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It is known that SARS-CoV-2 infection can result in gastrointestinal symptoms. For some, these symptoms may persist beyond acute infection, in what is known as ‘post-COVID syndrome’. We conducted a systematic review to examine the prevalence of persistent gastrointestinal symptoms and the incidence of new gastrointestinal illness following acute SARS-CoV-2 infection. We searched scientific literature using MedLine, SCOPUS, Embase, Europe PubMed Central, medRxiv and Google Scholar from December 2019 to October 2022. Two reviewers independently identified 28 eligible articles which followed participants for various gastrointestinal outcomes after acute SARS-CoV-2 infection. Study quality was assessed using the Joanna Briggs Institute Critical Appraisal Tools. The weighted pooled prevalence for persistent gastrointestinal symptom of any nature and duration was 10.7%, compared to 4.9% in healthy controls. For six studies at a low risk of methodological bias, the symptom prevalence ranged from 0.2% to 24.1% with a median follow-up time of 13 weeks. We also identified the presence of functional gastrointestinal disorders in historically SARS-CoV-2 exposed individuals. Our review has shown that, from a limited pool of mostly low-quality studies, previous SARS-CoV-2 exposure may be associated with ongoing gastrointestinal symptoms and the development of functional gastrointestinal illness. Furthermore, we show the need for high-quality research to better understand the SARS-CoV-2 association with gastrointestinal symptoms, particularly as population exposure to enteric infections returns to pre-COVID-19-restriction levels.HighlightsAcute SARS-CoV-2 infection can result in gastrointestinal symptomsThe burden of gastrointestinal illness after acute SARS-CoV-2 infection is not knownThis systematic review evaluates the evidence across 28 observational studiesMost studies identified are at high risk of bias and of low qualitySARS-CoV-2 exposure may be associated with new post-infection gastrointestinal illness

Sensitivity analysis.

NICE guidelines.

In the United Kingdom, women at moderate and high risk of breast cancer between the ages of 40 and 49 years are eligible for annual mammographic screening and preventive therapy with tamoxifen. Here, we estimate the numbers of women in a population eligible for this service and the proportion of breast cancers detected in this group compared with the whole population. Women N = 384), but the numbers of cancers in this group were not appreciably increased (N = 8). Systematic assessment of family history in primary care or through population-based screening will identify appreciable numbers of women in their forties, eligible for additional surveillance and chemoprevention Cancer Prev Res; 7(10); 993–1001. ©2014 AACR.

Background There is strong evidence for the co-occurrence of mental health conditions and alcohol problems, yet physical health outcomes among this group are not well characterised. This study aimed to identify clusters of physical health conditions and their associations with mental health and problematic alcohol use in England’s general population. Methods Cross-sectional analysis of the 2014 Adult Psychiatric Morbidity Survey (N = 7546) was conducted. The survey used standardised measures of problematic alcohol use and mental health conditions, including the Alcohol Use Disorders Identification Test (AUDIT) and the Clinical Interview Schedule-Revised. Participants self-reported any lifetime physical health conditions. Latent class analysis considered 12 common physical illnesses to identify clusters of multimorbidity. Multinomial logistic regression (adjusting for age, gender, ethnicity, education, and occupational grade) was used to explore associations between mental health, hazardous drinking (AUDIT 8 +), and co-occurring physical illnesses. Results Five clusters were identified with statistically distinct and clinically meaningful disease patterns: ‘Physically Healthy’ (76.62%), ‘Emerging Multimorbidity’ (3.12%), ‘Hypertension & Arthritis’ (14.28%), ‘Digestive & Bowel Problems’’ (3.17%), and ‘Complex Multimorbidity’ (2.8%). Having a mental health problem was associated with increased odds of ‘Digestive & Bowel Problems’ (adjusted multinomial odds ratio (AMOR) = 1.58; 95% CI [1.15–2.17]) and ‘Complex Multimorbidity’ (AMOR = 2.02; 95% CI [1.49–2.74]). Individuals with co-occurring mental health conditions and problematic alcohol use also had higher odds of ‘Digestive & Bowel Problems’ (AMOR = 2.64; 95% CI [1.68–4.15]) and ‘Complex Multimorbidity’ (AMOR = 2.62; 95% CI [1.61–4.23]). Conclusions Individuals with a mental health condition concurrent with problematic alcohol use experience a greater burden of physical illnesses, highlighting the need for timely treatment which is likely to include better integration of alcohol and mental health services.

Background The PCR quantification cycle (Cq) is a proxy measure of the viral load of a SARS-CoV-2-infected individual. Aim To investigate if Cq values vary according to different population characteristics, in particular demographic ones, and within the COVID-19 pandemic context, notably the SARS-CoV-2 type/variant individuals get infected with. Methods We considered all positive PCR results from Cheshire and Merseyside, England, between 6 November 2020 and 8 September 2021. Cq distributions were inspected with Kernel density estimates. Multivariable quantile regression models assessed associations between people’s features and Cq. Results We report Cq values for 188,821 SARS-CoV-2 positive individuals. Median Cqs increased with decreasing age for suspected wild-type virus and Alpha variant infections, but less so, if not, for Delta. For example, compared to 30–39-year-olds (median age group), 5–11-year-olds exhibited 1.8 (95% CI: 1.5 to 2.1), 2.2 (95% CI: 1.8 to 2.6) and 0.8 (95% CI: 0.6 to 0.9) higher median Cqs for suspected wild-type, Alpha and Delta positives, respectively, in multivariable analysis. 12–18-year-olds also had higher Cqs for wild-type and Alpha positives, however, not for Delta. Overall, in univariable analysis, suspected Delta positives reported 2.8 lower median Cqs than wild-type positives (95% CI: 2.7 to 2.8; p qs than wild type. Conclusions Wild-type- or Alpha-infected school-aged children (5–11-year-olds) might transmit less than adults (> 18 years old), but have greater mixing exposures. Smaller differences in viral loads with age occurred in suspected Delta infections. Suspected-Alpha- or Delta-infections involved higher viral loads than wild type, suggesting increased transmission risk. COVID-19 control strategies should consider age and dominant variant.

Objectives: No clear guidance exists on handling missing data at each stage of developing, validating and implementing a clinical prediction model (CPM). We aimed to review the approaches to handling missing data that underly the CPMs currently recommended for use in UK healthcare.Study Design and Setting: A descriptive cross-sectional meta-epidemiological study aiming to identify CPMs recommended by the National Institute for Health and Care Excellence (NICE), which summarized how missing data is handled across their pipelines.Results: A total of 23 CPMs were included through “sampling strategy.” Six missing data strategies were identified: complete case analysis (CCA), multiple imputation, imputation of mean values, k-nearest neighbours imputation, using an additional category for missingness, considering missing values as risk-factor-absent. 52% of the development articles and 48% of the validation articles did not report how missing data were handled. CCA was the most common approach used for development (40%) and validation (44%). At implementation, 57% of the CPMs required complete data entry, whilst 43% allowed missing values. Three CPMs had consistent paths in their pipelines.Conclusion: A broad variety of methods for handling missing data underly the CPMs currently recommended for use in UK healthcare. Missing data handling strategies were generally inconsistent. Better quality assurance of CPMs needs greater clarity and consistency in handling of missing data.

Background Structured Medication Reviews (SMRs) are intended to help deliver the NHS Long Term Plan for medicines optimisation in people living with multiple long-term conditions and polypharmacy. It is challenging to gather the information needed for these reviews due to poor integration of health records across providers and there is little guidance on how to identify those patients most urgently requiring review. Objective To extract information from scattered clinical records on how health and medications change over time, apply interpretable artificial intelligence (AI) approaches to predict risks of poor outcomes and overlay this information on care records to inform SMRs. We will pilot this approach in primary care prescribing audit and feedback systems, and co-design future medicines optimisation decision support systems. Design DynAIRx will target potentially problematic polypharmacy in three key multimorbidity groups, namely, people with (a) mental and physical health problems, (b) four or more long-term conditions taking ten or more drugs and (c) older age and frailty. Structured clinical data will be drawn from integrated care records (general practice, hospital, and social care) covering an ∼11m population supplemented with Natural Language Processing (NLP) of unstructured clinical text. AI systems will be trained to identify patterns of conditions, medications, tests, and clinical contacts preceding adverse events in order to identify individuals who might benefit most from an SMR. Discussion By implementing and evaluating an AI-augmented visualisation of care records in an existing prescribing audit and feedback system we will create a learning system for medicines optimisation, co-designed throughout with end-users and patients.

