20 research outputs found
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 ÂŁ1 invested
in the OxBRC generates an additional ÂŁ0.46 through income and job creation alone. Multiplicative
employment effects following a marginal investment in the OxBRC of ÂŁ98m 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 Purposeâ
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.
Methodsâ
A United Kingdom, multicenter, parallel-group, randomized, placebo-controlled trial. Patients â„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.
Resultsâ
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, 1.13â3.45; P=0.017), female sex (hazard ratio =2.13; 95% CI, 1.29â3.51; P=0.003), and fluoxetine (hazard ratio =2.00; 95% CI, 1.20â3.34; P=0.008) were independently associated with fractures.
Conclusionsâ
Most fractures resulted from falls. Although many fractures were serious, and likely to impair patientsâ 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 Registrationâ
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 40â69 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 1.06â1.55) and intake of sugar-sweetened beverages (> 2/day adj. HR 1.84, 95% CI 1.42â2.37) 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 patientsâ 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 2â15 days after stroke onset. Patients are randomised centrally via each trialsâ 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 â„ 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 â3.78%, 95% confidence interval â1.26% to â6.30%; 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 professionalsâ 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 Fluoxetineâa 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