Search results
Found 20034 matches for
©
Shutterstock
Clinical Prediction Models Incorporating Blood Test Trend for Cancer Detection: Systematic Review, Meta-Analysis, and Critical Appraisal.
BACKGROUND: Blood tests used to identify patients at increased risk of undiagnosed cancer are commonly used in isolation, primarily by monitoring whether results fall outside the normal range. Some prediction models incorporate changes over repeated blood tests (or trends) to improve individualized cancer risk identification, as relevant trends may be confined within the normal range. OBJECTIVE: Our aim was to critically appraise existing diagnostic prediction models incorporating blood test trends for the risk of cancer. METHODS: MEDLINE and EMBASE were searched until April 3, 2025 for diagnostic prediction model studies using blood test trends for cancer risk. Screening was performed by 4 reviewers. Data extraction for each article was performed by 2 reviewers independently. To critically appraise models, we narratively synthesized studies, including model building and validation strategies, model reporting, and the added value of blood test trends. We also reviewed the performance measures of each model, including discrimination and calibration. We performed a random-effects meta-analysis of the c-statistic for a trends-based prediction model if there were at least 3 studies validating the model. The risk of bias was assessed using the PROBAST (prediction model risk of bias assessment tool). RESULTS: We included 16 articles, with a total of 7 models developed and 14 external validation studies. In the 7 models derived, full blood count (FBC) trends were most commonly used (86%, n=7 models). Cancers modeled were colorectal (43%, n=3), gastro-intestinal (29%, n=2), nonsmall cell lung (14%, n=1), and pancreatic (14%, n=1). In total, 2 models used statistical logistic regression, 2 used joint modeling, and 1 each used XGBoost, decision trees, and random forests. The number of blood test trends included in the models ranged from 1 to 26. A total of 2 of 4 models were reported with the full set of coefficients needed to predict risk, with the remaining excluding at least one coefficient from their article or were not publicly accessible. The c-statistic ranged 0.69-0.87 among validation studies. The ColonFlag model using trends in the FBC was commonly externally validated, with a pooled c-statistic=0.81 (95% CI 0.77-0.85; n=4 studies) for 6-month colorectal cancer risk. Models were often inadequately tested, with only one external validation study assessing model calibration. All 16 studies scored a low risk of bias regarding predictor and outcome details. All but one study scored a high risk of bias in the analysis domain, with most studies often removing patients with missing data from analysis or not adjusting the derived model for overfitting. CONCLUSIONS: Our review highlights that blood test trends may inform further investigation for cancer. However, models were not available for most cancer sites, were rarely externally validated, and rarely assessed calibration when they were externally validated.
Assessing adherence to the UK Government’s sugar, salt, and calorie reduction targets by the highest-grossing restaurants’ menus in 2024: Dataset to support a cross-sectional study
This dataset contains per 100g and per serving nutritional information gathered from chained restaurants' online menus in 2024.
Involvement of episodic memory in language comprehension: Naturalistic comprehension pushes unrelated words closer in semantic space for at least 12 h
Recent experience with a word significantly influences its subsequent interpretation. For instance, encountering bank in a river-related context biases future interpretations toward ‘side of a river’ (vs. ‘financial bank’). To explain this effect, the episodic context account posits that episodic memory helps bind word meanings in the language input, creating a temporary, context-specific representation that can bias subsequent lexical interpretation. This account predicts that even unrelated words would be linked together in episodic memory, potentially altering their interpretation. In Experiments 1–3, participants read unrelated word pairs (e.g., sword—microwave, privacy—export) embedded in meaningful sentences, then completed a speeded relatedness judgement task after delays of 5 min, 20 min, or 12 h (including sleep). Results showed that sentence exposure increased the likelihood of the unrelated pairs being judged as related—a robust effect observed across all delay intervals. Experiment 4 showed that this exposure effect was abolished when words in a target pair were read in separate sentences, suggesting that the exposure effect may be dependent on lexical co-occurrence. Experiment 5, also with a 12-h delay (including sleep), additionally used an innovative word arrangement task to assess word relatedness without presenting the target pairs simultaneously or successively. In line with relatedness judgement, sentence exposure pushed the unrelated words closer in semantic space. Overall, our findings suggest that a context-specific representation, supported by episodic memory, is generated during language comprehension, and in turn, these representations can influence lexical interpretation for at least 12 h and across different linguistic circumstances. We argue that these representations endow the mental lexicon with the efficiency to deal with word burstiness and the dynamic nature of language.
