of the pandemic, but their increased problems largely subsided as the pandemic persisted. Twin analyses indicated that the aetiology of individual differences in mental health symptoms did not change during the lockdown (average heritability 33%); the average genetic correlation between T1 and T2-T5 was 0.95, indicating that genetic effects before the pandemic were substantially correlated with genetic effects up to a year later.

CONCLUSIONS

We conclude that on average the mental health of young adults in England and Wales has been remarkably resilient to the effects of the pandemic and associated lockdown.

Mid-regional proadrenomedullin, C-terminal proendothelin-1 values, and disease course are not different in critically ill SARS-CoV-2 pneumonia patients with obesity

Background/objectives

Patients affected by obesity and Coronavirus disease 2019, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), appear to have a higher risk for intensive care (ICU) admission. A state of low-grade chronic inflammation in obesity has been suggested as one of the underlying mechanisms. We investigated whether obesity is associated with differences in new inflammatory biomarkers mid-regional proadrenomedullin (MR-proADM), C-terminal proendothelin-1 (CT-proET-1), and clinical outcomes in critically ill patients with SARS-CoV-2 pneumonia.

Subjects/methods

A total of 105 critically ill patients with SARS-CoV-2 pneumonia were divided in patients with obesity (body mass index (BMI) ≥ 30 kg/m², n = 42) and patients without obesity (BMI < 30 kg/m², n = 63) and studied in a retrospective observational cohort study. MR-proADM, CT-proET-1 concentrations, and conventional markers of white blood count (WBC), C-reactive protein (CRP), and procalcitonin (PCT) were collected during the first 7 days.

Results

BMI was 33.5 (32–36.1) and 26.2 (24.7–27.8) kg/m² in the group with and without obesity. There were no significant differences in concentrations MR-proADM, CT-proET-1, WBC, CRP, and PCT at baseline and the next 6 days between patients with and without obesity. Only MR-proADM changed significantly over time (p = 0.039). Also, BMI did not correlate with inflammatory biomarkers (MR-proADM rho = 0.150, p = 0.125, CT-proET-1 rho = 0.179, p = 0.067, WBC rho = −0.044, p = 0.654, CRP rho = 0.057, p = 0.564, PCT rho = 0.022, p = 0.842). Finally, no significant differences in time on a ventilator, ICU length of stay, and 28-day mortality between patients with or without obesity were observed.

Conclusions

In critically ill patients with confirmed SARS-CoV-2 pneumonia, obesity was not associated with differences in MR-proADM, and CT-proET-1, or impaired outcome.

Trial registration

Netherlands Trial Register, NL8460.

Religious/spiritual beliefs and behaviours and study participation in a prospective cohort study (ALSPAC) in Southwest England

Background Longitudinal studies are key to understanding risk factors for health, well-being, and disease, yet associations may be biased if study invitation and participation are non-random. Religious/spiritual beliefs and behaviours (RSBB) are increasingly recognised as having potentially important relationships with health. However, it is unclear whether RSBB is associated with study participation. We examine whether RSBB is associated with participation in the longitudinal birth cohort ALSPAC (Avon Longitudinal Study of Parents and Children). Methods Three RSBB factors were used: religious belief (belief in God/a divine power; yes/not sure/no), religious affiliation (Christian/none/other), and religious attendance (frequency of attendance at a place of worship). Participation was measured in three ways: i) total number of questionnaires/clinics completed; ii) completion of the most recent questionnaire (in 2020); and iii) length of participation. Analyses were repeated for the ALSPAC mothers, their partners, and the study children, and were adjusted for relevant socio-demographic confounders. Results Religious attendance was positively associated with participation in all adjusted models in all three cohorts. For example, study mothers who attended a place of worship at least once a month on average completed two more questionnaires (out of a possible 50), had 50% greater odds of having completed the most recent questionnaire, and had 25% reduced risk of drop-out, relative to those who did not attend a place of worship. In the adjusted analyses, religious belief and attendance were not associated with participation. However, the majority of unadjusted models showed associations between RSBB and participation. Conclusion After adjusting for confounders, religious attendance –  not religious belief or affiliation – was associated with participation in ALSPAC. These results indicate that use of RSBB variables (and religious attendance in particular) may result in selection bias and spurious associations; these potential biases should be explored and discussed in future studies using these data.

Occupational differences in SARS-CoV-2 infection: analysis of the UK ONS COVID-19 infection survey

BACKGROUND

Concern remains about how occupational SARS-CoV-2 risk has evolved during the COVID-19 pandemic. We aimed to ascertain occupations with the greatest risk of SARS-CoV-2 infection and explore how relative differences varied over the pandemic.

