Can different patient satisfaction survey methods yield consistent results? Comparison of three surveys

Objective-To examine the consistency of survey estimates ofpatient satisfaction with interpersonal aspects of hospital experience. Design-Interview and postal surveys, evidence from three independent population surveys being compared. Setting-Scotland and Lothian. Subjects-Randomly selected members of the general adult population who had received hospital care in the past 12 months. Main outcome measures-Percentages of respondents dissatisfied with aspects of patient care. Results-For items covering respect for privacy, treatment with dignity, sensitivity to feelings, treatment as an individual, and clear explanation of care there was good agreement among the surveys despite differences in wording. But for items to do with being encouraged and given time to ask questions and being listened to by doctors there was substantial disagreement. Conclusions-Evidence regarding levels of patient dissatisfaction from national or local surveys should be calibrated against evidence from other surveys to improve reliability. Some important aspects of patient satisfaction seem to have been reliably estimated by surveys of all Scottish NHS users commissioned by the management executive, but certain questions may have underestimated the extent of dissatisfaction, possibly as a result of choice of wording

Using the Bayesian credible subgroups method to identify populations benefiting from treatment: An application to the Look AHEAD trial

Traditionally, subgroup analyses are used to assess whether patient characteristics moderate treatment effectiveness with general disregard for issues of multiplicity. Using data from The Action for Health in Diabetes (Look AHEAD) trial in the United States, we aim to identify a subgroup where all of its types of members experience a treatment benefit defined as reducing the likelihood of a major cardiovascular event under an intensive lifestyle and weight-loss intervention. We apply the credible subgroups method to a Bayesian logistic model with a conservative prior that is sceptical of large treatment effect heterogeneity. The covariate profiles for which there is sufficient evidence of treatment benefit are, coarsely, middle-aged women, in poor subjective general health and with moderately to poorly controlled diabetes. There is at least 80% posterior probability that the conditional average treatment effect is positive for all covariate profiles fitting this description, which account for 0.5% of trial participants. Conversely, the covariate profiles that are likely to be associated with no benefit are middle aged and older men in excellent subjective general health, with well-controlled diabetes. These profiles apply to less than 2% of trial participants. More information is required to determine treatment benefit or no benefit for the remainder of the trial population

A macroeconomic assessment of the impact of medical research expenditure: a case study of NIHR biomedical research centres

Quantifying the value of investment in medical research can inform decision-making on the prioritisation of research programmes. Existing methodologies to estimate the rate of return of medical research are inappropriate for early-phase translational research due to censoring of health benefits and time lags. A strategy to improve the process of translational research for patient benefit has been initiated as part of the UK National Institute for Health Research (NIHR) investment in Biomedical Research Centres (BRCs) in England. By providing a platform for partnership between universities, NHS trusts and industry, successful BRCs should reduce time lags within translational research whilst also providing an impetus for local economic growth through industry collaboration. We present a novel contribution in the assessment of early-phase biomedical research by estimating the impact of the Oxford Biomedical Research Centre (OxBRC) on income and job creation following the initial NIHR investment. We adopt a macroeconomic assessment approach using Input-Output Analysis to estimate the value of medical research in terms of income and job creation during the early pathway towards translational biomedical research. Inter-industry linkages are assessed by building a model economy for the South East England region to estimate the return on investment of the OxBRC. The results from the input-output model estimate that the return on investment in biomedical research within the OxBRC is 46%. Each £1 invested in the OxBRC generates an additional £0.46 through income and job creation alone. Multiplicative employment effects following a marginal investment in the OxBRC of £98m during the period 2007-2017 result in an estimated additional 196 full time equivalent positions being created within the local economy on top of direct employment within OxBRC. Results from input-output analyses can be used to inform the prioritisation of biomedical research programmes when compared against national minimum thresholds of investmen

