Consuming a diverse diet is essential to ensure an adequate intake of micronutrients. The aim of this study was to assess the nutritional status and dietary diversity of women of reproductive age (WRA) living in a marginalized community in rural Pakistan. Forty-seven WRA (35 ± 7 years old) who were not pregnant or lactating at enrollment, were recruited to participate in the study. Twenty-four-hour dietary recall interviews were conducted by the study nutritionist, and the data collected were used to create a minimum dietary diversity for women score (MDD-W) on five occasions during the monsoon and winter seasons (October to February). Nutritional status was assessed using anthropometry and biochemical markers of micronutrient status. Height and weight were used to determine body mass index (BMI), and mid-upper-arm circumference was measured. Plasma zinc, iron, and selenium concentrations were measured using inductively coupled mass spectrometry, and iron status was assessed using serum ferritin and blood hemoglobin concentrations. The mean (±SD) food group diversity score was 4 ± 1 with between 26% and 41% of participants achieving an MDD-W of 5. BMI was 27.2 ± 5.5 kg/m² with 28% obese, 34% overweight, and 6% underweight. The prevalence of zinc deficiency, based on plasma zinc concentration, was 29.8%; 17% of the participants had low plasma selenium levels; 8.5% were iron deficient; and 2% were suffering from iron deficiency anemia. The findings indicate that the women living in this community consume a diet that has a low diversity, consistent with a diet low in micronutrients, and that zinc deficiency is prevalent. Public health interventions aimed at increasing the dietary diversity of WRA are needed to improve the micronutrient intake, particularly of zinc, in this population.

Background: The intracluster correlation coefficient is a key input parameter for sample size determination in cluster-randomised trials. Sample size is very sensitive to small differences in the intracluster correlation coefficient, so it is vital to have a robust intracluster correlation coefficient estimate. This is often problematic because either a relevant intracluster correlation coefficient estimate is not available or the available estimate is imprecise due to being based on small-scale studies with low numbers of clusters. Misspecification may lead to an underpowered or inefficiently large and potentially unethical trial. Methods: We apply a Bayesian approach to produce an intracluster correlation coefficient estimate and hence propose sample size for a planned cluster-randomised trial of the effectiveness of a systematic voiding programme for post-stroke incontinence. A Bayesian hierarchical model is used to combine intracluster correlation coefficient estimates from other relevant trials making use of the wealth of intracluster correlation coefficient information available in published research. We employ knowledge elicitation process to assess the relevance of each intracluster correlation coefficient estimate to the planned trial setting. The team of expert reviewers assigned relevance weights to each study, and each outcome within the study, hence informing parameters of Bayesian modelling. To measure the performance of experts, agreement and reliability methods were applied. Results: The 34 intracluster correlation coefficient estimates extracted from 16 previously published trials were combined in the Bayesian hierarchical model using aggregated relevance weights elicited from the experts. The intracluster correlation coefficients available from external sources were used to construct a posterior distribution of the targeted intracluster correlation coefficient which was summarised as a posterior median with a 95% credible interval informing researchers about the range of plausible sample size values. The estimated intracluster correlation coefficient determined a sample size of between 450 (25 clusters) and 480 (20 clusters), compared to 500–600 from a classical approach. The use of quantiles, and other parameters, from the estimated posterior distribution is illustrated and the impact on sample size described. Conclusion: Accounting for uncertainty in an unknown intracluster correlation coefficient, trials can be designed with a more robust sample size. The approach presented provides the possibility of incorporating intracluster correlation coefficients from various cluster-randomised trial settings which can differ from the planned study, with the difference being accounted for in the modelling. By using expert knowledge to elicit relevance weights and synthesising the externally available intracluster correlation coefficient estimates, information is used more efficiently than in a classical approach, where the intracluster correlation coefficient estimates tend to be less robust and overly conservative. The intracluster correlation coefficient estimate constructed is likely to produce a smaller sample size on average than the conventional strategy of choosing a conservative intracluster correlation coefficient estimate. This may therefore result in substantial time and resources savings.

