Risk factors for positive and negative COVID-19 tests: a cautious and in-depth analysis of UK biobank data

Individual risk factors associated with SARS-CoV-2 infection during Alpha variant in high-income countries: a systematic review and meta-analysis

Objectives

This study aimed to systematically appraise risk factors associated with SARS-CoV-2 infection in high-income countries during the period of predominance of the Alpha variant (January 2020 to April 2021).

Methods

Four electronic databases were used to search observational studies. Literature search, study screening, data extraction and quality assessment were conducted by two authors independently. Meta-analyses were conducted for each risk factor, when appropriate.

Results

From 12,094 studies, 27 were included. The larger sample size was 17,288,532 participants, more women were included, and the age range was 18-117 years old. Meta-analyses identified men [Odds Ratio (OR): 1.23, 95% Confidence Interval (CI): 1.97-1.42], non-white ethnicity (OR: 1.63, 95% CI: 1.39-1.91), household number (OR: 1.08, 95% CI: 1.06-1.10), diabetes (OR: 1.22, 95% CI: 1.08-1.37), cancer (OR: 0.82, 95% CI: 0.68-0.98), cardiovascular diseases (OR: 0.92, 95% CI: 0.84-1.00), asthma (OR: 0.83, 95% CI: 0.75-0.92) and ischemic heart disease (OR: 0.82, 95% CI: 0.74-0.91) as associated with SARS-CoV-2 infection.

Conclusion

This study indicated several risk factors for SARS-CoV-2 infection. Due to the heterogeneity of the studies included, more studies are needed to understand the factors that increase the risk for SARS-CoV-2 infection.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021244148, PROSPERO registration number, CRD42021244148.

Outdoor air pollution as a risk factor for testing positive for SARS-CoV-2: A nationwide test-negative case-control study in the Netherlands

Air pollution is a known risk factor for several diseases, but the extent to which it influences COVID-19 compared to other respiratory diseases remains unclear. We performed a test-negative case-control study among people with COVID-19-compatible symptoms who were tested for SARS-CoV-2 infection, to assess whether their long- and short-term exposure to ambient air pollution (AAP) was associated with testing positive (vs. negative) for SARS-CoV-2. We used individual-level data for all adult residents in the Netherlands who were tested for SARS-CoV-2 between June and November 2020, when only symptomatic people were tested, and modeled ambient concentrations of PM10, PM2.5, NO2 and O3 at geocoded residential addresses. In long-term exposure analysis, we selected individuals who did not change residential address in 2017-2019 (1.7 million tests) and considered the average concentrations of PM10, PM2.5 and NO2 in that period, and different sources of PM (industry, livestock, other agricultural activities, road traffic, other Dutch sources, foreign sources). In short-term exposure analysis, individuals not changing residential address in the two weeks before testing day (2.7 million tests) were included in the analyses, thus considering 1- and 2-week average concentrations of PM10, PM2.5, NO2 and O3 before testing day as exposure. Mixed-effects logistic regression analysis with adjustment for several confounders, including municipality and testing week to account for spatiotemporal variation in viral circulation, was used. Overall, there was no statistically significant effect of long-term exposure to the studied pollutants on the odds of testing positive vs. negative for SARS-CoV-2. However, significant positive associations of long-term exposure to PM10 and PM2.5 from specifically foreign and livestock sources, and to PM10 from other agricultural sources, were observed. Short-term exposure to PM10 (adjusting for NO2) and PM2.5 were also positively associated with increased odds of testing positive for SARS-CoV-2. While these exposures seemed to increase COVID-19 risk relative to other respiratory diseases, the underlying biological mechanisms remain unclear. This study reinforces the need to continue to strive for better air quality to support public health.

A joint penalized spline smoothing model for the number of positive and negative COVID-19 tests

One of the key tools to understand and reduce the spread of the SARS-CoV-2 virus is testing. The total number of tests, the number of positive tests, the number of negative tests, and the positivity rate are interconnected indicators and vary with time. To better understand the relationship between these indicators, against the background of an evolving pandemic, the association between the number of positive tests and the number of negative tests is studied using a joint modeling approach. All countries in the European Union, Switzerland, the United Kingdom, and Norway are included in the analysis. We propose a joint penalized spline model in which the penalized spline is reparameterized as a linear mixed model. The model allows for flexible trajectories by smoothing the country-specific deviations from the overall penalized spline and accounts for heteroscedasticity by allowing the autocorrelation parameters and residual variances to vary among countries. The association between the number of positive tests and the number of negative tests is derived from the joint distribution for the random intercepts and slopes. The correlation between the random intercepts and the correlation between the random slopes were both positive. This suggests that, when countries increase their testing capacity, both the number of positive tests and negative tests will increase. A significant correlation was found between the random intercepts, but the correlation between the random slopes was not significant due to a wide credible interval.

Long-term air pollution exposure and risk of SARS-CoV-2 infection: A UK-wide cohort study

BACKGROUND

The association between air quality and risk of SARS-CoV-2 infection is poorly understood. We investigated this association using serological individual-level data adjusting for a wide range of confounders, in a large population-based cohort (COVIDENCE UK).

METHODS

We assessed the associations between long-term (2015-19) nitrogen dioxide (NO2) and fine particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), exposures with SARS-CoV-2 infection, level of antibody response among those infected, and COVID-19 disease severity. We used serological data from 10,489 participants in the COVIDENCE UK cohort, and estimated annual average air pollution exposure at each participant's home postcode.

RESULTS

After controlling for potential confounders, we found a positive association between 5-year NO2 and PM2.5 exposures and the risk of seropositivity: 10 unit increase in NO2 (μg/m3) was associated with an increasing risk of seropositivity by 1.092 (95% CI 1.02 to 1.17; p-for-trend 0.012). For PM2.5, 10 unit increase (μg/m3) was associated with an increasing risk of seropositivity by 1.65 (95% CI 1.015-2.68; p-for-trend 0·049). In addition, we found that NO2 was positively associated with higher antibody titres (p-for-trend 0·013) among seropositive participants, with no evidence of an association for PM2.5.

CONCLUSION

Our findings suggest that the long-term burden of air pollution increased the risks of SARS-CoV-2 infection and has important implications for future pandemic preparedness. This evidence strengthens the case for reducing long-term air pollution exposures to reduce the vulnerability of individuals to respiratory viruses.

Sociodemographic inequalities in the incidence of COVID-19 in National Household Sample Survey cohort, Brazil, 2020

OBJECTIVE

To verify the association between sociodemographic factors and the time until the occurrence of new cases of COVID-19 and positive tests for SARS-CoV-2 in Brazil, during the period from May to November 2020, based on a cohort of Brazilians participating in the COVID-19 National Household Sample Survey.

METHODS

A concurrent and closed cohort was created using monthly data from the PNAD COVID-19, carried out via telephone survey. A new case was defined based on the report of the occurrence of a flu-like syndrome, associated with loss of smell or taste; and positivity was defined based on the report of a positive test, among those who reported having been tested. Cox regression models were applied to verify associations. The analyzes took into account sample weighting, calibrated for age, gender and education distribution.

RESULTS

The cumulative incidence of cases in the overall fixed cohort was 2.4%, while that of positive tests in the fixed tested cohort was 27.1%. Higher incidences were observed in the North region, in females, in residents of urban areas and in individuals with black skin color. New positive tests occurred more frequently in individuals with less education and healthcare workers.

CONCLUSION

The importance of prospective national surveys is highlighted, contributing to detailed analyzes of social inequalities in reports focused on public health policies.

Physical Activity, Sleeping Problems, Weight, Feelings of Social Isolation, and Quality of Life of Older Adults After Coronavirus Infection: A Longitudinal Cohort Study

BACKGROUND

There is debate as to whether a coronavirus infection (SARS-CoV-2) affects older adults' physical activity, sleeping problems, weight, feelings of social isolation, and quality of life (QoL). We investigated differences in these outcomes between older adults with and without coronavirus infection over 180 days following infection.

METHODS

We included 6789 older adults (65+) from the Lifelines COVID-19 cohort study who provided data between April 2020 and June 2021. Older adults (65+) with and without coronavirus infection were matched on sex, age, education, living situation, body mass index, smoking status, vulnerable health, time of infection, and precoronavirus health outcome. Weighted linear mixed models, adjusted for strictness of governmental policy measures, were used to compare health outcomes after infection between groups.

RESULTS

In total, 309 participants were tested positive for coronavirus. Eight days after infection, older adults with a coronavirus infection engaged in less physical activity, had more sleeping problems, weighed less, felt more socially isolated, and had a lower QoL than those without an infection. Differences in weight, feelings of social isolation, and QoL were absent after 90 days. However, differences in physical activity were still present at 90 days following infection and sleeping problems were present at 180 days.

CONCLUSION

Our findings found negative associations of coronavirus infection with all the examined outcomes, which for physical activity persisted for 90 days and sleeping problems for 180 days. Magnitudes of estimated effects on physical activity and sleeping problems remain uncertain.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Prospective study design and data analysis in UK Biobank

Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank's study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide.

Disparities in Coronavirus Disease 2019 Clinical Outcomes and Vaccination Coverage Among Migrants With Human Immunodeficiency Virus in the PISCIS Cohort: A Population-Based Propensity Score-Matched Analysis

Background

Coronavirus disease 2019 (COVID-19) disproportionately affects migrants and ethnic minorities, including those with human immunodeficiency virus (HIV). Comprehensive studies are needed to understand the impact and risk factors.

Methods

Using data from the PISCIS cohort of people with HIV (PWH) in Catalonia, Spain, we investigated COVID-19 outcomes and vaccination coverage. Among 10 640 PWH we compared migrants and non-migrants assessing rates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing, diagnosis, and associated clinical outcomes through propensity score matching and multivariable Cox regression.

