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1.
Wellcome Open Res ; 6: 360, 2021.
Article in English | MEDLINE | ID: covidwho-1876163

ABSTRACT

Background: At the outset of the COVID-19 pandemic, there was no routine comprehensive hospital medicines data from the UK available to researchers. These records can be important for many analyses including the effect of certain medicines on the risk of severe COVID-19 outcomes. With the approval of NHS England, we set out to obtain data on one specific group of medicines, "high-cost drugs" (HCD) which are typically specialist medicines for the management of long-term conditions, prescribed by hospitals to patients. Additionally, we aimed to make these data available to all approved researchers in OpenSAFELY-TPP. This report is intended to support all studies carried out in OpenSAFELY-TPP, and those elsewhere, working with this dataset or similar data. Methods: Working with the North East Commissioning Support Unit and NHS Digital, we arranged for collation of a single national HCD dataset to help inform responses to the COVID-19 pandemic. The dataset was developed from payment submissions from hospitals to commissioners. Results: In the financial year (FY) 2018/19 there were 2.8 million submissions for 1.1 million unique patient IDs recorded in the HCD. The average number of submissions per patient over the year was 2.6. In FY 2019/20 there were 4.0 million submissions for 1.3 million unique patient IDs. The average number of submissions per patient over the year was 3.1. Of the 21 variables in the dataset, three are now available for analysis in OpenSafely-TPP: Financial year and month of drug being dispensed; drug name; and a description of the drug dispensed. Conclusions: We have described the process for sourcing a national HCD dataset, making these data available for COVID-19-related analysis through OpenSAFELY-TPP and provided information on the variables included in the dataset, data coverage and an initial descriptive analysis.

2.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-336821

ABSTRACT

Objective To 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. Design Population based cohort study, with the approval of NHS England using the OpenSAFELY platform. Setting Electronic health record data from 56.8 million NHS patients’ general practice records. Participants All 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 measure Monthly trends and between-practice variation for compliance with 13 PINCER measures between September 2019 and September 2021. Results The 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. Conclusion Good performance was maintained during the COVID-19 pandemic across a diverse range of widely evaluated measures of safe prescribing. Summary box WHAT IS ALREADY KNOWN ON THIS TOPIC Primary care services were substantially disrupted by the COVID-19 pandemic. Disruption to safe prescribing during the pandemic has not previously been evaluated. PINCER 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. WHAT THIS STUDY ADDS For the first time, we were able to successfully generate data on PINCER indicators for almost the whole population of England, in a single analysis. Our 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. Good performance was maintained across many PINCER indicators throughout the pandemic. Delays in delivering some medication-related blood test monitoring were evident though considerable recovery was made by the end of the study period.

3.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-335343

ABSTRACT

Background: Patients surviving hospitalisation for COVID-19 are thought to be at high risk of cardiometabolic and pulmonary complications, but quantification of that risk is limited. We aimed to describe the overall burden of these complications in people after discharge from hospital with COVID-19.   Methods: :  Working 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 pre-pandemic hospitalisation with pneumonia, and a frequency-matched cohort from the general population in 2019. We studied seven outcomes: deep vein thrombosis (DVT), pulmonary embolism (PE), ischaemic stroke, myocardial infarction (MI), heart failure, AKI and new type 2 diabetes mellitus (T2DM) diagnosis. Absolute rates were measured in each cohort and Fine and Gray models were used to estimate age/sex adjusted subdistribution hazard ratios comparing outcome risk between discharged COVID-19 patients and the two comparator cohorts. Results: :  Amongst the population of 77,347 patients discharged following hospitalisation with COVID-19, rates for the 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 higher risk of all outcomes compared to matched controls from the 2019 general population. Across the whole study period, the risk of outcomes was more similar when comparing patients discharged with COVID-19 to those discharged with pneumonia in 2019, although COVID-19 patients had higher risk of T2DM (15.2 versus 37.2 [rate per 1,000-person-years for COVID-19 versus pneumonia, respectively];SHR, 1.46 [95% CI: 1.31 - 1.63]).  Conclusions: :  Risk of cardiometabolic and pulmonary adverse outcomes is markedly raised following discharge from hospitalisation with COVID-19 compared to the general population. However, excess risks were similar to those seen following discharge post-pneumonia. Overall, this suggests a large additional burden on healthcare resources.

