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BackgroundIn England, national safety guidance recommends that ciclosporin, tacrolimus, and diltiazem are prescribed by brand name due to their narrow therapeutic windows and, in the case of tacrolimus, to reduce the chance of organ transplantation rejection. Various small studies have shown that changes to electronic health record (EHR) system interfaces can affect prescribing choices. ObjectiveOur objectives were to assess variation by EHR systems in breach of safety guidance around prescribing of ciclosporin, tacrolimus, and diltiazem, and to conduct user-interface research into the causes of such breaches. MethodsWe carried out a retrospective cohort study using prescribing data in English primary care. Participants were English general practices and their respective EHR systems. The main outcome measures were (1) the variation in ratio of safety breaches to adherent prescribing in all practices and (2) the description of observations of EHR system usage. ResultsA total of 2,575,411 prescriptions were issued in 2018 for ciclosporin, tacrolimus, and diltiazem (over 60 mg); of these, 316,119 prescriptions breached NHS guidance (12.27%). Breaches were most common among users of the EMIS EHR system (breaches in 18.81% of ciclosporin and tacrolimus prescriptions and in 17.99% of diltiazem prescriptions), but breaches were observed in all EHR systems. ConclusionsDesign choices in EHR systems strongly influence safe prescribing of ciclosporin, tacrolimus, and diltiazem, and breaches are prevalent in general practices in England. We recommend that all EHR vendors review their systems to increase safe prescribing of these medicines in line with national guidance. Almost all clinical practice is now mediated through an EHR system; further quantitative research into the effect of EHR system design on clinical practice is long overdue.

Author(s): Miriam Samuel 1,*, Robin Y. Park 2, Sophie V. Eastwood 3, Fabiola Eto 1, Caroline E. Morton 1,2, Daniel Stow 1, Sebastian Bacon 2, Amir Mehrkar 2, Jessica Morley [...], Background Obesity and rapid weight gain are established risk factors for noncommunicable diseases and have emerged as independent risk factors for severe disease following Coronavirus Disease 2019 (COVID-19) infection. Restrictions imposed to reduce COVID-19 transmission resulted in profound societal changes that impacted many health behaviours, including physical activity and nutrition, associated with rate of weight gain. We investigated which clinical and sociodemographic characteristics were associated with rapid weight gain and the greatest acceleration in rate of weight gain during the pandemic among adults registered with an English National Health Service (NHS) general practitioner (GP) during the COVID-19 pandemic. Methods and findings With the approval of NHS England, we used the OpenSAFELY platform inside TPP to conduct an observational cohort study of routinely collected electronic healthcare records. We investigated changes in body mass index (BMI) values recorded in English primary care between March 2015 and March 2022. We extracted data on 17,742,365 adults aged 18 to 90 years old (50.1% female, 76.1% white British) registered with an English primary care practice. We estimated individual rates of weight gain before ([delta]-prepandemic) and during ([delta]-pandemic) the pandemic and identified individuals with rapid weight gain (>0.5 kg/m.sup.2 /year) in each period. We also estimated the change in rate of weight gain between the prepandemic and pandemic period ([delta]-change = [delta]-pandemic-[delta]-prepandemic) and defined extreme accelerators as the 10% of individuals with the greatest increase in their rate of weight gain ([delta]-change [greater than or equal to]1.84 kg/m.sup.2 /year) between these periods. We estimated associations with these outcomes using multivariable logistic regression adjusted for age, sex, index of multiple deprivation (IMD), and ethnicity. P-values were generated in regression models. The median BMI of our study population was 27.8 kg/m.sup.2, interquartile range (IQR) [24.3, 32.1] in 2019 (March 2019 to February 2020) and 28.0 kg/m.sup.2, IQR [24.4, 32.6] in 2021. Rapid pandemic weight gain was associated with sex, age, and IMD. Male sex (male versus female: adjusted odds ratio (aOR) 0.76, 95% confidence interval (95% CI) [0.76, 0.76], p < 0.001), older age (e.g., 50 to 59 years versus 18 to 29 years: aOR 0.60, 95% CI [0.60, 0.61], p < 0.001]); and living in less deprived areas (least-deprived-IMD-quintile versus most-deprived: aOR 0.77, 95% CI [0.77, 0.78] p < 0.001) reduced the odds of rapid weight gain. Compared to white British individuals, all other ethnicities had lower odds of rapid pandemic weight gain (e.g., Indian versus white British: aOR 0.69, 95% CI [0.68, 0.70], p < 0.001). Long-term conditions (LTCs) increased the odds, with mental health conditions having the greatest effect (e.g., depression (aOR 1.18, 95% CI [1.17, 1.18], p < 0.001)). Similar characteristics increased odds of extreme acceleration in the rate of weight gain between the prepandemic and pandemic periods. However, changes in healthcare activity during the pandemic may have introduced new bias to the data. Conclusions We found female sex, younger age, deprivation, white British ethnicity, and mental health conditions were associated with rapid pandemic weight gain and extreme acceleration in rate of weight gain between the prepandemic and pandemic periods. Our findings highlight the need to incorporate sociodemographic, physical, and mental health characteristics when formulating research, policies, and interventions targeting BMI in the period of post pandemic service restoration and in future pandemic planning.