METHODS

Analysis of cohort data from the UK Office of National Statistics COVID-19 Infection Survey from April 2020 to November 2021. This survey is designed to be representative of the UK population and uses regular PCR testing. Cox and multilevel logistic regression were used to compare SARS-CoV-2 infection between occupational/sector groups, overall and by four time periods with interactions, adjusted for age, sex, ethnicity, deprivation, region, household size, urban/rural neighbourhood and current health conditions.

RESULTS

Based on 3 910 311 observations (visits) from 312 304 working age adults, elevated risks of infection can be seen overall for social care (HR 1.14; 95% CI 1.04 to 1.24), education (HR 1.31; 95% CI 1.23 to 1.39), bus and coach drivers (1.43; 95% CI 1.03 to 1.97) and police and protective services (HR 1.45; 95% CI 1.29 to 1.62) when compared with non-essential workers. By time period, relative differences were more pronounced early in the pandemic. For healthcare elevated odds in the early waves switched to a reduction in the later stages. Education saw raises after the initial lockdown and this has persisted. Adjustment for covariates made very little difference to effect estimates.

CONCLUSIONS

Elevated risks among healthcare workers have diminished over time but education workers have had persistently higher risks. Long-term mitigation measures in certain workplaces may be warranted.

Bias from questionnaire invitation and response in COVID-19 research: an example using ALSPAC

Background: Longitudinal studies are crucial for identifying potential risk factors for infection with, and consequences of, COVID-19, but relationships can be biased if they are associated with invitation and response to data collection. We describe factors relating to questionnaire invitation and response in COVID-19 questionnaire data collection in a multigenerational birth cohort (the Avon Longitudinal Study of Parents and Children, ALSPAC). Methods: We analysed online questionnaires completed between the beginning of the pandemic and easing of the first UK lockdown by participants with valid email addresses who had not actively disengaged from the study. We assessed associations of pre-pandemic sociodemographic, behavioural, anthropometric and health-related factors with: i) being sent a questionnaire; ii) returning a questionnaire; and iii) item response (for specific questions). Analyses were conducted in three cohorts: the index children born in the early 1990s (now young adults; 41 variables assessed), their mothers (35 variables) and the mothers' partners (27 variables). Results: Of 14,849 young adults, 41% were sent a questionnaire, of whom 57% returned one. Item response was >95%. In this cohort, 78% of factors were associated with being sent a questionnaire, 56% with returning one, and, as an example of item response, 20% with keyworker status response. For instance, children from mothers educated to degree-level had greater odds of being sent a questionnaire (OR=5.59; 95% CI=4.87-6.41), returning one (OR=1.60; 95% CI=1.31-1.95), and responding to items (e.g., keyworker status OR=1.65; 95% CI=0.88-3.04), relative to children from mothers with fewer qualifications. Invitation and response rates and associations were similar in all cohorts. Conclusions: These results highlight the importance of considering potential biases due to non-response when using longitudinal studies in COVID-19 research and interpreting results. We recommend researchers report response rates and factors associated with invitation and response in all COVID-19 observational research studies, which can inform sensitivity analyses.

Multi-ancestry Mendelian randomization of omics traits revealing drug targets of COVID-19 severity

BACKGROUND

Recent omic studies prioritised several drug targets associated with coronavirus disease 2019 (COVID-19) severity. However, little evidence was provided to systematically estimate the effect of drug targets on COVID-19 severity in multiple ancestries.

METHODS

In this study, we applied Mendelian randomization (MR) and colocalization approaches to understand the putative causal effects of 16,059 transcripts and 1608 proteins on COVID-19 severity in European and effects of 610 proteins on COVID-19 severity in African ancestry. We further integrated genetics, clinical and literature evidence to prioritise drug targets. Additional sensitivity analyses including multi-trait colocalization and phenome-wide MR were conducted to test for MR assumptions.

FINDINGS

MR and colocalization prioritized four protein targets, FCRL3, ICAM5, ENTPD5 and OAS1 that showed effect on COVID-19 severity in European ancestry. One protein target, SERPINA1 showed a stronger effect in African ancestry but much weaker effect in European ancestry (odds ratio [OR] in Africans=0.369, 95%CI=0.203 to 0.668, P = 9.96 × 10-4; OR in Europeans=1.021, 95%CI=0.901 to 1.157, P = 0.745), which suggested that increased level of SERPINA1 will reduce COVID-19 risk in African ancestry. One protein, ICAM1 showed suggestive effect on COVID-19 severity in both ancestries (OR in Europeans=1.152, 95%CI=1.063 to 1.249, P = 5.94 × 10-4; OR in Africans=1.481, 95%CI=1.008 to 2.176; P = 0.045). The OAS1, SERPINA1 and ICAM1 effects were replicated using updated COVID-19 severity data in the two ancestries respectively, where alternative splicing events in OAS1 and ICAM1 also showed marginal effects on COVID-19 severity in Europeans. The phenome-wide MR of the prioritised targets on 622 complex traits provided information on potential beneficial effects on other diseases and suggested little evidence of adverse effects on major complications.

