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Background and Objectives SARS-CoV-2 infection in children and young people (CYP) can lead to life threatening conditions including COVID-19, transmission to more vulnerable individuals, or even long COVID. Vaccination against COVID-19 reduces the chance of infection and transmission of the virus, but vaccine uptake in the UK has been shown to decrease with age. Approach We undertook a multistate-model approach to estimate hazard ratios on national cohorts constructed from linked health and administrative data, adjusting for several demographic factors. The models were applied to 3,433,483 CYP aged 5-17 years between 4th August 2021 and 31st May 2022. The results were combined in a random effects meta-analysis. Results Uptake of the first COVID-19 vaccine in CYP was lower compared to older age groups in the UK, and diminished further with subsequent doses (34%, 20% and 2% for 1st, 2nd, and booster dose respectively). Age of the CYP and vaccination status of the adults in the household were identified as important risk factors. For example, 5-11-year-olds were less likely to receive their first vaccine compared to 16-17-year-olds (adjusted Hazard Ratio [aHR]: 0.10 (95%CI: 0.06-0.19), and CYP in unvaccinated households were less likely to receive their first vaccine compared to partially vaccinated households (aHR: 0.19, 95%CI 0.13-0.29). Conclusions and Implications Our work highlights the need for targeted strategies to increase COVID-19 vaccine acceptance and uptake among CYP, especially considering the influence of parental consent and household factors. Further research could help identify risk factors in household types to investigate more direct associations, which may be influenced by factors such as living with vulnerable people, familial structure, or deprivation.

BackgroundNo single multimorbidity measure is validated for use in NHS (National Health Service) England’s General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR), the nationwide primary care data set created for COVID-19 pandemic research. The Cambridge Multimorbidity Score (CMMS) is a validated tool for predicting mortality risk, with 37 conditions defined by Read Codes. The GDPPR uses the more internationally used Systematized Nomenclature of Medicine clinical terms (SNOMED CT). We previously developed a modified version of the CMMS using SNOMED CT, but the number of terms for the GDPPR data set is limited making it impossible to use this version. ObjectiveWe aimed to develop and validate a modified version of CMMS using the clinical terms available for the GDPPR. MethodsWe used pseudonymized data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC), which has an extensive SNOMED CT list. From the 37 conditions in the original CMMS model, we selected conditions either with (1) high prevalence ratio (≥85%), calculated as the prevalence in the RSC data set but using the GDPPR set of SNOMED CT codes, divided by the prevalence included in the RSC SNOMED CT codes or (2) conditions with lower prevalence ratios but with high predictive value. The resulting set of conditions was included in Cox proportional hazard models to determine the 1-year mortality risk in a development data set (n=500,000) and construct a new CMMS model, following the methods for the original CMMS study, with variable reduction and parsimony, achieved by backward elimination and the Akaike information stopping criterion. Model validation involved obtaining 1-year mortality estimates for a synchronous data set (n=250,000) and 1-year and 5-year mortality estimates for an asynchronous data set (n=250,000). We compared the performance with that of the original CMMS and the modified CMMS that we previously developed using RSC data. ResultsThe initial model contained 22 conditions and our final model included 17 conditions. The conditions overlapped with those of the modified CMMS using the more extensive SNOMED CT list. For 1-year mortality, discrimination was high in both the derivation and validation data sets (Harrell C=0.92) and 5-year mortality was slightly lower (Harrell C=0.90). Calibration was reasonable following an adjustment for overfitting. The performance was similar to that of both the original and previous modified CMMS models. ConclusionsThe new modified version of the CMMS can be used on the GDPPR, a nationwide primary care data set of 54 million people, to enable adjustment for multimorbidity in predicting mortality in people in real-world vaccine effectiveness, pandemic planning, and other research studies. It requires 17 variables to produce a comparable performance with our previous modification of CMMS to enable it to be used in routine data using SNOMED CT.

Abstract Vaccines against COVID-19 and influenza can reduce the adverse outcomes caused by infections during pregnancy, but vaccine uptake among pregnant women has been suboptimal. We examined the COVID-19 and influenza vaccine uptake and disparities in pregnant women during the COVID-19 pandemic to inform vaccination interventions. We used data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre database in England and the Secure Anonymised Information Linkage Databank in Wales. The uptake of at least one dose of vaccine was 40.2% for COVID-19 and 41.8% for influenza among eligible pregnant women. We observed disparities in COVID-19 and influenza vaccine uptake, with socioeconomically deprived and ethnic minority groups showing lower vaccination rates. The suboptimal uptake of COVID-19 and influenza vaccines, especially in those from socioeconomically deprived backgrounds and Black, mixed or other ethnic groups, underscores the necessity for interventions to reduce vaccine hesitancy and enhance acceptance in pregnant women.

Abstract SARS-CoV-2 infection in children and young people (CYP) can lead to life-threatening COVID-19, transmission within households and schools, and the development of long COVID. Using linked health and administrative data, we investigated vaccine uptake among 3,433,483 CYP aged 5–17 years across all UK nations between 4th August 2021 and 31st May 2022. We constructed national cohorts and undertook multi-state modelling and meta-analysis to identify associations between demographic variables and vaccine uptake. We found that uptake of the first COVID-19 vaccine among CYP was low across all four nations compared to other age groups and diminished with subsequent doses. Age and vaccination status of adults living in the same household were identified as important risk factors associated with vaccine uptake in CYP. For example, 5–11 year-olds were less likely to receive their first vaccine compared to 16–17 year-olds (adjusted Hazard Ratio [aHR]: 0.10 (95%CI: 0.06–0.19)), and CYP in unvaccinated households were less likely to receive their first vaccine compared to CYP in partially vaccinated households (aHR: 0.19, 95%CI 0.13–0.29).

Summary: Background: UK COVID-19 vaccination policy has evolved to offering COVID-19 booster doses to individuals at increased risk of severe Illness from COVID-19. Building on our analyses of vaccine effectiveness of first, second and initial booster doses, we aimed to identify individuals at increased risk of severe outcomes (i.e., COVID-19 related hospitalisation or death) post the autumn 2022 booster dose. Methods: We undertook a national population-based cohort analysis across all four UK nations through linked primary care, vaccination, hospitalisation and mortality data. We included individuals who received autumn 2022 booster doses of BNT162b2 (Comirnaty) or mRNA-1273 (Spikevax) during the period September 1, 2022 to December 31, 2022 to investigate the risk of severe COVID-19 outcomes. Cox proportional hazard models were used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CIs) for the association between demographic and clinical factors and severe COVID-19 outcomes after the autumn booster dose. Analyses were adjusted for age, sex, body mass index (BMI), deprivation, urban/rural areas and comorbidities. Stratified analyses were conducted by vaccine type. We then conducted a fixed-effect meta-analysis to combine results across the four UK nations. Findings: Between September 1, 2022 and December 31, 2022, 7,451,890 individuals ≥18 years received an autumn booster dose. 3500 had severe COVID-19 outcomes (2.9 events per 1000 person-years). Being male (male vs female, aHR 1.41 (1.32–1.51)), older adults (≥80 years vs 18–49 years; 10.43 (8.06–13.50)), underweight (BMI