Background: COVID-19 pandemic restrictions may have influenced behaviours related to weight. Aim: To describe patterns of weight change among adults living in England with type 2 diabetes (T2D) and/or hypertension during the pandemic. Design and setting: An observational cohort study using the routinely collected health data of approximately 40% of adults living in England, accessed through the OpenSAFELY service inside TPP. Method: Clinical and sociodemographic characteristics associated with rapid weight gain (>0.5 kg/m2/year) were investigated using multivariable logistic regression. Results: Data were extracted on adults with T2D (n = 1 231 455, 43.9% female, and 76.0% White British) or hypertension (n = 3 558 405, 49.7% female, and 84.3% White British). Adults with T2D lost weight overall (median δ = −0.1 kg/m2/year [interquartile range {IQR} −0.7–0.4]). However, rapid weight gain was common (20.7%) and associated with the following: sex (male versus female: adjusted odds ratio [aOR] 0.78 [95% confidence interval {CI} = 0.77 to 0.79]); age (older age reduced odds, for example, aged 60–69 years versus 18–29 years: aOR 0.66 [95% CI = 0.61 to 0.71]); deprivation (least deprived Index of Multiple Deprivation [IMD] quintile versus most deprived IMD quintile: aOR 0.87 [95% CI = 0.85 to 0.89]); White ethnicity (Black versus White: aOR 0.95 [95% CI = 0.92 to 0.98]); mental health conditions (for example, depression: aOR 1.13 [95% CI = 1.12 to 1.15]); and diabetes treatment (non-insulin treatment versus no pharmacological treatment: aOR 0.68 [95% CI = 0.67 to 0.69]). Adults with hypertension maintained stable weight overall (median δ = 0.0 kg/m2/year [IQR −0.6–0.5]); however, rapid weight gain was common (24.7%) and associated with similar characteristics as in T2D. Conclusion: Among adults living in England with T2D and/or hypertension, rapid pandemic weight gain was more common among females, younger adults, those living in more deprived areas, and those with mental health conditions. [ABSTRACT FROM AUTHOR]

Background: The UK delivered its first “booster” COVID-19 vaccine doses in September 2021, initially to individuals at high risk of severe disease, then to all adults. The BNT162b2 Pfizer-BioNTech vaccine was used initially, then also Moderna mRNA-1273. Methods: With the approval of the National Health Service England, we used routine clinical data to estimate the effectiveness of boosting with BNT162b2 or mRNA-1273 compared with no boosting in eligible adults who had received two primary course vaccine doses. We matched each booster recipient with an unboosted control on factors relating to booster priority status and prior COVID-19 immunization. We adjusted for additional factors in Cox models, estimating hazard ratios up to 182 days (6 months) following booster dose. We estimated hazard ratios overall and within the following periods: 1–14, 15–42, 43–69, 70–97, 98–126, 127–152, and 155–182 days. Outcomes included a positive SARS-CoV-2 test, COVID-19 hospitalization, COVID-19 death, non-COVID-19 death, and fracture. Results: We matched 8,198,643 booster recipients with unboosted controls. Adjusted hazard ratios over 6-month follow-up were: positive SARS-CoV-2 test 0.75 (0.74, 0.75); COVID-19 hospitalization 0.30 (0.29, 0.31); COVID-19 death 0.11 (0.10, 0.14); non- COVID-19 death 0.22 (0.21, 0.23); and fracture 0.77 (0.75, 0.78). Estimated effectiveness of booster vaccines against severe COVID- 19-related outcomes peaked during the first 3 months following the booster dose. By 6 months, the cumulative incidence of positive SARS-CoV-2 test was higher in boosted than unboosted individuals. Conclusions: We estimate that COVID-19 booster vaccination, compared with no booster vaccination, provided substantial protection against COVID-19 hospitalization and COVID-19 death but only limited protection against positive SARS-CoV-2 test. Lower rates of fracture in boosted than unboosted individuals may suggest unmeasured confounding. Observational studies should report estimated vaccine effectiveness against nontarget and negative control outcomes. [ABSTRACT FROM AUTHOR]

