Background: Living with children has been associated with greater risks of SARS-CoV-2 infection, COVID-19 hospitalisation, and COVID-19 death. We examined how these associations varied during 2021–22 and according to the COVID-19 vaccination status of adults. Methods: We carried out a population-based cohort study, with the approval of NHS England. Primary care data and pseudonymously-linked hospital and death records from England, between 20th December 2020 and 21st February 2022, were used for adults (≥18 years) registered at a general practice on 20th December 2020. Adjusted hazard ratios (HRs) for SARS-CoV-2 infection, COVID-19 hospitalisation, or COVID-19 death, by presence of children in the household were calculated. Results: The cohort included 9,417,278 adults aged ≤65 years and 2,866,602 adults aged >65 years. Adults aged ≤65 years living with children of any age (versus no children) had greater risks of SARS-CoV-2 infection and COVID-19 hospitalisation (but not COVID-19 death), both when schools were open and closed (e.g. HR=1.50, 95% CI:1.49-1.51, for SARS-CoV-2 infection in the ‘Omicron dominant’ period, when schools were open, in adults living with children aged 0–11 years only). These associations also existed for adults aged >65 years, and there was some evidence that adults living with children also had greater risks of COVID-19 death. Vaccinated adults living with children had greater risks of SARS-CoV-2 infection, but lower risks of COVID-19 hospitalisation and death, than unvaccinated adults not living with children. Conclusions: In an era of widespread adult vaccination, adults living with children remained at increased risk of SARS-CoV-2 infection and COVID-19 hospitalisation.

Background NHS England guidance was issued in November 2020 for use of COVID Oximetry @home (informally named CO@h) to detect early deterioration of patients with COVID-19 in primary and community care settings. Methods With the approval of NHS England we conducted a retrospective cohort study between 4th October 2020 and 10th July 2021, using the primary care records for 57 million people registered at an English general practice. We identified patients with pulse oximetry coding (either specific CO@H codes or non-specific COPD005 codes) and described their characteristics. Results We identified 18,473 individuals with a CO@h code, and 1,581,665 with a non-specific COPD005 code related to pulse oximetry. Recording of CO@h codes varied according to patient demographics, region and practice software system (58.3 per 100,000 in TPP vs 13.2 in EMIS). Conclusion Our study shows that whilst CO@h codes were used in GP records, use of less specific COPD005 codes instead of new SNOMED CT codes for CO@h may have persisted. ### Competing Interest Statement All authors declare the following: BG has received research funding from the Bennett Foundation, the Laura and John Arnold Foundation, the NHS National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organisation, UKRI MRC, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; he has previously been a Non-Executive Director at NHS Digital; he also receives personal income from speaking and writing for lay audiences on the misuse of science. BMK is also employed by NHS England working on medicines policy and clinical lead for primary care medicines data ### Funding Statement The OpenSAFELY platform is principally funded by grants from: NHS England [2023-2025]; The Wellcome Trust (222097/Z/20/Z) [2020-2024]; MRC (MR/V015737/1) [2020-2021]. Additional contributions to OpenSAFELY have been funded by grants from: MRC via the National Core Study programme, Longitudinal Health and Wellbeing strand (MC\_PC\_20030, MC\_PC\_20059) [2020-2022] and the Data and Connectivity strand (MC\_PC\_20058) [2021-2022];NIHR and MRC via the CONVALESCENCE programme (COV-LT-0009, MC\_PC\_20051) [2021-2024]; NHS England via the Primary Care Medicines Analytics Unit [2021-2024]. The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, UK Health Security Agency (UKHSA), the Department of Health and Social Care, or other funders. Funders had no role in the study design, collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Health Research Authority (REC reference 20/LO/0651) and by the LSHTM Ethics Board (reference 21863). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Access to the underlying identifiable and potentially re-identifiable pseudonymised electronic health record data is tightly governed by various legislative and regulatory frameworks, and restricted by best practice. The data in the NHS England OpenSAFELY COVID-19 service is drawn from General Practice data across England where EMIS and TPP are the data processors. EMIS and TPP developers initiate an automated process to create pseudonymised records in the core OpenSAFELY database, which are copies of key structured data tables in the identifiable records. These pseudonymised records are linked onto key external data resources that have also been pseudonymised via SHA-512 one-way hashing of NHS numbers using a shared salt. University of Oxford, Bennett Institute for Applied Data Science developers and PIs, who hold contracts with NHS England, have access to the OpenSAFELY pseudonymised data tables to develop the OpenSAFELY tools. These tools in turn enable researchers with OpenSAFELY data access agreements to write and execute code for data management and data analysis without direct access to the underlying raw pseudonymised patient data, and to review the outputs of this code. All code for the full data management pipeline - from raw data to completed results for this analysis - and for the OpenSAFELY platform as a whole is available for review at github.com/OpenSAFELY.