Fluoxetine and fractures after stroke: exploratory analyses from the FOCUS trial

Background and Purpose— The FOCUS trial (Fluoxetine or Control Under Supervision) showed that fluoxetine did not improve modified Rankin Scale scores (mRS) but increased the risk of fractures. We aimed to describe the fractures, their impact on mRS and factors associated with fracture risk. Methods— A United Kingdom, multicenter, parallel-group, randomized, placebo-controlled trial. Patients ≥18 years with a clinical stroke and persisting deficit assessed 2 to 15 days after onset were eligible. Consenting patients were allocated fluoxetine 20 mg or matching placebo for 6 months. The primary outcome was the mRS at 6 months and secondary outcomes included fractures. Results— Sixty-five of 3127 (2.1%) patients had 67 fractures within 6 months of randomization; 43 assigned fluoxetine and 22 placebo. Fifty-nine (90.8%) had fallen and 26 (40%) had fractured their neck of femur. The effect of fluoxetine on mRS (common odds ratio =0.951) was not significantly altered by excluding fracture patients (common odds ratio =0.961). Cox proportional hazards modeling showed that only age >70 year (hazard ratio =1.97; 95% CI, 1.13–3.45; P=0.017), female sex (hazard ratio =2.13; 95% CI, 1.29–3.51; P=0.003), and fluoxetine (hazard ratio =2.00; 95% CI, 1.20–3.34; P=0.008) were independently associated with fractures. Conclusions— Most fractures resulted from falls. Although many fractures were serious, and likely to impair patients’ function, the increased fracture risk did not explain the lack of observed effect of fluoxetine on mRS. Only increasing age, female sex, and fluoxetine were independent predictors of fractures. Clinical Trial Registration— URL: http://www.controlled-trials.com. Unique identifier: ISRCTN83290762

The associations of sugar-sweetened, artificially sweetened and naturally sweet juices with all-cause mortality in 198,285 UK Biobank participants: a prospective cohort study

Background: Recent efforts to address the obesity epidemic have focused on sugar consumption, especially sugar-sweetened beverages. However, sugar takes many forms, is only one contributor to overall energy consumption and is correlated with other health-related lifestyle factors. The objective was to investigate the associations with allcause mortality of sugar- and artificially sweetened beverages and naturally sweet juices. Methods: Setting: UK Biobank, UK. Participants joined the UK Biobank study from 2006 to 2010 and were followed up until 2016; 198,285 men and women aged 40–69 years were eligible for this study (40% of the UK Biobank), of whom 3166 (1.6%) died over a mean of 7 years follow-up. Design: prospective population-based cohort study. Exposure variables: dietary consumption of sugar-sweetened beverages, artificially sweetened beverages, naturally sweet juices (100% fruit/vegetable juices) and total sugar intake, self-reported via 24-h dietary assessment tool completed between 2009 and 2012. Main outcome: all-cause mortality. Cox regression analyses were used to study the association between the daily intake of the above beverages and all-cause mortality. Models were adjusted for socio-demographic, economic, lifestyle and dietary confounders. Results: Total energy intake, total sugar intake and percentage of energy derived from sugar were comparable among participants who consumed > 2/day sugar-sweetened beverages and > 2/day fruit/vegetable juices (10,221 kJ/day versus 10,381 kJ/day; 183 g versus 190 g; 30.6% versus 31.0%). All-cause mortality was associated with total sugar intake (highest quintile adj. HR 1.28, 95% CI 1.06–1.55) and intake of sugar-sweetened beverages (> 2/day adj. HR 1.84, 95% CI 1.42–2.37) and remained so in sensitivity analyses. An association between artificially sweetened beverage intake and mortality did not persist after excluding deaths in the first 2 years of follow-up (landmark analysis) nor after excluding participants with recent weight loss. Furthermore, the inverse association between fruit/vegetable juice intake and mortality did not persist after additional adjustment for a diet quality score. Conclusions: Higher mortality is associated with sugar-sweetened beverages specifically. The lack of an adverse association with fruit/vegetable juices suggests that source of sugar may be important and the association with artificially sweetened beverage may reflect reverse causation. Conclusions: Higher mortality is associated with sugar-sweetened beverages specifically. The lack of an adverse association with fruit/vegetable juices suggests that source of sugar may be important and the association with artificially sweetened beverage may reflect reverse causation

Design and baseline characteristics of the PODOSA (Prevention of Diabetes & Obesity in South Asians) trial: a cluster, randomised lifestyle intervention in Indian and Pakistani adults with impaired glycaemia at high risk of developing type 2 diabetes