Aims: 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. Methods: 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. Results: 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%). Conclusions: 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. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic many patients were switched from warfarin to direct)acting oral anticoagulants (DOACs), which require the creatinine clearance (CrCl) calculated to ensure the correct dose is prescribed to avoid bleeding or reduced efficacy. Aim: To identify the study population proportion prescribed a DOAC. Of these, the proportion with recorded: weight, estimated glomerular filtration rate (eGFR), creatinine, CrCl and atrial fibrillation (AF). To analyse the proportion of patients with recorded AF and CrCl prescribed a recommended DOAC dose. Design & setting: A retrospective cohort study of 20.5 million adult NHS patients’ electronic health records (EHRs) in England in the OpenSAFELY-TPP platform (January 2018–February 2023). Method: Patients on DOACs were analysed for age, sex, recorded weight, eGFR, creatinine, CrCl and AF. Prescribed DOAC doses in patients with recorded AF were compared with recommended doses for recorded CrCl and determined as either recommended, higher than recommended (overdose), or lower than recommended (underdose). Results: In February 2023, weight, eGFR, creatinine, CrCl, and AF were recorded in 72.8%, 92.4%, 94.3%, 73.5%, and 73.9% of study population, respectively. Both AF and CrCl were recorded for 56.7% of patients. Of these, 86.2% received the recommended, and 13.8% non-recommended, DOAC doses. Conclusion: CrCl is not recorded for a substantial number of patients on DOACs. We recommend that national organisations tasked with safety, collectively update guidance on the appropriate weight to use in the Cockcroft–Gault equation, clarify that CrCl is not equivalent to eGFR, and work with GP clinical system suppliers to standardise the calculation of CrCl in the EHR. [ABSTRACT FROM AUTHOR]

Previous studies have demonstrated the association between antibiotic use and severe COVID-19 outcomes. This study aimed to explore detailed antibiotic exposure characteristics among COVID-19 patients. Using the OpenSAFELY platform, which integrates extensive health data and covers 40% of the population in England, the study analysed 3.16 million COVID-19 patients with at least two prior antibiotic prescriptions. These patients were compared to up to six matched controls without hospitalisation records. A machine learning model categorised patients into ten groups based on their antibiotic exposure history over the three years before their COVID-19 diagnosis. The study found that for COVID-19 patients, the total number of prior antibiotic prescriptions, diversity of antibiotic types, broad-spectrum antibiotic prescriptions, time between first and last antibiotics, and recent antibiotic use were associated with an increased risk of severe COVID-19 outcomes. Patients in the highest decile of antibiotic exposure had an adjusted odds ratio of 4.8 for severe outcomes compared to those in the lowest decile. These findings suggest a potential link between extensive antibiotic use and the risk of severe COVID-19. This highlights the need for more judicious antibiotic prescribing in primary care, primarily for patients with higher risks of infection-related complications, which may better offset the potential adverse effects of repeated antibiotic use. [ABSTRACT FROM AUTHOR]

Author(s): Krishnan Bhaskaran 1,*, Christopher T. Rentsch 1, George Hickman 2, William J. Hulme 2, Anna Schultze 1, Helen J. Curtis 2, Kevin Wing 1, Charlotte Warren-Gash 1, Laurie Tomlinson [...], Background There is concern about medium to long-term adverse outcomes following acute Coronavirus Disease 2019 (COVID-19), but little relevant evidence exists. We aimed to investigate whether risks of hospital admission and death, overall and by specific cause, are raised following discharge from a COVID-19 hospitalisation. Methods and findings With the approval of NHS-England, we conducted a cohort study, using linked primary care and hospital data in OpenSAFELY to compare risks of hospital admission and death, overall and by specific cause, between people discharged from COVID-19 hospitalisation (February to December 2020) and surviving at least 1 week, and (i) demographically matched controls from the 2019 general population; and (ii) people discharged from influenza hospitalisation in 2017 to 2019. We used Cox regression adjusted for age, sex, ethnicity, obesity, smoking status, deprivation, and comorbidities considered potential risk factors for severe COVID-19 outcomes. We included 24,673 postdischarge COVID-19 patients, 123,362 general population controls, and 16,058 influenza controls, followed for [less than or equal to]315 days. COVID-19 patients had median age of 66 years, 13,733 (56%) were male, and 19,061 (77%) were of white ethnicity. Overall risk of hospitalisation or death (30,968 events) was higher in the COVID-19 group than general population controls (fully adjusted hazard ratio [aHR] 2.22, 2.14 to 2.30, p < 0.001) but slightly lower than the influenza group (aHR 0.95, 0.91 to 0.98, p = 0.004). All-cause mortality (7,439 events) was