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Objectives To assess the association between routinely prescribed non-steroidal anti-inflammatory drugs (NSAIDs) and deaths from COVID-19 using OpenSAFELY, a secure analytical platform.Methods We conducted two cohort studies from 1 March to 14 June 2020. Working on behalf of National Health Service England, we used routine clinical data in England linked to death data. In study 1, we identified people with an NSAID prescription in the last 3 years from the general population. In study 2, we identified people with rheumatoid arthritis/osteoarthritis. We defined exposure as current NSAID prescription within the 4 months before 1 March 2020. We used Cox regression to estimate HRs for COVID-19 related death in people currently prescribed NSAIDs, compared with those not currently prescribed NSAIDs, accounting for age, sex, comorbidities, other medications and geographical region.Results In study 1, we included 536 423 current NSAID users and 1 927 284 non-users in the general population. We observed no evidence of difference in risk of COVID-19 related death associated with current use (HR 0.96, 95% CI 0.80 to 1.14) in the multivariable-adjusted model. In study 2, we included 1 708 781 people with rheumatoid arthritis/osteoarthritis, of whom 175 495 (10%) were current NSAID users. In the multivariable-adjusted model, we observed a lower risk of COVID-19 related death (HR 0.78, 95% CI 0.64 to 0.94) associated with current use of NSAID versus non-use.Conclusions We found no evidence of a harmful effect of routinely prescribed NSAIDs on COVID-19 related deaths. Risks of COVID-19 do not need to influence decisions about the routine therapeutic use of NSAIDs.https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.http://dx.doi.org/10.1136/annrheumdis-2020-219517

Background On December 8th 2020, NHS England administered the first COVID-19 vaccination as part of an ambitious vaccination programme during a global health emergency. Aims To develop a framework for detailed near-real-time monitoring of COVID-19 vaccine roll-out; to describe trends and variation in coverage by geographic area, and between key clinical and demographic patient groups. Methods Working on behalf of NHS England we used routine clinical data from 23.4 million patients to conduct a retrospective cohort study of comprehensive electronic health record data in NHS England, using the OpenSAFELY-TPP platform which covers approximately 40% of the general population in England with weekly data updates. We developed algorithms to identify key demographic and clinical sub-groups within this population and generated descriptive statistics on proportion of eligible patients receiving the vaccine among key Joint Committee on Vaccination and Immunisation (JCVI) target groups. Results Between December 8th and January 13th 961,580 people out of 23.4m in our dataset received a COVID-19 vaccine. Of 1,160,062 patients aged 80 or over and not living in a care home (currently targeted by JCVI) 476,375 had been vaccinated in total (41.1%). We observed a substantial divergence in vaccination by ethnicity within this group (White 42.5% vaccinated, Black 20.5%) and across rankings of deprivation (least deprived 44.7%, most deprived 37.9%). Patients with pre-existing medical conditions were equally likely, or more likely, to have received a vaccine across most co-morbidity groups with two exceptions: severe mental illness (30.3% vaccinated) and learning disability (28.1%). We identify substantial variation in vaccination among the over-80s between Sustainability and Transformation Partnerships (STPs; Range 12%-74%); lower vaccination rates among ethnic minority and deprived groups was observed in most but not all STPs. In the 70-79 age cohort 74,108 people (3.6%) had been vaccinated. 378,921 vaccine recipients under 70 and not identifiably resident in a care home were presumed to be health or social care workers; 32,174 recipients were identified as older aged care home residents (33.2% coverage). Of all those vaccinated, 169,472 had received a second dose (17.6%). Conclusions The NHS in England has rapidly delivered mass vaccination. We were able to deploy a data monitoring framework across small clinical subgroups using linked patient-level NHS data on 23.4 million people with very short delays from vaccine administration to completed analysis. Targeted activity may be needed to address lower vaccination rates observed among certain key groups: ethnic minorities, people living in areas of higher deprivation, and those with severe mental illness or learning disabilities. However we note that this data is only from the first preliminary weeks of the vaccination programme. Variation in vaccination coverage between groups and regions will have many complex drivers, the figures presented in this manuscript require thoughtful interpretation to support a rapidly evolving NHS vaccination campaign; we are sharing local level data with national and regional NHS teams on request.

Background The COVID-19 pandemic has disrupted healthcare activity globally. The NHS in England stopped most non-urgent work by March 2020, but later recommended that services should be restored to near-normal levels before winter where possible. The authors are developing the OpenSAFELY NHS Service Restoration Observatory , using data to describe changes in service activity during COVID-19, and reviewing signals for action with commissioners, researchers and clinicians. Here we report phase one: generating, managing, and describing the data. Objective To describe the volume and variation of coded clinical activity in English primary care across 23.8 million patients’ records, taking respiratory disease and laboratory procedures as key examples. Methods Working on behalf of NHS England we developed an open source software framework for data management and analysis to describe trends and variation in clinical activity across primary care EHR data on 23.8 million patients; and conducted a population cohort-based study to describe activity using CTV3 coding hierarchy and keyword searches from January 2019-September 2020. Results Much activity recorded in general practice declined to some extent during the pandemic, but largely recovered by September 2020, with some exceptions. There was a large drop in coded activity for commonly used laboratory tests, with broad recovery to pre-pandemic levels by September. One exception was blood coagulation tests such as International Normalised Ratio (INR), with a smaller reduction (median tests per 1000 patients in 2020: February 8.0; April 6.2; September 7.0). The overall pattern of recording for respiratory symptoms was less affected, following an expected seasonal pattern and classified as “no change” from the previous year. Respiratory tract infections exhibited a sustained drop compared with pre-pandemic levels, not returning to pre-pandemic levels by September 2020. Various COVID-19 codes increased through the period. We observed a small decline associated with high level codes for long-term respiratory conditions such as chronic obstructive pulmonary disease (COPD) and asthma. Asthma annual reviews experienced a small drop but since recovered, while COPD annual reviews remain below baseline. Conclusions We successfully delivered an open source software framework to describe trends and variation in clinical activity across an unprecedented scale of primary care data. The COVD-19 pandemic led to a substantial change in healthcare activity. Most laboratory tests showed substantial reduction, largely recovering to near-normal levels by September 2020, with some important tests less affected. Records of respiratory infections decreased with the exception of codes related to COVID-19, whilst activity of other respiratory disease codes was mixed. We are expanding the NHS Service Restoration Observatory in collaboration with clinicians, commissioners and researchers and welcome feedback.

Background Thromboembolism has been reported as a consequence of severe COVID-19. Although warfarin is a commonly used anticoagulant, it acts by antagonising vitamin K, which is low in patients with severe COVID-19. To date, the clinical evidence on the impact of regular use of warfarin on COVID-19-related thromboembolism is lacking. Methods On behalf of NHS England, we conducted a population-based cohort study investigating the association between warfarin and COVID-19 outcomes compared with direct oral anticoagulants (DOACs). We used the OpenSAFELY platform to analyse primary care data and pseudonymously linked SARS-CoV-2 antigen testing data, hospital admissions and death records from England. We used Cox regression to estimate hazard ratios (HRs) for COVID-19-related outcomes comparing warfarin with DOACs in people with non-valvular atrial fibrillation. We also conducted negative control outcome analyses (being tested for SARS-CoV-2 and non-COVID-19 death) to assess the potential impact of confounding. Results A total of 92,339 warfarin users and 280,407 DOAC users were included. We observed a lower risk of all outcomes associated with warfarin versus DOACs [testing positive for SARS-CoV-2, HR 0.73 (95% CI 0.68–0.79); COVID-19-related hospital admission, HR 0.75 (95% CI 0.68–0.83); COVID-19-related deaths, HR 0.74 (95% CI 0.66–0.83)]. A lower risk of negative control outcomes associated with warfarin versus DOACs was also observed [being tested for SARS-CoV-2, HR 0.80 (95% CI 0.79–0.81); non-COVID-19 deaths, HR 0.79 (95% CI 0.76–0.83)]. Conclusions Overall, this study shows no evidence of harmful effects of warfarin on severe COVID-19 disease.