Background Urinary incontinence affects around half of stroke survivors in the acute phase, and it often presents as a new problem after stroke or, if pre-existing, worsens significantly, adding to the disability and helplessness caused by neurological deficits. New management programmes after stroke are needed to address urinary incontinence early and effectively. Objective The Identifying Continence OptioNs after Stroke (ICONS)-II trial aimed to evaluate the clinical effectiveness and cost-effectiveness of a systematic voiding programme for urinary incontinence after stroke in hospital. Design This was a pragmatic, multicentre, individual-patient-randomised (1 : 1), parallel-group trial with an internal pilot. Setting Eighteen NHS stroke services with stroke units took part. Participants Participants were adult men and women with acute stroke and urinary incontinence, including those with cognitive impairment. Intervention Participants were randomised to the intervention, a systematic voiding programme, or to usual care. The systematic voiding programme comprised assessment, behavioural interventions (bladder training or prompted voiding) and review. The assessment included evaluation of the need for and possible removal of an indwelling urinary catheter. The intervention began within 24 hours of recruitment and continued until discharge from the stroke unit. Main outcome measures The primary outcome measure was severity of urinary incontinence (measured using the International Consultation on Incontinence Questionnaire) at 3 months post randomisation. Secondary outcome measures were taken at 3 and 6 months after randomisation and on discharge from the stroke unit. They included severity of urinary incontinence (at discharge and at 6 months), urinary symptoms, number of urinary tract infections, number of days indwelling urinary catheter was in situ, functional independence, quality of life, falls, mortality rate and costs. The trial statistician remained blinded until clinical effectiveness analysis was complete. Results The planned sample size was 1024 participants, with 512 allocated to each of the intervention and the usual-care groups. The internal pilot did not meet the target for recruitment and was extended to March 2020, with changes made to address low recruitment. The trial was paused in March 2020 because of COVID-19, and was later stopped, at which point 157 participants had been randomised (intervention, n = 79; usual care, n = 78). There were major issues with attrition, with 45% of the primary outcome data missing: 56% of the intervention group data and 35% of the usual-care group data. In terms of the primary outcome, patients allocated to the intervention group had a lower score for severity of urinary incontinence (higher scores indicate greater severity in urinary incontinence) than those allocated to the usual-care group, with means (standard deviations) of 8.1 (7.4) and 9.1 (7.8), respectively. Limitations The trial was unable to recruit sufficient participants and had very high attrition, which resulted in seriously underpowered results. Conclusions The internal pilot did not meet its target for recruitment and, despite recruitment subsequently being more promising, it was concluded that the trial was not feasible owing to the combined problems of poor recruitment, poor retention and COVID-19. The intervention group had a slightly lower score for severity of urinary incontinence at 3 months post randomisation, but this result should be interpreted with caution. Future work Further studies to assess the effectiveness of an intervention starting in or continuing into the community are required. Trial registration This trial is registered as ISRCTN14005026. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 31. See the NIHR Journals Library website for further project information.

We describe a flexible class of stochastic models that aim to capture key features of realistic patterns of animal movements observed in radio‐tracking and global positioning system telemetry studies. In the model, movements are represented as a diffusion‐based process evolving differently in heterogeneous regions. In this article, we extend the process of inference for heterogeneous movement models to the case in which boundaries of habitat regions are unknown and need to be estimated. Data augmentation is used in reconstructing the partition of the heterogeneous environment. The augmentation helps to diminish the impact of uncertainty about when and where the animal crosses habitat boundaries, and allows the extraction of additional information from the given observations. The approach to inference is Bayesian, using Markov chain Monte Carlo methods, allowing us to estimate both the parameters of the diffusion processes and the unknown boundaries. The suggested methodology is illustrated on simulated data and applied to real movement data from a radio‐tracking experiment on ibex. Some model checking and model choice issues are also discussed.

