Jon Massey

Background: The long-term effects of a COVID-19 diagnosis on respiratory outcomes by vaccination status, severity of COVID-19, and pre-existing conditions remain unclear. This study investigated the association between COVID-19 and respiratory conditions, considering these factors. Methods: OpenSAFELY-TPP was used to access linked data from 18 million people registered with GPs in England. Three cohorts were defined: pre-vaccination (wild-type/Alpha era), vaccinated and unvaccinated (Delta era). Adjusted hazard ratios (aHRs) compared the incidence of respiratory conditions after COVID-19 with that before or without COVID-19 for pneumonia, asthma, chronic obstructive pulmonary disease (COPD), and interstitial lung disease (ILD). Findings: Incidence of all outcomes was elevated during weeks 1-4 after COVID-19, compared with before or without COVID-19. Although attenuated beyond four weeks, incidence remained elevated for up to 5·5 years after COVID-19 for some outcomes, particularly following hospitalised COVID-19, e.g., COPD: pre-vaccination aHR 1·49[95% CI, 1·24-1·80], vaccinated 1·66[1·25-2·20], unvaccinated 2·15[1·41-3·28]. In most cases, aHRs were lower in vaccinated than unvaccinated people. Absolute incidence of pneumonia and ILD was higher among those with than without pre-existing respiratory conditions. Interpretation: Incidence of respiratory disease remains elevated particularly in the first month, irrespective of vaccination status and pre-existing conditions and for up to five years following hospitalised COVID-19. These findings have important implications for post-COVID clinical care, highlighting the need for long-term respiratory follow-up and healthcare service planning. Funding: UKRI Medical Research Council and UK National Institute for Health and Care Research.

Objectives: To evaluate temporal changes in the incidence and prevalence of 19 long-term conditions in England, quantifying the impact of the COVID-19 pandemic on diagnosis rates by disease, age group, sex, socioeconomic status, and ethnicity. Design: Observational cohort study. Setting: Primary care and hospital admission data, with the approval of NHS England. Participants: 27,132,190 individuals registered with general practices in England contributing data to the OpenSAFELY-TPP platform. Main outcomes measures: Temporal trends in age and sex-standardised incidence and prevalence were evaluated for 19 long-term conditions between April 1, 2016, and November 30, 2024. Differences between expected and observed incidence rates after the onset of the COVID-19 pandemic were compared using seasonal autoregressive integrated moving-average models. Results: Between March 2020 and November 2024, persistent large deficits in incident diagnoses were evident for depression (738,068 [28.0%] fewer diagnoses than expected; 95% CI 701,452 to 774,685), asthma (150,708 [16.0%] fewer diagnoses; 95% CI 133,300 to 168,117), COPD (84,084 [15.1%] fewer diagnoses; 95% CI 74,342 to 93,827), osteoporosis (78,891 [16.5%] fewer diagnoses; 95% CI 72,804 to 84,978) and psoriasis (56,231 [17.6%] fewer diagnoses; 95% CI 51,054 to 61,407). Conversely, post-pandemic diagnoses of chronic kidney disease (CKD) have increased by 32.7% above expected levels, corresponding to 325,996 additional diagnoses (95% CI 252,212 to 399,779). Dementia diagnoses have rebounded above pre-pandemic levels for individuals of White ethnicity and less deprived socioeconomic quintiles, but remain lower than expected for individuals from other ethnicities and more deprived communities. Conclusions: There has been a lasting and disproportionate impact of the pandemic on conditions including depression, asthma, COPD and osteoporosis, contrasting a post-pandemic surge in CKD diagnoses. Analyses stratified by ethnicity and socioeconomic status reveal inequity in the recovery from the pandemic, particularly for individuals with dementia. Importantly, this study demonstrates the potential for near real-time monitoring of disease epidemiology using routinely collected health data, informing strategies to enhance case detection and address healthcare disparities. Summary box: What is already known: Early in the COVID-19 pandemic, large deficits in newly-recorded diagnoses were reported for a range of long-term conditions, including chronic obstructive pulmonary disease (COPD), depression, asthma and rheumatoid arthritis. However, no population-level studies have evaluated whether diagnosis rates have subsequently recovered as healthcare services emerge from the pandemic, or compared the impact by disease, age group, sex, socioeconomic status, and ethnicity. What this study adds: Large deficits in diagnoses remain evident for conditions including depression, COPD, asthma, osteoporosis and psoriasis, contrasting a surge in new diagnoses of chronic kidney disease since 2022. Diagnosis rates for dementia have reached pre-pandemic levels for individuals of White ethnicity and those from less deprived socioeconomic communities, but remain lower than expected among individuals from other ethnicities and more deprived communities. This study demonstrates how routinely collected health data can transform monitoring of disease epidemiology in near real-time and highlight healthcare inequity.