Purpose To prospectively validate two risk scores to predict mortality (4C Mortality) and in-hospital deterioration (4C Deterioration) among adults hospitalised with COVID-19. Methods Prospective observational cohort study of adults (age ≥18 years) with confirmed or highly suspected COVID-19 recruited into the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) WHO Clinical Characterisation Protocol UK (CCP-UK) study in 306 hospitals across England, Scotland and Wales. Patients were recruited between 27 August 2020 and 17 February 2021, with at least 4 weeks follow-up before final data extraction. The main outcome measures were discrimination and calibration of models for in-hospital deterioration (defined as any requirement of ventilatory support or critical care, or death) and mortality, incorporating predefined subgroups. Results 76 588 participants were included, of whom 27 352 (37.4%) deteriorated and 12 581 (17.4%) died. Both the 4C Mortality (0.78 (0.77 to 0.78)) and 4C Deterioration scores (pooled C-statistic 0.76 (95% CI 0.75 to 0.77)) demonstrated consistent discrimination across all nine National Health Service regions, with similar performance metrics to the original validation cohorts. Calibration remained stable (4C Mortality: pooled slope 1.09, pooled calibration-in-the-large 0.12; 4C Deterioration: 1.00, –0.04), with no need for temporal recalibration during the second UK pandemic wave of hospital admissions. Conclusion Both 4C risk stratification models demonstrate consistent performance to predict clinical deterioration and mortality in a large prospective second wave validation cohort of UK patients. Despite recent advances in the treatment and management of adults hospitalised with COVID-19, both scores can continue to inform clinical decision making. Trial registration number NCT66726260.

Introduction Recurrent pulmonary exacerbations lead to progressive lung damage in cystic fibrosis (CF). Inhaled medications (mucoactive agents and antibiotics) help prevent exacerbations, but objectively measured adherence is low. We investigated whether a multi-component (complex) self-management intervention to support adherence would reduce exacerbation rates over 12 months.Methods Between October 2017 and May 2018, adults with CF (aged ≥16 years; 19 UK centres) were randomised to the intervention (data-logging nebulisers, a digital platform and behavioural change sessions with trained clinical interventionists) or usual care (data-logging nebulisers). Outcomes included pulmonary exacerbations (primary outcome), objectively measured adherence, body mass index (BMI), lung function (FEV1) and Cystic Fibrosis Questionnaire-Revised (CFQ-R). Analyses were by intent to treat over 12 months.Results Among intervention (n=304) and usual care (n=303) participants (51% female, median age 31 years), 88% completed 12-month follow-up. Mean exacerbation rate was 1.63/year with intervention and 1.77/year with usual care (adjusted ratio 0.96; 95% CI 0.83 to 1.12; p=0.64). Adjusted mean differences (95% CI) were in favour of the intervention versus usual care for objectively measured adherence (9.5% (8.6% to 10.4%)) and BMI (0.3 (0.1 to 0.6) kg/m2), with no difference for %FEV1 (1.4 (−0.2 to 3.0)). Seven CFQ-R subscales showed no between-group difference, but treatment burden reduced for the intervention (3.9 (1.2 to 6.7) points). No intervention-related serious adverse events occurred.Conclusions While pulmonary exacerbations and FEV1 did not show statistically significant differences, the intervention achieved higher objectively measured adherence versus usual care. The adherence difference might be inadequate to influence exacerbations, though higher BMI and lower perceived CF treatment burden were observed.Data availability statementData are available on reasonable request. Requests for patient level data and statistical code should be made to the corresponding author and will be considered by members of the original trial management group, including the chief investigators and members of clinical trials research unit, who will release data on a case-by-case basis. Data will be shared following the principles for sharing patient level data as described by Tudur Smith C, et al BMC Medicine 2015;13:298 (https://doi.org/10.1186/s12916-015-0532-z). The data will not contain any direct identifiers, and we will minimise indirect identifiers and remove free-text data to minimise the risk of identification.

Aims and MethodTo evaluate cardiovascular risk in a rural inpatient psychiatric unit over a one-year period. Care records were analysed for risk factor recording, and cardiovascular risks estimated using the QRISK3 calculator, which estimates 10-year risk of myocardial infarction or stroke.ResultsOf eligible patients, risk factor recording as part of routine care was completed in 86% of possible QRISK3 inputs, enabling QIRSK3 estimation in all eligible patients. QRISK3 for this group was raised (Relative risk: 3.8, 95%CI: 2.5 – 5.0). High risk of cardiovascular disease (QRISK3 score >10%) was detected in 28% of patients.Clinical ImplicationsThis service evaluation demonstrated significant unmet need for cardiovascular risk reduction that could be identified as part of routine care. An opportunity exists to integrate mental and physical healthcare by routinely assessing cardiovascular risk in psychiatric inpatients. Resources and training are needed to produce this risk information and act on it.

Introduction Care homes have been severely affected by the SARS-CoV-2 pandemic. Rapid antigen testing could identify most SARS-CoV-2 infected staff and visitors before they enter homes. We explored implementation of staff and visitor testing protocols using lateral flow devices (LFDs). Methods An evaluation of a SARS-CoV-2 LFD-based testing protocol in 11 care homes in Liverpool, UK, including staff and visitor testing, plus a qualitative exploratory study in nine of these homes. The proportion of pilot homes with outbreaks, and outbreak size, were compared to non-pilot homes in Liverpool. Adherence to testing protocols was evaluated. Fifteen staff were interviewed, and transcript data were thematically coded using an iterative analysis to identify and categorize factors influencing testing implementation. Results In total, 1,638 LFD rapid tests were performed on 407 staff. Protocol adherence was poor with 8.6% of staff achieving >75% protocol adherence, and 25.3% achieving $\ge$50%. Six care homes had outbreaks during the study. Compared to non-pilot care homes, there was no evidence of significant difference in the proportion of homes with outbreaks, or the size of outbreaks. Qualitative data showed difficulty implementing testing strategies due to excessive work burden. Factors influencing adherence related to test integration and procedural factors, socio-economic factors, cognitive overload and the emotional value of testing. Conclusion Implementation of staff and visitor care home LFD testing protocols was poorly adhered to and consequently did not reduce the number or scale of COVID-19 outbreaks. More focus is needed on the contextual and behavioural factors that influence protocol adherence.

Background The effectiveness and cost-effectiveness of biologic therapies for psoriasis are significantly compromised by variable treatment responses. Thus, more precise management of psoriasis is needed. Objectives We aim to identify subgroups of psoriasis patients treated with biologic therapies, based on changes in their disease activity over time, that may better inform patient management. Methods Here we apply latent class mixed modelling, to identify trajectory-based patient subgroups from longitudinal, routine clinical data on disease severity, as measured by the Psoriasis Area and Severity Index (PASI), from 3546 patients in the British Association of Dermatologists Biologics and Immunomodulators Register, as well as in an independent cohort of 2889 subjects pooled across four clinical trials. Results We discovered four, discrete classes of global response trajectories, each characterised in terms of time to response, size of effect and relapse. Each class was associated with differing clinical characteristics, e.g. Body Mass Index, baseline PASI, and prevalence of different manifestations. Our results were verified in a second cohort of clinical trials subjects, where similar trajectories following initiation of biologic therapy were identified. Further, we found differential associations of the genetic marker HLA-C*06:02 between our registry-identified trajectories. Conclusion These subgroups, defined by change in disease over time, may be indicative of distinct endotypes driven by different biological mechanisms and may help inform the management of patients with psoriasis. Future work will aim to further delineate these mechanisms by extensively characterising the subgroups with additional molecular and pharmacological data.