BackgroundIt is unclear if people with immune-mediated inflammatory diseases (IMIDs) (joint, bowel and skin) and on immune modifying therapy have increased risk of serious COVID-19 outcomes.MethodsWith the approval of NHS England we conducted a cohort study, using OpenSAFELY, analysingroutinely-collected primary care data linked to hospital admission, death and previously unavailable hospital prescription data. We used Cox regression (adjusting for confounders) to estimate hazard ratios (HR) comparing risk of COVID-19-death, death/critical care admission, and hospitalisation (March to September 2020) in: 1) people with IMIDs compared to the general population; and 2) people with IMIDs on targeted immune modifying drugs (e.g., biologics) compared to standard systemic treatment (e.g., methotrexate).FindingsWe identified 17,672,065 adults; of 1,163,438 (7%) with IMIDs, 19,119 people received targeted immune modifying drugs, and 200,813 received standard systemics. We saw evidence of increased COVID-19-death (HR 1.23, 95%CI 1.20, 1.27), and COVID-19 hospitalisation (HR 1.32, 95%CI 1.29, 1.35) in individuals with IMIDs overall compared to individuals without IMIDs of the same age, sex, deprivation and smoking status. We saw no evidence of increased COVID-19 deaths with targeted compared to standard systemic treatments (HR 1.03, 95%CI 0.80, 1.33). There was no evidence of increased COVID-19-related death in those prescribed TNF inhibitors, IL-12/23, IL7, IL-6 or JAK inhibitors compared to standard systemics. Rituximab was associated with increased COVID-19 death (HR 1.68, 95%CI 1.11, 2.56); however, this finding may relate to confounding.InterpretationCOVID-19 death and hospitalisation was higher in people with IMIDs. We saw no increased risk of adverse COVID-19 outcomes in those on most targeted immune modifying drugs for IMIDs compared to standard systemics.RESEARCH IN CONTEXTEvidence before this studyWe searched PubMed on May 19th, 2021, using the terms “COVID-19”, “SARS-CoV-2” and “rheumatoid arthritis”, “psoriatic arthritis” “ankylosing spondylitis”, “Crohn’s disease” “ulcerative colitis” “hidradenitis suppurativa” and “psoriasis”, to identify primary research articles examining severe COVID-19 outcome risk in individuals with immune-mediated inflammatory diseases (IMIDs) and those on immune modifying therapy. The studies identified (including matched cohort studies and studies in disease-specific registries) were limited by small sample sizes and number of outcomes. Most studies did not show a signal of increased adverse COVID-19 outcomes in those on targeted therapies, with the exception of rituximab. Additionally, disease-specific registries are subject to selection bias and lack denominator populations.Added value of the studyIn our large population-based study of 17 million individuals, including 1 million people with IMIDs and just under 200,000 receiving immune modifying medications, we saw evidence that people with IMIDs had an increased risk of COVID-19-related death compared to the general population after adjusting for potential confounders (age, sex, deprivation, smoking status) (HR 1.23, 95%CI 1.20, 1.27). We saw differences by IMID type, with COVID-19-related death being increased by the most in people with inflammatory joint disease (HR 1.47, 95%CI 1.40, 1.54). We also saw some evidence that those with IMIDs were more likely, compared to the general population, to have COVID-19-related critical care admission/death (HR 1.24, 95%CI 1.21, 1.28) and hospitalisation (HR 1.32, 95%CI 1.29, 1.35).Compared to people with IMIDs taking standard systemics, we saw no evidence of differences in severe COVID-19-related outcomes with TNF inhibitors, IL-17 inhibitors, IL-12/23 inhibitors, IL-6 inhibitors and JAK inhibitors. However, there was some evidence that rituximab was associated with an increased risk of COVID-19-related death (HR 1.68, 95%CI 1.11, 2.56) and death/critical care admission (HR 1.92, 95%CI 1.31, 2.81). We also saw evidence of an increase in COVID-19-related hospital admissions in people prescribed rituximab (HR 1.59, 95%CI 1.16, 2.18) or JAK inhibition (HR 1.81, 95%CI 1.09, 3.01) compared to those on standard systemics, although this could be related to worse underlying health rather than the drugs themselves, and numbers of events were small.This is the first study to our knowledge to use high-cost drug data on medicines supplied by hospitals at a national scale in England (to identify targeted therapies). The availability of these data fills an important gap in the medication record of those with more specialist conditions treated by hospitals creating an important opportunity to generate insights to these conditions and these medicationsImplications of all of the available evidenceOur study offers insights into future risk mitigation strategies and SARS-CoV-2 vaccination priorities for individuals with IMIDs, as it highlights that those with IMIDs and those taking rituximab may be at risk of severe COVID-19 outcomes. Critically, our study does not show a link between most targeted immune modifying medications compared to standard systemics and severe COVID-19 outcomes. However, the increased risk of adverse COVID-19 outcomes that we saw in people with IMIDs and those treated with rituximab merits further study.

BackgroundAll patients in England within vaccine priority groups were offered a COVID-19 vaccine by mid-April 2021. Clinical record systems contain codes to denote when such an offer has been declined by a patient (although these can in some cases be entered for a variety of other reasons including vaccination delay, or other administrative issues). We set out to describe the patterns of usage of codes for COVID-19 vaccines being declined.MethodsWith the approval of NHS England and using the full pseudonymised primary care records for 57.9 million NHS patients, we identified all patients in key vaccine priority groups: aged over 50, or over 16 and at increased risk from COVID-19 (Clinically Extremely Vulnerable [CEV] or otherwise “at risk”). We describe the proportion of patients recorded as declining a COVID-19 vaccination for each priority group, and by other clinical and demographic factors; whether patients recorded as “declined” subsequently went on to receive a vaccination; and the distribution of code usage across GP practices.ResultsOf 24.5 million patients in priority groups as of May 25th 2021, 89.2% had received a vaccine, 8.8% had neither a vaccination nor a decline recorded, and 663,033 (2.7%) had a decline code recorded. Of patients with a recorded decline, 125,587 (18.9%) were subsequently vaccinated. Subsequent vaccination was slightly more common in the South Asian population than other ethnicities (e.g. 32.3% vs 22.8%, over 65s). The proportion of declining-unvaccinated patients varied strongly with ethnicity (Black 15.3%, South Asian 5.6%, White 1.5% in over 80s); and was higher in patients from more deprived areas. COVID-19 vaccine decline codes were present in almost all practices (98.8%), but with wide variation between practices in rates of usage. Among all priority groups, declining-unvaccinated status was most common in CEV (3.3%).ConclusionsClinical codes indicative of COVID-19 vaccinations being declined are widely used in English general practice. They are substantially more common among Black and South Asian patients, and patients from more deprived areas. There is a need for more detailed survey and/or qualitative research with patients and clinicians to determine the most common reasons for these recorded declines.

National guidance was issued during the COVID-19 pandemic to switch patients on warfarin to direct oral anticoagulants (DOACs) where appropriate as these require less frequent blood testing. DOACs are not recommended for patients with mechanical heart valves. We conducted a retrospective cohort study of DOAC prescribing in people with a record of a mechanical heart valve between September 2019 and May 2021, and describe the characteristics of this population. We identified 15,457 individuals with a mechanical heart valve recorded in their records, of whom 1058 (6.8%) had been prescribed a DOAC during the study period. 767 individuals with a record of a mechanical heart valve were currently prescribed a DOAC as of May 31st 2021. This is suggestive of inappropriate prescribing of DOACs in individuals with mechanical heart valves. Direct alerts have been issued to clinicians through their EHR software informing the issue. We show that the OpenSAFELY platform can be used for rapid audit and feedback to mitigate the indirect health impacts of COVID-19 on the NHS. We will monitor changes in prescribing for this risk group over the following months.

BackgroundThere is concern about medium to long-term adverse outcomes following acute 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 FindingsWorking on behalf of NHS-England, we used 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-December 2020), and (i) demographically-matched controls from the 2019 general population; (ii) people discharged from influenza hospitalisation in 2017-19. We used Cox regression adjusted for personal and clinical characteristics.24,673 post-discharge COVID-19 patients, 123,362 general population controls, and 16,058 influenza controls were followed for ≤315 days. Overall risk of hospitalisation or death (30968 events) was higher in the COVID-19 group than general population controls (adjusted-HR 2.23, 2.14-2.31) but similar to the influenza group (adjusted-HR 0.94, 0.91-0.98). All-cause mortality (7439 events) was highest in the COVID-19 group (adjusted-HR 4.97, 4.58-5.40 vs general population controls and 1.73, 1.60-1.87 vs influenza controls). Risks for cause-specific outcomes were higher in COVID-19 survivors than general population controls, and largely comparable between COVID-19 and influenza patients. However, COVID-19 patients were more likely than influenza patients to be readmitted/die due to their initial infection/other lower respiratory tract infection (adjusted-HR 1.37, 1.22-1.54), and to experience mental health or cognitive-related admission/death (adjusted-HR 1.36, 1.01-2.83); in particular, COVID-19 survivors with pre-existing dementia had higher risk of dementia death. One limitation of our study is that reasons for hospitalisation/death may have been misclassified in some cases due to inconsistent use of codes.ConclusionsPeople 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, readmissions/death due to the initial infection, and dementia death, highlighting the importance of post-discharge monitoring.

