Adverse events following first and second dose COVID-19 vaccination in England, October 2020 to September 2021: a national vaccine surveillance platform self-controlled case series study

BackgroundPost-authorisation vaccine safety surveillance is well established for reporting common adverse events of interest (AEIs) following influenza vaccines, but not for COVID-19 vaccines.AimTo estimate the incidence of AEIs presenting to primary care following COVID-19 vaccination in England, and report safety profile differences between vaccine brands.MethodsWe used a self-controlled case series design to estimate relative incidence (RI) of AEIs reported to the national sentinel network, the Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub. We compared AEIs (overall and by clinical category) 7 days pre- and post-vaccination to background levels between 1 October 2020 and 12 September 2021.ResultsWithin 7,952,861 records, 781,200 individuals (9.82%) presented to general practice with 1,482,273 AEIs, 4.85% within 7 days post-vaccination. Overall, medically attended AEIs decreased post-vaccination against background levels. There was a 3–7% decrease in incidence within 7 days after both doses of Comirnaty (RI: 0.93; 95% CI: 0.91–0.94 and RI: 0.96; 95% CI: 0.94–0.98, respectively) and Vaxzevria (RI: 0.97; 95% CI: 0.95–0.98). A 20% increase was observed after one dose of Spikevax (RI: 1.20; 95% CI: 1.00–1.44). Fewer AEIs were reported as age increased. Types of AEIs, e.g. increased neurological and psychiatric conditions, varied between brands following two doses of Comirnaty (RI: 1.41; 95% CI: 1.28–1.56) and Vaxzevria (RI: 1.07; 95% CI: 0.97–1.78).ConclusionCOVID-19 vaccines are associated with a small decrease in medically attended AEI incidence. Sentinel networks could routinely report common AEI rates, contributing to reporting vaccine safety

A conceptual framework for characterising lifecourse determinants of multiple long-term condition multimorbidity

Objective: Social, biological and environmental factors in early-life, defined as the period from preconception until age 18, play a role in shaping the risk of multiple long-term condition multimorbidity. However, there is a need to conceptualise these early-life factors, how they relate to each other, and provide conceptual framing for future research on aetiology and modelling prevention scenarios of multimorbidity. We develop a conceptual framework to characterise the population-level domains of early-life determinants of future multimorbidity. Method: This work was conducted as part of the Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B) study. The conceptualisation of multimorbidity lifecourse determinant domains was shaped by a review of existing research evidence and policy, and co-produced with public involvement via two workshops. Results: Early-life risk factors incorporate personal, social, economic, behavioural and environmental factors, and the key domains discussed in research evidence, policy, and with public contributors included adverse childhood experiences, socioeconomics, the social and physical environment, and education. Policy recommendations more often focused on individual-level factors as opposed to the wider determinants of health discussed within the research evidence. Some domains highlighted through our co-production process with public contributors, such as religion and spirituality, health screening and check-ups, and diet, were not adequately considered within the research evidence or policy. Conclusions: This co-produced conceptualisation can inform research directions using primary and secondary data to investigate the early-life characteristics of population groups at risk of future multimorbidity, as well as policy directions to target public health prevention scenarios of early-onset multimorbidity

Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) – protocol for a research collaboration

Background: Most people living with multiple long-term condition multimorbidity (MLTC-M) are under 65 (defined as ‘early onset’). Earlier and greater accrual of long-term conditions (LTCs) may be influenced by the timing and nature of exposure to key risk factors, wider determinants or other LTCs at different life stages. We have established a research collaboration titled ‘MELD-B’ to understand how wider determinants, sentinel conditions (the first LTC in the lifecourse) and LTC accrual sequence affect risk of early-onset, burdensome MLTC-M, and to inform prevention interventions. Aim: Our aim is to identify critical periods in the lifecourse for prevention of early-onset, burdensome MLTC-M, identified through the analysis of birth cohorts and electronic health records, including artificial intelligence (AI)-enhanced analyses. Design: We will develop deeper understanding of ‘burdensomeness’ and ‘complexity’ through a qualitative evidence synthesis and a consensus study. Using safe data environments for analyses across large, representative routine healthcare datasets and birth cohorts, we will apply AI methods to identify early-onset, burdensome MLTC-M clusters and sentinel conditions, develop semi-supervised learning to match individuals across datasets, identify determinants of burdensome clusters, and model trajectories of LTC and burden accrual. We will characterise early-life (under 18 years) risk factors for early-onset, burdensome MLTC-M and sentinel conditions. Finally, using AI and causal inference modelling, we will model potential ‘preventable moments’, defined as time periods in the life course where there is an opportunity for intervention on risk factors and early determinants to prevent the development of MLTC-M. Patient and public involvement is integrated throughout

Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for healthcare policy and decision making