ObjectivesPublic involvement and engagement (PIE)) is playing an increasingly important role in big data initiatives and projects. It is therefore important to gain a deeper understanding of the different approaches used. ApproachThis study explores PIE using ethnographically-informed qualitative case studies. The case studies include: three citizen juries, each one carried out over eight days and that asked jurors to consider different real-world health data initiatives; and a public panel set up by a regional databank that carries out data linking. Data collection is ongoing and I will be continuing to carry out close observations of activities, and conducting semi-structured 1:1 interviews with those that organise and have taken part in the activities. ResultsData collection so far comprises completed observations at the citizen juries (~96 hours), ongoing observations of the public panel meetings (~15 hours), and thirty semi-structured 1:1 interviews with public contributors and other stakeholders about their experiences of the activities they were involved in. Early data analysis indicates key themes of: jurors feeling heard, but unsure whether anybody was listening; stakeholders being impressed by informed jurors, but raising concerns over contributors becoming too ‘expert’; how who is at the table and what information is presented impacts what is discussed; differences between online and in-person participation; and public involvement not being a substitute for informing the public about how their data is used. Conclusion‘Who’ is involved, and ‘how’ PPIE activities are designed and run can facilitate or constrain discussion, enhancing or limiting public contributions. If public involvement is to achieve its aims, including increasing trustworthiness, deeper consideration of these factors by those who seek the public’s views in their data projects is recommended.

Background: During the initial wave of the COVID-19 epidemic in England, several population characteristics were associated with increased risk of mortality - including, age, ethnicity, income deprivation, care home residence and housing conditions. In order to target control measures and plan for future waves of the epidemic, public health agencies need to understand how these vulnerabilities are distributed across and clustered within communities. We therefore generated an empirically informed vulnerability index for small areas across England based on predictors of mortality risk observed during the first wave. Methods: We performed a cross-sectional ecological analysis across 6,789 small areas in England. We assessed the association between COVID-19 mortality in each area and 5 vulnerability measures relating to ethnicity, poverty, prevalence of long-term health conditions, living in care homes and living in overcrowded housing, whilst accounting for the age profile of the population and the regional spread and duration of the epidemic. Estimates from multivariable Poisson regression models were used to derive a Small Area Vulnerability Index (SAVI) based on the association between these population vulnerability factors and COVID-19 mortality. Results: Four vulnerability measures were independently associated with age-adjusted COVID-19 mortality. Each standard deviation increase in the proportion of the population (1) living in care homes, (2) admitted to hospital in the past 5 years for a long-term health condition, (3) from an ethnic minority background and (4) living in overcrowded housing was associated with a 28%, [IRR=1.28, 95%CI 1.26 to 1.31], 19% [IRR=1.19, 95%CI 1.15 to 1.22], 8% [IRR=1.08, 95%CI 1.03 to 1.13] and 11% [IRR=1.11, 95%CI 1.06 to 1.15] increase in age-adjusted COVID-19 mortality rate respectively. Vulnerability to COVID-19 was noticeably higher in the North West, West Midlands, and North East regions, with high levels of vulnerability clustered in some communities. Conclusion: A second wave of the epidemic is likely to have more severe consequences for those communities identified as highly vulnerable by our index, with disproportionate affects in the North of England and the Midlands. Action is needed now to develop control measures to reduce vulnerability and increase resilience, in order to protect these communities and prevent further avoidable deaths, underpinned by proportionate allocation of resources.

Background To better understand domestic violence, data sources from multiple sectors such as police, justice, health, and welfare are needed. Linking police data to data collections from other agencies could provide unique insights and promote an all-of-government response to domestic violence. The New South Wales Police Force attends domestic violence events and records information in the form of both structured data and a free-text narrative, with the latter shown to be a rich source of information on the mental health status of persons of interest (POIs) and victims, abuse types, and sustained injuries. Objective This study aims to examine the concordance (ie, matching) between mental illness mentions extracted from the police’s event narratives and mental health diagnoses from hospital and emergency department records. Methods We applied a rule-based text mining method on 416,441 domestic violence police event narratives between December 2005 and January 2016 to identify mental illness mentions for POIs and victims. Using different window periods (1, 3, 6, and 12 months) before and after a domestic violence event, we linked the extracted mental illness mentions of victims and POIs to clinical records from the Emergency Department Data Collection and the Admitted Patient Data Collection in New South Wales, Australia using a unique identifier for each individual in the same cohort. Results Using a 2-year window period (ie, 12 months before and after the domestic violence event), less than 1% (3020/416,441, 0.73%) of events had a mental illness mention and also a corresponding hospital record. About 16% of domestic violence events for both POIs (382/2395, 15.95%) and victims (101/631, 16.01%) had an agreement between hospital records and police narrative mentions of mental illness. A total of 51,025/416,441 (12.25%) events for POIs and 14,802/416,441 (3.55%) events for victims had mental illness mentions in their narratives but no hospital record. Only 841 events for POIs and 919 events for victims had a documented hospital record within 48 hours of the domestic violence event. Conclusions Our findings suggest that current surveillance systems used to report on domestic violence may be enhanced by accessing rich information (ie, mental illness) contained in police text narratives, made available for both POIs and victims through the application of text mining. Additional insights can be gained by linkage to other health and welfare data collections.

BACKGROUND To better understand domestic violence, data sources from multiple sectors such as police, justice, health, and welfare are needed. Linking police data to databases from other agencies could provide unique insights and promote an all-of-government response to domestic violence. The New South Wales Police Force attends domestic violence events and records information in the form of both structured data and a free text narrative, with the latter shown to be a rich source of information. OBJECTIVE To examine the concordance (i.e., matching) between mental illness mentions extracted from the police’s event narratives with mental health diagnoses from hospital and emergency department records. METHODS Using different window periods (one/three/six/twelve months) before and after a domestic violence event, we automatically linked data from 416,441 police event narratives between December 2005 and January 2016 to clinical records from the Emergency Department Data Collection and the Admitted Patient Data Collection in New South Wales, Australia. RESULTS Using a two-year (i.e., twelve months before and after) window period, CONCLUSIONS Our findings reinforce the view that surveillance systems based solely on hospital presentations are very limited in terms of coverage, with hospitals seeing only a fraction of mental illness conditions arising from domestic violence situations. This demonstrates that police appear to have substantial visibility of mental illness in domestic violence covering scope and the severity of conditions. While the police data come with their own set of limitations, the coverage of mental health within domestic violence police narratives highlights the need to be able to link various data sources for large scale surveillance and reporting that could prove beneficial for vulnerable populations.

ObjectiveTo analyse the impact on hospital admissions for COVID-19 of large-scale, voluntary, public open access rapid testing for SARS-CoV-2 antigen in Liverpool (UK) between 6th November 2020 and 2nd January 2021.DesignSynthetic control analysis comparing hospital admissions for small areas in the intervention population to a group of control areas weighted to be similar in terms of prior COVID-19 hospital admission rates and socio-demographic factors.InterventionCOVID-SMART (Systematic Meaningful Asymptomatic Repeated Testing), a national pilot of large-scale, voluntary rapid antigen testing for people without symptoms of COVID-19 living or working in the City of Liverpool, deployed with the assistance of the British Army from the 6th November 2020 in an unvaccinated population. This pilot informed the UK roll-out of SARS-CoV-2 antigen rapid testing, and similar policies internationally.Main outcome measureWeekly COVID-19 hospital admissions for neighbourhoods in England.ResultsThe intensive introduction of COVID-SMART community testing was associated with a 43% (95% confidence interval: 29% to 57%) reduction in COVID-19 hospital admissions in Liverpool compared to control areas for the initial period of intensive testing with military assistance in national lockdown from 6th November to 3rd December 2020. A 25% (11% to 35%) reduction was estimated across the overall intervention period (6th November 2020 to 2nd January 2021), involving fewer testing centres, before England’s national roll-out of community testing, after adjusting for regional differences in Tiers of COVID-19 restrictions from 3rd December 2020 to 2nd January 2021.ConclusionsThe world’s first voluntary, city-wide SARS-CoV-2 rapid antigen testing pilot in Liverpool substantially reduced COVID-19 hospital admissions. Large scale asymptomatic rapid testing for SARS-CoV-2 can help reduce transmission and prevent hospital admissions.Summary boxWhat is already known on this topic–Previous studies on managing the spread of SARS-CoV-2 have identified asymptomatic transmission as significant challenges for controlling the pandemic.–Along with non-pharmaceutical measures, many countries rolled out population-based asymptomatic testing programmes to further limit transmission.–Evidence is required on whether large scale voluntary testing of communities for COVID-19 reduces severe disease, by breaking chains of transmission.What this study adds–The findings of this study suggest that large scale rapid antigen testing of communities for SARS-CoV-2, within an agile local public health campaign, can reduce transmission and prevent hospital admissions.–The results indicate that policy makers should integrate such testing into comprehensive, local public health programmes targeting high risk groups, supporting those required to isolate and adapting promptly to prevailing biological, behavioural and environmental circumstances.

