Bias assessment of a test-negative design study of COVID-19 vaccine effectiveness used in national policymaking.

National test-negative-case-control (TNCC) studies are used to monitor COVID-19 vaccine effectiveness in the UK. A questionnaire was sent to participants from the first published TNCC COVID-19 vaccine effectiveness study conducted by the UK Health Security Agency, to assess for potential biases and changes in behaviour related to vaccination. The original study included symptomatic adults aged ≥70 years testing for COVID-19 between 08/12/2020 and 21/02/2021. A questionnaire was sent to cases and controls tested from 1-21 February 2021. In this study, 8648 individuals responded to the questionnaire (36.5% response). Using information from the questionnaire to produce a combined estimate that accounted for all potential biases decreased the original vaccine effectiveness estimate after two doses of BNT162b2 from 88% (95% CI: 79-94%) to 85% (95% CI: 68-94%). Self-reported behaviour demonstrated minimal evidence of riskier behaviour after vaccination. These findings offer reassurance to policy makers and clinicians making decisions based on COVID-19 vaccine effectiveness TNCC studies.

Exploring the impact of mental health conditions on vaccine uptake in high-income countries: a systematic review.

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.

Household Tenure and Its Associations with Multiple Long-Term Conditions amongst Working-Age Adults in East London: A Cross-Sectional Analysis Using Linked Primary Care and Local Government Records.

Multiple long-term conditions (MLTCs) are influenced in extent and nature by social determinants of health. Few studies have explored associations between household tenure and different definitions of MLTCs. This study aimed to examine associations between household tenure and MLTCs amongst working-age adults (16 to 64 years old, inclusive). This cross-sectional study used the 2019−2020 wave of an innovative dataset that links administrative data across health and local government for residents of a deprived borough in East London. Three definitions of MLTCs were operationalised based on a list of 38 conditions. Multilevel logistic regression models were built for each outcome and adjusted for a range of health and sociodemographic factors. Compared to working-age owner-occupiers, odds of basic MLTCs were 36% higher for social housing tenants and 19% lower for private renters (OR 1.36; 95% CI 1.30−1.42; p < 0.001 and OR 0.81, 95% CI 0.77−0.84, p < 0.001, respectively). Results were consistent across different definitions of MLTCs, although associations were stronger for social housing tenants with physical-mental MLTCs. This study finds strong evidence that household tenure is associated with MLTCs, emphasising the importance of understanding household-level determinants of health. Resources to prevent and tackle MLTCs among working-age adults could be differentially targeted by tenure type.

Ethnic differences in the incidence of clinically diagnosed influenza: an England population-based cohort study 2008-2018.

Background: People of non-White ethnicity have a higher risk of severe outcomes following influenza infection. It is unclear whether this is driven by an increased risk of infection or complications. We therefore aimed to investigate the incidence of clinically diagnosed influenza/influenza-like illness (ILI) by ethnicity in England from 2008-2018. Methods: We used linked primary and secondary healthcare data (from the Clinical Practice Research Datalink [CPRD] GOLD and Aurum databases and Hospital Episodes Statistics Admitted Patient Care [HES APC]). We included patients with recorded ethnicity who were aged 40-64 years and did not have a chronic health condition that would render them eligible for influenza vaccination. ILI infection was identified from diagnostic codes in CPRD and HES APC. We calculated crude annual infection incidence rates by ethnic group. Multivariable Poisson regression models with random effects were used to estimate any ethnic disparities in infection risk. Our main analysis adjusted for age, sex, and influenza year. Results: A total of 3,735,308 adults aged 40-64 years were included in the study; 87.6% White, 5.2% South Asian, 4.2% Black, 1.9% Other, and 1.1% Mixed. We identified 102,316 ILI episodes recorded among 94,623 patients. The rate of ILI was highest in the South Asian (9.6 per 1,000 person-years), Black (8.4 per 1,000 person-years) and Mixed (6.9 per 1,000 person-years) ethnic groups. The ILI rate in the White ethnic group was 5.7 per 1,000 person-years. After adjustment for age sex and influenza year, higher incidence rate ratios (IRR) for ILI were seen for South Asian (1.70, 95% CI 1.66-1.75), Black (1.48, 1.44-1.53) and Mixed (1.22, 1.15-1.30) groups compared to White ethnicity. Conclusions: Our results suggest that influenza infection risk differs between White and non-White groups who are not eligible for routine influenza vaccination.

Use of non-steroidal anti-inflammatory drugs and risk of death from COVID-19: an OpenSAFELY cohort analysis based on two cohorts.

