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BackgroundThe COVID-19 pandemic has had a significant impact on delivery of NHS care. We have developed the OpenSAFELY Service Restoration Observatory (SRO) to describe this impact on primary care activity and monitor its recovery. ObjectivesTo develop key measures of primary care activity and describe the trends in these measures throughout the COVID-19 pandemic. MethodsWith the approval 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 electronic health record (EHR) data on 48 million adults. We developed SNOMED-CT codelists for key measures of primary care clinical activity selected by a expert clinical advisory group and conducted a population cohort-based study to describe trends and variation in these measures January 2019-December 2021, and pragmatically classified their level of recovery one year into the pandemic using the percentage change in the median practice level rate. ResultsWe produced 11 measures reflective of clinical activity in general practice. A substantial drop in activity was observed in all measures at the outset of the COVID-19 pandemic. By April 2021, the median rate had recovered to within 15% of the median rate in April 2019 in six measures. The remaining measures showed a sustained drop, ranging from a 18.5% reduction in medication reviews to a 42.0% reduction in blood pressure monitoring. Three measures continued to show a sustained drop by December 2021. ConclusionsThe COVID-19 pandemic was associated with a substantial change in primary care activity across the measures we developed, with recovery in most measures. We delivered an open source software framework to describe trends and variation in clinical activity across an unprecedented scale of primary care data. We will continue to expand the set of key measures to be routinely monitored using our publicly available NHS OpenSAFELY SRO dashboards with near real-time data.
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BackgroundThe UK COVID-19 vaccination programme delivered its first "booster" doses in September 2021, initially in groups at high risk of severe disease then across the adult population. The BNT162b2 Pfizer-BioNTech vaccine was used initially, with Moderna mRNA-1273 subsequently also used. MethodsWe used the OpenSAFELY-TPP database, covering 40% of English primary care practices and linked to national coronavirus surveillance, hospital episodes, and death registry data, to estimate the effectiveness of boosting with BNT162b2 compared with no boosting in eligible adults who had received two primary course vaccine doses between 16 September and 16 December 2021 when the Delta variant of SARS-CoV-2 was dominant. Follow up was for up to 10 weeks. Each booster recipient was matched with an unboosted control on factors relating to booster priority status and prior immunisation. Additional factors were adjusted for in Cox models estimating hazard ratios (HRs). Outcomes were positive SARS-CoV-2 test, COVID-19 hospitalisation, COVID-19 death and non-COVID-9 death. Booster vaccine effectiveness was defined as 1-HR. ResultsAmong 4,352,417 BNT162b2 booster recipients matched with unboosted controls, estimated effectiveness of a booster dose compared with two doses only was 50.7% (95% CI 50.1-51.3) for positive SARS-CoV-2 test, 80.1% (78.3-81.8) for COVID-19 hospitalisation, 88.5% (85.0-91.1) for COVID-19 death, and 80.3% (79.0-81.5) for non-COVID-19 death. Estimated effectiveness was similar among those who had received a BNT162b2 or ChAdOx1-S two-dose primary vaccination course, but effectiveness against severe COVID-19 was slightly lower in those classified as clinically extremely vulnerable (76.3% (73.1-79.1) for COVID-19 hospitalisation, and 85.1% (79.6-89.1) for COVID-19 death). Estimated effectiveness against each outcome was lower in those aged 18-65 years than in those aged 65 and over. ConclusionOur findings are consistent with strong protection of BNT162b2 boosting against positive SARS-CoV-2 test, COVID-19 hospitalisation, and COVID-19 death.