BackgroundResidents in care homes have been severely impacted by the COVID-19 pandemic. We describe trends in risk of mortality among care home residents compared to residents in private homes in England.MethodsOn behalf of NHS England, we used OpenSAFELY-TPP, an analytics platform running across the linked electronic health records of approximately a third of the English population, to calculate monthly age-standardised risks of death due to all causes and COVID-19 among adults aged >=65 years between 1/2/2019 and 31/03/2021. Care home residents were identified using linkage to the Care and Quality Commission.FindingsWe included 4,329,078 people aged 65 years or older on the 1st of February 2019, 2.2% of whom were classified as residing in a care or nursing home. Age-standardised mortality risks were approximately 10 times higher among care home residents compared to non-residents in February 2019 residents (CMF = 10.59, 95%CI = 9.51, 11.81 among women, CMF = 10.82, 95%CI = 9.89, 11.84 among men). This increased to more than 17 times in April 2020 (CMF = 17.52, 95%CI = 16.38, 18.74 among women, CMF = 18.12, 95%CI = 17.17 – 19.12 among men) before returning to pre-pandemic levels in June 2020. CMFs did not increase during the second wave, despite a rise in the absolute age-standardised COVID-19 mortality risks.InterpretationThe first COVID-19 wave had a disproportionate impact on care home residents in England compared to older private home residents. A degree of immunity, improved protective measures or changes in the underlying frailty of the populations may explain the lack of an increase in the relative mortality risks during the second wave. The care home population should be prioritised for measures aimed at controlling the spread of COVID-19.FundingMedical Research Council MR/V015737/1

BackgroundCOVID-19 has disproportionately affected minority ethnic populations in the UK. Our aim was to quantify ethnic differences in SARS-CoV-2 infection and COVID-19 outcomes during the first and second waves of the COVID-19 pandemic in England.MethodsWe conducted an observational cohort study of adults (aged ≥18 years) registered with primary care practices in England for whom electronic health records were available through the OpenSAFELY platform, and who had at least 1 year of continuous registration at the start of each study period (Feb 1 to Aug 3, 2020 [wave 1], and Sept 1 to Dec 31, 2020 [wave 2]). Individual-level primary care data were linked to data from other sources on the outcomes of interest: SARS-CoV-2 testing and positive test results and COVID-19-related hospital admissions, intensive care unit (ICU) admissions, and death. The exposure was self-reported ethnicity as captured on the primary care record, grouped into five high-level census categories (White, South Asian, Black, other, and mixed) and 16 subcategories across these five categories, as well as an unknown ethnicity category. We used multivariable Cox regression to examine ethnic differences in the outcomes of interest. Models were adjusted for age, sex, deprivation, clinical factors and comorbidities, and household size, with stratification by geographical region.FindingsOf 17 288 532 adults included in the study (excluding care home residents), 10 877 978 (62·9%) were White, 1 025 319 (5·9%) were South Asian, 340 912 (2·0%) were Black, 170 484 (1·0%) were of mixed ethnicity, 320 788 (1·9%) were of other ethnicity, and 4 553 051 (26·3%) were of unknown ethnicity. In wave 1, the likelihood of being tested for SARS-CoV-2 infection was slightly higher in the South Asian group (adjusted hazard ratio 1·08 [95% CI 1·07-1·09]), Black group (1·08 [1·06-1·09]), and mixed ethnicity group (1·04 [1·02-1·05]) and was decreased in the other ethnicity group (0·77 [0·76-0·78]) relative to the White group. The risk of testing positive for SARS-CoV-2 infection was higher in the South Asian group (1·99 [1·94-2·04]), Black group (1·69 [1·62-1·77]), mixed ethnicity group (1·49 [1·39-1·59]), and other ethnicity group (1·20 [1·14-1·28]). Compared with the White group, the four remaining high-level ethnic groups had an increased risk of COVID-19-related hospitalisation (South Asian group 1·48 [1·41-1·55], Black group 1·78 [1·67-1·90], mixed ethnicity group 1·63 [1·45-1·83], other ethnicity group 1·54 [1·41-1·69]), COVID-19-related ICU admission (2·18 [1·92-2·48], 3·12 [2·65-3·67], 2·96 [2·26-3·87], 3·18 [2·58-3·93]), and death (1·26 [1·15-1·37], 1·51 [1·31-1·71], 1·41 [1·11-1·81], 1·22 [1·00-1·48]). In wave 2, the risks of hospitalisation, ICU admission, and death relative to the White group were increased in the South Asian group but attenuated for the Black group compared with these risks in wave 1. Disaggregation into 16 ethnicity groups showed important heterogeneity within the five broader categories.InterpretationSome minority ethnic populations in England have excess risks of testing positive for SARS-CoV-2 and of adverse COVID-19 outcomes compared with the White population, even after accounting for differences in sociodemographic, clinical, and household characteristics. Causes are likely to be multifactorial, and delineating the exact mechanisms is crucial. Tackling ethnic inequalities will require action across many fronts, including reducing structural inequalities, addressing barriers to equitable care, and improving uptake of testing and vaccination.FundingMedical Research Council.

Background: Care home residents have been severely affected by the COVID-19 pandemic. Electronic Health Records (EHR) hold significant potential for studying the healthcare needs of this vulnerable population; however, identifying care home residents in EHR is not straightforward. We describe and compare three different methods for identifying care home residents in the newly created OpenSAFELY-TPP data analytics platform. Methods: Working on behalf of NHS England, we identified individuals aged 65 years or older potentially living in a care home on the 1st of February 2020 using (1) a complex address linkage, in which cleaned GP registered addresses were matched to old age care home addresses using data from the Care and Quality Commission (CQC); (2) coded events in the EHR; (3) household identifiers, age and household size to identify households with more than 3 individuals aged 65 years or older as potential care home residents. Raw addresses were not available to the investigators. Results: Of 4,437,286 individuals aged 65 years or older, 2.27% were identified as potential care home residents using the complex address linkage, 1.96% using coded events, 3.13% using household size and age and 3.74% using either of these methods. 53,210 individuals (32.0% of all potential care home residents) were classified as care home residents using all three methods. Address linkage had the largest overlap with the other methods; 93.3% of individuals identified as care home residents using the address linkage were also identified as such using either coded events or household age and size. Conclusion: We have described the partial overlap between three methods for identifying care home residents in EHR, and provide detailed instructions for how to implement these in OpenSAFELY-TPP to support research into the impact of the COVID-19 pandemic on care home residents.

Background Unsolicited feedback can solicit changes in prescribing. Objectives Determine whether a low-cost intervention increases clinicians’ engagement with data, and changes prescribing; with or without behavioural science techniques. Methods Randomized trial (ISRCTN86418238). The highest prescribing practices in England for broad-spectrum antibiotics were allocated to: feedback with behavioural impact optimization; plain feedback; or no intervention. Feedback was sent monthly for 3 months by letter, fax and email. Each included a link to a prescribing dashboard. The primary outcomes were dashboard usage and change in prescribing. Results A total of 1401 practices were randomized: 356 behavioural optimization, 347 plain feedback, and 698 control. For the primary engagement outcome, more intervention practices had their dashboards viewed compared with controls [65.7% versus 55.9%; RD 9.8%, 95% confidence intervals (CIs): 4.76% to 14.9%, P < 0.001]. More plain feedback practices had their dashboard viewed than behavioural feedback practices (69.1% versus 62.4%); but not meeting the P < 0.05 threshold (6.8%, 95% CI: −0.19% to 13.8%, P = 0.069). For the primary prescribing outcome, intervention practices possibly reduced broad-spectrum prescribing to a greater extent than controls (1.42% versus 1.12%); but again not meeting the P < 0.05 threshold (coefficient −0.31%, CI: −0.7% to 0.1%, P = 0.104). The behavioural impact group reduced broad-spectrum prescribing to a greater extent than plain feedback practices (1.63% versus 1.20%; coefficient 0.41%, CI: 0.007% to 0.8%, P = 0.046). No harms were detected. Conclusions Unsolicited feedback increased practices’ engagement with data, with possible slightly reduced antibiotic prescribing (P = 0.104). Behavioural science techniques gave greater prescribing effects. The modest effects on prescribing may reflect saturation from similar initiatives on antibiotic prescribing. Clinical Trial Registration ISRCTN86418238.

