Background: A younger age at onset of use of a specific substance is a well-documented risk-factor for a substance use disorder (SUD) related to that specific substance. However, the cross-substance relationship between a younger age at onset of alcohol use (AU) and nicotine use (NU) and the risk of cannabis use disorders (CUD) in adolescence and early adulthood remains unclear. Aims: To identify the sequence of and latency between initial AU/NU and initial cannabis use (CU). To investigate whether younger age at AU- and NU-onset is associated with any and earlier CU-onset and a higher risk of transition from first CU to CUD, taking into account externalizing disorders (ED) and parental substance use disorders as putative influential factors. Methods: Prospective-longitudinal community study with N = 3021 subjects (baseline age 14–24) and up to four assessment waves over up to ten years with additional direct parental and family history information. Substance use and CUD were assessed with the DSM-IV/M-CIDI. Results: Most subjects with CU reported AU (99%) and NU (94%). Among users of both substances, 93% reported AU prior to CU (87% for NU). After adjustment for ED and parental substance use disorders younger age at AU-onset was associated with any CU. Younger age at NU-onset was associated with earlier CU initiation. Younger age at AU- and NU-onset was not associated with a higher risk of CUD. Conclusions: The cross-substance relevance of younger age at first AU and NU for the risk of CUD is limited to early CU involvement.

Background Perinatal markers of prenatal development are associated with offspring psychiatric symptoms. However, there is little research investigating the specificity of perinatal markers for the development of specific disorders. This study aimed to explore if perinatal markers are specifically associated with adolescent substance use disorder (SUDs). Methods Adolescent participants from two study centers, one for SUD patients (n = 196) and one for general psychopathology (n = 307), were recruited for participation. Since the SUD participants presented with a number of comorbid disorders, we performed a 1-on-1 matching procedure, based on age, gender, and specific pattern of comorbid disorders. This procedure resulted in n = 51 participants from each group. From all participants and their mothers we recorded perinatal markers (mode of birth, weeks of completed pregnancy, birth weight, Apgar score after 5 min) as well as intelligence quotient (IQ). The SUD sample additionally filled out the Youth Safe Report (YSR) as well as the PQ-16 and the DUDIT. We aimed to distinguish the two groups (SUD sample vs. general psychiatric sample) based on the perinatal variables via a logistic regression analysis. Additionally, linear regressions were performed for the total group and the subgroups to assess the relationship between perinatal variables and IQ, YSR, DUDIT and PQ-16. Results The perinatal variables were not able to predict group membership (X2 [4] = 4.77, p = .312, Cox & Snell R² = 0.053). Odds ratios indicated a small increase in probability to belonging to the general psychiatric sample instead of the SUD sample if birth was completed via C-section. After Bonferroni-correction, the linear regression models showed no relation between perinatal markers and IQ (p = .60, R² = 0.068), YSR (p = .09, R² = 0.121), DUDIT (p = .65, R² = 0.020), and PQ-16 (p = .73, R² =0.021).

Over the past 150 years of neuroscientific research, the field has undergone a tremendous evolution. Starting out with lesion-based inference of brain function, functional neuroimaging, introduced in the late 1980s, and increasingly fine-grained and sophisticated methods and analyses now allow us to study the live neural correlates of complex behaviors in individuals and multiple agents simultaneously. Classically, brain-behavior coupling has been studied as an association of a specific area in the brain and a certain behavioral outcome. This has been a crucial first step in understanding brain organization. Social cognitive processes, as well as their neural correlates, have typically been regarded and studied as isolated functions and blobs of neural activation. However, as our understanding of the social brain as an inherently dynamic organ grows, research in the field of social neuroscience is slowly undergoing the necessary evolution from studying individual elements to how these elements interact and their embedding within the overall brain architecture. In this article, we review recent studies that investigate the neural representation of social cognition as interacting, complex, and flexible networks. We discuss studies that identify individual brain networks associated with social affect and cognition, interaction of these networks, and their relevance for disorders of social affect and cognition. This perspective on social cognitive neuroscience can highlight how a more fine-grained understanding of complex network (re-)configurations could improve our understanding of social cognitive deficits in mental disorders such as autism spectrum disorder and schizophrenia, thereby providing new impulses for methods of interventions.

