Long-term outdoor air pollution and COVID-19 mortality in London: an individual-level analysis (preprint)

Background: The risk of COVID-19 severity and mortality differs markedly by age, socio-demographic characteristics and pre-existing health status. Various studies have suggested that higher air pollution exposures also increase the likelihood of dying from COVID-19. Objectives: To assess the association between long-term outdoor air pollution (NO2, NOx, PM10 and PM2.5) concentrations and the risk of death involving COVID-19, using a large individual-level dataset. Methods: We used comprehensive individual-level data from the Office for National Statistics' Public Health Data Asset for September 2020 to January 2022 and London Air Quality Network modelled air pollution concentrations available for 2016. Using Cox proportional hazard regression models, we adjusted for potential confounders including age, sex, vaccination status, dominant virus variants, geographical factors (such as population density), ethnicity, area and household-level deprivation, and health comorbidities. Results: There were 737,356 confirmed COVID-19 cases including 9,315 COVID-related deaths. When only adjusting for age, sex, and vaccination status, there was an increased risk of dying from COVID-19 with increased exposure to all air pollutants studied (NO2: HR 1.07 [95% confidence interval: 1.04-1.12] per 10 g /m3; NOx: 1.05[1.02-1.09] per 20 g /m3; PM10: 1.32[1.15-1.51] per 10 g /m3; PM2.5: 1.29[1.12-1.49] per 5 g /m3). However, after adjustment including ethnicity and socio-economic factors the HRs were close to unity (NO2: 0.98[0.90-1.06]; NOx: 0.99[0.94-1.04]; PM10: 0.95[0.74-1.22]; PM2.5: 0.90[0.67-1.20]). Additional adjustment for dominant variant or pre-existing health comorbidities did not alter the results. Conclusions: Observed associations between long-term outdoor air pollution exposure and COVID-19 mortality in London are strongly confounded by geography, ethnicity and deprivation.

Risk of death following SARS-CoV-2 infection or COVID-19 vaccination in young people in England: a self-controlled case series study (preprint)

Objectives: To assess whether there is a change in the incidence of cardiac and all-cause death in young people following COVID-19 vaccination or SARS-CoV-2 infection in unvaccinated individuals. Design: Self-controlled case series. Setting: National, linked electronic health record data in England. Study population: Individuals aged 12-29 who had received at least one dose of COVID-19 vaccination and died between 8 December 2020 and 2 February 2022 and registered by 16 February 2022 within 12 weeks of COVID-19 vaccination; Individuals aged 12-29 who died within 12 weeks of testing positive for SARS-CoV-2. Main outcome measures: Cardiac and all-cause deaths occurring within 12 weeks of vaccination or SARS-CoV-2 infection. Results: Compared to the baseline period, there was no evidence of a change in the incidence of cardiac death in the six weeks after vaccination, whether for each of weeks 1 to 6 or the whole six-week period. There was a decrease in the risk of all-cause death in the first week after vaccination and no change in each of weeks 2 to 6 after vaccination or whole six-week period after vaccination. Subgroup analyses by sex, age, vaccine type, and last dose also showed no change in the risk of death in the first six weeks after vaccination. There was a large increase in the incidence of cardiac and all-cause death in the overall risk period after SARS-CoV-2 infection among the unvaccinated. Conclusion: There is no evidence of an association between COVID-19 vaccination and an increased risk of death in young people. By contrast, SARS-CoV-2 infection was associated with substantially higher risk of cardiac related death and all-cause death.

Ethnic differences in COVID-19 mortality in the second and third waves of the pandemic in England during the vaccine roll-out: a retrospective, population-based cohort study (preprint)

Objectives To assess whether ethnic differences in COVID-19 mortality in England have continued into the third wave and to what extent differences in vaccination rates contributed to excess COVID-19 mortality after accounting for other risk factors. Design Cohort study of 28.8 million adults using data from the Office for National Statistics Public Health Data Asset. Setting People living in private households or communal establishments in England. Participants 28,816,020 adults (47% male) aged 30-100 years in 2020 (mean age = 56), who were present at the 2011 Census and alive on 8 December 2020. Main outcome measures Death involving COVID-19 during the second (8 December 2020 to 12 June 2021) and third wave (13 June 2021 to 1 December 2021) of the pandemic. We calculated hazard ratios (HRs) separately for males to females to summarise the association between ethnic group and death involving COVID-19 in each wave, sequentially adjusting for age, residence type, geographical factors, sociodemographic characteristics, pre-pandemic health, and vaccination status. Results Age-adjusted HRs of death involving COVID-19 were higher for most ethnic minority groups than the White British group during both waves, particularly for groups with lowest vaccination rates (Bangladeshi, Pakistani, Black African and Black Caribbean). In both waves, HRs were attenuated after adjusting for geographical factors, sociodemographic characteristics, and pre-pandemic health. Further adjusting for vaccination status substantially reduced residual HRs for Black African, Black Caribbean, and Pakistani groups in the third wave. The only groups where fully-adjusted HRs remained elevated were the Bangladeshi group (men: 2.19, 95% CI 1.72 to 2.78; women: 2.12, 95% CI 1.58 to 2.86) and men from the Pakistani group (1.24, 95% CI 1.06 to 1.46). Conclusion Public health strategies to increase vaccination uptake in ethnic minority groups could reduce disparities in COVID-19 mortality that cannot be accounted for by pre-existing risk factors. What is already known on this topic Ethnic minority groups in England have been disproportionately affected by the COVID-19 pandemic during the first and second waves. COVID-19 vaccination uptake is also lower among many ethnic minority groups, particularly Bangladeshi, Black African, Black Caribbean, and Pakistani groups. There is a paucity of research into whether ethnic disparities in COVID-19 mortality have continued into the third wave and the extent to which differences in vaccination uptake contribute to differences in COVID-19 mortality. What this study adds Using linked data on 28.8 million adults in England, we find that rates of COVID-19 mortality have remained higher than the White British group for most ethnic minority groups during the vaccine roll-out, notably for the Bangladeshi, Black African, Black Caribbean, and Pakistani groups. After adjustment for geographical factors, sociodemographic characteristics, pre-pandemic health status, and vaccination status, the only groups with elevated rates of COVID-19 mortality during the third wave were the Bangladeshi group and men from the Pakistani group, suggesting that increasing vaccination uptake in ethnic minority groups could reduce ethnic disparities in COVID-19 mortality.