Objective: Network meta-analyses have extensively been used to compare the effectiveness of multiple interventions for healthcare policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are often interested in comparing and ranking the performance of multiple diagnostic tests, at varying levels of test thresholds, in one simultaneous analysis. Study design and setting: Motivated by an example of cognitive impairment diagnosis following stroke, we synthesized data from 13 studies assessing the efficiency of two diagnostic tests: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), at two test thresholds: MMSE <25/30 and <27/30, and MoCA <22/30 and <26/30. Using Markov Chain Monte Carlo (MCMC) methods, we fitted a bivariate network meta-analysis model incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study. Results: We developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Using this model, we found that MoCA at threshold <26/30 appeared to have the best true positive rate, whilst MMSE at threshold <25/30 appeared to have the best true negative rate. Conclusion: The combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making

Severe COVID-19 outcomes after full vaccination of primary schedule and initial boosters: pooled analysis of national prospective cohort studies of 30 million individuals in England, Northern Ireland, Scotland, and Wales

BackgroundCurrent UK vaccination policy is to offer future COVID-19 booster doses to individuals at high risk of serious illness from COVID-19, but it is still uncertain which groups of the population could benefit most. In response to an urgent request from the UK Joint Committee on Vaccination and Immunisation, we aimed to identify risk factors for severe COVID-19 outcomes (ie, COVID-19-related hospitalisation or death) in individuals who had completed their primary COVID-19 vaccination schedule and had received the first booster vaccine.MethodsWe constructed prospective cohorts across all four UK nations through linkages of primary care, RT-PCR testing, vaccination, hospitalisation, and mortality data on 30 million people. We included individuals who received primary vaccine doses of BNT162b2 (tozinameran; Pfizer–BioNTech) or ChAdOx1 nCoV-19 (Oxford–AstraZeneca) vaccines in our initial analyses. We then restricted analyses to those given a BNT162b2 or mRNA-1273 (elasomeran; Moderna) booster and had a severe COVID-19 outcome between Dec 20, 2021, and Feb 28, 2022 (when the omicron (B.1.1.529) variant was dominant). We fitted time-dependent Poisson regression models and calculated adjusted rate ratios (aRRs) and 95% CIs for the associations between risk factors and COVID-19-related hospitalisation or death. We adjusted for a range of potential covariates, including age, sex, comorbidities, and previous SARS-CoV-2 infection. Stratified analyses were conducted by vaccine type. We then did pooled analyses across UK nations using fixed-effect meta-analyses.FindingsBetween Dec 8, 2020, and Feb 28, 2022, 16 208 600 individuals completed their primary vaccine schedule and 13 836 390 individuals received a booster dose. Between Dec 20, 2021, and Feb 28, 2022, 59 510 (0·4%) of the primary vaccine group and 26 100 (0·2%) of those who received their booster had severe COVID-19 outcomes. The risk of severe COVID-19 outcomes reduced after receiving the booster (rate change: 8·8 events per 1000 person-years to 7·6 events per 1000 person-years). Older adults (≥80 years vs 18–49 years; aRR 3·60 [95% CI 3·45–3·75]), those with comorbidities (≥5 comorbidities vs none; 9·51 [9·07–9·97]), being male (male vs female; 1·23 [1·20–1·26]), and those with certain underlying health conditions—in particular, individuals receiving immunosuppressants (yes vs no; 5·80 [5·53–6·09])—and those with chronic kidney disease (stage 5 vs no; 3·71 [2·90–4·74]) remained at high risk despite the initial booster. Individuals with a history of COVID-19 infection were at reduced risk (infected ≥9 months before booster dose vs no previous infection; aRR 0·41 [95% CI 0·29–0·58]).InterpretationOlder people, those with multimorbidity, and those with specific underlying health conditions remain at increased risk of COVID-19 hospitalisation and death after the initial vaccine booster and should, therefore, be prioritised for additional boosters, including novel optimised versions, and the increasing array of COVID-19 therapeutics

Diagnostic test accuracy for COVID-19: systematic review and meta-analysis protocol.

Objective Accurate diagnosis of COVID-19 infection is paramount to initiating appropriate measures for reducing spread. We aim to conduct a systematic review and meta-analysis, augmented by linked electronic health records, to assess the diagnostic test accuracy for COVID-19. Approach We will search the following databases from November 2019 to February 2022: MEDLINE (PubMed), Embase, and Scopus, as well as reference lists of eligible studies and review articles. Keywords will relate to COVID-19 and diagnostic testing. Eligible studies will use an appropriate study design (e.g. prospective and retrospective cohort and case-control) to assess the accuracy of any COVID-19 diagnostic test (including thoracic imaging, mass spectrometry, and serological tests) in all healthcare and community settings. Studies of participants under 18 will be excluded. Data will be extracted using a piloted extraction form and bias will be assessed using the QUADAS-2 tool. Results Main outcomes will include frequency statistics, sensitivity and specificity, and positive and negative predictive value. Paired forest plots will be used to illustrate sensitivity and specificity across studies. We will pool data on sensitivity and specificity in a Bayesian framework using a bivariate random-effects logistic regression model, where appropriate. Uncertainty in the estimates will be represented using 95% credible intervals. A comparative framework will be developed to allow assessment of the comparative accuracy of diagnostic tests. Subgroup analyses will be undertaken for time since onset of symptoms, setting (including community and secondary care testing), and reference standard, where appropriate. Conclusion Results of this review will be combined with routinely collected electronic health records from the DECOVID database to inform relationships between tests and subgroups for healthcare decision-making. New methodology developed as part of this review will be generalizable to the evaluation of diagnostic test accuracy in other diseases

