Abstract Background Internalizing disorders and cardiometabolic disease are common conditions that frequently co-occur in later life and may be attributed to shared genetic influences. While phenotypic effects of polygenic liability of adult disorders may emerge early in life, studies have not investigated this in the context of multimorbidity. This study set out to investigate early manifestations of polygenic liability to adult internalizing-cardiometabolic multimorbidity (ICM-MM) in a UK population birth cohort. Methods We used data from 5,821 individuals in the Avon Longitudinal Study of Parents and Children (ALSPAC). We modelled trajectories of 12 mental and cardiometabolic health outcomes using mixed effects models, and investigated effects of adult ICM-MM polygenic liability on these trajectories. We also investigated associations of adult ICM-MM polygenic liability with circulating inflammatory proteins (Olink Target 96 Inflammation panel) at ages 9 and 24. Results Adult ICM-MM polygenic liability is associated with cardiometabolic traits and inflammation, and with changes in depressive symptoms and cardiometabolic traits over time in childhood through to early adulthood. A notable early life biological footprint is inflammation. We found that higher ICM-MM polygenic liability is consistently associated with higher interleukin-6 (IL6), tumor necrosis family superfamily member 14 (TNFSF14) and hepatocyte growth factor (HGF) levels in both childhood and early adulthood. Conclusions Adult ICM-MM polygenic liability manifests early in life through changes in mental and cardiometabolic health and blood biomarkers, especially in increases of circulating inflammatory proteins related to obesity, immune cell chemotaxis and migration that may contribute to disease pathogenesis by seeding inflammation in relevant tissues.

BACKGROUND:UK South Asian populations are at high risk of physical and mental health multimorbidity, which means they live with multiple long-term conditions. The life course emergence of multimorbidity, its underlying aetiology, and consequences for future health and mortality have yet to be studied in this population. METHODS AND FINDINGS:We studied Internalising (depression, anxiety, somatoform disorders) and Cardiometabolic (hypertension, obesity, type 2 diabetes, chronic kidney disease, dyslipidaemia) MultiMorbidity (ICM-MM): the lifetime occurrence of ≥1 internalising mental health condition AND ≥1 cardiometabolic condition in a longitudinal cohort of Genes and Health study participants with linked genetic and health data from 1st April 1997-24th November 2024. We used multi-state models to investigate trajectories in ICM-MM and risk of major cardiovascular or renal events (CVR) or non-CVR death. We used flexible parametric models to estimate baseline hazards for health state transitions, adjusting for sociodemographic factors, a polygenic risk score (PRS) for ICM-MM (ICM-MMPRS), and describe 10-year simulated health state probabilities. Over 10.2 years median follow-up of 23,554 British Bangladeshi and Pakistani participants (median baseline age 31.1 years, 12,934 [54.9%] women), 3,159 (13.4%) developed ICM-MM; 1,522 (6.5%) CVR; and there were 103 (0.4%) non-CVR deaths. Women were less likely to remain healthy, with higher probability of developing internalising conditions and subsequent ICM-MM, but lower risk of CVR than men. Younger age was associated with higher risk of developing internalising conditions. Bangladeshi ethnicity, higher deprivation, and smoking were all associated with higher probability of ICM-MM. 10-year CVR risk was highest for people who developed ICM-MM via the trajectory cardiometabolic-to-internalising (versus internalising-to-cardiometabolic) in mid-life (age 40). Higher ICM-MMPRS was associated with higher probability of ICM-MM via cardiometabolic conditions rather than internalising conditions. Our findings are based on routinely collected electronic health records from East London, which incompletely capture individual-level and time-varying risk factors, remission or recovery, and may not reflect British Bangladeshi and British Pakistani communities across the UK. The PRS was derived largely from GWAS of European ancestry populations, which may limit its transferability to this cohort. CONCLUSIONS:The burden of multimorbidity is high in British Bangladeshi and British Pakistani populations. Young Bangladeshi women are at high risk of ICM-MM, while men are at higher risk of CVR. Detection and intervention strategies for physical and mental health multimorbidity should be targeted early in the lifecourse, for those at highest risk.

