IntroductionCOVID-19 is commonly experienced as an acute illness, yet some people continue to have symptoms that persist for weeks, or months (commonly referred to as ‘long-COVID’). It remains ...unclear which patients are at highest risk of developing long-COVID. In this protocol, we describe plans to develop a prediction model to identify individuals at risk of developing long-COVID.Methods and analysisWe will use the national Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) platform, a population-level linked dataset of routine electronic healthcare data from 5.4 million individuals in Scotland. We will identify potential indicators for long-COVID by identifying patterns in primary care data linked to information from out-of-hours general practitioner encounters, accident and emergency visits, hospital admissions, outpatient visits, medication prescribing/dispensing and mortality. We will investigate the potential indicators of long-COVID by performing a matched analysis between those with a positive reverse transcriptase PCR (RT-PCR) test for SARS-CoV-2 infection and two control groups: (1) individuals with at least one negative RT-PCR test and never tested positive; (2) the general population (everyone who did not test positive) of Scotland. Cluster analysis will then be used to determine the final definition of the outcome measure for long-COVID. We will then derive, internally and externally validate a prediction model to identify the epidemiological risk factors associated with long-COVID.Ethics and disseminationThe EAVE II study has obtained approvals from the Research Ethics Committee (reference: 12/SS/0201), and the Public Benefit and Privacy Panel for Health and Social Care (reference: 1920-0279). Study findings will be published in peer-reviewed journals and presented at conferences. Understanding the predictors for long-COVID and identifying the patient groups at greatest risk of persisting symptoms will inform future treatments and preventative strategies for long-COVID.
Digital interventions with artificial intelligence (AI) can potentially support people with asthma to reduce the risk of exacerbation. Engaging patients throughout the development process is ...essential to ensure usability of the intervention for the end-users. Using our Connected for Asthma (C4A) intervention as an exemplar, we explore how patient involvement can shape a digital intervention. Seven Patient and Public Involvement (PPI) colleagues from the Asthma UK Centre for Applied Research participated in four advisory workshops to discuss how they would prefer to use/interact with AI to support living with their asthma, the benefit and caveats to use the AI that incorporated asthma monitoring and indoor/outdoor environmental data. Discussion focussed on the three most wanted use cases identified in our previous studies. PPI colleagues wanted AI to support data collection, remind them about self-management tasks, teach them about asthma environmental triggers, identify risk, and empower them to confidently look after their asthma whilst emphasising that AI does not replace clinicians. The discussion informed the key components in the next C4A interventions, including the approach to interacting with AI, the technology features and the research topics. Attendees highlighted the importance of considering health inequities, the presentation of data, and concerns about data accuracy, data privacy, security and ownership. We have demonstrated how patient roles can shift from that of 'user' (the traditional 'tester' of a digital intervention), to a co-design partner who shapes the next iteration of the intervention. Technology innovators should seek practical and feasible strategies to involve PPI colleagues throughout the development cycle of a digital intervention; supporting researchers to explore the barriers, concerns, enablers and advantages of implementing digital healthcare.