INTERPRETATION

Our study identified six proteins as showing putative causal effects on COVID-19 severity. OAS1 and SERPINA1 were targets of existing drugs in trials as potential COVID-19 treatments. ICAM1, ICAM5 and FCRL3 are related to the immune system. Across the six targets, OAS1 has no reliable instrument in African ancestry; SERPINA1, FCRL3, ICAM5 and ENTPD5 showed a different level of putative causal evidence in European and African ancestries, which highlights the importance of more powerful ancestry-specific GWAS and value of multi-ancestry MR in informing the effects of drug targets on COVID-19 across different populations. This study provides a first step towards clinical investigation of beneficial and adverse effects of COVID-19 drug targets.

FUNDING

No.

Racial and ethnic disparities in coronavirus disease 2019 disease incidence independent of comorbidities, among people with HIV in the United States

OBJECTIVES

To define the incidence of clinically detected coronavirus disease 2019 (COVID-19) in people with HIV (PWH) in the United States and evaluate how racial and ethnic disparities, comorbidities, and HIV-related factors contribute to risk of COVID-19.

DESIGN

Observational study within the CFAR Network of Integrated Clinical Systems cohort in seven cities during 2020.

METHODS

We calculated cumulative incidence rates of COVID-19 diagnosis among PWH in routine care by key characteristics including race/ethnicity, current and lowest CD4+ cell count, and geographic area. We evaluated risk factors for COVID-19 among PWH using relative risk regression models adjusted with disease risk scores.

RESULTS

Among 16 056 PWH in care, of whom 44.5% were black, 12.5% were Hispanic, with a median age of 52 years (IQR 40-59), 18% had a current CD4+ cell count less than 350 cells/μl, including 7% less than 200; 95.5% were on antiretroviral therapy (ART), and 85.6% were virologically suppressed. Overall in 2020, 649 PWH were diagnosed with COVID-19 for a rate of 4.94 cases per 100 person-years. The cumulative incidence of COVID-19 was 2.4-fold and 1.7-fold higher in Hispanic and black PWH respectively, than non-Hispanic white PWH. In adjusted analyses, factors associated with COVID-19 included female sex, Hispanic or black identity, lowest historical CD4+ cell count less than 350 cells/μl (proxy for CD4+ nadir), current low CD4+ : CD8+ ratio, diabetes, and obesity.

CONCLUSION

Our results suggest that the presence of structural racial inequities above and beyond medical comorbidities increased the risk of COVID-19 among PWH. PWH with immune exhaustion as evidenced by lowest historical CD4+ cell count or current low CD4+ : CD8+ ratio had greater risk of COVID-19.

Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization

Mendelian Randomisation (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants as instrumental variables (IVs) for the exposure. The effect estimates obtained from MR studies are often interpreted as the lifetime effect of the exposure in question. However, the causal effects of some exposures are thought to vary throughout an individual's lifetime with periods during which an exposure has a greater effect on a particular outcome. Multivariable MR (MVMR) is an extension of MR that allows for multiple, potentially highly related, exposures to be included in an MR estimation. MVMR estimates the direct effect of each exposure on the outcome conditional on all the other exposures included in the estimation. We explore the use of MVMR to estimate the direct effect of a single exposure at different time points in an individual's lifetime on an outcome. We use simulations to illustrate the interpretation of the results from such analyses and the key assumptions required. We show that causal effects at different time periods can be estimated through MVMR when the association between the genetic variants used as instruments and the exposure measured at those time periods varies. However, this estimation will not necessarily identify exact time periods over which an exposure has the most effect on the outcome. Prior knowledge regarding the biological basis of exposure trajectories can help interpretation. We illustrate the method through estimation of the causal effects of childhood and adult BMI on C-Reactive protein and smoking behaviour.

Association of smoking, lung function and COPD in COVID-19 risk: a two-step Mendelian randomization study

BACKGROUND AND AIMS

Smoking increases the risk of severe COVID-19, but whether lung function or chronic obstructive pulmonary disease (COPD) mediate the underlying associations is unclear. We conducted the largest Mendelian randomization study to date, to our knowledge, to address these questions.

DESIGN

Mendelian randomization study using summary statistics from genome-wide association studies (GWAS), FinnGen and UK Biobank. The main analysis was the inverse variance weighted method, and we included a range of sensitivity analyses to assess the robustness of the findings.

SETTING

GWAS which included international consortia, FinnGen and UK Biobank.

