The NHS visitor and migrant cost recovery programme - a threat to health?

BACKGROUND: In April 2014 the UK government launched the 'NHS Visitor and Migrant Cost Recovery Programme Implementation Plan' which set out a series of policy changes to recoup costs from 'chargeable' (largely non-UK born) patients. In England, approximately 75% of tuberculosis (TB) cases occur in people born abroad. Delays in TB treatment increase risk of morbidity, mortality and transmission in the community. We investigated whether diagnostic delay has increased since the Cost Recovery Programme (CRP) was introduced. METHODS: There were 3342 adult TB cases notified on the London TB Register across Barts Health NHS Trust between 1st January 2011 and 31st December 2016. Cases with missing relevant information were excluded. The median time between symptom onset and treatment initiation before and after the CRP was calculated according to birthplace and compared using the Mann Whitney test. Delayed diagnosis was considered greater or equal to median time to treatment for all patients (79 days). Univariable logistic regression was used to manually select exposure variables for inclusion in a multivariable model to test the association between diagnostic delay and the implementation of the CRP. RESULTS: We included 2237 TB cases. Among non-UK born patients, median time-to-treatment increased from 69 days to 89 days following introduction of CRP (p < 0.001). Median time-to-treatment also increased for the UK-born population from 75.5 days to 89.5 days (p = 0.307). The multivariable logistic regression model showed non-UK born patients were more likely to have a delay in diagnosis after the CRP (adjOR 1.37, 95% CI 1.13-1.66, p value 0.001). CONCLUSION: Since the introduction of the CRP there has been a significant delay for TB treatment among non-UK born patients. Further research exploring the effect of policies restricting access to healthcare for migrants is urgently needed if we wish to eliminate TB nationally

Practical approaches to Bayesian sample size determination in non-inferiority trials with binary outcomes.

Bayesian analysis of a non-inferiority trial is advantageous in allowing direct probability statements to be made about the relative treatment difference rather than relying on an arbitrary and often poorly justified non-inferiority margin. When the primary analysis will be Bayesian, a Bayesian approach to sample size determination will often be appropriate for consistency with the analysis. We demonstrate three Bayesian approaches to choosing sample size for non-inferiority trials with binary outcomes and review their advantages and disadvantages. First, we present a predictive power approach for determining sample size using the probability that the trial will produce a convincing result in the final analysis. Next, we determine sample size by considering the expected posterior probability of non-inferiority in the trial. Finally, we demonstrate a precision-based approach. We apply these methods to a non-inferiority trial in antiretroviral therapy for treatment of HIV-infected children. A predictive power approach would be most accessible in practical settings, because it is analogous to the standard frequentist approach. Sample sizes are larger than with frequentist calculations unless an informative analysis prior is specified, because appropriate allowance is made for uncertainty in the assumed design parameters, ignored in frequentist calculations. An expected posterior probability approach will lead to a smaller sample size and is appropriate when the focus is on estimating posterior probability rather than on testing. A precision-based approach would be useful when sample size is restricted by limits on recruitment or costs, but it would be difficult to decide on sample size using this approach alone

Erratum to: Models and impact of patient and public involvement in studies carried out by the Medical Research Council Clinical Trials Unit at University College London: findings from ten case studies (vol 17, 376, 2016)

This is an erratum to a previously published articles available here: http://discovery.ucl.ac.uk/1508429

Erratum to: 'Models and impact of patient and public involvement in studies carried out by the Medical Research Council Clinical Trials Unit at University College London: findings from ten case studies'.

Background Patient and public involvement (PPI) in studies carried out by the UK Medical Research Council Clinical Trials Unit (MRC CTU) at University College London varies by research type and setting. We developed a series of case studies of PPI to document and share good practice. Methods We used purposive sampling to identify studies representing the scope of research at the MRC CTU and different approaches to PPI. We carried out semi-structured interviews with staff and patient representatives. Interview notes were analysed descriptively to categorise the main aims and motivations for involvement; activities undertaken; their impact on the studies and lessons learned. Results We conducted 19 interviews about ten case studies, comprising one systematic review, one observational study and 8 randomised controlled trials in HIV and cancer. Studies were either open or completed, with start dates between 2003 and 2011. Interviews took place between March and November 2014 and were updated in summer 2015 where there had been significant developments in the study (i.e. if the study had presented results subsequent to the interview taking place). A wide range of PPI models, including representation on trial committees or management groups, community engagement, one-off task-focused activities, patient research partners and participant involvement had been used. Overall, interviewees felt that PPI had a positive impact, leading to improvements, for example in the research question; study design; communication with potential participants; study recruitment; confidence to carry out or complete a study; interpretation and communication of results; and influence on future research. Conclusions A range of models of PPI can benefit clinical studies. Researchers should consider different approaches to PPI, based on the desired impact and the people they want to involve. Use of multiple models may increase the potential impacts of PPI in clinical research

Improving clinical trial interpretation with ACCEPT analyses

Clements et al. propose ACceptability Curve Estimation using Probability above Threshold (ACCEPT) as a method of harmonizing interpretation across multiple trial types and maximizing the clinical utility of trial data

Improving clinical trial interpretation with ACCEPT analyses

Effective decision making from randomised controlled clinical trials relies on robust interpretation of the numerical results. However, the language we use to describe clinical trials can cause confusion both in trial design and in comparing results across trials. ACceptability Curve Estimation using Probability Above Threshold (ACCEPT) aids comparison between trials (even where of different designs) by harmonising reporting of results, acknowledging different interpretations of the results may be valid in different situations, and moving the focus from comparison to a pre-specified value to interpretation of the trial data. ACCEPT can be applied to historical trials or incorporated into statistical analysis plans for future analyses. An online tool enables ACCEPT on up to three trials simultaneously

Treatment estimands in clinical trials of patients hospitalised for COVID-19: ensuring trials ask the right questions

When designing a clinical trial, explicitly defining the treatment estimands of interest (that which is to be estimated) can help to clarify trial objectives and ensure the questions being addressed by the trial are clinically meaningful. There are several challenges when defining estimands. Here, we discuss a number of these in the context of trials of treatments for patients hospitalised with COVID-19 and make suggestions for how estimands should be defined for key outcomes. We suggest that treatment effects should usually be measured as differences in proportions (or risk or odds ratios) for outcomes such as death and requirement for ventilation, and differences in means for outcomes such as the number of days ventilated. We further recommend that truncation due to death should be handled differently depending on whether a patient- or resource-focused perspective is taken; for the former, a composite approach should be used, while for the latter, a while-alive approach is preferred. Finally, we suggest that discontinuation of randomised treatment should be handled from a treatment policy perspective, where non-adherence is ignored in the analysis (i.e. intention to treat)