BACKGROUND:Multimorbidity, also known as multiple long-term conditions, is a major public health concern. Internalising and CardioMetabolic MultiMorbidity (ICM-MM) is a common form of mental-physical health multimorbidity, yet its genetic predisposition is largely unknown. We examined the polygenic nature of ICM-MM by assessing single trait-specific polygenic risk scores (PRSTRAIT) and whether combining them could increase the proportion of variance in liability to ICM-MM explained by genetic variation. METHODS:We developed PRSTRAIT using PRS-CS and summary statistics from the largest trait-specific GWAS excluding UK Biobank (UKB). We evaluated PRSTRAIT on ICM-MM risk in 206 452 UKB participants (n = 39 311 (19.0%) with ICM-MM) using logistic regression adjusted for gender and 10 genetic principal components, defining ICM-MM as lifetime occurrence of: ≥1 internalising (depression, anxiety, somatoform disorder) traits AND ≥ 1 cardiometabolic traits (type 2 diabetes, obesity, hypertension, dyslipidemia, chronic kidney disease). We used elastic net regression in a 50% training sample to generate ICM-MM-PRSTRAIT: a weighted combination of PRSTRAIT targeting ICM-MM. RESULTS:The strongest associations were between ICM-MM and PRSTRAIT for depression and type 2 diabetes-both odds ratios (OR) 1.18, [95% confidence interval (CI) 1.17-1.20] per standard deviation increase in PRSTRAIT. ICM-MM-PRSTRAIT retained five PRSTRAIT, with stronger associations (OR = 1.31, [95%CI 1.29-1.34]) than any PRSTRAIT in the testing sample. DISCUSSION:Combining several PRS explains more variance in ICM-MM liability than single-trait PRSs alone. ICM-MM-PRSTRAIT is a measure of genetic risk that could be used to examine premorbid stages of ICM-MM in external and youth cohorts, supporting awareness of earlier presentation and potentially avoidance or intervention.

Internalising and CardioMetabolic MultiMorbidity (ICM-MM) is a common form of mental-physical health multimorbidity, yet its genetic predisposition is largely unknown. We examined the polygenic nature of ICM-MM by assessing single trait-specific polygenic risk scores (PRS TRAIT ) and whether combining them could increase the proportion of variance in liability to ICM-MM explained by genetic variation. We developed PRS TRAIT using PRS-CS and summary statistics from the largest trait-specific GWAS excluding UK Biobank (UKB). We evaluated PRS TRAIT on ICM-MM risk in 206,452 UKB participants (n=39,311 (19.0%) with ICM-MM) using logistic regression adjusted for gender and 10 genetic principal components, defining ICM-MM as lifetime occurrence of: ≥1 internalising (depression, anxiety, somatoform disorder) traits AND ≥1 cardiometabolic traits (type 2 diabetes, obesity, hypertension, dyslipidemia, chronic kidney disease). We trained an elastic net in a 50% subsample to generate ICM-MM-PRS TRAIT : a weighted combination of PRS TRAIT targeting ICM-MM. The strongest associations were between ICM-MM and PRS TRAIT for depression and type 2 diabetes - both odds ratios (OR) 1.18, [95% confidence interval (CI) 1.17–1.20] per standard deviation increase in PRS TRAIT . ICM-MM-PRS TRAIT retained five PRS TRAIT with stronger associations (OR=1.31, [95%CI 1.29–1.34]) than any PRS TRAIT in the validation sample. Combining several PRS explains more variance in ICM-MM liability than single-trait PRSs alone. ICM-MM-PRS TRAIT is a measure of genetic risk that could be used to examine premorbid stages of ICM-MM in external and youth cohorts, supporting awareness of earlier presentation and potentially avoidance or intervention.