BACKGROUND:COVID-19 restrictions led to increased reports of depressive symptoms in the general population and impacted health and social care services. We explored whether these changes affected antidepressant prescribing trends in the general population and those with learning disability or autism. METHODS:With the approval of NHS England, we used >24 million patients' primary care data from the OpenSAFELY-TPP platform. We used interrupted time series analysis to quantify trends in those prescribed and newly prescribed an antidepressant across key demographic and clinical subgroups, comparing pre-COVID-19 (January 2018-February 2020), COVID-19 restrictions (March 2020-February 2021) and recovery (March 2021-December 2022) periods. RESULTS:Prior to COVID-19 restrictions, antidepressant prescribing was increasing in the general population and in those with learning disability or autism. We did not find evidence that the pandemic was associated with a change in antidepressant prescribing trend in the general population (relative risk (RR) 1.00 (95% CI 0.97 to 1.02)), in those with autism (RR 0.99 (95% CI 0.97 to 1.01)) or in those with learning disability (RR 0.98 (95% CI 0.96 to 1.00)).New prescribing post restrictions was 13% and 12% below expected had COVID-19 not happened in both the general population and those with autism (RR 0.87 (95% CI 0.83 to 0.93), RR 0.88 (95% CI 0.83 to 0.92)), but not learning disability (RR 0.96 (95% CI 0.87 to 1.05)). CONCLUSIONS AND IMPLICATIONS:In this England study, we did not see an impact of COVID-19 on overall antidepressant prescribing, although unique trends were noted, such as trends in new antidepressant prescriptions which increased in care homes over the pandemic and decreased in the general population and those with autism since recovery.

Background Between December 2021 and June 2023, COVID-19 medicine delivery units (CMDUs) in England offered antiviral medicines and neutralising monoclonal antibodies (paxlovid, sotrovimab, molnupiravir, remdesivir, and casirivimab/imdevimab) to non-hospitalised individuals with COVID-19, identified at high risk of developing severe outcomes. In order to prescribe and supply medicines CMDUs were required to notify NHS England of every prescription via an electronic form. This data was supplied to OpenSAFELY, a secure analytics platform for electronic patient records, as the COVID-19 “Therapeutics” dataset. We aimed to explore the analytic potential of the dataset for research into the use and effectiveness of these therapeutics offered by CMDUs. Methods Working on behalf of NHS England, we assessed the content and data quality of the COVID-19 Therapeutics dataset within OpenSAFELY. We focused on therapeutics provided in outpatient settings by CMDUs. We described for each field the: data format, completeness and summarised its content. Results The COVID-19 Therapeutics dataset contained 18 columns and 58,590 rows of data, for 54,435 distinct patient IDs (92.9%) treated in outpatient settings. The dataset was well-structured, with completeness of almost all fields of 100%. The dataset included details on the specific treatment received, date administered, high-risk group(s) to which the patient belonged and the region in which they were assessed. Conclusion The COVID-19 Therapeutics dataset is well-structured, complete, and is suitable for research. It is linked to other data sources in OpenSAFELY (e.g., primary care), enabling important research on the impact of treatment and health disparities.

BACKGROUND:Clinically coded long COVID cases in electronic health records (EHRs) are incomplete, despite reports of rising cases of long COVID. AIM:To determine patient characteristics associated with clinically coded long COVID. DESIGN & SETTING:With the approval of NHS England, we conducted a cohort study using EHRs within the OpenSAFELY-TPP platform in England, to study patient characteristics associated with clinically coded long COVID from 29 January 2020 to 31 March 2022. METHOD:We summarised the distribution of characteristics for people with clinically coded long COVID. We estimated age-sex adjusted hazard ratios (aHRs) and fully aHRs for coded long COVID. Patient characteristics included demographic factors, and health behavioural and clinical factors. RESULTS:Among 17 986 419 adults, 36 886 (0.21%) were clinically coded with long COVID. Patient characteristics associated with coded long COVID included female sex, younger age (aged <60 years), obesity, living in less deprived areas, ever smoking, greater consultation frequency, and history of diagnosed asthma, mental health conditions, pre-pandemic post-viral fatigue, or psoriasis. These associations were attenuated following two doses of COVID-19 vaccines compared with before vaccination. Differences in the predictors of coded long COVID between the pre-vaccination and post-vaccination cohorts may reflect the different patient characteristics in these two cohorts rather than the vaccination status. Incidence of coded long COVID was higher in those with hospitalised COVID-19 than with those with non-hospitalised COVID-19. CONCLUSION:We identified variation in coded long COVID by patient characteristic. Results should be interpreted with caution as long COVID was likely under-recorded in EHRs.