highest in the COVID-19 group (aHR 4.82, 4.48 to 5.19 versus general population controls [p < 0.001] and 1.74, 1.61 to 1.88 versus influenza controls [p < 0.001]). Risks for cause-specific outcomes were higher in COVID-19 survivors than in general population controls and largely similar or lower in COVID-19 compared with influenza patients. However, COVID-19 patients were more likely than influenza patients to be readmitted or die due to their initial infection or other lower respiratory tract infection (aHR 1.37, 1.22 to 1.54, p < 0.001) and to experience mental health or cognitive-related admission or death (aHR 1.37, 1.02 to 1.84, p = 0.039); in particular, COVID-19 survivors with preexisting dementia had higher risk of dementia hospitalisation or death (age- and sex-adjusted HR 2.47, 1.37 to 4.44, p = 0.002). Limitations of our study were that reasons for hospitalisation or death may have been misclassified in some cases due to inconsistent use of codes, and we did not have data to distinguish COVID-19 variants. Conclusions In this study, we observed that people discharged from a COVID-19 hospital admission had markedly higher risks for rehospitalisation and death than the general population, suggesting a substantial extra burden on healthcare. Most risks were similar to those observed after influenza hospitalisations, but COVID-19 patients had higher risks of all-cause mortality, readmission or death due to the initial infection, and dementia death, highlighting the importance of postdischarge monitoring.

The article discusses a study which examined the effectiveness of two doses of coronavirus disease 2019 (COVID-19) vaccine beyond six months in England. Topics include increase in the cumulative incidence of receiving a third dose of vaccine during the eight weeks following eligibility, basis of the effectiveness of two doses during later comparison periods, and important factors in interpreting estimated vaccine effectiveness (VE).

Aims: In 2017, two distinct interventions were implemented in Ireland and England to reduce prescribing of lidocaine medicated plasters. In Ireland, restrictions on reimbursement were introduced through implementation of an application system for reimbursement. In England, updated guidance on items which should not be routinely prescribed in primary care, including lidocaine plasters, was published. This study aims to compare how the interventions impacted prescribing of lidocaine plasters in these countries. Methods: We conducted an interrupted time‐series study using general practice data. For Ireland, monthly dispensing data (2015–2019) from the means‐tested General Medical Services (GMS) scheme was used. For England, data covered all patients. Outcomes were the rate of dispensings, quantity and costs of lidocaine plasters, and we modelled level and trend changes from the first full month of the policy/guidance change. Results: Ireland had higher rates of lidocaine dispensings compared to England throughout the study period; this was 15.22/1000 population immediately pre‐intervention, and there was equivalent to a 97.2% immediate reduction following the intervention. In England, the immediate pre‐intervention dispensing rate was 0.36/1000, with an immediate reduction of 0.0251/1000 (a 5.8% decrease), followed by a small but significant decrease in the monthly trend relative to the pre‐intervention trend of 0.0057 per month. Conclusions: Among two different interventions aiming to decrease low‐value lidocaine plaster prescribing, there was a substantially larger impact in Ireland of reimbursement restriction compared to issuing guidance in England. However, this is in the context of much higher baseline rates of use in Ireland compared to England. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has disrupted healthcare activity across a broad range of clinical services. The NHS stopped non-urgent work in March 2020, later recommending services be restored to near-normal levels before winter where possible. Aim: To describe changes in the volume and variation of coded clinical activity in general practice across six clinical areas: cardiovascular disease, diabetes, mental health, female and reproductive health, screening and related procedures, and processes related to medication. Design and setting: With the approval of NHS England, a cohort study was conducted of 23.8 million patient records in general practice, in situ using OpenSAFELY. Method: Common primary care activities were analysed using Clinical Terms Version 3 codes and keyword searches from January 2019 to December 2020, presenting median and deciles of code usage across practices per month. Results: Substantial and widespread changes in