Health Complaints of school-aged children

E-print/Working paper (2023)

Key Findings: Irritability or bad temper is the most frequently experienced individual health complaint. Girls experience individual (and multiple) health complaints more often than boys. For girls, a ... [more ▼]

Key Findings: Irritability or bad temper is the most frequently experienced individual health complaint. Girls experience individual (and multiple) health complaints more often than boys. For girls, a steep augmentation can be observed between the ages of 11-12 and 13-14 years old in all health complaints; as for boys no age difference can be found for some of the areas, and a slow increase appears for others. [less ▲]

Substance Consumption of school-aged children

Diverse speeches and writings (2023)

Comparison of well-being perception between physical activity levels according with age in Luxembourg: Health Behavior in School-aged Children survey 2022

Poster (2023)

Purpose: Youth is a key life phase, as the behaviours established during this phase will mostly continue during adult live. Mental health is a state of well-being (WB) in which individuals realize their ... [more ▼]

Purpose: Youth is a key life phase, as the behaviours established during this phase will mostly continue during adult live. Mental health is a state of well-being (WB) in which individuals realize their own abilities, can cope with the usual stresses of life, can work productively, and is able to contribute to their community. Physical activity (PA) is associated with the improvement of physical, cognitive, and mental health. This study aimed to analyse adolescents’ state of WB in levels of PA across age, by gender in Luxembourg. Methods: A representative sample of 8117 adolescents (11-to-18-years-old) took part in the Health Behaviour in School Aged Children (HBSC) Luxembourg 2022 survey. WB was measured using the WHO-5 Well-being Index, and levels of PA were determined according to the time spend on and frequency of moderate-vigorous and vigorous PA (Highly active, Active, Low active and Not active). A two-way ANOVA stratified by gender was performed to analyse differences of the WB mean for PA levels and age group. Results: For both girls and boys, those Highly active reported better WB than those Not active, in all age groups (p<0.01, all age groups). For the intermediary levels of PA, different gender and age patterns were observed for the WB. While differences were observed between intermediary levels of PA among young girls, no statistically significant difference in mean WB was observed between intermediary levels of PA for girls aged 17 to 18 (highly active (mean=12.9, CI95%11.4-14.5), active (mean=12.4, CI95%11.8-12.9) and Low active (mean=11.80, CI95%11.3-13.2). In boys, only the age group 15-16 showed a difference between the highly active (mean=15.9, CI95%15.2-16.5) and active (mean=15.2, CI95%14.9-15.6) compared to the Low active (mean=13.3, CI95%12.8-13.9) and not active (mean=12.2, CI95%10.6-13.8). Conclusions: Higher PA level is a protective factor for the WB of adolescents in Luxembourg in all ages. This study reinforces the importance of PA to maintain higher levels of well-being through adolescence in Luxembourg. To increase WB in Luxembourg future actions could be taken in order to reinforce the adherence to PA WHO recommendations in Luxembourg in younger ages and promote any level of PA for older ages. [less ▲]

La consommation de Cannabis chez les jeunes d’âge scolaire (12-18 ans) au Luxembourg - Résultats de l'enquête HBSC de 2018

Report (2022)

Le cannabis est considéré comme la substance illicite la plus fréquemment consommée dans le monde. Sa consommation est jugée particulièrement dangereuse si elle est fréquente, fortement dosée et commence ... [more ▼]