ObjectiveTo examine if SARS-CoV-2 infections vary by vaccination status, if an individual had previously tested positive and by neighbourhood socioeconomic deprivation across the Delta and Omicron epidemic waves of SARS-CoV-2.DesignCohort study using electronic health recordsSettingCheshire and Merseyside, England (3rd June 2021 to 1st March 2022)Participants2.7M residentsMain Outcome measureRegistered positive test for SARS-CoV-2ResultsSocial inequalities in registered positive tests were dynamic during the study. Originally higher SARS-CoV-2 rates in the most socioeconomically deprived neighbourhoods changed to being higher in the least deprived neighbourhoods from the 1st September 2021. While the introduction of Omicron initially reset inequalities, they continued to be dynamic and inconsistent. Individuals who were fully vaccinated (two doses) were associated with fewer registered positive tests (e.g., between 1st September and 27th November 2021: (i) individuals engaged in testing – Hazards Ratio (HR) = 0.48, 95% Confidence Intervals (CIs) = 0.47-0.50; (ii) individuals engaged with healthcare - HR = 0.34, 95% CIs = 0.33-0.34). Individuals with a previous registered positive test were also less likely to have a registered positive test (e.g., between 1st September and 27th November 2021: (i) individuals engaged in testing - HR = 0.16, 95% CIs = 0.15-0.18; (ii) individuals engaged with healthcare - HR = 0.14, 95% CIs = 0.13-0.16). However, Omicron is disrupting these associations due to immune escape resulting in smaller effect sizes for both measures.ConclusionsChanging patterns of SARS-CoV-2 infections during the Delta and Omicron waves reveals a dynamic pandemic that continues to affect diverse communities in sometimes unexpected ways.

Big data, high-performance computing, and (deep) machine learning are increasingly becoming key to precision medicine—from identifying disease risks and taking preventive measures, to making diagnoses and personalizing treatment for individuals. Precision medicine, however, is not only about predicting risks and outcomes, but also about weighing interventions. Interventional clinical predictive models require the correct specification of cause and effect, and the calculation of so-called counterfactuals, that is, alternative scenarios. In biomedical research, observational studies are commonly affected by confounding and selection bias. Without robust assumptions, often requiring a priori domain knowledge, causal inference is not feasible. Data-driven prediction models are often mistakenly used to draw causal effects, but neither their parameters nor their predictions necessarily have a causal interpretation. Therefore, the premise that data-driven prediction models lead to trustable decisions/interventions for precision medicine is questionable. When pursuing intervention modelling, the bio-health informatics community needs to employ causal approaches and learn causal structures. Here we discuss how target trials (algorithmic emulation of randomized studies), transportability (the licence to transfer causal effects from one population to another) and prediction invariance (where a true causal model is contained in the set of all prediction models whose accuracy does not vary across different settings) are linchpins to developing and testing intervention models. Machine learning models are commonly used to predict risks and outcomes in biomedical research. But healthcare often requires information about cause–effect relations and alternative scenarios, that is, counterfactuals. Prosperi et al. discuss the importance of interventional and counterfactual models, as opposed to purely predictive models, in the context of precision medicine.

Covid-19 test-to-release from quarantine policies affect many lives. The SMART Release pilot was the foundation of these policies and an element of the world's largest population cohort study of community-wide, SARS-CoV-2 rapid antigen testing. The objective of the study was to evaluate daily lateral flow testing (LFT) as an alternative to 10-14 days quarantine for key worker contacts of known Covid-19 (or SARS-CoV-2 infection) cases.Prospective cohort study incorporating quantitative and qualitative research methods to consider how serial LFT compares with PCR testing to detect SARS-CoV-2 infections and to understand experiences/compliance with testing and the viability of this quarantine harm-reduction strategy. Participants were residents of the Liverpool area who were key workers at participating fire, police, NHS and local government organisations in Liverpool, and who were identified as close contacts of cases between December 2020 and August 2021. Thematic qualitative analysis was used to evaluate stakeholder meetings.Compliance with the daily testing regime was good across the three main organisations in this study with 96·9%, 93·7% and 92·8% compliance for Merseyside Police, Merseyside FireRescue Service and Alder Hey Children's Hospital respectively. Out of 1657 participants, 34 positive Covid-19 cases were identified and 3 undetected by the daily LFT regime. A total of 8291 workdays would have been lost to self-isolation but were prevented due to negative daily tests. Organisations reported that daily contact testing proved useful, flexible and well-tolerated initiative to sustain key worker services.Compliance with daily testing among key workers was high, helping sustain service continuity during periods of very high risk of staffing shortage. Services reported that the pilot was a "lifeline" and its successful delivery in Liverpool has been replicated elsewhere.This report is independent research commissioned by DHSC and part funded by DHSC and NIHR. Further funding was received from Liverpool City Council, the EPSRC and MRC.

In Australia, domestic violence reports are mostly based on data from the police, courts, hospitals, and ad hoc surveys. However, gaps exist in reporting information such as victim injuries, mental health status and abuse types. The police record details of domestic violence events as structured information (e.g., gender, postcode, ethnicity), but also in text narratives describing other details such as injuries, substance use, and mental health status. However, the voluminous nature of the narratives has prevented their use for surveillance purposes. We used a validated text mining methodology on 492,393 police-attended domestic violence event narratives from 2005 to 2016 to extract mental health mentions on persons of interest (POIs) (individuals suspected/charged with a domestic violence offense) and victims, abuse types, and victim injuries. A significant increase was observed in events that recorded an injury type (28.3% in 2005 to 35.6% in 2016). The pattern of injury and abuse types differed between male and female victims with male victims more likely to be punched and to experience cuts and bleeding and female victims more likely to be grabbed and pushed and have bruises. The four most common mental illnesses (alcohol abuse, bipolar disorder, depression schizophrenia) were the same in male and female POIs. An increase from 5.0% in 2005 to 24.3% in 2016 was observed in the proportion of events with a reported mental illness with an increase between 2005 and 2016 in depression among female victims. These findings demonstrate that extracting information from police narratives can provide novel insights into domestic violence patterns including confounding factors (e.g., mental illness) and thus enable policy responses to address this significant public health problem.

Objective: To assess associations between current use of SGLT2i, GLP-1RA and their combination and risk for MACCE and heart failure (HF) in people with type 2 diabetes. Research Design and Methods: In three nested case-control studies involving people with type 2 diabetes in England and Wales (primary care data from CPRD and SAIL Databank with linkage to hospital and mortality records), we matched each patient experiencing an event with up to 20 controls. Adjusted odds ratios for MACCE and HF among patients receiving SGLT2i or GLP-1RA regimens vs other combinations were estimated using conditional logistic regression and pooled using random-effects meta-analysis. Results: Among 336,334 people with type 2 diabetes and without cardiovascular disease, 18,531 (5.5%) experienced a MACCE. 17,451 (4.2%) experienced a HF event in a cohort of 411,206 with type 2 diabetes and without HF. Compared with other combination regimens, the adjusted pooled odds ratio and 95% confidence interval (CI) for MACCE associated with SGLT2i regimens was 0.82 (95% CI 0.73-0.92); with GLP-1RA regimens 0.93 (95% CI 0.81-1.06), and with the SGLT2i/GLP-1RA combination 0.70 (95% CI 0.50-0.98). Corresponding data for HF were: SGLT2i, 0.49 (95% CI 0.42-0.58); GLP-1RA, 0.82 (95% CI 0.71-0.95); and SGLT2i/GLP-1RA combination, 0.43 (95% CI 0.28-0.64). Conclusions: SGLT2i and SGLT2i/GLP-1RA combination regimens may be beneficial in primary prevention of MACCE and heart failure and GLP-1RA for heart failure. These data call for primary prevention trials using these agents and their combination.