Variation in prescription duration for long term conditions: a cohort study in English NHS primary care using OpenPrescribing.
BACKGROUND: Many patients receive routine medications for long-term conditions (LTCs). Doctors typically issue repeat prescriptions in one to three month durations, but England currently has no national guidance on the optimal duration. AIM: Describe current prescription durations for common LTCs in England, explore and visualise geographical variation, and identify practice factors associated with shorter prescribing duration to inform policy making. Design and Setting A retrospective cohort study of English GP prescribing data December 2018-November 2019 Methods: We calculated the duration of prescriptions for common LTCs in England including the medications ramipril, atorvastatin, simvastatin, levothyroxine and amlodipine . We assessed the level of variation between regional clinical commissioning groups (CCGs) and determined practice factors associated with different durations. RESULTS: Of the common medications included, 28-day (one-monthly) prescriptions accounted for 48.5% (2.5 billion) tablets/capsules issued, whilst 43.6% were issued for 56 days (two monthly). There was very wide regional variation in the proportion of 28-day prescriptions (7.2% to 95.0%). Practice dispensing status was the most likely predictor of prescription duration. The proportion of patients with LTCs and the electronic health record software used by a practice were also associated with prescription duration. CONCLUSIONS: One month prescription durations are common for patients taking medicines routinely for long term conditions, particularly in dispensing practices. Electronic health record configurations offer an opportunity to implement and evaluate new policies on repeat prescription duration in England.
Resilience of the acute sector in recovery from COVID-19 pressures
The COVID-19 pandemic had a profound impact on the management and delivery of acute healthcare. To tackle the pandemic, hospitals redesigned their organisational models to provide a rapid increase in acute care assessment and treatment capacity for patients with COVID-19 whilst also trying to maintain delivery of care for patients with non-COVID-19 healthcare needs. This capacity to adjust and recover after COVID-19 might be shaped by both measures taken by acute hospitals and wider hospital pre-pandemic characteristics. The aim of this study is to examine how hospital characteristics in acute care are associated with recovery of elective activity after the height of the COVID-19 pandemic compared to pre-pandemic levels. Using patient-level data from Hospital Episode Statistics aggregated at monthly-trust level for all English National Health Service (NHS) acute hospital trusts in 2019 and 2021, we estimate the associations between hospital recovery rate and hospital pre-pandemic characteristics by employing linear regressions of the proportional change over time in elective activity against a set of explanatory variables related to supply factors (e.g., hospital size, workforce, type of hospital, regional location), demand factors (e.g., population need, patient case-mix) and time factors. On average, English NHS acute hospital trusts did not fully recover from the COVID-19 pandemic in 2021. The results show that the explanatory variables are not systematically associated with hospital recovery rate, excepting regional differences. Hospital trusts not located in London, especially in the North of England, are associated with a lower recovery (less resilience) of total elective activity and orthopaedic and vascular surgical elective activity. The implication for policy development is that the evolution of hospital recovery rates in elective activity varied across English regions, especially for high-volume and high-risk elective specialties, with better recovery in London than elsewhere.
Navigating the complexities of end-stage kidney disease (ESKD) from risk factors to outcome: insights from the UK Biobank cohort
Background: The global prevalence of end-stage kidney disease (ESKD) is increasing despite optimal management of traditional risk factors such as hyperglycaemia, hypertension, and dyslipidaemia. This study examines the influence of cardiorenal risk factors, socioeconomic status, and ethnic and cardiovascular comorbidities on ESKD outcomes in the general population. Methods: This cross-sectional study analysed data from 502,408 UK Biobank study participants recruited between 2006 and 2010. Multivariable logistic regression models were fitted to assess risk factors for ESKD, with results presented as adjusted odds ratio (aOR) and 95% confidence intervals (95% CI). Results: A total of 1191 (0.2%) of the study participants reported ESKD. Diabetes increased ESKD risk by 62% [1.62 (1.36–1.93)], with early-onset diabetes (before age 40) conferring higher odds compared to later-onset (after age 40) [2.26 (1.57–3.24)]. Similarly, early-onset hypertension (before age 40), compared to later onset (after age 40), increased ESKD odds by 73% [1.73 (1.21–2.44)]. Cardiovascular comorbidities, including stroke, hypertension, myocardial infarction and angina, were strongly associated with ESKD [5.97 (3.99–8.72), 5.35 (4.38–6.56), 4.94 (3.56–6.78), and 4.89 (3.47–6.81)], respectively. Males were at 22% higher risk of ESKD than females [1.22 (1.04–1.43)]. Each additional year of diabetes duration increased ESKD odds by 2% [1.02 (1.01–1.03)]. Non-white ethnicity, compared to white and socioeconomically most deprived, compared to the least deprived quintiles, were at 70% and 83% higher odds of ESKD. Each unit of HbA1c rise increased the odds of ESKD by 2%. Compared to microalbuminuria, macroalbuminuria increased the odds of ESKD by almost 10-fold [9.47 (7.95–11.27)] while normoalbuminuria reduced the odds by 73% [0.27 (0.22–0.32)]. Conclusions: Early onset of diabetes and hypertension, male sex, non-white ethnicity, deprivation, poor glycaemic control, and prolonged hyperglycaemia are significant risk factors for ESKD. These findings highlight the complexity of ESKD and the need for multifactorial targeted interventions in high-risk populations. Clinical trial number: Not applicable.
