A two-arm, randomised feasibility trial using link workers to improve dental visiting in people with severe mental illness: a protocol paper.

BACKGROUND: People with severe mental illness (e.g. psychosis, bipolar disorder) experience poor oral health compared to the general population as shown by more decayed, missing and filled teeth and a higher prevalence of periodontal disease. Attending dental services allows treatment of oral health problems and support for prevention. However, people with severe mental illness face multiple barriers to attending routine dental appointments and often struggle to access care. Link work interventions use non-clinical support staff to afford vulnerable populations the capacity, opportunity, and motivation to navigate use of services. The authors have co-developed with service users a link work intervention for supporting people with severe mental illness to access routine dental appointments. The Mouth Matters in Mental Health Study aims to explore the feasibility and acceptability of this intervention within the context of a feasibility randomised controlled trial (RCT) measuring outcomes related to the recruitment of participants, completion of assessments, and adherence to the intervention. The trial will closely monitor the safety of the intervention and trial procedures. METHODS: A feasibility RCT with 1:1 allocation to two arms: treatment as usual (control) or treatment as usual plus a link work intervention (treatment). The intervention consists of six sessions with a link worker over 9 months. Participants will be adults with severe mental illness receiving clinical input from secondary care mental health service and who have not attended a planned dental appointment in the past 3 years. Assessments will take place at baseline and after 9 months. The target recruitment total is 84 participants from across three NHS Trusts. A subset of participants and key stakeholders will complete qualitative interviews to explore the acceptability of the intervention and trial procedures. DISCUSSION: The link work intervention aims to improve dental access and reduce oral health inequalities in people with severe mental illness. There is a dearth of research relating to interventions that attempt to improve oral health outcomes in people with mental illness and the collected feasibility data will offer insights into this important area. TRIAL REGISTRATION: The trial was preregistered on ISRCTN (ISRCTN13650779) and ClinicalTrials.gov (NCT05545228).

Statistical models to predict recruitment in clinical trials were rarely used by statisticians in UK and European networks.

OBJECTIVE: Identify the current practice for recruitment prediction and monitoring within clinical trials. STUDY DESIGN AND SETTING: Chief investigators (CIs) were surveyed to identify data sources and adjustments made to support recruitment prediction. Statisticians were surveyed to determine methods and adjustments used when predicting and monitoring recruitment. Participants were identified from the National Institute for Health Research recently funded studies, the UK Clinical Research Collaboration registered Clinical Trial Units network or by the European Clinical Research Infrastructure Network. RESULTS: A total of 51 CIs (UK = 32, ECRIN = 19) and 104 statisticians (UK = 51, ECRIN = 53) were contacted. Response rates varied (CIs UK = 53% ECRIN = 32%; statisticians UK = 98% ECRIN = 36%). Multiple data sources are used to support recruitment rates, most commonly audit data from multiple sites. Variation in individual site recruitment rates are frequently incorporated, but staggered site openings were featured more commonly among UK respondents. Simple prediction methods are preferred to rarely used statistical models. Lack of familiarity with statistical methods are barriers to their use with evidence needed to justify the time required to support their implementation. CONCLUSION: Simplistic methods will continue as the mainstay of prediction; however, generation of evidence supporting the benefits of complex statistical models should promote their implementations. Multiple data sources to support recruitment prediction are being used, and further work on the quality of these data is needed. Pressure to be optimistic about recruitment rates for the trial to be attractive to funders was felt by a sizable minority.

A systematic review describes models for recruitment prediction at the design stage of a clinical trial.

OBJECTIVE: Patient recruitment in clinical trials is challenging with failure to recruit to time and target sample size common. This may be caused by unanticipated problems or by overestimation of the recruitment rate. This study is a systematic review of statistical models to predict recruitment at the design stage of clinical trials. STUDY DESIGN AND SETTING: The Online Resource for Recruitment research in Clinical triAls database was searched to identify articles published between 2008 and 2016. Articles published before 2008 were identified from a relevant systematic review. Google search was used to find potential methods in gray literature. RESULTS: Thirteen eligible articles were identified of which, 11 focused on stochastic approaches, one on deterministic models, and one included both stochastic and deterministic methods. Models varied considerably in the factors included and in their complexity. Key aspects included their ability to condition on time; whether they used average or center-specific recruitment rates; and assumptions around center initiation rates. Lack of flexibility of some models restricts their implementation. CONCLUSION: Deterministic models require specification of few parameters but are likely unrealistic although easy to implement. Increasingly, stochastic models require greater parameter specification, which, along with greater complexity may be a barrier to their implementation.

DIABRISK-SL trial: further consideration of age and impact of imputations.

Type 2 diabetes mellitus (T2DM) is a major cause of morbidity and mortality worldwide. Early interventions may help to delay or prevent onset of cardiometabolic endpoints of clinical importance to T2DM patients.Wijesuriya et al. (BMC Med 15:146, 2017) published results of a randomised controlled trial in Sri Lanka testing the effect of two lifestyle modification programmes of varying intensity in participants aged 5-40 years with risk factors for T2DM. The intervention measured the impact of the two programmes on the primary composite endpoint consisting of various predictors of cardiometabolic disease. The authors concluded that the more intensive programme significantly reduced the incidence of predictors of cardiometabolic disease. Further, they delivered a large-scale intervention with restricted resources with widespread acceptance as demonstrated by the high uptake rate. However, we believe that further analysis is required to fully understand the potential for benefit, particularly in relation to age, retention and missing data.