To describe the design and baseline population characteristics of an adapted lifestyle intervention trial aimed at reducing weight and increasing physical activity in people of Indian and Pakistani origin at high risk of developing type 2 diabetes

The focus, affinity and effects trials studying the effect(s) of fluoxetine in patients with a recent stroke: statistical and health economic analysis plan for the trialsand for the individual patient data meta-analysis

Background: Small trials have suggested that fluoxetine may improve neurological recovery from stroke. FOCUS, AFFINITY and EFFECTS are a family of investigator-led, multicentre, parallel group, randomised, placebo-controlled trials which aim to determine whether the routine administration of fluoxetine (20 mg daily) for six months after an acute stroke improves patients’ functional outcome. Methods/Design: The core protocol for the three trials has been published (Mead et al., Trials 20:369, 2015). The trials include patients aged 18 years and older with a clinical diagnosis of stroke and persisting focal neurological deficits at randomisation 2–15 days after stroke onset. Patients are randomised centrally via each trials’ web-based randomisation system using a common minimisation algorithm. Patients are allocated fluoxetine 20 mg once daily or matching placebo capsules for six months. The primary outcome measure is the modified Rankin scale (mRS) at six months. Secondary outcomes include: living circumstances; the Stroke Impact Scale; EuroQol (EQ5D-5 L); the vitality subscale of the 36-Item Short Form Health Survey (SF36); diagnosis of depression; adherence to medication; serious adverse events including death and recurrent stroke; and resource use at six and 12 months and the mRS at 12 months. Discussion: Minor variations have been tailored to the national setting in the UK (FOCUS), Australia, New Zealand and Vietnam (AFFINITY) and Sweden (EFFECTS). Each trial is run and funded independently and will report its own results. A prospectively planned individual patient data meta-analysis of all three trials will provide the most precise estimate of the overall effect and establish whether any effects differ between trials or subgroups. This statistical analysis plan describes the core analyses for all three trials and that for the individual patient data meta-analysis. Recruitment and follow-up in the FOCUS trial is expected to be completed by the end of 2018. AFFINITY and EFFECTS are likely to complete follow-up in 2020. Trial registration: FOCUS: ISRCTN, ISRCTN83290762. Registered on 23 May 2012. EudraCT, 2011-005616-29. Registered on 3 February 2012. AFFINITY: Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Registered on 22 July 2011. EFFECTS: ISRCTN, ISRCTN13020412. Registered on 19 December 2014. Clinicaltrials.gov, NCT02683213. Registered on 2 February 2016. EudraCT, 2011-006130-16. Registered on 8 August 2014

Fluoxetine to improve functional outcomes in patients after acute stroke: the FOCUS RCT

Background: Our Cochrane review of selective serotonin inhibitors for stroke recovery indicated that fluoxetine may improve functional recovery, but the trials were small and most were at high risk of bias. Objectives: The Fluoxetine Or Control Under Supervision (FOCUS) trial tested the hypothesis that fluoxetine improves recovery after stroke. Design: The FOCUS trial was a pragmatic, multicentre, parallel-group, individually randomised, placebo-controlled trial. Setting: This trial took place in 103 UK hospitals. Participants: Patients were eligible if they were aged ≥ 18 years, had a clinical stroke diagnosis, with focal neurological deficits, between 2 and 15 days after onset. Interventions: Patients were randomly allocated 20 mg of fluoxetine once per day or the matching placebo for 6 months via a web-based system using a minimisation algorithm. Main outcome measures: The primary outcome was the modified Rankin Scale at 6 months. Patients, carers, health-care staff and the trial team were masked to treatment allocation. Outcome was assessed at 6 and 12 months after randomisation. Patients were analysed by their treatment allocation as specified in a published statistical analysis plan. Results: Between 10 September 2012 and 31 March 2017, we recruited 3127 patients, 1564 of whom were allocated fluoxetine and 1563 of whom were allocated placebo. The modified Rankin Scale score at 6 months was available for 1553 out of 1564 (99.3%) of those allocated fluoxetine and 1553 out of 1563 (99.4%) of those allocated placebo. The distribution across modified Rankin Scale categories at 6 months was similar in the two groups (common odds ratio adjusted for minimisation variables 0.951, 95% confidence interval 0.839 to 1.079; p = 0.439). Compared with placebo, patients who were allocated fluoxetine were less likely to develop a new episode of depression by 6 months [210 (13.0%) vs. 269 (16.9%), difference –3.78%, 95% confidence interval –1.26% to –6.30%; p = 0.003], but had more bone fractures [45 (2.9%) vs. 23 (1.5%), difference 1.41%, 95% confidence interval 0.38% to 2.43%; p = 0.007]. There were no statistically significant differences in any other recorded events at 6 or 12 months. Health economic analyses showed no differences between groups in health-related quality of life, hospital bed usage or health-care costs