PARTICIPANTS

The sample size ranged from 193 638 to 2 586 691.

MEASUREMENTS

Genetic determinants of life-time smoking index, lung function [e.g. forced expiratory volume in 1 sec (FEV₁ )], chronic obstructive pulmonary disease (COPD) and different severities of COID-19.

RESULTS

Smoking increased the risk of COVID-19 compared with population controls for overall COVID-19 [odds ratio (OR) = 1.19 per standard deviation (SD) of life-time smoking index, 95% confidence interval (CI) = 1.11-1.27], hospitalized COVID-19 (OR = 1.67, 95% CI = 1.42-1.97) or severe COVID-19 (OR = 1.48, 95% CI = 1.10-1.98), with directionally consistent effects from sensitivity analyses. Lung function and COPD liability did not appear to mediate these associations.

CONCLUSION

There is genetic evidence that smoking probably increases the risk of severe COVID-19 and possibly also milder forms of COVID-19. Decreased lung function and increased risk of chronic obstructive pulmonary disease do not seem to mediate the effect of smoking on COVID-19 risk.

Long COVID burden and risk factors in 10 UK longitudinal studies and electronic health records

The frequency of, and risk factors for, long COVID are unclear among community-based individuals with a history of COVID-19. To elucidate the burden and possible causes of long COVID in the community, we coordinated analyses of survey data from 6907 individuals with self-reported COVID-19 from 10 UK longitudinal study (LS) samples and 1.1 million individuals with COVID-19 diagnostic codes in electronic healthcare records (EHR) collected by spring 2021. Proportions of presumed COVID-19 cases in LS reporting any symptoms for 12+ weeks ranged from 7.8% and 17% (with 1.2 to 4.8% reporting debilitating symptoms). Increasing age, female sex, white ethnicity, poor pre-pandemic general and mental health, overweight/obesity, and asthma were associated with prolonged symptoms in both LS and EHR data, but findings for other factors, such as cardio-metabolic parameters, were inconclusive.

A theory-based practical solution to correct for sex-differential participation bias

Most genomic cohorts are retrospective where the exposures and outcomes are predetermined prior to sample collection. Therefore, a spurious association between an exposure and an outcome can arise if both variables affect study participation. Such concerns were raised in previous studies questioning the representativeness of the UK Biobank. Recently, a genome-wide association study (GWAS) on biological sex found many autosomal hits and non-negligible autosomal heritability which the authors attribute to selection bias. In this study, we propose a simple and a practical method that can overcome sex-driven selection bias based on theoretical analysis and simulations.

Illustrating How to Simulate Data From Directed Acyclic Graphs to Understand Epidemiologic Concepts

Simulation methods are a powerful set of tools that can allow researchers to better characterize phenomena from the real world. As such, the ability to simulate data represents a critical set of skills that epidemiologists should use to better understand epidemiologic concepts and ensure that they have the tools to continue to self-teach even when their formal instruction ends. Simulation methods are not always taught in epidemiology methods courses, whereas causal directed acyclic graphs (DAGs) often are. Therefore, this paper details an approach to building simulations from DAGs and provides examples and code for learning to perform simulations. We recommend using very simple DAGs to learn the procedures and code necessary to set up a simulation that builds on key concepts frequently of interest to epidemiologists (e.g., mediation, confounding bias, M bias). We believe that following this approach will allow epidemiologists to gain confidence with a critical skill set that may in turn have a positive impact on how they conduct future epidemiologic studies.

Clinical risk, sociodemographic factors, and SARS-CoV-2 infection over time in Ontario, Canada

We aimed to determine whether early public health interventions in 2020 mitigated the association of sociodemographic and clinical risk factors with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We conducted a population-based cohort study of all adults in Ontario, Canada who underwent testing for SARS-CoV-2 through December 31, 2020. The outcome was laboratory-confirmed SARS-CoV-2 infection, determined by reverse transcription polymerase chain reaction testing. Adjusted odds ratios (ORs) were determined for sociodemographic and clinical risk factors before and after the first-wave peak of the pandemic to assess for changes in effect sizes. Among 3,167,753 community-dwelling individuals, 142,814 (4.5%) tested positive. The association between age and SARS-CoV-2 infection risk varied over time (P-interaction < 0.0001). Prior to the first-wave peak, SARS-CoV-2 infection increased with age whereas this association reversed thereafter. Risk factors that persisted included male sex, residing in lower income neighborhoods, residing in more racially/ethnically diverse communities, immigration to Canada, hypertension, and diabetes. While there was a reduction in infection rates after mid-April 2020, there was less impact in regions with higher racial/ethnic diversity. Immediately following the initial peak, individuals living in the most racially/ethnically diverse communities with 2, 3, or ≥ 4 risk factors had ORs of 1.89, 3.07, and 4.73-fold higher for SARS-CoV-2 infection compared to lower risk individuals in their community (all P < 0.0001). In the latter half of 2020, this disparity persisted with corresponding ORs of 1.66, 2.48, and 3.70-fold higher, respectively. In the least racially/ethnically diverse communities, there was little/no gradient in infection rates across risk strata. Further efforts are necessary to reduce the risk of SARS-CoV-2 infection among the highest risk individuals residing in the most racially/ethnically diverse communities.