Background Epidemiological criminology refers to health issues affecting incarcerated and nonincarcerated offender populations, a group recognized as being challenging to conduct research with. Notwithstanding this, an urgent need exists for new knowledge and interventions to improve health, justice, and social outcomes for this marginalized population. Objective To better understand research outputs in the field of epidemiological criminology, we examined the lead author’s affiliation by analyzing peer-reviewed published outputs to determine countries and organizations (eg, universities, governmental and nongovernmental organizations) responsible for peer-reviewed publications. Methods We used a semiautomated approach to examine the first-author affiliations of 23,904 PubMed epidemiological studies related to incarcerated and offender populations published in English between 1946 and 2021. We also mapped research outputs to the World Justice Project Rule of Law Index to better understand whether there was a relationship between research outputs and the overall standard of a country’s justice system. Results Nordic countries (Sweden, Norway, Finland, and Denmark) had the highest research outputs proportional to their incarcerated population, followed by Australia. University-affiliated first authors comprised 73.3% of published articles, with the Karolinska Institute (Sweden) being the most published, followed by the University of New South Wales (Australia). Government-affiliated first authors were on 8.9% of published outputs, and prison-affiliated groups were on 1%. Countries with the lowest research outputs also had the lowest scores on the Rule of Law Index. Conclusions This study provides important information on who is publishing research in the epidemiological criminology field. This has implications for promoting research diversity, independence, funding equity, and partnerships between universities and government departments that control access to incarcerated and offending populations.

Background Twice weekly lateral flow tests (LFTs) for secondary school children was UK Government policy from 8 March 2021. We evaluate use of LFTs (both supervised at test centres, and home test kits) in school-aged children in Cheshire and Merseyside. Methods We report (i) number of LFT positives (ii) proportion of LFT positive with confirmatory reverse transcription polymerase chain reaction (PCR) test within 2 days, and (iii) agreement between LFT-positive and confirmatory PCR, and dependence of (i–iii) on COVID-19 prevalence. Findings 1 248 468 LFTs were taken by 211 255 12–18 years old, and 163 914 by 52 116 5–11 years old between 6 November 2020 and 31 July 2021. Five thousand three hundred and fourteen (2.5%) 12–18 years old and 1996 (3.8%) 5–11 years old returned LFT positives, with 3829 (72.1%) and 1535 (76.9%) confirmatory PCRs, and 3357 (87.7%) and 1383 (90.1%) confirmatory PCR-positives, respectively. Monthly proportions of LFT positive with PCR negative varied between 4.7% and 35.3% in 12–18 years old (corresponding proportion of all tests positive: 9.7% and 0.3%). Deprivation and non-White ethnicity were associated with reduced uptake of confirmatory PCR. Interpretation Substantial inequalities in confirmatory testing need more attention to avoid further disadvantage through education loss. When prevalence is low additional measures, including confirmatory testing, are needed. Local Directors of Public Health taking more control over schools testing may be needed. Funding DHSC, MRC, NIHR, EPSRC.

Background From January to May 2021 the alpha variant (B.1.1.7) of SARS-CoV-2 was the most commonly detected variant in the UK. Following this, the Delta variant (B.1.617.2) then became the predominant variant. The UK COVID-19 vaccination programme started on 8th December 2020. Prior to the Delta variant, most vaccine effectiveness studies focused on the alpha variant. We therefore aimed to estimate the effectiveness of the BNT162b2 (Pfizer-BioNTech) and the ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccines in preventing symptomatic and asymptomatic infection with respect to the Delta variant in a UK setting. Methods We used anonymised public health record data linked to infection data (PCR) using the Combined Intelligence for Population Health Action resource. We then constructed an SIR epidemic model to explain SARS-CoV-2 infection data across the Cheshire and Merseyside region of the UK. Vaccines were assumed to be effective after 21 days for 1 dose and 14 days for 2 doses. Results We determined that the effectiveness of the Oxford-AstraZeneca vaccine in reducing susceptibility to infection is 39% (95% credible interval [34, 43]) and 64% (95% credible interval [61, 67]) for a single dose and a double dose respectively. For the Pfizer-BioNTech vaccine, the effectiveness is 20% (95% credible interval [10, 28]) and 84% (95% credible interval [82, 86]) for a single-dose and a double dose respectively. Conclusion Vaccine effectiveness for reducing susceptibility to SARS-CoV-2 infection shows noticeable improvement after receiving two doses of either vaccine. Findings also suggest that a full course of the Pfizer-BioNTech provides the optimal protection against infection with the Delta variant. This reinforces the need to complete the full course programme to maximise individual protection and reduce transmission.

BackgroundIn 2020, a second wave of COVID-19 cases unevenly affected places in England leading to the introduction of a tiered system of controls with different geographical areas subject to different levels of restrictions. Whilst previous research has examined the impact of national lockdowns on transmission, there has been limited research examining the marginal effect of differences in localised restrictions or how these effects vary between socioeconomic contexts. We therefore examined how Tier 3 restrictions in England implemented between October-December 2020, which included additional restrictions on the hospitality sector and people meeting outdoors affected COVID-19 case rates, compared to Tier 2 restrictions, and how these effects varied by level of deprivation.MethodsWe used data on weekly reported COVID-19 cases for 7201 neighbourhoods in England and adjusted these for changing case-detection rates to provide an estimate of weekly SARS-CoV-2 infections in each neighbourhood. We identified those areas that entered Tier 3 restrictions at two time points in October and December, and constructed a synthetic control group of similar places that had entered Tier 2 restrictions, using calibration weights to match them on a wide range of covariates that may influence transmission. We then compared the change in weekly infections between those entering Tier 3 to the synthetic control group to estimate the proportional reduction of cases resulting from Tier 3 restrictions compared to Tier 2 restrictions, over a 4-week period. We further used interaction analysis to estimate whether this effect differed based on the level of socioeconomic deprivation in each neighbourhood and whether effects were modified by the prevalence of a new more infectious variant of SARS-CoV-2 (B.1.1.7) in each area.ResultsThe introduction of Tier 3 restrictions in October and December was associated with a 14% (95% CI 10% to 19%) and 20% (95% CI 13% to 29%) reduction in infections respectively, compared to the rates expected if only Tier 2 restrictions had been in place in those areas. We found that effects were similar across levels of deprivation and limited evidence that Tier 3 restrictions had a greater effect in areas where the new more infectious variant was more prevalent.DiscussionCompared to Tier 2 restrictions, additional restrictions on hospitality and meeting outdoors introduced in Tier 3 areas in England had a moderate effect on transmission and these restrictions did not appear to increase inequalities.