Background: Antimicrobial resistance (AMR) is a multifaceted global challenge, partly driven by inappropriate antibiotic prescribing. The objectives of this study were to evaluate the impact of the COVID-19 pandemic on treatment of common infections, develop risk prediction models and examine the effects of antibiotics on infection-related hospital admissions. Methods: With the approval of NHS England, we accessed electronic health records from The Phoenix Partnership (TPP) through OpenSAFELY platform. We included adult patients with primary care diagnosis of common infections, including lower respiratory tract infection (LRTI), upper respiratory tract infections (URTI), and lower urinary tract infection (UTI), from 1 January 2019 to 31 August 2022. We excluded patients with a COVID-19 record in the 90 days before to 30 days after the infection diagnosis. Risk prediction models using Cox proportional-hazard regression were developed for infection-related hospital admission in the 30 days after the common infection diagnosis. Results: We found 12,745,165 infection diagnoses from 1 January 2019 to 31 August 2022. Of them, 80,395 (2.05%) cases were admitted to the hospital during follow-up. Counts of hospital admission for infections dropped during COVID-19, for example LRTI from 3,950 in December 2019 to 520 in April 2020. Comparing those prescribed an antibiotic to those without, reduction in risk of hospital admission were largest with LRTI (adjusted hazard ratio (aHR) of 0.35; 95% confidence interval (CI), 0.35–0.36) and UTI (aHR 0.45; 95% CI, 0.44–0.46), compared to URTI (aHR 1.04; 95% CI, 1.03–1.06). Conclusions: A substantial variation in hospital admission risks between infections and patient groups was found. Antibiotics appeared more effective in preventing infection-related complications with LRTI and UTI, but not URTI. While this study has several limitations, the results indicate that a focus on risk-based antibiotic prescribing could help tackle AMR in primary care.

Background The COVID-19 pandemic disrupted cardiovascular disease management in primary care in England. Objective To describe the impact of the pandemic on blood pressure screening and hypertension management based on a national quality of care scheme (Quality and Outcomes Framework, QOF) across key demographic, regional and clinical subgroups. Methods With NHS England approval, a population-based cohort study was conducted using OpenSAFELY-TPP on 25.2 million NHS patients registered at general practices (March 2019 to March 2023). We examined monthly changes in recorded blood pressure screening in the preceding 5 years in patients aged ≥45 years and recorded the hypertension prevalence and the percentage of patients treated to target (≤140/90 mmHg for patients aged ≤79 years and ≤150/90 mmHg for patients aged ≥80 years) in the preceding 12 months. Results The percentage of patients aged ≥45 years who had blood pressure screening recorded in the preceding 5 years decreased from 90% (March 2019) to 85% (March 2023). Recorded hypertension prevalence was relatively stable at 15% throughout the study period. The percentage of patients with a record of hypertension treated to target in the preceding 12 months reduced from a maximum of 71% (March 2020) to a minimum of 47% (February 2021) in patients aged ≤79 years and from 85% (March 2020) to a minimum of 58% (February 2021) in patients aged ≥80 years before recovery. Blood pressure screening rates in the preceding 5 years remained stable in older people, patients with recorded learning disability or care home status. Conclusions The pandemic substantially disrupted hypertension management QOF indicators, which is likely attributable to general reductions of blood pressure measurement including screening. OpenSAFELY can be used to continuously monitor changes in national quality-of-care schemes to identify changes in key clinical subgroups early and support prioritisation of recovery from care disrupted by COVID-19.