This article examines the stability of symptoms, syndromes, and diagnoses of specific anxiety and depressive disorders, as well as diagnostic shifts from one syndrome to another over time. Using retrospective and longitudinal prospective data from the baseline and first follow-up investigation (19.7 months later) of the Early Developmental Stages of Psychopathology Study (EDSP), we focus on establishing stability measures for early stages of mental disorders in a community sample of adolescents aged 14 to 17 years at baseline. The results are as follows: (1) Although only about 30% developed a full-blown DSM-IV disorder, psychopathological syndromes are widespread in adolescents: 15% of the population aged 14 to 17 at baseline were not affected by at least some clinically relevant symptoms of mental disorders either throughout their previous life or throughout the follow-up period. (2) The likelihood of staying free of symptoms and threshold disorders during follow-up was highest among subjects who were completely well at baseline. The probability of a positive outcome decreased as a function of severity of baseline diagnostic status. (3) There was a considerable degree of fluctuation not only in the diagnostic status and severity of specific disorders, but also in terms of complete remissions and shifts from one syndrome and disorder to another. (4) Anxiety disorders, overall, slightly differ with regard to the persistence and stability of the diagnostic status from depressive disorders. (5) However, there were remarkable differences between specific types of anxiety and depressive disorders. Consistent with other longitudinal epidemiological studies in the general population, this study finds that the syndromes and diagnoses of mental disorders have a strong tendency to wax and wane over time in this age group.

Background: When assessing the association between a factor X and a subsequent outcome Y in observational studies, the question that arises is what are the variables to adjust for to reduce bias due to confounding for causal inference on the effect of X on Y. Disregarding such factors is often a source of overestimation because these variables may affect both X and Y. On the other hand, adjustment for such variables can also be a source of underestimation because such variables may be the causal consequence of X and part of the mechanism that leads from X to Y. Methods: In this paper, we present a simple method to compute control variables in the presence of age at onset data on both X and a set of other variables. Using these age at onset data, control variables are computed that adjust only for conditions that occur prior to X. This strategy can be used in prospective as well as in survival analysis. Our method is motivated by an argument based on the counterfactual model of a causal effect. Results: The procedure is exemplified by examining of the relation between panic attack and the subsequent incidence of MDD. Conclusions: The results reveal that the adjustment for all other variables, irrespective of their temporal relation to X, can yield a false negative result (despite unconsidered confounders and other sources of bias).

Background: When assessing the association between a factor X and a subsequent outcome Y in observational studies, the question that arises is what are the variables to adjust for to reduce bias due to confounding for causal inference on the effect of X on Y. Disregarding such factors is often a source of overestimation because these variables may affect both X and Y. On the other hand, adjustment for such variables can also be a source of underestimation because such variables may be the causal consequence of X and part of the mechanism that leads from X to Y. Methods: In this paper, we present a simple method to compute control variables in the presence of age at onset data on both X and a set of other variables. Using these age at onset data, control variables are computed that adjust only for conditions that occur prior to X. This strategy can be used in prospective as well as in survival analysis. Our method is motivated by an argument based on the counterfactual model of a causal effect. Results: The procedure is exemplified by examining of the relation between panic attack and the subsequent incidence of MDD. Conclusions: The results reveal that the adjustment for all other variables, irrespective of their temporal relation to X, can yield a false negative result (despite unconsidered confounders and other sources of bias).

This article examines the stability of symptoms, syndromes, and diagnoses of specific anxiety and depressive disorders, as well as diagnostic shifts from one syndrome to another over time. Using retrospective and longitudinal prospective data from the baseline and first follow-up investigation (19.7 months later) of the Early Developmental Stages of Psychopathology Study (EDSP), we focus on establishing stability measures for early stages of mental disorders in a community sample of adolescents aged 14 to 17 years at baseline. The results are as follows: (1) Although only about 30% developed a full-blown DSM-IV disorder, psychopathological syndromes are widespread in adolescents: 15% of the population aged 14 to 17 at baseline were not affected by at least some clinically relevant symptoms of mental disorders either throughout their previous life or throughout the follow-up period. (2) The likelihood of staying free of symptoms and threshold disorders during follow-up was highest among subjects who were completely well at baseline. The probability of a positive outcome decreased as a function of severity of baseline diagnostic status. (3) There was a considerable degree of fluctuation not only in the diagnostic status and severity of specific disorders, but also in terms of complete remissions and shifts from one syndrome and disorder to another. (4) Anxiety disorders, overall, slightly differ with regard to the persistence and stability of the diagnostic status from depressive disorders. (5) However, there were remarkable differences between specific types of anxiety and depressive disorders. Consistent with other longitudinal epidemiological studies in the general population, this study finds that the syndromes and diagnoses of mental disorders have a strong tendency to wax and wane over time in this age group.

No description available.

No description available.

