ObjectiveTo estimate prevalence of suicidal ideation and suicidal ideation with a plan in each surveyed country and to examine cross-national differences in associated risk factors.MethodsWe analysed data of students aged 13–17 years who participated in the 2003–2012 Global School-based Health Surveys in 32 countries, of which 29 are low- and middle-income. We used random effects meta-analysis to generate regional and overall pooled estimates. Multivariable logistic regression was used to estimate risk ratios for the associated risk factors. Population attributable fractions were estimated based on adjusted risk ratios and the prevalence of the determinants within each exposure level.FindingsAcross all countries, the pooled 12-month prevalence of suicide ideation were 16.2% (95% confidence interval, CI: 15.6 to 16.7) among females and 12.2% (95% CI: 11.7 to 12.7) among males and ideation with a plan were 8.3% (95% CI: 7.9 to 8.7) among females and 5.8% (95% CI: 5.5 to 6.1) among males. Suicide ideation in the WHO Region of the Americas was higher in females than males, with an estimated prevalence ratio of 1.70 (95% CI: 1.60 to 1.81), while this ratio was 1.04 (95% CI: 0.98 to 1.10) in the WHO African Region. Factors associated with suicidal ideation in most countries included experiences of bullying and physical violence, loneliness, limited parental support and alcohol and tobacco use.ConclusionThe prevalence of adolescent suicidal behaviours varies across countries, yet a consistent set of risk factors of suicidal behaviours emerged across all regions and most countries.

Aim  To explore factors associated with students’ attitudes towards their peers with disabilities. Method  All 7th grade students (aged 12−13y) from 12 schools in the Toulouse area were invited to participate (n=1509). Attitudes were measured using the Chedoke‐McMaster Attitudes Towards Children with Handicaps (CATCH) questionnaire (affective, behavioural, cognitive, and total scores). Personal characteristics, including KIDSCREEN quality of life scores, were recorded. Data regarding information about disabilities received from parents and the media and acquaintance with people with disabilities constituted the ‘disability knowledge’ factors. The characteristics of the schools were obtained from the local education authority. Multivariate multilevel linear regression analyses were conducted to explore the associations between CATCH scores and these three groups of factors. Results  Responses from 1135 students (612 females, 523 males; mean age 12y 8mo SD 7mo; age range 10y 8mo–15y) were studied (75.2% of the students approached). Factors independently associated with more positive attitudes were being a female, having a good quality of life, being friends with a child with disabilities, or having received information about disabilities from parents and the media. Presence in the school of a special class for children with cognitive disabilities was independently associated with more negative attitudes. Interpretation  This cross‐sectional study identified different personal and environmental factors upon which interventions aimed at improving students’ attitudes towards their peers with disabilities could be based.

Longitudinal studies are essential tools in medical research. In these studies, variables are not restricted to single measurements but can be seen as variable-trajectories, either single or joint. Thus, an important question concerns the identification of homogeneous patient trajectories.kml and kml3d are R packages providing an implementation of k-means designed to work specifically on trajectories (kml) or on joint trajectories (kml3d). They provide various tools to work on longitudinal data: imputation methods for trajectories (nine classic and one original), methods to define starting conditions in k-means (four classic and three original) and quality criteria to choose the best number of clusters (four classic and one original). In addition, they offer graphic facilities to "visualize" the trajectories, either in 2D (single trajectory) or 3D (joint-trajectories). The 3D graph representing the mean joint-trajectories of each cluster can be exported through L A T E X in a 3D dynamic rotating PDF graph (Figures 1 and 9).

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