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BackgroundThe 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. AimsUsing routinely collected data, our aim was to describe changes in the volume and variation of coded clinical activity in general practice in: (i) cardiovascular disease, (ii) diabetes, (iii) mental health, (iv) female and reproductive health, (v) screening, and (vi) processes related to medication. Design and settingWith the approval of NHS England, we conducted a cohort study of 23.8 million patient records in general practice, in-situ using OpenSAFELY. MethodsWe selected common primary care activity using CTV3 codes and keyword searches from January 2019 - December 2020, presenting median and deciles of code usage across practices per month. ResultsWe identified substantial and widespread changes in clinical activity in primary care 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, e.g. "Depression interim review" (median across practices in December 2020 -41.6% compared to December 2019). ConclusionsGranular NHS GP 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 key measures identified here as well as further studies, using primary care data to monitor and mitigate the indirect health impacts of Covid-19 on the NHS. How this fits inDuring the COVID-19 pandemic, routine healthcare services in England faced significant disruption, and NHS England recommended restoring NHS services to near-normal levels before winter 2020. Our previous report covered the disruption and recovery in pathology tests and respiratory activity: here we describe an additional six areas of common primary care activity. We found most activities exhibited significant reductions during pandemic wave 1 (with most recovering to near-normal levels by December); however many important aspects of care - especially those of a more time-critical nature - were maintained throughout the pandemic. We recommend key measures for ongoing monitoring and further investigation of the impacts on health inequalities, to help measure and mitigate the ongoing indirect health impacts of COVID-19 on the NHS.
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ObjectiveTo describe the impact of the COVID-19 pandemic on safe prescribing, using the PINCER prescribing indicators; to implement complex prescribing indicators at national scale using GP data. DesignPopulation based cohort study, with the approval of NHS England using the OpenSAFELY platform. SettingElectronic health record data from 56.8 million NHS patients general practice records. ParticipantsAll NHS patients registered at a GP practice using TPP or EMIS computer systems and recorded as at risk of at least one potentially hazardous PINCER indicator between September 2019 and September 2021. Main outcome measureMonthly trends and between-practice variation for compliance with 13 PINCER measures between September 2019 and September 2021. ResultsThe indicators were successfully implemented across GP data in OpenSAFELY. Hazardous prescribing remained largely unchanged during the COVID-19 pandemic, with only small reductions in achievement of the PINCER indicators. There were transient delays in blood test monitoring for some medications, particularly ACE inhibitors. All indicators exhibited substantial recovery by September 2021. We identified 1,813,058 patients at risk of at least one hazardous prescribing event. ConclusionGood performance was maintained during the COVID-19 pandemic across a diverse range of widely evaluated measures of safe prescribing. Summary box O_TEXTBOXWHAT IS ALREADY KNOWN ON THIS TOPICO_LIPrimary care services were substantially disrupted by the COVID-19 pandemic. C_LIO_LIDisruption to safe prescribing during the pandemic has not previously been evaluated. C_LIO_LIPINCER is a nationally adopted programme of activities that aims to identify and correct hazardous prescribing in GP practices, by conducting manual audit on subgroups of practices. C_LI WHAT THIS STUDY ADDSO_LIFor the first time, we were able to successfully generate data on PINCER indicators for almost the whole population of England, in a single analysis. C_LIO_LIOur study is the most comprehensive assessment of medication safety during the COVID-19 pandemic in England, covering 95% of the population using well-validated measures. C_LIO_LIGood performance was maintained across many PINCER indicators throughout the pandemic. C_LIO_LIDelays in delivering some medication-related blood test monitoring were evident though considerable recovery was made by the end of the study period. C_LI C_TEXTBOX
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The SARS-CoV-2 Omicron variant is increasing in prevalence around the world. Accurate estimation of disease severity associated with Omicron is critical for pandemic planning. We found lower risk of accident and emergency (AE) attendance following SARS-CoV-2 infection with Omicron compared to Delta (HR: 0.39 (95% CI: 0.30 - 0.51; P<.0001). For AE attendances that lead to hospital admission, Omicron was associated with an 85% lower hazard compared with Delta (HR: 0.14 (95% CI: 0.09 - 0.24; P<.0001)). Conflicts of InterestsNothing to declare. Funding statementThis work was supported by the Medical Research Council MR/V015737/1. TPP provided technical expertise and infrastructure within their data centre pro bono in the context of a national emergency. Rosalind Eggo is funded by HDR UK (grant: MR/S003975/1), MRC (grant: MC_PC 19065), NIHR (grant: NIHR200908).