clinical activity in primary care were identified since the onset of the COVID-19 pandemic, with generally good recovery by December 2020. A few exceptions showed poor recovery and warrant further investigation, such as mental health (for example, for 'Depression interim review' the median occurrences across practices in December 2020 was down by 41.6% compared with December 2019). Conclusion: Granular NHS general practice data at population-scale can be used to monitor disruptions to healthcare services and guide the development of mitigation strategies. The authors are now developing real-time monitoring dashboards for the key measures identified in this study, as well as further studies using primary care data to monitor and mitigate the indirect health impacts of COVID-19 on the NHS. [ABSTRACT FROM AUTHOR]

The response of many governments to the COVID-19 pandemic has involved measures to control within- and between-household transmission, providing motivation to improve understanding of the absolute and relative risks in these contexts. Here, we perform exploratory, residual-based, and transmission-dynamic household analysis of the Office for National Statistics COVID-19 Infection Survey data from 26 April 2020 to 15 July 2021 in England. This provides evidence for: (i) temporally varying rates of introduction of infection into households broadly following the trajectory of the overall epidemic and vaccination programme; (ii) susceptible-Infectious transmission probabilities of within-household transmission in the 15-35% range; (iii) the emergence of the Alpha and Delta variants, with the former being around 50% more infectious than wildtype and 35% less infectious than Delta within households; (iv) significantly (in the range of 25-300%) more risk of bringing infection into the household for workers in patient-facing roles pre-vaccine; (v) increased risk for secondary school-age children of bringing the infection into the household when schools are open; (vi) increased risk for primary school-age children of bringing the infection into the household when schools were open since the emergence of new variants. [ABSTRACT FROM AUTHOR]

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) alpha variant (B.1.1.7) is associated with higher transmissibility than wild-type virus, becoming the dominant variant in England by January 2021. We aimed to describe the severity of the alpha variant in terms of the pathway of disease from testing positive to hospital admission and death. Methods With the approval of NHS England, we linked individual-level data from primary care with SARS-CoV-2 community testing, hospital admission, and Office for National Statistics all-cause death data. We used testing data with S-gene target failure as a proxy for distinguishing alpha and wild-type cases, and stratified Cox proportional hazards regression to compare the relative severity of alpha cases with wild-type diagnosed from 16 November 2020 to 11 January 2021. Results Using data from 185 234 people who tested positive for SARS-CoV-2 in the community (alpha = 93 153; wild-type = 92 081), in fully adjusted analysis accounting for individual-level demographics and comorbidities as well as regional variation in infection incidence, we found alpha associated with 73% higher hazards of all-cause death (adjusted hazard ratio [aHR]: 1.73; 95% confidence interval [CI]: 1.41–2.13; P  < .0001) and 62% higher hazards of hospital admission (1.62; 1.48–1.78; P  < .0001) compared with wild-type virus. Among patients already admitted to the intensive care unit, the association between alpha and increased all-cause mortality was smaller and the CI included the null (aHR: 1.20; 95% CI:.74–1.95; P  = .45). Conclusions The SARS-CoV-2 alpha variant is associated with an increased risk of both hospitalization and mortality than wild-type virus. [ABSTRACT FROM AUTHOR]

• COVID-19 vaccines save lives, but it is important to understand rare adverse events. • We found a small increased rate of GBS and Bell's palsy after a first dose of ChAdOx1 vaccine. • If these relationships are causal, the absolute attributable risks are small. • No increase in GBS was found after the second dose of ChAdOx1. • All results were reassuring for the safety of mRNA vaccines. We investigated the potential association of COVID-19 vaccination with three acute neurological events: Guillain-Barré syndrome (GBS), transverse myelitis and Bell's palsy. With the approval of NHS England we analysed primary care data from >17 million patients in England linked to emergency care, hospital admission and mortality records in the OpenSAFELY platform. Separately for each vaccine brand, we used a self-controlled case