ABSTRACT
We describe SARS-CoV-2 infection and COVID-19 vaccine uptake in Scotland in a prospective cohort of all pregnant women in Scotland drawn from national databases. As of mid-October 2021, the Covid-19 in pregnancy in Scotland (COPS) cohort included linked data on a total of 139,136 pregnancies in 126,749 women. Up to September 30, 2021, a total of 22,779 COVID-19 vaccinations had been administered to 16,229 pregnant women. Vaccine coverage was substantially lower in pregnant women than in the general female population of reproductive age (23.7% of women giving birth in September 2021 were fully vaccinated compared to 74.9 % in women 18-44 years). Of the 4,274 cases of COVID-19 in pregnancy (confirmed by SARS-CoV-2 viral reverse transcriptase polymerase chain reaction) between December 2020 (the month the COVID-19 vaccination programme started in Scotland) and September 2021 inclusive, 629 women (14.7%) were admitted to hospital and 89 (2.1%) were admitted to critical care. Of the COVID-19 cases occurring in pregnant women, 81.7% (3,491/4,274; 95% CI 80.5-82.8) were in unvaccinated women. Of the COVID-19 associated hospital admissions, 93.0% (585/629; 95% CI 90.7-94.8) were in women who were unvaccinated at the time of COVID-19 diagnosis. Of the COVID-19 associated critical care admissions 98.9% (88/89; 95% CI 93.9-100) were in women who were unvaccinated at the time of COVID-19 diagnosis. The extended perinatal mortality rate for women who gave birth within 28 days of COVID-19 diagnosis was 15.9 per 1000 births (95% CI 7.8 to 31.0; background rate in 2020 6.3 per 1,000 total births [95% CI 5.7-7.1]; background rate 2019 5.7 per 1,000 total births [95% CI 5.0-6.4]). All baby deaths occurred after pregnancies in women who were unvaccinated at the time of COVID-19 diagnosis. Addressing low vaccine uptake rates in pregnant women is imperative to protect the health of women and babies.
Subject(s)
COVID-19ABSTRACT
Background: The QCovid algorithm is a risk prediction tool for COVID-19 hospitalisation and mortality that can be used to stratify patients by risk into vulnerability groups . We carried out an external validation of the QCovid algorithm in Scotland.Methods: We established a national COVID-19 data platform using individual level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription polymerase chain reaction (RT-PCR) virology testing, hospitalisation and mortality data. We assessed the performance of the QCovid algorithm in predicting COVID-19 hospitalisation and deaths in our dataset for two time periods: 1 March, 2020 to 30 April, 2020, and 1 May, 2020 to 30 June, 2020.Findings: Our dataset comprised 5,384,819 individuals, representing 99% of the estimated population (5,463,300) resident in Scotland in 2020. The algorithm showed excellent calibration in both time periods with close correspondence between observed and predicted risks. Harrell ’s C for deaths in males and females in the first period was 0.946 (95% CI: 0.941 - 0.951) and 0.925 (95% CI: 0.919 - 0.931) respectively. Harrell’s C for hospitalisations in males and females in the first period was 0.809 (95% CI: 0.801 - 0.817) and 0.816 (95% CI: 0.808 - 0.823) respectively.Interpretation: The QCovid algorithm shows high levels of external validity in predicting the risk of COVID- 19 hospitalisation and death in the population of Scotland.Funding: Medical Research Council, National Institute for Health Research Health Technology Assessment Programme, funded through the UK Research and Innovation Industrial Strategy Challenge Fund Health Data Research UK.Declaration of Interests: Dr. Hippisley-Cox reports grants from MRC, grants from Wellcome Trrust, grants from NIHR, during the conduct of the study; other from ClinRisk Ltd, outside the submitted work. Dr. Sheikh reports grants from NIHR, grants from MRC, grants from HRR UK, during the conduct of the study. All other authors report no conflict of interest.Ethics Approval Statement: Ethical permission for this study was granted from South East Scotland Research Ethics Committee 02 [12/SS/0201]. The Public Benefit and Privacy Panel Committee of Public Health Scotland, approved the linkage and analysis of the de-identified datasets for this project [1920-0279].
