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1.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-327690

ABSTRACT

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.

2.
Nat Commun ; 12(1): 6250, 2021 10 29.
Article in English | MEDLINE | ID: covidwho-1493099

ABSTRACT

Understanding the trajectory, duration, and determinants of antibody responses after SARS-CoV-2 infection can inform subsequent protection and risk of reinfection, however large-scale representative studies are limited. Here we estimated antibody response after SARS-CoV-2 infection in the general population using representative data from 7,256 United Kingdom COVID-19 infection survey participants who had positive swab SARS-CoV-2 PCR tests from 26-April-2020 to 14-June-2021. A latent class model classified 24% of participants as 'non-responders' not developing anti-spike antibodies, who were older, had higher SARS-CoV-2 cycle threshold values during infection (i.e. lower viral burden), and less frequently reported any symptoms. Among those who seroconverted, using Bayesian linear mixed models, the estimated anti-spike IgG peak level was 7.3-fold higher than the level previously associated with 50% protection against reinfection, with higher peak levels in older participants and those of non-white ethnicity. The estimated anti-spike IgG half-life was 184 days, being longer in females and those of white ethnicity. We estimated antibody levels associated with protection against reinfection likely last 1.5-2 years on average, with levels associated with protection from severe infection present for several years. These estimates could inform planning for vaccination booster strategies.


Subject(s)
Antibodies, Viral/immunology , Antibody Formation/immunology , COVID-19/immunology , SARS-CoV-2/pathogenicity , Adult , Aged , Antibody Formation/physiology , Bayes Theorem , Female , Humans , Immunoglobulin G/metabolism , Male , Middle Aged , SARS-CoV-2/immunology
3.
Lancet Digit Health ; 3(7): e425-e433, 2021 07.
Article in English | MEDLINE | ID: covidwho-1246269

ABSTRACT

BACKGROUND: Public policy measures and clinical risk assessments relevant to COVID-19 need to be aided by risk prediction models that are rigorously developed and validated. We aimed to externally validate a risk prediction algorithm (QCovid) to estimate mortality outcomes from COVID-19 in adults in England. METHODS: We did a population-based cohort study using the UK Office for National Statistics Public Health Linked Data Asset, a cohort of individuals aged 19-100 years, based on the 2011 census and linked to Hospital Episode Statistics, the General Practice Extraction Service data for pandemic planning and research, and 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 periods were used: (1) Jan 24 to April 30, 2020, and (2) May 1 to July 28, 2020. We assessed the performance of the QCovid algorithms using measures of discrimination and calibration. Using predicted 90-day risk of COVID-19 death, we calculated r2 values, Brier scores, and measures of discrimination and calibration with corresponding 95% CIs over the two time periods. FINDINGS: We included 34 897 648 adults aged 19-100 years resident in England. 26 985 (0·08%) COVID-19 deaths occurred during the first period and 13 177 (0·04%) during the second. The algorithms had good discrimination and calibration in both periods. In the first period, they explained 77·1% (95% CI 76·9-77·4) of the variation in time to death in men and 76·3% (76·0-76·6) in women. The D statistic was 3·761 (3·732-3·789) for men and 3·671 (3·640-3·702) for women and Harrell's C was 0·935 (0·933-0·937) for men and 0·945 (0·943-0·947) for women. Similar results were obtained for 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 period was 65·94% for men and 71·67% for women. INTERPRETATION: The QCovid population-based risk algorithm performed well, showing high levels of discrimination for COVID-19 deaths in men and women for both time periods. QCovid has the potential to be dynamically updated as the pandemic evolves and, therefore, has potential use in guiding national policy. FUNDING: UK National Institute for Health Research.


Subject(s)
Algorithms , COVID-19/mortality , Risk Assessment/statistics & numerical data , Adult , Aged , Aged, 80 and over , Cohort Studies , Databases, Factual , England/epidemiology , Female , Humans , Male , Middle Aged , Pandemics , SARS-CoV-2 , Young Adult
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