Background: Antibiotic treatment duration may be longer than sometimes needed. Stopping antibiotics early, rather than completing pre-set antibiotic courses, may help reduce unnecessary exposure to antibiotics and antimicrobial resistance (AMR). Aim: To identify clinicians' and patients' views on stopping antibiotics when better (SAWB) for urinary tract infections (UTIs), and to explore comparisons with other acute infections. Design & setting: An exploratory qualitative study with general practice clinicians and patients in England. Method: Primary care clinicians and patients who had recent UTI experience were recruited in England. Remote one-to one interviews with clinicians and patients, and one focus group with patients, were conducted. Data were audiorecorded, transcribed, and analysed thematically. Results: Eleven clinicians (seven GPs) and 19 patients (14 with experience of recurrent and/or chronic UTIs) were included. All participants considered SAWB unfamiliar and contradictory to well-known advice to complete antibiotic courses, but were interested in the evidence for risks and benefits of SAWB. Clinicians were amenable if evidence and guidelines supported it, whereas patients were more averse because of concerns about the risk of UTI recurrence and/or complications and AMR. Participants viewed SAWB as potentially more appropriate for longer antibiotic courses and other infections (with longer courses and lower risk of recurrence and/or complications). Participants stressed the need for unambiguous advice and SAWB as part of shared decision making and personalised advice. Conclusion: Patients were less accepting of SAWB, whereas clinicians were more amenable to it. Patients and clinicians require good evidence that this novel approach to self-determining antibiotic duration is safe and beneficial. If evidence based, SAWB should be offered with an explanation of why the advice differs from the \u2018complete the course\u2019 instruction, and a clear indication of when exactly to stop antibiotics should be given.
\n \n\n \n \nIntroduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19. Likewise, there is some evidence that inhaled corticosteroids could also reduce its severity, opening the possibility that nicotine and inhaled steroids could be used as treatments. Methods In this prospective cohort study, we will link English general practice records from the QResearch database to Public Health England\u2019s database of SARS-CoV-2 positive tests, Hospital Episode Statistics, admission to intensive care units, and death from COVID-19 to identify our outcomes: hospitalisation, ICU admission, and death due to COVID. Using Cox regression, we will perform sequential adjustment for potential confounders identified by separate directed acyclic graphs to: Assess the association between smoking and COVID-19 disease severity, and how that changes on adjustment for smoking-related comorbidity. More closely characterise the association between smoking and severe COVID-19 disease by assessing whether the association is modified by age (as a proxy of length of smoking), gender, ethnic group, and whether people have asthma or COPD. Assess for evidence of a dose-response relation between smoking intensity and disease severity, which would help create a case for causality. Examine the association between former smokers who are using NRT or are vaping and disease severity. Examine whether pre-existing respiratory disease is associated with severe COVID-19 infection. Assess whether the association between chronic obstructive pulmonary disease (COPD) and asthma and COVID-19 disease severity is modified by age, gender, ethnicity, and smoking status. Assess whether the use of inhaled corticosteroids is associated with severity of COVID-19 disease. To assess whether the association between use of inhaled corticosteroids and severity of COVID-19 disease is modified by the number of other airways medications used (as a proxy for severity of condition) and whether people have asthma or COPD. Conclusions This representative population sample will, to our knowledge, present the first comprehensive examination of the association between smoking, nicotine use without smoking, respiratory disease, and severity of COVID-19. We will undertake several sensitivity analyses to examine the potential for bias in these associations.