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BackgroundThe rate at which COVID-19 vaccine effectiveness wanes over time is crucial for vaccination policies, but is incompletely understood with conflicting results from different studies. MethodsThis cohort study, using the OpenSAFELY-TPP database and approved by NHS England, included individuals without prior SARS-CoV-2 infection assigned to vaccines priority groups 2-12 defined by the UK Joint Committee on Vaccination and Immunisation. We compared individuals who had received two doses of BNT162b2 or ChAdOx1 with unvaccinated individuals during six 4-week comparison periods, separately for subgroups aged 65+ years; 16-64 years and clinically vulnerable; 40-64 years and 18-39 years. We used Cox regression, stratified by first dose eligibility and geographical region and controlled for calendar time, to estimate adjusted hazard ratios (aHRs) comparing vaccinated with unvaccinated individuals, and quantified waning vaccine effectiveness as ratios of aHRs per-4-week period. The outcomes were COVID-19 hospitalisation, COVID-19 death, positive SARS-CoV-2 test, and non-COVID-19 death. FindingsThe BNT162b2, ChAdOx1 and unvaccinated groups comprised 1,773,970, 2,961,011 and 2,433,988 individuals, respectively. Waning of vaccine effectiveness was similar across outcomes and vaccine brands: e.g. in the 65+ years subgroup ratios of aHRs versus unvaccinated for COVID-19 hospitalisation, COVID-19 death and positive SARS-CoV-2 test ranged from 1.23 (95% CI 1.15-1.32) to 1.27 (1.20-1.34) for BNT162b2 and 1.16 (0.98-1.37) to 1.20 (1.14-1.27) for ChAdOx1. Despite waning, rates of COVID-19 hospitalisation and COVID-19 death were substantially lower among vaccinated individuals compared to unvaccinated individuals up to 26 weeks after second dose, with estimated aHRs <0.20 (>80% vaccine effectiveness) for BNT162b2, and <0.26 (>74%) for ChAdOx1. By weeks 23-26, rates of SARS-CoV-2 infection in fully vaccinated individuals were similar to or higher than those in unvaccinated individuals: aHRs ranged from 0.85 (0.78-0.92) to 1.53 (1.07-2.18) for BNT162b2, and 1.21 (1.13-1.30) to 1.99 (1.94-2.05) for ChAdOx1. InterpretationThe rate at which estimated vaccine effectiveness waned was strikingly consistent for COVID-19 hospitalisation, COVID-19 death and positive SARS-CoV-2 test, and similar across subgroups defined by age and clinical vulnerability. If sustained to outcomes of infection with the Omicron variant and to booster vaccination, these findings will facilitate scheduling of booster vaccination doses.
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ObjectivesAscertain patient eligibility status and describe coverage of antivirals and neutralising monoclonal antibodies (nMAB) as treatment for COVID-19 in community settings in England. DesignCohort study, approved by NHS England. SettingRoutine clinical data from 23.4m people linked to data on COVID-19 infection and treatment, within the OpenSAFELY-TPP database. ParticipantsNon-hospitalised COVID-19 patients at high-risk of severe outcomes. InterventionsNirmatrelvir/ritonavir (Paxlovid), sotrovimab, molnupiravir, casirivimab or remdesivir, administered in the community by COVID-19 Medicine Delivery Units. ResultsWe identified 102,170 non-hospitalised patients with COVID-19 between 11th December 2021 and 28th April 2022 at high-risk of severe outcomes and therefore potentially eligible for antiviral and/or nMAB treatment. Of these patients, 18,210 (18%) received treatment; sotrovimab, 9,340 (51%); molnupiravir, 4,500 (25%); Paxlovid, 4,290 (24%); casirivimab, 50 (<1%); and remdesivir, 20 (<1%). The proportion of patients treated increased from 8% (180/2,380) in the first week of treatment availability to 22% (420/1870) in the latest week. The proportion treated varied by high risk group, lowest in those with Liver disease (12%; 95% CI 11 to 13); by treatment type, with sotrovimab favoured over molnupiravir/Paxlovid in all but three high risk groups: Down syndrome (36%; 95% CI 31 to 40), Rare neurological conditions (46%; 95% CI 44 to 48), and Primary immune deficiencies (49%; 95% CI 48 to 51); by ethnicity, from Black (10%; 95% CI 9 to 11) to White (18%; 95% CI 18 to 19); by NHS Region, from 11% (95% CI 10 to 12) in Yorkshire and the Humber to 23% (95% CI 22 to 24) in the East of England); and by deprivation level, from 12% (95% CI 12 to 13) in the most deprived areas to 21% (95% CI 21 to 22) in the least deprived areas. There was also lower coverage among unvaccinated patients (5%; 95% CI 4 