Le cannabis est considéré comme la substance illicite la plus fréquemment consommée dans le monde. Sa consommation est jugée particulièrement dangereuse si elle est fréquente, fortement dosée et commence à l'adolescence. En raison de ses effets nocifs sur la santé et la société, ce rapport a pour objectif d’examiner le nombre de jeunes qui consomment du cannabis au Luxembourg, ainsi que les facteurs sociodémographiques, de risque et de protection associés à cette consommation, afin d'illustrer les possibilités d'interventions promotion de la santé et de prévention de comportement addictifs. Les données présentées proviennent de l'enquête Health Behaviour in School-aged Children - HBSC de 2018. L’analyse porte sur les données de 6 880 jeunes de l‘enseignement secondaire âgés de 12 à 18 ans. Pour répondre à l’objectif, les jeunes ont été répartis en 3 groupes : 1) les jeunes qui n'ont jamais consommé du cannabis ; 2) les jeunes qui ont consommé du cannabis dans leur vie, mais pas au cours des 30 derniers jours (consommation passée) et ; 3) les jeunes qui ont consommé du cannabis au cours des 30 derniers jours (consommation actuelle). Les analyses descriptives montrent que parmi les participants, 82 % n'ont jamais consommé du cannabis, 9 % en ont consommé dans le passé et 9 % en consomment actuellement. À l’aide des analyses de régression logistique multinomiales, le lien entre la consommation de cannabis et une série de variables sociodémographiques, des comportements à risque, le soutien social, le milieu scolaire et la santé et le bien-être a été analysé, afin d'explorer les facteurs sociodémographiques, de risque et de protections associés à la consommation de cannabis. Les seuls facteurs qui restent significatifs pour la consommation de cannabis sont l'âge, le genre, la situation familiale, la consommation de tabac, la consommation d'alcool, la participation à des bagarres, la perception du soutien de la part des parents et des enseignants par les élèves. Toutefois, l'âge et la consommation actuelle de tabac sont les facteurs les plus importants. Ainsi, en ce qui concerne les facteurs sociodémographiques, les garçons, les adolescents les plus âgés et ceux qui ne vivent pas avec leurs deux parents sont plus enclins à faire partie des groupes qui ont consommé du cannabis. L’augmentation des comportements à risque (fréquence plus élevée de consommation de tabac, d'alcool et de participation à des bagarres) se traduit par une plus grande probabilité de consommation de cannabis. De plus, les jeunes qui ont un comportement à risque ont tendance à opter pour d'autres comportements à risque. En revanche, le soutien perçu de la famille et le soutien perçu des enseignants sont des facteurs protégeant de la consommation de cannabis. Ainsi, les élèves qui se sentent soutenus par leurs parents et leurs enseignants sont moins enclins à consommer du cannabis. Il est donc pertinent d'adapter les méthodes de prévention de la consommation de cannabis en fonction de l'âge et du genre, et d’axer les approches de prévention sur ceux qui considèrent sa consommation comme l’un des éléments d'un comportement à risque global plutôt que celles ciblant uniquement cette substance. En effet, les modèles de prévention qui visent à promouvoir un comportement global plus soucieux de la santé semblent être plus efficaces. [less ▲]

Item response theory and differential test functioning analysis of the HBSC-Symptom-Checklist across 46 countries

in BMC Medical Research Methodology (2022), 22(253),

Background The Symptom Checklist (SCL) developed by the Health Behaviour in School-aged Children (HBSC) study is a non-clinical measure of psychosomatic complaints (e.g., headache and feeling low) that ... [more ▼]

Background The Symptom Checklist (SCL) developed by the Health Behaviour in School-aged Children (HBSC) study is a non-clinical measure of psychosomatic complaints (e.g., headache and feeling low) that has been used in numerous studies. Several studies have investigated the psychometric characteristics of this scale; however, some psychometric properties remain unclear, among them especially a) dimensionality, b) adequacy of the Graded Response Model (GRM), and c) measurement invariance across countries. Methods Data from 229,906 adolescents aged 11, 13 and 15 from 46 countries that participated in the 2018 HBSC survey were analyzed. Adolescents were selected using representative sampling and surveyed by questionnaire in the classroom. Dimensionality was investigated using exploratory graph analysis. In addition, we investigated whether the GRM provided an adequate description of the data. Reliability over the latent variable continuum and differential test functioning across countries were also examined. Results Exploratory graph analyses showed that SCL can be considered as one-dimensional in 16 countries. However, a comparison of the unidimensional with a post-hoc bifactor GRM showed that deviation from a hypothesized one-dimensional structure was negligible in most countries. Multigroup invariance analyses supported configural and metric invariance, but not scalar invariance across 32 countries. Alignment analysis showed non-invariance especially for the items irritability, feeling nervous/bad temper and feeling low. Conclusion HBSC-SCL appears to represent a consistent and reliable unidimensional instrument across most countries. This bodes well for population health analyses that rely on this scale as an early indicator of mental health status. [less ▲]

Problematische Nutzung sozialer Medien von Kindern und Jugendlichen im Schulalter in Luxembourg – Ergebnisse der HBSC Umfrage 2018

Report (2022)

Insbesondere unter Jugendlichen hat die Nutzung sozialer Medien in den letzten Jahren zugenommen. Wenn die Nutzung Merkmale einer Sucht aufweist (z.B. Gewöhnung und sozialer Rückzug), dann wird von einer ... [more ▼]