This article presents a free and open source toolkit that supports the semi-automated checking of research outputs (SACRO) for privacy disclosure within secure data environments. SACRO is a framework that applies best-practice principles-based statistical disclosure control (SDC) techniques on-the-fly as researchers conduct their analyses. SACRO is designed to assist human checkers rather than seeking to replace them as with current automated rules-based approaches. The toolkit is composed of a lightweight Python package that sits over well-known analysis tools that produce outputs such as tables, plots, and statistical models. This package adds functionality to (i) automatically identify potentially disclosive outputs against a range of commonly used disclosure tests; (ii) apply optional disclosure mitigation strategies as requested; (iii) report reasons for applying SDC; and (iv) produce simple summary documents trusted research environment staff can use to streamline their workflow and maintain auditable records. This creates an explicit change in the dynamics so that SDC is something done with researchers rather than to them, and enables more efficient communication with checkers. A graphical user interface supports human checkers by displaying the requested output and results of the checks in an immediately accessible format, highlighting identified issues, potential mitigation options, and tracking decisions made. The major analytical programming languages used by researchers (Python, R, and Stata) are supported by providing front-end packages that interface with the core Python back-end. Source code, packages, and documentation are available under MIT license at https://github.com/AI-SDC/ACRO

COVID-19 is associated with higher rates of gastrointestinal diseases, though the duration of this effect, and the role of vaccination and COVID-19 severity remain uncertain. To investigate the relationship between COVID-19 and gastrointestinal diseases, by vaccination status in hospitalised and non-hospitalised patients. With NHS England approval, OpenSAFELY-TPP was used to access linked data from 24 million English adults. We defined three cohorts: “Pre-vaccination” (January 2020-June 2021), “vaccinated” and “unvaccinated” (June-December 2021). We estimated adjusted hazard ratios (aHRs) comparing the incidence of 10 gastrointestinal diseases, including upper and lower gastrointestinal bleeding, after versus before or without a COVID-19 diagnosis overall and by COVID-19 severity. COVID-19 diagnosis was associated with elevated incidence of gastrointestinal diseases, particularly after hospitalised disease. In the pre-vaccination cohort (n=18,422,781), the adjusted hazard ratios (aHRs) for upper gastrointestinal bleeding after hospitalised COVID-19 were 20.1 (95% CI 18.4–22.1) in weeks 1–4 and 1.90 (1.63–2.22) in weeks 53–102. The corresponding aHRs after non-hospitalised COVID-19 were 1.92 (1.74–2.11) and 1.34 (1.23– 1.45) respectively. Across time periods, aHRs for gastrointestinal diseases were lower in vaccinated (n=14,948,727) than in unvaccinated (n=3,479,043) individuals. These patterns were similar across gastrointestinal diseases. The incidence of gastrointestinal disease is elevated for up to two years among individuals hospitalised following a COVID-19 diagnosis. Increases in incidence are attenuated among vaccinated individuals. Monitoring for gastrointestinal diseases after severe COVID-19 and promotion of vaccination in vulnerable groups are key to reducing long-term burden. Acute symptoms of COVID-19 include gastrointestinal problems, and there is existing evidence that those with a COVID-19 diagnosis are more likely to experience long-term gastrointestinal complications, especially if they were hospitalised. Previous studies were either limited in size, lacked representativeness of the population, or had short follow-up (e.g. during hospitalisation). The role of vaccination, COVID-19 severity or peoples’ characteristics (e.g. age or sex) were not considered. Based on population-wide data on 18.4 million individuals, rates of 10 gastrointestinal diseases are elevated after COVID-19, compared with rates before or without COVID-19 diagnosis. The elevation in rates of gastrointestinal diseases after COVID-19 diagnosis was less marked in people who were vaccinated before COVID-19 diagnosis. The elevation in rates was markedly higher, and persisted for up to two years, among individuals hospitalised with COVID-19, compared with those who were not hospitalised. We show for the first time that the incidence of both acute and chronic gastrointestinal diseases remains elevated for up to two years after severe COVID-19 leading to hospitalisation. Our findings highlight the protective role of COVID-19 vaccination in reducing severe COVID-19 and its subsequent long-term gastrointestinal complications, reinforcing the importance of vaccination as a public health measure. Individuals who had severe COVID-19, regardless of vaccination status, should be prioritised for clinical monitoring for gastrointestinal diseases.

INTRODUCTION:SARS-CoV-2 infection is associated with an increased risk of venous thromboembolism. Data are lacking on how this risk altered during the COVID-19 pandemic and following vaccination. We aimed to evaluate the 90-day risk of postoperative venous thromboembolism during the pandemic. METHODS:We performed a retrospective cohort study of patients having abdominal, obstetric, orthopaedic, cardiac, thoracic or vascular surgical procedures using the OpenSAFELY-TPP platform. Crude 90-day risks of venous thromboembolism were calculated and crude and adjusted hazard ratios were derived from individual Cox proportional hazards models. RESULTS:In total, 1,800,540 procedures were performed with 15,390 individual venous thromboembolic events recorded within 90 days. The highest crude absolute risk was in the Alpha wave at 1.2%. Postoperative SARS-CoV-2 infection was associated with a 4.4-fold increase in relative risk of 90-day venous thromboembolism (adjusted hazard ratio 4.42, 95%CI 4.21-4.64) compared with those without. Recent SARS-CoV-2 infection was associated with an increased risk of venous thromboembolism (adjusted hazard ratio 4.03, 95%CI 3.78-4.30) compared with those without. Patients who were unvaccinated had the highest relative risk for 90-day venous thromboembolism. A single dose of vaccine was associated with a 20% relative risk reduction of venous thromboembolism (adjusted hazard ratio 0.80, 95%CI 0.76-0.84). DISCUSSION:SARS-CoV-2 infection status and vaccination history were associated with 90-day venous thromboembolism risk, with both recent and postoperative SARS-CoV-2 infection associated with an increased risk, whilst one dose of vaccine reduced the risk.

