Risk of COVID-19 death in adults who received booster COVID-19 vaccinations: national retrospective cohort study on 14.6 million people in England (preprint)

Background: The emergence of the COVID-19 vaccination has been critical in changing the course of the COVID-19 pandemic, with estimates suggesting vaccinations have prevented millions of deaths worldwide. To ensure protection remains high in groups at high-risk, booster vaccinations in the UK have been targeted based on age and clinical vulnerabilities. We sought to identify adults at increased risk of COVID-19 death, and compared to non-COVID-19 risk, despite having received a booster dose as part of the 2022 autumn vaccination campaign in England. Methods: We undertook a national retrospective cohort study using data from the 2021 Census linked to electronic health records. We fitted cause-specific Cox regression to examine the association between a range of health conditions and the risk of COVID-19 death and all-other-cause death for adults aged 50-100-years in England vaccinated with a booster in autumn 2022. Findings: Our total population was 14,644,570 people; there were 6,800 COVID-19 deaths (52. and 150,075 non-COVID-19 deaths. Having learning disabilities or Down Syndrome (hazard ratio=5.07;conficence interval=3.69-6.98), pulmonary hypertension or fibrosis(2.88;2.43-3.40), motor neuron disease, multiple sclerosis, myasthenia or Huntington's disease (2.94, 1.82-4.74), cancer of blood and bone marrow (3.11;2.72-3.56), Parkinson's disease (2.74;2.34-3.20), lung or oral cancer (2.57;2.04 to 3.24), dementia (2.64;2.46 to 2.83) or liver cirrhosis (2.65;1.95 to 3.59) was associated with an increased risk of COVID-19 death. Individuals with cancer of the blood or bone marrow, chronic kidney disease, cystic fibrosis, pulmonary hypotension or fibrosis, or rheumatoid arthritis or systemic lupus erythematosus had a significantly higher risk of COVID-19 death relative to other causes of death compared with individuals who did not have diagnoses of these comorbidities. Interpretation: We identify groups who are at increased risk of COVID-19 death relative to non-COVID-19 deaths. Vulnerable groups should continue to be prioritised for COVID-19 booster doses to minimise the risk of COVID-19 deaths.

Confirmed SARS-CoV-2 infection in Scottish neonates 2020-2022: a national, population-based cohort study (preprint)

ObjectiveTo examine infants in Scotland aged 0-27 days with confirmed SARS-CoV-2 infection; the risk of neonatal infection by factors including maternal infection status and gestation at birth; and the need for hospital admission among infected neonates. DesignPopulation-based cohort study. Setting and populationAll live births in Scotland, 1 March 2020 to 31 January 2022. ResultsThere were 141 neonates with confirmed SARS-CoV-2 infection over the study period, giving an overall infection rate of 153 per 100,000 live births (141/92,009). Among infants born to women with confirmed infection around the time of birth, the infection rate was 1,811 per 100,000 live births (15/828). Nearly two-thirds (92/141, 65.2%) of babies with confirmed neonatal infection had an associated admission to neonatal or (more commonly) paediatric care. Of those admitted to hospital, 6/92 (6.5%) infants were admitted to neonatal or paediatric intensive care, however none of these six had COVID-19 recorded as the main diagnosis underlying their admission. There were no neonatal deaths among babies with confirmed infection. Implications and relevanceConfirmed neonatal SARS-CoV-2 infection is uncommon. Secular trends in the neonatal infection rate broadly follow those seen in the general population, albeit at a lower level. Maternal infection at birth increases the risk of neonatal infection, but most babies with neonatal infection are born to women without confirmed infection. A high proportion of neonates with confirmed infection are admitted to hospital, with resulting implications for the baby, family, and services, although their outcomes are generally good. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIThe incidence of SARS-CoV-2 infection in neonates is low, but some studies have suggested that age under 1 month is a risk factor for severe infection requiring admission to intensive care. C_LIO_LIAlmost all the studies of neonatal SARS-CoV-2 have focused on the transmission risk from SARS-CoV-2 positive women to their offspring and data are lacking on the level of neonatal SARS-CoV-2 infection in the whole population. C_LI What this study addsO_LIThis study includes all babies with confirmed SARS-CoV-2 in the neonatal period in Scotland during the first 22 months of the COVID-19 pandemic. C_LIO_LIConfirmed neonatal SARS-CoV-2 infection is uncommon, but a high proportion of neonates with confirmed infection are admitted to hospital. C_LIO_LIConfirmed maternal SARS-CoV-2 infection around the time of birth substantially increases the risk of neonatal infection, although the absolute risk of neonatal infection remains low (<2%) and most babies with neonatal infection are born to women without confirmed infection. C_LIO_LIOutcomes for neonates with confirmed SARS-CoV-2 infection are good; only 6.5% (6/92) of admitted neonates required intensive care, and COVID-19 was not the primary diagnosis recorded for these babies. There were no neonatal deaths among babies with confirmed infection. C_LI

