Parental use of aggressive discipline, specifically corporal punishment (CP) and psychological aggression (PA), has been shown to increase the risk of problem behaviours in children and adolescents. A major gap in the research concerns our lack of understanding regarding how CP and PA contribute to adverse development in adulthood. Because these techniques do not occur in isolation, a number of protective factors were also considered. Both CP and PA were associated with lower levels of parental warmth/support and responsiveness, and more inconsistency in discipline; whereas high induction was associted with more frequent CP and PA. CP predicted later partner violence, and PA predicted anxiety and lower self-esteem in adulthood, even after the effects of positive parenting were considered. These findings suggest that not only do CP and PA tend to occur within environments that are less conducive to positive development, but also predict problematic outcomes in adulthood even after protective factors are considered.

Parental use of aggressive discipline, specifically corporal punishment (CP) and psychological aggression (PA), has been shown to increase the risk of problem behaviours in children and adolescents. A major gap in the research concerns our lack of understanding regarding how CP and PA contribute to adverse development in adulthood. Because these techniques do not occur in isolation, a number of protective factors were also considered. Both CP and PA were associated with lower levels of parental warmth/support and responsiveness, and more inconsistency in discipline; whereas high induction was associted with more frequent CP and PA. CP predicted later partner violence, and PA predicted anxiety and lower self-esteem in adulthood, even after the effects of positive parenting were considered. These findings suggest that not only do CP and PA tend to occur within environments that are less conducive to positive development, but also predict problematic outcomes in adulthood even after protective factors are considered.

O objetivo da presente tese foi o de propor um modelo de identificação de características somatomotoras em escolares para a prospecção de possíveis talentos esportivos para o Rugby XV. Para tanto fizeram parte da amostra 195 escolares do sexo masculino com idades entre 16 e 17 anos e 32 atletas do sexo masculino da seleção brasileira de Rugby menores de 18 anos, subdividos em dois grupos Forwards (n=17) e Backs (n=15). Este estudo foi realizado a partir de uma base de dados de medidas e testes em escolares proveniente do Projeto Esporte Brasil (PROESP-Br) e da base de dados da Confederação Brasileira de Rugby. Os dados utilizados foram Estatura (E), Envergadura (EV), Massa Corporal (MC), Força Explosiva de Membros Inferiores (FEMI), Força Explosiva de Membros Superiores (FEMS), Velocidade (VEL), Agilidade (AGIL) e Aptidão Cardiorrespiratória (ACR). Para todos os grupos medidas e testes foram feitas conforme o protocolo da bateria de testes do PROESP-Br, com exceção da ACR dos atletas que foi através do Yoyo test. Para agrupar as unidades de ACR recorremos a equação de Bergmann et al. (2014) e Barnett et al. (1993) transformando as unidades em volume máximo de oxigênio (VO2máx.). Para analisar os dados recorremos à Análise da Função Discriminante (AFD). Para cumprir com os pressupostos da AFD utilizamos a ANOVA para identificar se os resultados eram diferentes entre os grupos. A primeira comparação foi entre os grupos de Forwards e de Backs através da ANOVA, todas as variáveis apresentaram diferenças estatísticas, com exceção FEMI. Na comparação entre Forwards e Escolares houve diferença estatisticamente significativa em todas as variáveis. Já quando comparamos Backs com Escolares não houve diferença somente na E e na EV. Os dados para comparação Forwards e Escolares demonstrou que a função foi significativa (p≤0,05), teve uma boa correlação canônica (0,687) e ainda apresentou um Lambda de Wilks médio (0,528), esse conjunto de informações direciona a afirmar que essa função tem um boa relação com as variáveis analisadas e difere com poder significativo os grupos estudados. A função de Backs e Escolares apresentou resultados semelhantes - (p≤0,05), correlação canônica (0,638) e Lambda de Wilk médio (0,593) tendo um desfecho igual ao grupo dos Fowards e Escolares. A analise dos coeficientes estandardizados que indicaram a MC, VO2máx. e a VEL como as variáveis que mais se destacam na discriminação Forwards e Escolares. O mesmo ocorre para Backs