Background COVID-19 lockdowns led to increased reports of depressive symptoms in the general population and impacted the health and social care services of people with learning disability and autism. We explored whether the COVID-19 pandemic had an impact on antidepressant prescribing trends within these and the general population. Methods With the approval of NHS England, we used >24 million patients primary care data from the OpenSAFELY-TPP platform. We identified patients with learning disability or autism and used an interrupted time series analysis to quantify trends in those prescribed and newly prescribed an antidepressant across key demographic and clinical subgroups, comparing pre-COVID-19 (January 2018-February 2020), COVID-19 lockdown (March 2020-February 2021) and the recovery period (March 2021-December 2022). Results Prior to COVID-19 lockdown, antidepressant prescribing was increasing at 0.3% (95% CI 0.2% to 0.3%) patients per month, in the general population and in those with learning disability, and 0.3% (95% CI 0.2% to 0.4%) in those with autism. We did not find evidence that the pandemic was associated with a change in trend of antidepressant prescribing in the general population (RR 1.00 (95% CI 0.97 to 1.02)), in those with autism (RR 0.99 (95% CI 0.97 to 1.01)), or in those with learning disability (RR 0.98 (95% CI 0.96 to 1.00)). New prescribing post lockdown was 13% and 12% below expected if COVID-19 had not happened in both the general population and those with autism (RR 0.87 (95% CI 0.83 to 0.93), RR 0.88 (95% CI 0.83 to 0.92))), but not learning disability (RR 0.96 (95% CI 0.87 to 1.05)). Conclusions and Implications Pre-COVID-19, antidepressant prescribing was increasing at 0.3% per month. While we did not see an impact of COVID-19 on overall prescribing in the general population, prescriptions to those aged 0-19, 20-29, and new prescriptions were lower than pre-COVID-19 trends would have predicted, but tricyclics and new prescriptions in care homes were higher than expected. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The OpenSAFELY Platform is supported by grants from the Wellcome Trust (222097/Z/20/Z) and MRC (MR/V015737/1, MC\_PC\_20059, MR/W016729/1). In addition, development of OpenSAFELY has been funded by the Longitudinal Health and Wellbeing strand of the National Core Studies programme (MC\_PC\_20030: MC\_PC\_20059), the NIHR funded CONVALESCENCE programme (COV-LT-0009), NIHR (NIHR135559, COV-LT2-0073), and the Data and Connectivity National Core Study funded by UK Research and Innovation (MC\_PC\_20058) and Health Data Research UK (HDRUK2021.000). BG has also received funding from: the Bennett Foundation, the Wellcome Trust, NIHR Oxford Biomedical Research Centre, NIHR Applied Research Collaboration Oxford and Thames Valley, the Mohn-Westlake Foundation; all Bennett Institute staff are supported by BGs grants on this work. BMK is also employed by NHS England working on medicines policy and clinical lead for primary care medicines data. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: London - City & East Research Ethics Committee gave ethical approval for this work. REC reference 20/LO/0651. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The dataset analysed within OpenSAFELY is based on > 24 million people currently registered with GP surgeries using TPP SystmOne software. All data were linked, stored and analysed securely using the OpenSAFELY platform, https://www.opensafely.org/, as part of the NHS England OpenSAFELY COVID-19 service. Data include pseudonymised data such as coded diagnoses, medications and physiological parameters. No free text data are included. All code is shared openly for review and re-use under MIT open licence. Detailed pseudonymised patient data is potentially re-identifiable and therefore not shared. Data management and analysis was performed using Python 3. All code for data management and analysis, as well as codelists, is shared openly for inspection and re-use.

Electronic health records (EHRs) and other administrative health data are increasingly used in research to generate evidence on the effectiveness, safety, and utilisation of medical products and services, and to inform public health guidance and policy. Reproducibility is a fundamental step for research credibility and promotes trust in evidence generated from EHRs. At present, ensuring research using EHRs is reproducible can be challenging for researchers. Research software platforms can provide technical solutions to enhance the reproducibility of research conducted using EHRs. In response to the COVID-19 pandemic, we developed the secure, transparent, analytic open-source software platform OpenSAFELY designed with reproducible research in mind. OpenSAFELY mitigates common barriers to reproducible research by: standardising key workflows around data preparation; removing barriers to code-sharing in secure analysis environments; enforcing public sharing of programming code and codelists; ensuring the same computational environment is used everywhere; integrating new and existing tools that encourage and enable the use of reproducible working practices; and providing an audit trail for all code that is run against the real data to increase transparency. This paper describes OpenSAFELY’s reproducibility-by-design approach in detail.