to 7), those with dementia (5%; 95% CI 4 to 6) and care home residents (6%; 95% CI 5 to 6). ConclusionsUsing the OpenSAFELY platform we were able to identify patients who were potentially eligible to receive treatment and assess the coverage of these new treatments amongst these patients. Targeted activity may be needed to address apparent lower treatment coverage observed among certain groups, in particular (at present): different NHS regions, socioeconomically deprived areas, and care homes. What is already known about this topicSince the emergence of COVID-19, a number of approaches to treatment have been tried and evaluated. These have mainly consisted of treatments such as dexamethasone, which were used in UK hospitals,from early on in the pandemic to prevent progression to severe disease. Until recently (December 2021), no treatments have been widely used in community settings across England. What this study addsFollowing the rollout of antiviral medicines and neutralising monoclonal antibodies (nMABs) as treatment for patients with COVID-19, we were able to identify patients who were potentially eligible to receive antivirals or nMABs and assess the coverage of these new treatments amongst these patients, in as close to real-time as the available data flows would support. While the proportion of the potentially eligible patients receiving treatment increased over time, rising from 8% (180/2,380) in the first week of the roll out to 22% (420/1870) in the last week of April 2022, there were variations in coverage between key clinical, geographic, and demographic subgroup. How this study might affect research, practice, or policyTargeted activity may therefore be needed to address lower treatment rates observed among certain geographic areas and key groups including ethnic minorities, people living in areas of higher deprivation, and in care homes.
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BackgroundWhile the vaccines against COVID-19 are considered to be highly effective, COVID-19 vaccine breakthrough is likely and a small number of people will still fall ill, be hospitalised, or die from COVID-19, despite being fully vaccinated. With the continued increase in numbers of positive SARS-CoV-2 tests, describing the characters of individuals who have experienced a COVID-19 vaccine breakthrough could be hugely important in helping to determine who may be at greatest risk. MethodWith the approval of NHS England we conducted a retrospective cohort study using routine clinical data from the OpenSAFELY TPP database of fully vaccinated individuals, linked to secondary care and death registry data, and described the characteristics of those experiencing a COVID-19 vaccine breakthrough. ResultsAs of 01st November 2021, a total of 15,436,455 individuals were identified as being fully vaccinated against COVID-19, with a median follow-up time of 149 days (IQR: 107-179). From within this population, a total of 577245 (<4%) individuals reported a positive SARS-CoV-2 test. For every 1000 years of patient follow-up time, the corresponding incidence rate was 98.02 (95% CI 97.9-98.15). There were 16,120 COVID-19-related hospital admissions, 1,100 COVID-19 critical care admission patients and 3,925 COVID-19-related deaths; corresponding incidence rates of 2.72 (95% C 2.7-2.74), 0.19 (95% C 0.18-0.19) and 0.66 (95% C 0.65-0.67), respectively. When broken down by the initial priority group, higher rates of hospitalisation and death were seen in those in care homes and those over 80 years of age. Comorbidities with the highest rates of breakthrough COVID-19 included chronic kidney disease, dialysis, transplant, haematological malignancy, and immunocompromised. ConclusionThe majority of COVID-19 vaccine breakthrough cases in England were mild with relatively few fully vaccinated individuals being hospitalised or dying as a result. However, some concerning differences in rates of breakthrough cases were identified in several clinical and demographic groups. While it is important to note that these findings are simply descriptive and cannot be used to answer why certain groups have higher rates of COVID-19 breakthrough than others, the emergence of the Omicron variant of COVID-19 coupled with the continued increase in numbers of positive SARS-CoV-2 tests are concerning. As numbers of fully vaccinated individuals increases and follow-up time lengthens, so too will the number of COVID-19 breakthrough cases. Additional analyses, aimed at identifying individuals at higher risk, are therefore required.