e Escolares. Os coeficientes estruturantes apresentam as variáveis em ordem hierárquica para os dois modelos, (1) Forwards e Escolares: MC, FEMS, VEL, AGIL, EV, E, VO2 máx., e FEMI; (2) Backs e Escolares: VEL, FEMS, AGIL, VO2 máx., FEMI, MC, E e EV. Os valores médios de cada grupo na função discriminante 1 foram: Fowards (3,188) e Escolares (-0,278). Na função discriminante 2 foram: Backs (2,974) e Escolares (-0,229). Assim, podemos considerar um sujeito com vocação motora quando o Y da equação [Y = (B1.X1) + (B2.X2) + (B3.X3) + (B4.X4) + (B5.X5) + (B6.X6) + (B7.X7) + (B8.X8) + C], for maior que 1,216 para Forward e maior que 1,418 para Backs, ao utilizarmos as respectivos modelos matemáticos. Conclui-se que, é possível identificar através de um modelo matemático quais são as variáveis que discriminam os sujeitos com características motoras semelhantes a de atletas de Rugby do grupo de Escolares. / The aim of this thesis was to identify in the set of measures and tests of Projeto Esporte Brasil motor performance indicators in order to develop parameters and methods for the exploration of possible sporting talents to Rugby Union. For this, the sample was composed by 195 male students aged between 16 and 17 years and 32 male athletes of the Brazilian national Rugby team under 18, subdivided into two groups forwards ( n = 17 ) and Backs ( n = 15 ). This study was conducted from a database of measurements and tests in schoolchildren from the Projeto Esporte Brasil (PROESP -Br) and the database of the Brazilian Confederation of Rugby . The data used were: Stature (S) , Wingspan (W) , Body Mass (BM) , Power Strength Leg Length (PSL) , Power Strength of Upper Limb (PSU) , Speed (SPD) , Agility (AGIL) and Cardiorespiratory Fitness (CRF). For all groups measurements and tests were performed according to the protocol of the test battery PROESP-Br, with the exception of CRF of athletes who went through the Yoyo test. The CRF results were considered from VO2 máx.. For this, the equations proposed for Bergmann et al. (2014) and Barnett et al. (1993) were used to 9 minutes run/walk test and yo-yo test, respectively. To analyze the data we used the discriminant function analysis (DFA). To meet the assumptions of DFA we used the ANOVA to identify whether the results were different between the groups. The first comparison was between groups of Forwards and Backs by ANOVA, all variables showed statistical differences, except PSL. Forwards and Schoolchildren comparison showed statistically significant differences in all variables. When Backs and Schoolchildren were compared no difference were found only in S and W. The data presented in the DFA for comparison between Forwards and Schoolchildren showed that the function was significant (p ≤ 0.05), had a canonical significant correlation (0.687) and also presented a Lambda Wilks average (0.528). This set of information directs state that this function has a good relationship with the variables analyzed and differs the studied groups with significant power. The FDA results indicate that Backs and Schoolchildren showed similar results - (p ≤ 0.05), significant canonical correlation (0.638) and Lambda Wilks average (0.593) having an equal outcome to the group Fowards and Schoolchildren. The analysis of the standardized coefficients indicated that the BM, VO2max. and SPD as the variables that stand out in discrimination Forwards and Schoolchildren. The same occurs for Backs and Schoolchildren. The structural coefficients showed the variables in hierarchical order for the two models, (1) Forwards and Schoolchildren: BM, PSU, SPD, AGIL, W, S, VO2max , and PSL, (2) Backs and Schoolchildren: SPD, PSU, AGIL, VO2max., PSL, BM , S and W . The mean values of each group in the discriminant function 1 were: Forwards (3,188) and Schoolchildren (-0.278). In discriminant function 2 were: Backs (2,974) and Schoolchildren (-0.229). Thus, we can consider a subject with motor vocation when the Y of the equation [Y = ( B1.X1 ) + ( B2.X2 ) + ( B3.X3 ) + ( B4.X4 ) + ( B5.X5 ) + ( B6.X6 ) + ( B7.X7 ) + ( B8.X8 ) + C], is greater than 1,418 to be a Forward and greater to 1,216 for backs, when we use the respective mathematical models . Therefore, this is possible to determine by a mathematical model which variables evaluated by PROESP-Br that discriminate subjects with similar motor characteristics of the athletes in the group of Rugby School. Thus, this model can be an important tool for the composition of a development program athletes long term parameter.