\n \n\n \n \nBackground: Viral epidemics or pandemics of acute respiratory infections (ARIs) pose a global threat. Examples are influenza (H1N1) caused by the H1N1pdm09 virus in 2009, severe acute respiratory syndrome (SARS) in 2003, and coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 in 2019. Antiviral drugs and vaccines may be insufficient to prevent their spread. This is an update of a Cochrane Review last published in 2020. We include results from studies from the current COVID-19 pandemic. Objectives: To assess the effectiveness of physical interventions to interrupt or reduce the spread of acute respiratory viruses. Search methods: We searched CENTRAL, PubMed, Embase, CINAHL, and two trials registers in October 2022, with backwards and forwards citation analysis on the new studies. Selection criteria: We included randomised controlled trials (RCTs) and cluster-RCTs investigating physical interventions (screening at entry ports, isolation, quarantine, physical distancing, personal protection, hand hygiene, face masks, glasses, and gargling) to prevent respiratory virus transmission. Data collection and analysis: We used standard Cochrane methodological procedures. Main results: We included 11\u00a0new RCTs and cluster-RCTs (610,872 participants) in this update, bringing the total number of RCTs to 78. Six of the new trials were conducted during the COVID-19 pandemic; two from Mexico, and one each from Denmark, Bangladesh, England, and Norway. We identified four\u00a0ongoing studies, of which one is completed, but unreported, evaluating masks concurrent with the COVID-19 pandemic. Many studies were conducted during non-epidemic influenza periods. Several were conducted during the 2009 H1N1 influenza pandemic, and others in epidemic influenza seasons up to 2016. Therefore, many studies were conducted in the context of lower respiratory viral circulation and transmission compared to COVID-19. The included studies were conducted in heterogeneous settings, ranging from suburban schools to hospital wards in high-income countries; crowded inner city settings in low-income countries; and an immigrant neighbourhood in a high-income country. Adherence with interventions was low in many studies. The risk of bias for the RCTs and cluster-RCTs was mostly high or unclear. Medical/surgical masks compared to no masks. We included 12 trials (10 cluster-RCTs) comparing medical/surgical masks versus no masks to prevent the spread of viral respiratory illness (two trials with healthcare workers and 10 in the community). Wearing masks in the community probably makes little or no difference to the outcome of influenza-like illness (ILI)/COVID-19 like illness compared to not wearing masks (risk ratio (RR) 0.95, 95% confidence interval (CI) 0.84 to 1.09; 9 trials, 276,917 participants; moderate-certainty evidence. Wearing masks in the community probably makes little or no difference to the outcome of laboratory-confirmed influenza/SARS-CoV-2 compared to not wearing masks (RR 1.01, 95% CI 0.72 to 1.42; 6 trials, 13,919 participants; moderate-certainty evidence). Harms were rarely measured and poorly reported (very low-certainty evidence). N95/P2 respirators compared to medical/surgical masks. We pooled trials comparing N95/P2 respirators with medical/surgical masks (four in healthcare settings and one in a household setting). We are very uncertain on the effects of N95/P2 respirators compared with medical/surgical masks on the outcome of clinical respiratory illness (RR 0.70, 95% CI 0.45 to 1.10; 3 trials, 7779 participants; very low-certainty evidence). N95/P2 respirators compared with medical/surgical masks may be effective for ILI (RR 0.82, 95% CI 0.66 to 1.03; 5 trials, 8407 participants; low-certainty evidence). Evidence is limited by imprecision and heterogeneity for these subjective outcomes. The use of a N95/P2 respirators compared to medical/surgical masks probably makes little or no difference for the objective and more precise outcome of laboratory-confirmed influenza infection (RR 1.10, 95% CI 0.90 to 1.34; 5 trials, 8407 participants; moderate-certainty evidence). Restricting pooling to healthcare workers made no difference to the overall findings. Harms were poorly measured and reported, but discomfort wearing medical/surgical masks or N95/P2 respirators was mentioned in several studies (very low-certainty evidence). One previously reported ongoing RCT\u00a0has now been published and observed that medical/surgical masks were non-inferior to N95 respirators in a large study of 1009 healthcare workers in four countries providing direct care to COVID-19 patients. Hand hygiene compared to control. Nineteen trials compared hand hygiene interventions with controls with sufficient data to include in meta-analyses. Settings included\u00a0schools, childcare centres and homes. Comparing hand hygiene interventions with controls (i.e. no intervention),\u00a0there was a 14% relative reduction in the number of people with ARIs in the hand hygiene group (RR 0.86, 95% CI 0.81 to 0.90; 9 trials, 52,105 participants; moderate-certainty evidence), suggesting a probable benefit. In absolute terms this benefit would result in a reduction from 380 events per 1000 people to 327 per 1000 people (95% CI 308 to 342). When considering the more strictly defined outcomes of ILI and laboratory-confirmed influenza, the estimates of effect for ILI (RR 0.94, 95% CI 0.81 to 1.09; 11 trials, 34,503 participants; low-certainty evidence), and laboratory-confirmed influenza (RR 0.91, 95% CI 0.63 to 1.30; 8 trials, 8332 participants; low-certainty evidence), suggest the intervention made little or no\u00a0difference. We pooled 19 trials (71, 210 participants) for the composite outcome of ARI or ILI or influenza, with each study only contributing once and the most comprehensive outcome reported. Pooled data showed that hand hygiene may be beneficial with an 11% relative reduction of respiratory illness (RR 0.89, 95% CI 0.83 to 0.94; low-certainty evidence), but with high heterogeneity. In absolute terms this benefit