Recruiting South Asians to a lifestyle intervention trial: experiences and lessons from PODOSA (Prevention of Diabetes & Obesity in South Asians)

Background: Despite the growing emphasis on the inclusion of ethnic minority patients in research, there is little published on the recruitment of these populations especially to randomised, community based, lifestyle intervention trials in the UK. Methods: We share our experience of recruitment to screening in the PODOSA (Prevention of Diabetes and Obesity in South Asians) trial, which screened 1319 recruits (target 1800) for trial eligibility. A multi-pronged recruitment approach was used. Enrolment via the National Health Service included direct referrals from health care professionals and written invitations via general practices. Recruitment within the community was carried out by both the research team and through our partnerships with local South Asian groups and organisations. Participants were encouraged to refer friends and family throughout the recruitment period. Results: Health care professionals referred only 55 potential participants. The response to written invitations via general practitioners was 5.2%, lower than reported in other general populations. Community orientated, personal approaches for recruitment were comparatively effective yielding 1728 referrals (82%) to the screening stage. Conclusions: The PODOSA experience shows that a community orientated, personal approach for recruiting South Asian ethnic minority populations can be successful in a trial setting. We recommend that consideration is given to cover recruitment costs associated with community engagement and other personalised approaches. Researchers should consider prioritising approaches that minimise interference with professionals’ work and, particularly in the current economic climate, keep costs to a minimum. The lessons learned in PODOSA should contribute to future community based trials in South Asians

Update to the focus, affinity and effects trials studying the effect(s) of fluoxetine in patients with a recent stroke: statistical analysis plan for the trials and for the individual patient data meta-analysis

Background: Three large trials of fluoxetine for stroke recovery (FOCUS (fluoxetine or control under supervision), AFFINITY (the Assessment oF FluoxetINe In sTroke recovery) and EFFECTS (Efficacy oF Fluoxetine—a randomisEd Controlled Trial in Stroke)) have been collaboratively designed with the same basic protocol to facilitate an individual patient data analysis (IPDM). The statistical analysis plan for the three individual trials has already been reported in Trials, including a brief description of the IPDM. In this protocol, we describe in detail how we will perform the IPDM. Methods/design: Data from EFFECTS and AFFINITY will be transferred securely to the FOCUS statistician, who will perform a one-stage IPDM and a two-stage IPDM. For the one-stage IPDM, data will be combined into a single data set and the same analyses performed as described for the individual trials. For the two-stage IPDM, the results for the three individual trials will be combined using fixed effects meta-analyses. The primary and secondary outcome domains for the IPDM are the same as for individual trials. We will also perform analyses according to several subgroups including country of recruitment, ethnicity and trial. We will also explore the effects of fluoxetine on our primary and secondary outcomes in subgroups defined by combinations of characteristics. We also describe additional research questions that will be addressed using the combined data set, and published subsequently, including predictors of important post-stroke problems such as seizures, low mood and bone fractures. Discussion: An IPDM of our three large trials of fluoxetine for stroke recovery will allow us to provide the most precise estimates of any risks and benefits of fluoxetine vs placebo, to detect reliably a smaller overall effect size than those detectable by the individual trials, to better determine the effects of fluoxetine vs placebo in subgroups of patients and outcomes and to broaden the generalisability of the results. Also, we may identify differences in treatment effects between studies