Background Depression is associated with alterations in immuno-metabolic biomarkers, but it remains unclear whether these alterations are limited to specific markers, and whether there are subtypes of depression and depressive symptoms which are associated with specific patterns of immuno-metabolic dysfunction. Methods To investigate whether immuno-metabolic biomarkers could be used to profile subtypes of depression, we applied regression, clustering, and machine learning to a dataset comprising depression diagnosis, depressive and anxiety symptoms, and blood-based immunological and metabolic biomarkers (n = 118). We measured inflammatory proteins, cell counts, lipids, hormones, and metabolites from up to n = 4161 participants (2363 female, 337 with depression) aged 24 years from the Avon Longitudinal Study of Parents and Children birth cohort. Results Depression at age 24 was associated with both altered concentrations of immuno-metabolic markers, and increased extreme-valued inflammatory markers. Inflammatory and metabolic biomarkers show distinct, opposing associations with somatic and anxiety symptoms. We identified two latent components representing the relationship between blood biomarkers, symptoms, and covariates, one characterised by higher somatic symptoms and inflammatory markers (neutrophils, WBC, IL-6), and the other characterised by higher anxiety and worry and lower inflammatory markers (CRP, WBC, IL-6). Individuals with higher somatic-inflammatory component scores had greater depressive symptoms severity over the next five years. Immuno-metabolic biomarkers predicted depression diagnosis (Balanced Accuracy = 0.580) and depression with high somatic symptoms (Balanced Accuracy = 0.575) better than chance, but not depression with high anxiety symptoms (Balanced Accuracy = 0.479). Conclusions Alterations in immuno-metabolic homeostasis is present in young adults with depression well before the typical age of onset of cardiometabolic diseases. The relationships between affective symptoms and blood immuno-metabolic biomarkers indicate two biotypes of depressive symptoms (somatic-inflamed vs anxious-non-inflamed). These patterns are relevant for prognosis and prediction, highlighting the potential usefulness of immuno-metabolic biomarkers for depression subtyping.

Depression is associated with immunological and metabolic alterations, but immunometabolic characteristics of developmental trajectories of depressive symptoms remain unclear. Studies of longitudinal trends of depressive symptoms in young people could provide insight into aetiological mechanisms and heterogeneity behind depression, and origins of possible common cardiometabolic comorbidities for depression. Using depressive symptoms scores measured on 10 occasions between ages 10 and 25 years in the Avon Longitudinal Study of Parents and Children (n = 7302), we identified four distinct trajectories: low-stable (70% of the sample), adolescent-limited (13%), adulthood-onset (10%) and adolescent-persistent (7%). We examined associations of these trajectories with: i) anthropometric, cardiometabolic and psychiatric phenotypes using multivariable regression (n = 1565-2828); ii) 67 blood immunological proteins and 57 metabolomic features using empirical Bayes moderated linear models (n = 2059 and n = 2240 respectively); and iii) 28 blood cell counts and biochemical measures using multivariable regression (n = 2246). Relative to the low-stable group, risk of depression and anxiety in adulthood was higher for all other groups, especially in the adolescent-persistent (RRdepression=13.11, 95% CI 9.59-17.90; RRGAD = 11.77, 95% CI 8.58-16.14) and adulthood-onset (RRdepression=6.25, 95% CI 4.50-8.68; RRGAD = 4.66, 95% CI 3.29-6.60) groups. The three depression-related trajectories vary in their immunometabolic profile, with evidence of little or no alterations in the adolescent-limited group. The adulthood-onset group shows widespread classical immunometabolic changes (e.g., increased immune cell counts and insulin resistance), while the adolescent-persistent group is characterised by higher BMI both in childhood and adulthood with few other immunometabolic changes. These findings point to distinct mechanisms and prevention opportunities for adverse cardiometabolic profile in different groups of young people with depression.

