Hearing loss is associated with poor cognitive performance and incident dementia and may contribute to cognitive decline. Treating hearing loss with hearing aids may ameliorate cognitive decline. The ...purpose of this study was to test whether use of hearing aids was associated with better cognitive performance, and if this relationship was mediated via social isolation and/or depression. Structural equation modelling of associations between hearing loss, cognitive performance, social isolation, depression and hearing aid use was carried out with a subsample of the UK Biobank data set (n = 164,770) of UK adults aged 40 to 69 years who completed a hearing test. Age, sex, general health and socioeconomic status were controlled for as potential confounders. Hearing aid use was associated with better cognition, independently of social isolation and depression. This finding was consistent with the hypothesis that hearing aids may improve cognitive performance, although if hearing aids do have a positive effect on cognition it is not likely to be via reduction of the adverse effects of hearing loss on social isolation or depression. We suggest that any positive effects of hearing aid use on cognition may be via improvement in audibility or associated increases in self-efficacy. Alternatively, positive associations between hearing aid use and cognition may be accounted for by more cognitively able people seeking and using hearing aids. Further research is required to determine the direction of association, if there is any direct causal relationship between hearing aid use and better cognition, and whether hearing aid use results in reduction in rates of cognitive decline measured longitudinally.
Digital cognitive behavioral therapy (dCBT) is a scalable and effective intervention for treating insomnia. Most people with insomnia, however, seek help because of the daytime consequences of poor ...sleep, which adversely affects quality of life.
To investigate the effect of dCBT for insomnia on functional health, psychological well-being, and sleep-related quality of life and to determine whether a reduction in insomnia symptoms was a mediating factor.
This online, 2-arm, parallel-group randomized trial comparing dCBT for insomnia with sleep hygiene education (SHE) evaluated 1711 participants with self-reported symptoms of insomnia. Participants were recruited between December 1, 2015, and December 1, 2016, and dCBT was delivered using web and/or mobile channels plus treatment as usual; SHE comprised a website and a downloadable booklet plus treatment as usual. Online assessments took place at 0 (baseline), 4 (midtreatment), 8 (posttreatment), and 24 (follow-up) weeks. Programs were completed within 12 weeks after inclusion.
Primary outcomes were scores on self-reported measures of functional health (Patient-Reported Outcomes Measurement Information System: Global Health Scale; range, 10-50; higher scores indicate better health); psychological well-being (Warwick-Edinburgh Mental Well-being Scale; range, 14-70; higher scores indicate greater well-being); and sleep-related quality of life (Glasgow Sleep Impact Index; range, 1-100; higher scores indicate greater impairment). Secondary outcomes comprised mood, fatigue, sleepiness, cognitive failures, work productivity, and relationship satisfaction. Insomnia was assessed with the Sleep Condition Indicator (range: 0-32; higher scores indicate better sleep).
Of the 1711 participants included in the intention-to-treat analysis, 1329 (77.7%) were female, mean (SD) age was 48.0 (13.8) years, and 1558 (91.1%) were white. Use of dCBT was associated with a small improvement in functional health compared with SHE (adjusted difference 95% CI at week 4, 0.90 0.40-1.40; week 8, 1.76 1.24-2.28; week 24, 1.76 1.22-2.30) and psychological well-being (adjusted difference 95% CI at week 4, 1.04 0.28-1.80; week 8, 2.68 1.89-3.47; week 24, 2.95 2.13-3.76), and with a large improvement in sleep-related quality of life (at week 4, -8.76 -11.83 to -5.69; week 8, -17.60 -20.81 to -14.39; week 24, -18.72 -22.04 to -15.41) (all P < .01). A large improvement in insomnia mediated these outcomes (range mediated, 45.5%-84.0%).
Use of dCBT is effective in improving functional health, psychological well-being, and sleep-related quality of life in people reporting insomnia symptoms. A reduction in insomnia symptoms mediates these improvements. These results confirm that dCBT improves both daytime and nighttime aspects of insomnia, strengthening existing recommendations of CBT as the treatment of choice for insomnia.
isrctn.org identifier: ISRCTN60530898.