Results

The cohort (mean age, 43 years; 83.5% male) included 57.4% (3053) Latin American migrants. Migrants with HIV (MWH) had fewer SARS-CoV-2 tests (67.8% vs 72.1%, P < .0001) but similar COVID-19 diagnoses (29.2% vs 29.4%, P = .847) compared to Spanish natives. Migrants had lower complete vaccination (78.9% vs 85.1%, P < .0001) and booster doses (63.0% vs 65.5%, P = .027). COVID-19 hospitalizations (8.1% vs 5.1%, P < .0001) and intensive care unit (ICU) admissions (2.9% vs 1.2%, P < .0001) were higher among migrants, with similar hospitalization duration (5.5 vs 4.0 days, P = .098) and mortality (3 [0.2%] vs 6 [0.4%], P = .510). Age ≥40 years, CD4 counts <200 cells/μL, ≥2 comorbidities, and incomplete/nonreception of the SARS-CoV-2 vaccine increased the risk of severe COVID-19 among migrants.

Conclusions

MWH had lower rates of SARS-CoV-2 testing and vaccination coverage, although the rates of COVID-19 diagnosis were similar between migrants and non-migrants. Rates of COVID-19-associated hospitalizations and ICU admissions were higher among migrants in comparison with non-migrants, with similar hospitalization duration and mortality. These findings can inform policies to address disparities in future pandemic responses for MWH.

Impact of the COVID-19 Pandemic on Physical Activity among Mostly Older, Overweight Black Women Living in the Rural Alabama Black Belt

Despite well-documented global declines in physical activity (PA) during the COVID-19 pandemic, little is known regarding the specific impact among underserved, rural Alabama counties. This is concerning as this region was already disproportionately burdened by inactivity and related chronic diseases and was among the hardest hit by COVID-19. Thus, the current study examined the effect of COVID-19 on PA in four rural Alabama counties. An ancillary survey was administered between March 2020 and August 2021 to the first cohort (N = 171) of participants enrolled in a larger PA trial. Main outcomes of this survey included the perceived impact of COVID-19 on PA, leisure-time PA, and social cognitive theory (SCT) constructs at 3 months. Almost half of the participants reported being less active during the pandemic (49.7%) and endorsed that COVID-19 made PA more difficult (47.4%), citing concerns such as getting sick from exercising outside of the home (70.4%) and discomfort wearing a face mask while exercising (58%). Perceived COVID-19 impact on PA was significantly associated with education, household dependents, and gender (p's < 0.05). More women, parents, and college graduates reported that the COVID-19 pandemic made PA more difficult. Overall, there were no significant associations between PA, SCT constructs, or perceived COVID-19 impact on PA scores at 3 months. While the pandemic made PA difficult for many participants, these barriers were not associated with leisure-time PA levels or related theoretical mechanisms of action, which bodes well for the success of our ongoing intervention efforts and the resiliency of these communities.

Changes in symptoms and characteristics of COVID-19 patients across different variants: two years study using neural network analysis

BACKGROUND

Considering the fact that COVID-19 has undergone various changes over time, its symptoms have also varied. The aim of this study is to describe and compare the changes in personal characteristics, symptoms, and underlying conditions of individuals infected with different strains of COVID-19.

METHODS

This descriptive-analytical study was conducted on 46,747 patients who underwent PCR testing during a two-year period from February 22, 2020 to February 23, 2022, in South Khorasan province, Iran. Patient characteristics and symptoms were extracted based on self-report and the information system. The data were analyzed using logistic regression and artificial neural network approaches. The R software was used for analysis and a significance level of 0.05 was considered for the tests.

RESULTS

Among the 46,747 cases analyzed, 23,239 (49.7%) were male, and the mean age was 51.48 ± 21.41 years. There was a significant difference in symptoms among different variants of the disease (p < 0.001). The factors with a significant positive association were myalgia (OR: 2.04; 95% CI, 1.76 - 2.36), cough (OR: 1.93; 95% CI, 1.68-2.22), and taste or smell disorder (OR: 2.62; 95% CI, 2.1 - 3.28). Additionally, aging was found to increase the likelihood of testing positive across the six periods.

CONCLUSION

We found that older age, myalgia, cough and taste/smell disorder are better factors compared to dyspnea or high body temperature, for identifying a COVID-19 patient. As the disease evolved, chills and diarrhea, demonstrated prognostic strength as in Omicron.

A cohort study on the biochemical and haematological parameters of Italian blood donors as possible risk factors of COVID-19 infection and severe disease in the pre- and post-Omicron period

To investigate the association between biochemical and blood parameters collected before the pandemic in a large cohort of Italian blood donors with the risk of infection and severe disease. We also focused on the differences between the pre- and post-Omicron spread in Italy (i.e., pre- and post-January 01, 2022) on the observed associations. We conducted an observational cohort study on 13750 blood donors was conducted using data archived up to 5 years before the pandemic. A t-test or chi-squared test was used to compare differences between groups. Hazard ratios with 95% confidence intervals for SARS-CoV-2 infection and severe disease were estimated using Cox proportional hazards models. Subgroup analyses stratified by sex, age and epidemic phase of first infection (pre- and post-Omicron spread) were examined. We confirmed a protective effect of groups B and O, while groups A and AB had a higher likelihood of infection and severe disease. However, these associations were only significant in the pre-Omicron period. We found an opposite behavior after Omicron spread, with the O phenotype having a higher probability of infection. When stratified by variant, A antigen appeared to protect against Omicron infection, whereas it was associated with an increased risk of infection by earlier variants. We were able to stratify for the SARS CoV-2 dominant variant, which revealed a causal association between blood group and probability of infection, as evidenced by the strong effect modification observed between the pre- and post-Omicron spread. The mechanism by which group A acts on the probability of infection should consider this strong effect modification.

Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data

For the long term control of an infectious disease such as COVID-19, it is crucial to identify the most likely individuals to become infected and the role that differences in demographic characteristics play in the observed patterns of infection. As high-volume surveillance winds down, testing data from earlier periods are invaluable for studying risk factors for infection in detail. Observed changes in time during these periods may then inform how stable the pattern will be in the long term. To this end we analyse the distribution of cases of COVID-19 across Scotland in 2021, where the location (census areas of order 500-1,000 residents) and reporting date of cases are known. We consider over 450,000 individually recorded cases, in two infection waves triggered by different lineages: B.1.1.529 ("Omicron") and B.1.617.2 ("Delta"). We use random forests, informed by measures of geography, demography, testing and vaccination. We show that the distributions are only adequately explained when considering multiple explanatory variables, implying that case heterogeneity arose from a combination of individual behaviour, immunity, and testing frequency. Despite differences in virus lineage, time of year, and interventions in place, we find the risk factors remained broadly consistent between the two waves. Many of the observed smaller differences could be reasonably explained by changes in control measures.

Air pollution exposure, SARS-CoV-2 infection, and immune response in a cohort of healthcare workers of a large university hospital in Milan, Italy

Several studies have examined the possible relationship between air pollutants and the risk of COVID-19 but most returned controversial findings. We tried to assess the association between (short- and long-term) exposure to particulate and gaseous pollutants, SARS-CoV-2 infections, and immune response in a population of healthcare workers (HCWs) with well-characterized individual data. We collected occupational and clinical characteristics of all HCWs who performed a nasopharyngeal swab (NPS) for detecting SARS-CoV-2 at the Policlinico Hospital in Milan (Lombardy, Italy) between February 24, 2020 (day after first documented case of COVID-19 in our hospital) and December 26, 2020 (day before start of the vaccination campaign). Each subject was assigned daily average levels of particulate matter ≤10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) retrieved from the air quality monitoring station closest to his/her residential address. Air pollution data were treated as time-dependent variables, generating person-days at risk. Multivariate Poisson regression models were fit to evaluate the rate of positive NPS and to assess the association between air pollution and antibody titer among NPS-positive HCWs. Among 3712 included HCWs, 635 (17.1%) had at least one positive NPS. A 10 μg/m3 increase in NO2 average concentration in the four days preceding NPS was associated with a higher risk of testing positive [Incidence Rate Ratio (IRR) = 1.08, 95% confidence interval (CI): 1.01; 1.16)]. When considering a 1 μg/m3 increase in 2019 annual NO2 average, we observed a higher risk of infection (IRR: 1.02, 95%CI: 1.00; 1.03) and an increased antibody titer (+2.4%, 95%CI: 1.1; 3.6%). Findings on PM10 and O3 were less consistent and, differently from NO2, were not confirmed in multipollutant models. Our study increases the body of evidence suggesting an active role of air pollution exposure on SARS-CoV-2 infection and confirms the importance of implementing pollution reduction policies to improve public health.

Biological age estimation using circulating blood biomarkers

Biological age captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for biological age estimation. This study aims to improve biological age estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank (n = 306,116). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk (C-Index = 0.778; 95% CI [0.767-0.788]), which outperforms the well-known blood-biomarker based PhenoAge model (C-Index = 0.750; 95% CI [0.739-0.761]), providing a C-Index lift of 0.028 representing an 11% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual's mortality risk. Values ranged between 20-years younger and 20-years older than individuals' chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of Biological Age, available to the general population.

Periodontitis and risk of immune-mediated systemic conditions: A systematic review and meta-analysis

INTRODUCTION

The aim of this review is to examine and quantify the long-term risk of immune-mediated systemic conditions in people with periodontitis compared to people without periodontitis.