O objetivo da presente tese foi o de propor um modelo de identificação de características somatomotoras em escolares para a prospecção de possíveis talentos esportivos para o Rugby XV. Para tanto fizeram parte da amostra 195 escolares do sexo masculino com idades entre 16 e 17 anos e 32 atletas do sexo masculino da seleção brasileira de Rugby menores de 18 anos, subdividos em dois grupos Forwards (n=17) e Backs (n=15). Este estudo foi realizado a partir de uma base de dados de medidas e testes em escolares proveniente do Projeto Esporte Brasil (PROESP-Br) e da base de dados da Confederação Brasileira de Rugby. Os dados utilizados foram Estatura (E), Envergadura (EV), Massa Corporal (MC), Força Explosiva de Membros Inferiores (FEMI), Força Explosiva de Membros Superiores (FEMS), Velocidade (VEL), Agilidade (AGIL) e Aptidão Cardiorrespiratória (ACR). Para todos os grupos medidas e testes foram feitas conforme o protocolo da bateria de testes do PROESP-Br, com exceção da ACR dos atletas que foi através do Yoyo test. Para agrupar as unidades de ACR recorremos a equação de Bergmann et al. (2014) e Barnett et al. (1993) transformando as unidades em volume máximo de oxigênio (VO2máx.). Para analisar os dados recorremos à Análise da Função Discriminante (AFD). Para cumprir com os pressupostos da AFD utilizamos a ANOVA para identificar se os resultados eram diferentes entre os grupos. A primeira comparação foi entre os grupos de Forwards e de Backs através da ANOVA, todas as variáveis apresentaram diferenças estatísticas, com exceção FEMI. Na comparação entre Forwards e Escolares houve diferença estatisticamente significativa em todas as variáveis. Já quando comparamos Backs com Escolares não houve diferença somente na E e na EV. Os dados para comparação Forwards e Escolares demonstrou que a função foi significativa (p≤0,05), teve uma boa correlação canônica (0,687) e ainda apresentou um Lambda de Wilks médio (0,528), esse conjunto de informações direciona a afirmar que essa função tem um boa relação com as variáveis analisadas e difere com poder significativo os grupos estudados. A função de Backs e Escolares apresentou resultados semelhantes - (p≤0,05), correlação canônica (0,638) e Lambda de Wilk médio (0,593) tendo um desfecho igual ao grupo dos Fowards e Escolares. A analise dos coeficientes estandardizados que indicaram a MC, VO2máx. e a VEL como as variáveis que mais se destacam na discriminação Forwards e Escolares. O mesmo ocorre para Backs e Escolares. Os coeficientes estruturantes apresentam as variáveis em ordem hierárquica para os dois modelos, (1) Forwards e Escolares: MC, FEMS, VEL, AGIL, EV, E, VO2 máx., e FEMI; (2) Backs e Escolares: VEL, FEMS, AGIL, VO2 máx., FEMI, MC, E e EV. Os valores médios de cada grupo na função discriminante 1 foram: Fowards (3,188) e Escolares (-0,278). Na função discriminante 2 foram: Backs (2,974) e Escolares (-0,229). Assim, podemos considerar um sujeito com vocação motora quando o Y da equação [Y = (B1.X1) + (B2.X2) + (B3.X3) + (B4.X4) + (B5.X5) + (B6.X6) + (B7.X7) + (B8.X8) + C], for maior que 1,216 para Forward e maior que 1,418 para Backs, ao utilizarmos as respectivos modelos matemáticos. Conclui-se que, é possível identificar através de um modelo matemático quais são as variáveis que discriminam os sujeitos com características motoras semelhantes a de atletas de Rugby do grupo de Escolares. / The aim of this thesis was to identify in the set of measures and tests of Projeto Esporte Brasil motor performance indicators in order to develop parameters and methods for the exploration of possible sporting talents to Rugby Union. For this, the sample was composed by 195 male students aged between 16 and 17 years and 32 male athletes of the Brazilian national Rugby team under 18, subdivided into two groups forwards ( n = 17 ) and Backs ( n = 15 ). This study was conducted from a database of measurements and tests in schoolchildren from the Projeto Esporte Brasil (PROESP -Br) and the database of the Brazilian Confederation of Rugby . The data used were: Stature (S) , Wingspan (W) , Body Mass (BM) , Power Strength Leg Length (PSL) , Power Strength of Upper Limb (PSU) , Speed (SPD) , Agility (AGIL) and Cardiorespiratory Fitness (CRF). For all groups measurements and tests were performed according to the protocol of the test battery PROESP-Br, with the exception of CRF of athletes who went through the Yoyo test. The CRF results were considered from VO2 máx.. For this, the equations proposed for Bergmann et al. (2014) and Barnett et al. (1993) were used to 9 minutes run/walk test and yo-yo test, respectively. To analyze the data we used the discriminant function analysis (DFA). To meet the assumptions of DFA we used the ANOVA to identify whether the results were different between the groups. The first comparison was between groups of Forwards and Backs by ANOVA, all variables showed statistical differences, except PSL. Forwards and Schoolchildren comparison showed statistically significant differences in all variables. When Backs and Schoolchildren were compared no difference were found only in S and W. The data presented in the DFA for comparison between Forwards and Schoolchildren showed that the function was significant (p ≤ 0.05), had a canonical significant correlation (0.687) and also presented a Lambda Wilks average (0.528). This set of information directs state that this function has a good relationship with the variables analyzed and differs the studied groups with significant power. The FDA results indicate that Backs and Schoolchildren showed similar results - (p ≤ 0.05), significant canonical correlation (0.638) and Lambda Wilks average (0.593) having an equal outcome to the group Fowards and Schoolchildren. The analysis of the standardized coefficients indicated that the BM, VO2max. and SPD as the variables that stand out in discrimination Forwards and Schoolchildren. The same occurs for Backs and Schoolchildren. The structural coefficients showed the variables in hierarchical order for the two models, (1) Forwards and Schoolchildren: BM, PSU, SPD, AGIL, W, S, VO2max , and PSL, (2) Backs and Schoolchildren: SPD, PSU, AGIL, VO2max., PSL, BM , S and W . The mean values of each group in the discriminant function 1 were: Forwards (3,188) and Schoolchildren (-0.278). In discriminant function 2 were: Backs (2,974) and Schoolchildren (-0.229). Thus, we can consider a subject with motor vocation when the Y of the equation [Y = ( B1.X1 ) + ( B2.X2 ) + ( B3.X3 ) + ( B4.X4 ) + ( B5.X5 ) + ( B6.X6 ) + ( B7.X7 ) + ( B8.X8 ) + C], is greater than 1,418 to be a Forward and greater to 1,216 for backs, when we use the respective mathematical models . Therefore, this is possible to determine by a mathematical model which variables evaluated by PROESP-Br that discriminate subjects with similar motor characteristics of the athletes in the group of Rugby School. Thus, this model can be an important tool for the composition of a development program athletes long term parameter.