Das Hauptanliegen dieser Arbeit besteht in der Erweiterung des Kenntnisstandes zur Bedeutung des Berufs im Hinblick auf Prävalenz bzw. Risiko einer Psychischen und Verhaltensstörung (PVS) bei Frauen. Es gibt zahlreiche deskriptive Aussagen von Krankenkassen zum Auftreten verschiedener Diagnosegruppen oder Einzeldiagnosen, die sich bezüglich der Berufe jedoch auf die Gesamtheit der PVS beschränken. Die Analysen im Rahmen dieser Arbeit beruhen auf den Daten der weiblichen Versicherten der AOK PLUS für die Jahre 2007 bis 2011 mit insgesamt 2.113.083 Versichertenjahren. Es werden inferenzstatistische Aussagen getroffen, die auf Ergebnissen explorativer Voranalysen beruhen. Den berufsbezogenen Analysen werden zunächst Auswertungen zum Alter vorangestellt (Abschnitt 4). Erwartungsgemäß sind die Unterschiede zwischen den Altersgruppen für jede betrachtete Diagnosegruppe signifikant, jedoch weisen die Diagnosegruppen nicht dieselbe Altersabhängigkeit auf. Mit zunehmendem Alter zeigen sich zudem längere Erkrankungsdauern. Die Berufe im Datenbestand der AOK PLUS sind mittels der Klassifikation KldB 1988 [1] codiert (ca. 330 3-Steller). Für die berufsbezogenen Analysen wird in Abschnitt 5 zunächst die Frage geklärt, ob sich mittels Aggregationen der 3-Steller eine geeignetere Systematik finden lässt. Bis auf die Zusammenfassung einiger weniger 3-Steller ist das nicht der Fall. Basierend auf diesen Ergebnissen wird in Abschnitt 6 ein zweistufiges Vorgehen gewählt. In einem ersten Schritt werden für jede betrachtete PVS-Diagnose (bzw. Diagnosegruppe) die 21 Berufe mit den größten Quoten (AU-Personen pro 1.000 Versichertenjahre) ermittelt. Dabei werden zusätzlich altersstandardisierte Quoten berechnet. In einem zweiten Schritt werden die Ergebnisse des ersten Schrittes mittels Binärer Logistischer Regression (BLR) mathematisch-statistisch abgesichert. Dabei wird die Gesamtheit der nicht ausgewählten Berufe (also alle außer den 21) als Referenzgruppe verwendet. Für alle 21 ausgewählten Berufe werden dann die Odds Ratios gegenüber der Referenzgruppe in Verbindung mit einer Aussage zur Signifikanz ermittelt. Daraus resultiert schließlich eine Rangreihe der „Risikoberufe“, sortiert nach den unteren Grenzen der Konfidenzintervalle. Das Alter wird in diesem Schritt durch Einbezug als potentielle Einflussgröße berücksichtigt. Die Ergebnisse (Abschnitte 6 und 7) zeigen, dass Frauen besonders von PVS betroffen sind, wenn sie als Schienenfahrzeugführer (711), Soldaten, Grenzschützer, Polizisten, Sicherheitskontrolleure (801, 803), Krankenversicherungsfachleute (693), Telefonisten (734) sowie als Fachschul-, Berufsschul- und Werklehrer (874) arbeiten. Für Einzeldiagnosen und Diagnosegruppen ergibt sich ebenfalls ein differenziertes Bild. Bei den Erkrankungsdauern in Abhängigkeit vom Beruf zeigen sich keine substantiellen Auffälligkeiten.:1 Einleitung 7 2 Problemlage und Fragestellungen 8 2.1 LITERATURÜBERSICHT 8 2.1.1 Allgemeine Aussagen und Kosten 8 2.1.2 Aussagen zu Diagnosegruppen und Einzeldiagnosen der PVS 10 2.1.3 Aussagen zum Alter 12 2.1.4 Aussagen zum Beruf 13 2.1.5 Berufe als Risikofaktor für psychische Fehlbelastung 19 2.1.6 Psychischen Gesundheit von