We present the main findings of the 5th National Audit Project (NAP5) on accidental awareness during general anaesthesia (AAGA). Incidences were estimated using reports of accidental awareness as the ...numerator, and a parallel national anaesthetic activity survey to provide denominator data. The incidence of certain/probable and possible accidental awareness cases was ∼1:19 600 anaesthetics (95% confidence interval 1:16 700–23 450). However, there was considerable variation across subtypes of techniques or subspecialities. The incidence with neuromuscular block (NMB) was ∼1:8200 (1:7030–9700), and without, it was ∼1:135 900 (1:78 600–299 000). The cases of AAGA reported to NAP5 were overwhelmingly cases of unintended awareness during NMB. The incidence of accidental awareness during Caesarean section was ∼1:670 (1:380–1300). Two-thirds (82, 66%) of cases of accidental awareness experiences arose in the dynamic phases of anaesthesia, namely induction of and emergence from anaesthesia. During induction of anaesthesia, contributory factors included: use of thiopental, rapid sequence induction, obesity, difficult airway management, NMB, and interruptions of anaesthetic delivery during movement from anaesthetic room to theatre. During emergence from anaesthesia, residual paralysis was perceived by patients as accidental awareness, and commonly related to a failure to ensure full return of motor capacity. One-third (43, 33%) of accidental awareness events arose during the maintenance phase of anaesthesia, mostly due to problems at induction or towards the end of anaesthesia. Factors increasing the risk of accidental awareness included: female sex, age (younger adults, but not children), obesity, anaesthetist seniority (junior trainees), previous awareness, out-of-hours operating, emergencies, type of surgery (obstetric, cardiac, thoracic), and use of NMB. The following factors were not risk factors for accidental awareness: ASA physical status, race, and use or omission of nitrous oxide. We recommend that an anaesthetic checklist, to be an integral part of the World Health Organization Safer Surgery checklist, is introduced as an aid to preventing accidental awareness. This paper is a shortened version describing the main findings from NAP5—the full report can be found at http://www.nationalauditprojects.org.uk/NAP5_home.
Undervaccination (receiving fewer than the recommended number of SARS-CoV-2 vaccine doses) could be associated with increased risk of severe COVID-19 outcomes—ie, COVID-19 hospitalisation or ...death—compared with full vaccination (receiving the recommended number of SARS-CoV-2 vaccine doses). We sought to determine the factors associated with undervaccination, and to investigate the risk of severe COVID-19 outcomes in people who were undervaccinated in each UK nation and across the UK.
We used anonymised, harmonised electronic health record data with whole population coverage to carry out cohort studies in England, Northern Ireland, Scotland, and Wales. Participants were required to be at least 5 years of age to be included in the cohorts. We estimated adjusted odds ratios for undervaccination as of June 1, 2022. We also estimated adjusted hazard ratios (aHRs) for severe COVID-19 outcomes during the period June 1 to Sept 30, 2022, with undervaccination as a time-dependent exposure. We combined results from nation-specific analyses in a UK-wide fixed-effect meta-analysis. We estimated the reduction in severe COVID-19 outcomes associated with a counterfactual scenario in which everyone in the UK was fully vaccinated on June 1, 2022.
The numbers of people undervaccinated on June 1, 2022 were 26 985 570 (45·8%) of 58 967 360 in England, 938 420 (49·8%) of 1 885 670 in Northern Ireland, 1 709 786 (34·2%) of 4 992 498 in Scotland, and 773 850 (32·8%) of 2 358 740 in Wales. People who were younger, from more deprived backgrounds, of non-White ethnicity, or had a lower number of comorbidities were less likely to be fully vaccinated. There was a total of 40 393 severe COVID-19 outcomes in the cohorts, with 14 156 of these in undervaccinated participants. We estimated the reduction in severe COVID-19 outcomes in the UK over 4 months of follow-up associated with a counterfactual scenario in which everyone was fully vaccinated on June 1, 2022 as 210 (95% CI 94–326) in the 5–15 years age group, 1544 (1399–1689) in those aged 16–74 years, and 5426 (5340–5512) in those aged 75 years or older. aHRs for severe COVID-19 outcomes in the meta-analysis for the age group of 75 years or older were 2·70 (2·61–2·78) for one dose fewer than recommended, 3·13 (2·93–3·34) for two fewer, 3·61 (3·13–4·17) for three fewer, and 3·08 (2·89–3·29) for four fewer.
Rates of undervaccination against COVID-19 ranged from 32·8% to 49·8% across the four UK nations in summer, 2022. Undervaccination was associated with an elevated risk of severe COVID-19 outcomes.
UK Research and Innovation National Core Studies: Data and Connectivity.
