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ObjectiveTo estimate vaccine effectiveness (VE) for preventing COVID-19 hospital admission in women first infected with SARS-CoV-2 during pregnancy, and assess how this compares to VE among women of reproductive age who were not pregnant when first infected. DesignPopulation-based cohort study using national, linked Census and administrative data. SettingEngland, United Kingdom, from 8th December 2020 to 31st August 2021. Participants815,4777 women aged 18 to 45 years (mean age, 30.4 years) who had documented evidence of a first SARS-CoV-2 infection in NHS Test and Trace data or Hospital Episode Statistics. Main outcome measuresA hospital inpatient episode where COVID-19 was recorded as the primary diagnosis. Cox proportional hazards models, adjusted for calendar time of infection and sociodemographic factors related to vaccine uptake and risk of severe COVID-19, were used to estimate VE as the complement of the hazard ratio for COVID-19 hospital admission. ResultsCompared with unvaccinated pregnant women, the adjusted rate of COVID-19 hospital admission was 76% (95% confidence interval 69% to 82%) lower for single-vaccinated pregnant women and 83% (75% to 88%) lower for double-vaccinated pregnant women. These estimates were similar to those found for non-pregnant women: 79% (76% to 81%) for single-vaccinated and 82% (80% to 83%) for double-vaccinated. Among those vaccinated more than 90 days before infection, being double-vaccinated was associated with a greater reduction in risk than being single-vaccinated. ConclusionsCOVID-19 vaccination is associated with reduced rates of severe illness in pregnant women infected with SARS-CoV-2, and the reduction in risk is similar to that for non-pregnant women. Waning of vaccine effectiveness occurs more quickly after one dose of a vaccine than two doses. What is already known on this topicBeing pregnant is a risk factor for severe illness and mortality following infection with SARS-CoV-2. Existing evidence suggests that COVID-19 vaccines are effective for preventing severe outcomes in pregnant women. However, research directly comparing vaccine effectiveness between pregnant and non-pregnant women of reproductive age at the population level are lacking. What this study addsOur study provides real-world evidence that COVID-19 vaccination reduces the risk of hospital admission by a similar amount for both women infected with SARS-CoV-2 during pregnancy and women who were not pregnant when infected, during the Alpha and Delta dominant periods in England.
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ObjectiveTo examine socio-demographic disparities in SARS-CoV-2 case rates during the second (Alpha) and third (Delta) waves of the COVID-19 pandemic. DesignRetrospective, population-based cohort study. SettingResident population of England. Participants39,006,194 people aged 10 years and over who were enumerated at the 2011 Census, registered with the National Health Service (NHS) and alive on 1 September 2020. Main outcome measuresTesting positive for SARS-CoV-2 during the second wave (1 September 2020 to 22 May 2021) or third wave (23 May to 10 December 2021) of the pandemic. We calculated age-standardised case rates by socio-demographic characteristics and used logistic regression models to estimate adjusted odds ratios (ORs). ResultsDuring the study period, 5,767,584 individuals tested positive for SARS-CoV-2. In the second wave, the fully-adjusted odds of having a positive test, relative to the White British group, were highest for the Bangladeshi (OR: 1.88, 95% CI 1.86 to 1.90) and Pakistani (1.81, 1.79 to 1.82) ethnic groups. Relative to the Christian group, Muslim and Sikh religious groups had fully-adjusted ORs of 1.58 (1.57 to 1.59) and 1.74 (1.72 to 1.76), respectively. Greater area deprivation, disadvantaged socio-economic position, living in a care home and low English language proficiency were also associated with higher odds of having a positive test. However, the disparities between groups varied over time. Being Christian, White British, non-disabled, and from a more advantaged socio-economic position were all associated with increased odds of testing positive during the third wave. ConclusionThere are large socio-demographic disparities on SARS-CoV-2 cases which have varied between different waves of the pandemic. Research is now urgently needed to understand why these disparities exist to inform policy interventions in future waves or pandemics. What is already known on this topicPeople with pre-existing health conditions or disability, ethnic minority groups, the elderly, some religious groups, people with low socio-economic status, and those living in deprived areas have been disproportionately affected by the COVID-19 pandemic in terms of risk of infection and adverse outcomes. What this study addsUsing linked data on 39 million people in England, we found that during the second wave, COVID-19 case rates were highest among the Bangladeshi and Pakistani ethnic groups, the Muslim religious group, individuals from deprived areas and of low socio-economic position; during the third wave, being Christian, White British, non-disabled, and from a more advantaged socio-economic position were all associated with increased odds of receiving a positive test Adjusting for geographical factors, socio-demographic characteristics, and pre-pandemic health status explained some, but not all, of the excess risk When stratifying the dataset by broad age groups, the odds of receiving a positive test remained higher among the Bangladeshi and Pakistani ethnic groups aged 65 years and over during the third wave, which may partly explain the continued elevated mortality rates in these groups
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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.