Summary Background Induction of a clinical complete response with chemoradiotherapy, followed by observation via a watch-and-wait approach, has emerged as a management option for patients with rectal ...cancer. We aimed to address the shortage of evidence regarding the safety of the watch-and-wait approach by comparing oncological outcomes between patients managed by watch and wait who achieved a clinical complete response and those who had surgical resection (standard care). Methods Oncological Outcomes after Clinical Complete Response in Patients with Rectal Cancer (OnCoRe) was a propensity-score matched cohort analysis study, that included patients of all ages diagnosed with rectal adenocarcinoma without distant metastases who had received preoperative chemoradiotherapy (45 Gy in 25 daily fractions with concurrent fluoropyrimidine-based chemotherapy) at a tertiary cancer centre in Manchester, UK, between Jan 14, 2011, and April 15, 2013. Patients who had a clinical complete response were offered management with the watch-and-wait approach, and patients who did not have a complete clinical response were offered surgical resection if eligible. We also included patients with a clinical complete response managed by watch and wait between March 10, 2005, and Jan 21, 2015, across three neighbouring UK regional cancer centres, whose details were obtained through a registry. For comparative analyses, we derived one-to-one paired cohorts of watch and wait versus surgical resection using propensity-score matching (including T stage, age, and performance status). The primary endpoint was non-regrowth disease-free survival from the date that chemoradiotherapy was started, and secondary endpoints were overall survival, and colostomy-free survival. We used a conservative p value of less than 0·01 to indicate statistical significance in the comparative analyses. Findings 259 patients were included in our Manchester tertiary cancer centre cohort, 228 of whom underwent surgical resection at referring hospitals and 31 of whom had a clinical complete response, managed by watch and wait. A further 98 patients were added to the watch-and-wait group via the registry. Of the 129 patients managed by watch and wait (median follow-up 33 months IQR 19–43), 44 (34%) had local regrowths (3-year actuarial rate 38% 95% CI 30–48); 36 (88%) of 41 patients with non-metastatic local regrowths were salvaged. In the matched analyses (109 patients in each treatment group), no differences in 3-year non-regrowth disease-free survival were noted between watch and wait and surgical resection (88% 95% CI 75–94 with watch and wait vs 78% 63–87 with surgical resection; time-varying p=0·043). Similarly, no difference in 3-year overall survival was noted (96% 88–98 vs 87% 77–93; time-varying p=0·024). By contrast, patients managed by watch and wait had significantly better 3-year colostomy-free survival than did those who had surgical resection (74% 95% CI 64–82 vs 47% 37–57; hazard ratio 0·445 95% CI 0·31–0·63; p<0·0001), with a 26% (95% CI 13–39) absolute difference in patients who avoided permanent colostomy at 3 years between treatment groups. Interpretation A substantial proportion of patients with rectal cancer managed by watch and wait avoided major surgery and averted permanent colostomy without loss of oncological safety at 3 years. These findings should inform decision making at the outset of chemoradiotherapy. Funding Bowel Disease Research Foundation.
Aims
Contemporary data describing type 2 diabetes prevalence, incidence and mortality are limited. We aimed to (1) estimate annual incidence and prevalence rates of type 2 diabetes in the UK between ...2004 and 2014, (2) examine relationships between observed rates with age, gender, socio‐economic status and geographic region, and (3) assess how temporal changes in incidence and all‐cause mortality rates influence changes in prevalence.
Methods
Type 2 diabetes patients aged ≥16 years between January 2004 and December 2014 were identified using the Clinical Practice Research Datalink (CPRD). Up to 5 individuals without diabetes were matched to diabetes patients based on age, gender and the general practice. Annual incidence, prevalence and mortality rates were calculated per 10 000 person‐years at risk (95% CI). Survival models compared mortality rates in patients with and without type 2 diabetes.
Results
Prevalence rates of type 2 diabetes increased from 3.21% (3.19; 3.22) in 2004 to 5.26% (5.24; 5.29) in 2014. Incidence rates remained stable, overall, throughout the study period. Higher incidence and prevalence rates were related to male gender and deprivation. Individuals with type 2 diabetes were associated with higher risk of mortality (Hazard ratio 1.26 1.20; 1.32). Mortality rates declined in patients with and without diabetes throughout the study period. The incidence and prevalence of type 2 diabetes in patients aged 16 to 34 years increased over time.
Conclusions
The rising prevalence of type 2 diabetes in the UK over the last decade is probably explained by patients living longer rather than by increasing incidence of type 2 diabetes.
This study
) investigated life expectancy and cause-specific mortality rates associated with type 2 diabetes and
) quantified these relationships in ethnic subgroups.