Background: There were substantial reductions in asthma exacerbations during the COVID-19 pandemic for reasons that remain poorly understood. We investigated changes in modifiable risk factors which ...might help explain the reductions in asthma exacerbations. Methods: Multilevel generalised linear mixed models were fitted to examine changes in modifiable risk factors for asthma exacerbations during 2020–2022, compared to pre-pandemic year (2019), using observational, routine data from general practices in the Oxford-Royal College of General Practitioners Research and Surveillance Centre. Asthma exacerbations were defined as any of GP recorded: asthma exacerbations, prescriptions of prednisolone, accident and emergency department attendance or hospitalisation for asthma. Modifiable risk factors of interest were ownership of asthma self-management plan, asthma annual review, inhaled-corticosteroid (ICS) prescriptions, influenza vaccinations and respiratory-tract-infections (RTI). Findings: Compared with 2019 (n = 550,995), in 2020 (n = 565,956) and 2022 (n = 562,167) (p < 0.05): asthma exacerbations declined from 67.1% to 51.9% and 61.1%, the proportion of people who had: asthma exacerbations reduced from 20.4% to 15.1% and 18.5%, asthma self-management plans increased from 28.6% to 37.7% and 55.9%; ICS prescriptions increased from 69.9% to 72.0% and 71.1%; influenza vaccinations increased from 14.2% to 25.4% and 55.3%; current smoking declined from 15.0% to 14.5% and 14.7%; lower-RTI declined from 10.5% to 5.3% and 8.1%; upper-RTI reduced from 10.7% to 5.8% and 7.6%. There was cluster effect of GP practices on asthma exacerbations (p = 0.001). People with asthma were more likely (p < 0.05) to have exacerbations if they had LRTI (seven times(x)), had URTI and ILI (both twice), were current smokers (1.4x), PPV vaccinated (1.3x), seasonal flu vaccinated (1.01x), took ICS (1.3x), had asthma reviews (1.09x). People with asthma were less likely to have exacerbations if they had self-management plan (7%), and were partially (4%) than fully COVID-19 vaccinated. Interpretation: We have identified changes in modifiable risk factors for asthma exacerbation that need to be maintained in the post-pandemic era. Funding: Asthma UK Centre for Applied Research and Health Data Research UK.
There were substantial reductions in asthma exacerbations during the COVID-19 pandemic for reasons that remain poorly understood. We investigated changes in modifiable risk factors which might help ...explain the reductions in asthma exacerbations.
Multilevel generalised linear mixed models were fitted to examine changes in modifiable risk factors for asthma exacerbations during 2020–2022, compared to pre-pandemic year (2019), using observational, routine data from general practices in the Oxford-Royal College of General Practitioners Research and Surveillance Centre. Asthma exacerbations were defined as any of GP recorded: asthma exacerbations, prescriptions of prednisolone, accident and emergency department attendance or hospitalisation for asthma. Modifiable risk factors of interest were ownership of asthma self-management plan, asthma annual review, inhaled-corticosteroid (ICS) prescriptions, influenza vaccinations and respiratory-tract-infections (RTI).
Compared with 2019 (n = 550,995), in 2020 (n = 565,956) and 2022 (n = 562,167) (p < 0.05): asthma exacerbations declined from 67.1% to 51.9% and 61.1%, the proportion of people who had: asthma exacerbations reduced from 20.4% to 15.1% and 18.5%, asthma self-management plans increased from 28.6% to 37.7% and 55.9%; ICS prescriptions increased from 69.9% to 72.0% and 71.1%; influenza vaccinations increased from 14.2% to 25.4% and 55.3%; current smoking declined from 15.0% to 14.5% and 14.7%; lower-RTI declined from 10.5% to 5.3% and 8.1%; upper-RTI reduced from 10.7% to 5.8% and 7.6%. There was cluster effect of GP practices on asthma exacerbations (p = 0.001). People with asthma were more likely (p < 0.05) to have exacerbations if they had LRTI (seven times(x)), had URTI and ILI (both twice), were current smokers (1.4x), PPV vaccinated (1.3x), seasonal flu vaccinated (1.01x), took ICS (1.3x), had asthma reviews (1.09x). People with asthma were less likely to have exacerbations if they had self-management plan (7%), and were partially (4%) than fully COVID-19 vaccinated.