Background The National Health Service (NHS) Long Term Plan, published in 2019, committed to ensuring that every patient in England has the right to digital-first primary care by 2023-2024. The COVID-19 pandemic and infection prevention and control measures accelerated work by the NHS to enable and stimulate the use of online consultation (OC) systems across all practices for improved access to primary care. Objective We aimed to explore general practice coding activity associated with the use of OC systems in terms of trends, COVID-19 effect, variation, and quality. Methods With the approval of NHS England, the OpenSAFELY platform was used to query and analyze the in situ electronic health records of suppliers The Phoenix Partnership (TPP) and Egton Medical Information Systems, covering >53 million patients in >6400 practices, mainly in 2019-2020. Systematized Medical Nomenclature for Medicine–Clinical Terminology (SNOMED-CT) codes relevant to OC systems and written OCs were identified including eConsultation. Events were described by volumes and population rates, practice coverage, and trends before and after the COVID-19 pandemic. Variation was characterized among practices, by sociodemographics, and by clinical history of long-term conditions. Results Overall, 3,550,762 relevant coding events were found in practices using TPP, with the code eConsultation detected in 84.56% (2157/2551) of practices. Activity related to digital forms of interaction increased rapidly from March 2020, the onset of the pandemic; namely, in the second half of 2020, >9 monthly eConsultation coding events per 1000 registered population were registered compared to <1 a year prior. However, we found large variations among regions and practices: December 2020 saw the median practice have 0.9 coded instances per 1000 population compared to at least 36 for the highest decile of practices. On sociodemographics, the TPP cohort with OC instances, when compared (univariate analysis) to the cohort with general practitioner consultations, was more predominantly female (661,235/1,087,919, 60.78% vs 9,172,833/17,166,765, 53.43%), aged 18 to 40 years (349,162/1,080,589, 32.31% vs 4,295,711/17,000,942, 25.27%), White (730,389/1,087,919, 67.14% vs 10,887,858/17,166,765, 63.42%), and less deprived (167,889/1,068,887, 15.71% vs 3,376,403/16,867,074, 20.02%). Looking at the eConsultation code through multivariate analysis, it was more commonly recorded among patients with a history of asthma (adjusted odds ratio [aOR] 1.131, 95% CI 1.124-1.137), depression (aOR 1.144, 95% CI 1.138-1.151), or atrial fibrillation (aOR 1.119, 95% CI 1.099-1.139) when compared to other patients with general practitioner consultations, adjusted for long-term conditions, age, and gender. Conclusions We successfully queried general practice coding activity relevant to the use of OC systems, showing increased adoption and key areas of variation during the pandemic at both sociodemographic and clinical levels. The work can be expanded to support monitoring of coding quality and underlying activity. This study suggests that large-scale impact evaluation studies can be implemented within the OpenSAFELY platform, namely looking at patient outcomes.

AimsThe COVID‐19 pandemic caused significant disruption to routine activity in primary care. Medication reviews are an important primary care activity ensuring safety and appropriateness of prescribing. A disruption could have significant negative implications for patient care. Using routinely collected data, our aim was first to describe codes used to record medication review activity and then to report the impact of COVID‐19 on the rates of medication reviews.MethodsWith the approval of NHS England, we conducted a cohort study of 20 million adult patient records in general practice, in‐situ using the OpenSAFELY platform. For each month, between April 2019 and March 2022, we report the percentage of patients with a medication review coded monthly and in the previous 12 months with breakdowns by regional, clinical and demographic subgroups and those prescribed high‐risk medications.ResultsIn April 2019, 32.3% of patients had a medication review coded in the previous 12 months. During the first COVID‐19 lockdown, monthly activity decreased (−21.1% April 2020), but the 12‐month rate was not substantially impacted (−10.5% March 2021). The rate of structured medication review in the last 12 months reached 2.9% by March 2022, with higher percentages in high‐risk groups (care home residents 34.1%, age 90+ years 13.1%, high‐risk medications 10.2%). The most used medication review code was Medication review done 314530002 (59.5%).ConclusionsThere was a substantial reduction in the monthly rate of medication reviews during the pandemic but rates recovered by the end of the study period. Structured medication reviews were prioritized for high‐risk patients.

ObjectivesTo investigate the effect of the COVID‐19 pandemic on prostate cancer incidence, prevalence, and mortality in England.Patients and MethodsWith the approval of NHS England and using the OpenSAFELY‐TPP dataset of 24 million patients, we undertook a cohort study of men diagnosed with prostate cancer. We visualised monthly rates in prostate cancer incidence, prevalence, and mortality per 100 000 adult men from January 2015 to July 2023. To assess the effect of the pandemic, we used generalised linear models and the pre‐pandemic data to predict the expected rates from March 2020 as if the pandemic had not occurred. The 95% confidence intervals (CIs) of the predicted values were used to estimate the significance of the difference between the predicted and observed rates.ResultsIn 2020, there was a drop in recorded incidence by 4772 (31%) cases (15 550 vs 20 322; 95% CI 19 241–21 403). In 2021, the incidence started to recover, and the drop was 3148 cases (18%, 17 950 vs 21 098; 95% CI 19 740–22 456). By 2022, the incidence returned to the levels that would be expected. During the pandemic, the age at diagnosis shifted towards older men. In 2020, the average age was 71.6 (95% CI 71.5–71.8) years, in 2021 it was 71.8 (95% CI 71.7–72.0) years as compared to 71.3 (95% CI 71.1–71.4) years in 2019.ConclusionsGiven that our dataset represents 40% of the population, we estimate that proportionally the pandemic led to 20 000 missed prostate cancer diagnoses in England alone. The increase in incidence recorded in 2023 was not enough to account for the missed cases. The prevalence of prostate cancer remained lower throughout the pandemic than expected. As the recovery efforts continue, healthcare should focus on finding the men who were affected. The research should focus on investigating the potential harms to men diagnosed at older age.