Objective To investigate whether risk of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and outcomes of coronavirus disease 2019 (covid-19) differed between adults living with and without children during the first two waves of the UK pandemic. Design Population based cohort study, on behalf of NHS England. Setting Primary care data and pseudonymously linked hospital and intensive care admissions and death records from England, during wave 1 (1 February to 31 August 2020) and wave 2 (1 September to 18 December 2020). Participants Two cohorts of adults (18 years and over) registered at a general practice on 1 February 2020 and 1 September 2020. Main outcome measures Adjusted hazard ratios for SARS-CoV-2 infection, covid-19 related admission to hospital or intensive care, or death from covid-19, by presence of children in the household. Results Among 9 334 392 adults aged 65 years and under, during wave 1, living with children was not associated with materially increased risks of recorded SARS-CoV-2 infection, covid-19 related hospital or intensive care admission, or death from covid-19. In wave 2, among adults aged 65 years and under, living with children of any age was associated with an increased risk of recorded SARS-CoV-2 infection (hazard ratio 1.06 (95% confidence interval 1.05 to 1.08) for living with children aged 0-11 years; 1.22 (1.20 to 1.24) for living with children aged 12-18 years) and covid-19 related hospital admission (1.18 (1.06 to 1.31) for living with children aged 0-11; 1.26 (1.12 to 1.40) for living with children aged 12-18). Living with children aged 0-11 was associated with reduced risk of death from both covid-19 and non-covid-19 causes in both waves; living with children of any age was also associated with lower risk of dying from non-covid-19 causes. For adults 65 years and under during wave 2, living with children aged 0-11 years was associated with an increased absolute risk of having SARS-CoV-2 infection recorded of 40-60 per 10 000 people, from 810 to between 850 and 870, and an increase in the number of hospital admissions of 1-5 per 10 000 people, from 160 to between 161 and 165. Living with children aged 12-18 years was associated with an increase of 160-190 per 10 000 in the number of SARS-CoV-2 infections and an increase of 2-6 per 10 000 in the number of hospital admissions. Conclusions In contrast to wave 1, evidence existed of increased risk of reported SARS-CoV-2 infection and covid-19 outcomes among adults living with children during wave 2. However, this did not translate into a materially increased risk of covid-19 mortality, and absolute increases in risk were small.

BackgroundPatients with COVID-19 are thought to be at higher risk of cardiometabolic and pulmonary complications, but quantification of that risk is limited. We aimed to describe the overall burden of these complications in survivors of severe COVID-19.MethodsWorking on behalf of NHS England, we used linked primary care records, death certificate and hospital data from the OpenSAFELY platform. We constructed three cohorts: patients discharged following hospitalisation with COVID-19, patients discharged following hospitalisation with pneumonia in 2019, and a frequency-matched cohort from the general population in 2019. We studied eight cardiometabolic and pulmonary outcomes. Absolute rates were measured in each cohort and Cox regression models were fitted to estimate age/sex adjusted hazard ratios comparing outcome rates between discharged COVID-19 patients and the two comparator cohorts.ResultsAmongst the population of 31,716 patients discharged following hospitalisation with COVID-19, rates for majority of outcomes peaked in the first month post-discharge, then declined over the following four months. Patients in the COVID-19 population had markedly increased risk of all outcomes compared to matched controls from the 2019 general population, especially for pulmonary embolism (HR 12.86; 95% CI: 11.23 - 14.74). Outcome rates were more similar when comparing patients discharged with COVID-19 to those discharged with pneumonia in 2019, although COVID-19 patients had increased risk of type 2 diabetes (HR 1.23; 95% CI: 1.05 - 1.44).InterpretationCardiometabolic and pulmonary adverse outcomes are markedly raised following hospitalisation for COVID-19 compared to the general population. However, the excess risks were more comparable to those seen following hospitalisation with pneumonia. Identifying patients at particularly high risk of outcomes would inform targeted preventive measures.FundingWellcome, Royal Society, National Institute for Health Research, National Institute for Health Research Oxford Biomedical Research Centre, UK Medical Research Council, UK Research and Innovation, Health and Safety Executive.

BackgroundThe COVID-19 pandemic has disrupted healthcare activity globally. The NHS in England stopped most non-urgent work by March 2020, but later recommended that services should be restored to near-normal levels before winter where possible. The authors are developing the OpenSAFELY NHS Service Restoration Observatory, using data to describe changes in service activity during COVID-19, and reviewing signals for action with commissioners, researchers and clinicians. Here we report phase one: generating, managing, and describing the data.ObjectiveTo describe the volume and variation of coded clinical activity in English primary care across 23.8 million patients’ records, taking respiratory disease and laboratory procedures as key examples.MethodsWorking on behalf of NHS England we developed an open source software framework for data management and analysis to describe trends and variation in clinical activity across primary care EHR data on 23.8 million patients; and conducted a population cohort-based study to describe activity using CTV3 coding hierarchy and keyword searches from January 2019-September 2020.ResultsMuch activity recorded in general practice declined to some extent during the pandemic, but largely recovered by September 2020, with some exceptions. There was a large drop in coded activity for commonly used laboratory tests, with broad recovery to pre-pandemic levels by September. One exception was blood coagulation tests such as International Normalised Ratio (INR), with a smaller reduction (median tests per 1000 patients in 2020: February 8.0; April 6.2; September 7.0). The overall pattern of recording for respiratory symptoms was less affected, following an expected seasonal pattern and classified as “no change” from the previous year. Respiratory tract infections exhibited a sustained drop compared with pre-pandemic levels, not returning to pre-pandemic levels by September 2020. Various COVID-19 codes increased through the period. We observed a small decline associated with high level codes for long-term respiratory conditions such as chronic obstructive pulmonary disease (COPD) and asthma. Asthma annual reviews experienced a small drop but since recovered, while COPD annual reviews remain below baseline.ConclusionsWe successfully delivered an open source software framework to describe trends and variation in clinical activity across an unprecedented scale of primary care data. The COVD-19 pandemic led to a substantial change in healthcare activity. Most laboratory tests showed substantial reduction, largely recovering to near-normal levels by September 2020, with some important tests less affected. Records of respiratory infections decreased with the exception of codes related to COVID-19, whilst activity of other respiratory disease codes was mixed. We are expanding the NHS Service Restoration Observatory in collaboration with clinicians, commissioners and researchers and welcome feedback.

BackgroundEarly in the COVID-19 pandemic the NHS recommended that appropriate patients anticoagulated with warfarin should be switched to direct acting oral anticoagulants (DOACs), requiring less frequent blood testing. Subsequently, a national safety alert was issued regarding patients being inappropriately co-prescribed two anticoagulants following a medication change, and associated monitoring.ObjectiveTo describe which people were switched from warfarin to DOACs; identify potentially unsafe co-prescribing of anticoagulants; and assess whether abnormal clotting results have become more frequent during the pandemic.MethodsWorking on behalf of NHS England we conducted a population cohort based study using routine clinical data from >17 million adults in England.Results20,000 of 164,000 warfarin patients (12.2%) switched to DOACs between March and May 2020, most commonly to edoxaban and apixaban. Factors associated with switching included: older age, recent renal function test, higher number of recent INR tests recorded, atrial fibrillation diagnosis and care home residency. There was a sharp rise in co-prescribing of warfarin and DOACs from typically 50-100 per month to 246 in April 2020, 0.06% of all people receiving a DOAC or warfarin. INR testing fell by 14% to 506.8 patients tested per 1000 warfarin patients each month. We observed a very small increase in elevated INRs (n=470) during April compared with January (n=420).ConclusionsIncreased switching of anticoagulants from warfarin to DOACs was observed at the outset of the COVID-19 pandemic in England following national guidance. There was a small but substantial number of people co-prescribed warfarin and DOACs during this period. Despite a national safety alert on the issue, a widespread rise in elevated INR test results was not found. Primary care has responded rapidly to changes in patient care during the COVID-19 pandemic.

