BackgroundMany quality improvement initiatives in healthcare employ educational outreach visits, integrating academic detailing to bridge evidence-practice gaps and accelerate knowledge translation. Replicability of their outcomes in different contexts varies, and what makes some visiting programmes more successful than others is unclear.ObjectiveWe conducted a realist synthesis to develop theories of what makes educational outreach visiting integrating academic detailing work, for whom, under which circumstances and why, focusing on the clinician-visitor interaction when influencing prescribing of medicines in ambulatory care settings.MethodsThe realist review was performed in accordance with RAMESES standards. An initial programme theory was generated, academic databases and grey literature were screened for documents with detail on contexts, intervention and outcomes. Using realist logic of analysis, data from 43 documents were synthesised in the generation of a refined programme theory, supported by additional theoretical frameworks of learning and communication.ResultsTwenty-seven interdependent context-mechanism-outcome configurations explain how clinicians engage with educational outreach visits integrating academic detailing through programme design, what matters in programme design and the educational visitor-clinician interaction and how influence extends beyond the visit. They suggest that in addition to relevance, credibility and trustworthiness of a visit\u2019s contents, communication and clinical skills of educational visitors, the relationship between the educational visitor and clinician, built on a dialogue of learning from and sense-making with each other, creates conditions of critical thinking which are conducive to facilitating prescribing practice change when necessary.ConclusionThis realist synthesis elucidates that the quality of clinician-educational visitor interactions is pivotal to educational outreach visiting programmes. Building and sustaining relationships, and establishing an open dialogue are important; neglecting these undermines the impact of visits. Educational visitors can facilitate clinicians\u2019 reflection on practice and influence their prescribing. Clinicians value the discussion of individualised, tailored information and advice they can translate into their practice.PROSPERO registration numberCRD42021258199.
\n \n\n \n \nBACKGROUND: Simple blood tests can play an important role in identifying patients for cancer investigation. The current evidence base is limited almost entirely to tests used in isolation. However, recent evidence suggests combining multiple types of blood tests and investigating trends in blood test results over time could be more useful to select patients for further cancer investigation. Such trends could increase cancer yield and reduce unnecessary referrals. We aim to explore whether trends in blood test results are more useful than symptoms or single blood test results in selecting primary care patients for cancer investigation. We aim to develop clinical prediction models that incorporate trends in blood tests to identify the risk of cancer. METHODS: Primary care electronic health record data from the English Clinical Practice Research Datalink Aurum primary care database will be accessed and linked to cancer registrations and secondary care datasets. Using a cohort study design, we will describe patterns in blood testing (aim 1) and explore associations between covariates and trends in blood tests with cancer using mixed-effects, Cox, and dynamic models (aim 2). To build the predictive models for the risk of cancer, we will use dynamic risk modelling (such as multivariate joint modelling) and machine learning, incorporating simultaneous trends in multiple blood tests, together with other covariates (aim 3). Model performance will be assessed using various performance measures, including c-statistic and calibration plots. DISCUSSION: These models will form decision rules to help general practitioners find patients who need a referral for further investigation of cancer. This could increase cancer yield, reduce unnecessary referrals, and give more patients the opportunity for treatment and improved outcomes.
\n \n\n \n \nBACKGROUND: Approaches to addressing unwarranted variation in health care service delivery have traditionally relied on the prospective identification of activities and outcomes, based on a hypothesis, with subsequent reporting against defined measures. Practice-level prescribing data in England are made publicly available by the National Health Service (NHS) Business Services Authority for all general practices. There is an opportunity to adopt a more data-driven approach to capture variability and identify outliers by applying hypothesis-free, data-driven algorithms to national data sets. OBJECTIVE: This study aimed to develop and apply a hypothesis-free algorithm to identify unusual prescribing behavior in primary care data at multiple administrative levels in the NHS in England and to visualize these results using organization-specific interactive dashboards, thereby demonstrating proof of concept for prioritization approaches. METHODS: Here we report a new data-driven approach to quantify how \"unusual\" the prescribing rates of a particular chemical within an organization are as compared to peer organizations, over a period of 6 months (June-December 2021). This is followed by a ranking to identify which chemicals are the most notable outliers in each organization. These outlying chemicals are calculated for all practices, primary care networks, clinical commissioning groups, and sustainability and transformation partnerships in England. Our results are presented via organization-specific interactive dashboards, the iterative development of which has been informed by user feedback. RESULTS: We developed interactive dashboards for every practice (n=6476) in England, highlighting the unusual prescribing of 2369 chemicals (dashboards are also provided for 42 sustainability and transformation partnerships, 106 clinical commissioning groups, and 1257 primary care networks). User feedback and internal review of case studies demonstrate that our methodology identifies prescribing behavior that sometimes warrants further investigation or is a known issue. CONCLUSIONS: Data-driven approaches have the potential to overcome existing biases with regard to the planning and execution of audits, interventions, and policy making within NHS organizations, potentially revealing new targets for improved health care service delivery. We present our dashboards as a proof of concept for generating candidate lists to aid expert users in their interpretation of prescribing data and prioritize further investigations and qualitative research in terms of potential targets for improved performance.