ObjectiveTo investigate the effect of the covid-19 pandemic on the number of patients with group A streptococcal infections and related antibiotic prescriptions.DesignRetrospective cohort study in England using OpenSAFELY-TPP.SettingPrimary care practices in England that used TPP SystmOne software, 1 January 2018 to 31 March 2023, with the approval of NHS England.ParticipantsPatients registered at a TPP practice at the start of each month of the study period. Patients with missing data for sex or age were excluded, resulting in a population of 23 816 470 in January 2018, increasing to 25 541 940 by March 2023.Main outcome measuresMonthly counts and crude rates of patients with group A streptococcal infections (sore throat or tonsillitis, scarlet fever, and invasive group A streptococcal infections), and recommended firstline, alternative, and reserved antibiotic prescriptions linked with a group A streptococcal infection before (pre-April 2020), during, and after (post-April 2021) covid-19 restrictions. Maximum and minimum count and rate for each infectious season (time from September to August), as well as the rate ratio of the 2022-23 season compared with the last comparably high season (2017-18).ResultsThe number of patients with group A streptococcal infections, and antibiotic prescriptions linked to an indication of group A streptococcal infection, peaked in December 2022, higher than the peak in 2017-18. The rate ratios for monthly sore throat or tonsillitis (possible group A streptococcal throat infection), scarlet fever, and invasive group A streptococcal infection in 2022-23 relative to 2017-18 were 1.39 (95% confidence interval (CI) 1.38 to 1.40), 2.68 (2.59 to 2.77), and 4.37 (2.94 to 6.48), respectively. The rate ratio for prescriptions of first line, alternative, and reserved antibiotics to patients with group A streptococcal infections in 2022-23 relative to 2017-18 were 1.37 (95% CI 1.35 to 1.38), 2.30 (2.26 to 2.34), and 2.42 (2.24 to 2.61), respectively. For individual antibiotic prescriptions in 2022-23, azithromycin showed the greatest relative increase versus 2017-18, with a rate ratio of 7.37 (6.22 to 8.74). This finding followed a marked decrease in the recording of patients with group A streptococcal infections and associated prescriptions during the period of covid-19 restrictions where the maximum count and rates were lower than any minimum rates before the covid-19 pandemic.ConclusionsRecording of rates of scarlet fever, sore throat or tonsillitis, and invasive group A streptococcal infections, and associated antibiotic prescribing, peaked in December 2022. Primary care data can supplement existing infectious disease surveillance through linkages with relevant prescribing data and detailed analysis of clinical and demographic subgroups.

Infection with SARS-CoV-2 is associated with an increased risk of arterial and venous thrombotic events, but the implications of vaccination for this increased risk are uncertain. With the approval of NHS England, we quantified associations between COVID-19 diagnosis and cardiovascular diseases in different vaccination and variant eras using linked electronic health records for ~40% of the English population. We defined a ‘pre-vaccination’ cohort (18,210,937 people) in the wild-type/Alpha variant eras (January 2020-June 2021), and ‘vaccinated’ and ‘unvaccinated’ cohorts (13,572,399 and 3,161,485 people respectively) in the Delta variant era (June-December 2021). We showed that the incidence of each arterial thrombotic, venous thrombotic and other cardiovascular outcomes was substantially elevated during weeks 1-4 after COVID-19, compared with before or without COVID-19, but less markedly elevated in time periods beyond week 4. Hazard ratios were higher after hospitalised than non-hospitalised COVID-19 and higher in the pre-vaccination and unvaccinated cohorts than the vaccinated cohort. COVID-19 vaccination reduces the risk of cardiovascular events after COVID-19 infection. People who had COVID-19 before or without being vaccinated are at higher risk of cardiovascular events for at least two years.