METHODS

Medline, EMBASE and Cochrane databases were searched up to June 2022 using keywords and MeSH headings. The 'Risk of Bias in Non-Randomised Studies of Interventions' tool was used to assess bias. Cohort studies comparing incident metabolic/autoimmune/inflammatory diseases in periodontitis to healthy controls were included. Meta-analysis and meta-regression quantified risks and showed impact of periodontitis diagnosis type and severity.

RESULTS

The search retrieved 3354 studies; 166 studies were eligible for full-text screening, and 30 studies were included for review. Twenty-seven studies were eligible for meta-analysis. The risks of diabetes, rheumatoid arthritis (RA) and osteoporosis were increased in people with periodontitis compared to without periodontitis (diabetes-relative risk [RR]: 1.22, 95% CI: 1.13-1.33; RA-RR: 1.27, 95% CI: 1.07-1.52; osteoporosis-RR: 1.40, 95% CI: 1.12-1.75). Risk of diabetes showed gradient increase by periodontitis severity (moderate-RR = 1.20, 95% CI = 1.11-1.31; severe-RR = 1.34, 95% CI = 1.10-1.63).

CONCLUSION

People with moderate-to-severe cases of periodontitis have the highest risk of developing diabetes, while the effect of periodontal severity on risk of other immune-mediated systemic conditions requires further investigation. More homologous evidence is required to form robust conclusions regarding periodontitis-multimorbidity associations.

Time-sensitive testing pressures and COVID-19 outcomes: are socioeconomic inequalities over the first year of the pandemic explained by selection bias?

BACKGROUND

There are many ways in which selection bias might impact COVID-19 research. Here we focus on selection for receiving a polymerase-chain-reaction (PCR) SARS-CoV-2 test and how known changes to selection pressures over time may bias research into COVID-19 infection.

METHODS

Using UK Biobank (N = 420,231; 55% female; mean age = 66.8 [SD = 8·11]) we estimate the association between socio-economic position (SEP) and (i) being tested for SARS-CoV-2 infection versus not being tested (ii) testing positive for SARS-CoV-2 infection versus testing negative and (iii) testing negative for SARS-CoV-2 infection versus not being tested. We construct four distinct time-periods between March 2020 and March 2021, representing distinct periods of testing pressures and lockdown restrictions and specify both time-stratified and combined models for each outcome. We explore potential selection bias by examining associations with positive and negative control exposures.

RESULTS

The association between more disadvantaged SEP and receiving a SARS-CoV-2 test attenuated over time. Compared to individuals with a degree, individuals whose highest educational qualification was a GCSE or equivalent had an OR of 1·27 (95% CI: 1·18 to 1·37) in March-May 2020 and 1·13 (95% CI: 1.·10 to 1·16) in January-March 2021. The magnitude of the association between educational attainment and testing positive for SARS-CoV-2 infection increased over the same period. For the equivalent comparison, the OR for testing positive increased from 1·25 (95% CI: 1·04 to 1·47), to 1·69 (95% CI: 1·55 to 1·83). We found little evidence of an association between control exposures, and any considered outcome.

CONCLUSIONS

The association between SEP and SARS-CoV-2 testing changed over time, highlighting the potential of time-specific selection pressures to bias analyses of COVID-19. Positive and negative control analyses suggest that changes in the association between SEP and SARS-CoV-2 infection over time likely reflect true increases in socioeconomic inequalities.

Psychosocial predictors of COVID-19 infection in UK biobank (N = 104 201)

BACKGROUND

Since the outbreak of COVID-19, data on its psychosocial predictors are limited. We therefore aimed to explore psychosocial predictors of COVID-19 infection at the UK Biobank (UKB).

METHODS

This was a prospective cohort study conducted among UKB participants.

RESULTS

The sample size was N = 104 201, out of which 14 852 (14.3%) had a positive COVID-19 test. The whole sample analysis showed significant interactions between sex and several predictor variables. Among females, absence of college/university degree [odds ratio (OR) 1.55, 95% confidence interval (CI) 1.45-1.66] and socioeconomic deprivation (OR 1.16 95% CI 1.11-1.21) were associated with higher odds of COVID-19 infection, while history of psychiatric consultation (OR 0.85 95% CI 0.77-0.94) with lower odds. Among males, absence of college/university degree (OR 1.56, 95% CI 1.45-1.68) and socioeconomic deprivation (OR 1.12, 95% CI 1.07-1.16) were associated with higher odds, while loneliness (OR 0.87, 95% CI 0.78-0.97), irritability (OR 0.91, 95% CI 0.83-0.99) and history of psychiatric consultation (OR 0.85, 95% CI 0.75-0.97) were associated with lower odds.

CONCLUSION

Sociodemographic factors predicted the odds of COVID-19 infection equally among male and female participants, while psychological factors had differential impacts.

Risk factors for SARS-CoV-2 infection during the early stages of the COVID-19 pandemic: a systematic literature review

Introduction

Identifying SARS-CoV-2 infection risk factors allows targeted public health and social measures (PHSM). As new, more transmissible variants of concern (VoC) emerge, vaccination rates increase and PHSM are eased, it is important to understand any potential change to infection risk factors. The aim of this systematic literature review is to describe the risk factors for SARS-CoV-2 infection by VoC.

Methods

A literature search was performed in MEDLINE, PubMed and Embase databases on 5 May 2022. Eligibility included: observational studies published in English after 1 January 2020; any age group; the outcome of SARS-CoV-2 infection; and any potential risk factors investigated in the study. Results were synthesized into a narrative summary with respect to measures of association, by VoC. ROBINS-E tool was utilized for risk of bias assessment.

Results

Of 6,197 studies retrieved, 43 studies were included after screening. Common risk factors included older age, minority ethnic group, low socioeconomic status, male gender, increased household size, occupation/lower income level, inability to work from home, public transport use, and lower education level. Most studies were undertaken when the ancestral strain was predominant. Many studies had some selection bias due to testing criteria and limited laboratory capacity.

Conclusion

Understanding who is at risk enables the development of strategies that target priority groups at each of the different stages of a pandemic and helps inform vaccination strategies and other interventions which may also inform public health responses to future respiratory infection outbreaks. While it was not possible to determine changes to infection risk by recent VoC in this review, the risk factors identified will add to the overall understanding of the groups who are at greatest risk of infection in the early stages of a respiratory virus outbreak.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022330706, PROSPERO [CRD42022330706].

The public health impact of poor sleep on severe COVID-19, influenza and upper respiratory infections

BACKGROUND

Poor sleep is associated with an increased risk of infections and all-cause mortality but the causal direction between poor sleep and respiratory infections has remained unclear. We examined if poor sleep contributes as a causal risk factor to respiratory infections.

METHODS

We used data on insomnia, influenza and upper respiratory infections (URIs) from primary care and hospital records in the UK Biobank (N ≈ 231,000) and FinnGen (N ≈ 392,000). We computed logistic regression to assess association between poor sleep and infections, disease free survival hazard ratios, and performed Mendelian randomization analyses to assess causality.

FINDINGS

Utilizing 23 years of registry data and follow-up, we discovered that insomnia diagnosis associated with increased risk for infections (FinnGen influenza Cox's proportional hazard (CPH) HR = 4.34 [3.90, 4.83], P = 4.16 × 10-159, UK Biobank influenza CPH HR = 1.54 [1.37, 1.73], P = 2.49 × 10-13). Mendelian randomization indicated that insomnia causally predisposed to influenza (inverse-variance weighted (IVW) OR = 1.65, P = 5.86 × 10-7), URI (IVW OR = 1.94, P = 8.14 × 10-31), COVID-19 infection (IVW OR = 1.08, P = 0.037) and risk of hospitalization from COVID-19 (IVW OR = 1.47, P = 4.96 × 10-5).

INTERPRETATION

Our findings indicate that chronic poor sleep is a causal risk factor for contracting respiratory infections, and in addition contributes to the severity of respiratory infections. These findings highlight the role of sleep in maintaining sufficient immune response against pathogens.

FUNDING

Instrumentarium Science Foundation, Academy of Finland, Signe and Ane Gyllenberg Foundation, National Institutes of Health.

Effectiveness of and Inequalities in COVID-19 Epidemic Control Strategies in Hungary: A Nationwide Cross-Sectional Study

INTRODUCTION

Before the mass vaccination, epidemiological control measures were the only means of containing the COVID-19 epidemic. Their effectiveness determined the consequences of the COVID-19 epidemic. Our study evaluated the impact of sociodemographic, lifestyle, and clinical factors on patient-reported epidemiological control measures.

METHODS

A nationwide representative sample of 1008 randomly selected adults were interviewed in person between 15 March and 30 May 2021. The prevalence of test-confirmed SARS-CoV-2 infection was 12.1%, of testing was 33.7%, and of contact tracing among test-confirmed infected subjects was 67.9%. The vaccination coverage was 52.4%.

RESULTS

According to the multivariable logistic regression models, the occurrence of infection was not influenced by sociodemographic and lifestyle factors or by the presence of chronic disease. Testing was more frequent among middle-aged adults (aOR = 1.53, 95% CI 1.10-2.13) and employed adults (aOR = 2.06, 95% CI 1.42-3.00), and was more frequent among adults with a higher education (aOR_secondary = 1.93, 95% CI 1.20-3.13; aOR_tertiary = 3.19, 95% CI 1.81-5.63). Contact tracing was more frequently implemented among middle-aged (aOR41-7y = 3.33, 95% CI 1.17-9.45) and employed (aOR = 4.58, 95% CI 1.38-15.22), and those with chronic diseases (aOR = 5.92, 95% CI 1.56-22.47). Positive correlation was observed between age groups and vaccination frequency (aOR41-70y = 2.94, 95% CI 2.09-4.15; aOR71+y = 14.52, 95% CI 7.33-28.77). Higher than primary education (aOR_secondary = 1.69, 95% CI 1.08-2.63; aOR_tertiary = 4.36, 95% CI 2.46-7.73) and the presence of a chronic disease (aOR = 2.58, 95% CI 1.75-3.80) positively impacted vaccination. Regular smoking was inversely correlated with vaccination (aOR = 0.60; 95% CI 0.44-0.83).