Developing a digital phenotype to subdivide adult immunosuppressed COVID-19 outcomes within the English Primary Care Sentinel Network
Background: Adults classified as immunosuppressed have been disproportionately affected by the COVID-19 pandemic. Compared to the immunocompetent, certain patients are at increased risk of suboptimal vaccine response and adverse health outcomes if infected. However, there has been insufficient work to pinpoint where these risks concentrate within the immunosuppressed spectrum; surveillance efforts typically treat the immunosuppressed as a single entity, leading to wide confidence intervals. A clinically meaningful and computerised medical record (CMR) compatible method to subdivide immunosuppressed COVID-19 data is urgently needed. Methods: We conducted a rapid scoping review into COVID-19 mortality across UK immunosuppressed categories to assess if differential mortality risk was a viable means of subdivision. We converted the risk hierarchy that surfaced into a pilot digital phenotype—a valueset and series of ontological rules ready to extract immunosuppressed patients from CMR data and stratify outcomes of interest in COVID-19 surveillance dataflows. Results: The rapid scoping review returned COVID-19 mortality data for all immunosuppressed subgroups assessed and revealed significant heterogeneity across the spectrum. There was a clear distinction between heightened COVID-19 mortality in haematological malignancy and transplant patients and mortality that approached the immunocompetent baseline amongst cancer therapy recipients, autoimmune patients, and those with HIV. This process, complemented by expert clinical input, informed the curation of the five-part digital phenotype now ready for testing in real-world data; its ontological rules will enable mutually exclusive, hierarchical extraction with nuanced time and treatment conditions. Unique categorisations have been introduced, including ‘Bone Marrow Compromised’ and those dedicated to differentiating prescriptions related and unrelated to cancer. Codification was supported by existing reference sets of medical codes; absent or redundant codes had to be resolved manually. Discussion: Although this work is in its earliest phases, the development process we report has been highly informative. Systematic review, clinical consensus building, and implementation studies will test the validity of our results and address criticisms of the rapid scoping exercise they are predicated on. Conclusion: Comprehensive testing for COVID-19 has differentiated mortality risks across the immunosuppressed spectrum. This risk hierarchy has been codified into a digital phenotype for differentiated COVID-19 surveillance; this marks a step towards the needs-based management of these patients that is urgently required.