Background Ethnicity is known to be an important correlate of health outcomes, particularly during the COVID-19 pandemic, where some ethnic groups were shown to be at higher risk of infection and adverse outcomes. The recording of patients’ ethnic groups in primary care can support research and efforts to achieve equity in service provision and outcomes; however the coding of ethnicity is known to present complex challenges. We therefore set out to describe ethnicity coding in detail with a view to supporting the use of this data in a wide range of settings, as part of wider efforts to robustly describe and define methods of using administrative data. Methods We describe the completeness and consistency of primary care ethnicity recording in the OpenSAFELY-TPP database, containing linked primary care and hospital records in >25 million patients in England. We also compared the ethnic breakdown in OpenSAFELY-TPP with that of the 2021 UK census. Results 78.2% of patients registered in OpenSAFELY-TPP on 1 January 2022 had their ethnicity recorded in primary care records, rising to 92.5% when supplemented with hospital data. The completeness of ethnicity recording was higher for women than for men. The rate of primary care ethnicity recording ranged from 77% in the South East of England to 82.2% in the West Midlands. Ethnicity recording rates were higher in patients with chronic or other serious health conditions. For each of the five broad ethnicity groups, primary care recorded ethnicity was within 2.9 percentage points of the population rate as recorded in the 2021 Census for England as a whole. For patients with multiple ethnicity records, 98.7% of the latest recorded ethnicities matched the most frequently coded ethnicity. Patients whose latest recorded ethnicity was categorised as Other were most likely to have a discordant ethnicity recording (32.2%). Conclusions Primary care ethnicity data in OpenSAFELY is present for over three quarters of all patients, and combined with data from other sources can achieve a high level of completeness. The overall distribution of ethnicities across all English OpenSAFELY-TPP practices was similar to the 2021 Census, with some regional variation. This report identifies the best available codelist for use in OpenSAFELY and similar electronic health record data.

The COVID-19 pandemic caused significant disruption to routine activity in primary care. Medication reviews are an important primary care activity ensuring safety and appropriateness of prescribing. A disruption could have significant negative implications for patient care. Using routinely collected data, our aim was first to describe codes used to record medication review activity and then to report the impact of COVID-19 on the rates of medication reviews. With the approval of NHS England, we conducted a cohort study of 20 million adult patient records in general practice, in-situ using the OpenSAFELY platform. For each month, between April 2019 and March 2022, we report the percentage of patients with a medication review coded monthly and in the previous 12 months with breakdowns by regional, clinical and demographic subgroups and those prescribed high-risk medications. In April 2019, 32.3% of patients had a medication review coded in the previous 12 months. During the first COVID-19 lockdown, monthly activity decreased (−21.1% April 2020), but the 12-month rate was not substantially impacted (−10.5% March 2021). The rate of structured medication review in the last 12 months reached 2.9% by March 2022, with higher percentages in high-risk groups (care home residents 34.1%, age 90+ years 13.1%, high-risk medications 10.2%). The most used medication review code was Medication review done 314530002 (59.5%). There was a substantial reduction in the monthly rate of medication reviews during the pandemic but rates recovered by the end of the study period. Structured medication reviews were prioritized for high-risk patients.

Background Body mass index (BMI) has been identified as a risk factor for clinical outcomes in patients with COVID-19. Studies identifying this risk have used electronic health record (EHR) platforms in which clinical conditions must be properly identified. We set out to define and evaluate various methods of deriving BMI measurements in OpenSAFELY-TPP, an EHR platform that has been used in many studies relating to the COVID-19 pandemic. Methods With the approval of NHS England, we use routine clinical data from >22 million patients in England to define four derivations of BMI. We compare the number of patients with each type of BMI measurement and the number of measurements themselves. We also examine the plausibility of each derivation by looking at the distribution of measurements and counting values out of the expected range. To evaluate how frequently the BMI derivations are recorded, we track the number of new measurements recorded over time and the average time between updates in patients with multiple measurements. Results Primary constraints in creating the optimal BMI derivation is coverage, accuracy, and computational complexity. BMI derivations calculated from height and weight contain a few extreme outliers that affect aggregated statistics. SNOMED-recorded BMI records are more accurate on average and offer better coverage across the population. The canonical OpenSAFELY definition – which uses calculated BMI as a first instance and SNOMED-recorded BMI if missing – offers the best coverage, but contains the same extreme outliers found in calculated BMI and is the most computationally expensive of all methods. Conclusions Across all derivations, some cleaning should be performed to drop implausible outliers. Using calculated BMI on its own does not offer the best coverage or accuracy. In choosing between SNOMED-recorded BMI and the current OpenSAFELY implementation, users should decide whether they would like to maximise computational efficiency or coverage.