Abstract Background Depressive symptoms in childhood and adolescence are associated with an elevated risk of depression and other psychiatric diagnoses in adulthood. Therefore, delineating developmental trajectories of depressive symptoms can inform strategies for early identification and prevention. Depression in adults is associated with immunometabolic alterations and disease; however, the immunometabolic signature of developmental trajectories of depressive symptoms remains unknown. Examining a potentially broad biosignature of these trajectories can shed light on markers of and dysregulations in biological pathways that may be involved in disorder pathogenesis, persistence, and help identify predictive biomarkers. Aims & Objective This study aims to characterise developmental trajectories of depressive symptoms from adolescence to early adulthood and to identify proteomic, metabolomic and biochemical biomarkers associated with these trajectories. Method Depressive symptoms were assessed in the Avon Longitudinal Study of Parents and Children (ALSPAC) [1, 2] using the 13-item Short Mood and Feelings Questionnaire [3] across 10 time points between 10 and 25 years. Blood samples were collected at face-to-face examinations at 24 years of age; circulating proteins were measured using the Target 96 Inflammation panel (Olink Analysis Service, Uppsala, Sweden) and metabolites using a 1H-NMR spectroscopy-based platform (Nightingale Health, Helsinki, Finland). In addition, full blood count and blood biochemistry tests were conducted. We characterised depressive symptom trajectories in a subsample with data from at least three time points (n=7302) using latent class trajectory modelling, and then estimated posterior probabilities and trajectory class membership in the full sample (n=9595 where estimation was possible). We examined associations between trajectory class membership with 67 inflammation proteins and 71 metabolites using linear models with empirical Bayes moderation and with 28 blood count and biochemical measures using linear regressions (n=2256). Results We identified four distinct depressive symptom trajectories – stable-low (69.6%), adolescent-limited (13.3%), adolescent-persistent (7.0%), and adulthood-onset (10.0%). Relative to the stable-low trajectory class, distinct immunometabolic profiles were identified for the three ‘atypical’ trajectory classes, with some overlap in proteomic markers observed between the adolescent-persistent and adulthood-onset classes (fibroblast growth factor 21 [FGF-21], hepatic growth factor [HGF] and eotaxin [CCL11]). The adulthood-onset class showed widespread immunometabolic alterations, including elevated ApoB/ApoA1 ratio, insulin and neutrophils among others. Discussion & Conclusion We show that distinct developmental trajectories of depressive symptoms exist from adolescence to early adulthood, which are characterised by distinct immunometabolic changes. In particular, our findings suggest there are widespread immunometabolic alterations in the adulthood-onset class, including raised systemic inflammation and disruptions in glucose and lipid metabolism, which is consistent with an elevated cardiometabolic risk profile. These results provide some insights into markers of and biological pathways that may play a role in the emergence of depressive symptoms. These results may have implications on preventive and early intervention strategies. References 1.Boyd, A., Golding, J., Macleod, J., Lawlor, D. A., Fraser, A., Henderson, J. et al. (2013). Cohort Profile: The ‘Children of the 90s’— the index offspring of the Avon Longitudinal Study of Parents and Children. International Journal of Epidemiology, 42(1), 111-127. 2.Northstone, K., Lewcock, M., Groom, A., Boyd, A., Macleod, J., Timpson, N. et al. (2019). The Avon Longitudinal Study of Parents and Children (ALSPAC): an update on the enrolled sample of index children in 2019 [version 1; peer review: 2 approved]. Wellcome Open Research, 4(51). 3.Angold, A., Costello, E. J., Messer, S. C., Pickles, A. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5(4), 237-249.

Background: Electronic health care databases are widely used for epidemiological studies. However, they may contain inactive records of individuals no longer participating in the health care system. These inactive records create a methodological challenge as they systematically appear as unexposed with no recorded outcomes. Given the widespread health care system engagement during the COVID-19 pandemic, the English National Health Service (NHS), which hosts a national pandemic planning and research dataset with linkage to COVID-19 vaccination and emergency care data, makes it an ideal setting to identify the extent of overrepresentation due to inactive health care records and assess ways to mitigate them. Objective: The objective of this study is to report any differences between the general practitioner-registered adult population size based on health care records compared to census estimates for England and to apply methodology that could be used to correct for such differences. Methods: We compared the number of adult patients within the General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) with a valid general practitioner registration as of 1st October 2021, with estimates published by the Office for National Statistics (ONS) for the English population. We used an approach adapted from a weighting method to correct for non-response bias in surveys and down-weighted individuals with no evidence of recent activity in their records. Results: There were 61,194,033 registered NHS patients (in the GDPPR) compared with 56,550,138 in the ONS census-based population. De-duplication on NHS number reduced the population to 57,876,641, including 46,835,968 adults, with the biggest overrepresented group aged 30-45 years. Of the 46,835,986, 1,121,954 (2.4%) individuals had their initial weights down-weighted due to non-engagement with the health care system since January 2019. The down-weighting removed most of the differences between NHS and ONS populations. Conclusions: There are notable differences in the adult population size as per GDPPR when compared to census estimates. While the overall population size in the GDPPR data was seen to be inflated when compared to ONS census estimates, this was differential with respect to sociodemographic variables. A weighting-based approach can be applied to correct for the inflated denominator. Not correcting for it in large health care datasets, including the English NHS data, could introduce selection bias in epidemiological studies.