CONCLUSIONS

The survey indicated that testing, contact tracing, and vaccination were seriously influenced by socioeconomic position; less so by chronic disease prevalence and very minimally by lifestyle. The etiological role of socioeconomic inequalities in epidemic measure implementation likely generated socioeconomic inequality in COVID-19-related complication and death rates.

Particulate matter air pollution and COVID-19 infection, severity, and mortality: A systematic review and meta-analysis

Ecological evidence links ambient particulate matter ≤2.5 mm (PM_2.5) and the rate of COVID-19 infections, severity, and deaths. However, such studies are unable to account for individual-level differences in major confounders like socioeconomic status and often rely on imprecise measures of PM_2.5. We conducted a systematic review of case-control and cohort studies, which rely on individual-level data, searching Medline, Embase, and the WHO COVID-19 database up to 30 June 2022. Study quality was evaluated using the Newcastle-Ottawa Scale. Results were pooled with a random effects meta-analysis, with Egger's regression, funnel plots, and leave-one-out/trim-and-fill sensitivity analyses to account for publication bias. N = 18 studies met inclusion criteria. A 10 μg/m³ increase in PM_2.5 was associated with 66 % (95 % CI: 1.31-2.11) greater odds of COVID-19 infection (N = 7) and 127 % (95 % CI: 1.41-3.66) odds of severe illness (hospitalisation, ICU admission, or requiring respiratory support) (N = 6). Pooled mortality results (N = 5) indicated increased deaths due to PM_2.5 but were non-significant (OR 1.40; 0.94 to 2.10). Most studies were rated "good" quality (14/18 studies), though there were numerous methodological issues; few used individual-level data to adjust for socioeconomic status (4/18 studies), instead using area-based indicators (11/18 studies) or no such adjustments (3/18 studies). Most severity (9/10 studies) and mortality studies (5/6 studies) were based on people already diagnosed COVID-19, potentially introducing collider bias. There was evidence of publication bias in studies of infection (p = 0.012) but not severity (p = 0.132) or mortality (p = 0.100). While methodological limits and evidence of bias require cautious interpretation of the findings, we found compelling evidence that PM_2.5 increases the risk of COVID-19 infection and severe disease, and weaker evidence of an increase in mortality risk.

A Literature Review of the Effects of Air Pollution on COVID-19 Health Outcomes Worldwide: Statistical Challenges and Data Visualization

Several peer-reviewed papers and reviews have examined the relationship between exposure to air pollution and COVID-19 spread and severity. However, many of the existing reviews on this topic do not extensively present the statistical challenges associated with this field, do not provide comprehensive guidelines for future researchers, and review only the results of a relatively small number of papers. We reviewed 139 papers, 127 of which reported a statistically significant positive association between air pollution and adverse COVID-19 health outcomes. Here, we summarize the evidence, describe the statistical challenges, and make recommendations for future research. To summarize the 139 papers with data from geographical locations around the world, we also present anopen-source data visualization tool that summarizes these studies and allows the research community to contribute evidence as new research papers are published.

Prioritization of COVID-19 risk factors in July 2020 and February 2021 in the UK

BACKGROUND

Risk for COVID-19 positivity and hospitalization due to diverse environmental and sociodemographic factors may change as the pandemic progresses.

METHODS

We investigated the association of 360 exposures sampled before COVID-19 outcomes for participants in the UK Biobank, including 9268 and 38,837 non-overlapping participants, sampled at July 17, 2020 and February 2, 2021, respectively. The 360 exposures included clinical biomarkers (e.g., BMI), health indicators (e.g., doctor-diagnosed diabetes), and environmental/behavioral variables (e.g., air pollution) measured 10-14 years before the COVID-19 time periods.

RESULTS

Here we show, for example, "participant having son and/or daughter in household" was associated with an increase in incidence from 20% to 32% (risk difference of 12%) between timepoints. Furthermore, we find age to be increasingly associated with COVID-19 positivity over time from Risk Ratio [RR] (per 10-year age increase) of 0.81 to 0.6 (hospitalization RR from 1.18 to 2.63, respectively).

CONCLUSIONS

Our data-driven approach demonstrates that time of pandemic plays a role in identifying risk factors associated with positivity and hospitalization.

COVID-19 infection in nursing staff: A cohort study

AIM

This study aims to identify the working conditions of Brazilian nursing professionals and the risk factors for these professionals to become infected by coronavirus disease.

BACKGROUND

Understanding the factors that affected nursing professionals during the pandemic can support better nursing management.

DESIGN

This is a quantitative, cross-sectional survey study.

METHODS

Data collection was carried out between February and March 2022 in Brazil. All nursing professionals registered in the national database received by e-mail the study instrument with the data collection variables: professionals' sociodemographic and comorbid, professional and institutional characteristics, and professionals' health conditions and disease-related aspects for COVID-19.

RESULTS

Four thousand eight hundred sixty-two nursing professionals reported a lack of personal protective equipment for patient care, and 4424 were infected by coronavirus disease. The risk factors to become infected were having cardiovascular disease, being under 60 years of age, living in the northern region, using public transportation, working in a hospital, an emergency department or reference institution for COVID-19, living with an infected person and lack of respirators or waterproof aprons.

CONCLUSION

Multiple risk factors for infection with SARS-CoV-2 were demonstrated for the nursing professionals during the pandemic, highlighting current and future pandemics factors that are modifiable in a worthwhile time frame to minimize nurses' infection risks, such as inadequate working conditions associated with lack of essential personal protective equipment.

Increased suicidal ideation and suicide attempts in COVID-19 patients in the United States: Statistics from a large national insurance billing database

Emerging research suggests suicidality may have increased during the COVID-19 pandemic. This cross-sectional study aimed to advance understanding of suicide risk during the pandemic through novel use of a large insurance database. Using logistic regression across time-points, we estimated the effect of exposure to SARS-CoV-2 infection on rates of suicidal ideation and suicide attempts in infected individuals versus uninfected controls during the pandemic (March 2020 - September 2021). In uninfected individuals, we estimated the effect of exposure to the pandemic period versus the pre-pandemic control period (January 2017 to February 2020) on suicidality rates. We also investigated within-pandemic temporal patterns of suicidality. All patients with data in the UnitedHealth Group claims during those intervals were included. ICD-10 codes defined suicidality measures. There were 525,312,717 (62.3% over age 45, 57.7% female) included encounters. From the pandemic subsample (32.8%), 1.7% were COVID+. Adjusted odds ratios showed that COVID+ patients were significantly more likely to have suicidal ideation and suicide attempts than COVID- patients. Among COVID- patients, adjusted odds of suicidality were significantly lower during versus prior to the pandemic. Results were unfortunately limited by the absence of data on deaths by suicide. Further research should examine how SARS-CoV-2 infection may influence suicidality.

Exploring the impact of selection bias in observational studies of COVID-19: a simulation study

BACKGROUND

Non-random selection of analytic subsamples could introduce selection bias in observational studies. We explored the potential presence and impact of selection in studies of SARS-CoV-2 infection and COVID-19 prognosis.

METHODS

We tested the association of a broad range of characteristics with selection into COVID-19 analytic subsamples in the Avon Longitudinal Study of Parents and Children (ALSPAC) and UK Biobank (UKB). We then conducted empirical analyses and simulations to explore the potential presence, direction and magnitude of bias due to this selection (relative to our defined UK-based adult target populations) when estimating the association of body mass index (BMI) with SARS-CoV-2 infection and death-with-COVID-19.

RESULTS

In both cohorts, a broad range of characteristics was related to selection, sometimes in opposite directions (e.g. more-educated people were more likely to have data on SARS-CoV-2 infection in ALSPAC, but less likely in UKB). Higher BMI was associated with higher odds of SARS-CoV-2 infection and death-with-COVID-19. We found non-negligible bias in many simulated scenarios.

CONCLUSIONS

Analyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depend on the outcome definition, the true effect of the risk factor and the assumed selection mechanism; these are likely to differ between studies with different target populations. Bias due to sample selection is a key concern in COVID-19 research based on national registry data, especially as countries end free mass testing. The framework we have used can be applied by other researchers assessing the extent to which their results may be biased for their research question of interest.

Occupational differences in COVID-19 incidence, severity, and mortality in the United Kingdom: Available data and framework for analyses

There are important differences in the risk of SARS-CoV-2 infection and death depending on occupation. Infections in healthcare workers have received the most attention, and there are clearly increased risks for intensive care unit workers who are caring for COVID-19 patients. However, a number of other occupations may also be at an increased risk, particularly those which involve social care or contact with the public. A large number of data sets are available with the potential to assess occupational risks of COVID-19 incidence, severity, or mortality. We are reviewing these data sets as part of the Partnership for Research in Occupational, Transport, Environmental COVID Transmission (PROTECT) initiative, which is part of the National COVID-19 Core Studies. In this report, we review the data sets available (including the key variables on occupation and potential confounders) for examining occupational differences in SARS-CoV-2 infection and COVID-19 incidence, severity and mortality. We also discuss the possible types of analyses of these data sets and the definitions of (occupational) exposure and outcomes. We conclude that none of these data sets are ideal, and all have various strengths and weaknesses. For example, mortality data suffer from problems of coding of COVID-19 deaths, and the deaths (in England and Wales) that have been referred to the coroner are unavailable. On the other hand, testing data is heavily biased in some periods (particularly the first wave) because some occupations (e.g. healthcare workers) were tested more often than the general population. Random population surveys are, in principle, ideal for estimating population prevalence and incidence, but are also affected by non-response. Thus, any analysis of the risks in a particular occupation or sector (e.g. transport), will require a careful analysis and triangulation of findings across the various available data sets.