BackgroundHydroxychloroquine has been shown to inhibit entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into epithelial cells in vitro, but clinical studies found no evidence of reduced mortality when treating patients with COVID-19. We aimed to evaluate the effectiveness of hydroxychloroquine for prevention of COVID-19 mortality, as opposed to treatment for the disease.MethodsWe did a prespecified observational, population-based cohort study using national primary care data and linked death registrations in the OpenSAFELY platform, which covers approximately 40% of the general population in England, UK. We included all adults aged 18 years and older registered with a general practice for 1 year or more on March 1, 2020. We used Cox regression to estimate the association between ongoing routine hydroxychloroquine use before the COVID-19 outbreak in England (considered as March 1, 2020) compared with non-users of hydroxychloroquine and risk of COVID-19 mortality among people with rheumatoid arthritis or systemic lupus erythematosus. Model adjustment was informed by a directed acyclic graph.FindingsBetween Sept 1, 2019, and March 1, 2020, of 194 637 people with rheumatoid arthritis or systemic lupus erythematosus, 30 569 (15·7%) received two or more prescriptions of hydroxychloroquine. Between March 1 and July 13, 2020, there were 547 COVID-19 deaths, 70 among hydroxychloroquine users. Estimated standardised cumulative COVID-19 mortality was 0·23% (95% CI 0·18 to 0·29) among users and 0·22% (0·20 to 0·25) among non-users; an absolute difference of 0·008% (−0·051 to 0·066). After accounting for age, sex, ethnicity, use of other immunosuppressive drugs, and geographical region, no association with COVID-19 mortality was observed (HR 1·03, 95% CI 0·80 to 1·33). We found no evidence of interactions with age or other immunosuppressive drugs. Quantitative bias analyses indicated that our observed associations were robust to missing information for additional biologic treatments for rheumatological disease. We observed similar associations with the negative control outcome of non-COVID-19 mortality.InterpretationWe found no evidence of a difference in COVID-19 mortality among people who received hydroxychloroquine for treatment of rheumatological disease before the COVID-19 outbreak in England. Therefore, completion of randomised trials investigating pre-exposure prophylactic use of hydroxychloroquine for prevention of severe outcomes from COVID-19 are warranted.FundingMedical Research Council.

BackgroundClose contact with children may provide cross-reactive immunity to SARs-CoV-2 due to more frequent prior coryzal infections from seasonal coronaviruses. Alternatively, close contact with children may increase risk of SARs-CoV-2 infection. We investigated whether risk of infection with SARs-CoV-2 and severe outcomes differed between adults living with and without children.MethodsWorking on behalf of NHS England, we conducted a population-based cohort study using primary care data and pseudonymously-linked hospital and intensive care admissions, and death records, from patients registered in general practices representing 40% of England. Using multivariable Cox regression, we calculated fully-adjusted hazard ratios (HR) of outcomes from 1st February-3rd August 2020 comparing adults living with and without children in the household.FindingsAmong 9,157,814 adults ≤65 years, living with children 0-11 years was not associated with increased risks of recorded SARS-CoV-2 infection, COVID-19 related hospital or ICU admission but was associated with reduced risk of COVID-19 death (HR 0.75, 95%CI 0.62-0.92). Living with children aged 12-18 years was associated with a small increased risk of recorded SARS-CoV-2 infection (HR 1.08, 95%CI 1.03-1.13), but not associated with other COVID-19 outcomes. Living with children of any age was also associated with lower risk of dying from non-COVID-19 causes. Among 2,567,671 adults >65 years there was no association between living with children and outcomes related to SARS-CoV-2. We observed no consistent changes in risk following school closure.InterpretationFor adults living with children there is no evidence of an increased risk of severe COVID-19 outcomes. These findings have implications for determining the benefit-harm balance of children attending school in the COVID-19 pandemic.FundingThis work was supported by the Medical Research Council MR/V015737/1.Research in contextEvidence before this studyWe searched MEDLINE on 19th October 2020 for population-based epidemiological studies comparing the risk of SARS-CoV-2 infection and COVID-19 disease in people living with and without children. We searched for articles published in 2020, with abstracts available, and terms “(children or parents or dependants) AND (COVID or SARS-CoV-2 or coronavirus) AND (rate or hazard or odds or risk), in the title, abstract or keywords. 244 papers were identified for screening but none were relevant. One additional study in preprint was identified on medRxiv and found a reduced risk of hospitalisation for COVID-19 and a positive SARS-CoV-2 infection among adult healthcare workers living with children.Added value of this studyThis is the first population-based study to investigate whether the risk of recorded SARS-CoV-2 infection and severe outcomes from COVID-19 differ between adults living in households with and without school-aged children during the UK pandemic. Our findings show that for adults living with children there is no evidence of an increased risk of severe COVID-19 outcomes although there may be a slightly increased risk of recorded SARS-CoV-2 infection for working-age adults living with children aged 12 to 18 years. Working-age adults living with children 0 to 11 years have a lower risk of death from COVID-19 compared to adults living without children, with the effect size being comparable to their lower risk of death from any cause. We observed no consistent changes in risk of recorded SARS-CoV-2 infection and severe outcomes from COVID-19 comparing periods before and after school closure.Implications of all the available evidenceOur results demonstrate no evidence of serious harms from COVID-19 to adults in close contact with children, compared to those living in households without children. This has implications for determining the benefit-harm balance of children attending school in the COVID-19 pandemic.

Background In 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. Objective Our 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. Methods We 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. Results A 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. Conclusions Design 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.

BackgroundCOVID-19 has had a disproportionate impact on ethnic minority populations, both in the UK and internationally. To date, much of the evidence has been derived from studies within single healthcare settings, mainly those hospitalised with COVID-19. Working on behalf of NHS England, the aim of this study was to identify ethnic differences in the risk of COVID-19 infection, hospitalisation and mortality using a large general population cohort in England.MethodsWe conducted an observational cohort study using linked primary care records of 17.5 million adults between 1 February 2020 and 3 August 2020. Exposure was self-reported ethnicity collapsed into the 5 and 16 ethnicity categories of the English Census. Multivariable Cox proportional hazards regression was used to identify ethnic differences in the risk of being tested and testing positive for SARS-CoV-2 infection, COVID-19 related intensive care unit (ICU) admission, and COVID-19 mortality, adjusted for socio-demographic factors, clinical co-morbidities, geographic region, care home residency, and household size.ResultsA total of 17,510,002 adults were included in the study; 63% white (n=11,030,673), 6% south Asian (n=1,034,337), 2% black (n=344,889), 2% other (n=324,730), 1% mixed (n=172,551), and 26% unknown (n=4,602,822). After adjusting for measured explanatory factors, south Asian, black, and mixed groups were marginally more likely to be tested (south Asian HR 1.08, 95%CI 1.07-1.09; black HR 1.08; 95%CI 1.06-1.09, mixed HR 1.03, 95%CI 1.01-1.05), and substantially more likely to test positive for SARS-CoV-2 compared with white adults (south Asian HR 2.02. 95% CI 1.97-2.07; black HR 1.68, 95%CI 1.61-1.76; mixed HR 1.46, 95%CI 1.36-1.56). The risk of being admitted to ICU for COVID-19 was substantially increased in all ethnic minority groups compared with white adults (south Asian HR 2.22, 95%CI 1.96-2.52; black HR 3.07, 95%CI 2.61-3.61; mixed HR 2.86, 95%CI 2.19-3.75, other HR 2.86, 95%CI 2.31-3.63). Risk of COVID-19 mortality was increased by 25-56% in ethnic minority groups compared with white adults (south Asian HR 1.27, 95%CI 1.17-1.38; black HR 1.55, 95%CI 1.38-1.75; mixed HR 1.40, 95%CI 1.12-1.76; other HR 1.25, 95%CI 1.05-1.49).We observed heterogeneity of associations after disaggregation into detailed ethnic groupings; Indian and African groups were at higher risk of all outcomes; Pakistani, Bangladeshi and Caribbean groups were less or equally likely to be tested for SARS-CoV-2, but at higher risk of all other outcomes, Chinese groups were less likely to be tested for and test positive for SARS-CoV-2, more likely to be admitted to ICU, and equally likely to die from COVID-19.ConclusionsWe found evidence of substantial ethnic inequalities in the risk of testing positive for SARS-CoV-2, ICU admission, and mortality, which persisted after accounting for explanatory factors, including household size. It is likely that some of this excess risk is related to factors not captured in clinical records such as occupation, experiences of structural discrimination, or inequitable access to health and social services. Prioritizing linkage between health, social care, and employment data and engaging with ethnic minority communities to better understand their lived experiences is essential for generating evidence to prevent further widening of inequalities in a timely and actionable manner.

Coronavirus disease 2019 (COVID-19) has rapidly affected mortality worldwide1. There is unprecedented urgency to understand who is most at risk of severe outcomes, and this requires new approaches for the timely analysis of large datasets. Working on behalf of NHS England, we created OpenSAFELY-a secure health analytics platform that covers 40% of all patients in England and holds patient data within the existing data centre of a major vendor of primary care electronic health records. Here we used OpenSAFELY to examine factors associated with COVID-19-related death. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19-related deaths. COVID-19-related death was associated with: being male (hazard ratio (HR) 1.59 (95% confidence interval 1.53-1.65)); greater age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared with people of white ethnicity, Black and South Asian people were at higher risk, even after adjustment for other factors (HR 1.48 (1.29-1.69) and 1.45 (1.32-1.58), respectively). We have quantified a range of clinical factors associated with COVID-19-related death in one of the largest cohort studies on this topic so far. More patient records are rapidly being added to OpenSAFELY, we will update and extend our results regularly.