This was a cohort study using ...Clinical Practice Research Datalink data from 383 general practices in England with linked hospitalization and mortality records. A total of 187,968 patients with incident type 2 diabetes from 1998 to 2015 were matched to 908,016 control subjects. Abridged life tables estimated years of life lost, and a competing risk survival model quantified cause-specific hazard ratios (HRs).
A total of 40,286 deaths occurred in patients with type 2 diabetes. At age 40, white men with diabetes lost 5 years of life and white women lost 6 years compared with those without diabetes. A loss of between 1 and 2 years was observed for South Asians and blacks with diabetes. At age older than 65 years, South Asians with diabetes had up to 1.1 years' longer life expectancy than South Asians without diabetes. Compared with whites with diabetes, South Asians with diabetes had lower adjusted risks for mortality from cardiovascular (HR 0.82; 95% CI 0.75, 0.89), cancer (HR 0.43; 95% CI 0.36, 0.51), and respiratory diseases (HR 0.60; 95% CI 0.48, 0.76). A similar pattern was observed in blacks with diabetes compared with whites with diabetes.
Type 2 diabetes was associated with more years of life lost among whites than among South Asians or blacks, with older South Asians experiencing longer life expectancy compared with South Asians without diabetes. The findings support optimized cardiovascular disease risk factor management, especially in whites with type 2 diabetes.
To determine the prevalence of autism spectrum disorder (ASD) in Neurofibromatosis Type 1 (NF1).
Second-phase population-based epidemiologic study using an allcase NF1 registry in a defined UK 4.1 ...million population area. A total of 109 (52.7%) of 207 responders from the initial screening phase were grouped by using the parent-rated Social Responsiveness Scale (SRS) as significant ASD (SRS≥76; n = 32), moderate ASD (SRS ≥ 60<76; n = 29), or non-ASD (SRS <60, n = 48). Twenty-three cases from the significant ASD group, 16 from moderate ASD, and 8 from non-ASD (total n = 47), invited proportionately by random selection, were seen for detailed confirmatory ascertainment. Assessments on Autism Diagnostic Interview-Revised, Autism Diagnostic Observation Scale-Generic, and verbal IQ were combined by using standard Collaborative Program for Excellence in Autism criteria into an ASD categorization for each case (ASD, broad ASD with partial features, non-ASD). A preplanned weighted analysis was used to derive prevalence estimates for the whole population.
Fourteen (29.5%) of 47 showed ASD, 13 (27.7%) broad ASD, and 20 (42.5%) non-ASD. The ASD/broad ASD group showed male predominance (1.7:1.0), but did not differ significantly from the non-ASD group on IQ, age, socioeconomic status, inheritance, physical severity, or education. The population prevalence estimate is 24.9% ASD (95% confidence interval 13.1%-42.1%) and 20.8% broad ASD (95% confidence interval 10.0%-38.1%); a total of 45.7% showing some ASD spectrum phenotype.
Findings indicate high prevalence of ASD in NF1, with implications for clinical practice and further research into NF1 as a single-gene model for autism.
In the presence of heterogeneous treatment effects, it is desirable to divide patients into subgroups based on their expected response to treatment. This is formalised via a personalised treatment ...recommendation: an algorithm that uses biomarker measurements to select treatments. It could be that multiple, rather than single, biomarkers better predict these subgroups. However, finding the optimal combination of multiple biomarkers can be a difficult prediction problem.
We described three parametric methods for finding the optimal combination of biomarkers in a personalised treatment recommendation, using randomised trial data: a regression approach that models outcome using treatment by biomarker interactions; an approach proposed by Kraemer that forms a combined measure from individual biomarker weights, calculated on all treated and control pairs; and a novel modification of Kraemer's approach that utilises a prognostic score to sample matched treated and control subjects. Using Monte Carlo simulations under multiple data-generating models, we compare these approaches and draw conclusions based on a measure of improvement under a personalised treatment recommendation compared to a standard treatment. The three methods are applied to data from a randomised trial of home-delivered pragmatic rehabilitation versus treatment as usual for patients with chronic fatigue syndrome (the FINE trial). Prior analysis of this data indicated some treatment effect heterogeneity from multiple, correlated biomarkers.
The regression approach outperformed Kraemer's approach across all data-generating scenarios. The modification of Kraemer's approach leads to improved treatment recommendations, except in the case where there was a strong unobserved prognostic biomarker. In the FINE example, the regression method indicated a weak improvement under its personalised treatment recommendation algorithm.