Objective To analyse the impact of voluntary rapid testing for SARS-CoV-2 antigen in Liverpool city on covid-19 related hospital admissions. Design Synthetic control analysis comparing hospital admissions for small areas in the intervention population with a group of control areas weighted to be similar for past covid-19 related hospital admission rates and sociodemographic factors. Setting Liverpool city, UK, 6 November 2020 to 2 January 2021, under the intervention of Covid-SMART (systematic meaningful asymptomatic repeated testing) voluntary, open access supervised self-testing with lateral flow devices, compared with control areas selected from the rest of England. Population General population of Liverpool (n=498 042) and a synthetic control population from the rest of England. Main outcome measure Weekly covid-19 related hospital admissions for neighbourhoods in England. Results The introduction of community testing was associated with a 43% (95% confidence interval 29% to 57%) reduction (146 (96 to 192) in total) in covid-19 related hospital admissions in Liverpool compared with the synthetic control population (non-adjacent set of neighbourhoods with aggregate trends in covid-19 hospital admissions similar to Liverpool) for the initial period of intensive testing with military assistance in national lockdown from 6 November to 3 December 2020. A 25% (11% to 35%) reduction (239 (104 to 333) in total) was estimated across the overall intervention period (6 November 2020 to 2 January 2021), involving fewer testing centres, before England’s national roll-out of community testing, after adjusting for regional differences in tiers of covid-19 restrictions from 3 December 2020 to 2 January 2021. Conclusions The city-wide pilot of community based asymptomatic testing for SARS-CoV-2 was associated with substantially reduced covid-19 related hospital admissions. Large scale asymptomatic rapid testing for SARS-CoV-2 could help reduce transmission and prevent hospital admissions.

Background: SARS-CoV-2 is frequently shed in the stool of patients hospitalised with COVID-19. The rate of faecal shedding of SARS-CoV-2 among individuals in the community, and its potential to contribute to spread of disease, is unknown. Methods: In this prospective, observational cohort study among households in Liverpool, UK, participants underwent weekly nasal/throat swabbing to detect SARS-CoV-2 virus, over a 12-week period from enrolment starting July 2020. Participants that tested positive for SARS-CoV-2 were asked to provide a stool sample three and 14 days later. In addition, in October and November 2020, during a period of high community transmission, stool sampling was undertaken to determine the prevalence of SARS-CoV-2 faecal shedding among all study participants. SARS-CoV-2 RNA was detected using Real-Time PCR. Findings: A total of 434 participants from 176 households were enrolled. Eighteen participants (4·2%: 95% confidence interval [CI] 2·5-6·5%) tested positive for SARS-CoV-2 virus on nasal/throat swabs and of these, 3/17 (18%: 95% CI 4-43%) had SARS-CoV-2 detected in stool. Two of three participants demonstrated ongoing faecal shedding of SARS-CoV-2 , without associated gastrointestinal symptoms, after testing negative for SARS-CoV-2 in respiratory samples. Among 165/434 participants without SARS-CoV-2 infection and who took part in the prevalence study, none had detectable SARS-CoV-2 in stool. There was no demonstrable household transmission of SARS-CoV-2 among households containing a participant with faecal shedding. Interpretation: Faecal shedding of SARS-CoV-2 occurred among participants in the community with confirmed SARS-CoV-2 infection. However, during a period of high community transmission, faecal shedding of SARS-CoV-2 was not detected among participants without SARS-CoV-2 infection. It is unlikely that the faecal-oral route plays a significant role in household and community transmission of SARS-CoV-2 . Funding: NIHR Health Protection Research Unit (HPRU) in Gastrointestinal Infections, NIHR HPRU in Emerging and Zoonotic Infections, Centre of Excellence in Infectious Disease Research, and Alder Hey Charity. Declaration of Interest: NF reports research grant support from the Alder Hey Charity. MIG reports other financial or non-financial interests in V-PLEX Th17 Panel 1 Human Kit. LT reports research grant support from NIHR HPRU in Emerging and Zoonotic Infections related to this study. Unrelated to this study LT also reports fees paid to University of Liverpool from Eisai for providing a lecture on COVID-19 and cancer. WS reports scholarship for doctoral study at the University of Liverpool from the Ministry of Finance, Republic of Indonesia through the Indonesia Endowment Fund for Education program. DH, NAC, ERA, TS, KS and NMV have nothing to disclose. Ethical Approval: The study has received approval from the NHS Research Ethics Committee; REC Reference: 20/HRA/2297, IRAS Number: 283464.

BackgroundThe COVID-19 pandemic created the need for very large scale, rapid testing to prevent and contain transmission of the SARS-CoV-2 virus. Lateral flow device (LFD) immunoassays meet this need by indicating the presence of SARS-CoV-2 antigen from nose/throat swab washings in 30 minutes without laboratory processing, and can be manufactured quickly at low cost. Since March 2021, UK schools have asked pupils without symptoms to test twice weekly. Pupils have posted on social media about using soft drinks to create positive results. The aim of this study was to systematically test a variety soft drinks to determine whether they can cause false “false positive” LFD results.MethodsThis study used 14 soft drinks and 4 artificial sweeteners to determine the outcome of misusing them as analyte for the Innova SARS-CoV-2 antigen rapid qualitative LFD. The pH value, sugar content and ingredients of each sample are described. The LFD results were double read and a subset was repeated using the same devices and fake analytes but differently sourced.FindingsOne sample (1/14; 7%), spring water, produced a negative result. Ten drinks (10/14; 71%) produced a positive or weakly positive result. Three samples (3/14; 21%) produced void results, mostly the fruit concentrate drinks. There was no apparent correlation between the pH value (pH 5.0 in 13/14, 93%; pH 6.5 in 1/14; 7%) or the sugar content (range 0-10.7 grams per 100mls) of the drinks and their LFD result. The 4 artificial sweeteners all produced negative results. A subset of the results was fully replicated with differently sourced materials.InterpretationSeveral soft drinks can be misused to give false positive SARS-CoV-2 LFD results. Daily LFD testing should be performed first thing in the morning, prior to the consumption of any food or drinks, and supervised where feasible.FundingThis work was self-funded by author LO and the LFD were gifted for use in this study.Research in contextEvidence before this studyLateral flow devices (LFD) for SARS-CoV-2 antigen testing have been used extensively in the UK and internationally in COVID-19 pandemic responses, providing rapid testing at low costRecent reports from young people on social media suggested soft drinks might be misused as LFD analyte and produce a seemingly positive resultAdded value of this studyVarious common soft drinks used as fake analyte can produce false positive SARS-CoV-2 LFD resultsArtificial sweeteners alone in fake analyte solution did not produce false positive resultsImplications of all the available evidenceSoft drinks misused as analyte can produce false “false positive” SARS-CoV-2 LFD resultsDaily testing is best done first thing in the morning, prior to any food or drink, and under supervision where possible

BackgroundPolicy simulation models (PSMs) have been used extensively to shape health policies before real-world implementation and evaluate post-implementation impact. This systematic review aimed to examine best practices, identify common pitfalls in tobacco control PSMs and propose a modelling quality assessment framework.MethodsWe searched five databases to identify eligible publications from July 2013 to August 2019. We additionally included papers from Feirman et al for studies before July 2013. Tobacco control PSMs that project tobacco use and tobacco-related outcomes from smoking policies were included. We extracted model inputs, structure and outputs data for models used in two or more included papers. Using our proposed quality assessment framework, we scored these models on population representativeness, policy effectiveness evidence, simulated smoking histories, included smoking-related diseases, exposure-outcome lag time, transparency, sensitivity analysis, validation and equity.FindingsWe found 146 eligible papers and 25 distinct models. Most models used population data from public or administrative registries, and all performed sensitivity analysis. However, smoking behaviour was commonly modelled into crude categories of smoking status. Eight models only presented overall changes in mortality rather than explicitly considering smoking-related diseases. Only four models reported impacts on health inequalities, and none offered the source code. Overall, the higher scored models achieved higher citation rates.ConclusionsWhile fragments of good practices were widespread across the reviewed PSMs, only a few included a ‘critical mass’ of the good practices specified in our quality assessment framework. This framework might, therefore, potentially serve as a benchmark and support sharing of good modelling practices.