O objetivo da presente tese foi o de propor um modelo de identificação de características somatomotoras em escolares para a prospecção de possíveis talentos esportivos para o Rugby XV. Para tanto fizeram parte da amostra 195 escolares do sexo masculino com idades entre 16 e 17 anos e 32 atletas do sexo masculino da seleção brasileira de Rugby menores de 18 anos, subdividos em dois grupos Forwards (n=17) e Backs (n=15). Este estudo foi realizado a partir de uma base de dados de medidas e testes em escolares proveniente do Projeto Esporte Brasil (PROESP-Br) e da base de dados da Confederação Brasileira de Rugby. Os dados utilizados foram Estatura (E), Envergadura (EV), Massa Corporal (MC), Força Explosiva de Membros Inferiores (FEMI), Força Explosiva de Membros Superiores (FEMS), Velocidade (VEL), Agilidade (AGIL) e Aptidão Cardiorrespiratória (ACR). Para todos os grupos medidas e testes foram feitas conforme o protocolo da bateria de testes do PROESP-Br, com exceção da ACR dos atletas que foi através do Yoyo test. Para agrupar as unidades de ACR recorremos a equação de Bergmann et al. (2014) e Barnett et al. (1993) transformando as unidades em volume máximo de oxigênio (VO2máx.). Para analisar os dados recorremos à Análise da Função Discriminante (AFD). Para cumprir com os pressupostos da AFD utilizamos a ANOVA para identificar se os resultados eram diferentes entre os grupos. A primeira comparação foi entre os grupos de Forwards e de Backs através da ANOVA, todas as variáveis apresentaram diferenças estatísticas, com exceção FEMI. Na comparação entre Forwards e Escolares houve diferença estatisticamente significativa em todas as variáveis. Já quando comparamos Backs com Escolares não houve diferença somente na E e na EV. Os dados para comparação Forwards e Escolares demonstrou que a função foi significativa (p≤0,05), teve uma boa correlação canônica (0,687) e ainda apresentou um Lambda de Wilks médio (0,528), esse conjunto de informações direciona a afirmar que essa função tem um boa relação com as variáveis analisadas e difere com poder significativo os grupos estudados. A função de Backs e Escolares apresentou resultados semelhantes - (p≤0,05), correlação canônica (0,638) e Lambda de Wilk médio (0,593) tendo um desfecho igual ao grupo dos Fowards e Escolares. A analise dos coeficientes estandardizados que indicaram a MC, VO2máx. e a VEL como as variáveis que mais se destacam na discriminação Forwards e Escolares. O mesmo ocorre para Backs e Escolares. Os coeficientes estruturantes apresentam as variáveis em ordem hierárquica para os dois modelos, (1) Forwards e Escolares: MC, FEMS, VEL, AGIL, EV, E, VO2 máx., e FEMI; (2) Backs e Escolares: VEL, FEMS, AGIL, VO2 máx., FEMI, MC, E e EV. Os valores médios de cada grupo na função discriminante 1 foram: Fowards (3,188) e Escolares (-0,278). Na função discriminante 2 foram: Backs (2,974) e Escolares (-0,229). Assim, podemos considerar um sujeito com vocação motora quando o Y da equação [Y = (B1.X1) + (B2.X2) + (B3.X3) + (B4.X4) + (B5.X5) + (B6.X6) + (B7.X7) + (B8.X8) + C], for maior que 1,216 para Forward e maior que 1,418 para Backs, ao utilizarmos as respectivos modelos matemáticos. Conclui-se que, é possível identificar através de um modelo matemático quais são as variáveis que discriminam os sujeitos com características motoras semelhantes a de atletas de Rugby do grupo de Escolares. / The aim of this thesis was to identify in the set of measures and tests of Projeto Esporte Brasil motor performance indicators in order to develop parameters and methods for the exploration of possible sporting talents to Rugby Union. For this, the sample was composed by 195 male students aged between 16 and 17 years and 32 male athletes of the Brazilian national Rugby team under 18, subdivided into two groups forwards ( n = 17 ) and Backs ( n = 15 ). This study was conducted from a database of measurements and tests in schoolchildren from the Projeto Esporte Brasil (PROESP -Br) and the database of the Brazilian Confederation of Rugby . The data used were: Stature (S) , Wingspan (W) , Body Mass (BM) , Power Strength Leg Length (PSL) , Power Strength of Upper Limb (PSU) , Speed (SPD) , Agility (AGIL) and Cardiorespiratory Fitness (CRF). For all groups measurements and tests were performed according to the protocol of the test battery PROESP-Br, with the exception of CRF of athletes who went through the Yoyo test. The CRF results were considered from VO2 máx.. For this, the equations proposed for Bergmann et al. (2014) and Barnett et al. (1993) were used to 9 minutes run/walk test and yo-yo test, respectively. To analyze the data we used the discriminant function analysis (DFA). To meet the assumptions of DFA we used the ANOVA to identify whether the results were different between the groups. The first comparison was between groups of Forwards and Backs by ANOVA, all variables showed statistical differences, except PSL. Forwards and Schoolchildren comparison showed statistically significant differences in all variables. When Backs and Schoolchildren were compared no difference were found only in S and W. The data presented in the DFA for comparison between Forwards and Schoolchildren showed that the function was significant (p ≤ 0.05), had a canonical significant correlation (0.687) and also presented a Lambda Wilks average (0.528). This set of information directs state that this function has a good relationship with the variables analyzed and differs the studied groups with significant power. The FDA results indicate that Backs and Schoolchildren showed similar results - (p ≤ 0.05), significant canonical correlation (0.638) and Lambda Wilks average (0.593) having an equal outcome to the group Fowards and Schoolchildren. The analysis of the standardized coefficients indicated that the BM, VO2max. and SPD as the variables that stand out in discrimination Forwards and Schoolchildren. The same occurs for Backs and Schoolchildren. The structural coefficients showed the variables in hierarchical order for the two models, (1) Forwards and Schoolchildren: BM, PSU, SPD, AGIL, W, S, VO2max , and PSL, (2) Backs and Schoolchildren: SPD, PSU, AGIL, VO2max., PSL, BM , S and W . The mean values of each group in the discriminant function 1 were: Forwards (3,188) and Schoolchildren (-0.278). In discriminant function 2 were: Backs (2,974) and Schoolchildren (-0.229). Thus, we can consider a subject with motor vocation when the Y of the equation [Y = ( B1.X1 ) + ( B2.X2 ) + ( B3.X3 ) + ( B4.X4 ) + ( B5.X5 ) + ( B6.X6 ) + ( B7.X7 ) + ( B8.X8 ) + C], is greater than 1,418 to be a Forward and greater to 1,216 for backs, when we use the respective mathematical models . Therefore, this is possible to determine by a mathematical model which variables evaluated by PROESP-Br that discriminate subjects with similar motor characteristics of the athletes in the group of Rugby School. Thus, this model can be an important tool for the composition of a development program athletes long term parameter.

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This paper holds a study of Swedish inequality from 1900-1985 with specific focus on the half-decades of 1900-1905 and 1980-1985. Subsequently constructed data and contemporary news outlets are presented to develop further understanding upon how inequality was depicted in comparison to data. Through a comparison of the time-periods, the developed understanding and changed stance of the public on the issue of inequality is examined. This method of examination gives new insight upon how the widening knowledge of inequality during the 1900s, through the development of micro-economic measurements, as well as more complete tax returns, has affected the depiction of the issue. The results of the study suggest that news articles of the latter period (1980-1985), tend to be more data-based than during the former time-period, where general assumptions were more frequent. It additionally suggests that the decreased inequality over the 20th century did not lessen the discussion of inequality, but rather the opposite. The news articles furthermore do not discuss inequality in relation to the presented data - the long-term changes are not clearly stated in the news outlets - but are in conclusion unconsciously considered, through the general knowledge of current inequality, through tax levels etc.