Occupational differences in COVID-19 incidence, severity, and mortality in the United Kingdom: Available data and framework for analyses

There are important differences in the risk of SARS-CoV-2 infection and death depending on occupation. Infections in healthcare workers have received the most attention, and there are clearly increased risks for intensive care unit workers who are caring for COVID-19 patients. However, a number of other occupations may also be at an increased risk, particularly those which involve social care or contact with the public. A large number of data sets are available with the potential to assess occupational risks of COVID-19 incidence, severity, or mortality. We are reviewing these data sets as part of the Partnership for Research in Occupational, Transport, Environmental COVID Transmission (PROTECT) initiative, which is part of the National COVID-19 Core Studies. In this report, we review the data sets available (including the key variables on occupation and potential confounders) for examining occupational differences in SARS-CoV-2 infection and COVID-19 incidence, severity and mortality. We also discuss the possible types of analyses of these data sets and the definitions of (occupational) exposure and outcomes. We conclude that none of these data sets are ideal, and all have various strengths and weaknesses. For example, mortality data suffer from problems of coding of COVID-19 deaths, and the deaths (in England and Wales) that have been referred to the coroner are unavailable. On the other hand, testing data is heavily biased in some periods (particularly the first wave) because some occupations (e.g. healthcare workers) were tested more often than the general population. Random population surveys are, in principle, ideal for estimating population prevalence and incidence, but are also affected by non-response. Thus, any analysis of the risks in a particular occupation or sector (e.g. transport), will require a careful analysis and triangulation of findings across the various available data sets.

The longitudinal impact of the COVID-19 pandemic on health behaviors, psychosocial factors, and cognitive functioning in older adults

Background: Older adults are at a greater risk for contracting and experiencing severe illness from COVID-19 and may be further affected by pandemic-related precautions (e.g., social distancing and isolation in quarantine). However, the longitudinal impact of the COVID-19 pandemic on older adults is unclear. The current study examines changes in health behaviors, psychosocial factors, and cognitive functioning in a large sample of older adults using a pre-pandemic baseline and longitudinal follow-up throughout 9 months of the COVID-19 pandemic. Methods: One hundred and eighty-nine older adults (ages 65-89) were recruited from a multisite clinical trial to complete additional virtual assessments during the COVID-19 pandemic. Mixed effects models evaluated changes in health behaviors, psychosocial factors, and cognitive functioning during the pandemic compared to a pre-pandemic baseline and over the course of the pandemic (i.e., comparing the first and last COVID-19 timepoints). Results: Compared to their pre-pandemic baseline, during the pandemic, older adults reported worsened sleep quality, perceived physical health and functioning, mental health, slight increases in depression and apathy symptoms, reduced social engagement/perceived social support, but demonstrated better performance on objective cognitive tasks of attention and working memory. Throughout the course of the pandemic, these older adults reported continued worsening of perceived physical health and function, fewer depression symptoms, and they demonstrated improved cognitive performance. It is important to note that changes on self-report mood measures and cognitive performance were relatively small regarding clinical significance. Education largely served as a protective factor, such that greater years of education was generally associated with better outcomes across domains. Conclusions: The present study provides insights into the longitudinal impact of the COVID-19 pandemic on health behaviors, psychosocial factors, and cognitive functioning in a population disproportionately affected by the virus. Replicating this study design in a demographically representative older adult sample is warranted to further inform intervention strategies targeting older adults negatively impacted by the COVID-19 pandemic.

Risk factors for SARS-CoV-2 infection after primary vaccination with ChAdOx1 nCoV-19 or BNT162b2 and after booster vaccination with BNT162b2 or mRNA-1273: A population-based cohort study (COVIDENCE UK)

Background

Little is known about how demographic, behavioural, and vaccine-related factors affect risk of post-vaccination SARS-CoV-2 infection. We aimed to identify risk factors for SARS-CoV-2 infection after primary and booster vaccinations.

Methods

This prospective, population-based, UK study in adults (≥16 years) vaccinated against SARS-CoV-2 assessed risk of breakthrough SARS-CoV-2 infection up to February, 2022, for participants who completed a primary vaccination course (ChAdOx1 nCoV-19 or BNT162b2) and those who received a booster dose (BNT162b2 or mRNA-1273). Cox regression models explored associations between sociodemographic, behavioural, clinical, pharmacological, and nutritional factors and test-positive breakthrough infection, adjusted for local weekly SARS-CoV-2 incidence.

Findings

1051 (7·1%) of 14 713 post-primary participants and 1009 (9·5%) of 10 665 post-booster participants reported breakthrough infection, over a median follow-up of 203 days (IQR 195–216) and 85 days (66–103), respectively. Primary vaccination with ChAdOx1 (vs BNT162b2) was associated with higher risk of infection in both post-primary analysis (adjusted hazard ratio 1·63, 95% CI 1·41–1·88) and after an mRNA-1273 booster (1·26 [1·00–1·57] vs BNT162b2 primary and booster). Lower risk of infection was associated with older age (post-primary: 0·97 [0·96–0·97] per year; post-booster: 0·97 [0·97–0·98]), whereas higher risk of infection was associated with lower educational attainment (post-primary: 1·78 [1·44–2·20] for primary/secondary vs postgraduate; post-booster: 1·46 [1·16–1·83]) and at least three weekly visits to indoor public places (post-primary: 1·36 [1·13–1·63] vs none; post-booster: 1·29 [1·07–1·56]).

Interpretation

Vaccine type, socioeconomic status, age, and behaviours affect risk of breakthrough infection after primary and booster vaccinations.

Funding

Barts Charity, UK Research and Innovation Industrial Strategy Challenge Fund.

Association of Household Deprivation, Comorbidities, and COVID-19 Hospitalization in Children in Germany, January 2020 to July 2021

IMPORTANCE

Adults in disadvantaged socioeconomic positions have elevated risks of a severe course of COVID-19, but it is unclear whether this holds true for children.

OBJECTIVE

To investigate whether young people from disadvantaged households have a higher risk of COVID-19 hospitalization and whether differences were associated with comorbidities that predispose children to severe courses.

DESIGN, SETTING, AND PARTICIPANTS

This population-based cohort study included all children and adolescents (aged 0-18 years) who were enrolled in a statutory health insurance carrier in Germany during the observation period of January 1, 2020, to July 13, 2021. Logistic regressions were calculated to compare children from households with and without an indication of poverty. Age, sex, days under observation, nationality, and comorbidities (eg, obesity, diabetes) were controlled for to account for explanatory factors.

EXPOSURES

Disadvantage on the household level was assessed by the employment status of the insurance holder (ie, employed, long- or short-term unemployed, low-wage employment, economically inactive). Socioeconomic characteristics of the area of residence were also assessed.

MAIN OUTCOMES AND MEASURES

Daily hospital diagnoses of COVID-19 (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes U07.1 and U07.2) were recorded. Comorbidities were assessed using inpatient and outpatient diagnoses contained in the insurance records.

RESULTS

A total of 688 075 children and adolescents were included, with a mean (SD) age of 8.3 (5.8) years and 333 489 (48.4%) female participants. COVID-19 hospital diagnosis was a rare event (1637 participants [0.2%]). Children whose parents were long-term unemployed were 1.36 (95% CI, 1.22-1.51) times more likely than those with employed parents to be hospitalized. Elevated odds were also found for children whose parents had low-wage employment (odds ratio, 1.29; 95% CI, 1.05-1.58). Those living in low-income areas had 3.02 (95% CI, 1.73-5.28) times higher odds of hospitalization than those in less deprived areas. Comorbidities were associated with hospitalization, but their adjustment did not change main estimates for deprivation.

CONCLUSIONS AND RELEVANCE

In this cohort study, children who had parents who were unemployed and those who lived in low-income areas were at higher risk of COVID-19 hospitalization. This finding suggests that attention must be paid to children with SARS-CoV-2 from vulnerable families and closer monitoring should be considered. A number of explanatory factors, including comorbidities, were taken into account, but their analysis yielded no clear picture about underlying processes.

Concordance of SARS-CoV-2 Antibody Results during a Period of Low Prevalence

Accurate, highly specific immunoassays for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed to evaluate seroprevalence. This study investigated the concordance of results across four immunoassays targeting different antigens for sera collected at the beginning of the SARS-CoV-2 pandemic in the United States. Specimens from All of Us participants contributed between January and March 2020 were tested using the Abbott Architect SARS-CoV-2 IgG (immunoglobulin G) assay (Abbott) and the EuroImmun SARS-CoV-2 enzyme-linked immunosorbent assay (ELISA) (EI). Participants with discordant results, participants with concordant positive results, and a subset of concordant negative results by Abbott and EI were also tested using the Roche Elecsys anti-SARS-CoV-2 (IgG) test (Roche) and the Ortho-Clinical Diagnostics Vitros anti-SARS-CoV-2 IgG test (Ortho). The agreement and 95% confidence intervals were estimated for paired assay combinations. SARS-CoV-2 antibody concentrations were quantified for specimens with at least two positive results across four immunoassays. Among the 24,079 participants, the percent agreement for the Abbott and EI assays was 98.8% (95% confidence interval, 98.7%, 99%). Of the 490 participants who were also tested by Ortho and Roche, the probability-weighted percentage of agreement (95% confidence interval) between Ortho and Roche was 98.4% (97.9%, 98.9%), that between EI and Ortho was 98.5% (92.9%, 99.9%), that between Abbott and Roche was 98.9% (90.3%, 100.0%), that between EI and Roche was 98.9% (98.6%, 100.0%), and that between Abbott and Ortho was 98.4% (91.2%, 100.0%). Among the 32 participants who were positive by at least 2 immunoassays, 21 had quantifiable anti-SARS-CoV-2 antibody concentrations by research assays. The results across immunoassays revealed concordance during a period of low prevalence. However, the frequency of false positivity during a period of low prevalence supports the use of two sequentially performed tests for unvaccinated individuals who are seropositive by the first test. IMPORTANCE What is the agreement of commercial SARS-CoV-2 immunoglobulin G (IgG) assays during a time of low coronavirus disease 2019 (COVID-19) prevalence and no vaccine availability? Serological tests produced concordant results in a time of low SARS-CoV-2 prevalence and no vaccine availability, driven largely by the proportion of samples that were negative by two immunoassays. The CDC recommends two sequential tests for positivity for future pandemic preparedness. In a subset analysis, quantified antinucleocapsid and antispike SARS-CoV-2 IgG antibodies do not suggest the need to specify the antigen targets of the sequential assays in the CDC's recommendation because false positivity varied as much between assays targeting the same antigen as it did between assays targeting different antigens.

