Moderate certainty evidence supports use of nicotine electronic cigarettes to quit smoking combustible cigarettes. However, there is less certainty regarding how long people continue to use e-cigarettes after smoking cessation attempts. We set out to synthesise data on the proportion of people still using e-cigarettes or other study products at 6 months or longer in studies of e-cigarettes for smoking cessation. We updated Cochrane searches (November 2021). For the first time, we meta-analysed prevalence of continued e-cigarette use among individuals allocated to e-cigarette conditions, and among those individuals who had successfully quit smoking. We updated meta-analyses comparing proportions continuing product use among individuals allocated to use nicotine e-cigarettes and other treatments. We included 19 studies (n = 7787). The pooled prevalence of continued e-cigarette use at 6 months or longer was 54% (95% CI: 46% to 61%, I² 86%, N = 1482) in participants assigned to e-cigarette conditions. Of participants who had quit combustible cigarettes overall 70% were still using e-cigarettes at six months or longer (95% CI: 53% to 82%, I² 73%, N = 215). Heterogeneity in direction of effect precluded meta-analysis comparing long-term use of nicotine e-cigarettes with NRT. More people were using nicotine e-cigarettes at longest follow-up compared to non-nicotine e-cigarettes, but CIs included no difference (risk ratio 1.15, 95% CI: 0.94 to 1.41, n = 601). The levels of continued e-cigarette use observed may reflect the success of e-cigarettes as a quitting tool. Further research is needed to establish drivers of variation in and implications of continued use of e-cigarettes.

Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings. The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests.

To critically appraise and evaluate the diagnostic value of symptoms and signs in identifying UTI in older adult outpatients, using evidence from observational studies. We searched Medline and Medline in process, Embase and Web of Science, from inception up to September 2017. We included studies assessing the diagnostic accuracy of symptoms and/or signs in predicting UTI in outpatients aged 65 years and above. Study quality was assessed using the QUADAS-2 tool. We identified 15 eligible studies of variable quality, with a total of 12,039 participants (range 65-4259), and assessed the diagnostic accuracy of 66 different symptoms and signs in predicting UTI. A number of symptoms and signs typically associated with UTI, such as nocturia, urgency and abnormal vital signs, were of limited use in older adult outpatients. Inability to perform a number of acts of daily living were predictors of UTI: For example, disability in feeding oneself, + ve LR: 11.8 (95% CI 5.51-25.2) and disability in washing one's hands and face, + ve LR: 6.84 (95% CI 4.08-11.5). The limited evidence of varying quality shows that a number of symptoms and signs traditionally associated with UTI may have limited diagnostic value in older adult outpatients.

Background: The number of prediction models for self-harm and suicide has grown substantially in recent years. However, their potential role in improving assessment of suicide risk is debated. In this systematic review, we provide an overview and critical appraisal of the predictive performance and methodological quality of prognostic risk models for self-harm and suicide. Methods: We searched MEDLINE, EMBASE, PsycINFO, CINAHL, and Global Health from inception to 30/11/2021. The search was updated on 25/10/2024 to include new external validations. We included studies describing the development and/or external validation of statistical models for predicting risk of non-fatal self-harm and/or death by suicide. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results: We included 91 articles describing the development of 167 models and 29 external validations. Most models predicted risk of self-harm (76 models), followed by suicide (51 models), and the composite outcome of suicide or non-fatal self-harm (40 models). Only 8% of developed models (14/167) were externally validated, and 17% (28/167) were presented in a format enabling validation or use by others. The reported C indices ranged from 0.61 to 0.97 (median 0.82) in development studies and from 0.60 to 0.86 (median 0.81) in external validations. Calibration was assessed for 9% of models (15/167) in development studies and 31% of external validations (9/29). Of these, the OxMIS and Simon models showed adequate discrimination and calibration performance in external validation. All model development studies, and all but two external validations, were at high risk of bias. This was mainly driven by inappropriate or incomplete evaluation of predictive performance (180/196, 92%), insufficient sample sizes (151/196, 77%), inappropriate handling of missing data (129/196, 66%), and not adequately accounting for overfitting and optimism during model development (106/167, 63%). Conclusions: Despite skepticism about the feasibility and accuracy of self-harm and suicide risk prediction and assessment, we have identified five models with good predictive performance in external validation. Avoidable sources of research waste include an oversupply of unvalidated prediction models addressing similar research questions, and shortcomings in study design, conduct, and statistical analysis. To address these, new research must prioritise methodological rigour and focus on external validation and updating existing models. Complete, transparent, and accurate reporting is essential, with model presentation in a format that enables independent validation.