series design to estimate the incidence rate ratio for each outcome in the period following vaccination (4–42 days for GBS, 4–28 days for transverse myelitis and Bell's palsy) compared to a within-person baseline, using conditional Poisson regression. Among 7,783,441 ChAdOx1 vaccinees, there was an increased rate of GBS (N = 517; incidence rate ratio 2·85; 95% CI2·33–3·47) and Bell's palsy (N = 5,350; 1·39; 1·27–1·53) following a first dose of ChAdOx1 vaccine, corresponding to 11.0 additional cases of GBS and 17.9 cases of Bell's palsy per 1 million vaccinees if causal. For GBS this applied to the first, but not the second, dose. There was no clear evidence of an association of ChAdOx1 vaccination with transverse myelitis (N = 199; 1·51; 0·96–2·37). Among 5,729,152 BNT162b2 vaccinees, there was no evidence of any association with GBS (N = 283; 1·09; 0·75–1·57), transverse myelitis (N = 109; 1·62; 0·86–3·03) or Bell's palsy (N = 3,609; 0·89; 0·76–1·03). Among 255,446 mRNA-1273 vaccine recipients there was no evidence of an association with Bell's palsy (N = 78; 0·88, 0·32–2·42). COVID-19 vaccines save lives, but it is important to understand rare adverse events. We observed a short-term increased rate of Guillain-Barré syndrome and Bell's palsy after first dose of ChAdOx1 vaccine. The absolute risk, assuming a causal effect attributable to vaccination, was low. [ABSTRACT FROM AUTHOR]

Background: In March 2022, a concerning rise in cases of unexplained pediatric hepatitis was reported in multiple countries. Cases were defined as acute hepatitis with serum transaminases >500 U/L (aspartate transaminase [AST] or alanine transaminase [ALT]) in children aged 16 years or younger. We explored a simple federated data analytics method to search for evidence of unreported cases using routinely held data. We conducted a pragmatic survey to analyze changes in the proportion of hospitalized children with elevated AST or ALT over time. In addition, we studied the feasibility of using routinely collected clinical laboratory results to detect or follow-up the outbreak of an infectious disease., Objective: We explored a simple federated data analytics method to search for evidence of unreported cases using routinely held data., Methods: We provided hospitals with a simple computational tool to enable laboratories to share nondisclosive summary-level data. Summary statistics for AST and ALT measurements were collected from the last 10 years across all age groups. Measurements were considered elevated if ALT or AST was >200 U/L. The rate of elevated AST or ALT test for 3-week- to 5-year-olds was compared between a period of interest in which cases of hepatitis were reported (December 1, 2021, to August 31, 2022) and a prepandemic baseline period (January 1, 2012, to December 31, 2019). We calculated a z score, which measures the extent to which the rate for elevated ALT or AST was higher or lower in the period of interest compared to a baseline period, for the 3-week- to 5-year-olds., Results: Our approach of sharing a simple software tool for local use enabled rapid, federated data analysis. A total of 34 hospitals in the United Kingdom, the Netherlands, Ireland, and Curaçao were asked to contribute summary data, and 30 (88%) submitted their data. For all locations combined, the rate of elevated AST or ALT measurements in the period of interest was not elevated (z score=-0.46; P=.64). Results from individual regions were discordant, with a higher rate of elevated AST or ALT values in the Netherlands (z score=4.48; P<.001), driven by results from a single center in Utrecht. We did not observe any clear indication of changes in primary care activity or test results in the same period., Conclusions: Hospital laboratories collect large amounts of data on a daily basis that can potentially be of use for disease surveillance, but these are currently not optimally used. Federated analytics using nondisclosive, summary-level laboratory data sharing was successful, safe, and efficient. The approach holds potential as a tool for pandemic surveillance in future outbreaks. Our findings do not indicate the presence of a broader outbreak of mild hepatitis cases among young children, although there was an increase in elevated AST or ALT values locally in the Netherlands., (© Maaike C Swets, Steven R Kerr, Brian MacKenna, Louis Fisher, Merel van Wijnen, Diederik Brandwagt, Paul W Schenk, Pieter Fraaij, Leonardus G Visser, Sebastian Bacon, Amir Mehrkar, Alistair Nichol, Patrick Twomey, Philippa C Matthews, ISARIC4C Hepatitis Study Group, Malcolm G Semple, Geert H Groeneveld, Ben Goldacre, Iain Jones, J Kenneth Baillie. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org).)