Determinants of Antibody Responses to SARS-CoV-2 Vaccines: Population-Based Longitudinal Study (COVIDENCE UK)

Antibody responses to SARS-CoV-2 vaccines vary for reasons that remain poorly understood. A range of sociodemographic, behavioural, clinical, pharmacologic and nutritional factors could explain these differences. To investigate this hypothesis, we tested for presence of combined IgG, IgA and IgM (IgGAM) anti-Spike antibodies before and after 2 doses of ChAdOx1 nCoV-19 (ChAdOx1, AstraZeneca) or BNT162b2 (Pfizer-BioNTech) in UK adults participating in a population-based longitudinal study who received their first dose of vaccine between December 2020 and July 2021. Information on sixty-six potential sociodemographic, behavioural, clinical, pharmacologic and nutritional determinants of serological response to vaccination was captured using serial online questionnaires. We used logistic regression to estimate multivariable-adjusted odds ratios (aORs) for associations between independent variables and risk of seronegativity following two vaccine doses. Additionally, percentage differences in antibody titres between groups were estimated in the sub-set of participants who were seropositive post-vaccination using linear regression. Anti-spike antibodies were undetectable in 378/9101 (4.2%) participants at a median of 8.6 weeks post second vaccine dose. Increased risk of post-vaccination seronegativity associated with administration of ChAdOx1 vs. BNT162b2 (adjusted odds ratio (aOR) 6.6, 95% CI 4.2−10.4), shorter interval between vaccine doses (aOR 1.6, 1.2−2.1, 6−10 vs. >10 weeks), poor vs. excellent general health (aOR 3.1, 1.4−7.0), immunodeficiency (aOR 6.5, 2.5−16.6) and immunosuppressant use (aOR 3.7, 2.4−5.7). Odds of seronegativity were lower for participants who were SARS-CoV-2 seropositive pre-vaccination (aOR 0.2, 0.0−0.6) and for those taking vitamin D supplements (aOR 0.7, 0.5−0.9). Serologic responses to vaccination did not associate with time of day of vaccine administration, lifestyle factors including tobacco smoking, alcohol intake and sleep, or use of anti-pyretics for management of reactive symptoms after vaccination. In a sub-set of 8727 individuals who were seropositive post-vaccination, lower antibody titres associated with administration of ChAdOx1 vs. BNT162b2 (43.4% lower, 41.8−44.8), longer duration between second vaccine dose and sampling (12.7% lower, 8.2−16.9, for 9−16 weeks vs. 2−4 weeks), shorter interval between vaccine doses (10.4% lower, 3.7−16.7, for <6 weeks vs. >10 weeks), receiving a second vaccine dose in October−December vs. April−June (47.7% lower, 11.4−69.1), older age (3.3% lower per 10-year increase in age, 2.1−4.6), and hypertension (4.1% lower, 1.1−6.9). Higher antibody titres associated with South Asian ethnicity (16.2% higher, 3.0−31.1, vs. White ethnicity) or Mixed/Multiple/Other ethnicity (11.8% higher, 2.9−21.6, vs. White ethnicity), higher body mass index (BMI; 2.9% higher, 0.2−5.7, for BMI 25−30 vs. <25 kg/m2) and pre-vaccination seropositivity for SARS-CoV-2 (105.1% higher, 94.1−116.6, for those seropositive and experienced COVID-19 symptoms vs. those who were seronegative pre-vaccination). In conclusion, we identify multiple determinants of antibody responses to SARS-CoV-2 vaccines, many of which are modifiable.

A Risk Prediction Model and Risk Score of SARS-CoV-2 Infection Following Healthcare-Related Exposure

Hospital workers are at high risk of contact with COVID-19 patients. Currently, there is no evidence-based, comprehensive risk assessment tool for healthcare-related exposure; so, we aimed to identify independent factors related to COVID-19 infection in hospital workers following workplace exposure(s) and construct a risk prediction model. We analyzed the COVID-19 contact tracing dataset from 15 July to 31 December 2021 using multiple logistic regression analysis, considering exposure details, demographics, and vaccination history. Of 7146 included exposures to confirmed COVID-19 patients, 229 (4.2%) had subsequently tested positive via RT-PCR. Independent risk factors for a positive test were having symptoms (adjusted odds ratio 4.94, 95%CI 3.83−6.39), participating in an unprotected aerosol-generating procedure (aOR 2.87, 1.66−4.96), duration of exposure >15 min (aOR 2.52, 1.82−3.49), personnel who did not wear a mask (aOR 2.49, 1.75−3.54), exposure to aerodigestive secretion (aOR 1.5, 1.03−2.17), index patient not wearing a mask (aOR 1.44, 1.01−2.07), and exposure distance <1 m without eye protection (aOR 1.39, 1.02−1.89). High-potency vaccines and high levels of education protected against infection. A risk model and scoring system with good discrimination power were built. Having symptoms, unprotected exposure, lower education level, and receiving low potency vaccines increased the risk of laboratory-confirmed COVID-19 following healthcare-related exposure events.

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.

Regional disparities in SARS-CoV-2 infections by labour market indicators: a spatial panel analysis using nationwide German data on notified infections

Background

Regional labour markets and their properties are named as potential reasons for regional variations in levels of SARS-CoV-2 infections rates, but empirical evidence is missing.

Methods

Using nationwide data on notified laboratory-confirmed SARS-CoV-2 infections, we calculated weekly age-standardised incidence rates (ASIRs) for working-age populations at the regional level of Germany’s 400 districts. Data covered nearly 2 years (March 2020 till December 2021), including four main waves of the pandemic. For each of the pandemic waves, we investigated regional differences in weekly ASIRs according to three regional labour market indicators: (1) employment rate, (2) employment by sector, and (3) capacity to work from home. We use spatial panel regression analysis, which incorporates geospatial information and accounts for regional clustering of infections.

Results

For all four pandemic waves under study, we found that regions with higher proportions of people in employment had higher ASIRs and a steeper increase of infections during the waves. Further, the composition of the workforce mattered: rates were higher in regions with larger secondary sectors or if opportunities of working from home were comparatively low. Associations remained consistent after adjusting for potential confounders, including a proxy measure of regional vaccination progress.

Conclusions

If further validated by studies using individual-level data, our study calls for increased intervention efforts to improve protective measures at the workplace, particularly among workers of the secondary sector with no opportunities to work from home. It also points to the necessity of strengthening work and employment as essential components of pandemic preparedness plans.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07643-5.

Air pollution, SARS-CoV-2 incidence and COVID-19 mortality in Rome - a longitudinal study

Chronic exposure to ambient air pollution has been related to increased mortality in the general population [1]. After the outbreak of the SARS-CoV-2 pandemic in 2019, there has been a fast proliferation of epidemiological studies linking ambient air pollution to coronavirus disease 2019 (COVID-19) incidence or adverse prognosis [2]. It has been hypothesised that ambient air pollution might increase human vulnerability to viruses by reducing immune defences, promoting a low-level chronic inflammatory state, or leading to chronic diseases [3]. Most studies have applied ecological designs, and failed to account for key individual-level or area-level determinants of COVID-19 spread or severity, such as demographic characteristics of the studied populations, socioeconomic or clinical susceptibility, and area-level proxies of disease spread such as mobility or population density [4].

Long-term exposure to air pollution (PM2.5 and NO2) was associated with COVID-19 mortality, but not with SARS-CoV-2 incidence, in a large observational population-based cohort of >1.5 million subjects in Rome, Italy https://bit.ly/3zZjjSC

Health-Related Quality of Life of Moroccan COVID-19 Survivors: A Case-Control Study

Background: Research on COVID-19 has mostly focused on transmission, mortality and morbidity associated with the virus. However, less attention has been given to its impact on health-related quality of life (HRQoL) of patients with COVID-19. Therefore, this study aimed to determine the demographic and clinical risk factors associated with COVID-19 and evaluate its impact on the HRQoL of COVID-19 survivors. Methods: A case-control study was carried out between September 2021 and March 2022 on 1105 participants. A total of 354 were COVID-19 survivors and 751 were the control group. The HRQoL was assessed using both EQ-5D-5L and SF-6D generic instruments. Results: The average age of all participants was 56.17 ± 15.46. Older age, urban area, tobacco use, presence of chronic diseases especially type 1 diabetes, kidney and cardiovascular diseases were significantly associated with COVID-19. The COVID-19 survivors had significantly lower HRQoL (EQ-VAS = 50.89) compared to the control group (EQ-VAS = 63.36) (p-value < 0.0001). Pain/ discomfort and anxiety/depression were the most negatively affected by COVID-19 (p-value < 0.0001). Conclusions: The findings from this study could help healthcare professionals and policy makers to better understand the HRQoL sequelae among the COVID-19 survivors and contribute to develop tailored interventions.