Association of Statins for Primary Prevention of Cardiovascular Diseases With Hospitalization for COVID-19: A Nationwide Matched Population-Based Cohort Study

Background There is little evidence on the relationship between statin use and the risk of hospitalization attributable to COVID-19. Methods and Results The French National Healthcare Data System database was used to conduct a matched-cohort study. For each adult aged ≥40 years receiving statins for the primary prevention of cardiovascular diseases, one nonuser was randomly selected and matched for year of birth, sex, residence area, and comorbidities. The association between statin use and hospitalization for COVID-19 was examined using conditional Cox proportional hazards models, adjusted for baseline characteristics, comorbidities, and long-term medications. Its association with in-hospital death from COVID-19 was also explored. All participants were followed up from February 15, 2020, to June 15, 2020. The matching procedure generated 2 058 249 adults in the statin group and 2 058 249 in the control group, composed of 46.6% of men with a mean age of 68.7 years. Statin users had a 16% lower risk of hospitalization for COVID-19 than nonusers (adjusted hazard ratio [HR], 0.84; 95% CI, 0.81-0.88). All types of statins were significantly associated with a lower risk of hospitalization, with the adjusted HR ranging from 0.75 for fluvastatin to 0.89 for atorvastatin. Low- and moderate-intensity statins also showed a lower risk compared with nonusers (HR, 0.78 [95% CI, 0.71-0.86] and HR, 0.84 [95% CI, 0.80-0.89], respectively), whereas high-intensity statins did not (HR, 1.01; 95% CI, 0.86-1.18). We found similar results with in-hospital death from COVID-19. Conclusions Our findings support that the use of statins for primary prevention is associated with lower risks of hospitalization for COVID-19 and of in-hospital death from COVID-19.

Extending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy

False negative rates of severe acute respiratory coronavirus 2 diagnostic tests, together with selection bias due to prioritized testing can result in inaccurate modeling of COVID-19 transmission dynamics based on reported "case" counts. We propose an extension of the widely used Susceptible-Exposed-Infected-Removed (SEIR) model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number R 0 as a function of false negative rates of the diagnostic tests and selection probabilities for getting tested. Analyzing data from the first two waves of the pandemic in India, we show that correcting for misclassification and selection leads to more accurate prediction in a test sample. We provide estimates of undetected infections and deaths between April 1, 2020 and August 31, 2021. At the end of the first wave in India, the estimated under-reporting factor for cases was at 11.1 (95% CI: 10.7,11.5) and for deaths at 3.58 (95% CI: 3.5,3.66) as of February 1, 2021, while they change to 19.2 (95% CI: 17.9, 19.9) and 4.55 (95% CI: 4.32, 4.68) as of July 1, 2021. Equivalently, 9.0% (95% CI: 8.7%, 9.3%) and 5.2% (95% CI: 5.0%, 5.6%) of total estimated infections were reported on these two dates, while 27.9% (95% CI: 27.3%, 28.6%) and 22% (95% CI: 21.4%, 23.1%) of estimated total deaths were reported. Extensive simulation studies demonstrate the effect of misclassification and selection on estimation of R 0 and prediction of future infections. A R-package SEIRfansy is developed for broader dissemination.

Clinical Severity of SARS-CoV-2 Omicron Variant Compared with Delta among Hospitalized COVID-19 Patients in Belgium during Autumn and Winter Season 2021-2022

This retrospective multi-center matched cohort study assessed the risk for severe COVID-19 (combination of severity indicators), intensive care unit (ICU) admission, and in-hospital mortality in hospitalized patients when infected with the Omicron variant compared to when infected with the Delta variant. The study is based on a causal framework using individually-linked data from national COVID-19 registries. The study population consisted of 954 COVID-19 patients (of which, 445 were infected with Omicron) above 18 years old admitted to a Belgian hospital during the autumn and winter season 2021-2022, and with available viral genomic data. Patients were matched based on the hospital, whereas other possible confounders (demographics, comorbidities, vaccination status, socio-economic status, and ICU occupancy) were adjusted for by using a multivariable logistic regression analysis. The estimated standardized risk for severe COVID-19 and ICU admission in hospitalized patients was significantly lower (RR = 0.63; 95% CI (0.30; 0.97) and RR = 0.56; 95% CI (0.14; 0.99), respectively) when infected with the Omicron variant, whereas in-hospital mortality was not significantly different according to the SARS-CoV-2 variant (RR = 0.78, 95% CI (0.28-1.29)). This study demonstrates the added value of integrated genomic and clinical surveillance to recognize the multifactorial nature of COVID-19 pathogenesis.