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ObjectivesTo compare the effectiveness of the BNT162b2 mRNA (Pfizer-BioNTech) and the ChAdOx1 (Oxford-AstraZeneca) COVID-19 vaccines against infection and COVID-19 disease in health and social care workers. DesignCohort study, emulating a comparative effectiveness trial. SettingLinked primary care, hospital, and COVID-19 surveillance records available within the OpenSAFELY-TPP research platform. Participants317,341 health and social care workers vaccinated between 4 January and 28 February 2021, registered with a GP practice using the TPP SystmOne clinical information system in England, and not clinically extremely vulnerable. InterventionsVaccination with either BNT162b2 or ChAdOx1 administered as part of the national COVID-19 vaccine roll-out. Main outcome measuresRecorded SARS-CoV-2 positive test, or COVID-19 related Accident and Emergency attendance or hospital admission occurring within 20 weeks of vaccination. ResultsThe cumulative incidence of each outcome was similar for both vaccines during the first 20 weeks post-vaccination. The cumulative incidence of recorded SARS-CoV-2 infection 6 weeks after vaccination with BNT162b2 was 19.2 per 1000 people (95%CI 18.6 to 19.7) and with ChAdOx1 was 18.9 (95%CI 17.6 to 20.3), representing a difference of -0.24 per 1000 people (95%CI -1.71 to 1.22). The difference in the cumulative incidence per 1000 people of COVID-19 accident and emergency attendance at 6 weeks was 0.01 per 1000 people (95%CI -0.27 to 0.28). For COVID-19 hospital admission, this difference was 0.03 per 1000 people (95%CI -0.22 to 0.27). ConclusionsIn this cohort of healthcare workers where we would not anticipate vaccine type to be related to health status, we found no substantial differences in the incidence of SARS-CoV-2 infection or COVID-19 disease up to 20 weeks after vaccination. Incidence dropped sharply after 3-4 weeks and there were very few COVID-19 hospital attendance and admission events after this period. This is in line with expected onset of vaccine-induced immunity, and suggests strong protection against COVID-19 disease for both vaccines.
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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 CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed on May 19th, 2021, using the terms "COVID-19", "SARS-CoV-2" and "rheumatoid arthritis", "psoriatic arthritis" "ankylosing spondylitis", "Crohns 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 medications Implications 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.
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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.
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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
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BackgroundLong COVID is a term to describe new or persistent symptoms at least four weeks after onset of acute COVID-19. Clinical codes to describe this phenomenon were released in November 2020 in the UK, but it is not known how these codes have been used in practice. MethodsWorking on behalf of NHS England, we used OpenSAFELY data encompassing 96% of the English population. We measured the proportion of people with a recorded code for long COVID, overall and by demographic factors, electronic health record software system, and week. We also measured variation in recording amongst practices. ResultsLong COVID was recorded for 23,273 people. Coding was unevenly distributed amongst practices, with 26.7% of practices having not used the codes at all. Regional variation was high, ranging between 20.3 per 100,000 people for East of England (95% confidence interval 19.3-21.4) and 55.6 in London (95% CI 54.1-57.1). The rate was higher amongst women (52.1, 95% CI 51.3-52.9) compared to men (28.1, 95% CI 27.5-28.7), and higher amongst practices using EMIS software (53.7, 95% CI 52.9-54.4) compared to TPP software (20.9, 95% CI 20.3-21.4). ConclusionsLong COVID coding in primary care is low compared with early reports of long COVID prevalence. This may reflect under-coding, sub-optimal communication of clinical terms, under-diagnosis, a true low prevalence of long COVID diagnosed by clinicians, or a combination of factors. We recommend increased awareness of diagnostic codes, to facilitate research and planning of services; and surveys of clinicians experiences, to complement ongoing patient surveys.