ImportanceThere has been speculation that non-steroidal anti-inflammatory drugs (NSAIDs) may negatively affect coronavirus disease 2019 (COVID-19) outcomes, yet clinical evidence is limited.ObjectiveTo assess the association between NSAID use and deaths from COVID-19 using OpenSAFELY, a secure analytical platform.DesignTwo cohort studies (1stMarch-14thJune 2020).SettingWorking on behalf of NHS England, we used routine clinical data from >17 million patients in England linked to death data from the Office for National Statistics.ParticipantsStudy 1: General population (people with an NSAID prescription in the last three years). Study 2: people with rheumatoid arthritis/osteoarthritis.ExposuresCurrent NSAID prescription within the 4 months before 1stMarch 2020.Main Outcome and MeasureWe used Cox regression to estimate hazard ratios (HRs) for COVID-19 related death in people currently prescribed NSAIDs, compared with those not currently prescribed NSAIDs, adjusting for age, sex, comorbidities and other medications.ResultsIn Study 1, we included 535,519 current NSAID users and 1,924,095 non-users in the general population. The crude HR for current use was 1.25 (95% CI, 1.07–1.46), versus non-use. We observed no evidence of difference in risk of COVID-19 related death associated with current use (HR, 0.95, 95% CI, 0.80–1.13) in the fully adjusted model.In Study 2, we included 1,711,052 people with rheumatoid arthritis/osteoarthritis, of whom 175,631 (10%) were current NSAID users. The crude HR for current use was 0.43 (95% CI, 0.36–0.52), versus non-use. In the fully adjusted model, we observed a lower risk of COVID-19 related death (HR, 0.78, 95% CI, 0.65–0.94) associated with current use of NSAID versus non-use.Conclusion and RelevanceWe found no evidence of a harmful effect of NSAIDs on COVID-19 related deaths. Risks of COVID-19 do not need to influence decisions about therapeutic use of NSAIDs.

BackgroundElectronic health record (EHR) systems are used by clinicians to record patients’ medical information, and support clinical activities such as prescribing. In England, healthcare professionals are advised to ‘prescribe generically’ because generic drugs are usually cheaper than branded alternatives, and have fixed reimbursement costs. ‘Ghost-branded generics’ are a new category of medicines savings, caused by prescribers specifying a manufacturer for a generic product, often resulting in a higher reimbursement price compared with the true generic.AimTo describe time trends and practice factors associated with excess medication costs from ghost-branded generic prescribing.Design and settingRetrospective cohort study of English GP prescribing data and EHR deployment data.MethodA retrospective cohort study was conducted, based on data from the OpenPrescribing.net database from May 2013 to May 2019. Total spending on ghost-branded generics across England was calculated, and excess spend on ghost-branded generics calculated as a percentage of all spending on generics for every CCG and general practice in England, for every month in the study period.ResultsThere were 31.8 million ghost-branded generic items and £9.5 million excess cost in 2018, compared with 7.45 million ghost-branded generic items and £1.3 million excess cost in 2014. Most excess costs were associated with one EHR, SystmOne, and it was identified that SystmOne offered ghost-branded generic options as the default. After informing the vendor, the authors monitored for subsequent change in costs, and report a rapid decrease in ghost-branded generic expenditure.ConclusionA design choice in a commonly used EHR has led to £9.5 million in avoidable excess prescribing costs for the NHS in 1 year. Notifying the vendor led to a change in user interface and a rapid, substantial spend reduction. This finding illustrates that EHR user interface design has a substantial impact on the quality, safety, and cost-effectiveness of clinical practice; this should be a priority for quantitative research.

BackgroundIt is unclear whether HIV infection is associated with risk of COVID-19 death. We aimed to investigate this in a large-scale population-based study in England.MethodsWorking on behalf of NHS England, we used the OpenSAFELY platform to analyse routinely collected electronic primary care data linked to national death registrations. People with a primary care record for HIV infection were compared to people without HIV. COVID-19 death was defined by ICD-10 codes U07.1 or U07.2 anywhere on the death certificate. Cox regression models were used to estimate the association between HIV infection and COVID-19 death, initially adjusted for age and sex, then adding adjustment for index of multiple deprivation and ethnicity, and finally for a broad range of comorbidities. Interaction terms were added to assess effect modification by age, sex, ethnicity, comorbidities and calendar time.Results17.3 million adults were included, of whom 27,480 (0.16%) had HIV recorded. People living with HIV were more likely to be male, of black ethnicity, and from a more deprived geographical area than the general population. There were 14,882 COVID-19 deaths during the study period, with 25 among people with HIV. People living with HIV had nearly three-fold higher risk of COVID-19 death than those without HIV after adjusting for age and sex (HR=2.90, 95% CI 1.96-4.30). The association was attenuated but risk remained substantially raised, after adjustment for deprivation and ethnicity (adjusted HR=2.52, 1.70-3.73) and further adjustment for comorbidities (HR=2.30, 1.55-3.41). There was some evidence that the association was larger among people of black ethnicity (HR = 3.80, 2.15-6.74, compared to 1.64, 0.92-2.90 in non-black individuals, p-interaction=0.045)InterpretationHIV infection was associated with a markedly raised risk of COVID-19 death in a country with high levels of antiretroviral therapy coverage and viral suppression; the association was larger in people of black ethnicity.

BackgroundEarly descriptions of the coronavirus outbreak showed a lower prevalence of asthma and COPD than was expected for people diagnosed with COVID-19, leading to speculation that inhaled corticosteroids (ICS) may protect against infection with SARS-CoV-2, and development of serious sequelae. We evaluated the association between ICS and COVID-19 related death using linked electronic health records in the UK.MethodsWe conducted cohort studies on two groups of people (COPD and asthma) using the OpenSAFELY platform to analyse data from primary care practices linked to national death registrations. People receiving an ICS were compared to those receiving alternative respiratory medications. Our primary outcome was COVID-19 related death.FindingsWe identified 148,588 people with COPD and 817,973 people with asthma receiving relevant respiratory medications in the four months prior to 01 March 2020. People with COPD receiving ICS were at a greater risk of COVID-19 related death compared to those receiving a long-acting beta agonist (LABA) and a long-acting muscarinic antagonist (LAMA) (adjusted HR = 1.38, 95% CI = 1.08 – 1.75). People with asthma receiving high dose ICS were at an increased risk of death compared to those receiving a short-acting beta agonist (SABA) only (adjusted HR = 1.52, 95%CI = 1.08 – 2.14); the adjusted HR for those receiving low-medium dose ICS was 1.10 (95% CI = 0.82 – 1.49). Quantitative bias analyses indicated that an unmeasured confounder of only moderate strength of association with exposure and outcome could explain the observed associations in both populations.InterpretationThese results do not support a major role of ICS in protecting against COVID-19 related deaths. Observed increased risks of COVID-19 related death among people with COPD and asthma receiving ICS can be plausibly explained by unmeasured confounding due to disease severity.FundingThis work was supported by the Medical Research Council MR/V015737/1.

BackgroundEstablishing who is at risk from a novel rapidly arising cause of death, and why, requires a new approach to epidemiological research with very large datasets and timely data. Working on behalf of NHS England we therefore set out to deliver a secure and pseudonymised analytics platform inside the data centre of a major primary care electronic health records vendor establishing coverage across detailed primary care records for a substantial proportion of all patients in England. The following results are preliminary.Data sourcesPrimary care electronic health records managed by the electronic health record vendor TPP, pseudonymously linked to patient-level data from the COVID-19 Patient Notification System (CPNS) for death of hospital inpatients with confirmed COVID-19, using the new OpenSAFELY platform.Population17,425,445 adults.Time period1st Feb 2020 to 25th April 2020.Primary outcomeDeath in hospital among people with confirmed COVID-19.MethodsCohort study analysed by Cox-regression to generate hazard ratios: age and sex adjusted, and multiply adjusted for co-variates selected prospectively on the basis of clinical interest and prior findings.ResultsThere were 5683 deaths attributed to COVID-19. In summary after full adjustment, death from COVID-19 was strongly associated with: being male (hazard ratio 1.99, 95%CI 1.88-2.10); older age and deprivation (both with a strong gradient); uncontrolled diabetes (HR 2.36 95% CI 2.18-2.56); severe asthma (HR 1.25 CI 1.08-1.44); and various other prior medical conditions. Compared to people with ethnicity recorded as white, black people were at higher risk of death, with only partial attenuation in hazard ratios from the fully adjusted model (age-sex adjusted HR 2.17 95% CI 1.84-2.57; fully adjusted HR 1.71 95% CI 1.44-2.02); with similar findings for Asian people (age-sex adjusted HR 1.95 95% CI 1.73-2.18; fully adjusted HR 1.62 95% CI 1.431.82).ConclusionsWe have quantified a range of clinical risk factors for death from COVID-19, some of which were not previously well characterised, in the largest cohort study conducted by any country to date. People from Asian and black groups are at markedly increased risk of in-hospital death from COVID-19, and contrary to some prior speculation this is only partially attributable to pre-existing clinical risk factors or deprivation; further research into the drivers of this association is therefore urgently required. Deprivation is also a major risk factor with, again, little of the excess risk explained by co-morbidity or other risk factors. The findings for clinical risk factors are concordant with policies in the UK for protecting those at highest risk. Our OpenSAFELY platform is rapidly adding further NHS patients’ records; we will update and extend these results regularly.