Accelerated waning of the humoral response to SARS-CoV-2 vaccines in obesity (preprint)

Obesity is associated with an increased risk of severe Covid-19. However, the effectiveness of SARS-CoV-2 vaccines in people with obesity is unknown. Here we studied the relationship between body mass index (BMI), hospitalization and mortality due to Covid-19 amongst 3.5 million people in Scotland. Vaccinated people with severe obesity (BMI>40 kg/m2) were significantly more likely to experience hospitalization or death from Covid-19. Excess risk increased with time since vaccination. To investigate the underlying mechanisms, we conducted a prospective longitudinal study of the immune response in a clinical cohort of vaccinated people with severe obesity. Compared with normal weight controls, six months after their second vaccine dose, significantly more people with severe obesity had unquantifiable titres of neutralizing antibody against authentic SARS-CoV-2 virus, reduced frequencies of antigen-experienced SARS-CoV-2 Spike-binding B cells, and a dissociation between anti-Spike antibody levels and neutralizing capacity. Neutralizing capacity was restored by a third dose of vaccine, but again declined more rapidly in people with severe obesity. We demonstrate that waning of SARS-CoV-2 vaccine-induced humoral immunity is accelerated in people with severe obesity and associated with increased hospitalization and mortality from breakthrough infections. Given the prevalence of obesity, our findings have significant implications for global public health.

Temporal sequencing in multimorbidity using population-scale linked data for 1.7 million individuals with 20-year follow-up (preprint)

Multimorbidity is defined as the coexistence of two or more chronic health conditions in an individual. The objective of this study was to examine how diseases in a cluster of physical-mental health multimorbidity with a high all-cause mortality (psychosis, diabetes, and congestive heart failure) develop and coexist over time, and to assess the associated impact of different temporal sequences on mortality. Population-scale, individual-level, anonymised, linked, demographic, administrative and electronic health record data were modelled using multi-state models for 1,675,585 individuals over a 20-year period (2000–2019). Cox regression models were used to estimate baseline hazards for transitions between states, adjusted for gender, age, and area-level deprivation. Our findings suggest that the order of disease acquisition in physical-mental health multimorbidity had an important impact and complex relationship on patient mortality. Individuals developing diabetes, psychosis, and congestive heart failure, in that order, had an increased all-cause mortality rate compared to the development of the same conditions in a different order, resulting in the highest loss in expectation of life of 13 years at age 50 compared to the general population. Congestive heart failure as a single condition and in combination with psychosis had an equally high loss in expectation of life. Identification and therapeutic targets for psychosis and congestive heart failure may be beneficial within 5 years following an initial diagnosis of diabetes. The use of multi-state models offers a flexible framework to assess temporal sequences of diseases and associated patient outcomes, and allows identification of potential risk factors, screening opportunities, and therapeutic targets in multimorbidity.

COVID-19 vaccination rates and SARS-CoV-2 infection in pregnant women in Scotland (preprint)

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.