Zusammenfassung Hintergrund Der Konsum von Alkohol ist unter Jugendlichen weitverbreitet und stellt in Europa noch vor Nikotin die am häufigsten konsumierte abhängigkeitserzeugende Substanz dar. Die individuellen und gesellschaftlichen Konsequenzen jugendlichen Alkoholgebrauchs sind weitreichend, so gilt dieser als wichtigster Risikofaktor für Morbidität und Mortalität bei jungen Menschen in der westlichen Welt (Rehm et al., 2006). Vor diesem Hintergrund hat in Deutschland und anderen europäischen Ländern seit Beginn der 2000er Jahre der besorgniserregende Trend steigender Zahlen stationär behandelter jugendlicher Alkohol-intoxikationen Aufmerksamkeit erhalten. Unbehandelt stellt eine Alkoholintoxikation mit sehr hoher Blutalkoholkonzentration eine lebensbedrohliche Situation dar. Die entsprechenden Behandlungszahlen für Minderjährige haben sich im Zeitraum von 2000 bis 2012 mehr als verdreifacht. Verglichen mit anderen Altersgruppen stellte die Gruppe der 15- bis 20-Jährigen im genannten Zeitraum die mit Abstand größte Altersgruppe dar, gemessen an der Zahl der Behandlungsfälle pro 100 000 Gleichaltriger. Für diese junge Patientenpopulation liegen jedoch noch keine aussagekräftigen Daten darüber vor, welche Faktoren mit problematischem habituellen Alkoholkonsum assoziiert sind und in welchem Ausmaß bereits Alkoholgebrauchsstörungen bestehen. Ebenso wenig existieren Informationen über längerfristige Entwicklungsverläufe der betroffenen Kinder und Jugendlichen, was jedoch ausschlaggebend für die Identifizierung besonders gefährdeter Patienten ist. Fragestellungen Auf der Grundlage der aktuellen Literatur ergeben sich für die vorliegende Arbeit folgende Fragen: - Welche biopsychosozialen Faktoren sind mit problematischem habituellen Alkoholkonsum bei Fällen jugendlicher Alkoholintoxikationen assoziiert? - Sind betroffene Jugendliche im Vergleich zu Peers aus der Allgemeinbevölkerung gefährdeter hinsichtlich Alkoholgebrauchsstörungen und Substanzgebrauch? - Haben Kinder und Jugendliche, die aufgrund einer Alkoholintoxikation stationär behandelt wurden, ein erhöhtes Risiko, im jungen Erwachsenenalter eine Alkoholgebrauchsstörung und andere psychosoziale Probleme zu entwickeln, verglichen mit minderjährigen Patienten, die aus nicht-alkoholassoziierten Gründen behandelt wurden? - Welche Faktoren, die zum Zeitpunkt der stationär behandelten Alkoholintoxikation im Kindes- bzw. Jugendalter bekannt sind, sagen psychosoziale Outcome-Variablen im jungen Erwachsenenalter voraus? Die genannten Fragen sollen mit drei Studien beantwortet werden. Material und Methode Studie 1: In dieser naturalistischen Querschnittsuntersuchung wurden 65 Kinder und Jugendliche (Durchschnittsalter: MW (SD) 15,2 (1,6) Jahre), die stationär aufgrund von Alkoholintoxikation in den beiden Dresdner Kinderkliniken behandelt wurden, am Tag nach der Aufnahme am Krankenbett mündlich und schriftlich befragt. Die Datenerhebung erfolgte zwischen Mai 2009 und September 2010. Neben der Prävalenz des Substanzkonsums und von Alkoholgebrauchsstörungen, wurden auch bekannte Risikofaktoren für problematischen Alkoholgebrauch erhoben, wie das wahrgenommene elterliche Erziehungsverhalten, die Familienanamnese für Alkoholprobleme, deviante Verhaltensweisen der Freunde, Alkoholtoleranz und Alter des Alkoholerstkonsums. Studie 2 und Studie 3: Beide Studien entstanden im Rahmen des RiScA-Projekts (Risiko- und Schutzfaktoren bei Alkoholvergiftungen im Kindes- und Jugendalter) und beziehen sich auf dieselbe Stichprobe. In diesem Projekt wurden die Krankenakten von 1603 Fällen von jugendlichen Alkoholintoxikationen, die im Zeitraum von 2000 bis 2007 in einem von fünf Kinderkliniken (Universitätsklinikum Carl Gustav Carus, Städtisches Klinikum Dresden-Neustadt, HELIOS Klinikum Pirna, Universitätsmedizin Rostock, Münchner Klinikum Schwabing) stationär behandelt wurden, analysiert (Intoxikationsgruppe). Die Akten wurden systematisch hinsichtlich geeigneter Prädiktorvariablen, die die psychosoziale Langzeitentwicklung nach der Krankenhausbehandlung vorhersagen sollen, ausgewertet. Die Langzeitkatamnese wurde realisiert, indem die ehemaligen Patienten zur Teilnahme an einem standardisierten Telefoninterview eingeladen wurden. Zusätzlich wurde eine hinsichtlich Alter, Geschlecht und geographischer Region gematchte Kontrollgruppe telefonisch interviewt, die im selben Untersuchungszeitraum und in den selben Kinderkliniken stationär behandelt wurde aufgrund anderer, nicht-alkoholassoziierter Gründe. In der Intoxikationsgruppe nahmen 277 (Durchschnittsalter: MW (SD) 24,3 (2,3) Jahre) und in der Kontrollgruppe 116 junge Erwachsene (MW (SD) 23,9 (2,1) Jahre) am Telefoninterview teil (Beobachtungszeitraum seit der Krankenhausbehandlung: 5–13 Jahre (MW (SD) 8,3 (2,3) Jahre)). Folgende Outcome-Variablen des Langzeitverlaufs wurden erhoben: Konsum von Alkohol, Tabak und illegalen Substanzen, Vorliegen von Alkoholgebrauchsstörungen, allgemeine Lebenszufriedenheit, Delinquenz und Inanspruchnahme psychiatrischer/psychotherapeutischer Behandlungen. Ergebnisse Studie 1: Es zeigten sich erhöhte Prävalenzwerte für Alkoholabhängigkeit und -missbrauch, Rauschtrinken, regelmäßiges Rauchen und Gebrauch illegaler Drogen im Vergleich zu Daten aus der Allgemeinbevölkerung. Kein Unterschied wurde jedoch in Bezug auf die Häufigkeit des Alkoholkonsums, verglichen mit der Allgemeinbevölkerung, gefunden. Weiterhin zeigte sich, dass Patienten, die problematischen habituellen Alkoholkonsum betrieben – verglichen mit Patienten, auf die dies nicht zutraf – signifikant häufiger deviante Verhaltensweisen bei ihrem Freundeskreis, eine signifikant höhere Anzahl von Familienmitgliedern, die ein Alkoholproblem haben, berichteten, eine höhere Alkoholtoleranz, gemessen durch den Self-Rating of the Effects of Alcohol (SRE) - Fragebogen, erreichten und häufiger elterliche Erziehungsverhaltensweisen berichteten, die durch weniger Aufsicht gekennzeichnet waren. Studie 2: Die Probanden der Intoxikationsgruppe betrieben signifikant häufiger problematischen habituellen Alkoholkonsum, berichteten häufiger delinquente Verhaltensweisen und den Gebrauch illegaler Drogen und erfüllten häufiger die DSM-5-Kriterien einer schweren Alkoholgebrauchsstörung verglichen mit der Kontrollgruppe. Die Mehrheit der Probanden berichtete jedoch keine psychosozialen Belastungen. Keine signifikanten Unterschiede zwischen beiden Gruppen zeigten sich beim Bildungsgrad, regelmäßigen Rauchen, der allgemeinen Lebenszufriedenheit und der Inanspruchnahme von psychiatrischer oder psychotherapeutischer Behandlungen. Studie 3: 277 junge Erwachsene (Rücklaufquote: 22,7%) konnten nach durchschnittlich 8 Jahren nach der Klinikbehandlung befragt werden. Es zeigte sich mittels logistischer und linearer Regressionsmodelle, dass vor allem die Variablen männliches Geschlecht, Gebrauch illegaler Drogen und deviante Verhaltensweisen im Jugendalter, wie mehrmaliges Schulschwänzen oder Weglaufen von Zuhause, psychosoziale Outcome-Maße im jungen Erwachsenenalter, wie Rauschtrinken, Alkoholabhängigkeit, den Gebrauch illegaler Drogen und eine geringe allgemeine Lebenszufriedenheit signifikant vorhersagten. Die durch die Regressionsmodelle erklärte Varianz der im jungen Erwachsenenalter erhobenen psychosozialen Outcome-Variablen lag jedoch nur bei 13 bis 24%. Schlussfolgerungen Für Kinder und Jugendliche, die mit einer Alkoholintoxikation stationär behandelt wurden, besteht ein erhöhtes Risiko, bis zum jungen Erwachsenenalter eine schwere Alkoholgebrauchsstörung oder andere psychosoziale Probleme entwickelt zu haben. Allerdings war die Mehrheit der Probanden sowohl im Kindes- und Jugendalter (Studie 1) als auch im jungen Erwachsenenalter (Studie 2 und 3) nicht von Alkoholgebrauchs-störungen oder anderen negativen Entwicklungsaspekten betroffen. Der überwiegende Teil der betroffenen Kinder und Jugendlichen war scheinbar unerfahren im Gebrauch von Alkohol und/oder besaß eine geringe Alkoholtoleranz. Es kann jedoch nicht ausgeschlossen werden, dass eine besonders belastete Subgruppe existiert, die bereits Suchterkrankungen und/oder andere Verhaltensprobleme aufweisen, wobei das exzessive Trinkverhalten, das der stationären Behandlung vorausging, möglicherweise auf zugrundeliegende Entwicklungsgefährdungen hinweist. Interventionen sollten daher das problematische Trinkverhalten nicht losgelöst vom sozialen Umfeld, wie beispielsweise den Eltern, betrachten. Um besonders gefährdete Kinder und Jugendliche besser identifizieren zu können, sollten vor allem die Verlaufsprädiktoren männliches Geschlecht, Gebrauch illegaler Drogen und Verhaltensauffälligkeiten wie Schulschwänzen und Weglaufen von zu Hause (Studie 3) z. B. in Präventions- und Interventions-maßnahmen Beachtung finden. / Summary Background Alcohol use by young people is widespread and alcohol is the most frequently used psychoactive substance in Europe still before nicotine. Individual and societal consequences of adolescent alcohol use are far reaching, as it is considered to be the most important risk factor for morbidity and mortality among young people in the Western world (Rehm et al., 2006). Against this background, the worrying trend of increasing numbers of adolescents admitted to inpatient treatment with alcohol intoxication (AIA) since the 2000s has gained attention in Germany and other European countries. Alcohol intoxication with a high level of blood alcohol concentration can be life-threatening if untreated. The number of AIA more than tripled in the period from 2000 to 2012 and compared with all other age groups, 15- to 20-year-olds were the largest group measured in terms of the number of treated individuals per 100 000 peers. However, meaningful empirical data concerning the prevalence of alcohol use disorders (AUD) in AIA and which factors are associated with problematic habitual alcohol use in this patient population are lacking. Moreover, the long-term psychosocial development of AIA is still unknown, while this information is crucial for identifying high-risk patients. Research questions The present thesis addresses the following research questions: Study 1: Which biopsychosocial factors are associated with problematic habitual alcohol use in cases of AIA? Are the prevalences of substance use and AUDs in AIA elevated compared