A Remotely Delivered GLP-1RA–Supported Specialist Weight Management Program in Adults Living With Obesity: Retrospective Service Evaluation
Background Limited access to specialist weight management services restricts the implementation of novel pharmacotherapies for obesity such as glucagon-like peptide-1 receptor agonists (GLP-1RAs) in the UK National Health Service (NHS). Second Nature, a commercial digital health company, offers a remotely delivered program combining a GLP-1RA medication (semaglutide) with digital behavioral support, potentially providing a scalable solution. However, evidence for long-term effectiveness in this real-world context is limited. Objective This study aimed to evaluate the 12-month effectiveness, feasibility, acceptability, and potential cost-effectiveness of the remotely delivered, semaglutide-supported weight management program by Second Nature. Methods This retrospective service evaluation analyzed data from participants who initiated the program between September and December 2023. The primary outcome was weight change at 12 months among participants with available data (completers). Secondary outcomes included retention, program engagement (measured by views of the Home screen in the app), behavioral changes, side effects, participant experience (qualitative analysis), and a comparative cost analysis against an NHS specialist weight management service. An “active subscription” was defined as maintaining a paid subscription for the full 12-month period. Descriptive statistics and paired 2-tailed t tests evaluated outcomes. Results Data from 341 participants were included at baseline (282/341, 82.7% women; mean age 49, SD 11.1 years; mean baseline BMI 37.9, SD 6.9 kg/m2). At 12 months, 39.6% (135/206) maintained an active subscription, while 60.4% (206/341) became inactive. Weight data at 12 months were available for 179 participants (52.5% of the baseline cohort; 100% of active and 19.4% of inactive participants). Among completers who maintained an active subscription, the mean weight loss was 20.0 kg (SD 8.7 kg; P<.001), representing 19.1% of starting weight. Overall, 77.7% (139/179) of completers achieved ≥10% weight loss and 61.5% (110/179) achieved ≥15%. Program engagement declined over time. Side effects also decreased, with 69.6% (81/116) of respondents reporting none by month 12. Most participants completing the 12-month survey reported positive (41/120, 34.2%) or neutral (68/120, 56.7%) experiences. Conclusions This evaluation suggests that remotely delivered GLP-1RA–supported weight management can achieve significant weight loss in participants remaining engaged for 12 months. However, the high rate of withdrawal limits generalizability. The program appears feasible, acceptable, and potentially cost-effective for completers. Further research, ideally in public health care settings using intent-to-treat analyses, is needed to confirm clinical outcomes, assess sustained results, and understand factors influencing retention.
The cost-effectiveness of specialist hospital discharge and intermediate care services for patients who are homeless
Background: Recognising the diverse healthcare needs of the population, there is a growing emphasis on tailoring hospital discharge processes to address the unique challenges faced by individuals who are homeless, aiming to enhance the efficiency and effectiveness of post-hospitalisation care for this vulnerable demographic. This study aimed to evaluate the costs and consequences of specialist hospital discharge and intermediate care (support after discharge) services for people who are homeless in England. Methods: We estimated the comparative costs and consequences of different types of specialist care provided by 17 homeless hospital discharge and intermediate care services. We compared ‘clinically-led’ (multidisciplinary) services with those that were ‘housing-led’ (uniprofessional). A retrospective observational study was conducted to estimate effectiveness and costs for two'intervention groups'(clinically-led and housing-led) and a previously published RCT for'standard care'. Use of resources data for specialist care was sourced through linkage with Hospital Episode Statistics. The measure of effectiveness was the number of bed days avoided (in terms of hospital stays for all readmissions in the follow-up period) per homeless user. Additional secondary analysis of three services looked at quality-adjusted life years (QALYs) and service delivery costs. The perspective adopted was NHS in England. Results: Data from the comparative analysis showed that specialist homeless hospital discharge (HHD) care is likely to be cost-effective compared with standard care. Patients accessing specialist care use fewer bed days per year (including both planned and unplanned readmissions). Patients using specialist care have more planned readmissions to hospital and, overall, use more NHS resources than those who use standard care. We interpret this as a positive outcome indicating that specialist care is likely to work more effectively than standard care to improve access to healthcare for this marginalised group. Specialist care remained cost-effective over a range of sensitivity analyses. Secondary analyses of three specific schemes found better QALY outcomes, but results are not generalisable to all 17 schemes. Conclusion: Specialist HHD services are likely to be cost-effective for the NHS compared with standard care, although further research is needed to access patient level data for both costs and outcomes to conduct a rigorous statistical analysis between groups and address possible underlying biases due to data coming from non-randomised study design.
Reforming the funding of long-Term care for older people: Costs and distributional impacts of planned changes in England
Reforms to the means tests in England for state-financed long-Term care were planned for implementation in 2025. They included a lifetime limit (cap) on how much an individual must contribute to their care, with the state meeting subsequent care costs. We present projections of the costs and distributional impacts of these reforms for older people, using two linked simulation models which draw on a wide range of data. We project that by 2038 public spending on long-Term care for older people in England would be about 14% higher than without the reforms. While the main direct beneficiaries of the lifetime cap would have been the better off who currently receive no state help with their care costs, the reforms also treated capital assets more generously than the current system, helping people with more modest incomes and wealth. When analysing the impacts of the reforms it is therefore important to consider the whole reform package. Our results depend on a range of assumptions, and the impacts of the reforms would be sensitive to the levels of the cap and other reformed parameters of the means test on implementation.