Insbesondere unter Jugendlichen hat die Nutzung sozialer Medien in den letzten Jahren zugenommen. Wenn die Nutzung Merkmale einer Sucht aufweist (z.B. Gewöhnung und sozialer Rückzug), dann wird von einer problematischen Nutzung sozialer Medien gesprochen – im Folgenden PSMU genannt. Dieser Kurzbericht gibt einen Überblick darüber, wie häufig PSMU unter luxemburgischen Schülern vorkommt und welche Merkmale mit einem höheren Risiko für PSMU einhergehen. Dazu wurden Daten der luxemburgischen Health Behavior in School-aged Children (HBSC)-Studie 2018 ausgewertet, an der 8 687 Jugendliche im Alter von 11 bis 18 Jahren teilnahmen. Laut dieser Umfrage liegt die Häufigkeit von PSMU in dieser Altersgruppe bei 5,9 %. Eine Reihe von Merkmalen aus den Bereichen Soziodemografie, soziale Unterstützung, Wohlbefinden und Mediennutzung wurden als potenzielle Risikofaktoren untersucht. PSMU tritt häufiger bei Mädchen und jüngeren Schülern sowie bei Schülern mit Migrationshintergrund auf. Bei Schülern, die bei beiden Elternteilen aufwachsen, ist PSMU seltener im Vergleich zu Schülern, die bei Alleinerziehenden oder in anderen Familienkonstellationen aufwachsen. Ein Vergleich der Risikofaktoren hat ergeben, dass das Alter, Cybermobbing, Stress, psychosomatische Beschwerden, eine Vorliebe für Online-Interaktion und die Intensität der Kommunikation über elektronische Medien die wichtigsten Risikofaktoren sind. Das Risiko für einen problematischen Umgang mit sozialen Medien ist somit höher bei jüngeren Schülern; Schülern, die andere online mobben; gestressten Schülern; Schülern mit häufigen psychosomatischen Beschwerden; Schülern, die Online-Kommunikation gegenüber einer Kommunikation in der realen Welt vorziehen sowie Schülern, die elektronische Medien generell häufig nutzen. Speziell zur Prävention von PSMU haben sich noch keine Maßnahmen etabliert, aber es gibt gut erforschte Maßnahmen zur Prävention von Internetsucht, die sich in abgewandelter Form möglicherweise auch bei PSMU eignen. [less ▲]

Types of health-related behaviours: a cluster analysis of the Luxembourgish HBSC data

Scientific Conference (2021, June)

Background: Although it is known that health behaviours, socio-demographic variables and outcomes correlate, it is rarely investigated if there are typical patterns of these variables among the research ... [more ▼]

Background: Although it is known that health behaviours, socio-demographic variables and outcomes correlate, it is rarely investigated if there are typical patterns of these variables among the research subjects. Objectives: To find out whether the students can be divided into distinct groups based on their health behaviour and whether these groups differ in other ways (outcomes and socio-demographics). Method: In step 1, a hierarchical cluster analysis was carried out to determine the number of groups and to identify the cluster centres. In step 2, this information was entered as the initial values of a cluster centre analysis. In step 3, the clusters were characterised using additional variables. Results: The 8065 students surveyed could be divided into 5 distinct groups based on their data on smoking, drinking, soft drinks, exercising, fighting and bullying, with cluster 1 and cluster 5 representing the strongest contrast. Cluster 1 comprises students whose health behaviour is generally positive. It is the largest cluster with 49.5% of students. Cluster 5 comprises students whose behaviour is consistently negative. It is the smallest cluster with 7.1% of students. Students in cluster 2 are close to average on many variables, but their dental health is problematic because they frequently consume soft drinks and rarely brush their teeth. Students in cluster 3 are physically inactive, their mental health is poor, but they are also rarely injured. The students in cluster 4 stand out because of their aggressive behaviour. Conclusion: With the help of cluster analysis, it is possible to categorise the students into a small number of groups based on their health behaviour. These groups are coherent in terms of health behaviour, many outcome variables and socio-demographic variables. [less ▲]

What is problematic about binary questions on gender in health surveys – a missing answer analysis

in Cogent Medicine (2020, December 04)

Background: In many studies, participants who do not state their gender are excluded from the analysis. This may be appropriate if they do not answer the questionnaire seriously. However, some ... [more ▼]