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ObjectivesWe investigated the role of routinely prescribed oral anticoagulants (OACs) in COVID-19 outcomes, comparing current OAC use versus non-use in Study 1; and warfarin versus direct oral anticoagulants (DOACs) in Study 2. DesignTwo cohort studies, on behalf of NHS England. SettingPrimary care data and pseudonymously-linked SARS-CoV-2 antigen testing data, hospital admissions, and death records from England. ParticipantsStudy 1: 70,464 people with atrial fibrillation (AF) and CHA{square}DS{square}-VASc score of 2. Study 2: 372,746 people with non-valvular AF. Main outcome measuresTime to test for SARS-CoV-2, testing positive for SARS-CoV-2, COVID-19 related hospital admission, COVID-19 deaths or non-COVID-19 deaths in Cox regression. ResultsIn Study 1, we included 52,416 current OAC users and 18,048 non-users. We observed no difference in risk of being tested for SARS-CoV-2 associated with current use (adjusted HR, 1.01, 95%CI, 0.96 to 1.05) versus non-use. We observed a lower risk of testing positive for SARS-CoV-2 (adjusted HR, 0.73, 95%CI, 0.60 to 0.90), and COVID-19 deaths (adjusted HR, 0.69, 95%CI, 0.49 to 0.97) associated with current use versus non-use. In Study 2, we included 92,339 warfarin users and 280,407 DOAC users. We observed a lower risk of COVID-19 deaths (adjusted HR, 0.79, 95%CI, 0.76 to 0.83) associated with warfarin versus DOACs. Similar associations were found for all other outcomes. ConclusionsAmong people with AF and a CHA{square}DS{square}-VASc score of 2, those receiving OACs had a lower risk of receiving a positive COVID-19 test and severe COVID-19 outcomes than non-users; this might be explained by a causal effect of OACs in preventing severe COVID-19 outcomes or more cautious behaviours leading to reduced infection risk. There was no evidence of a higher risk of severe COVID-19 outcomes associated with warfarin versus DOACs in people with non-valvular AF regardless of CHA{square}DS{square}-VASc score. Key pointsO_ST_ABSWhat is already known on this topicC_ST_ABSO_LICurrent studies suggest that prophylactic or therapeutic anticoagulant use, particularly low molecular weight heparin, lower the risk of pulmonary embolism and mortality during hospitalisation among patients with COVID-19. C_LIO_LIReduced vitamin K status has been reported to be correlated with severity of COVID-19. This could mean that warfarin, as a vitamin K antagonist, is associated with more severe COVID-19 disease than non-vitamin K anticoagulants. C_LI What this study addsO_LIIn 70,464 people with atrial fibrillation, at the threshold of being treated with an OAC based on risk of stroke, we observed a lower risk of testing positive for SARS-CoV-2 and COVID-19 related deaths associated with routinely prescribed OACs, relative to non-use. C_LIO_LIThis might be explained by OACs preventing severe COVID-19 outcomes, or more cautious behaviours and environmental factors reducing the risk of SARS-CoV-2 infection in those taking OACs. C_LIO_LIIn 372,746 people with non-valvular atrial fibrillation, there was no evidence of a higher risk of severe COVID-19 outcomes associated with warfarin compared with DOACs. C_LI
ABSTRACT
The B.1.1.7 variant of concern (VOC) is increasing in prevalence across Europe. Accurate estimation of disease severity associated with this VOC is critical for pandemic planning. We found increased risk of death for VOC compared with non-VOC cases in England (HR: 1.67 (95% CI: 1.34 - 2.09; P<.0001). Absolute risk of death by 28-days increased with age and comorbidities. VOC has potential to spread faster with higher mortality than the pandemic to date.
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ObjectivesTo assess the association between learning disability and risk of hospitalisation and mortality from COVID-19 in England among adults and children. DesignWorking on behalf of NHS England, two cohort studies using patient-level data for >17 million people from primary care electronic health records were linked with death data from the Office for National Statistics and hospitalization data from NHS Secondary Uses Service using the OpenSAFELY platform. SettingGeneral practices in England which use TPP software. ParticipantsParticipants were males and females, aged up to 105 years, from two cohorts: (1) wave 1, registered with a TPP practice as of 1st March 2020 and followed until 31st August, 2020; (2) wave 2 registered 1st September 2020 and followed until 31st December 2020 (for admissions) or 8th February 2021 (for deaths). The main exposure group was people included on a general practice learning disability register (LDR), with a subgroup of people classified as having profound or severe learning disability. We also identified patients with Down syndrome and cerebral palsy (whether or not on the learning disability register). Main outcome measures(i) COVID-19 related death, (ii) COVID-19 related hospitalisation. Non-COVID-19 related death was also explored. ResultsIn wave 1, of 14,301,415 included individuals aged 16 and over, 90,095 (0.63%) were identified as being on the LDR. 30,173 COVID-related hospital admissions, 13,919 COVID-19 related deaths and 69,803 non-COVID deaths occurred; of which 538 (1.8%), 221 (1.6%) and 596 (0.85%) were among individuals on the LDR, respectively. In wave 2, 27,611 COVID-related hospital admissions, 17,933 COVID-19 related deaths and 54,171 non-COVID deaths occurred; of which 383 (1.4%), 260 (1.4%) and 470 (0.87%) were among individuals on the LDR. Wave 1 hazard ratios for individuals on the LDR, adjusted for age, sex, ethnicity and geographical location, were 5.3 (95% confidence interval (CI) 4.9, 5.8) for COVID-19 related hospital admissions and 8.2 (95% CI: 7.1, 9.4) for COVID-19 related death. Wave 2 produced similar estimates. Associations were stronger among those classed as severe-profound and among those in residential care. Down syndrome and cerebral palsy were associated with increased hazard of both events in both waves; Down syndrome to a much greater extent. Hazards of non-COVID-19 related death followed similar patterns with weaker associations. ConclusionsPeople with learning disabilities have markedly increased risks of hospitalisation and mortality from COVID-19. This raised risk is over and above that seen for non-COVID causes of death. Ensuring prompt access to Covid-19 testing and health care and consideration of prioritisation for COVID-19 vaccination and other targeted preventive measures are warranted.