Relationship of cardiorenal risk factors with albuminuria based on age, smoking, glycaemic status and BMI: a retrospective cohort study of the UK Biobank data.
INTRODUCTION: Smoking is harmful, and its cessation is recommended to prevent chronic kidney disease, which often begins with abnormal leakage of albumin in the urine, called albuminuria. Smoking cessation's effect on albuminuria depends on the pack-years smoked, length of abstinence, body mass index (BMI) and glycosylated haemoglobin (HbA1c). Using the UK Biobank data, we examined the relationship between these cardiorenal variables and albuminuria. METHODS: For this study, we selected a UK Biobank cohort with urinary albumin concentration (UAC) in the first and second visits. Participants were divided into progressor and regressor groups, where progressors were defined as those with increased UAC value, and regressors were those with decreased UAC value. Three different logistic regression models were fitted. In model 1, with a cohort design, we explored the impact of a change in age, HbA1c and BMI between the first and second visits and the UAC. In model 2 and 3, in a cross-sectional design, we explored which cardiorenal risk factors were associated with a rise or fall of UAC at the time point of the second visit. Results are expressed in OR and 95% CI. RESULTS: The prevalence of albuminuria was highest in ex-smokers who started smoking between the ages of 13 and 18. With a mean duration of 51 months, there was no statistically significant relationship between smoking status and BMI with albuminuria. Each year of ageing and each unit of increase in HbA1c (mmol/mol) increased the odds of progression of albuminuria by 20% and 3%, respectively. In ex-smokers, at the time point of the second visit, each year of smoking increased, and each year of abstinence decreased the odds by 4% and 6%, respectively. CONCLUSION: Smokers should be supported to stop smoking and remain abstinent despite short-term weight gain. Childhood smoking should be actively discouraged.
Creating a Modified Version of the Cambridge Multimorbidity Score to Predict Mortality in People Older Than 16 Years: Model Development and Validation
Background: No single multimorbidity measure is validated for use in NHS (National Health Service) England’s General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR), the nationwide primary care data set created for COVID-19 pandemic research. The Cambridge Multimorbidity Score (CMMS) is a validated tool for predicting mortality risk, with 37 conditions defined by Read Codes. The GDPPR uses the more internationally used Systematized Nomenclature of Medicine clinical terms (SNOMED CT). We previously developed a modified version of the CMMS using SNOMED CT, but the number of terms for the GDPPR data set is limited making it impossible to use this version. Objective: We aimed to develop and validate a modified version of CMMS using the clinical terms available for the GDPPR. Methods: We used pseudonymized data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC), which has an extensive SNOMED CT list. From the 37 conditions in the original CMMS model, we selected conditions either with (1) high prevalence ratio (≥85%), calculated as the prevalence in the RSC data set but using the GDPPR set of SNOMED CT codes, divided by the prevalence included in the RSC SNOMED CT codes or (2) conditions with lower prevalence ratios but with high predictive value. The resulting set of conditions was included in Cox proportional hazard models to determine the 1-year mortality risk in a development data set (n=500,000) and construct a new CMMS model, following the methods for the original CMMS study, with variable reduction and parsimony, achieved by backward elimination and the Akaike information stopping criterion. Model validation involved obtaining 1-year mortality estimates for a synchronous data set (n=250,000) and 1-year and 5-year mortality estimates for an asynchronous data set (n=250,000). We compared the performance with that of the original CMMS and the modified CMMS that we previously developed using RSC data. Results: The initial model contained 22 conditions and our final model included 17 conditions. The conditions overlapped with those of the modified CMMS using the more extensive SNOMED CT list. For 1-year mortality, discrimination was high in both the derivation and validation data sets (Harrell C=0.92) and 5-year mortality was slightly lower (Harrell C=0.90). Calibration was reasonable following an adjustment for overfitting. The performance was similar to that of both the original and previous modified CMMS models. Conclusions: The new modified version of the CMMS can be used on the GDPPR, a nationwide primary care data set of 54 million people, to enable adjustment for multimorbidity in predicting mortality in people in real-world vaccine effectiveness, pandemic planning, and other research studies. It requires 17 variables to produce a comparable performance with our previous modification of CMMS to enable it to be used in routine data using SNOMED CT.