Uptake of infant and pre-school immunisations in Scotland and England during the COVID-19 pandemic: an observational study of routinely collected data (preprint)

BackgroundIn 2020, the COVID-19 pandemic and control measures such as national lockdowns threatened to disrupt routine childhood immunisation programmes. Initial reports from the early weeks of lockdown in the UK and worldwide suggested that uptake could fall putting children at risk from multiple other infectious diseases. In Scotland and England, enhanced surveillance of national data for childhood immunisations was established to inform and rapidly assess the impact of the pandemic on infant and preschool immunisation uptake rates. Methods and findingsWe undertook an observational study using routinely collected data for the year prior to the pandemic (2019), and immediately before, during and after the first period of the UK lockdown in 2020. Data were obtained for Scotland from the Public Health Scotland "COVID19 wider impacts on the health care system" dashboard (https://scotland.shinyapps.io/phs-covid-wider-impact/) and for England from ImmForm. Five vaccinations delivered at different ages were evaluated; three doses of the 6-in-1 DTaP/IPV/Hib/HepB vaccine and two doses of MMR. Uptake in the periods in 2020 compared to that in the baseline year of 2019 using binary logistic regression analysis. For Scotland, we analysed timely uptake of immunisations, defined as uptake within four weeks of the child becoming eligible by age for each immunisation and data were also analysed by geographical region and indices of deprivation. For both Scotland and England, we assessed whether immunisations were up to date at approximately 6 months (all doses 6-in-1) and 16-18 months (first MMR) of age. We found that uptake rates within four weeks of eligibility in Scotland for all the five vaccine visits were higher during the 2020 lockdown period than in 2019. The difference ranged from 1.3% for the first dose of the 6-in-1 vaccine (95.3 vs 94%, OR 1.28, CI 1.18-1.39) to 14.3% for the second MMR dose (66.1 vs 51.8 %, OR 1.8, CI 1.74-1.87). Significant increases in uptake were seen across all deprivation levels, though, for MMR, there was evidence of greater improvement for children living in the least deprived areas. In England, fewer children who had been due to receive their immunisations during the lockdown period were up to date at 6 months (6-in-1) or 18 months (first dose MMR). The fall in percentage uptake ranged from 0.5% for first 6-in1 (95.8 vs 96.3%, OR 0.89, CI 0.86-0.91) to 2.1% for third 6-in-1 (86.6 vs 88.7%, OR 0.82, CI 0.81-0.83). ConclusionsThis study suggests that the national lockdown in Scotland was associated with a positive effect on timely childhood immunisation uptake, however in England a lower percentage of children were up to date at 6 and 18 months. Reason for the improve uptake in Scotland may include active measures taken to promote immunisation at local and national level during this period. Promoting immunisation uptake and addressing potential vaccine hesitancy is particularly important given the ongoing pandemic and COVID-19 vaccination campaigns.

An External Validation of the QCovid Risk Prediction Algorithm for Risk of Mortality from COVID-19 in Adults: National Validation Cohort Study in Scotland (preprint)

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].

Effectiveness of First Dose of COVID-19 Vaccines Against Hospital Admissions in Scotland: National Prospective Cohort Study of 5.4 Million People (preprint)

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).

Pristine annotations-based multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19 (preprint)

The COVID-19 pandemic continues to spread and impact the well-being of the global population. The front-line modalities including computed tomography (CT) and X-ray play an important role for triaging COVID patients. Considering the limited access of resources (both hardware and trained personnel) and decontamination considerations, CT may not be ideal for triaging suspected subjects. Artificial intelligence (AI) assisted X-ray based applications for triaging and monitoring require experienced radiologists to identify COVID patients in a timely manner and to further delineate the disease region boundary are seen as a promising solution. Our proposed solution differs from existing solutions by industry and academic communities, and demonstrates a functional AI model to triage by inferencing using a single x-ray image, while the deep-learning model is trained using both X-ray and CT data. We report on how such a multi-modal training improves the solution compared to X-ray only training. The multi-modal solution increases the AUC (area under the receiver operating characteristic curve) from 0.89 to 0.93 and also positively impacts the Dice coefficient (0.59 to 0.62) for localizing the pathology. To the best our knowledge, it is the first X-ray solution by leveraging multi-modal information for the development.

Informing the public health response to COVID-19 (and lessons learnt for future pandemics): a systematic review of risk factors for disease, severity, and mortality. (preprint)

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.