Differences in COVID-19 vaccination coverage by occupation in England: a national linked data study (preprint)

Background: Monitoring differences in COVID-19 vaccination uptake in different groups is crucial to help inform the policy response to the pandemic. A key gap is the absence of data on uptake by occupation. Methods: Using nationwide population-level data, we calculated the proportion of people who had received two doses of a COVID-19 vaccine (assessed on 31 August 2021) by detailed occupational categories in adults aged 40-64 and estimated adjusted odds ratios to examine whether these differences were driven by occupation or other factors, such as education. We also examined whether vaccination rates differed by ability to work from home. Results: Our study population included 14,298,147 adults 40-64. Vaccination rates differed markedly by occupation, being higher in administrative and secretarial occupations (90.8%); professional occupations (90.7%); and managers, directors and senior officials (90.6%); and lowest (83.1%) in people working in elementary occupations. We found substantial differences in vaccination rates looking at finer occupational groups even after adjusting for confounding factors, such as education. Vaccination rates were higher in occupations which can be done from home and lower in those which cannot. Many occupations with low vaccination rates also involved contact with the public or with vulnerable people. Conclusions: Increasing vaccination coverage in occupations with low vaccination rates is crucial to help protecting the public and control infection, especially in occupations that cannot be done from home and involve contacts with the public. Policies such as 'work from home if you can' may only have limited future impact on hospitalisations and deaths

Estimating the effectiveness of first dose of COVID-19 vaccine against mortality in England: a quasi-experimental study (preprint)

BackgroundEstimating real-world vaccine effectiveness is vital to assess the impact of the vaccination programme on the pandemic and inform the ongoing policy response. However, estimating vaccine effectiveness using observational data is inherently challenging because of the non-randomised design and the potential for unmeasured confounding. MethodsWe used a Regression Discontinuity Design (RDD) to estimate vaccine effectiveness against COVID-19 mortality in England, exploiting the discontinuity in vaccination rates resulting from the UKs age-based vaccination priority groups. We used the fact that people aged 80 or over were prioritised for the vaccine roll-out in the UK to compare the risk of COVID-19 and non-COVID-19 death in people aged 75-79 and 80-84. FindingsThe prioritisation of vaccination of people aged 80 or above led to a large discrepancy in vaccination rates in people 80-84 compared to those 75-79 at the beginning of the vaccination campaign. We found a corresponding difference in COVID-19 mortality, but not in non-COVID-19 mortality, suggesting that our approach appropriately addresses the issue of unmeasured confounding factors. Our results suggest that the first vaccine dose reduced the risk of COVID-19 death by 52.6% (95% Cl 26.6-84.2) in those aged 80. InterpretationsOur results support existing evidence that a first dose of a COVID-19 vaccine has a strong protective effect against COVID-19 mortality in older adults. The RDD estimate of vaccine effectiveness is comparable to previously published studies using different methods, suggesting that unmeasured confounding factors are unlikely to substantially bias these studies. FundingOffice for National Statistics. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for studies reporting on the real-world effectiveness of the COVID-19 vaccination on risk of death using terms such as "COVID-19", "vaccine effectiveness", "mortality" and "death". The relevant published studies on this topic report vaccine effectiveness estimates against risk of death ranging from 64.2% to 98.7%, for varying times post-vaccination. All of these are observational studies and therefore potentially subject to bias from unmeasured confounding. We found no studies that used a quasi-experimental method such as regression discontinuity design, which is not subject to bias from unmeasured confounding, to calculate the effectiveness of the COVID-19 vaccination on risk of COVID-19 death, or on other outcomes such as hospitalisation or infection. Added value of this studyThe estimates of vaccine effectiveness based on observational data may be biased by unmeasured confounding. This study uses a regression discontinuity design to estimate vaccine effectiveness, exploiting the fact that the vaccination campaign in the UK was rolled out following age-based priority groups. This enables the calculation of an unbiased estimate of the effectiveness of the COVID-19 vaccine against risk of death. The vaccine effectiveness estimate of 52.6% (95% Cl 26.6-84.2) is slightly lower but similar to previously published estimates, therefore suggesting that these estimates are not substantially affected by unmeasured confounding factors and confirming the effectiveness of the COVID-19 vaccine against risk of COVID-19 death. Implications of all the available evidenceObtaining an unbiased estimate of COVID-19 vaccine effectiveness is of vital importance in informing policy for lifting COVID-19 related measures. The regression discontinuity design provides confidence that the existing estimates from observational studies are unlikely to be substantially biased by unmeasured confounding.

