Zerebrale Probleme in der pädiatrischen Intensivmedizin [Cerebral problems in pediatric intensive care]

This paper presents a review of different methods enabling the monitoring of cerebral function in neonatal and paediatric intensive care. EEG, evoked potentials, conventional radiological studies, computerized tomography, ultrasound, intracranial pressure measurements, nuclear magnetic resonance, Doppler ultrasound, radioisotope studies, angiography, infra-red spectral analysis and last, but not least, clinical examination produce information regarding the neurological state of the patient which must be critically analysed in order to ensure optimal management of the case. Unfortunately, and in spite of impressive progress in non-invasive monitoring of the cerebral function, we are still forced to make important medical and ethical decisions without precise information about the neurological state of our patients.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Attachment and temperament in early childhood; implications for later behavior problems.

In the context of a French validation study, the Child Behavior Checklist (CBCL) was administered to more than 3000 French speaking mothers of 5-year-old children. Scores were factor-analyzed. Principal components analysis revealed four dimensions: externalizing and internalizing behavior problems, immaturity and somatoform disorders. Another sample of 40 mothers participated in a longitudinal study, filling in the CBCL when their children were 5 years old. These children had been observed previously in the Strange Situation (SSP) at 21 months. Several dichotomous variables derived from the SSP (e.g. secure versus insecure, proximal versus distal interaction with the mother, avoidant behavior) have been used as predictors of the four dimensions extracted from the CBCL. Hierarchical regressions showed that proximal behaviors with the mother, which reflect temperamental characteristics independently of the quality of attachment, predicted internalizing problems, whereas avoidance of the mother, or insecure-avoidant attachment, predicted internalizing as well as externalizing problems at 5 years of age. These results show that attachment and temperament, as assessed by the SSP, may each have specific implications for later behavior problems.

Risky single occasion drinking frequency and alcohol-related consequences: can abstinence during early adulthood lead to alcohol problems?

QUESTIONS UNDER STUDY: the main purpose of this longitudinal study was to determine the impact of risky single occasion drinking (RSOD) frequency on alcohol dependence and drinking consequences reported 15 months later. METHODS: As a baseline sample, 5,990 young men were assessed on their drinking habits including the frequency of RSOD. Of them, 5,196 were reassessed at follow-up 15 months later on RSOD frequency, alcohol dependence and alcohol related consequences in thze interceding year. Drop out biases were investigated. RESULTS: Around 45% of the baseline participants reported regular RSOD (every month or more frequently). Despite the fact that RSOD distribution was generally stable during the initial sample, 47.4% reported a variation of their RSOD frequency 15 months later. Around 25% of the sample reported reduced RSOD frequency. Nonetheless, occasional RS drinkers were more likely to become regular (monthly) RSO drinkers at follow up. Daily and weekly RSOD were associated with high proportions of alcohol dependence and detrimental consequences of drinking. Surprisingly, abstainers at baseline were more likely to be at risk of alcohol dependence and consequences at follow up than non-RSO drinkers. CONCLUSIONS: Despite the fact that alcohol abstinence is logically the best way to avoid the detrimental consequences of alcohol drinking, abstainers at baseline reported as many problems due to alcohol use at follow up as occasional or monthly RSO drinkers. The few participants who had become RSO drinkers during the follow up period were indeed likely to engage in detrimental behaviour. Non-RSO drinkers had the fewest problems due to alcohol use. This substantiates the early occurrence of drinking consequences among inexperienced RSO drinkers.

Health problems among detainees in Switzerland: a study using the ICPC-2 classification.

BACKGROUND: Little is known about the health status of prisoners in Switzerland. The aim of this study was to provide a detailed description of the health problems presented by detainees in Switzerland's largest remand prison. METHODS: In this retrospective cross-sectional study we reviewed the health records of all detainees leaving Switzerland's largest remand prison in 2007. The health problems were coded using the International Classification for Primary Care (ICPC-2). Analyses were descriptive, stratified by gender. RESULTS: A total of 2195 health records were reviewed. Mean age was 29.5 years (SD 9.5); 95% were male; 87.8% were migrants. Mean length of stay was 80 days (SD 160). Illicit drug use (40.2%) and mental health problems (32.6%) were frequent, but most of these detainees (57.6%) had more generic primary care problems, such as skin (27.0%), infectious diseases (23.5%), musculoskeletal (19.2%), injury related (18.3%), digestive (15.0%) or respiratory problems (14.0%). Furthermore, 7.9% reported exposure to violence during arrest by the police. CONCLUSION: Morbidity is high in this young, predominantly male population of detainees, in particular in relation to substance abuse. Other health problems more commonly seen in general practice are also frequent. These findings support the further development of coordinated primary care and mental health services within detention centers.

Variations régionales du recours aux soins: quelques problèmes méthodologiques. [Regional variations in health services: various methodological problems].

Many studies show strong variation of health consumption between regions, suggesting that theses variations are related to the uncertainty of medical practice or to other factors related to health services or patients attitude. However the statistical interpretation of these variations is far from easy: apart from usual and specific information bias, there are statistical problems when observing incidence of events like health care consumption: it is in fact a rare event, which is observed within small population, and among regions with unequal number of person. Therefore, most of the variation reported might be well explained by a purely statistical phenomenon. This paper presents some aspects of this variability for three common indicators of variation, and suggest the use of ad hoc simulation to get statistical criteria.

Qualité des statistiques de causes de décès: problèmes actuels et perspectives. [Quality of statistics on causes of death: current problems and perspectives].

Statistics of causes of death remain an important source of epidemiological data for the evaluation of various medical and health problems. The improvement of analytical techniques and, above all, the transformation of demographic and morbid structures of populations have prompted researchers in the field to give more importance to the quality of death certificates. After describing the data collection system presently used in Switzerland, the paper discusses various indirect estimations of the quality of Swiss data and reviews the corresponding international literature.