Assessing Smoking Status and Risk of SARS-CoV-2 Infection: A Machine Learning Approach among Veterans

The role of smoking in the risk of SARS-CoV-2 infection is unclear. We used a retrospective cohort design to study data from veterans’ Electronic Medical Record to assess the impact of smoking on the risk of SARS-CoV-2 infection. Veterans tested for the SARS-CoV-2 virus from 02/01/2020 to 02/28/2021 were classified as: Never Smokers (NS), Former Smokers (FS), and Current Smokers (CS). We report the adjusted odds ratios (aOR) for potential confounders obtained from a cascade machine learning algorithm. We found a 19.6% positivity rate among 1,176,306 veterans tested for SARS-CoV-2 infection. The positivity proportion among NS (22.0%) was higher compared with FS (19.2%) and CS (11.5%). The adjusted odds of testing positive for CS (aOR:0.51; 95%CI: 0.50, 0.52) and FS (aOR:0.89; 95%CI:0.88, 0.90) were significantly lower compared with NS. Four pre-existing conditions, including dementia, lower respiratory infections, pneumonia, and septic shock, were associated with a higher risk of testing positive, whereas the use of the decongestant drug phenylephrine or having a history of cancer were associated with a lower risk. CS and FS compared with NS had lower risks of testing positive for SARS-CoV-2. These findings highlight our evolving understanding of the role of smoking status on the risk of SARS-CoV-2 infection.

Using machine learning to predict COVID-19 infection and severity risk among 4510 aged adults: a UK Biobank cohort study

Many risk factors have emerged for novel 2019 coronavirus disease (COVID-19). It is relatively unknown how these factors collectively predict COVID-19 infection risk, as well as risk for a severe infection (i.e., hospitalization). Among aged adults (69.3 ± 8.6 years) in UK Biobank, COVID-19 data was downloaded for 4510 participants with 7539 test cases. We downloaded baseline data from 10 to 14 years ago, including demographics, biochemistry, body mass, and other factors, as well as antibody titers for 20 common to rare infectious diseases in a subset of 80 participants with 124 test cases. Permutation-based linear discriminant analysis was used to predict COVID-19 risk and hospitalization risk. Probability and threshold metrics included receiver operating characteristic curves to derive area under the curve (AUC), specificity, sensitivity, and quadratic mean. Model predictions using the full cohort were marginal. The "best-fit" model for predicting COVID-19 risk was found in the subset of participants with antibody titers, which achieved excellent discrimination (AUC 0.969, 95% CI 0.934-1.000). Factors included age, immune markers, lipids, and serology titers to common pathogens like human cytomegalovirus. The hospitalization "best-fit" model was more modest (AUC 0.803, 95% CI 0.663-0.943) and included only serology titers, again in the subset group. Accurate risk profiles can be created using standard self-report and biomedical data collected in public health and medical settings. It is also worthwhile to further investigate if prior host immunity predicts current host immunity to COVID-19.

Cancers and COVID-19 Risk: A Mendelian Randomization Study

Simple Summary

During the COVID-19 pandemic, cancer patients are regarded as a highly vulnerable population. Given the unavoidable bias and unmeasured confounders in observational studies, the causal effects of cancers on COVID-19 outcomes are largely unknown. In the study, we tried to evaluate the causal effects of cancers on COVID-19 outcomes using the Mendelian randomization (MR) approach. No strong evidence was observed to support a causal role of cancer in COVID-19 development. Previous observational correlations between cancers and COVID-19 outcomes were likely confounded. Large and well-conducted epidemiological studies are required to determine whether cancers causally contribute to increased risk of COVID-19.

Abstract

Observational studies have shown increased COVID-19 risk among cancer patients, but the causality has not been proven yet. Mendelian randomization analysis can use the genetic variants, independently of confounders, to obtain causal estimates which are considerably less confounded. We aimed to investigate the causal associations of cancers with COVID-19 outcomes using the MR analysis. The inverse-variance weighted (IVW) method was employed as the primary analysis. Sensitivity analyses and multivariable MR analyses were conducted. Notably, IVW analysis of univariable MR revealed that overall cancer and twelve site-specific cancers had no causal association with COVID-19 severity, hospitalization or susceptibility. The corresponding p-values for the casual associations were all statistically insignificant: overall cancer (p = 0.34; p = 0.42; p = 0.69), lung cancer (p = 0.60; p = 0.37; p = 0.96), breast cancer (p = 0.43; p = 0.74; p = 0.43), endometrial cancer (p = 0.79; p = 0.24; p = 0.83), prostate cancer (p = 0.54; p = 0.17; p = 0.58), thyroid cancer (p = 0.70; p = 0.80; p = 0.28), ovarian cancer (p = 0.62; p = 0.96; p = 0.93), melanoma (p = 0.79; p = 0.45; p = 0.82), small bowel cancer (p = 0.09; p = 0.08; p = 0.19), colorectal cancer (p = 0.85; p = 0.79; p = 0.30), oropharyngeal cancer (p = 0.31; not applicable, NA; p = 0.80), lymphoma (p = 0.51; NA; p = 0.37) and cervical cancer (p = 0.25; p = 0.32; p = 0.68). Sensitivity analyses and multivariable MR analyses yielded similar results. In conclusion, cancers might have no causal effect on increasing COVID-19 risk. Further large-scale population studies are needed to validate our findings.

Interaction of Vitamin D and Corticosteroid Use in Hospitalized COVID-19 Patients: A Potential Explanation for Inconsistent Findings in the Literature

Vitamin D is an important immune-modulator with anti-inflammatory properties. While this prohormone has been studied extensively in the prevention and treatment of COVID-19, findings have been inconsistent regarding its overall benefit in patients hospitalized with COVID-19. Most studies to date have been observational in nature, not accounting for the use of corticosteroids. Furthermore, the few randomized clinical trials designed to examine the effect of vitamin D supplementation on COVID-19 outcomes have been relatively small and thus insufficiently powered to assure a balance of corticosteroid use between study arms. The current perspective addresses the interaction of vitamin D and corticosteroids as a potential explanation for the divergent results reported in the literature. Future research on vitamin D and COVID-19 will benefit by considering this interaction, especially among hospitalized patients requiring oxygen and mechanical ventilation.

Assessing sex differential in COVID-19 mortality rate by age and polymerase chain reaction test results: an Iranian multi-center study

BACKGROUND

The aim of this study is to evaluate the sex differential effect in the COVID-19 mortality by different age groups and polymerase chain reaction (PCR) test results.

RESEARCH DESIGN

In a multicenter cross-sectional study from 55 hospitals in Tehran, Iran, patients were categorized as positive, negative, and suspected cases.

RESULTS

A total of 25,481 cases (14,791 males) were included in the study with a mortality rate of 12.0%. The mortality rates in positive, negative, and suspected cases were 20.55%, 9.97%, and 7.31%, respectively. Using a Cox regression model, sex had a significant effect on the hazard of death due to COVID-19 in adult and senior male patients having positive and suspected PCR test results. However, sex was not found as significant factor for mortality in patients with a negative PCR test in different age groups.

CONCLUSIONS

Regardless of other risk factors, we found that the effect of sex on COVID-19 mortality varied significantly in different age groups. Therefore, appropriate strategies should be designed to protect adult and senior males from this deadly infectious disease. Furthermore, owing to the considerable death rate of COVID-19 patients with negative test results, new policies should be launched to increase the accuracy of diagnosis tests.

Occupation and Educational Attainment Characteristics Associated With COVID-19 Mortality by Race and Ethnicity in California

IMPORTANCE

Racial and ethnic inequities in COVID-19 mortality may be driven by occupation and education, but limited evidence has assessed these mechanisms.

OBJECTIVE

To estimate whether occupational characteristics or educational attainment explained the associations between race and ethnicity and COVID-19 mortality.

DESIGN, SETTING, AND PARTICIPANTS

This population-based retrospective cohort study of Californians aged 18 to 65 years linked COVID-19 deaths to population estimates within strata defined by race and ethnicity, gender, age, nativity in the US, region of residence, education, and occupation. Analysis was conducted from September 2020 to February 2022.

EXPOSURES

Education and occupational characteristics associated with COVID-19 exposure (essential sector, telework option, wages).

MAIN OUTCOMES AND MEASURES

All confirmed COVID-19 deaths in California through February 12, 2021. The study estimated what COVID-19 mortality would have been if each racial and ethnic group had (1) the COVID-19 mortality risk associated with the education and occupation distribution of White people and (2) the COVID-19 mortality risk associated with the lowest-risk educational and occupational positions.

RESULTS

Of 25 235 092 participants (mean [SD] age, 40 [14] years; 12 730 395 [50%] men), 14 783 died of COVID-19, 8 125 565 (32%) had a Bachelor's degree or higher, 13 345 829 (53%) worked in essential sectors, 11 783 017 (47%) could not telework, and 12 812 095 (51%) had annual wages under $51 700. COVID-19 mortality ranged from 15 deaths per 100 000 for White women and Asian women to 139 deaths per 100 000 for Latinx men. Accounting for differences in age, nativity, and region of residence, if all races and ethnicities had the COVID-19 mortality associated with the occupational characteristics of White people (sector, telework, wages), COVID-19 mortality would be reduced by 10% (95% CI, 6% to 14%) for Latinx men, but increased by 5% (95% CI, -8% to 17%) for Black men. If all working-age Californians had the COVID-19 mortality associated with the lowest-risk educational and occupational position (Bachelor's degree, nonessential, telework, and highest wage quintile), there would have been 43% fewer COVID-19 deaths among working-age adults (8441 fewer deaths; 95% CI, 32%-54%), with the largest absolute risk reductions for Latinx men (3755 deaths averted; 95% CI, 3304-4255 deaths) and Latinx women (2329 deaths averted; 95% CI, 2038-2621 deaths).

