Age and gender differences in health-related quality of life of children and adolescents in Europe: a multilevel analysis

OBJECTIVES: To determine age and gender differences in health-related quality of life (HRQOL) in children and adolescents across 12 European countries using a newly developed HRQOL measure (KIDSCREEN). METHODS: The KIDSCREEN-52 questionnaire was filled in by 21,590 children and adolescents aged 8-18 from 12 countries. We used multilevel regression analyses to model the hierarchical structure of the data. In addition, effect sizes were computed to test for gender differences within each age group. RESULTS: Children generally showed better HRQOL than adolescents (P < 0.001). While boys and girls had similar HRQOL at young age, girls' HRQOL declined more than boys' (P < 0.001) with increasing age, depending on the HRQOL scale. There was significant variation between countries both at the youngest age and for age trajectories. CONCLUSIONS: For the first time, gender and age differences in children's and adolescents' HRQOL across Europe were assessed using a comprehensive and standardised instrument. Gender and age differences exist for most HRQOL scales. Differences in HRQOL across Europe point to the importance of national contexts for youth's well-being.

Parameter-free extraction of the functional network topology from intracranial EEG recordings: a time-resolved study of graph properties in focal onset seizures

Rationale: Focal onset epileptic seizures are due to abnormal interactions between distributed brain areas. By estimating the cross-correlation matrix of multi-site intra-cerebral EEG recordings (iEEG), one can quantify these interactions. To assess the topology of the underlying functional network, the binary connectivity matrix has to be derived from the cross-correlation matrix by use of a threshold. Classically, a unique threshold is used that constrains the topology [1]. Our method aims to set the threshold in a data-driven way by separating genuine from random cross-correlation. We compare our approach to the fixed threshold method and study the dynamics of the functional topology. Methods: We investigate the iEEG of patients suffering from focal onset seizures who underwent evaluation for the possibility of surgery. The equal-time cross-correlation matrices are evaluated using a sliding time window. We then compare 3 approaches assessing the corresponding binary networks. For each time window: * Our parameter-free method derives from the cross-correlation strength matrix (CCS)[2]. It aims at disentangling genuine from random correlations (due to finite length and varying frequency content of the signals). In practice, a threshold is evaluated for each pair of channels independently, in a data-driven way. * The fixed mean degree (FMD) uses a unique threshold on the whole connectivity matrix so as to ensure a user defined mean degree. * The varying mean degree (VMD) uses the mean degree of the CCS network to set a unique threshold for the entire connectivity matrix. * Finally, the connectivity (c), connectedness (given by k, the number of disconnected sub-networks), mean global and local efficiencies (Eg, El, resp.) are computed from FMD, CCS, VMD, and their corresponding random and lattice networks. Results: Compared to FMD and VMD, CCS networks present: *topologies that are different in terms of c, k, Eg and El. *from the pre-ictal to the ictal and then post-ictal period, topological features time courses that are more stable within a period, and more contrasted from one period to the next. For CCS, pre-ictal connectivity is low, increases to a high level during the seizure, then decreases at offset. k shows a ‘‘U-curve’’ underlining the synchronization of all electrodes during the seizure. Eg and El time courses fluctuate between the corresponding random and lattice networks values in a reproducible manner. Conclusions: The definition of a data-driven threshold provides new insights into the topology of the epileptic functional networks.