4.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-335281

ABSTRACT

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 Interests Nothing to declare. Funding statement This 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).

5.
Br J Gen Pract ; 2022 Feb 16.
Article in English | MEDLINE | ID: covidwho-1810374

ABSTRACT

BACKGROUND: Early evidence has shown that anticoagulant reduces the risk of thrombotic events in those infected with COVID-19. However, evidence of the role of routinely prescribed oral anticoagulants (OACs) in COVID-19 outcomes is limited. AIM: To investigate the association between OACs and COVID-19 outcomes in those with atrial fibrillation and a CHA2DS2-VASc score of 2. DESIGN AND SETTING: On behalf of NHS England, a population-based cohort study was conducted. METHOD: The study used primary care data and pseudonymously-linked SARS-CoV-2 antigen testing data, hospital admissions, and death records from England. Cox regression was used to estimate hazard ratios (HRs) for COVID-19 outcomes comparing people with current OAC use versus non-use, accounting for age, sex, comorbidities, other medications, deprivation, and general practice. RESULTS: Of 71 103 people with atrial fibrillation and a CHA2DS2-VASc score of 2, there were 52 832 current OAC users and 18 271 non-users. No difference in risk of being tested for SARS-CoV-2 was associated with current use (adjusted HR [aHR] 0.99, 95% confidence interval [CI] = 0.95 to 1.04) versus non-use. A lower risk of testing positive for SARS-CoV-2 (aHR 0.77, 95% CI = 0.63 to 0.95) and a marginally lower risk of COVID-19-related death (aHR, 0.74, 95% CI = 0.53 to 1.04) were associated with current use versus non-use. CONCLUSION: Among those at low baseline stroke risk, people receiving OACs had a lower risk of testing positive for SARS-CoV-2 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 unmeasured confounding, including more cautious behaviours leading to reduced infection risk.

6.
Lancet Reg Health Eur ; 14: 100295, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1747703

ABSTRACT

BACKGROUND: Residents in care homes have been severely impacted by COVID-19. We describe trends in the mortality risk among residents of care homes compared to private homes. METHODS: On behalf of NHS England we used OpenSAFELY-TPP 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 Care and Quality Commission data. FINDINGS: We included 4,340,648 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 those in private housing in February 2019: comparative mortality figure (CMF) = 10.59 (95%CI = 9.51, 11.81) among women, and 10.87 (9.93, 11.90) among men. By April 2020 these relative differences had increased to more than 17 times with CMFs of 17.57 (16.43, 18.79) among women and 18.17 (17.22, 19.17) among men. CMFs did not increase during the second wave, despite a rise in the absolute age-standardised COVID-19 mortality risks. INTERPRETATION: COVID-19 has had a disproportionate impact on the mortality of care home residents in England compared to older residents of private homes, but only in the first wave. This may be explained by a degree of acquired immunity, improved protective measures or changes in the underlying frailty of the populations. The care home population should be prioritised for measures aimed at controlling COVID-19. FUNDING: Medical Research Council MR/V015737/1.