Subject(s)
COVID-19ABSTRACT
Background: The BNT162b2 mRNA (Pfizer-BioNTech) and ChAdOx1 (Oxford-AstraZeneca) COVID-19 vaccines have demonstrated high efficacy against infection in phase 3 clinical trials and are now being used in national vaccination programmes in the UK and several other countries. There is an urgent need to study the ‘real-world’ effects of these vaccines. The aim of our study was to estimate the effectiveness of the first dose of these COVID-19 vaccines in preventing hospital admissions.Methods: We conducted a prospective cohort study using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) database comprising of linked vaccination, primary care, Real-Time Polymerase Chain Reaction (RT-PCR) testing, hospitalisation and mortality records for 5.4 million people in Scotland (covering ~99% of population). A time-dependent Cox model and Poisson regression models were fitted to estimate effectiveness against COVID-19 related hospitalisation (defined as 1- Adjusted Hazard Ratio) following the first dose of vaccine.Findings: The first dose of the BNT162b2 vaccine was associated with a vaccine effect of 85% (95% confidence interval [CI] 76 to 91) for COVID-19 related hospitalisation at 28-34 days post-vaccination. Vaccine effect at the same time interval for the ChAdOx1 vaccine was 94% (95% CI 73 to 99). Results of combined vaccine effect for prevention of COVID-19 related hospitalisation were comparable when restricting the analysis to those aged ≥80 years (81%; 95% CI 65 to 90 at 28-34 days post-vaccination).Interpretation: A single dose of the BNT162b2 mRNA and ChAdOx1 vaccines resulted in substantial reductions in the risk of COVID-19 related hospitalisation in Scotland.Funding: UK Research and Innovation (Medical Research Council); Research and Innovation Industrial Strategy Challenge Fund; Health Data Research UK.Conflict of Interest: AS is a member of the Scottish Government Chief Medical Officer’s COVID-19Advisory Group and the New and Emerging Respiratory Virus Threats (NERVTAG) Risk Stratification Subgroup. CRS declares funding from the MRC, NIHR, CSO and New Zealand Ministry for Business, Innovation and Employment and Health Research Council during the conduct of this study. SVK is co-chair of the Scottish Government’s Expert Reference Group on COVID-19 and ethnicity, is a member of the Scientific Advisory Group on Emergencies (SAGE) subgroup on ethnicity and acknowledges funding from a NRS Senior Clinical Fellowship, MRC and CSO. All other authors report no conflicts of interest.Ethical Approval: Approvals were obtained from the National Research Ethics Service Committee, Southeast Scotland 02 (reference number: 12/SS/0201) and Public Benefit and Privacy Panel for Health and Social Care (reference number: 1920-0279).
Subject(s)
COVID-19 , EmergenciesABSTRACT
BackgroundSevere Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has challenged public health agencies globally. In order to effectively target government responses, it is critical to identify the individuals most at risk of coronavirus disease-19 (COVID-19), developing severe clinical signs, and mortality. We undertook a systematic review of the literature, to present the current status of scientific knowledge in these areas and describe the need for unified global approaches, moving forwards, as well as lessons learnt for future pandemics. MethodsMedline, Embase and Global Health were searched to the end of April 2020, as well as the Web of Science. Search terms were specific to the SARS-CoV-2 virus and COVID-19. Comparative studies of risk factors from any setting, population group and in any language were included. Titles, abstracts and full texts were screened by two reviewers and extracted in duplicate into a standardised form. Data were extracted on risk factors for COVID-19 disease, severe disease, or death and were narratively and descriptively synthesised. Results1,238 papers were identified post-deduplication. 33 met our inclusion criteria, of which 26 were from China. Six assessed the risk of contracting the disease, 20 the risk of having severe disease and ten the risk of dying. Age, gender and co-morbidities were commonly assessed as risk factors. The weight of evidence showed increasing age to be associated with severe disease and mortality, and general comorbidities with mortality. Only seven studies presented multivariable analyses and power was generally limited. A wide range of definitions were used for disease severity. ConclusionsThe volume of literature generated in the short time since the appearance of SARS-CoV-2 has been considerable. Many studies have sought to document the risk factors for COVID-19 disease, disease severity and mortality; age was the only risk factor based on robust studies and with a consistent body of evidence. Mechanistic studies are required to understand why age is such an important risk factor. At the start of pandemics, large, standardised, studies that use multivariable analyses are urgently needed so that the populations most at risk can be rapidly protected. This review was registered on PROSPERO as CRD42020177714.