would result in a reduction from 200 events per 1000 people to 178 per 1000 people (95% CI 166 to 188). Few trials measured and reported harms (very low-certainty evidence). We found no RCTs on gowns and gloves, face shields, or screening at entry ports. Authors' conclusions: The high risk of bias in the trials, variation in outcome measurement, and relatively low adherence with the interventions during the studies hampers drawing firm conclusions. There were additional RCTs during the pandemic related to physical interventions but a relative paucity given the importance of the question of masking and its relative effectiveness and the concomitant measures of mask adherence which would be highly relevant to the measurement of effectiveness, especially in the elderly and in young children. There is uncertainty about the effects of face masks. The low to moderate certainty of evidence means our confidence in the effect estimate is limited, and that the true effect may be different from the observed estimate of the effect. The pooled results of RCTs did not show a clear reduction in respiratory viral infection with the use of medical/surgical masks. There were no clear differences between the use of medical/surgical masks compared with N95/P2 respirators in healthcare workers when used in routine care to reduce respiratory viral infection. Hand hygiene is likely to modestly reduce the burden of respiratory illness, and although this effect was also present when ILI and laboratory-confirmed influenza were analysed separately, it was not found to be a significant difference for the latter two outcomes. Harms associated with physical interventions were under-investigated. There is a need\u00a0for large, well-designed RCTs addressing the effectiveness of many of these interventions in multiple settings and populations, as well as the impact of adherence on effectiveness, especially in those most at risk of ARIs.
\n \n\n \n \nBackground: 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.\u00a0 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.\u00a0 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.
\n \n\n \n \nBackground: Primary care has been described as the \u2018bedrock\u2019 of the National Health Service (NHS) accounting for approximately 90% of patient contacts but is facing significant challenges. Against a backdrop of a rapidly ageing population with increasingly complex health challenges, policy-makers have encouraged primary care commissioners to increase the usage of data when making commissioning decisions. Purported benefits include cost savings and improved population health. However, research on evidence-based commissioning has concluded that commissioners work in complex environments and that closer attention should be paid to the interplay of contextual factors and evidence use. The aim of this review was to understand how and why primary care commissioners use data to inform their decision making, what outcomes this leads to, and understand what factors or contexts promote and inhibit their usage of data. Methods: We developed initial programme theory by identifying barriers and facilitators to using data to inform primary care commissioning based on the findings of an exploratory literature search and discussions with programme implementers. We then located a range of diverse studies by searching seven databases as well as grey literature. Using a realist approach, which has an explanatory rather than a judgemental focus, we identified recurrent patterns of outcomes and their associated contexts and mechanisms related to data usage in primary care commissioning to form context-mechanism-outcome (CMO) configurations. We then developed a revised and refined programme theory. Results: Ninety-two studies met the inclusion criteria, informing the development of 30 CMOs. Primary care commissioners work in complex and demanding environments, and the usage of data are promoted and inhibited by a wide range of contexts including specific commissioning activities, commissioners\u2019 perceptions and skillsets, their relationships with external providers of data (analysis), and the characteristics of data themselves. Data are used by commissioners not only as a source of evidence but also as a tool for stimulating commissioning improvements and as a warrant for convincing others about decisions commissioners wish to make. Despite being well-intentioned users of data, commissioners face considerable challenges when trying to use them, and have developed a range of strategies to deal with \u2018imperfect\u2019 data. Conclusions: There are still considerable barriers to using data in certain contexts. Understanding and addressing these will be key in light of the government\u2019s ongoing commitments to using data to inform policy-making, as well as increasing integrated commissioning.
\n \n\n \n \nBackground: The coronavirus disease 2019 (COVID-19) vaccination programme in England was extended to include all adolescents and children by April 2022. The aim of this paper is to describe trends and variation in vaccine coverage in different clinical and demographic groups amongst adolescents and children in England by August 2022. Methods: With the approval of NHS England, a cohort study was conducted of 3.21 million children and adolescents' records in general practice in England,\u00a0 in situ\u00a0and within the infrastructure of the electronic health record software vendor TPP using OpenSAFELY. Vaccine coverage across various demographic (sex, deprivation index and ethnicity) and clinical (risk status) populations is described. Results: Coverage is higher amongst adolescents than it is amongst children, with 53.5% adolescents and 10.8% children having received their first dose of the COVID-19 vaccine. Within those groups, coverage varies by ethnicity, deprivation index and risk status; there is no evidence of variation by sex. Conclusion: First dose COVID-19 vaccine coverage is shown to vary amongst various demographic and clinical groups of children and adolescents.