OBJECTIVES: To assess the association between routinely prescribed non-steroidal anti-inflammatory drugs (NSAIDs) and deaths from COVID-19 using OpenSAFELY, a secure analytical platform. METHODS: We conducted two cohort studies from 1 March to 14 June 2020. Working on behalf of National Health Service England, we used routine clinical data in England linked to death data. In study 1, we identified people with an NSAID prescription in the last 3 years from the general population. In study 2, we identified people with rheumatoid arthritis/osteoarthritis. We defined exposure as current NSAID prescription within the 4 months before 1 March 2020. We used Cox regression to estimate HRs for COVID-19 related death in people currently prescribed NSAIDs, compared with those not currently prescribed NSAIDs, accounting for age, sex, comorbidities, other medications and geographical region. RESULTS: In study 1, we included 536 423 current NSAID users and 1 927 284 non-users in the general population. We observed no evidence of difference in risk of COVID-19 related death associated with current use (HR 0.96, 95% CI 0.80 to 1.14) in the multivariable-adjusted model. In study 2, we included 1 708 781 people with rheumatoid arthritis/osteoarthritis, of whom 175 495 (10%) were current NSAID users. In the multivariable-adjusted model, we observed a lower risk of COVID-19 related death (HR 0.78, 95% CI 0.64 to 0.94) associated with current use of NSAID versus non-use. CONCLUSIONS: We found no evidence of a harmful effect of routinely prescribed NSAIDs on COVID-19 related deaths. Risks of COVID-19 do not need to influence decisions about the routine therapeutic use of NSAIDs.

A comprehensive high cost drugs dataset from the NHS in England - An OpenSAFELY-TPP Short Data Report.

Background: At the outset of the COVID-19 pandemic, there was no routine comprehensive hospital medicines data from the UK available to researchers. These records can be important for many analyses including the effect of certain medicines on the risk of severe COVID-19 outcomes. With the approval of NHS England, we set out to obtain data on one specific group of medicines, "high-cost drugs" (HCD) which are typically specialist medicines for the management of long-term conditions, prescribed by hospitals to patients. Additionally, we aimed to make these data available to all approved researchers in OpenSAFELY-TPP. This report is intended to support all studies carried out in OpenSAFELY-TPP, and those elsewhere, working with this dataset or similar data. Methods: Working with the North East Commissioning Support Unit and NHS Digital, we arranged for collation of a single national HCD dataset to help inform responses to the COVID-19 pandemic. The dataset was developed from payment submissions from hospitals to commissioners. Results: In the financial year (FY) 2018/19 there were 2.8 million submissions for 1.1 million unique patient IDs recorded in the HCD. The average number of submissions per patient over the year was 2.6. In FY 2019/20 there were 4.0 million submissions for 1.3 million unique patient IDs. The average number of submissions per patient over the year was 3.1. Of the 21 variables in the dataset, three are now available for analysis in OpenSafely-TPP: Financial year and month of drug being dispensed; drug name; and a description of the drug dispensed. Conclusions: We have described the process for sourcing a national HCD dataset, making these data available for COVID-19-related analysis through OpenSAFELY-TPP and provided information on the variables included in the dataset, data coverage and an initial descriptive analysis.

Household and area-level social determinants of multimorbidity: a systematic review.

BACKGROUND: No clear synthesis of evidence examining household and area-level social determinants of multimorbidity exists. This study aimed to systematically review the existing literature on associations between household and area-level social determinants of health (SDoH) and multimorbidity prevalence or incidence in the general population. METHODS: Six databases (MedLine, EMBASE, PsychINFO, Web of Science, CINAHL Plus and Scopus) were searched. The search was limited to peer-reviewed studies conducted in high-income countries and published in English between 2010 and 2019. A second reviewer screened all titles with abstracts and a subset of full texts. Study quality was assessed and protocol pre-registered (CRD42019135281). RESULTS: 41 studies spanning North America, Europe and Australasia were included. Household income and area-level deprivation were the most explored with fairly consistent findings. The odds of multimorbidity were up to 4.4 times higher for participants with the lowest level of income compared with the highest level. Those living in the most deprived areas had the highest prevalence or incidence of multimorbidity (pooled OR 1.42, 95% CI 1.41 to 1.42). Associations between deprivation and multimorbidity differed by age and multimorbidity type. Findings from the few studies investigating household tenure, household composition and area-level rurality were mixed and contradictory; homeownership and rurality were associated with increased and decreased multimorbidity, while living alone was found to be associated with a higher risk of multimorbidity and not associated. CONCLUSION: Improving our understanding of broader social determinants of multimorbidity-particularly at the household level-could help inform strategies to tackle multimorbidity.

Learning from Adverse Events in Obstetrics: Is a Standardized Computer Tool an Effective Strategy for Root Cause Analysis?