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ObjectivesTo compare approaches for obtaining relative and absolute estimates of risk of 28-day COVID-19 mortality for adults in the general population of England in the context of changing levels of circulating infection. DesignThree designs were compared. (A) case-cohort which does not explicitly account for the time-changing prevalence of COVID-19 infection, (B) 28-day landmarking, a series of sequential overlapping sub-studies incorporating time-updating proxy measures of the prevalence of infection, and (C) daily landmarking. Regression models were fitted to predict 28-day COVID-19 mortality. SettingWorking on behalf of NHS England, we used clinical data from adult patients from all regions of England held in the TPP SystmOne electronic health record system, linked to Office for National Statistics (ONS) mortality data, using the OpenSAFELY platform. ParticipantsEligible participants were adults aged 18 or over, registered at a general practice using TPP software on 1st March 2020 with recorded sex, postcode and ethnicity. 11,972,947 individuals were included, and 7,999 participants experienced a COVID-19 related death. The study period lasted 100 days, ending 8th June 2020. PredictorsA range of demographic characteristics and comorbidities were used as potential predictors. Local infection prevalence was estimated with three proxies: modelled based on local prevalence and other key factors; rate of A&E COVID-19 related attendances; and rate of suspected COVID-19 cases in primary care. Main outcome measuresCOVID-19 related death. ResultsAll models discriminated well between patients who did and did not experience COVID-19 related death, with C-statistics ranging from 0.92-0.94. Accurate estimates of absolute risk required data on local infection prevalence, with modelled estimates providing the best performance. ConclusionsReliable estimates of absolute risk need to incorporate changing local prevalence of infection. Simple models can provide very good discrimination and may simplify implementation of risk prediction tools in practice.
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BackgroundThere has been extensive speculation about the relationship between COVID-19 and various cardiometabolic and pulmonary conditions. This a complex question: COVID-19 may cause a cardiometabolic or respiratory event; admission for a clinical event may result in hospital-acquired SARS-CoV-2 infection; both may contribute to a patient surpassing the threshold for presenting to services; and the presence of a pandemic may change whether patients present to services at all. To inform analysis of these questions, we set out to describe the overall rate of various key clinical events over time, and their relationship with COVID-19. MethodsWorking on behalf of NHS England, we used data from the OpenSAFELY platform containing data from approximately 40% of the population of England. We selected the whole adult population of 17m patients and within this identified two further mutually exclusive groups: patients who tested positive for SARS-CoV-2 in the community; and patients hospitalised with COVID-19. We report counts of death, DVT, PE, ischaemic stroke, MI, heart failure, AKI and diabetic ketoacidosis in each month between February 2019 and October 2020 within each of: the general population, community SARS-CoV-2 cases, and hospitalised patients with COVID-19. Outcome events were defined using hospitalisations, GP records and cause of death data. ResultsFor all outcomes except death there was a lower count of events in April 2020 compared to April 2019. For most outcomes the minimum count of events was in April 2020, where the decrease compared to April 2019 in events ranged from 5.9% (PE) to 40.0% (heart failure). Despite hospitalised COVID-19 patients making up just 0.14% of the population in April 2020, these patients accounted for an extremely high proportion of cardiometabolic and respiratory events in that month (range of proportions 10.3% (DVT) to 33.5% (AKI)). InterpretationWe observed a substantial drop in the incidence of cardiometabolic and pulmonary events in the non-COVID-19 general population, but high occurrence of COVID-19 among patients with these events. Shortcomings in routine NHS secondary care data, especially around the timing and order of events, make causal interpretations challenging. We caution that the intermediate findings reported here should be used to inform the design and interpretation of any studies using a general population comparator to evaluate the relationship between COVID-19 and other clinical events.