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

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

Background The COVID-19 pandemic disrupted healthcare delivery, including difficulty accessing in-person care, which may have increased the need for strong pharmacological pain relief. Methods With NHS England approval, we used routine clinical data from >20 million general practice adult patients in OpenSAFELY-TPP. Using interrupted time series analysis, we quantified prevalent and new opioid prescribing prior to the COVID-19 pandemic (January 2018-February 2020), and during lockdown (March 2020-March 2021) and recovery periods (April 2021-June 2022), overall and stratified by demographics (age, sex, deprivation, ethnicity, geographic region) and to people in care homes. Outcomes The median number of people prescribed an opioid per month was 50.9 per 1000 patients prior to the pandemic. There was little change in prevalent prescribing during the pandemic, except for a temporary increase in March 2020. We observed a 9.8% (95%CI -14.5%, -6.5%) reduction in new opioid prescribing from March 2020, sustained to June 2022 for all demographic groups except people 80+ years. Among care home residents, in April 2020 new opioid prescribing increased by 112.5% (95%CI 92.2%, 134.9%) and parenteral opioid prescribing increased by 186.3% (95%CI 153.1%, 223.9%). Interpretation New opioid prescribing increased among older people and care home residents, likely reflecting use to treat end-of-life COVID-19 symptoms, but decreased among most other groups. Further research is needed to understand what is driving the reduction in new opioid prescribing and its relation to changes to health care provision during the pandemic. Funding The OpenSAFELY Platform is supported by grants from the Wellcome Trust (222097/Z/20/Z) and MRC (MR/V015737/1, MC_PC_20059, MR/W016729/1). In addition, development of OpenSAFELY has been funded by the Longitudinal Health and Wellbeing strand of the National Core Studies programme (MC_PC_20030: MC_PC_20059), the NIHR funded CONVALESCENCE programme (COV-LT-0009), NIHR (NIHR135559, COV-LT2-0073), and the Data and Connectivity National Core Study funded by UK Research and Innovation (MC_PC_20058) and Health Data Research UK (HDRUK2021.000). The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, UK Health Security Agency (UKHSA) or the Department of Health and Social Care. Evidence before this study We searched Pubmed for publications between 1 March 2020 and 8 January 2023 using the following search terms: (“COVID-19” OR “SARS-CoV-2”) AND (“United Kingdom” OR “England” OR “Britain” OR “Scotland” OR “Wales”) AND (“opioid”). We also searched the reference list of relevant articles. We included research studies (excluding conference abstracts and editorials) that quantified opioid prescribing or use in the United Kingdom during the COVID-19 pandemic. Studies focussed solely on opioid substitution therapy for treatment of opioid use disorder were excluded. We identified four studies. One described opioid use among a cohort of people on a waiting list for hip or knee arthroplasty in Scotland (n=548) and found higher rates of long-term opioid use during the COVID-19 pandemic compared with historical controls. The second study quantified changes in opioid prescribing using English aggregate prescription data. This study found no changes in opioid prescribing after the start of the COVID-19 pandemic. The third study of 1.3 million people with rheumatic and musculoskeletal diseases found a decrease in new opioid users among people with certain conditions, but not in the number of overall prescriptions. The last study of 34,711 people newly diagnosed with cancer and 30,256 who died of cancer in Wales found increases in strong opioid prescribing in both populations. Added value of this study This is the largest study (>20 million patients) of opioid prescribing during the COVID-19 pandemic in a representative sample of the population of England. We used person-level data to quantify changes in the number of people prescribed opioids and identified that prevalent opioid prescribing changed little, with the exception of a temporary increase at the start of the first lockdown. However, we also identified meaningful reductions in new opioid prescribing. While our findings confirm previous studies quantifying variation in opioid prescribing by sex, ethnicity, region and deprivation, we showed that changes to new prescribing during the COVID-19 pandemic were experienced approximately similarly across these subgroups. The exceptions were older people and people in care homes. The latter group experienced substantial increases in new opioid prescribing (especially parenteral opioids, which are used in palliative care) coinciding with periods of greatest COVID-19 morbidity and mortality. Implications of all the available evidence The COVID-19 pandemic resulted in substantial disruptions to the healthcare system. Despite concerns that difficulty or delays in providing care during the pandemic may have led to shifts from non-pharmacological treatments to greater opioid prescribing, we observed no increases in prescribing prevalence in most demographic groups in England. The one major exception is people residing in care homes, where the observed prescribing patterns suggest use to treat end of life symptoms, consistent with best practice. However, our findings do not preclude increased prescribing in high risk subgroups, such as people on procedure waiting lists. Further research to quantify changes in this population is warranted.

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

Objective To determine whether periods of disruption were associated with increased ‘avoidable’ hospital admissions and wider social inequalities in England. Design Observational repeated cross-sectional study. Setting England (January 2019 to March 2022). Participants With the approval of NHS England we used individual-level electronic health records from OpenSAFELY, which covered ~40% of general practices in England (mean monthly population size 23.5 million people). Primary and secondary outcome measures We estimated crude and directly age-standardised rates for potentially preventable unplanned hospital admissions: ambulatory care sensitive conditions and urgent emergency sensitive conditions. We considered how trends in these outcomes varied by three measures of social and spatial inequality: neighbourhood socioeconomic deprivation, ethnicity and geographical region. Results There were large declines in avoidable hospitalisations during the first national lockdown (March to May 2020). Trends increased post-lockdown but never reached 2019 levels. The exception to these trends was for vaccine-preventable ambulatory care sensitive admissions which remained low throughout 2020–2021. While trends were consistent by each measure of inequality, absolute levels of inequalities narrowed across levels of neighbourhood socioeconomic deprivation, Asian ethnicity (compared with white ethnicity) and geographical region (especially in northern regions). Conclusions We found no evidence that periods of healthcare disruption from the COVID-19 pandemic resulted in more avoidable hospitalisations. Falling avoidable hospital admissions has coincided with declining inequalities most strongly by level of deprivation, but also for Asian ethnic groups and northern regions of England.

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