Background: Failure to report the results of a clinical trial can distort the evidence base for clinical practice, breaches researchers' ethical obligations to participants, and represents an important source of research waste. The Food and Drug Administration Amendments Act (FDAAA) of 2007 now requires sponsors of applicable trials to report their results directly onto ClinicalTrials.gov within 1 year of completion. The first trials covered by the Final Rule of this act became due to report results in January, 2018. In this cohort study, we set out to assess compliance. Methods: We downloaded data for all registered trials on ClinicalTrials.gov each month from March, 2018, to September, 2019. All cross-sectional analyses in this manuscript were performed on data extracted from ClinicalTrials.gov on Sept 16, 2019; monthly trends analysis used archived data closest to the 15th day of each month from March, 2018, to September, 2019. Our study cohort included all applicable trials due to report results under FDAAA. We excluded all non-applicable trials, those not yet due to report, and those given a certificate allowing for delayed reporting. A trial was considered reported if results had been submitted and were either publicly available, or undergoing quality control review at ClinicalTrials.gov. A trial was considered compliant if these results were submitted within 1 year of the primary completion date, as required by the legislation. We described compliance with the FDAAA 2007 Final Rule, assessed trial characteristics associated with results reporting using logistic regression models, described sponsor-level reporting, examined trends in reporting, and described time-to-report using the Kaplan-Meier method. Findings: 4209 trials were due to report results; 1722 (40·9%; 95% CI 39·4–42·2) did so within the 1-year deadline. 2686 (63·8%; 62·4–65·3) trials had results submitted at any time. Compliance has not improved since July, 2018. Industry sponsors were significantly more likely to be compliant than non-industry, non-US Government sponsors (odds ratio [OR] 3·08 [95% CI 2·52–3·77]), and sponsors running large numbers of trials were significantly more likely to be compliant than smaller sponsors (OR 11·84 [9·36–14·99]). The median delay from primary completion date to submission date was 424 days (95% CI 412–435), 59 days higher than the legal reporting requirement of 1 year. Interpretation: Compliance with the FDAAA 2007 is poor, and not improving. To our knowledge, this is the first study to fully assess compliance with the Final Rule of the FDAAA 2007. Poor compliance is likely to reflect lack of enforcement by regulators. Effective enforcement and action from sponsors is needed; until then, open public audit of compliance for each individual sponsor may help. We will maintain updated compliance data for each individual sponsor and trial at fdaaa.trialstracker.net.

BackgroundPrescribing high doses of methotrexate increases the potentially fatal risk of toxicity. To minimise risk, it is recommended that only 2.5 mg tablets are used.AimTo describe trends in GP prescribing of methotrexate over time; the harm associated with methotrexate errors at a national level; ascertain variation between practices and clinical commissioning groups (CCGs) in their implementation of the safety guidance; and map current variations at CCG and practice level.Design and settingA retrospective cohort study of English GP prescribing data (August 2010–April 2018), and data acquired via freedom of information (FOI) requests.MethodThe main outcome measures were: variation in ratio of non-adherent/adherent prescribing, geographically and over time, between practices and CCGs; and description of responses to FOI requests.ResultsOf 7349 practices in England, 1689 prescribed both 2.5 mg and 10 mg tablets to individual patients in 2017, breaching national guidance. In April 2018, 697 practices (≥90th percentile) prescribed >14.3% of all methotrexate as 10 mg tablets, likewise breaching national guidance. The 66 practices at ≥99th percentile gave >52.4% of all prescribed methotrexate in the form of 10 mg tablets. The prescribing of 10 mg tablets fell during the study period, with 10 mg tablets as a proportion of all prescribed methotrexate tablets falling from 9.1% to 3.4%. Twenty-one deaths caused by methotrexate poisoning were reported from 1993–2017 in England and Wales.ConclusionThe prevalence of unsafe methotrexate prescribing has reduced but remains common, with substantial variation between practices and CCGs. The authors recommend investment in better strategies around implementation. As 21 deaths that occurred from 1993–2017 in England and Wales were attributed to methotrexate poisoning, the coroners’ reports for these deaths should be reviewed to identify recurring themes.

Objectives To ascertain compliance rates with the European Commission’s requirement that all trials on the EU Clinical Trials Register (EUCTR) post results to the registry within 12 months of completion (final compliance date 21 December 2016); to identify features associated with non-compliance; to rank sponsors by compliance; and to build a tool for live ongoing audit of compliance. Design Retrospective cohort study. Setting EUCTR. Participants 7274 of 11 531 trials listed as completed on EUCTR and where results could be established as due. Main outcome measure Publication of results on EUCTR. Results Of 7274 trials where results were due, 49.5% (95% confidence interval 48.4% to 50.7%) reported results. Trials with a commercial sponsor were substantially more likely to post results than those with a non-commercial sponsor (68.1% v 11.0%, adjusted odds ratio 23.2, 95% confidence interval 19.2 to 28.2); as were trials by a sponsor who conducted a large number of trials (77.9% v 18.4%, adjusted odds ratio 18.4, 15.3 to 22.1). More recent trials were more likely to report results (per year odds ratio 1.05, 95% confidence interval 1.03 to 1.07). Extensive evidence was found of errors, omissions, and contradictory entries in EUCTR data that prevented ascertainment of compliance for some trials. Conclusions Compliance with the European Commission requirement for all trials to post results on to the EUCTR within 12 months of completion has been poor, with half of all trials non-compliant. EU registry data commonly contain inconsistencies that might prevent even regulators assessing compliance. Accessible and timely information on the compliance status of each individual trial and sponsor may help to improve reporting rates.

Objectives EBM DataLab is a team that aims to produce tools from data, rather than just academic publications. We have a multidisciplinary approach that combines the skills of software engineers, clinicians and academics. In this workshop we will give an overview covering how to make effective interactive services driven by data, through: Providing an overview of the software and services which can be used to create your own data–driven tools. Demonstrating examples from the work of the EBM DataLab. Providing an opportunity for attendees to discuss their own ideas or experiences related to data–driven tools. Method In this session you will learn the very basics about a range of useful software and services including Python, pandas and iPython notebooks, GitHub, Google Analytics, Google Sheets, Google Forms, API’s (for Scopus, PubMed, and Web of Science), SQL databases such as BigQuery and Postgres, and graphical tools such as Tableau and d3. We will demonstrate the creative use of these tools with worked examples from our recent output including the Retractobot, various trials trackers, OpenPrescribing long term trends, the Drug Tariff explorer, and more. Results Attendees should leave this workshop with a better grasp of how to identify user needs, select the appropriate software or services for the job, design tools to be deliverable and impactful, manage the development cycle from prototyping to launch, and carry out the basic process of user testing. Conclusions Simple yet engaging presentation of data that allows people to act on it can be a critical step in disseminating evidence and improving quality of care. Live updating dynamic tools can keep information at the cutting-edge and provide a platform that provides real and enduring value to users. There are great resources available that can allow researchers to quickly and efficiently turn their findings into public-facing tools. We hope this workshop will empower attendees to begin presenting and sharing their data in new and effective ways in order to promote positive change in their respective fields.