Background: With recent changes in the United Kingdom’s clinical practice for diabetes mellitus care, contemporary estimates of sex disparities in cardiovascular risk and risk factor management are needed. Methods: In this retrospective cohort study, using the Clinical Practice Research Datalink linked to hospital and death records for people in England, we identified 79 985 patients with incident type 2 diabetes mellitus (T2DM) between 2006 to 2013 matched to 386 547 patients without diabetes mellitus. Sex-stratified Cox models were used to assess cardiovascular risk. Results: Compared with women without T2DM, women with T2DM had a higher cardiovascular event risk (adjusted hazard ratio, 1.20 [95% confidence interval, 1.12–1.28]) with similar corresponding data in men (hazard ratio, 1.12 [1.06–1.19]), leading to a nonsignificant higher relative risk in women (risk ratio, 1.07 [0.98–1.17]). However, some important sex differences in the management of risk factors were observed. Compared with men with T2DM, women with T2DM were more likely to be obese, hypertensive, and have hypercholesterolemia, but were less likely to be prescribed lipid-lowering medication and angiotensin-converting enzyme inhibitors, especially if they had cardiovascular disease. Conclusions: Compared with men developing T2DM, women with T2DM do not have a significantly higher relative increase in cardiovascular risk, but ongoing sex disparities in prescribing should prompt heightened efforts to improve the standard and equity of diabetes mellitus care in women and men.

Background/Aims: The assessment of disease severity in people with diabetes enables identification of patients in need for intensive and targeted therapies, and could help optimise the allocation of healthcare resources. We aimed to identify and appraise measures that have used clinical data to establish systems for grading Type 2 diabetes severity. Methods: MEDLINE, Embase and PubMed were searched between inception-June 2018. Data from studies on diabetes-specific severity grading measures derived by medical data in adult patients with Type 2 diabetes were extracted. Studies reporting only on other diabetes forms were excluded. Detailed characteristics of the eligible severity measures, including design, severity domains, and the association between severity and health-related outcomes, were identified following independent screening. Results: Eighteen severity measures in 17 papers including 32,314 participants were included. Measures’ designs were: diabetes severity index (N = 8 measures); severity categories (N = 7 measures); complications’ count (N = 2 measures); or severity checklist (N = 1 measure). The included domains differed greatly across the identified measures. Glucose or HbA1c levels and/or diabetes-related complications were included in 89% of the measures. Two severity measures were validated in a separate study population. Higher levels of diabetes severity were associated with poorer cognitive function, higher healthcare expenses, and significantly increased risks for hospitalisation and death. Conclusion: Increasing diabetes severity is associated with greater risks for adverse outcomes. Despite that our findings demonstrate the suitability of health records to assess diabetes severity, the clinical uptake of existing measures is poor. The need to advance diabetes severity assessment is important to develop actionable measures that would help benchmark efficient clinical services.

Background Large-scale asymptomatic testing of communities in Liverpool (UK) for SARS-CoV-2 was used as a public health tool for containing COVID-19. The aim of the study is to explore social and spatial inequalities in uptake and case-detection of rapid lateral flow SARS-CoV-2 antigen tests (LFTs) offered to people without symptoms of COVID-19. Methods Linked pseudonymised records for asymptomatic residents in Liverpool who received a LFT for COVID-19 between 6th November 2020 to 31st January 2021 were accessed using the Combined Intelligence for Population Health Action resource. Bayesian Hierarchical Poisson Besag, York, and Mollie models were used to estimate ecological associations for uptake and positivity of testing. Findings 214 525 residents (43%) received a LFT identifying 5192 individuals as positive cases of COVID-19 (1.3% of tests were positive). Uptake was highest in November when there was military assistance. High uptake was observed again in the week preceding Christmas and was sustained into a national lockdown. Overall uptake were lower among males (e.g. 40% uptake over the whole period), Black Asian and other Minority Ethnic groups (e.g. 27% uptake for ‘Mixed' ethnicity) and in the most deprived areas (e.g. 32% uptake in most deprived areas). These population groups were also more likely to have received positive tests for COVID-19. Models demonstrated that uptake and repeat testing were lower in areas of higher deprivation, areas located further from test sites and areas containing populations less confident in the using Internet technologies. Positive tests were spatially clustered in deprived areas. Interpretation Large-scale voluntary asymptomatic community testing saw social, ethnic, digital and spatial inequalities in uptake. COVID-19 testing and support to isolate need to be more accessible to the vulnerable communities most impacted by the pandemic, including non-digital means of access. Funding Department of Health and Social Care (UK) and Economic and Social Research Council.

Testing for SARS-CoV-2 internationally has focused on COVID-19 diagnosis among symptomatic individuals using reverse transcriptase polymerase chain reaction (PCR) tests. Recently, however, SARS-CoV-2 antigen rapid lateral flow tests (LFT) have been rolled out in several countries for testing asymptomatic individuals in public health programmes. Validation studies for LFT have been largely cross-sectional, reporting sensitivity, specificity and predictive values of LFT relative to PCR. However, because PCR detects genetic material left behind for a long period when the individual is no longer infectious, these statistics can under-represent sensitivity of LFT for detecting infectious individuals, especially when sampling asymptomatic populations. LFTs (intended to detect individuals with live virus) validated against PCR (intended to diagnose infection) are not reporting against a gold standard of equivalent measurements. Instead, these validation studies have reported relative performance statistics that need recalibrating to the purpose for which LFT is being used. We present an approach to this recalibration.We derive a formula for recalibrating relative performance statistics from LFT vs PCR validation studies to give likely absolute sensitivity of LFT for detecting individuals with live virus. We show the differences between widely reported apparent sensitivities of LFT and its absolute sensitivity as a test of presence of live virus. After accounting for within-individual viral kinetics and epidemic dynamics within asymptomatic populations we show that a highly performant test of live virus should show a LFT-to-PCR relative sensitivity of less than 50% in conventional validation studies, which after re-calibration would be an absolute sensitivity of more than 80%.Further studies are needed to ascertain the absolute sensitivity of LFT as a test of infectiousness in COVID-19 responses. These studies should include sampling for viral cultures and longitudinal series of LFT and PCR, ideally in cohorts sampled from both contacts of cases and the general population.

Irene Petersen, 1 Alexander Crozier, 2 Iain Buchan, 3 Michael J Mina, 4, 5 Jonathan W Bartlett 6 1Department of Primary Care & Population Health, University College London, London, UK; 2Division of Biosciences, University College London, London, UK; 3Institute of Population Health, University of Liverpool, Liverpool, UK; 4Department of Epidemiology, Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA; 5Department of Pathology, Clinical Microbiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 6Department of Mathematical Sciences, University of Bath, Bath, UKCorrespondence: Irene Petersen Email i.petersen@ucl.ac.ukABSTRACT: Testing for SARS-CoV-2 internationally has focused on COVID-19 diagnosis among symptomatic individuals using reverse transcriptase polymerase chain reaction (PCR) tests. Recently, however, SARS-CoV-2 antigen rapid lateral flow tests (LFT) have been rolled out in several countries for testing asymptomatic individuals in public health programmes. Validation studies for LFT have been largely cross-sectional, reporting sensitivity, specificity and predictive values of LFT relative to PCR. However, because PCR detects genetic material left behind for a long period when the individual is no longer infectious, these statistics can under-represent the sensitivity of LFT for detecting infectious individuals, especially when sampling asymptomatic populations. LFTs (intended to detect individuals shedding SARS-CoV-2 antigens) validated against PCR (intended to diagnose infection) are not reporting against a gold standard of equivalent measurements. Instead, these validation studies have reported relative performance statistics that need recalibrating to the purpose for which LFT is being used. We present an approach to this recalibration. We derive a formula for recalibrating relative performance statistics from LFT vs PCR validation studies to give likely absolute sensitivity of LFT for detecting individuals who are shedding shedding SARS-CoV-2 antigens. We contrast widely reported apparent sensitivities of LFT with recalibrated absolute sensitivity for detecting individuals shedding SARS-CoV-2 antigens. After accounting for within-individual viral kinetics and epidemic dynamics within asymptomatic populations we show that a highly performant test for SARS-CoV-2 antigen should show LFT-to-PCR relative sensitivity of less than 50% in conventional validation studies, which after re-calibration would be an absolute sensitivity of more than 80%. Further studies are needed to ascertain the absolute sensitivity of LFT as a test of infectiousness in COVID-19 responses. These studies should include longitudinal series of LFT and PCR, ideally in cohorts sampled from both contacts of cases and the general population.Keywords: rapid test, PCR, validation, recalibration, lateral flow tests

