Ethnic group differences in overweight and obese children and young people in England: cross sectional survey

Aims: To determine the percentage of children and young adults who are obese or overweight within different ethnic and socioeconomic groups.Methods: Secondary analysis of data on 5689 children and young adults aged 2 - 20 years from the 1999 Health Survey for England.Results: Twenty three per cent of children (n = 1311) were overweight, of whom 6% ( n = 358) were obese. More girls than boys were overweight ( 24% v 22%). Afro-Caribbean girls were more likely to be overweight ( odds ratio 1.73, 95% CI 1.29 to 2.33), and Afro-Caribbean and Pakistani girls were more likely to be obese than girls in the general population ( odds ratios 2.74 ( 95% CI 1.74 to 4.31) and 1.71 ( 95% CI 1.06 to 2.76), respectively). Indian and Pakistani boys were more likely to be overweight ( odds ratios 1.55 ( 95% CI 1.12 to 2.17) and 1.36 ( 95% CI 1.01 to 1.83), respectively). There were no significant differences in the prevalence of obese and overweight children from different social classes.Conclusion: The percentage of children and young adults who are obese and overweight differs by ethnic group and sex, but not by social class. British Afro-Caribbean and Pakistani girls have an increased risk of being obese and Indian and Pakistani boys have an increased risk of being overweight than the general population. These individuals may be at greater combined cumulative risk of morbidity and mortality from cardiovascular disease and so may be a priority for initiatives to target groups of children at particular risk of obesity

A comparison of methods for analysing multiple outcome measures in randomised controlled trials using a simulation study

Multiple primary outcomes are sometimes collected and analysed in randomised controlled trials (RCTs), and are used in favour of a single outcome. By collecting multiple primary outcomes, it is possible to fully evaluate the effect that an intervention has for a given disease process. A simple approach to analysing multiple outcomes is to consider each outcome separately, however, this approach does not account for any pairwise correlations between the outcomes. Any cases with missing values must be ignored, unless an additional imputation step is performed. Alternatively, multivariate methods that explicitly model the pairwise correlations between the outcomes may be more efficient when some of the outcomes have missing values. In this paper, we present an overview of relevant methods that can be used to analyse multiple outcome measures in RCTs, including methods based on multivariate multilevel (MM) models. We perform simulation studies to evaluate the bias in the estimates of the intervention effects and the power of detecting true intervention effects observed when using selected methods. Different simulation scenarios were constructed by varying the number of outcomes, the type of outcomes, the degree of correlations between the outcomes and the proportions and mechanisms of missing data. We compare multivariate methods to univariate methods with and without multiple imputation. When there are strong correlations between the outcome measures (ρ > .4), our simulation studies suggest that there are small power gains when using the MM model when compared to analysing the outcome measures separately. In contrast, when there are weak correlations (ρ < .4), the power is reduced when using univariate methods with multiple imputation when compared to analysing the outcome measures separately

Risk prediction in multicentre studies when there is confounding by cluster or informative cluster size

BACKGROUND: Clustered data arise in research when patients are clustered within larger units. Generalised Estimating Equations (GEE) and Generalised Linear Models (GLMM) can be used to provide marginal and cluster-specific inference and predictions, respectively. METHODS: Confounding by Cluster (CBC) and Informative cluster size (ICS) are two complications that may arise when modelling clustered data. CBC can arise when the distribution of a predictor variable (termed ‘exposure’), varies between clusters causing confounding of the exposure-outcome relationship. ICS means that the cluster size conditional on covariates is not independent of the outcome. In both situations, standard GEE and GLMM may provide biased or misleading inference, and modifications have been proposed. However, both CBC and ICS are routinely overlooked in the context of risk prediction, and their impact on the predictive ability of the models has been little explored. We study the effect of CBC and ICS on the predictive ability of risk models for binary outcomes when GEE and GLMM are used. We examine whether two simple approaches to handle CBC and ICS, which involve adjusting for the cluster mean of the exposure and the cluster size, respectively, can improve the accuracy of predictions. RESULTS: Both CBC and ICS can be viewed as violations of the assumptions in the standard GLMM; the random effects are correlated with exposure for CBC and cluster size for ICS. Based on these principles, we simulated data subject to CBC/ICS. The simulation studies suggested that the predictive ability of models derived from using standard GLMM and GEE ignoring CBC/ICS was affected. Marginal predictions were found to be mis-calibrated. Adjusting for the cluster-mean of the exposure or the cluster size improved calibration, discrimination and the overall predictive accuracy of marginal predictions, by explaining part of the between cluster variability. The presence of CBC/ICS did not affect the accuracy of conditional predictions. We illustrate these concepts using real data from a multicentre study with potential CBC. CONCLUSION: Ignoring CBC and ICS when developing prediction models for clustered data can affect the accuracy of marginal predictions. Adjusting for the cluster mean of the exposure or the cluster size can improve the predictive accuracy of marginal predictions