Psychotic disorder is associated with altered levels of various inflammatory markers in blood, but existing studies have typically focused on a few selected biomarkers, have not examined specific symptom domains notably negative symptoms, and are based on individuals with established/chronic illness. Based on data from young people aged 24 years from the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK birth cohort, we have examined the associations of 67 plasma immune/inflammatory proteins assayed using the Olink Target 96 Inflammation panel with psychotic disorder, positive (any psychotic experiences and definite psychotic experiences) and negative symptoms, using linear models with empirical Bayes estimation. The analyses included between 2317 and 2854 individuals. After adjustment for age, sex, body mass index and smoking and correction for multiple testing, positive symptoms and psychotic disorder were consistently associated with upregulation of CDCP1 and IL-6, and psychotic disorder was additionally associated with upregulation of MMP-10. Negative symptoms were associated with upregulation of CDCP1 and TRAIL. CDCP1 and MMP-10 are novel markers of psychosis identified in this study, and are involved in immune regulation, immune cell activation/migration, blood-brain barrier disruption, and extracellular matrix abnormalities. Our findings highlight psychosis symptom domains have overlapping and distinct immune associations, and support a role of inflammation and immune dysfunction in the pathogenesis of psychosis.

Depression is a complex and multifactorial disorder that has genetic and environmental influences. Genome-wide association studies have shown that common genetic variants are implicated in depression. These common variants, when combined into polygenic risk scores, are associated with depression case status, severity and age of onset. However, less is known about how genetic risk affects change in depression symptoms longitudinally. Furthermore, psychiatric disorders are comorbid and recent studies have shown genetic risk is shared between them, but the association between this shared polygenic risk and how depression manifests and changes over time is not yet understood.We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC). Self-reported depressive symptoms were assessed on 10 occasions between the ages of 10 and 25 using the 13-item Short Mood and Feelings Questionnaire. Polygenic risk scores (PRS) for major depressive disorder (MDD), anxiety (ANX), neuroticism (NEU), and schizophrenia (SCZ) were computed with PRSice-2 using summary statistics from recent genome-wide associations studies, in which ALSPAC was not included. Additionally, we used genomic structural equation modelling (GSEM) to create a multi-trait PRS of MDD, ANX, NEU, SCZ, bipolar disorder, autism spectrum disorder, and attention deficit hyperactivity disorder, to capture the spectrum of psychopathology and explored how this genetic risk score was associated with depression trajectories.We modelled depression trajectories using generalised additive models, with age as the time metric, and included sex, age-sex interaction, PRS, age-PRS interaction, and the first 10 principal components as predictors. We ran separate models for each PRS.Depression trajectories for those in the top and bottom deciles of MDD and NEU PRS start to show divergence around mid- to late-adolescence with higher genetic risk associated with worse trajectories. With the multi-trait PRS, differences emerge as early as childhood, again with higher genetic risk indicative of worse trajectories. In these three models, the separation between the trajectories then continues to increase into adulthood. No clear pattern of separation was observed with the ANX or SCZ PRS.These findings suggest that psychiatric PRS are associate with (and may influence) the longitudinal course of depressive symptoms from childhood into early adulthood. The multi-trait PRS was superior to PRS of individual psychiatricdisorders in delineating depression trajectories in association with genetic risk. One interpretation is that a spectrum of psychiatric genetic risk could underpin developmental differences in depression trajectories.

Background People with multiple health conditions are more likely to have poorer health outcomes and greater care and service needs; a reliable measure of multimorbidity would inform management strategies and resource allocation. Aim To develop and validate a modified version of the Cambridge Multimorbidity Score in an extended age range, using clinical terms that are routinely used in electronic health records across the world (Systematized Nomenclature of Medicine — Clinical Terms, SNOMED CT). Design and setting Observational study using diagnosis and prescriptions data from an English primary care sentinel surveillance network between 2014 and 2019. Method In this study new variables describing 37 health conditions were curated and the associations modelled between these and 1-year mortality risk using the Cox proportional hazard model in a development dataset ( n = 300 000). Two simplified models were then developed — a 20-condition model as per the original Cambridge Multimorbidity Score and a variable reduction model using backward elimination with Akaike information criterion as the stopping criterion. The results were compared and validated for 1-year mortality in a synchronous validation dataset ( n = 150 000), and for 1-year and 5-year mortality in an asynchronous validation dataset ( n = 150 000). Results The final variable reduction model retained 21 conditions, and the conditions mostly overlapped with those in the 20-condition model. The model performed similarly to the 37- and 20-condition models, showing high discrimination and good calibration following recalibration. Conclusion This modified version of the Cambridge Multimorbidity Score allows reliable estimation using clinical terms that can be applied internationally across multiple healthcare settings.