BackgroundIn 2020, a second wave of COVID-19 cases unevenly affected places in England leading to the introduction of a tiered system of controls with different geographical areas subject to different levels of restrictions. Whilst previous research has examined the impact of national lockdowns on transmission, there has been limited research examining the marginal effect of differences in localised restrictions or how these effects vary between socioeconomic contexts. We therefore examined how Tier 3 restrictions in England implemented between October-December 2020, which included additional restrictions on the hospitality sector and people meeting outdoors affected COVID-19 case rates, compared to Tier 2 restrictions, and how these effects varied by level of deprivation.MethodsWe used data on weekly reported COVID-19 cases for 7201 neighbourhoods in England and adjusted these for changing case-detection rates to provide an estimate of weekly SARS-CoV-2 infections in each neighbourhood. We identified those areas that entered Tier 3 restrictions at two time points in October and December, and constructed a synthetic control group of similar places that had entered Tier 2 restrictions, using calibration weights to match them on a wide range of covariates that may influence transmission. We then compared the change in weekly infections between those entering Tier 3 to the synthetic control group to estimate the proportional reduction of cases resulting from Tier 3 restrictions compared to Tier 2 restrictions, over a 4-week period. We further used interaction analysis to estimate whether this effect differed based on the level of socioeconomic deprivation in each neighbourhood and whether effects were modified by the prevalence of a new more infectious variant of SARS-CoV-2 (B.1.1.7) in each area.ResultsThe introduction of Tier 3 restrictions in October and December was associated with a 14% (95% CI 10% to 19%) and 20% (95% CI 13% to 29%) reduction in infections respectively, compared to the rates expected if only Tier 2 restrictions had been in place in those areas. We found that effects were similar across levels of deprivation and limited evidence that Tier 3 restrictions had a greater effect in areas where the new more infectious variant was more prevalent.InterpretationAdditional restrictions on hospitality and meeting outdoors introduced in Tier 3 areas in England had a moderate effect on transmission and these restrictions did not appear to increase inequalities, having a similar impact across areas with differing levels of socioeconomic deprivation. Where transmission risks vary between geographical areas a tiered approach of local restrictions on outdoor mixing and hospitality can contribute to control of SARS-CoV-2 and is unlikely to increases inequalities in transmission.

BACKGROUND Tobacco control models are mathematical models predicting tobacco-related outcomes in defined populations. The policy simulation model is considered as a subcategory of tobacco control models simulating the potential outcomes of tobacco control policy options. However, we could not identify any existing tool specifically designed to assess the quality of tobacco control models. OBJECTIVE The aims of this systematic methodology review are to: (1) identify best modeling practices, (2) highlight common pitfalls, and (3) develop recommendations to assess the quality of tobacco control policy simulation models. Crucially, these recommendations can empower model users to assess the quality of current and future modeling studies, potentially leading to better tobacco policy decision-making for the public. This protocol describes the planned systematic review stages, paper inclusion and exclusion criteria, data extraction, and analysis. METHODS Two reviewers searched five databases (Embase, EconLit, PsycINFO, PubMed, and CINAHL Plus) to identify eligible studies published between July 2013 and August 2019. We included papers projecting tobacco-related outcomes with a focus on tobacco control policies in any population and setting. Eligible papers were independently screened by two reviewers. The data extraction form was designed and piloted to extract model structure, data sources, transparency, validation, and other qualities. We will use a narrative synthesis to present the results by summarizing model trends, analyzing model approaches, and reporting data input and result quality. We will propose recommendations to assess the quality of tobacco control policy simulation models using the findings from this review and related literature. RESULTS Data collection is in progress. Results are expected to be completed and submitted for publication by April 2021. CONCLUSIONS This systematic methodological review will summarize the best practices and pitfalls existing among tobacco control policy simulation models and present a recommendation list of a high-quality tobacco control simulation model. A more standardized and quality-assured tobacco control policy simulation model will benefit modelers, policymakers, and the public on both model building and decision making. CLINICALTRIAL PROSPERO International Prospective Register of Systematic Reviews CRD42020178146; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020178146 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/26854

Psychosis is a major mental illness with first onset in young adults. The prognosis is poor in around half of the people affected, and difficult to predict. The few tools available to predict prognosis have major weaknesses which limit their use in clinical practice. We aimed to develop and validate a risk prediction model of symptom nonremission in first-episode psychosis. Our development cohort consisted of 1027 patients with first-episode psychosis recruited between 2005 and 2010 from 14 early intervention services across the National Health Service in England. Our validation cohort consisted of 399 patients with first-episode psychosis recruited between 2006 and 2009 from a further 11 English early intervention services. The one-year nonremission rate was 52% and 54% in the development and validation cohorts, respectively. Multivariable logistic regression was used to develop a risk prediction model for nonremission, which was externally validated. The prediction model showed good discrimination C-statistic of 0.73 (0.64, 0.81) and adequate calibration with intercept alpha of –0.014 (–0.34, 0.31) and slope beta of 0.85 (0.42, 1.27). Our model improved the net-benefit by 16% at a risk threshold of 50% compared to the strategy of treating all, equivalent to 16 more detected nonremitted first-episode psychosis individuals per 100 without incorrectly classifying remitted cases. Once prospectively validated, our first episode psychosis prediction model could help identify patients at increased risk of nonremission at initial clinical contact.

Background: The Innova SARS-CoV-2 antigen rapid lateral flow test (LFT) offers fast detection of COVID-19 cases. This study assesses the performance of LFT in the general population attending asymptomatic testing centres. Methods: Observational cohort study to compare LFT with reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR) results based on two self-administrated swab samples per participant taken within minutes among individuals declaring no classic symptoms, attending asymptomatic testing sites in Liverpool, UK, between 6th and 29th of November 2020. Findings: 5869 individuals attended 48 testing sites in Liverpool. The relative sensitivity of LFT versus RT-qPCR, excluding void tests, was 40∙0% (95% CI: 28∙5 to 52∙4; 28/70). The specificity was 99∙9% (99∙8 to 99∙99; 5431/5434), positive predictive value was 90∙3% (74∙2 to 98∙0; 28/31) and negative predictive value was 99∙2% (99∙0 to 99∙4; 5431/5473). For cases with RT-qPCR cycle threshold CT 10 6 RNA copies/ml), sensitivity was 90∙9% (58∙7 to 99∙8; 10/11), for CT 10 4 RNA copies/ml), sensitivity was 69∙4% (51∙9 to 83∙7; 25/36), and for CT >24∙4 (viral load Interpretation: Innova LFT is a helpful tool for identifying infections among people who declare no symptoms of COVID-19, particularly those with high viral load and so more likely to infect others. The number of cases with lower CT (indicating higher viral load) missed by LFT, although small, should be considered, with due caution over using single LFT in high-consequence settings. Clear and accurate communication with the public about how to interpret test results is important. Further research is needed to understand how infectiousness is reflected in the viral antigen shedding detected by LFT versus the viral loads approximated by RT-qPCR. Funding: This evaluation was commissioned by the UK Department of Health and Social Care. Declaration of Interests: This evaluation was commissioned by the UK Department of Health and Social Care. IEB, MGF, MGS, DMH, GB and CPC received grant funding from the UK Department of Health and Social Care to evaluate LFT in the Liverpool pilot discussed in this article. IEB reports fees from AstraZeneca as chief data scientist adviser via Liverpool University and a senior investigator grant from the National Institute for Health Research (NIHR) outside the submitted work. MGS is Chair of the Infectious Disease Scientific Advisory Board and a minority shareholder in Integrum Scientific LLC, Greensboro, NC, USA, a company that has interests in COVID-19 testing but not with lateral flow technology, and reports grants from the NIHR, the Medical Research Council, and the Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool. Ethics Approval Statement: The University of Liverpool provided secondary data analysis as part of a UK national service evaluation with data collection by the UK Department of Health and Social Care (DHSC; Sponsor) As per the National Health Service (NHS) Research Authority guidelines, this work did not require ethical approval.