Frauen: Der Beruf als Modulator 21 2.1.7 Gesamteinschätzung zum Kenntnisstand 21 2.2 FRAGESTELLUNGEN 23 2.2.1 Zum Alter 24 2.2.2 Zur Systematik der Berufe 25 2.2.3 Zum Beruf 26 2.2.4 Zur Relation der Frage- und Zielstellungen 26 3 Datenbasis und methodische Aspekte 27 3.1 DATENBASIS 27 3.2 INDIKATOREN, DESKRIPTIVE STATISTIKEN 28 3.3 SYSTEMATIK DER BERUFE 29 3.4 MATHEMATISCH-STATISTISCHE VERFAHREN 30 3.4.1 Altersstandardisierung 30 3.4.2 Chi-Quadrat- und MANTEL-HAENSZEL-Test 31 3.4.3 Binäre Logistische Regression (BLR) 31 3.4.4 Mathematisch-statistische Aussagen zu Erkrankungsdauern 33 3.4.5 Signifikanzniveau und Darstellung von Signifikanzaussagen 35 4 F-Diagnosen und Z73 nach Alter 36 4.1 BETROFFENENQUOTEN 36 4.2 ERKRANKUNGSDAUERN 39 5 Systematiken zum Beruf im Vergleich 43 5.1 BERUFSORDNUNG 44 5.2 SYSTEMATIK NACH BLOSSFELD 45 5.3 ZUSAMMENGEFASSTE BERUFSGRUPPEN NACH SUGA 46 5.4 BERUFSGRUPPEN 47 5.5 PARTIELL AGGREGIERTE BERUFE 48 6 F-Diagnosen und Z73 nach Beruf und Alter 49 6.1 DIAGNOSEHAUPTGRUPPE F00-99 PSYCHISCHE UND VERHALTENSSTÖRUNGEN 49 6.1.1 AU-Personen 49 6.1.2 Erkrankungsdauern 54 6.2 DIAGNOSEGRUPPEN IM VERGLEICH 55 6.3 DIAGNOSEGRUPPE F10-19 PSYCHISCHE UND VERHALTENSSTÖRUNGEN DURCH PSYCHOTROPE SUBSTANZEN 56 6.3.1 Gesamtgruppe F10-19 – AU-Personen 56 6.3.2 Diagnosen F10 und F17 – AU-Personen 59 6.3.3 AU-Personen – die auffälligsten Berufe 60 6.3.4 Erkrankungsdauern 61 6.4 DIAGNOSEGRUPPE F30-39 AFFEKTIVE STÖRUNGEN 62 6.4.1 Gesamtgruppe F30-39 – AU-Personen 62 6.4.2 Diagnosen F32 und F33 – AU-Personen 64 6.4.3 AU-Personen – die auffälligsten Berufe 66 6.4.4 Erkrankungsdauern 66 6.5 DIAGNOSEGRUPPE F40-48 NEUROTISCHE, BELASTUNGS- UND SOMATOFORME STÖRUNGEN 68 6.5.1 Gesamtgruppe F40-48 – AU-Personen 68 6.5.2 Diagnosen F41, F43, F45, F48 – AU-Personen 70 6.5.3 AU-Personen – die auffälligsten Berufe 73 6.5.4 Erkrankungsdauern 74 6.6 DIAGNOSE Z73 PROBLEME BEI DER LEBENSBEWÄLTIGUNG (BURN-OUT) 75 6.6.1 AU-Personen 75 6.6.2 Erkrankungsdauern 76 6.7 AU-PERSONEN – DIE AUFFÄLLIGSTEN BERUFE IM GESAMTKONTEXT 77 7 Diskussion der Ergebnisse 83 7.1 METHODISCHE ASPEKTE 83 7.1.1 Einordnung der Analysen im Sinne der Epidemiologie 83 7.1.2 Bezugsbasis Versichertenjahre versus Versicherte 85 7.1.3 Zur Altersstandardisierung 86 7.1.4 Zur Binären Logistischen Regression 88 7.1.5 Binäre logistische Regression versus MANTEL-HAENSZEL Test und Anmerkungen zum Signifikanzniveau 89 7.1.6 Zur Auswahl der Zielgrößen AU-Personen und Erkrankungsdauern 89 7.2 INHALTLICHE ASPEKTE 91 7.2.1 Zum Alter 91 7.2.1.1 Betroffenenquoten 91 7.2.1.2 Erkrankungsdauern 92 7.2.2 Zu den Berufen 93 7.2.2.1 Zu F00-99 – Gesamtheit der PVS – Erkrankungsrisiko 93 7.2.2.2 Zu F00-99 – Gesamtheit der PVS – Erkrankungsdauern 98 7.2.2.3 Zu den Diagnosegruppen F30-39 Affektive Störungen und F40-48 Neurotische, Belastungs- und somatoforme Störungen – Erkrankungsrisiko 99 7.2.2.4 Zur Diagnose F10 PVS durch Alkohol – Erkrankungsrisiko 103 7.2.2.5 Zur Diagnose F17 – PVS durch Tabak 106 7.2.2.6 Zu Diagnose Z73 – Burn-out-Syndrom 108 7.2.3 Zusammenfassung 109 7.3 AUSBLICK 110 8 Literatur 112 9 Verzeichnis der Abkürzungen 122 10 Verzeichnis der Abbildungen 124 11 Verzeichnis der Tabellen 126 Anlagen 128