7.
Diagn Progn Res ; 6(1): 6, 2022 Feb 24.
Article in English | MEDLINE | ID: covidwho-1702772

ABSTRACT

BACKGROUND: Obtaining accurate estimates of the risk of COVID-19-related death in the general population is challenging in the context of changing levels of circulating infection. METHODS: We propose a modelling approach to predict 28-day COVID-19-related death which explicitly accounts for COVID-19 infection prevalence using a series of sub-studies from new landmark times incorporating time-updating proxy measures of COVID-19 infection prevalence. This was compared with an approach ignoring infection prevalence. The target population was adults registered at a general practice in England in March 2020. The outcome was 28-day COVID-19-related death. Predictors included demographic characteristics and comorbidities. Three proxies of local infection prevalence were used: model-based estimates, rate of COVID-19-related attendances in emergency care, and rate of suspected COVID-19 cases in primary care. We used data within the TPP SystmOne electronic health record system linked to Office for National Statistics mortality data, using the OpenSAFELY platform, working on behalf of NHS England. Prediction models were developed in case-cohort samples with a 100-day follow-up. Validation was undertaken in 28-day cohorts from the target population. We considered predictive performance (discrimination and calibration) in geographical and temporal subsets of data not used in developing the risk prediction models. Simple models were contrasted to models including a full range of predictors. RESULTS: Prediction models were developed on 11,972,947 individuals, of whom 7999 experienced COVID-19-related death. All models discriminated well between individuals who did and did not experience the outcome, including simple models adjusting only for basic demographics and number of comorbidities: C-statistics 0.92-0.94. However, absolute risk estimates were substantially miscalibrated when infection prevalence was not explicitly modelled. CONCLUSIONS: Our proposed models allow absolute risk estimation in the context of changing infection prevalence but predictive performance is sensitive to the proxy for infection prevalence. Simple models can provide excellent discrimination and may simplify implementation of risk prediction tools.

8.
Clin Infect Dis ; 2021 Sep 06.
Article in English | MEDLINE | ID: covidwho-1706197

ABSTRACT

BACKGROUND: The SARS-CoV-2 alpha variant (B.1.1.7) is associated with higher transmissibility than wild type virus, becoming the dominant variant in England by January 2021. We aimed to describe the severity of the alpha variant in terms of the pathway of disease from testing positive to hospital admission and death. METHODS: With the approval of NHS England, we linked individual-level data from primary care with SARS-CoV-2 community testing, hospital admission, and ONS all-cause death data. We used testing data with S-gene target failure as a proxy for distinguishing alpha and wild-type cases, and stratified Cox proportional hazards regression to compare the relative severity of alpha cases compared to wild type diagnosed from 16th November 2020 to 11th January 2021. RESULTS: Using data from 185,234 people who tested positive for SARS-CoV-2 in the community (alpha=93,153; wild-type=92,081), in fully adjusted analysis accounting for individual-level demographics and comorbidities as well as regional variation in infection incidence, we found alpha associated with 73% higher hazards of all-cause death (aHR: 1.73 (95% CI 1.41 - 2.13; P<.0001)) and 62% higher hazards of hospital admission (aHR: 1.62 ((95% CI 1.48 - 1.78; P<.0001), compared to wild-type virus. Among patients already admitted to ICU, the association between alpha and increased all-cause mortality was smaller and the confidence interval included the null (aHR: 1.20 (95% CI 0.74 - 1.95; P=0.45)). CONCLUSIONS: The SARS-CoV-2 alpha variant is associated with an increased risk of both hospitalisation and mortality than wild-type virus.

9.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-322685

ABSTRACT

On March 11th 2020, the World Health Organization characterised COVID-19 as a pandemic. Responses to containing the spread of the virus have relied heavily on policies involving restricting contact between people. Evolving policies regarding shielding and individual choices about restricting social contact will rely heavily on perceived risk of poor outcomes from COVID-19. In order to make informed decisions, both individual and collective, good predictive models are required.   For outcomes related to an infectious disease, the performance of any risk prediction model will depend heavily on the underlying prevalence of infection in the population of interest. Incorporating measures of how this changes over time may result in important improvements in prediction model performance.  This protocol reports details of a planned study to explore the extent to which incorporating time-varying measures of infection burden over time improves the quality of risk prediction models for COVID-19 death in a large population of adult patients in England. To achieve this aim, we will compare the performance of different modelling approaches to risk prediction, including static cohort approaches typically used in chronic disease settings and landmarking approaches incorporating time-varying measures of infection prevalence and policy change, using COVID-19 related deaths data linked to longitudinal primary care electronic health records data within the OpenSAFELY secure analytics platform.

10.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-320963

ABSTRACT

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.