OBJECTIVE: Adverse events occur in up to 10% of obstetric cases, and up to one half of these could be prevented. Case reviews and root cause analysis using a structured tool may help health care providers to learn from adverse events and to identify trends and recurring systems issues. We sought to establish the reliability of a root cause analysis computer application called Standardized Clinical Outcome Review (SCOR). METHODS: We designed a mixed methods study to evaluate the effectiveness of the tool. We conducted qualitative content analysis of five charts reviewed by both the traditional obstetric quality assurance methods and the SCOR tool. We also determined inter-rater reliability by having four health care providers review the same five cases using the SCOR tool. RESULTS: The comparative qualitative review revealed that the traditional quality assurance case review process used inconsistent language and made serious, personalized recommendations for those involved in the case. In contrast, the SCOR review provided a consistent format for recommendations, a list of action points, and highlighted systems issues. The mean percentage agreement between the four reviewers for the five cases was 75%. The different health care providers completed data entry and assessment of the case in a similar way. Missing data from the chart and poor wording of questions were identified as issues affecting percentage agreement. CONCLUSION: The SCOR tool provides a standardized, objective, obstetric-specific tool for root cause analysis that may improve identification of risk factors and dissemination of action plans to prevent future events.

Objectif : Des événements indésirables se manifestent dans jusqu'à 10 % des cas obstétricaux et jusqu'à la moitié de ces événements sont évitables. Les analyses de cas et l'analyse des causes fondamentales au moyen d'un outil structuré pourraient aider les fournisseurs de soins à tirer des leçons des événements indésirables et à identifier les tendances et les problèmes systémiques récurrents. Nous avons cherché à établir la fiabilité d'un logiciel d'analyse des causes fondamentales connu sous le nom de Standardized Clinical Outcome Review (SCOR). Méthodes : Nous avons conçu une étude faisant appel à des méthodes mixtes pour évaluer l'efficacité de l'outil. Nous avons mené une analyse qualitative du contenu de cinq dossiers ayant été analysés tant au moyen des méthodes traditionnelles d'assurance de la qualité en obstétrique qu'au moyen de l'outil SCOR. Nous avons également déterminé la fidélité interévaluateurs en demandant à quatre fournisseurs de soins d'analyser les cinq mêmes dossiers au moyen de l'outil SCOR. Résultats : L'analyse qualitative comparative a révélé que le processus traditionnel d'assurance de la qualité dans le cadre de l'analyse des cas utilisait un langage hétérogène et formulait de sérieuses recommandations personnalisées à l'endroit des intervenants du dossier. En revanche, l'analyse au moyen de l'outil SCOR fournissait un format uniforme pour les recommandations et une liste de points de décision, en plus de faire ressortir les problèmes systémiques. Le taux moyen d'entente (en pourcentage) entre les quatre évaluateurs pour les cinq dossiers en question était de 75 %. Les autres fournisseurs de soins ont procédé à la saisie des données et à l'évaluation des dossiers de façon semblable. L'absence de certaines données dans les dossiers et la mauvaise formulation des questions ont été identifiées comme étant des problèmes affectant le taux d'entente. Conclusion : L'outil SCOR permet la tenue d'une analyse des causes fondamentales de façon standardisée, objective et centrée sur l'obstétrique, ce qui pourrait améliorer l'identification des facteurs de risque et la dissémination des plans d'action pour la prévention de futurs événements.

Next-generation tag sequencing for cancer gene expression profiling.

We describe a new method, Tag-seq, which employs ultra high-throughput sequencing of 21 base pair cDNA tags for sensitive and cost-effective gene expression profiling. We compared Tag-seq data to LongSAGE data and observed improved representation of several classes of rare transcripts, including transcription factors, antisense transcripts, and intronic sequences, the latter possibly representing novel exons or genes. We observed increases in the diversity, abundance, and dynamic range of such rare transcripts and took advantage of the greater dynamic range of expression to identify, in cancers and normal libraries, altered expression ratios of alternative transcript isoforms. The strand-specific information of Tag-seq reads further allowed us to detect altered expression ratios of sense and antisense (S-AS) transcripts between cancer and normal libraries. S-AS transcripts were enriched in known cancer genes, while transcript isoforms were enriched in miRNA targeting sites. We found that transcript abundance had a stronger GC-bias in LongSAGE than Tag-seq, such that AT-rich tags were less abundant than GC-rich tags in LongSAGE. Tag-seq also performed better in gene discovery, identifying >98% of genes detected by LongSAGE and profiling a distinct subset of the transcriptome characterized by AT-rich genes, which was expressed at levels below those detectable by LongSAGE. Overall, Tag-seq is sensitive to rare transcripts, has less sequence composition bias relative to LongSAGE, and allows differential expression analysis for a greater range of transcripts, including transcripts encoding important regulatory molecules.