\n \n\n \n \nThe COVID-19 vaccines were developed and rigorously evaluated in randomized trials during 2020. However, important questions, such as the magnitude and duration of protection, their effectiveness against new virus variants, and the effectiveness of booster vaccination, could not be answered by randomized trials and have therefore been addressed in observational studies. Analyses of observational data can be biased because of confounding and because of inadequate design that does not consider the evolution of the pandemic over time and the rapid uptake of vaccination. Emulating a hypothetical \"target trial\" using observational data assembled during vaccine rollouts can help manage such potential sources of bias. This article describes 2 approaches to target trial emulation. In the sequential approach, on each day, eligible persons who have not yet been vaccinated are matched to a vaccinated person. The single-trial approach sets a single baseline at the start of the rollout and considers vaccination as a time-varying variable. The nature of the confounding depends on the analysis strategy: Estimating \"per-protocol\" effects (accounting for vaccination of initially unvaccinated persons after baseline) may require adjustment for both baseline and \"time-varying\" confounders. These issues are illustrated by using observational data from 2\u2009780\u2009931 persons in the United Kingdom aged 70 years or older to estimate the effect of a first dose of a COVID-19 vaccine. Addressing the issues discussed in this article should help authors of observational studies provide robust evidence to guide clinical and policy decisions.
\n \n\n \n \nBACKGROUND: The COVID-19 pandemic has disrupted healthcare activity across a broad range of clinical services. The NHS stopped non-urgent work in March 2020, later recommending services be restored to near-normal levels before winter where possible. AIM: To describe changes in the volume and variation of coded clinical activity in general practice across six clinical areas: cardiovascular disease, diabetes, mental health, female and reproductive health, screening and related procedures, and processes related to medication. DESIGN AND SETTING: With the approval of NHS England, a cohort study was conducted of 23.8 million patient records in general practice, in situ using OpenSAFELY. METHOD: Common primary care activities were analysed using Clinical Terms Version 3 codes and keyword searches from January 2019 to December 2020, presenting median and deciles of code usage across practices per month. RESULTS: Substantial and widespread changes in clinical activity in primary care were identified since the onset of the COVID-19 pandemic, with generally good recovery by December 2020. A few exceptions showed poor recovery and warrant further investigation, such as mental health (for example, for 'Depression interim review' the median occurrences across practices in December 2020 was down by 41.6% compared with December 2019). CONCLUSION: Granular NHS general practice data at population-scale can be used to monitor disruptions to healthcare services and guide the development of mitigation strategies. The authors are now developing real-time monitoring dashboards for the key measures identified in this study, as well as further studies using primary care data to monitor and mitigate the indirect health impacts of COVID-19 on the NHS.