Objectives: To examine the impact of the COVID-19 pandemic on deprivation-related inequalities in hospitalisations for cardiovascular disease (CVD) conditions in Denmark and England between March 2018 and December 2021., Design: Time-series studies in England and Denmark., Setting: With the approval of National Health Service England, we used English primary care electronic health records, linked to secondary care and death registry data through the OpenSAFELY platform and nationwide Danish health registry data., Participants: We included adults aged 18 and over without missing age, sex or deprivation information. On 1 March 2020, 16 234 700 people in England and 4 491 336 people in Denmark met the inclusion criteria., Primary Outcome Measures: Hospital admissions with the primary reason for myocardial infarction (MI), ischaemic or haemorrhagic stroke, heart failure and venous thromboembolism (VTE)., Results: We saw deprivation gradients in monthly CVD hospitalisations in both countries, with differences more pronounced in Denmark. Based on pre-pandemic trends, in England, there were an estimated 2608 fewer admissions than expected for heart failure in the most deprived quintile during the pandemic compared with an estimated 979 fewer admissions in the least deprived quintile. For all other outcomes, there was little variation by deprivation quintile. In Denmark, there were an estimated 1013 fewer admissions than expected over the pandemic for MI in the most deprived quintile compared with 619 in the least deprived quintile. Similar trends were seen for stroke and VTE, though absolute numbers were smaller. Heart failure admissions were similar to pre-pandemic levels with little variation by deprivation quintile., Conclusions: Overall, we did not find that the pandemic substantially worsened pre-existing deprivation-related differences in CVD hospitalisations, though there were exceptions in both countries., Competing Interests: Competing interests: REC has personal shares in AstraZeneca (AZ) unrelated to this work. BM is also employed by National Health Service ( NHS) England (all declarations are openly available at: https://www.whopaysthisdoctor.org/doctor/491/active ). JH has grant funding from UKRI and the Wellcome Trust, has a patent with Juli Health unrelated to this work and has received consultancy fees from Juli Health and the Wellcome Trust unrelated to this work. RM is supported by Barts Charity (MGU0504), receives salary contributions from Genes & Health and has received consultancy fees from Amgen. JKQ has grants from MRC, HDR UK, GlaxoSmithKline (GSK), BI, Asthma + Lung UK and AZ and has received fees from GSK, Evidera, AZ and Insmed. SL was co-founder and co-chair of the RECORD steering committee and has a leadership role at Health Data Research UK. KM has received consultancy fees from Amgen. LAT has grant funding from MRC, the Wellcome Trust, has consulted for Bayer and is on the MHRA expert advisory group (Women’s health) and is a member of four non-industry funded trial advisory committees (unpaid). AYSW is funded by British Heart Foundation (FS/19/19/34175) and AIR@InnoHK administered by Innovation and Technology Commission. AM has received consultancy fees from induction health and is a member of RCGP health informatics group and the NHS Digital GP data Professional Advisory Group. Department of Clinical Epidemiology, Aarhus University, receives funding for other studies from companies in the form of research grants to (and administered by) Aarhus University. None of these studies have any relation to the present study. All other authors declare no competing interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Objectives: Long-term sickness absence from employment has negative consequences for the economy and can lead to widened health inequalities. Sick notes (also called 'fit notes') are issued by general practitioners when a person cannot work for health reasons for more than 7 days. We quantified the sick note rate in people with evidence of COVID-19 in 2020, 2021 and 2022, as an indication of the burden for people recovering from COVID-19., Design: Cohort study., Setting: With National Health Service (NHS) England approval, we used routine clinical data (primary care, hospital and COVID-19 testing records) within the OpenSAFELY-TPP database., Participants: People 18-64 years with a recorded positive test or diagnosis of COVID-19 in 2020 (n=365 421), 2021 (n=1 206 555) or 2022 (n=1 321 313); general population matched in age, sex and region in 2019 (n=3 140 326), 2020 (n=3 439 534), 2021 (n=4 571 469) and 2022 (n=4 818 870); people hospitalised with pneumonia in 2019 (n=29 673)., Primary Outcome Measure: Receipt of a sick note in primary care., Results: Among people with a positive SARS-CoV-2 test or COVID-19 diagnosis, the sick note rate was 4.88 per 100 person-months (95% CI 4.83 to 4.93) in 2020, 2.66 (95% CI 2.64 to 2.67) in 2021 and 1.73 (95% CI 1.72 to 1.73) in 2022. Compared with the age, sex and region-matched general population, the adjusted HR for receipt of a sick note over the entire follow-up period (up to 10 months) was 4.07 (95% CI 4.02 to 4.12) in 2020 decreasing to 1.57 (95% CI 1.56 to 1.58) in 2022. The HR was highest in the first 30 days postdiagnosis in all years. Among people hospitalised with COVID-19, after adjustment, the sick note rate was lower than in people hospitalised with pneumonia., Conclusions: Given the under-recording of postacute COVID-19-related symptoms, these findings contribute a valuable perspective on the long-term effects of COVID-19. Despite likely underestimation of the sick note rate, sick notes were issued more frequently to people with COVID-19 compared with those without, even in an era when most people are vaccinated. Most sick notes occurred in the first 30 days postdiagnosis, but the increased risk several months postdiagnosis may provide further evidence of