Convexity subarachnoid haemorrhage has a high risk of intracerebral haemorrhage in suspected cerebral amyloid angiopathy

The risk of future symptomatic intracerebral haemorrhage (sICH) remains uncertain in patients with acute convexity subarachnoid haemorrhage (cSAH) associated with suspected cerebral amyloid angiopathy (CAA). We assessed the risk of future sICH in patients presenting to our comprehensive stroke service with acute non-traumatic cSAH due to suspected CAA, between 2011 and 2016. We conducted a systematic search and pooled analysis including our cohort and other published studies including similar cohorts. Our hospital cohort included 20 patients (mean age 69 years; 60% male); 12 (60%) had probable CAA, and 6 (30%) had possible CAA according to the modified Boston criteria; two did not meet CAA criteria because of age <55 years, but were judged likely to be due to CAA. Fourteen patients (70%) had cortical superficial siderosis; 12 (60%) had cerebral microbleeds. Over a mean follow-up period of 19 months, 2 patients (9%) suffered sICH, both with probable CAA (annual sICH risk for probable CAA 8%). In a pooled analysis including our cohort and eight other studies (n = 172), the overall sICH rate per patient-year was 16% (95% CI 11-24%). In those with probable CAA (n = 104), the sICH rate per patient-year was 19% (95% CI 13-27%), compared to 7% (95% CI 3-15%) for those without probable CAA (n = 72). Patients with acute cSAH associated with suspected CAA are at high risk of future sICH (16% per patient-year); probable CAA might carry the highest risk

Are multiple primary outcomes analysed appropriately in randomised controlled trials? A review

To review how multiple primary outcomes are currently considered in the analysis of randomised controlled trials. We briefly describe the methods available to safeguard the inferences and to raise awareness of the potential problems caused by multiple outcomes

Systematic review and meta-analysis of clinical effectiveness of self-management interventions in Parkinson’s disease

BACKGROUND: Parkinson's disease is a complex neurodegenerative condition with significant impact on quality of life (QoL), wellbeing and function. The objective of this review is to evaluate the clinical effectiveness of self-management interventions for people with Parkinson's disease, taking a broad view of self-management and considering effects on QoL, wellbeing and function. METHODS: Systematic searches of four databases (MEDLINE, Embase, PsycINFO, Web of Science) were conducted for studies evaluating self-management interventions for people with Parkinson's disease published up to 16th November 2020. Original quantitative studies of adults with idiopathic Parkinson's disease were included, whilst studies of atypical Parkinsonism were excluded. Full-text articles were independently assessed by two reviewers, with data extracted by one reviewer and reliability checked by a second reviewer, then synthesised through a narrative approach and, for sufficiently similar studies, a meta-analysis of effect size was conducted (using a random-effects meta-analysis with restricted maximum likelihood method pooled estimate). Interventions were subdivided into self-management components according to PRISMS Taxonomy. Risk of bias was examined with the Cochrane Risk of Bias 2 (RoB2) tool or ROBIN-I tool as appropriate. RESULTS: Thirty-six studies were included, evaluating a diverse array of interventions and encompassing a range of study designs (RCT n = 19; non-randomised CT n = five; within subject pre- and post-intervention comparisons n = 12). A total of 2884 participants were assessed in studies across ten countries, with greatest output from North America (14 studies) and UK (six studies). Risk of bias was moderate to high for the majority of studies, mostly due to lack of participant blinding, which is not often practical for interventions of this nature. Only four studies reported statistically significant improvements in QoL, wellbeing or functional outcomes for the intervention compared to controls. These interventions were group-based self-management education and training programmes, either alone, combined with multi-disciplinary rehabilitation, or combined with Cognitive Behaviour Therapy; and a self-guided community-based exercise programme. Four of the RCTs evaluated sufficiently similar interventions and outcomes for meta-analysis: these were studies of self-management education and training programmes evaluating QoL (n = 478). Meta-analysis demonstrated no significant difference between the self-management and the control groups with a standardised mean difference (Hedges g) of - 0.17 (- 0.56, 0.21) p = 0.38. By the GRADE approach, the quality of this evidence was deemed "very low" and the effect of the intervention is therefore uncertain. Components more frequently observed in effective interventions, as per PRISMS taxonomy analysis, were: information about resources; training or rehearsing psychological strategies; social support; and lifestyle advice and support. The applicability of these findings is weakened by the ambiguous and at times overlapping nature of self-management components. CONCLUSION: Approaches and outcomes to self-management interventions in Parkinson's disease are heterogenous. There are insufficient high quality RCTs in this field to show effectiveness of self-management interventions in Parkinson's disease. Whilst it is not possible to draw conclusions on specific intervention components that convey effectiveness, there are promising findings from some studies, which could be targeted in future evaluations