Whose story is it? Mental health consumer and carer views on carer participation in research

Abstract BACKGROUND: Mental health carers contribute a unique set of perspectives and lived experiences to research; however, national research ethics guidelines do not specifically address the issues that affect informal carers as participants. OBJECTIVE: This study sought to explore Australian mental health consumer and carer views on the ethical conduct of research involving mental health carers. DESIGN: A public forum (n = 14; consumer = 5, carer = 9) and a subsequent series of interviews (n = 10; consumer = 5, carer = 4, both = 1) were conducted to investigate consumer and carer views on mental health research ethics. Data collection and analysis drew strongly on methodological features of grounded theory. RESULTS: Conducting research involving carers and consumer-carer relationships raises potential concerns related to story ownership. Lived experience stories have shared and separate elements; thus, it is important to consider potential risks to the privacy of non-participants and of social harm to participants' relationships when conducting research in this space. These risks could be minimized and managed through communication between researchers and participants, and within relationships. CONCLUSIONS: When conducting research involving carers and consumer-carer relationships, researchers may need to facilitate the negotiation of information-sharing boundaries within relationships and the safe and confidential telling of shared stories.ACACIA is supported by ACT Health [contract numbers 2013.21920.590, 2015.27504.340]. At the time of the research, MB was supported by Australian Research Council Discovery Early Career Researcher Award [DE150100637]

The Hospital Frailty Risk Score (HFRS) applied to primary data: protocol for a systematic review

Introduction Frailty is characterised by vulnerability to adverse health outcomes and increases with age. Many frailty risk scores have been developed. One important example is the Hospital Frailty Risk Score (HFRS) which has the potential to be widely used and automatically calculated which will provide accurate assessment of frailty in a time/cost-effective manner. This systematic review, therefore, seeks to describe the HFRS use since its publication in 2018. Methods and analysis The proposed systematic review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We will include published original peer-reviewed articles, preprints, conference proceedings and letters to the editor reporting primary data where there is an English language abstract available from 1 January 2018 to 30 June 2022. Databases to be searched are MEDLINE, EMBASE and Web of Science. Additional studies from, for example, the reference of the included studies will be identified and assessed for potential inclusion. Two independent reviewers will perform and assess the following: (1) eligibility of the included studies, (2) critical appraisal using the Cochrane Risk of Bias in Non-randomized Studies of Interventions tool, and (3) data extraction using a predefined form. Disagreements will be resolved through discussions or by involvement of a third reviewer. It may be possible to undertake a meta-analysis if there are sufficient studies reporting effect measures in homogenous populations and/or settings. Effect sizes will be calculated using meta-analysis methods and expressed as risk ratios or ORs with 95% CIs. Ethics and dissemination No ethical approval is required for this systematic review as it will use secondary data only. The results of the systematic review will be submitted for publication in recognised peer-reviewed journals related to frailty and geriatric care and will be widely disseminated through conferences, congresses, seminars, symposia and scientific meetings

Direct and indirect effects of the COVID-19 pandemic on mortality: an individual-level population-scale analysis.

Objectives The COVID-19 pandemic has had a detrimental impact on healthcare utilisation, resulting in increased mortality both directly and indirectly associated with COVID-19. We aimed to assess the impact of the COVID-19 pandemic on all-cause and disease-specific mortality and further explore the impact of potential inequalities, deprivation status and ethnicity. Approach Population-scale, individual-level, anonymised linked, routinely-collected electronic health records from demographic and administrative sources were used for two cohorts: i) C19-COHORT16 included individuals alive and resident in Wales on the 1st January 2016 with follow-up until death, break-in Welsh residency, or 31st December 2019; ii) C19-COHORT20 included individuals alive and resident in Wales on 1st January 2020 with follow-up until death, break-in Welsh residency, or study end. We used time-series analysis to investigate trends in mortality over time. We fitted negative binomial models to estimate expected all-cause and disease-specific mortality and compared these estimates to observed mortality in C19-COHORT20. Results Excess all-cause and COVID19-related deaths were higher during the period where the alpha variant was dominant. The trend in deaths decreased during the omicron dominant period. The Asian population had increased mortality during the period where the delta variant was dominant. Mortality was increased for most deprived groups compared to least deprived groups, however, the magnitude of this effect remained unchanged during the pandemic. COVID-19 indirectly affected cancer, circulatory, trauma, digestive and mental health related deaths, with a higher than expected mortality. The majority of trauma related deaths occurred early on in the pandemic, where a higher than expected number of deaths occurred outside of an NHS establishment. Mortality associated with respiratory disease (unrelated to COVID-19) was significantly lower than expected during the COVID-19 pandemic. Conclusion Increased all-cause and disease-specific mortality was observed during the COVID-19 pandemic. Excess deaths may be a result of reduced healthcare utilisation, delayed investigation and/or treatment of chronic diseases. As healthcare systems recover from COVID-19, investigation of mortality trends will play a central role in healthcare planning, utilisation and resource use