Phenotype execution and modeling architecture to support disease surveillance and real-world evidence studies: English sentinel network evaluation
Objective: To evaluate Phenotype Execution and Modelling Architecture (PhEMA), to express sharable phenotypes using Clinical Quality Language (CQL) and intensional Systematised Nomenclature of Medicine (SNOMED) Clinical Terms (CT) Fast Healthcare Interoperability Resources (FHIR) valuesets, for exemplar chronic disease, sociodemographic risk factor, and surveillance phenotypes. Method: We curated 3 phenotypes: Type 2 diabetes mellitus (T2DM), excessive alcohol use, and incident influenza-like illness (ILI) using CQL to define clinical and administrative logic. We defined our phenotypes with valuesets, using SNOMED's hierarchy and expression constraint language, and CQL, combining valuesets and adding temporal elements where needed. We compared the count of cases found using PhEMA with our existing approach using convenience datasets. We assessed our new approach against published desiderata for phenotypes. Results: The T2DM phenotype could be defined as 2 intensionally defined SNOMED valuesets and a CQL script. It increased the prevalence from 7.2% to 7.3%. Excess alcohol phenotype was defined by valuesets that added qualitative clinical terms to the quantitative conceptual definitions we currently use; this change increased prevalence by 58%, from 1.2% to 1.9%. We created an ILI valueset with SNOMED concepts, adding a temporal element using CQL to differentiate new episodes. This increased the weekly incidence in our convenience sample (weeks 26-38) from 0.95 cases to 1.11 cases per 100 000 people. Conclusions: Phenotypes for surveillance and research can be described fully and comprehensibly using CQL and intensional FHIR valuesets. Our use case phenotypes identified a greater number of cases, whilst anticipated from excessive alcohol this was not for our other variable. This may have been due to our use of SNOMED CT hierarchy. Our new process fulfilled a greater number of phenotype desiderata than the one that we had used previously, mostly in the modeling domain. More work is needed to implement that sharing and warehousing domains.
Disparities in COVID-19 mortality amongst the immunosuppressed: A systematic review and meta-analysis for enhanced disease surveillance
Background: Effective disease surveillance, including that for COVID-19, is compromised without a standardised method for categorising the immunosuppressed as a clinical risk group. Methods: We conducted a systematic review and meta-analysis to evaluate whether excess COVID-associated mortality compared to the immunocompetent could meaningfully subdivide the immunosuppressed. Our study adhered to UK Immunisation against infectious disease (Green Book) criteria for defining and categorising immunosuppression. Using OVID (EMBASE, MEDLINE, Transplant Library, and Global Health), PubMed, and Google Scholar, we examined relevant literature between the entirety of 2020 and 2022. We selected for cohort studies that provided mortality data for immunosuppressed subgroups and immunocompetent comparators. Meta-analyses, grey literature and any original works that failed to provide comparator data or reported all-cause or paediatric outcomes were excluded. Odds Ratios (OR) and 95% confidence intervals (CI) of COVID-19 mortality were meta-analysed by immunosuppressed category and subcategory. Subgroup analyses differentiated estimates by effect measure, country income, study setting, level of adjustment, use of matching and publication year. Study screening, extraction and bias assessment were performed blinded and independently by two researchers; conflicts were resolved with the oversight of a third researcher. PROSPERO registration number is CRD42022360755. Findings: We identified 99 unique studies, incorporating data from 1,542,097 and 56,248,181 unique immunosuppressed and immunocompetent patients with COVID-19 infection, respectively. Compared to immunocompetent people (pooled OR, 95%CI), solid organ transplants (2.12, 1.50-2.99) and malignancy (2.02, 1.69-2.42) patients had a very high risk of COVID-19 mortality. Patients with rheumatological conditions (1.28, 1.13-1.45) and HIV (1.20, 1.05-1.36) had just slightly higher risks than the immunocompetent baseline. Case type, setting income and mortality data matching and adjustment were significant modifiers of excess immunosuppressed mortality for some immunosuppressed subgroups. Interpretation: Excess COVID-associated mortality among the immunosuppressed compared to the immunocompetent was seen to vary significantly across subgroups. This novel means of subdivision has prospective benefit for targeting patient triage, shielding and vaccination policies during periods of high disease transmission.