The method proposed by Kraemer does not perform better than a regression approach for combining multiple biomarkers. All methods are sensitive to misspecification of the parametric models.
The development of the capability and capacity to evaluate the outcomes of trials of complex interventions is a key priority of the National Institute for Health Research (NIHR) and the Medical ...Research Council (MRC). The evaluation of complex treatment programmes for mental illness (e.g. cognitive-behavioural therapy for depression or psychosis) not only is a vital component of this research in its own right but also provides a well-established model for the evaluation of complex interventions in other clinical areas. In the context of efficacy and mechanism evaluation (EME) there is a particular need for robust methods for making valid causal inference in explanatory analyses of the mechanisms of treatment-induced change in clinical outcomes in randomised clinical trials.
The key objective was to produce statistical methods to enable trial investigators to make valid causal inferences about the mechanisms of treatment-induced change in these clinical outcomes. The primary objective of this report is to disseminate this methodology, aiming specifically at trial practitioners.
The three components of the research were (1) the extension of instrumental variable (IV) methods to latent growth curve models and growth mixture models for repeated-measures data; (2) the development of designs and regression methods for parallel trials; and (3) the evaluation of the sensitivity/robustness of findings to the assumptions necessary for model identifiability. We illustrate our methods with applications from psychological and psychosocial intervention trials, keeping the technical details to a minimum, leaving the reporting of the more theoretical and mathematically demanding results for publication in appropriate specialist journals.
We show how to estimate treatment effects and introduce methods for EME. We explain the use of IV methods and principal stratification to evaluate the role of putative treatment effect mediators and therapeutic process measures. These results are extended to the analysis of longitudinal data structures. We consider the design of EME trials. We focus on designs to create convincing IVs, bearing in mind assumptions needed to attain model identifiability. A key area of application that has become apparent during this work is the potential role of treatment moderators (predictive markers) in the evaluation of treatment effect mechanisms for personalised therapies (stratified medicine). We consider the role of targeted therapies and multiarm trials and the use of parallel trials to help elucidate the evaluation of mediators working in parallel.
In order to demonstrate both efficacy and mechanism, it is necessary to (1) demonstrate a treatment effect on the primary (clinical) outcome, (2) demonstrate a treatment effect on the putative mediator (mechanism) and (3) demonstrate a causal effect from the mediator to the outcome. Appropriate regression models should be applied for (3) or alternative IV procedures, which account for unmeasured confounding, provided that a valid instrument can be identified. Stratified medicine may provide a setting where such instruments can be designed into the trial. This work could be extended by considering improved trial designs, sample size considerations and measurement properties.
The project presents independent research funded under the MRC-NIHR Methodology Research Programme (grant reference G0900678).
A downwards secular trend in the incidence of cardiovascular disease (CVD) in England was identified through previous work and the literature. Risk prediction models for primary prevention of CVD do ...not model this secular trend, this could result in over prediction of risk for individuals in the present day. We evaluate the effects of modelling this secular trend, and also assess whether it is driven by an increase in statin use during follow up.
We derived a cohort of patients (1998-2015) eligible for cardiovascular risk prediction from the Clinical Practice Research Datalink with linked hospitalisation and mortality records (N = 3,855,660). Patients were split into development and validation cohort based on their cohort entry date (before/after 2010). The calibration of a CVD risk prediction model developed in the development cohort was tested in the validation cohort. The calibration was also assessed after modelling the secular trend. Finally, the presence of the secular trend was evaluated under a marginal structural model framework, where the effect of statin treatment during follow up is adjusted for.
Substantial over prediction of risks in the validation cohort was found when not modelling the secular trend. This miscalibration could be minimised if one was to explicitly model the secular trend. The reduction in risk in the validation cohort when introducing the secular trend was 35.68 and 33.24% in the female and male cohorts respectively. Under the marginal structural model framework, the reductions were 33.31 and 32.67% respectively, indicating increasing statin use during follow up is not the only the cause of the secular trend.
Inclusion of the secular trend into the model substantially changed the CVD risk predictions. Models that are being used in clinical practice in the UK do not model secular trend and may thus overestimate the risks, possibly leading to patients being treated unnecessarily. Wider discussion around the modelling of secular trends in a risk prediction framework is needed.
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