\n \n\n \n \nBACKGROUND: Antihypertensives are effective at reducing the risk of cardiovascular disease, but limited data exist quantifying their association with serious adverse events, particularly in older people with frailty. This study aimed to examine this association using nationally representative electronic health record data. METHODS AND FINDINGS: This was a retrospective cohort study utilising linked data from 1,256 general practices across England held within the Clinical Practice Research Datalink between 1998 and 2018. Included patients were aged 40+ years, with a systolic blood pressure reading between 130 and 179 mm Hg, and not previously prescribed antihypertensive treatment. The main exposure was defined as a first prescription of antihypertensive treatment. The primary outcome was hospitalisation or death within 10 years from falls. Secondary outcomes were hypotension, syncope, fractures, acute kidney injury, electrolyte abnormalities, and primary care attendance with gout. The association between treatment and these serious adverse events was examined by Cox regression adjusted for propensity score. This propensity score was generated from a multivariable logistic regression model with patient characteristics, medical history and medication prescriptions as covariates, and new antihypertensive treatment as the outcome. Subgroup analyses were undertaken by age and frailty. Of 3,834,056 patients followed for a median of 7.1 years, 484,187 (12.6%) were prescribed new antihypertensive treatment in the 12 months before the index date (baseline). Antihypertensives were associated with an increased risk of hospitalisation or death from falls (adjusted hazard ratio [aHR] 1.23, 95% confidence interval (CI) 1.21 to 1.26), hypotension (aHR 1.32, 95% CI 1.29 to 1.35), syncope (aHR 1.20, 95% CI 1.17 to 1.22), acute kidney injury (aHR 1.44, 95% CI 1.41 to 1.47), electrolyte abnormalities (aHR 1.45, 95% CI 1.43 to 1.48), and primary care attendance with gout (aHR 1.35, 95% CI 1.32 to 1.37). The absolute risk of serious adverse events with treatment was very low, with 6 fall events per 10,000 patients treated per year. In older patients (80 to 89 years) and those with severe frailty, this absolute risk was increased, with 61 and 84 fall events per 10,000 patients treated per year (respectively). Findings were consistent in sensitivity analyses using different approaches to address confounding and taking into account the competing risk of death. A strength of this analysis is that it provides evidence regarding the association between antihypertensive treatment and serious adverse events, in a population of patients more representative than those enrolled in previous randomised controlled trials. Although treatment effect estimates fell within the 95% CIs of those from such trials, these analyses were observational in nature and so bias from unmeasured confounding cannot be ruled out. CONCLUSIONS: Antihypertensive treatment was associated with serious adverse events. Overall, the absolute risk of this harm was low, with the exception of older patients and those with moderate to severe frailty, where the risks were similar to the likelihood of benefit from treatment. In these populations, physicians may want to consider alternative approaches to management of blood pressure and refrain from prescribing new treatment.
\n \n\n \n \nBACKGROUND: Nicotine receptor partial agonists may help people to stop smoking by a combination of maintaining moderate levels of dopamine to counteract withdrawal symptoms (acting as an agonist) and reducing smoking satisfaction (acting as an antagonist). This is an update of a Cochrane Review first published in 2007. OBJECTIVES: To assess the effectiveness of nicotine receptor partial agonists, including varenicline and cytisine, for smoking cessation. SEARCH METHODS: We searched the Cochrane Tobacco Addiction Group's Specialised Register in April 2022 for trials, using relevant terms in the title or abstract, or as keywords. The register is compiled from searches of CENTRAL, MEDLINE, Embase, and PsycINFO.\u00a0 SELECTION CRITERIA: We included randomised controlled trials that compared the treatment drug with placebo, another smoking cessation drug, e-cigarettes, or no medication. We excluded trials that did not report a minimum follow-up period of six months from baseline. DATA COLLECTION AND ANALYSIS: We followed standard Cochrane methods. Our main outcome was abstinence from smoking at longest follow-up using the most rigorous definition of abstinence, preferring biochemically validated rates where reported. We pooled risk ratios (RRs), using the Mantel-Haenszel fixed-effect model. We also reported the number of people reporting serious adverse events (SAEs). MAIN RESULTS: We included 75 trials of 45,049 people; 45 were new for this update. We