Objectives NHS England recently announced a consultation seeking to discourage the use of treatments it considers to be low-value. We set out to produce an interactive data resource to show savings in each NHS general practice and to assess the current use of these treatments, their change in use over time, and the extent and reasons for variation in such prescribing. Design Cross-sectional analysis. Setting English primary care. Participants English general practices. Main outcome measures We determined the cost per 1000 patients for prescribing of each of 18 treatments identified by NHS England for each month from July 2012 to June 2017, and also aggregated over the most recent year to assess total cost and variation among practices. We used mixed effects linear regression to determine factors associated with cost of prescribing. Results Spend on low-value treatments was £153.5 m in the last year, across 5.8 m prescriptions (mean, £26 per prescription). Among individual treatments, liothyronine had the highest prescribing cost at £29.6 m, followed by trimipramine (£20.2 m). Over time, the overall total number of low-value prescriptions decreased, but the cost increased, although this varied greatly between treatments. Three treatment areas increased in cost and two increased in volume, all others reduced in cost and volume. Annual practice level spending varied widely (median, £2262 per thousand patients; interquartile range £1439 to £3298). Proportion of patients over 65 was strongly associated with low-value prescribing, as was Clinical Commissioning Group. Our interactive data tool was deployed to OpenPrescribing.net where monthly updated figures and graphs can be viewed. Conclusions Prescribing of low-value treatments is extensive but varies widely by treatment, geographic area and individual practice. Despite a fall in prescription numbers, the overall cost of prescribing for low-value items has risen. Prescribing behaviour is clustered by Clinical Commissioning Group, which may represent variation in the optimisation efficiency of medicines, or in some cases access inequality.

Objectives Prescribing of homeopathy still occurs in a small minority of English general practices. We hypothesised that practices that prescribe any homeopathic preparations might differ in their prescribing of other drugs. Design Cross-sectional analysis. Setting English primary care. Participants English general practices. Main outcome measures We identified practices that made any homeopathy prescriptions over six months of data. We measured associations with four prescribing and two practice quality indicators using multivariable logistic regression. Results Only 8.5% of practices (644) prescribed homeopathy between December 2016 and May 2017. Practices in the worst-scoring quartile for a composite measure of prescribing quality (>51.4 mean percentile) were 2.1 times more likely to prescribe homeopathy than those in the best category (Conclusions Even infrequent homeopathy prescribing is strongly associated with poor performance on a range of prescribing quality measures, but not with overall patient recommendation or quality outcomes framework score. The association is unlikely to be a direct causal relationship, but may reflect underlying practice features, such as the extent of respect for evidence-based practice, or poorer stewardship of the prescribing budget.

BackgroundMinimising prescription costs while maintaining quality is a core element of delivering high-value healthcare. There are various strategies to achieve savings, but almost no research to date on determining the most effective approach. We describe a new method of identifying potential savings due to large national variations in drug cost, including variation in generic drug cost, and compare these with potential savings from an established method (generic prescribing).MethodsWe used English National Health Service (NHS) Digital prescribing data, from October 2015 to September 2016. Potential cost savings were calculated by determining the price per unit (eg, pill, millilitre) for each drug and dose within each general practice. This was compared against the same cost for the practice at the lowest cost decile to determine achievable savings. We compared these price-per-unit savings to the savings possible from generic switching and determined the chemicals with the highest savings nationally. A senior pharmacist manually assessed whether a random sample of savings were practically achievable.ResultsWe identified a theoretical maximum of £410 million of savings over 12 months. £273 million of these savings were for individual prescribing changes worth over £50 per practice per month (mean annual saving £33 433 per practice); this compares favourably with generic switching, where only £35 million of achievable savings were identified. The biggest savings nationally were on glucose blood testing reagents (£12 million), fluticasone propionate (£9 million) and venlafaxine (£8 million). Approximately half of all savings were deemed practically achievable.DiscussionWe have developed a new method to identify and enable large potential cost savings within NHS community prescribing. Given the current pressures on the NHS, it is vital that these potential savings are realised. Our tool enabling doctors to achieve these savings is now launched in pilot form at OpenPrescribing.net. However, savings could potentially be achieved more simply through national policy change.

BackgroundThere is substantial disagreement about whether gluten-free foods should be prescribed on the NHS. We aim to describe time trends, variation and factors associated with prescribing gluten-free foods in England.MethodsWe described long-term national trends in gluten-free prescribing, and practice and Clinical Commissioning Group (CCG) level monthly variation in the rate of gluten-free prescribing (per 1000 patients) over time. We used a mixed effect poisson regression model to determine factors associated with gluten-free prescribing rate.ResultsThere were 1.3 million gluten-free prescriptions between July 2016 and June 2017, down from 1.8 million in 2012/13, with a corresponding cost reduction from £25.4m to £18.7m. There was substantial variation in prescribing rates among practices (range 0 to 148 prescriptions per 1000 patients, interquartile range 7.3 to 31.8), driven in part by substantial variation at the CCG level, likely due to differences in prescribing policy. Practices in the most deprived quintile of deprivation score had a lower prescribing rate than those in the highest quintile (incidence rate ratio 0.89, 95% confidence interval 0.87-0.91). This is potentially a reflection of the lower rate of diagnosed coeliac disease in more deprived populations.ConclusionGluten-free prescribing is in a state of flux, with substantial clinically unwarranted variation between practices and CCGs.Strengths and weaknesses of the studyWe were able to measure the prescribing of gluten-free foods across all prescribing in England, eliminating bias. We also removed seasonal variation by aggregating savings over 12 months.As well as gluten-free prescribing variation at practice and CCG level, we have described long-term prescribing trends at national level, back to 1998.Using the available data, we were unable to look at gluten-free prescribing at prescriber level, or investigate factors associated with prescribing to individual patients

BackgroundRoutine accessible audit of prescribing data presents significant opportunities to identify cost-saving opportunities. NHS England recently announced a consultation seeking to discourage use of medicines it considers to be low-value. We set out to produce an interactive data resource to show savings in each NHS general practice, and to assess the current use of these medicines, their change in use over time, and the extent and reasons for variation in such prescribing.ResultsThe total NHS spend on all low-value medicines identified by NHS England was £153.5m in the last year, across 5.8m prescriptions (mean £26 per prescription). Among individual medications, liothyronine had the highest prescribing cost at £29.6m, followed by trimipramine (£20.2m) and gluten-free foods (£18.7m). Over time, the overall total number of low-value prescriptions decreased, but the cost increased, although this varied greatly between medications. Annual practice level spending varied widely (median, £2,262 per thousand patients, IQR £1,439 to £3,298). The proportion of patients over 65 showed the strongest association with low-value prescribing; CCG was also strongly associated. Our interactive data tool was deployed to OpenPrescribing.net where monthly updated figures and graphs can be viewed.ConclusionsPrescribing of low-value medications is extensive but varies widely by medication, geographic area and individual practice. Despite a fall in prescription numbers, the overall cost of prescribing for low-value items has risen. Prescribing behaviour is clustered by CCG, which may represent variation in medicines optimisation efficiency, or in some cases access inequality.AbbreviationsGPGeneral PracticeNHSNational Health ServiceNICENational Institute for Health and Care ExcellencePCAPrescription cost AnalysisQOFQuality Outcomes Framework

BackgroundMinimising prescription costs while maintaining quality is a core element of delivering high value healthcare. There are various strategies to achieve savings, but almost no research to date on determining the most effective approach. We describe a new method of identifying potential savings due to large national variations in drug cost, including variation in generic drug cost; and compare these with potential savings from an established method (generic prescribing).MethodsWe used English NHS Digital prescribing data, from October 2015 to September 2016. Potential cost savings were calculated by determining the price-per-unit (e.g. pill, ml) for each drug and dose within each general practice. This was compared against the same cost for the practice at the lowest cost decile, to determine achievable savings. We compared these price-per-unit savings to the savings possible from generic switching; and determined the chemicals with the highest savings nationally. A senior pharmacist manually assessed whether a random sample of savings were practically achievable.ResultsWe identified a theoretical maximum of £410M of savings over 12 months. £273M of these savings were for individual prescribing changes worth over £50 per practice per month; this compares favorably with generic switching, where only £35M of achievable savings were identified. The biggest savings nationally were on glucose blood testing reagents (£12M), fluticasone propionate (£9M) and venlafaxine (£8M). Approximately half of all savings were deemed practically achievable.DiscussionWe have developed a new method to identify and enable large potential cost savings within NHS community prescribing. Given the current pressures on the NHS, it is vital that these potential savings are realised. Our tool enabling doctors to achieve these savings is now launched in pilot form. However savings could potentially be achieved more simply through national policy change.AbbreviationsBNFBritish National FormularyCCGClinical Commissioning GroupGPGeneral PracticeMRModified ReleaseNHSNational Health ServiceNICNet Ingredient CostNP8Non-Part VIII, i.e. drugs not listed in Part VIII of the Drug TariffPPUPrice-Per-Unit