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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. [ABSTRACT FROM AUTHOR]

Objectives: We investigated SARS-CoV-2 infection trends, risk of SARS-CoV-2 infection and COVID-19 vaccination uptake among school staff, students and their household members in Wales, UK. Design: Seven-day average of SARS-CoV-2 infections and polymerase chain reaction tests per 1000 people daily, cumulative incidence of COVID-19 vaccination uptake and multi-level Poisson models with time-varying covariates. Setting: National electronic cohort between September 2020 and May 2022 when several variants were predominant in the UK (Alpha, Delta and Omicron). Participants: School students aged 4 to 10/11 years (primary school and younger middle school, n = 238,163), and 11 to 15/16 years (secondary school and older middle school, n = 182,775), school staff in Wales (n = 47,963) and the household members of students and staff (n = 697,659). Main outcome measures: SARS-CoV-2 infection and COVID-19 vaccination uptake. Results: School students had a sustained period of high infection rates compared with household members after August 2021. Primary schedule vaccination uptake was highest among staff (96.3%) but lower for household members (72.2%), secondary and older middle school students (59.8%), and primary and younger middle school students (3.3%). Multi-level Poisson models showed that vaccination was associated with a lower risk of SARS-CoV-2 infection. The Delta variant posed a greater infection risk for students than the Alpha variant. However, Omicron was a larger risk for staff and household members. Conclusions: Public health bodies should be informed of the protection COVID-19 vaccines afford, with more research being required for younger populations. Furthermore, schools require additional support in managing new, highly transmissible variants. Further research should examine the mechanisms between child deprivation and SARS-CoV-2 infection. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]