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Black and minority ethnic groups were at raised risk of dying from COVID-19 during the first few months of the COVID-19 epidemic in England. We aimed to investigate whether ethnic inequalities in COVID-19 deaths were similar in the more recent "second wave" of the epidemic. Working on behalf of NHS England, we used primary care and linked ONS mortality data within the OpenSAFELY platform. All adults in the database at 1st September 2020 and with at least 1 year of prior follow-up and a record of ethnicity were included. The outcome was COVID-19-related death (death with COVID-19 listed as a cause of death on the death certificate). Follow-up was to 9th November 2020. Hazard ratios for ethnicity were calculated using Cox regression models adjusted for age and sex, and then further adjusted for deprivation. 13,223,154 people were included. During the study period, people of South Asian ethnicity were at higher risk of death due to COVID-19 than white people after adjusting for age and sex (HR = 3.47, 95% CI 2.99-4.03); the association attenuated somewhat on further adjustment for index of multiple deprivation (HR = 2.86, 2.46-3.33, Table 2). In contrast with the first wave of the epidemic, we found little evidence of a raised risk in black or other ethnic groups compared to white (HR for black vs white = 1.28, 0.87-1.88 adjusted for age and sex; and 1.01, 0.69-1.49 further adjusted for deprivation). Our findings suggest that ethnic inequalities in the risk of dying COVID-19-related death have changed between the first and early second wave of the epidemic in England. O_TBL View this table: org.highwire.dtl.DTLVardef@987a5org.highwire.dtl.DTLVardef@1a8a141org.highwire.dtl.DTLVardef@1f2de56org.highwire.dtl.DTLVardef@1e2f9b8org.highwire.dtl.DTLVardef@78bfcc_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 2:C_FLOATNO O_TABLECAPTIONAssociation between ethnicity and COVID-19 death 1st Sept - 9th Nov 2020 C_TABLECAPTION C_TBL
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BackgroundOn December 8th 2020, NHS England administered the first COVID-19 vaccination as part of an ambitious vaccination programme during a global health emergency. AimsTo describe trends and variation in vaccine coverage by key clinical and demographic groups; to create a framework for near-real-time monitoring of vaccine coverage in key subgroups. MethodsWorking on behalf of NHS England we analysed 57.9 million patient records in situ and in near-real-time within the infrastructure of the Electronic Health Record (EHR) software vendors EMIS and TPP using OpenSAFELY. We describe vaccine coverage and time trends across a range of demographic and fine-grained clinical subgroups in eight Joint Committee on Vaccination and Immunisation (JCVI) priority cohorts. Results20,852,692 patients (36%) received a COVID-19 vaccine between December 8th 2020 and March 17th 2021. Of patients aged [≥]80 not in a care home (JCVI group 2) 94.7% received a vaccine, but with substantial variation by ethnicity (White 96.2% vaccinated, Black 68.3%) and deprivation (least deprived 96.6%, most deprived 90.7%). Overall, patients with pre-existing medical conditions were equally or more likely to be vaccinated with two exceptions: severe mental illness (89.5% vaccinated) and learning disability (91.4%). 275,205 vaccine recipients were identified as care home residents (priority group 1; 91.2% coverage). 1,257,914 (6.0%) recipients have had a second dose. Detailed characteristics of recipients in all cohorts are reported. ConclusionsThe NHS in England has rapidly delivered mass vaccination. We were able to deploy a data monitoring framework using publicly auditable methods and a secure, in-situ processing model, using linked but pseudonymised patient-level NHS data on 57.9 million patients with very short delays from vaccine administration to completed analysis. Targeted activity may be needed to address lower vaccination coverage observed among certain key groups: ethnic minorities, those living in deprived areas, and people with severe mental illness or learning disabilities.