Background Several SARS-CoV-2 vaccines have been shown to provide protection against COVID-19 hospitalization and death. However, some evidence suggests that notable waning in effectiveness against these outcomes occurs within months of vaccination. We undertook a pooled analysis across the four nations of the UK to investigate waning in vaccine effectiveness (VE) and relative vaccine effectiveness (rVE) against severe COVID-19 outcomes. Methods We carried out a target trial design for first/second doses of ChAdOx1(Oxford-AstraZeneca) and BNT162b2 (Pfizer-BioNTech) with a composite outcome of COVID-19 hospitalization or death over the period 8 December 2020 to 30 June 2021. Exposure groups were matched by age, local authority area and propensity for vaccination. We pooled event counts across the four UK nations. Results For Doses 1 and 2 of ChAdOx1 and Dose 1 of BNT162b2, VE/rVE reached zero by approximately Days 60-80 and then went negative. By Day 70, VE/rVE was -25% (95% CI: -80 to 14) and 10% (95% CI: -32 to 39) for Doses 1 and 2 of ChAdOx1, respectively, and 42% (95% CI: 9 to 64) and 53% (95% CI: 26 to 70) for Doses 1 and 2 of BNT162b2, respectively. rVE for Dose 2 of BNT162b2 remained above zero throughout and reached 46% (95% CI: 13 to 67) after 98 days of follow-up. Conclusions We found strong evidence of waning in VE/rVE for Doses 1 and 2 of ChAdOx1, as well as Dose 1 of BNT162b2. This evidence may be used to inform policies on timings of additional doses of vaccine.

Background Post-authorisation vaccine safety surveillance is well established for reporting common adverse events of interest (AEIs) following influenza vaccines, but not for COVID-19 vaccines. Aim To estimate the incidence of AEIs presenting to primary care following COVID-19 vaccination in England, and report safety profile differences between vaccine brands. Methods We 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. Results Within 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). Conclusion COVID-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.

OBJECTIVES To estimate the incidence of adverse events of interest (AEIs) after receiving their first and second doses of coronavirus disease 2019 (COVID-19) vaccinations, and to report the safety profile differences between the different COVID-19 vaccines. DESIGN We used a self-controlled case series design to estimate the relative incidence (RI) of AEIs reported to the Oxford-Royal College of General Practitioners national sentinel network. We compared the AEIs that occurred seven days before and after receiving the COVID-19 vaccinations to background levels between 1 October 2020 and 12 September 2021. SETTING England, UK. PARTICIPANTS Individuals experiencing AEIs after receiving first and second doses of COVID-19 vaccines. MAIN OUTCOME MEASURES AEIs determined based on events reported in clinical trials and in primary care during post-license surveillance. RESULTS A total of 7,952,861 individuals were vaccinated with COVID-19 vaccines within the study period. Among them, 781,200 individuals (9.82%) presented to general practice with 1,482,273 AEIs. Within the first seven days post-vaccination, 4.85% of all the AEIs were reported. There was a 3-7% decrease in the overall RI of AEIs in the seven days after receiving both doses of Pfizer-BioNTech BNT162b2 (RI = 0.93; 95% CI: 0.91-0.94) and 0.96; 95% CI: 0.94-0.98), respectively) and Oxford-AstraZeneca ChAdOx1 (RI = 0.97; 95% CI: 0.95-0.98) for both doses), but a 20% increase after receiving the first dose of Moderna mRNA-1273 (RI = 1.20; 95% CI: 1.00-1.44)). CONCLUSIONS COVID-19 vaccines are associated with a small decrease in the incidence of medically attended AEIs. Sentinel networks could routinely report common AEI rates, which could contribute to reporting vaccine safety.