Background: Psychological problems post-stroke are common and debilitating, yet insufficient evidence-based psychological support exists for stroke survivors, either in stroke or general mental health services. Many stroke survivors with significant needs remain unsupported. To address this problem, we need pathways to identify, treat and manage psychological difficulties after stroke. The Accelerating Delivery of Psychological Therapies after Stroke (ADOPTS) study aimed to explore the feasibility of collaboratively developing, implementing and evaluating intervention packages (IPs) to facilitate access to, and increase the provision of, psychological support post-stroke. Methods: Stakeholder groups were formed across four sites in north-west England, comprising stroke and psychological services, to collaboratively develop site-specific IPs incorporating a psychological care pathway, staff training, a staff manual for stroke-specific psychological support and supervision. A feasibility stepped-wedge cluster randomised trial recruited patients admitted with stroke during the usual care (pre-implementation of the IP) and intervention (post-implementation) periods. The feasibility of IP implementation and their potential usefulness were evaluated through assessing wellbeing and the support received, and through a process evaluation incorporating interviews with staff, patients and carers. Feasibility evaluation included the recruitment rate and attrition rate; exploratory analysis (mixed-effects linear or logistic regression models) was used to assess the ‘promise’ of the intervention in achieving psychological distress outcomes (mood (PHQ-9), anxiety (GAD-7)), assessed using validated measures at 6 weeks and 6 months. Results: IPs were collaboratively developed at each site but implementation took longer than the per-study-protocol duration of three months. Nineteen training sessions (152 attendees) were delivered for nursing, therapy, NHS Talking Therapies and voluntary staff. Nursing staff were underrepresented due to difficulties with releasing staff. Manuals were developed for each site, incorporating a mood screening and referral algorithm, but these were not finalised at one site. Stroke and NHS Talking Therapies champions were identified in each site to facilitate cross-service staff supervision. A total of 270 patients were recruited over 14 months (133 usual care, 137 intervention), with 227 and 198 at 6 weeks and 6 months, respectively. Stroke staff found the training, manual and pathway helpful, and reported greater confidence in managing and referring psychological issues. NHS Talking Therapies staff found the training useful for adapting their therapy. However, the intervention took longer to implement in all sites, requiring an additional time period to be added to the stepped-wedge design. Conclusions: It is feasible to collaboratively develop and implement IPs for post-stroke psychological support. However, an alternative to the stepped-wedge design used here would be more appropriate for a future study. This study was registered in ISRCTN—the UK’s Clinical Study Registry (trial registration: ISRCTN12868810, registration date: 4 February 2016).

Background/Aims: Sample size determination for cluster randomised trials is challenging because it requires robust estimation of the intra-cluster correlation coefficient. Typically, the sample size is chosen to provide a certain level of power to reject the null hypothesis in a two-sample hypothesis test. This relies on the minimal clinically important difference and estimates for the overall standard deviation, the intra-cluster correlation coefficient and, if cluster sizes are assumed to be unequal, the coefficient of variation of the cluster size. Varying any of these parameters can have a strong effect on the required sample size. In particular, it is very sensitive to small differences in the intra-cluster correlation coefficient. A relevant intra-cluster correlation coefficient estimate is often not available, or the available estimate is imprecise due to being based on studies with low numbers of clusters. If the intra-cluster correlation coefficient value used in the power calculation is far from the unknown true value, this could lead to trials which are substantially over- or under-powered. Methods: In this article, we propose a hybrid approach using Bayesian assurance to determine the sample size for a cluster randomised trial in combination with a frequentist analysis. Assurance is an alternative to traditional power, which incorporates the uncertainty on key parameters through a prior distribution. We suggest specifying prior distributions for the overall standard deviation, intra-cluster correlation coefficient and coefficient of variation of the cluster size, while still utilising the minimal clinically important difference. We illustrate the approach through the design of a cluster randomised trial in post-stroke incontinence and compare the results to those obtained from a standard power calculation. Results: We show that assurance can be used to calculate a sample size based on an elicited prior distribution for the intra-cluster correlation coefficient, whereas a power calculation discards all of the information in the prior except for a single point estimate. Results show that this approach can avoid misspecifying sample sizes when the prior medians for the intra-cluster correlation coefficient are very similar, but the underlying prior distributions exhibit quite different behaviour. Incorporating uncertainty on all three of the nuisance parameters, rather than only on the intra-cluster correlation coefficient, does not notably increase the required sample size. Conclusion: Assurance provides a better understanding of the probability of success of a trial given a particular minimal clinically important difference and can be used instead of power to produce sample sizes that are more robust to parameter uncertainty. This is especially useful when there is difficulty obtaining reliable parameter estimates.