rated 22 at low risk of bias, 18 at high risk, and 35 at unclear risk. We found moderate-certainty evidence (limited by heterogeneity) that cytisine helps more people to quit smoking than placebo (RR 1.30, 95% confidence interval (CI) 1.15 to 1.47; I2 = 83%; 4 studies, 4623 participants), and no evidence of a difference in the number reporting SAEs (RR 1.04, 95% CI 0.78 to 1.37; I2 = 0%; 3 studies, 3781 participants; low-certainty evidence). SAE evidence was limited by imprecision. We found no data on neuropsychiatric or cardiac SAEs. We found high-certainty evidence that varenicline helps more people to quit than placebo (RR 2.32, 95% CI 2.15 to 2.51; I2 = 60%, 41 studies, 17,395 participants), and moderate-certainty evidence that people taking varenicline are more likely to report SAEs than those not taking it (RR 1.23, 95% CI 1.01 to 1.48; I2 = 0%; 26 studies, 14,356 participants). While point estimates suggested increased risk of cardiac SAEs (RR 1.20, 95% CI 0.79 to 1.84; I2 = 0%; 18 studies, 7151 participants; low-certainty evidence), and decreased risk of neuropsychiatric SAEs (RR 0.89, 95% CI 0.61 to 1.29; I2 = 0%; 22 studies, 7846 participants; low-certainty evidence), in both cases evidence was limited by imprecision, and confidence intervals were compatible with both benefit and harm. Pooled results from studies that randomised people to receive cytisine or varenicline showed that more people in the varenicline arm quit smoking (RR 0.83, 95% CI 0.66 to 1.05; I2 = 0%; 2 studies, 2131 participants; moderate-certainty evidence) and reported SAEs (RR 0.67, 95% CI 0.44 to 1.03; I2 = 45%; 2 studies, 2017 participants; low-certainty evidence). However, the evidence was limited by imprecision, and confidence intervals incorporated the potential for benefit from either cytisine or varenicline. We found no data on neuropsychiatric or cardiac SAEs. We found high-certainty evidence that varenicline helps more people to quit than bupropion (RR 1.36, 95% CI 1.25 to 1.49; I2 = 0%; 9 studies, 7560 participants), and no clear evidence of difference in rates of SAEs (RR 0.89, 95% CI 0.61 to 1.31; I2 = 0%; 5 studies, 5317 participants), neuropsychiatric SAEs (RR 1.05, 95% CI 0.16 to 7.04; I2 = 10%; 2 studies, 866 participants), or cardiac SAEs (RR 3.17, 95% CI 0.33 to 30.18; I2 = 0%; 2 studies, 866 participants). Evidence of harms was of low certainty, limited by imprecision. We found high-certainty evidence that varenicline helps more people to quit than a single form of nicotine replacement therapy (NRT) (RR 1.25, 95% CI 1.14 to 1.37; I2 = 28%; 11 studies, 7572 participants), and low-certainty evidence, limited by imprecision, of fewer reported SAEs (RR 0.70, 95% CI 0.50 to 0.99; I2 = 24%; 6 studies, 6535 participants). We found no data on neuropsychiatric or cardiac SAEs. We found no clear evidence of a difference in quit rates between varenicline and dual-form NRT (RR 1.02, 95% CI 0.87 to 1.20; I2 = 0%; 5 studies, 2344 participants; low-certainty evidence, downgraded because of imprecision). While pooled point estimates suggested increased risk of SAEs (RR 2.15, 95% CI 0.49 to 9.46; I2 = 0%; 4 studies, 1852 participants) and neuropsychiatric SAEs (RR 4.69, 95% CI 0.23 to 96.50; I2 not estimable as events only in 1 study; 2 studies, 764 participants), and reduced risk of cardiac SAEs (RR 0.32, 95% CI 0.01 to 7.88; I2 not estimable as events only in 1 study; 2 studies, 819 participants), in all three cases evidence was of low certainty and confidence intervals were very wide, encompassing both substantial harm and benefit. AUTHORS' CONCLUSIONS: Cytisine and varenicline both help more people to quit smoking than placebo or no medication. Varenicline is more effective at helping people to quit smoking than bupropion, or a single form of NRT, and may be as or more effective than dual-form NRT. People taking varenicline are probably more likely to experience SAEs than those not taking it, and while there may be increased risk of cardiac SAEs and decreased risk of neuropsychiatric SAEs, evidence was compatible with both benefit and harm. Cytisine may lead to fewer people reporting SAEs than varenicline. Based on studies that directly compared cytisine and varenicline, there may be a benefit from varenicline for quitting smoking, however further evidence could strengthen this finding or demonstrate a benefit from cytisine. Future trials should test the effectiveness and safety of cytisine compared with varenicline and other pharmacotherapies, and should also test variations in dose and duration. There is limited benefit to be gained from more trials testing the effect of standard-dose varenicline compared with placebo for smoking cessation. Further trials on varenicline should test variations in dose and duration, and compare varenicline with e-cigarettes for smoking cessation.