Background: COVID-19 vaccines have been shown to be highly effective against hospitalisation and death following COVID-19 infection. COVID-19 vaccine effectiveness estimates against severe endpoints among individuals with clinical conditions that place them at increased risk of critical disease are limited. Methods: We used English primary care medical record data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre sentinel network (N > 18 million). Data were linked to the National Immunisation Management Service database, Second Generation Surveillance System for virology test data, Hospital Episode Statistics, and death registry data. We estimated adjusted vaccine effectiveness (aVE) against COVID-19 infection followed by hospitalisation and death among individuals in specific clinical risk groups using a cohort design during the delta-dominant period. We also report mortality statistics and results from our antibody surveillance in this population. Findings: aVE against severe endpoints was high, 14-69d following a third dose aVE was 96.4% (95.1%-97.4%) and 97.9% (97.2%-98.4%) for clinically vulnerable people given a Vaxzevria and Comirnaty primary course respectively. Lower aVE was observed in the immunosuppressed group: 88.6% (79.1%-93.8%) and 91.9% (85.9%-95.4%) for Vaxzevria and Comirnaty respectively. Antibody levels were significantly lower among the immunosuppressed group than those not in this risk group across all vaccination types and doses. The standardised case fatality rate within 28 days of a positive test was 3.9/1000 in people not in risk groups, compared to 12.8/1000 in clinical risk groups. Waning aVE with time since 2nd dose was also demonstrated, for example, Comirnaty aVE against hospitalisation reduced from 96.0% (95.1-96.7%) 14-69days post-dose 2-82.9% (81.4-84.2%) 182days+ post-dose 2. Interpretation: In all clinical risk groups high levels of vaccine effectiveness against severe endpoints were seen. Reduced vaccine effectiveness was noted among the immunosuppressed group. (c) 2023 Published by Elsevier Ltd on behalf of The British Infection Association.

BACKGROUND:From September 2021, Health Care Workers (HCWs) in Wales began receiving a COVID-19 booster vaccination. This is the first dose beyond the primary vaccination schedule. Given the emergence of new variants, vaccine waning vaccine, and increasing vaccination hesitancy, there is a need to understand booster vaccine uptake and subsequent breakthrough in this high-risk population. METHODS:We conducted a prospective, national-scale, observational cohort study of HCWs in Wales using anonymised, linked data from the SAIL Databank. We analysed uptake of COVID-19 booster vaccinations from September 2021 to February 2022, with comparisons against uptake of the initial primary vaccination schedule. We also analysed booster breakthrough, in the form of PCR-confirmed SARS-Cov-2 infection, comparing to the second primary dose. Cox proportional hazard models were used to estimate associations for vaccination uptake and breakthrough regarding staff roles, socio-demographics, household composition, and other factors. RESULTS:We derived a cohort of 73,030 HCWs living in Wales (78% female, 60% 18-49 years old). Uptake was quickest amongst HCWs aged 60 + years old (aHR 2.54, 95%CI 2.45-2.63), compared with those aged 18-29. Asian HCWs had quicker uptake (aHR 1.18, 95%CI 1.14-1.22), whilst Black HCWs had slower uptake (aHR 0.67, 95%CI 0.61-0.74), compared to white HCWs. HCWs residing in the least deprived areas were slightly quicker to have received a booster dose (aHR 1.12, 95%CI 1.09-1.16), compared with those in the most deprived areas. Strongest associations with breakthrough infections were found for those living with children (aHR 1.52, 95%CI 1.41-1.63), compared to two-adult only households. HCWs aged 60 + years old were less likely to get breakthrough infections, compared to those aged 18-29 (aHR 0.42, 95%CI 0.38-0.47). CONCLUSION:Vaccination uptake was consistently lower among black HCWs, as well as those from deprived areas. Whilst breakthrough infections were highest in households with children.