11.
The Lancet regional health. Europe ; 14:100295-100295, 2022.
Article in English | EuropePMC | ID: covidwho-1615360

ABSTRACT

Background Residents in care homes have been severely impacted by COVID-19. We describe trends in the mortality risk among residents of care homes compared to private homes. Methods On behalf of NHS England we used OpenSAFELY-TPP 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 Care and Quality Commission data. Findings We included 4,340,648 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 those in private housing in February 2019: comparative mortality figure (CMF) = 10.59 (95%CI = 9.51, 11.81) among women, and 10.87 (9.93, 11.90) among men. By April 2020 these relative differences had increased to more than 17 times with CMFs of 17.57 (16.43, 18.79) among women and 18.17 (17.22, 19.17) among men. CMFs did not increase during the second wave, despite a rise in the absolute age-standardised COVID-19 mortality risks. Interpretation COVID-19 has had a disproportionate impact on the mortality of care home residents in England compared to older residents of private homes, but only in the first wave. This may be explained by a degree of acquired immunity, improved protective measures or changes in the underlying frailty of the populations. The care home population should be prioritised for measures aimed at controlling COVID-19. Funding Medical Research Council MR/V015737/1

12.
Br J Gen Pract ; 72(714): e63-e74, 2022 01.
Article in English | MEDLINE | ID: covidwho-1592598

ABSTRACT

BACKGROUND: The COVID-19 pandemic has disrupted healthcare activity. The NHS stopped non-urgent work in March 2020, later recommending services be restored to near-normal levels before winter where possible. AIM: To describe the volume and variation of coded clinical activity in general practice, taking respiratory disease and laboratory procedures as examples. DESIGN AND SETTING: Working on behalf of NHS England, a cohort study was conducted of 23.8 million patient records in general practice, in situ using OpenSAFELY. METHOD: Activity using Clinical Terms Version 3 codes and keyword searches from January 2019 to September 2020 are described. RESULTS: Activity recorded in general practice declined during the pandemic, but largely recovered by September. There was a large drop in coded activity for laboratory tests, with broad recovery to pre-pandemic levels by September. One exception was the international normalised ratio test, with a smaller reduction (median tests per 1000 patients in 2020: February 8.0; April 6.2; September 6.9). The pattern of recording for respiratory symptoms was less affected, following an expected seasonal pattern and classified as 'no change'. Respiratory infections exhibited a sustained drop, not returning to pre-pandemic levels by September. Asthma reviews experienced a small drop but recovered, whereas chronic obstructive pulmonary disease reviews remained below baseline. CONCLUSION: An open-source software framework was delivered 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, with some important tests less affected and recording of respiratory disease codes was mixed.


Subject(s)
COVID-19 , Cohort Studies , England/epidemiology , Humans , Pandemics , Primary Health Care , SARS-CoV-2 , State Medicine
13.
Br J Gen Pract ; 72(714): e51-e62, 2022 01.
Article in English | MEDLINE | ID: covidwho-1592597

ABSTRACT

BACKGROUND: On 8 December 2020 NHS England administered the first COVID-19 vaccination. AIM: To describe trends and variation in vaccine coverage in different clinical and demographic groups in the first 100 days of the vaccine rollout. DESIGN AND SETTING: With the approval of NHS England, a cohort study was conducted of 57.9 million patient records in general practice in England, in situ and within the infrastructure of the electronic health record software vendors EMIS and TPP using OpenSAFELY. METHOD: Vaccine coverage across various subgroups of Joint Committee on Vaccination and Immunisation (JCVI) priority cohorts is described. RESULTS: A total of 20 852 692 patients (36.0%) received a vaccine between 8 December 2020 and 17 March 2021. Of patients aged ≥80 years not in a care home (JCVI group 2) 94.7% received a vaccine, but with substantial variation by ethnicity (White 96.2%, Black 68.3%) and deprivation (least deprived 96.6%, most deprived 90.7%). Patients with pre-existing medical conditions were more likely to be vaccinated with two exceptions: severe mental illness (89.5%) and learning disability (91.4%). There were 275 205 vaccine recipients who were identified as care home residents (JCVI group 1; 91.2% coverage). By 17 March, 1 257 914 (6.0%) recipients had a second dose. CONCLUSION: The NHS rapidly delivered mass vaccination. In this study a data-monitoring framework was deployed using publicly auditable methods and a secure in situ processing model, using linked but pseudonymised patient-level NHS data for 57.9 million patients. Targeted activity may be needed to address lower vaccination coverage observed among certain key groups.