Background: In many studies, participants who do not state their gender are excluded from the analysis. This may be appropriate if they do not answer the questionnaire seriously. However, some participants may have understandable reasons for not reporting their gender, e.g. questioning their gender identity. Research question: How many students and which students do not answer the question on gender? Methods: We analyzed data of the Health Behaviour in School-aged Children study from Ireland, France, Hungary, Scotland, Belgium (Flemish) and Luxembourg (n = 40,053). To explore the reasons for non-response, we divided the participants into 3 groups: 1. Responders answered both socio- demographic questions (age and gender) 2. age non-responders did not answer the question on age. 3. Gender non-responders answered the question on age, but not the one on gender. Results: 311 out of 40,053 (0.8%) pupils aged 11–18 did not report their gender. About 40% of them did not answer the age question either. However, the other 60% belong to the group of gender non-responders and this group is disadvantaged compared to responders: they report lower self-rated health, more health complaints, less family support and more substance use (alcohol, tobacco, cannabis). 1.9% of pupils did not answer the question about age. These age non-responders answered the questionnaire more selectively overall and skipped more questions. Conclusion: The data suggest that the reasons for age non-response and gender non-response are different. For age non-responders, the fear of de-anonymization seems to be the reason for not indicating their age. Not answering the question on gender is rare. If the participants answered the question on age, but not the question on gender, then the variable gender is missing not at random. The health problems of gender non-responders correspond to the health problems of gender non-conforming adolescents. Thus, the question arises if the group of gender non-responders should be included in the analysis and if the question on gender should be asked differently in the future [less ▲]

Trends from 2006-2018 in Health, Health Behaviour, Health Outcomes and Social Context of Adolescents in Luxembourg

Report (2020)

This report shows how 30 health indicators developed in the four Luxembourg HBSC surveys conducted in 2006, 2010, 2014 and 2018. There were positive trends especially in the health behaviour of the pupils ... [more ▼]

This report shows how 30 health indicators developed in the four Luxembourg HBSC surveys conducted in 2006, 2010, 2014 and 2018. There were positive trends especially in the health behaviour of the pupils: they smoke less and drink less alcohol. They also report more frequently that they brush their teeth regularly, eat more fruit and fewer sweets and consume fewer soft drinks. From 2006-2018, however, there were also deteriorations. For example, more pupils feel stressed from school and rate the climate among classmates worse. In addition, there are more pupils who are overweight and exercise less and more pupils report having psychosomatic health complaints. [less ▲]

Cross-National Time Trends in Adolescent Mental Well-Being From 2002 to 2018 and the Explanatory Role of Schoolwork Pressure

in Journal of Adolescent Health (2020), 66

Purpose: Previous research has shown inconsistent time trends in adolescent mental well-being, but potential underlying mechanisms for such trends are yet to be examined. This study investigates cross ... [more ▼]

Purpose: Previous research has shown inconsistent time trends in adolescent mental well-being, but potential underlying mechanisms for such trends are yet to be examined. This study investigates cross-national time trends in adolescent mental well-being (psychosomatic health complaints and life satisfaction) in mainly European countries and the extent to which time trends in schoolwork pressure explain these trends. Methods: Data from 915,054 adolescents from 36 countries (50.8% girls; meanage ¼ 13.54; standard deviationage ¼ 1.63) across five Health Behaviour in School-aged Children surveys (2002, 2006, 2010, 2014, and 2018) were included in the analyses. Hierarchical multilevel models estimated cross-national trends in adolescent mental well-being and schoolwork pressure. We also tested whether schoolwork pressure could explain these trends in mental well-being. Results: A small linear increase over time in psychosomatic complaints and schoolwork pressure was found. No change in life satisfaction emerged. Furthermore, there was large cross-country variation in the prevalence of, and trends over time in, adolescent mental well-being and schoolwork pressure. Overall, declines in well-being and increases in schoolwork pressure were apparent in the higher income countries. Across countries, the small increase in schoolwork pressure over time partly explained the decline in psychosomatic health complaints. Conclusions: Our findings do not provide evidence for substantial declines in mental well-being among adolescents. Yet, the small declines in mental well-being and increases in schoolwork pressure appear to be quite consistent across high-income countries. This calls for the attention of public health professionals and policy-makers. Country differences in trends in both adolescent mental well-being outcomes and schoolwork pressure were considerable, which requires caution regarding the cross-national generalization of national trends. [less ▲]