CONCLUSIONS AND RELEVANCE

In this population-based cohort study of working-age California adults, occupational disadvantage was associated with excess COVID-19 mortality for Latinx men. For all racial and ethnic groups, excess risk associated with low-education, essential, on-site, and low-wage jobs accounted for a substantial fraction of COVID-19 mortality.

Factors Associated with an Outbreak of COVID-19 in Oilfield Workers, Kazakhstan, 2020

From March to May 2020, 1306 oilfield workers in Kazakhstan tested positive for SARS-CoV-2. We conducted a case-control study to assess factors associated with SARS-CoV-2 transmission. The cases were PCR-positive for SARS-CoV-2 during June–September 2020. Controls lived at the same camp and were randomly selected from the workers who were PCR-negative for SARS-CoV-2. Data was collected telephonically by interviewing the oil workers. The study had 296 cases and 536 controls with 627 (75%) men, and 527 (63%) were below 40 years of age. Individual factors were the main drivers of transmission, with little contribution by environmental factors. Of the twenty individual factors, rare hand sanitizer use, travel before shift work, and social interactions outside of work increased SARS-CoV-2 transmission. Of the twenty-two environmental factors, only working in air-conditioned spaces was associated with SARS-CoV-2 transmission. Communication messages may enhance workers’ individual responsibility and responsibility for the safety of others to reduce SARS-CoV-2 transmission.

Using Andersen's model of health care utilization to assess factors associated with COVID-19 testing among adults in nine low-and middle-income countries: an online survey

Background

This study aimed to investigate, using Andersen’s model of health care utilization, factors associated with COVID-19 testing among adults in nine low- and middle- income countries.

Methods

In between 10 December 2020 and 9 February 2021, an online survey was organized in nine low- and middle-income countries. In total 10,183 adults (median age 45 years, interquartile range 33–57 years, range 18–93 years), including 6470 from Brazil, 1738 Malaysia, 1124 Thailand, 230 Bangladesh, 219 DR Congo, 159 Benin, 107 Uganda, 81 Malawi and 55 from Mali participated in the study. COVID-19 testing/infection status was assessed by self-report.

Results

Of the 10,183 participants, 40.3% had ever tested for COVID-19, 7.3% tested positive, and 33.0% tested negative. In an adjusted logistic regression model, predisposing factors (residing in Brazil, postgraduate education), enabling/disabling factors (urban residence, higher perceived economic status, being a student or worker in the health care sector, and moderate or severe psychological distress), and need factors (having at least one chronic condition) increased the odds of COVID-19 testing. Among those who were tested, participants residing in Bangladesh, those who had moderate to severe psychological distress were positively associated with COVID-19 positive diagnosis. Participants who are residing in Malaysia and Thailand, and those who had higher education were negatively associated with a COVID-19 positive diagnosis. Considering all participants, higher perceived economic status, being a student or worker in the health sector, and moderate or severe psychological distress were positively associated with a COVID-19 positive diagnosis, and residing in Malaysia, Thailand or five African countries was negatively associated with a COVID-19 positive diagnosis.

Conclusion

A high rate of COVID-19 testing among adults was reported in nine low-and middle-income countries. However, access to testing needs to be increased in Africa. Moreover, COVID-19 testing programmes need to target persons of lower economic status and education level who are less tested but most at risk for COVID-19 infection.

Determinants of pre-vaccination antibody responses to SARS-CoV-2: a population-based longitudinal study (COVIDENCE UK)

BACKGROUND

Prospective population-based studies investigating multiple determinants of pre-vaccination antibody responses to SARS-CoV-2 are lacking.

METHODS

We did a prospective population-based study in SARS-CoV-2 vaccine-naive UK adults recruited between May 1 and November 2, 2020, without a positive swab test result for SARS-CoV-2 prior to enrolment. Information on 88 potential sociodemographic, behavioural, nutritional, clinical and pharmacological risk factors was obtained through online questionnaires, and combined IgG/IgA/IgM responses to SARS-CoV-2 spike glycoprotein were determined in dried blood spots obtained between November 6, 2020, and April 18, 2021. We used logistic and linear regression to estimate adjusted odds ratios (aORs) and adjusted geometric mean ratios (aGMRs) for potential determinants of SARS-CoV-2 seropositivity (all participants) and antibody titres (seropositive participants only), respectively.

RESULTS

Of 11,130 participants, 1696 (15.2%) were seropositive. Factors independently associated with  higher risk of SARS-CoV-2 seropositivity included frontline health/care occupation (aOR 1.86, 95% CI 1.48-2.33), international travel (1.20, 1.07-1.35), number of visits to shops and other indoor public places (≥ 5 vs. 0/week: 1.29, 1.06-1.57, P-trend = 0.01), body mass index (BMI) ≥ 25 vs. < 25 kg/m² (1.24, 1.11-1.39), South Asian vs. White ethnicity (1.65, 1.10-2.49) and alcohol consumption ≥15 vs. 0 units/week (1.23, 1.04-1.46). Light physical exercise associated with  lower risk (0.80, 0.70-0.93, for ≥ 10 vs. 0-4 h/week). Among seropositive participants, higher titres of anti-Spike antibodies associated with factors including BMI ≥ 30 vs. < 25 kg/m² (aGMR 1.10, 1.02-1.19), South Asian vs. White ethnicity (1.22, 1.04-1.44), frontline health/care occupation (1.24, 95% CI 1.11-1.39), international travel (1.11, 1.05-1.16) and number of visits to shops and other indoor public places (≥ 5 vs. 0/week: 1.12, 1.02-1.23, P-trend = 0.01); these associations were not substantially attenuated by adjustment for COVID-19 disease severity.

CONCLUSIONS

Higher alcohol consumption and lower light physical exercise represent new modifiable risk factors for SARS-CoV-2 infection. Recognised associations between South Asian ethnic origin and obesity and higher risk of SARS-CoV-2 seropositivity were independent of other sociodemographic, behavioural, nutritional, clinical, and pharmacological factors investigated. Among seropositive participants, higher titres of anti-Spike antibodies in people of South Asian ancestry and in obese people were not explained by greater COVID-19 disease severity in these groups.

Childhood obesity and risk of SARS-CoV-2 infection

Objective

To estimate the association between childhood obesity and the risk of SARS-CoV-2 infection in a cohort followed from 4 to 12 years of age.

Methods

The data were obtained from two independent sources: the Longitudinal Childhood Obesity Study (ELOIN) and the epidemiological surveillance system data from the Community of Madrid (Spain), which served to identify the population within the cohort with confirmed SARS-CoV-2 infection. The SARS-CoV-2 registry was cross-checked with the cohort population at 11–12 years of age. A total of 2018 eligible participants were identified in the cohort, who underwent physical examinations at 4, 6, and 9 years of age during which weight, height, and waist circumference were recorded. General obesity (GO) was determined according to the WHO-2007 criteria whereas abdominal obesity (AO) was defined based on the International Diabetes Federation (IDF) criteria. The relative risks (RRs) of infection were estimated using a Poisson regression model and adjusted by sociodemographic variables, physical activity, and perceived health reported by the parents.

Results

The accumulated incidence of SARS-CoV-2 infection was 8.6% (95% CI: 7.3–9.8). The estimated RR of SARS-CoV-2 infection was 2.53 (95% CI: 1.56–4.10) and 2.56 (95% CI: 1.55–4.21) for children 4–9 years old with stable GO and AO, respectively, compared with those who did not present GO.

Conclusions

Childhood obesity is an independent risk factor for SARS-CoV-2 infection. This study provides new evidence that indicates that obesity increases the vulnerability of the paediatric population to infectious diseases.

The Avon Longitudinal Study of Parents and Children - A resource for COVID-19 research: Antibody testing results, April - June 2021

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective population-based cohort which recruited pregnant women in 1990-1992 and has followed these women, their partners (Generation 0; G0) and their offspring (Generation 1; G1) ever since. The study reacted rapidly and repeatedly to the coronavirus disease 2019 (COVID-19) pandemic, deploying multiple online questionnaires and a previous home-based antibody test in October 2020. A second antibody test, in collaboration with ten other longitudinal population studies, was completed by 4,622 ALSPAC participants between April and June 2021. Of 4,241 participants with a valid spike protein antibody test result (8.2% were void), indicating antibody response to either COVID-19 vaccination or natural infection, 3,172 were positive (74.8%). Generational differences were substantial, with 2,463/2,555 G0 participants classified positive (96.4%) compared to 709/1,686 G1 participants (42.1%). Of 4,199 participants with a valid nucleocapsid antibody test result (9.2% were void), suggesting potential and recent natural infection, 493 were positive (11.7%); 248/2,526 G0 participants (9.8%) and 245/1,673 G1 participants (14.6%) tested positive, respectively. We also compare results for this round of testing to that undertaken in October 2020. Future work will combine these test results with additional sources of data to identify participants' COVID-19 infection and vaccination status. These ALSPAC COVID-19 serology data are being complemented with linkage to health records and Public Health England pillar testing results as they become available, in addition to four previous questionnaire waves and a prior antibody test. Data have been released as an update to the previous COVID-19 datasets. These comprise: 1) a standard dataset containing all participant responses to all four previous questionnaires with key sociodemographic factors; and 2) individual participant-specific release files enabling bespoke research across all areas supported by the study. This data note describes the second ALSPAC antibody test and the data obtained from it.