Subject(s)
COVID-19 Vaccines , COVID-19 , Cohort Studies , Humans , Primary Health Care , SARS-CoV-2 , Vaccination
14.
2021.
Preprint in English | Other preprints | ID: ppcovidwho-296145

ABSTRACT

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.

15.
Open Heart ; 8(2)2021 11.
Article in English | MEDLINE | ID: covidwho-1523054

ABSTRACT

BACKGROUND: Early in the COVID-19 pandemic, the National Health Service (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 coprescribed two anticoagulants following a medication change and associated monitoring. OBJECTIVE: To describe which people were switched from warfarin to DOACs; identify potentially unsafe coprescribing of anticoagulants; and assess whether abnormal clotting results have become more frequent during the pandemic. METHODS: With the approval of NHS England, we conducted a cohort study using routine clinical data from 24 million NHS patients in England. RESULTS: 20 000 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 coprescribing 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. International normalised ratio (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). CONCLUSIONS: Increased 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 coprescribed 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.


Subject(s)
Anticoagulants/administration & dosage , Blood Coagulation/drug effects , COVID-19 , Drug Substitution/standards , Factor Xa Inhibitors/administration & dosage , Practice Guidelines as Topic/standards , Practice Patterns, Physicians'/standards , State Medicine/standards , Warfarin/administration & dosage , Aged , Anticoagulants/adverse effects , Blood Coagulation Tests , Drug Monitoring , Drug Prescriptions , Drug Substitution/adverse effects , Drug Utilization/standards , England , Factor Xa Inhibitors/adverse effects , Female , Humans , Male , Middle Aged , Patient Safety , Primary Health Care/standards , Retrospective Studies , Risk Assessment , Risk Factors , Warfarin/adverse effects
16.
Br J Gen Pract ; 72(714): e63-e74, 2022 01.
Article in English | MEDLINE | ID: covidwho-1505838

ABSTRACT

BACKGROUND: The COVID-19 pandemic has disrupted healthcare activity. The NHS stopped non-urgent work in March 2020, later recommending services be restored to near-normal levels before winter where possible. AIM: To describe the volume and variation of coded clinical activity in general practice, taking respiratory disease and laboratory procedures as examples. DESIGN AND SETTING: Working on behalf of NHS England, a cohort study was conducted of 23.8 million patient records in general practice, in situ using OpenSAFELY. METHOD: Activity using Clinical Terms Version 3 codes and keyword searches from January 2019 to September 2020 are described. RESULTS: Activity recorded in general practice declined during the pandemic, but largely recovered by September. There was a large drop in coded activity for laboratory tests, with broad recovery to pre-pandemic levels by September. One exception was the international normalised ratio test, with a smaller reduction (median tests per 1000 patients in 2020: February 8.0; April 6.2; September 6.9). The pattern of recording for respiratory symptoms was less affected, following an expected seasonal pattern and classified as 'no change'. Respiratory infections exhibited a sustained drop, not returning to pre-pandemic levels by September. Asthma reviews experienced a small drop but recovered, whereas chronic obstructive pulmonary disease reviews remained below baseline. CONCLUSION: An open-source software framework was delivered 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, with some important tests less affected and recording of respiratory disease codes was mixed.