\n \n\n \n \nBackground and aimsBehavioural smoking cessation trials have employed comparators that vary considerably between trials. Although some previous meta-analyses made attempts to account for variability in comparators, these relied on subsets of trials and incomplete data on comparators. This study aimed to estimate the relative effectiveness of (individual) smoking cessation interventions, while accounting for variability in comparators using comprehensive data on experimental and comparator interventions.MethodsSystematic review and meta-regression including 172 randomised controlled trials with at least 6 months' follow-up and biochemically verified smoking cessation. Authors were contacted to obtain unpublished information. This information was coded in terms of active content and attributes of the study population and methods. Meta-regression was used to create a model predicting smoking cessation outcomes. This model was used to re-estimate intervention effects, as if all interventions have been evaluated against the same comparators. Outcome measures included log odds of smoking cessation for the meta-regression models and smoking cessation differences and ratios to compare relative effectiveness.ResultsThe meta-regression model predicted smoking cessation rates well (pseudo R2 =.44). Standardizing the comparator had substantial impact on conclusions regarding the (relative) effectiveness of trials and types of intervention. Compared with a 'no support comparator', self-help was 1.33 times (95% confidence interval [CI]=1.16-1.49), brief physician advice 1.61 times (95%CI=1.31-1.90) nurse individual counselling 1.76 times (95%CI=1.62-1.90), psychologist individual counselling 2.04 times (95%CI=1.95-2.15), and individual group psychologist interventions 2.06 times (95%CI=1.92-2.20) more effective. Notably, more elaborate experimental interventions (e.g., psychologist counselling) were typically compared with more elaborate comparators, masking their effectiveness.ConclusionsComparator variability and underreporting of comparators obscures the interpretation, comparison and generalisability of behavioural smoking cessation trials. Comparator variability should thus be taken into account when interpreting and synthesising evidence from trials. Otherwise, policy makers, practitioners and researchers may draw incorrect conclusions about the (cost)effectiveness of smoking cessation interventions and their constituent components.
\n \n\n \n \nThe Cochrane Tobacco Addiction Group has created risk of bias tools which are topic-agnostic. In 2012 the Cochrane Tobacco Addiction Group created guidance specific to considerations for reviews of randomized controlled trials of tobacco cessation interventions, building on existing Cochrane tools. The guidance covers issues relating to selection bias, performance bias, detection bias, attrition bias, and selective reporting. In this paper, we set out to make this guidance publicly available, so that others can use and cite it. We provide advice for using this tool to appraise trials critically as a systematic reviewer. We also provide guidance for triallists on ways to use this tool to improve trial design and reporting.
\n \n\n \n \nIntroduction: There is strong evidence that type 2 diabetes (T2D) remission can be achieved by adopting a low-energy diet achieved through total dietary replacement products. There is promising evidence that low-carbohydrate diets can achieve remission of T2D. The Dietary Approaches to the Management of type 2 Diabetes (DIAMOND) programme combines both approaches in a behaviourally informed low-energy, low-carbohydrate diet for people with T2D, delivered by nurses in primary care. This trial compares the effectiveness of the DIAMOND programme to usual care in inducing remission of T2D and in reducing risk of cardiovascular disease. Methods and analysis: We aim to recruit 508 people in 56 practices with T2D diagnosed within 6 years, who are demographically representative of the UK population. We will allocate general practices, based on ethnicity and socioeconomic status, to provide usual care for diabetes or offer the DIAMOND programme. Participants in practices offering DIAMOND will see the nurse seven times over 6 months. At baseline, 6 months, and 1 year we will measure weight, blood pressure, HbA1c, lipid profile and risk of fatty liver disease. The primary outcome is diabetes remission at 1 year, defined as HbA1c < 48 mmol/mol and off glucose-lowering medication for at least 6 months. Thereafter, we will assess whether people resume treatment for diabetes and the incidence of microvascular and macrovascular disease through the National Diabetes Audit. Data will be analysed using mixed-effects generalised linear models. This study has been approved by the National Health Service Health Research Authority Research Ethics Committee (Ref: 22/EM/0074). Trial Registration number: ISRCTN46961767.
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