Clinical outcomes associated with SARS-CoV-2 Omicron (B.1.1.529) variant and BA.1/BA.1.1 or BA.2 subvariant infection in southern California

ABTRACT

Epidemiologic surveillance has revealed decoupling of COVID-19 hospitalizations and deaths from case counts following emergence of the Omicron (B.1.1.529) SARS-CoV-2 variant globally. However, assessment of the relative severity of Omicron variant infections presents challenges because of differential acquired immune protection against Omicron and prior variants, and because longer-term changes have occurred in testing and healthcare practices. Here we show that Omicron variant infections were associated with substantially reduced risk of progression to severe clinical outcomes relative to time-matched Delta (B.1.617.2) variant infections within a large, integrated healthcare system in southern California. Adjusted hazard ratios (aHRs) for any hospital admission, symptomatic hospital admission, intensive care unit admission, mechanical ventilation, and death comparing cases with Omicron versus Delta variant infection were 0.59 (95% confidence interval: 0.51-0.69), 0.59 (0.51-0.68), 0.50 (0.29-0.87), 0.36 (0.18-0.72), and 0.21 (0.10-0.44) respectively. This reduced severity could not be explained by differential history of prior infection among cases with Omicron or Delta variant infection, and was starkest among cases not previously vaccinated against COVID-19 (aHR=0.40 [0.33-0.49] for any hospital admission and 0.14 [0.07-0.28] for death). Infections with the Omicron BA.2 subvariant were not associated with differential risk of severe outcomes in comparison to BA.1/BA.1.1 subvariant infections. Lower risk of severe clinical outcomes among cases with Omicron variant infection should inform public health response amid establishment of the Omicron variant as the dominant SARS-CoV-2 lineage globally.

Renin-Angiotensin Aldosterone System Inhibitors and COVID-19: A Systematic Review and Meta-Analysis Revealing Critical Bias Across a Body of Observational Research

Background Renin-angiotensin aldosterone system (RAAS) inhibitor-COVID-19 studies, observational in design, appear to use biased methods that can distort the interaction between RAAS inhibitor use and COVID-19 risk. This study assessed the extent of bias in that research and reevaluated RAAS inhibitor-COVID-19 associations in studies without critical risk of bias. Methods and Results Searches were performed in MEDLINE, EMBASE, and CINAHL databases (December 1, 2019 to October 21, 2021) identifying studies that compared the risk of infection and/or severe COVID-19 outcomes between those using or not using RAAS inhibitors (ie, angiotensin-converting enzyme inhibitors or angiotensin II type-I receptor blockers). Weighted hazard ratios (HR) and 95% CIs were extracted and pooled in fixed-effects meta-analyses, only from studies without critical risk of bias that assessed severe COVID-19 outcomes. Of 169 relevant studies, 164 had critical risks of bias and were excluded. Ultimately, only two studies presented data relevant to the meta-analysis. In 1 351 633 people with uncomplicated hypertension using a RAAS inhibitor, calcium channel blocker, or thiazide diuretic in monotherapy, the risk of hospitalization (angiotensin-converting enzyme inhibitor: HR, 0.76; 95% CI, 0.66-0.87; P<0.001; angiotensin II type-I receptor blockers: HR, 0.86; 95% CI, 0.77-0.97; P=0.015) and intubation or death (angiotensin-converting enzyme inhibitor: HR, 0.64; 95% CI, 0.48-0.85; P=0.002; angiotensin II type-I receptor blockers: HR, 0.74; 95% CI, 0.58-0.95; P=0.019) with COVID-19 was lower in those using a RAAS inhibitor. However, these protective effects are probably not clinically relevant. Conclusions This study reveals the critical risk of bias that exists across almost an entire body of COVID-19 research, raising an important question: Were research methods and/or peer-review processes temporarily weakened during the surge of COVID-19 research or is this lack of rigor a systemic problem that also exists outside pandemic-based research? Registration URL: www.crd.york.ac.uk/prospero/; Unique identifier: CRD42021237859.