Sociodemographic inequality in COVID-19 vaccination coverage amongst elderly adults in England: a national linked data study (preprint)

Objective: To examine inequalities in COVID-19 vaccination rates amongst elderly adults in England Design: Cohort study Setting: People living in private households and communal establishments in England Participants: 6,829,643 adults aged 70 years or above (mean 78.7 years, 55.2% female) who were alive on 15 March 2021. Main outcome measures: Having received the first dose of a vaccine against COVID-19 by 15 March 2021. We calculated vaccination rates and estimated unadjusted and adjusted odds ratios using logistic regression models. Results: By 15 March 2021, 93.2% of people living in England aged 70 years and over had received at least one dose of a COVID-19 vaccine. While vaccination rates differed across all factors considered apart from sex, the greatest disparities were seen between ethnic and religious groups. The lowest rates were in people of Black African and Black Caribbean ethnic backgrounds, where only 67.2% and 73.9% had received a vaccine, with adjusted odds of not being vaccinated at 5.01 (95% CI 4.86 - 5.16) and 4.85 (4.75 - 4.96) times greater than the White British group. The proportion of individuals self-identifying as Muslim and Buddhist who had received a vaccine was 79.1% and 84.1%, respectively. Older age, greater area deprivation, less advantaged socio-economic position (proxied by living in a rented home), being disabled and living either alone or in a multi-generational household were also associated with higher odds of not having received the vaccine. Conclusion: People disproportionately affected seem most hesitant to COVID-19 vaccinations. Policy Interventions to improve these disparities are urgently needed.

An external validation of the QCovid risk prediction algorithm for risk of mortality from COVID-19 in adults: national validation cohort study in England (preprint)

BackgroundTo externally validate a risk prediction algorithm (QCovid) to estimate mortality outcomes from COVID-19 in adults in England. MethodsPopulation-based cohort study using the ONS Public Health Linked Data Asset, a cohort based on the 2011 Census linked to Hospital Episode Statistics, the General Practice Extraction Service Data for pandemic planning and research, radiotherapy and systemic chemotherapy records. The primary outcome was time to COVID-19 death, defined as confirmed or suspected COVID-19 death as per death certification. Two time periods were used: (a) 24th January to 30th April 2020; and (b) 1st May to 28th July 2020. We evaluated the performance of the QCovid algorithms using measures of discrimination and calibration for each validation time period. FindingsThe study comprises 34,897,648 adults aged 19-100 years resident in England. There were 26,985 COVID-19 deaths during the first time-period and 13,177 during the second. The algorithms had good calibration in the validation cohort in both time periods with close correspondence of observed and predicted risks. They explained 77.1% (95% CI: 76.9% to 77.4%) of the variation in time to death in men in the first time-period (R2); the D statistic was 3.76 (95% CI: 3.73 to 3.79); Harrells C was 0.935 (0.933 to 0.937). Similar results were obtained for women, and in the second time-period. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths in the first time period was 65.9% for men and 71.7% for women. People in the top 20% of predicted risks of death accounted for 90.8% of all COVID-19 deaths for men and 93.0% for women. InterpretationThe QCovid population-based risk algorithm performed well, showing very high levels of discrimination for COVID-19 deaths in men and women for both time periods. It has the potential to be dynamically updated as the pandemic evolves and therefore, has potential use in guiding national policy. FundingNational Institute of Health Research RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSPublic policy measures and clinical risk assessment relevant to COVID-19 need to be aided by rigorously developed and validated risk prediction models. A recent living systematic review of published risk prediction models for COVID-19 found most models are subject to a high risk of bias with optimistic reported performance, raising concern that these models may be unreliable when applied in practice. A population-based risk prediction model, QCovid risk prediction algorithm, has recently been developed to identify adults at high risk of serious COVID-19 outcomes, which overcome many of the limitations of previous tools. Added value of this studyCommissioned by the Chief Medical Officer for England, we validated the novel clinical risk prediction model (QCovid) to identify risks of short-term severe outcomes due to COVID-19. We used national linked datasets from general practice, death registry and hospital episode data for a population-representative sample of over 34 million adults. The risk models have excellent discrimination in men and women (Harrells C statistic>0.9) and are well calibrated. QCovid represents a new, evidence-based opportunity for population risk-stratification. Implications of all the available evidenceQCovid has the potential to support public health policy, from enabling shared decision making between clinicians and patients in relation to health and work risks, to targeted recruitment for clinical trials, and prioritisation of vaccination, for example.