Background Vaccination is an essential public health intervention to reduce morbidity and mortality from infectious diseases. Despite being at higher at risk of infectious diseases, health inequalities towards vaccine uptake in people with mental health issues have not been systematically appraised. Methods We searched 7 databases from 1994 to 26/03/2021. We included all studies with a relative measure of effect comparing a group with a mental health issue to a control group. All studies covering any mental health issue were eligible with no constraints to study population, vaccine type or region, provided in a high-income country for comparability of health care systems. The study outcomes were synthesised by study population, mental health issue and type of vaccine. Results From 4,069 titles, 23 eligible studies from 12 different countries were identified, focusing on adults (n = 13) or children (n = 4) with mental health issues, siblings of children with mental health issues (n = 2), and mothers with mental health issue and vaccine uptake in their children (n = 6). Most studies focused on depression (n = 12), autism, anxiety, or alcoholism (n = 4 respectively). Many studies were at high risk of selection bias. Discussion Mental health issues were associated with considerably lower vaccine uptake in some contexts such as substance use disorder, but findings were heterogeneous overall and by age, mental health issue or types of vaccine. Only individuals with mental health issues and physical comorbidities had consistently higher uptake in comparison to other adults. Mental health should be considered as a health inequality for vaccine uptake but more context specific research is needed focusing more on specific mental health issues and subgroups of the population to understand who misses vaccination and why.

Routinely collected electronic health records (EHR) offer a valuable opportunity to carry out research on immunisation uptake, effectiveness and safety, using large and representative samples of the population. However, using EHR presents challenges for identifying vaccinated and unvaccinated cohorts. Some vaccinations are delivered in different care settings, so may not be fully recorded in primary care EHR. In contrast to other drugs, they do not require electronic prescription in many settings, which may lead to ambiguous coding of vaccination status and timing. Additionally, for childhood vaccination, there may be other challenges of identifying the study population eligible for vaccination due to changes in immunisation schedules over time, different vaccine indications depending on the context (e.g., tetanus vaccination after exposure) and the lack of full dates of birth in many databases of data confidentiality restrictions.In this paper, we described our approach to tackling methodological issues related to identifying childhood immunisations in the Clinical Practice Research Datalink (CPRD) Aurum, a UK primary care dataset of EHR, as an example, and we introduce a comprehensive algorithm to support high-quality studies of childhood vaccination. We showed that a broad variety of considerations is important to identify vaccines in EHR and offer guidance on decisions to ascertain the vaccination status, such as considering data source and delivery systems (e.g., primary or secondary care), using a wide range of medical codes in combination to identify vaccination events, and using appropriate wash-out periods and quality checks to deal with issues of over-recording and back dating in EHR.Our algorithm reproduced estimates of vaccination coverage which are comparable to official national estimates in England. This paper aims to improve transparency, quality, comparability and reproducibility of studies on immunisations.

Background: Acute Kidney Injury (AKI) is a multifactorial condition which presents a substantial burden to healthcare systems. There is limited evidence on whether it is seasonal. We sought to investigate the seasonality of AKI hospitalisations in England and use unsupervised machine learning to explore clustering of underlying comorbidities, to gain insights for future intervention. Methods: We used Hospital Episodes Statistics linked to the Clinical Practice Research Datalink to describe the overall incidence of AKI admissions between 2015-2019 weekly by demographic and admission characteristics. We carried out dimension reduction on 850 diagnosis codes using multiple correspondence analysis and applied k-means clustering to classify patients. We phenotype each group based on the dominant characteristics and describe the seasonality of AKI admissions by these different phenotypes. Findings: Between 2015-2019, weekly AKI admissions peaked in winter, with additional summer peaks related to periods of extreme heat. Winter seasonality was more evident in those diagnosed with AKI on admission. From the cluster classification we describe six phenotypes of people admitted to hospital with AKI. Among these, seasonality of AKI admissions was observed among people who we described as having a multimorbid phenotype, established risk factor phenotype, and general AKI phenotype. Interpretation: We demonstrate winter seasonality of AKI admissions in England, particularly among those with AKI diagnosed on admission, suggestive of community triggers. Differences in seasonality between phenotypes suggests some groups may be more likely to develop AKI as a result of these factors. This may be driven by underlying comorbidity profiles or reflect differences in uptake of seasonal interventions such as vaccines. Funding: This study was funded by the National Institute for Health and Care Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, a partnership between UK Health Security Agency (UKHSA), Imperial College London, and London School of Hygiene and Tropical Medicine. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR, UK Department of Health or UKHSA.