Postpandemic Sentinel Surveillance of Respiratory Diseases in the Context of the World Health Organization Mosaic Framework: Protocol for a Development and Evaluation Study Involving the English Primary Care Network 2023-2024
Background: Prepandemic sentinel surveillance focused on improved management of winter pressures, with influenza-like illness (ILI) being the key clinical indicator. The World Health Organization (WHO) global standards for influenza surveillance include monitoring acute respiratory infection (ARI) and ILI. The WHO’s mosaic framework recommends that the surveillance strategies of countries include the virological monitoring of respiratory viruses with pandemic potential such as influenza. The Oxford-Royal College of General Practitioner Research and Surveillance Centre (RSC) in collaboration with the UK Health Security Agency (UKHSA) has provided sentinel surveillance since 1967, including virology since 1993. Objective: We aim to describe the RSC’s plans for sentinel surveillance in the 2023-2024 season and evaluate these plans against the WHO mosaic framework. Methods: Our approach, which includes patient and public involvement, contributes to surveillance objectives across all 3 domains of the mosaic framework. We will generate an ARI phenotype to enable reporting of this indicator in addition to ILI. These data will support UKHSA’s sentinel surveillance, including vaccine effectiveness and burden of disease studies. The panel of virology tests analyzed in UKHSA’s reference laboratory will remain unchanged, with additional plans for point-of-care testing, pneumococcus testing, and asymptomatic screening. Our sampling framework for serological surveillance will provide greater representativeness and more samples from younger people. We will create a biomedical resource that enables linkage between clinical data held in the RSC and virology data, including sequencing data, held by the UKHSA. We describe the governance framework for the RSC. Results: We are co-designing our communication about data sharing and sampling, contextualized by the mosaic framework, with national and general practice patient and public involvement groups. We present our ARI digital phenotype and the key data RSC network members are requested to include in computerized medical records. We will share data with the UKHSA to report vaccine effectiveness for COVID-19 and influenza, assess the disease burden of respiratory syncytial virus, and perform syndromic surveillance. Virological surveillance will include COVID-19, influenza, respiratory syncytial virus, and other common respiratory viruses. We plan to pilot point-of-care testing for group A streptococcus, urine tests for pneumococcus, and asymptomatic testing. We will integrate test requests and results with the laboratory-computerized medical record system. A biomedical resource will enable research linking clinical data to virology data. The legal basis for the RSC’s pseudonymized data extract is The Health Service (Control of Patient Information) Regulations 2002, and all nonsurveillance uses require research ethics approval. Conclusions: The RSC extended its surveillance activities to meet more but not all of the mosaic framework’s objectives. We have introduced an ARI indicator. We seek to expand our surveillance scope and could do more around transmissibility and the benefits and risks of nonvaccine therapies.
The impact of COVID-19 lockdowns on primary care contact among vulnerable populations in England: a controlled interrupted time series study.
BACKGROUND: UK COVID-19 lockdowns significantly affected primary care access and delivery. Little is known about whether lockdowns disproportionally impacted vulnerable groups, including people who misuse substances, domestic violence or abuse victims, those with intellectual disability, and children with safeguarding concerns. AIM: To evaluate the impact of UK COVID-19 lockdowns on primary care contact rates among vulnerable groups. DESIGN & SETTING: Natural experimental design using all registered patients in the OpenSAFELY platform. METHOD: With approval from NHS England, we conducted controlled interrupted time-series analyses on 24million patients in England between September2019-September2021. RESULTS: Pre-pandemic, primary care consultation rates were 110.1 per 1000 patients per week. Following the initiation of the first lockdown (23/03/2020), there was a large reduction of 29-61 contacts per 1000 patients per week among vulnerable and general population groups. For patients with alcohol misuse, aged ≥14 years with intellectual disability, and children with safeguarding concerns, this reduction was significantly more extreme than corresponding general populations (relative rate difference -23.8 [95% confidence interval -39.8,-7.7], -24.6 [-38.8,-10.5], and -15.4 [-26.9,-3.8], respectively). Following the final lockdown (29/03/2021), all groups had consulting rates exceeding pre-pandemic rates (with increases more marked in vulnerable populations), except those only including children. CONCLUSION: Analyses suggested a larger short-term impact of the first COVID-19 lockdown on primary care contact for some vulnerable groups, compared to the general population; differential impacts persisted through subsequent lockdowns and beyond for some vulnerable groups. There is a need to examine drivers of these differences to enable more equitable primary care access and provision.