the long-term impact., Competing Interests: Competing interests: Over the past 5 years, BG has received research funding from the Laura and John Arnold Foundation, the NHS National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, 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 (HDRUK), the Health Foundation and the WHO; he also receives personal income from speaking and writing for lay audiences on the misuse of science. CB is an employee of TPP. BM is also employed by NHS England working on medicines policy and clinical lead for primary care medicines data., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Background: Numeric results of pathology tests are sometimes returned as a range rather than a precise value, e.g. "<10". In health data research, test result values above or below clinical threshold values are often used to categorise patients into groups; however comparators (<, > etc) are typically stored separately to the numeric values and often ignored, but may influence interpretation., Methods: With the approval of NHS England we used routine clinical data from 24 million patients in OpenSAFELY to identify pathology tests with comparators commonly attached to result values. For each test we report: the proportion returned with comparators present, split by comparator type and geographic region; the specific numeric result values returned with comparators, and the associated reference limits., Results: We identified 11 common test codes where at least one in four results had comparators. Three codes related to glomerular filtration rate (GFR) tests/calculations, with 31-45% of results returned with "≥" comparators. At least 90% of tests with numeric values 60 and 90 represented ranges (≥60 and ≥90 respectively) rather than exact values. The other tests - four blood tests (Nucleated red blood cell count, Plasma C reactive protein, Tissue transglutaminase immunoglobulin A, and Rheumatoid factor), two urine tests (albumin/microalbumin) and two faecal tests (calprotectin and quantitative faecal immunochemical test) - were returned with "≤" comparators (29-86%)., Conclusions: Comparators appear commonly in certain pathology tests in electronic health records. For most common affected tests, we expect there to be minimal implications for researchers for most use-cases. However, care should be taken around whether results falling exactly on clinical threshold values should be considered "normal" or "abnormal". Results from GFR tests/calculations cannot reliably distinguish between mild kidney disease (stage G2, 60-<90) versus healthy kidney function (90+). More broadly, health data researchers using numeric test result values should consider the impact of comparators., Competing Interests: Competing interests: Over the past five years 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, 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 (HDRUK), the Health Foundation, and the World Health Organisation; he also receives personal income from speaking and writing for lay audiences on the misuse of science., (Copyright: © 2024 Curtis HJ et al.)

Background: We have previously demonstrated that opioid prescribing increased by 127% between 1998 and 2016. New policies aimed at tackling this increasing trend have been recommended by public health bodies, and there is some evidence that progress is being made., Objective: We sought to extend our previous work and develop a data-driven approach to identify general practices and clinical commissioning groups (CCGs) whose prescribing data suggest that interventions to reduce the prescribing of opioids may have been successfully implemented., Methods: We analyzed 5 years of prescribing data (December 2014 to November 2019) for 3 opioid prescribing measures-total opioid prescribing as oral morphine equivalent per 1000 registered population, the number of high-dose opioids prescribed per 1000 registered population, and the number of high-dose opioids as a percentage of total opioids prescribed. Using a data-driven approach, we applied a modified version of our change detection Python library to identify reductions in these measures over time, which may be consistent with the successful implementation of an intervention to reduce opioid prescribing. This analysis was carried out for general practices and CCGs, and organizations were ranked according to the change in prescribing rate., Results: We identified a reduction in total opioid prescribing in 94 (49.2%) out of 191 CCGs, with a median reduction of 15.1 (IQR 11.8-18.7; range 9.0-32.8) in total oral morphine equivalence per 1000 patients. We present data for the 3 CCGs and practices demonstrating the biggest reduction in opioid prescribing for each of the 3 opioid prescribing measures. We observed a 40% proportional drop (8.9% absolute reduction) in the regular prescribing of high-dose opioids (measured as a percentage of regular opioids) in the highest-ranked CCG (North Tyneside); a 99% drop in this same measure was found in several practices (44%-95% absolute reduction). Decile plots demonstrate that CCGs exhibiting large reductions in opioid prescribing do so via slow and gradual reductions over a long period of time (typically over a period of 2 years); in contrast, practices exhibiting large reductions do so rapidly over a much shorter period of time., Conclusions: By applying 1 of our existing analysis tools to a national data set, we were able to identify rapid and maintained changes in opioid prescribing within practices and CCGs and rank organizations by the magnitude of reduction. Highly ranked organizations are candidates for further qualitative research into intervention design and implementation., (©Lisa EM Hopcroft, Helen J Curtis, Richard Croker, Felix Pretis, Peter Inglesby, David Evans, Sebastian Bacon, Ben Goldacre, Alex J Walker, Brian MacKenna. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 05.06.2024.)