A loss-of-function IFNAR1 allele in Polynesia underlies severe viral diseases in homozygotes

Globally, autosomal recessive IFNAR1 deficiency is a rare inborn error of immunity underlying susceptibility to live attenuated vaccine and wild-type viruses. We report seven children from five unrelated kindreds of western Polynesian ancestry who suffered from severe viral diseases. All the patients are homozygous for the same nonsense IFNAR1 variant (p.Glu386*). This allele encodes a truncated protein that is absent from the cell surface and is loss-of-function. The fibroblasts of the patients do not respond to type I IFNs (IFN-α2, IFN-ω, or IFN-β). Remarkably, this IFNAR1 variant has a minor allele frequency >1% in Samoa and is also observed in the Cook, Society, Marquesas, and Austral islands, as well as Fiji, whereas it is extremely rare or absent in the other populations tested, including those of the Pacific region. Inherited IFNAR1 deficiency should be considered in individuals of Polynesian ancestry with severe viral illnesses.

Severity of infection with the SARS-CoV-2 B.1.1.7 lineage among hospitalized COVID-19 patients in Belgium

INTRODUCTION

The pathogenesis of COVID-19 depends on the interplay between host characteristics, viral characteristics and contextual factors. Here, we compare COVID-19 disease severity between hospitalized patients in Belgium infected with the SARS-CoV-2 variant B.1.1.7 and those infected with previously circulating strains.

METHODS

The study is conducted within a causal framework to study the severity of SARS-CoV-2 variants by merging surveillance registries in Belgium. Infection with SARS-CoV-2 B.1.1.7 ('exposed') was compared to infection with previously circulating strains ('unexposed') in terms of the manifestation of severe COVID-19, intensive care unit (ICU) admission, or in-hospital mortality. The exposed and unexposed group were matched based on the hospital and the mean ICU occupancy rate during the patient's hospital stay. Other variables identified as confounders in a Directed Acyclic Graph (DAG) were adjusted for using regression analysis. Sensitivity analyses were performed to assess the influence of selection bias, vaccination rollout, and unmeasured confounding.

RESULTS

We observed no difference between the exposed and unexposed group in severe COVID-19 disease or in-hospital mortality (RR = 1.15, 95% CI [0.93-1.38] and RR = 0.92, 95% CI [0.62-1.23], respectively). The estimated standardized risk to be admitted in ICU was significantly higher (RR = 1.36, 95% CI [1.03-1.68]) when infected with the B.1.1.7 variant. An age-stratified analysis showed that among the younger age group (≤65 years), the SARS-CoV-2 variant B.1.1.7 was significantly associated with both severe COVID-19 progression and ICU admission.

CONCLUSION

This matched observational cohort study did not find an overall increased risk of severe COVID-19 or death associated with B.1.1.7 infection among patients already hospitalized. There was a significant increased risk to be transferred to ICU when infected with the B.1.1.7 variant, especially among the younger age group. However, potential selection biases advocate for more systematic sequencing of samples from hospitalized COVID-19 patients.

Hospitalization and mortality in patients with COVID-19 with or at risk of type 2 diabetes: data from five health systems in Pennsylvania and Maryland

OBJECTIVE

To identify the demographic and clinical characteristics associated with adverse COVID-19 outcomes across a 12-month period in 2020 and 2021.

RESEARCH DESIGN AND METHODS

We conducted a retrospective cohort study using electronic health records from five academic health systems in Pennsylvania and Maryland, including patients with COVID-19 with type 2 diabetes or at risk of type 2 diabetes. Patients were classified based on 30-day outcomes: (1) no hospitalization; (2) hospitalization only; or (3) a composite measure including admission to the intensive care unit (ICU), intubation, or death. Analyses were conducted in patients with type 2 diabetes and patients at risk of type 2 diabetes separately.

RESULTS

We included 15 725 patients with COVID-19 diagnoses between March 2020 and February 2021. Older age and higher Charlson Comorbidity Index scores were associated with higher odds of adverse outcomes, while COVID-19 diagnoses later in the study period were associated with lower odds of severe outcomes. In patients with type 2 diabetes, individuals on insulin treatment had higher odds for ICU/intubation/death (OR=1.59, 95% CI 1.27 to 1.99), whereas those on metformin had lower odds (OR=0.56, 95% CI 0.45 to 0.71). Compared with non-Hispanic White patients, Hispanic patients had higher odds of hospitalization in patients with type 2 diabetes (OR=1.73, 95% CI 1.36 to 2.19) or at risk of type 2 diabetes (OR=1.77, 95% CI 1.43 to 2.18.) CONCLUSIONS: Adults who were older, in racial minority groups, had multiple chronic conditions or were on insulin treatment had higher risks for severe COVID-19 outcomes. This study reinforced the urgency of preventing COVID-19 and its complications in vulnerable populations.

TRIAL REGISTRATION NUMBER

NCT02788903.