Subject(s)
COVID-19 , Cohort Studies , England/epidemiology , Humans , Pandemics , Primary Health Care , SARS-CoV-2 , State Medicine
17.
Br J Gen Pract ; 72(714): e51-e62, 2022 01.
Article in English | MEDLINE | ID: covidwho-1505837

ABSTRACT

BACKGROUND: On 8 December 2020 NHS England administered the first COVID-19 vaccination. AIM: To describe trends and variation in vaccine coverage in different clinical and demographic groups in the first 100 days of the vaccine rollout. DESIGN AND SETTING: With the approval of NHS England, a cohort study was conducted of 57.9 million patient records in general practice in England, in situ and within the infrastructure of the electronic health record software vendors EMIS and TPP using OpenSAFELY. METHOD: Vaccine coverage across various subgroups of Joint Committee on Vaccination and Immunisation (JCVI) priority cohorts is described. RESULTS: A total of 20 852 692 patients (36.0%) received a vaccine between 8 December 2020 and 17 March 2021. Of patients aged ≥80 years not in a care home (JCVI group 2) 94.7% received a vaccine, but with substantial variation by ethnicity (White 96.2%, Black 68.3%) and deprivation (least deprived 96.6%, most deprived 90.7%). Patients with pre-existing medical conditions were more likely to be vaccinated with two exceptions: severe mental illness (89.5%) and learning disability (91.4%). There were 275 205 vaccine recipients who were identified as care home residents (JCVI group 1; 91.2% coverage). By 17 March, 1 257 914 (6.0%) recipients had a second dose. CONCLUSION: The NHS rapidly delivered mass vaccination. In this study a data-monitoring framework was deployed using publicly auditable methods and a secure in situ processing model, using linked but pseudonymised patient-level NHS data for 57.9 million patients. Targeted activity may be needed to address lower vaccination coverage observed among certain key groups.


Subject(s)
COVID-19 Vaccines , COVID-19 , Cohort Studies , Humans , Primary Health Care , SARS-CoV-2 , Vaccination
18.
J Hematol Oncol ; 14(1): 172, 2021 10 19.
Article in English | MEDLINE | ID: covidwho-1477441

ABSTRACT

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.


Subject(s)
Anticoagulants/therapeutic use , COVID-19/epidemiology , Thromboembolism/drug therapy , Thromboembolism/virology , Warfarin/therapeutic use , Administration, Oral , Adolescent , Adult , Aged , Aged, 80 and over , Anticoagulants/pharmacology , COVID-19/blood , COVID-19/drug therapy , COVID-19/virology , Cohort Studies , England/epidemiology , Humans , Middle Aged , SARS-CoV-2/isolation & purification , Thromboembolism/blood , Thromboembolism/epidemiology , Treatment Outcome , Young Adult
19.
Wellcome Open Res ; 6: 90, 2021.
Article in English | MEDLINE | ID: covidwho-1395316

ABSTRACT

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.

20.
Clin Infect Dis ; 2021 Sep 06.
Article in English | MEDLINE | ID: covidwho-1393220

ABSTRACT

BACKGROUND: The SARS-CoV-2 alpha variant (B.1.1.7) is associated with higher transmissibility than wild type virus, becoming the dominant variant in England by January 2021. We aimed to describe the severity of the alpha variant in terms of the pathway of disease from testing positive to hospital admission and death. METHODS: With the approval of NHS England, we linked individual-level data from primary care with SARS-CoV-2 community testing, hospital admission, and ONS all-cause death data. We used testing data with S-gene target failure as a proxy for distinguishing alpha and wild-type cases, and stratified Cox proportional hazards regression to compare the relative severity of alpha cases compared to wild type diagnosed from 16th November 2020 to 11th January 2021. RESULTS: Using data from 185,234 people who tested positive for SARS-CoV-2 in the community (alpha=93,153; wild-type=92,081), in fully adjusted analysis accounting for individual-level demographics and comorbidities as well as regional variation in infection incidence, we found alpha associated with 73% higher hazards of all-cause death (aHR: 1.73 (95% CI 1.41 - 2.13; P<.0001)) and 62% higher hazards of hospital admission (aHR: 1.62 ((95% CI 1.48 - 1.78; P<.0001), compared to wild-type virus. Among patients already admitted to ICU, the association between alpha and increased all-cause mortality was smaller and the confidence interval included the null (aHR: 1.20 (95% CI 0.74 - 1.95; P=0.45)). CONCLUSIONS: The SARS-CoV-2 alpha variant is associated with an increased risk of both hospitalisation and mortality than wild-type virus.

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