Importance Associations have been found between COVID-19 and subsequent mental illness in both hospital- and population-based studies. However, evidence regarding which mental illnesses are associated with COVID-19 by vaccination status in these populations is limited. Objective To determine which mental illnesses are associated with diagnosed COVID-19 by vaccination status in both hospitalized patients and the general population. Design, Setting, and ParticipantsThis study was conducted in 3 cohorts, 1 before vaccine availability followed during the wild-type/Alpha variant eras (January 2020-June 2021) and 2 (vaccinated and unvaccinated) during the Delta variant era (June-December 2021). With National Health Service England approval, OpenSAFELY-TPP was used to access linked data from 24 million people registered with general practices in England using TPP SystmOne. People registered with a GP in England for at least 6 months and alive with known age between 18 and 110 years, sex, deprivation index information, and region at baseline were included. People were excluded if they had COVID-19 before baseline. Data were analyzed from July 2022 to June 2024. Exposure Confirmed COVID-19 diagnosis recorded in primary care secondary care, testing data, or the death registry. Main Outcomes and Measures Adjusted hazard ratios (aHRs) comparing the incidence of mental illnesses after diagnosis of COVID-19 with the incidence before or without COVID-19 for depression, serious mental illness, general anxiety, posttraumatic stress disorder, eating disorders, addiction, self-harm, and suicide. Results The largest cohort, the pre-vaccine availability cohort, included 18 648 606 people (9 363 710 [50.2%] female and 9 284 896 [49.8%] male) with a median (IQR) age of 49 (34-64) years. The vaccinated cohort included 14 035 286 individuals (7 308 556 [52.1%] female and 6 726 730 [47.9%] male) with a median (IQR) age of 53 (38-67) years. The unvaccinated cohort included 3 242 215 individuals (1 363 401 [42.1%] female and 1 878 814 [57.9%] male) with a median (IQR) age of 35 (27-46) years. Incidence of most outcomes was elevated during weeks 1 through 4 after COVID-19 diagnosis, compared with before or without COVID-19, in each cohort. Incidence of mental illnesses was lower in the vaccinated cohort compared with the pre-vaccine availability and unvaccinated cohorts: aHRs for depression and serious mental illness during weeks 1 through 4 after COVID-19 were 1.93 (95% CI, 1.88-1.98) and 1.49 (95% CI, 1.41-1.57) in the pre-vaccine availability cohort and 1.79 (95% CI, 1.68-1.90) and 1.45 (95% CI, 1.27-1.65) in the unvaccinated cohort compared with 1.16 (95% CI, 1.12-1.20) and 0.91 (95% CI, 0.85-0.98) in the vaccinated cohort. Elevation in incidence was higher and persisted longer after hospitalization for COVID-19. Conclusions and Relevance In this study, incidence of mental illnesses was elevated for up to a year following severe COVID-19 in unvaccinated people. These findings suggest that vaccination may mitigate the adverse effects of COVID-19 on mental health.

Background Some studies have shown that the incidence of type 2 diabetes increases after a diagnosis of COVID-19, although the evidence is not conclusive. However, the effects of the COVID-19 vaccine on this association, or the effect on other diabetes subtypes, are not clear. We aimed to investigate the association between COVID-19 and incidence of type 2, type 1, gestational and non-specific diabetes, and the effect of COVID-19 vaccination, up to 52 weeks after diagnosis. Methods In this retrospective cohort study, we investigated the diagnoses of incident diabetes following COVID-19 diagnosis in England in a pre-vaccination, vaccinated, and unvaccinated cohort using linked electronic health records. People alive and aged between 18 years and 110 years, registered with a general practitioner for at least 6 months before baseline, and with available data for sex, region, and area deprivation were included. Those with a previous COVID-19 diagnosis were excluded. We estimated adjusted hazard ratios (aHRs) comparing diabetes incidence after COVID-19 diagnosis with diabetes incidence before or in the absence of COVID-19 up to 102 weeks after diagnosis. Results were stratified by COVID-19 severity (categorised as hospitalised or non-hospitalised) and diabetes type. Findings 16 669 943 people were included in the pre-vaccination cohort (Jan 1, 2020-Dec 14, 2021), 12 279 669 in the vaccinated cohort, and 3 076 953 in the unvaccinated cohort (both June 1-Dec 14, 2021). In the pre-vaccination cohort, aHRs for the incidence of type 2 diabetes after COVID-19 (compared with before or in the absence of diagnosis) declined from 4 center dot 30 (95% CI 4 center dot 06-4 center dot 55) in weeks 1-4 to 1 center dot 24 (1 center dot 14-1.35) in weeks 53-102. aHRs were higher in unvaccinated people (8 center dot 76 [7 center dot 49-10 center dot 25]) than in vaccinated people (1 center dot 66 [1 center dot 50-1 center dot 84]) in weeks 1-4 and in patients hospitalised with COVID-19 (pre-vaccination cohort 28 center dot 3 [26 center dot 2-30 center dot 5]) in weeks 1-4 declining to 2 center dot 04 [1 center dot 72-2 center dot 42] in weeks 53-102) than in those who were not hospitalised (1 center dot 95 [1 center dot 78-2 center dot 13] in weeks 1-4 declining to 1 center dot 11 [1 center dot 01-1 center dot 22] in weeks 53-102). Type 2 diabetes persisted for 4 months after COVID-19 in around 60% of those diagnosed. Patterns were similar for type 1 diabetes, although excess incidence did not persist beyond 1 year after a COVID-19 diagnosis. Interpretation Elevated incidence of type 2 diabetes after COVID-19 is greater, and persists for longer, in people who were hospitalised with COVID-19 than in those who were not, and is markedly less apparent in people who have been vaccinated against COVID-19. Testing for type 2 diabetes after severe COVID-19 and the promotion of vaccination are important tools in addressing this public health problem. Copyright (c) 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.