Susceptibility to COVID-19 Diagnosis in People with Down Syndrome Compared to the General Population: Matched-Cohort Study Using Primary Care Electronic Records in the UK

BACKGROUND

During the COVID-19 pandemic, people with Down syndrome (DS) have experienced a more severe disease course and higher mortality rates than the general population. It is not yet known whether people with DS are more susceptible to being diagnosed with COVID-19.

OBJECTIVE

To explore whether DS is associated with increased susceptibility to COVID-19.

DESIGN

Matched-cohort study design using anonymised primary care electronic health records from the May 2021 release of Clinical Practice Research Datalink (CPRD) Aurum.

SETTING

Electronic health records from approximately 1400 general practices (GPs) in England.

PARTICIPANTS

8854 people with DS and 34,724 controls matched for age, gender and GP who were registered on or after the 29th January 2020.

MEASUREMENTS

The primary outcome was COVID-19 diagnosis between January 2020 and May 2021. Conditional logistic regression models were fitted to estimate associations between DS and COVID-19 diagnosis, adjusting for comorbidities.

RESULTS

Compared to controls, people with DS were more likely to be diagnosed with COVID-19 (7.4% vs 5.6%, p ≤ 0.001, odds ratio (OR) = 1.35; 95% CI = 1.23-1.48). There was a significant interaction between people with DS and a chronic respiratory disease diagnosis excluding asthma and increased odds of a COVID-19 diagnosis (OR = 1.71; 95% CI = 1.20-2.43), whilst adjusting for a number of comorbidities.

CONCLUSION

Individuals with DS are at increased risk for contracting COVID-19. Those with underlying lung conditions are particularly vulnerable during viral pandemics and should be prioritised for vaccinations.

Neurodevelopmental Outcomes at 1 Year in Infants of Mothers Who Tested Positive for SARS-CoV-2 During Pregnancy

Importance

Epidemiologic studies suggest maternal immune activation during pregnancy may be associated with neurodevelopmental effects in offspring.

Objective

To evaluate whether in utero exposure to SARS-CoV-2 is associated with risk for neurodevelopmental disorders in the first 12 months after birth.

Design, Setting, and Participants

This retrospective cohort study examined live offspring of all mothers who delivered between March and September 2020 at any of 6 Massachusetts hospitals across 2 health systems. Statistical analysis was performed from October to December 2021.

Exposures

Maternal SARS-CoV-2 infection confirmed by a polymerase chain reaction test during pregnancy.

Main Outcomes and Measures

Neurodevelopmental disorders determined from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnostic codes over the first 12 months of life; sociodemographic and clinical features of mothers and offspring; all drawn from the electronic health record.

Results

The cohort included 7772 live births (7466 pregnancies, 96% singleton, 222 births to SARS-CoV-2 positive mothers), with mean (SD) maternal age of 32.9 (5.0) years; offspring were 9.9% Asian (772), 8.4% Black (656), and 69.0% White (5363); 15.1% (1134) were of Hispanic ethnicity. Preterm delivery was more likely among exposed mothers: 14.4% (32) vs 8.7% (654) (P = .003). Maternal SARS-CoV-2 positivity during pregnancy was associated with greater rate of neurodevelopmental diagnoses in unadjusted models (odds ratio [OR], 2.17 [95% CI, 1.24-3.79]; P = .006) as well as those adjusted for race, ethnicity, insurance status, offspring sex, maternal age, and preterm status (adjusted OR, 1.86 [95% CI, 1.03-3.36]; P = .04). Third-trimester infection was associated with effects of larger magnitude (adjusted OR, 2.34 [95% CI, 1.23-4.44]; P = .01).

Conclusions and Relevance

This cohort study of SARS-CoV-2 exposure in utero found preliminary evidence that maternal SARS-CoV-2 may be associated with neurodevelopmental sequelae in some offspring. Prospective studies with longer follow-up duration will be required to exclude confounding and confirm these associations.

Clarifying the causes of consistent and inconsistent findings in genetics

As research in genetics has advanced, some findings have been unexpected or shown to be inconsistent between studies or datasets. The reasons these inconsistencies arise are complex. Results from genetic studies can be affected by various factors including statistical power, linkage disequilibrium, quality control, confounding and selection bias, as well as real differences from interactions and effect modifiers, which may be informative about the mechanisms of traits and disease. Statistical artefacts can manifest as differences between results but they can also conceal underlying differences, which implies that their critical examination is important for understanding the underpinnings of traits. In this review, we examine these factors and outline how they can be identified and conceptualised with structural causal models. We explain the consequences they have on genetic estimates, such as genetic associations, polygenic scores, family‐ and genome‐wide heritability, and describe methods to address them to aid in the estimation of true effects of genetic variation. Clarifying these factors can help researchers anticipate when results are likely to diverge and aid researchers' understanding of causal relationships between genes and complex traits.