We have identified changes in modifiable risk factors for asthma exacerbation that need to be maintained in the post-pandemic era.
Asthma UK Centre for Applied Research and Health Data Research UK.
The 5th National Audit Project (NAP5) of the Royal College of Anaesthetists and the Association of Anaesthetists of Great Britain and Ireland into accidental awareness during general anaesthesia ...(AAGA) yielded data related to psychological aspects from the patient, and the anaesthetist, perspectives; patients' experiences ranged from isolated auditory or tactile sensations to complete awareness. A striking finding was that 75% of experiences were for <5 min, yet 51% of patients 95% confidence interval (CI) 43–60% experienced distress and 41% (95% CI 33–50%) suffered longer term adverse effect. Distress and longer term harm occurred across the full range of experiences but were particularly likely when the patient experienced paralysis (with or without pain). The patient's interpretation of what is happening at the time of the awareness seemed central to later impact; explanation and reassurance during suspected AAGA or at the time of report seemed beneficial. Quality of care before the event was judged good in 26%, poor in 39%, and mixed in 31%. Three-quarters of cases of AAGA (75%) were judged preventable. In 12%, AAGA care was judged good and the episode not preventable. The contributory and human factors in the genesis of the majority of cases of AAGA included medication, patient, and education/training. The findings have implications for national guidance, institutional organization, and individual practice. The incidence of ‘accidental awareness' during sedation (∼1:15 000) was similar to that during general anaesthesia (∼1:19 000). The project raises significant issues about information giving and consent for both sedation and anaesthesia. We propose a novel approach to describing sedation from the patient's perspective which could be used in communication and consent. Eight (6%) of the patients had resorted to legal action (12, 11%, to formal complaint) at the time of reporting. NAP5 methodology provides a standardized template that might usefully inform the investigation of claims or serious incidents related to AAGA.
Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors ...associated with its development.
In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98–99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status.
Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n = 1,092, 0.02%), followed by free text (n = 8,368, 0.2%), sick notes (n = 14,469, 0.3%), and the operational definition (n = 64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38–67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4–26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p < 0.0001) more prevalent in positive COVID-19 patients than in matched negative controls. In a case-crossover analysis, 16.4% of those identified by the operational definition had similar healthcare patterns recorded before testing positive.
The prevalence of long COVID presenting in general practice was estimated to be 0.02–1.7%, depending on the measure used. Due to challenges in diagnosing long COVID and inconsistent recording of information in EHRs, the true prevalence of long COVID is likely to be higher. The operational definition provided a novel approach but relied on a restricted set of symptoms and may misclassify individuals with pre-existing health conditions. Further research is needed to refine and validate this approach.
Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.
Background: Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk ...factors associated with its development. Methods: In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98–99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status. Findings: Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n = 1,092, 0.02%), followed by free text (n = 8,368, 0.2%), sick notes (n = 14,469, 0.3%), and the operational definition (n = 64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38–67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4–26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p < 0.0001) more prevalent in positive COVID-19 patients than in matched negative controls. In a case-crossover analysis, 16.4% of those identified by the operational definition had similar healthcare patterns recorded before testing positive. Interpretation: The prevalence of long COVID presenting in general practice was estimated to be 0.02–1.7%, depending on the measure used. Due to challenges in diagnosing long COVID and inconsistent recording of information in EHRs, the true prevalence of long COVID is likely to be higher. The operational definition provided a novel approach but relied on a restricted set of symptoms and may misclassify individuals with pre-existing health conditions. Further research is needed to refine and validate this approach. Funding: Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.
PDF