\n \n\n \n \nDrug shortages make it difficult or impossible to meet the therapeutic needs of individual patients or populations. In the first part of this review we proposed an operational definition that incorporates the processes by which products are manufactured, the causes of shortages and stock-outs (local shortages), and the contributory factors. Here we discuss causes and possible solutions. Drug shortages have complex causes, and a single cause cannot always be identified. Reasons include lack or shortage of raw materials, manufacturing difficulties, regulatory and political actions, voluntary recalls, just-in-time inventory systems, halts in production for financial or other business reasons, low demand (e.g. orphan products, reduced usage), mergers, market shifts (e.g. diversion to home markets), and unexpected increases in demand (e.g. improved diagnosis, new trial information, epidemics and pandemics, inappropriate use, off-label use). Potential solutions are as diverse as the potential causes. Prevention is hard, because shortages are not easily predicted. Everyone in the supply chain is involved in anticipating and managing shortages, with responsibilities for preventing them or at least trying to mitigate their effects. This includes manufacturers and suppliers, particularly of generic formulations, pharmacists, prescribers, patients, and governments. Solutions can therefore be linked to the causes and classified according to where the responsibility for implementing them lies.
\n \n\n \n \nObjective: To describe the trial results reporting behavior of leading European non-commercial sponsors by country and over time. Study design and setting: Cross-sectional analysis describing results reporting rates to the European Clinical Trials Registry among the top sponsors across Europe as of May 2021 and a comparison of reporting trends for a cohort of major sponsors between January 2018 and May 2021. Results: Fifty-nine highly active sponsors from 10 countries and 9 collaborative groups had 1,916 trials due to report, representing 14% of all due trials on the registry (n = 13,709); of these, 1,058 had reported results (55.2%). Sponsors in the UK, Belgium and Germany had the highest compliance at 94%, 69% and 58%; those in Spain, France and the Netherlands, had the lowest, ranging from 4% to 21%. Collaborative groups had a reporting rate of 50%. In the major sponsors cohort (n = 49), those with no reporting to the registry decreased from 27 (55%) in 2018 to 10 (20%) in 2021. Thirteen of these sponsors (27%) reached a 90-100% reporting rate in 2021 compared to 0 in 2018. Conclusion: Compliance with EU regulations by non-commercial sponsors is highly variable between countries. Enforcement of EU reporting regulations should be prioritized.
\n \n\n \n \nBackground: 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.
\n \n\n \n \nBackground/Aims: The European Union Clinical Trials Register is a public facing portal containing information on trials of medicinal products conducted under the purview of the European Union regulatory system. As of September 2021, the registry holds information on over 40,000 trials. Given its distinct regulatory purpose, and results reporting requirements, the European Union Clinical Trials Register should be a valuable open-source hub for trial information. Past work examining the European Union Clinical Trials Register has suggested that data quality on the registry may be lacking. We therefore set out to examine the quality and availability of trial data on the registry with a focus on areas that fall under the authority of regulators in each European Union/European Economic Area country. Methods: Using data scraped from the full European Union Clinical Trials Register public dataset, we examined the extent of issues with three areas of trial data availability linked to European Union regulations. We examined whether there is evidence for missing registration of protocols in the public database, whether information on the completion of clinical trials is being made available and how often the results of trials are posted to the registry. We assessed each area overall, and examined variation between national regulators and over time. Results: Major issues with the availability of expected protocols and information on trial completion were focused in a few countries. Overall, when comparing enrolment countries from tabular results to available registrations, 26,932 of 31,118 (86.5%) expected protocols were available and 22 of 30 (73%) countries had over 90% of expected protocols available. The majority of missing protocols, totalling 2764 (66%), were from just three countries: France, Norway and Poland. Evidence for this issue is further supported by data on trends in new registrations by country over time. Low availability of data on trial completion is also most pronounced in a minority of countries, like Spain and the Netherlands, with consistent trends for missingness over time. Finally, overall results availability is substantially worse among the 23,623 trials with a single registered European Union protocol (n = 6259, 26.5%) compared to 13,897 of those taking place in multiple countries (n = 8423, 60.6%). Reporting for single-protocol trials was consistently low across both time and location. Conclusion: Deficiencies in the public availability of trial protocols, trial completion information and summary results complicate the utility of the European Union Clinical Trials Register for research, transparency and accountability efforts. Users of the registry would benefit from a more complete and accurate accounting of the European research environment via the official European Union registry. We recommend regulators at the national and pan-national level undertake routine audits of approved trials to ensure national-level issues are proactively and transparently identified, documented and addressed.
\n \n\n \n \nOn 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 Open SAFELY secure analytics platform.
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