Objectives: We analyzed hepatitis B surface antigen (HBsAg) screening and seropositivity within a network of 419 general practices representative of all regions of England.Methods: Information was extracted using pseudonymized registration data. Predictors of HBsAg ser-opositivity were explored in models that considered age, gender, ethnicity, time at the current practice, practice location and associated deprivation index, and presence of nationally endorsed screen indicators including pregnancy, men who have sex with men (MSM), history of injecting drug use (IDU), close HBV contact or imprisonment, and diagnosis of blood-borne or sexually transmitted infections.Results: Among 6,975,119 individuals, 192,639 (2.8 %) had a screening record, including 3.6-38.6 % of those with a screen indicator, and 8065 (0.12 %) had a seropositive record. The odds of seropositivity were highest in London, in the most deprived neighborhoods, among minority ethnic groups, and in people with screen indicators. Seroprevalence exceeded 1 % in people from high-prevalence countries, MSM, close HBV con-tacts, and people with a history of IDU or a recorded diagnosis of HIV, HCV, or syphilis. Overall, 1989/8065 (24.7 %) had a recorded referral to specialist hepatitis care.Conclusions: In England, HBV infection is associated with poverty. There are unrealized opportunities to promote access to diagnosis and care for those affected.(c) 2023 The Authors. Published by Elsevier Ltd on behalf of The British Infection Association. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Background COVID-19 vaccines approved in the UK are highly effective in general population cohorts, however, data on effectiveness amongst individuals with clinical conditions that place them at increased risk of severe disease are limited. Methods We used GP electronic health record data, sentinel virology swabbing and antibody testing within a cohort of 712 general practices across England to estimate vaccine antibody response and vaccine effectiveness against medically attended COVID-19 amongst individuals in clinical risk groups using cohort and test-negative case control designs. Findings There was no reduction in S-antibody positivity in most clinical risk groups, however reduced S-antibody positivity and response was significant in the immunosuppressed group. Reduced vaccine effectiveness against clinical disease was also noted in the immunosuppressed group; after a second dose, effectiveness was moderate (Pfizer: 59.6%, 95%CI 18.0-80.1%; AstraZeneca 60.0%, 95%CI -63.6-90.2%). Interpretation In most clinical risk groups, immune response to primary vaccination was maintained and high levels of vaccine effectiveness were seen. Reduced antibody response and vaccine effectiveness were seen after 1 dose of vaccine amongst a broad immunosuppressed group, and second dose vaccine effectiveness was moderate. These findings support maximising coverage in immunosuppressed individuals and the policy of prioritisation of this group for third doses.

BACKGROUND:The Data and Connectivity COVID-19 Vaccines Pharmacovigilance (DaC-VaP) UK-wide collaboration was created to monitor vaccine uptake and effectiveness and provide pharmacovigilance using routine clinical and administrative data. To monitor these, pooled analyses may be needed. However, variation in terminologies present a barrier as England uses the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), while the rest of the United Kingdom uses the Read v2 terminology in primary care. The availability of data sources is not uniform across the United Kingdom. OBJECTIVE:This study aims to use the concept mappings in the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to identify common concepts recorded and to report these in a repeated cross-sectional study. We planned to do this for vaccine coverage and 2 adverse events of interest (AEIs), cerebral venous sinus thrombosis (CVST) and anaphylaxis. We identified concept mappings to SNOMED CT, Read v2, the World Health Organization's International Classification of Disease Tenth Revision (ICD-10) terminology, and the UK Dictionary of Medicines and Devices (dm+d). METHODS:Exposures and outcomes of interest to DaC-VaP for pharmacovigilance studies were selected. Mappings of these variables to different terminologies used across the United Kingdom's devolved nations' health services were identified from the Observational Health Data Sciences and Informatics (OHDSI) Automated Terminology Harmonization, Extraction, and Normalization for Analytics (ATHENA) online browser. Lead analysts from each nation then confirmed or added to the mappings identified. These mappings were then used to report AEIs in a common format. We reported rates for windows of 0-2 and 3-28 days postvaccine every 28 days. RESULTS:We listed the mappings between Read v2, SNOMED CT, ICD-10, and dm+d. For vaccine exposure, we found clear mapping from OMOP to our clinical terminologies, though dm+d had codes not listed by OMOP at the time of searching. We found a list of CVST and anaphylaxis codes. For CVST, we had to use a broader cerebral venous thrombosis conceptual approach to include Read v2. We identified 56 SNOMED CT codes, of which we selected 47 (84%), and 15 Read v2 codes. For anaphylaxis, our refined search identified 60 SNOMED CT codes and 9 Read v2 codes, of which we selected 10 (17%) and 4 (44%), respectively, to include in our repeated cross-sectional studies. CONCLUSIONS:This approach enables the use of mappings to different terminologies within the OMOP CDM without the need to catalogue an entire database. However, Read v2 has less granular concepts than some terminologies, such as SNOMED CT. Additionally, the OMOP CDM cannot compensate for limitations in the clinical coding system. Neither Read v2 nor ICD-10 is sufficiently granular to enable CVST to be specifically flagged. Hence, any pooled analysis will have to be at the less specific level of cerebrovascular venous thrombosis. Overall, the mappings within this CDM are useful, and our method could be used for rapid collaborations where there are only a limited number of concepts to pool.