to adolescents from the general population? Study 2: Do AIA have an increased risk to develop AUD and other adverse psychosocial outcomes in young adulthood compared to their peers? Study 3: Which variables at the time of hospital admission in adolescence predict psychosocial outcomes in young adulthood in cases of AIA? Methods Study 1: In this cross-sectional naturalistic study 65 AIA (M and SD age 15.2 ± 1.6 yr) completed questionnaires and were interviewed the morning after admission. The study was conducted between May 2009 and September 2010 in both pediatric hospitals in the City of Dresden, Germany, i.e., the University Hospital Carl Gustav Carus and the Municipal Hospital Dresden-Neustadt. Assessment included substance use, alcohol use disorders and known risk factors for problematic alcohol use such as perceived parental monitoring, family history of alcohol problems, deviant peer affiliations, alcohol tolerance, and age of first drink. Study2: Study 2 and study 3 originated from the RiScA project (Risk and Protective Factors in the Context of Acute Alcohol Intoxication in Childhood and Adolescence; German: Risiko- und Schutzfaktoren nach Alkoholvergiftungen im Kindes- und Jugendalter). In this study, 277 young adults (mean age 24.3 years (SD 2.3)) who had been hospitalized due to alcohol intoxication in one of five pediatric departments (University Hospital Carl Gustav Carus, Municipal Hospital Dresden-Neustadt, HELIOS Hospital Pirna, Municipal Hospital Schwabing, University Medicine Rostock) 5 – 13 years ago (mean 8.3 years (SD 2.3)) between 2000 and 2007 participated in a standardized telephone interview. The interview was also conducted with a control group consisting of 116 former patients (mean age 23.9 years (SD 2.1)) matched in terms of age, sex and geographic region who were admitted to the same hospitals in the same period due to conditions other than alcohol intoxication in adolescence. Long-term outcome measures included current DSM-5 AUD, drinking patterns, illicit substance use, regular smoking, general life satisfaction, use of mental health treatment, and delinquency. Study 3: 1603 cases of AIA who had been hospitalized in the same period and in the same hospitals as in Study 2 were identified. These former patients were invited to participate in a standardized telephone interview in young adulthood collecting the following long-term psychosocial outcomes: binge-drinking, alcohol abuse and alcohol dependence (according to DSM-IV), illicit substance use, general life satisfaction, delinquency, and use of psychiatric or psychotherapeutic treatment. Medical records of AIA were retrospectively analyzed extracting potential variables predicting psychosocial long-term outcomes in young adulthood. Results Study 1: Elevated prevalence rates were found for alcohol abuse, alcohol dependence, binge drinking, regular smoking, and illicit substance in AIA compared to adolescents from the general population. No difference was found with respect to frequency of alcohol use compared with the general population. Exploratory analyses revealed that deviant peer affiliations, a positive family history of alcoholism, an elevated alcohol tolerance, and a parenting style characterized by less supervision and strictness were associated with problematic habitual alcohol use. Study 2: AIA had a significantly elevated risk to engage in problematic habitual alcohol use, to exhibit delinquent behaviors, and to use illicit substances in young adulthood compared to the control group. Severe AUD also occurred considerably more often in the AIA than the control group. However, the majority of participants did not report adverse psychosocial outcomes. No significant differences between the study groups were found regarding level of education, regular smoking, general life satisfaction and use of psychiatric or psychotherapeutic treatment. Study 3: 277 young adults (response rate: 22.7%) could be interviewed 5–13 (mean 8.3 (SD 2.3)) years after hospital treatment. Logistic and linear regression models revealed that being male, using illicit substances and truancy or runaway behavior in adolescence predicted binge drinking, alcohol dependence, use of illicit substances and poor general life satisfaction in young adulthood, explaining between 13 and 24% of the variance for the different outcome variables. Conclusions AIA are at an elevated risk to develop severe AUD or other psychosocial problems in young adulthood. However, the majority of participants both in childhood and adolescence (Study 1) and young adulthood (Study 2 and 3) was not affected by AUD or other developmental hazards. It seems that the greater part of AIA were inexperienced drinkers and / or had a low alcohol tolerance. However, it cannot be ruled out that a particularly vulnerable subgroup exist which already developed AUD and / or other behavioral problems. To better identify high-risk AIA, the predictor variables being male, illicit substance use and behavioral problems such as truancy or runaway behavior in adolescence (Study 3) should be practically included in prevention programs and brief interventions.