Examining determinants of early and late age at onset in panic disorder: An admixture analysis

Lee Tibi; Patricia van Oppen; Idan M. Aderka; Anton J. L. M. van Balkom; Neeltje M. Batelaan; Philip Spinhoven; Brenda W. Penninx; Gideon E. Anholt

doi:10.1016/j.jpsychires.2013.09.001

Examining determinants of early and late age at onset in panic disorder: An admixture analysis

Lee Tibi^*, Patricia van Oppen, Idan M. Aderka, Anton J. L. M. van Balkom, Neeltje M. Batelaan, Philip Spinhoven, Brenda W. Penninx, Gideon E. Anholt

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

22 Citations (Scopus)

Abstract

Past research demonstrated that age at onset might account for different clinical and etiological characteristics in panic disorder (PD). However, prior research relied on arbitrary choices of age cut-offs. Using a data-driven validated method, this study aimed to examine differences between early and late onset PD in various determinants. Admixture analysis was used to determine the best fitting model of age at onset distribution in PD. Data was collected from 511 individuals (ages 18-65) with PD diagnoses, who participated in the Netherlands Study of Depression and Anxiety (NESDA). DSM-IV comorbidities and various measures of childhood adversities, suicidal behavior, anxiety and depressive symptoms were assessed. The best fitting cut-off score between early and late age at onset groups was 27 years (early age at onset

Original language	English
Pages (from-to)	1870-1875
Number of pages	6
Journal	Journal of Psychiatric Research
Volume	47
Issue number	12
DOIs	https://doi.org/10.1016/j.jpsychires.2013.09.001
Publication status	Published - Dec-2013

Keywords

Panic disorder
Age at onset
Risk factors
DIAGNOSTIC INTERVIEW CIDI
CHILDHOOD LIFE EVENTS
OF-ONSET
ANXIETY DISORDERS
TREATMENT RESPONSE
COMORBIDITY
DEPRESSION
RELIABILITY
OUTPATIENTS
INVENTORY

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@article{3feb93a3ec604af68067699553237c5a,

title = "Examining determinants of early and late age at onset in panic disorder: An admixture analysis",

abstract = "Past research demonstrated that age at onset might account for different clinical and etiological characteristics in panic disorder (PD). However, prior research relied on arbitrary choices of age cut-offs. Using a data-driven validated method, this study aimed to examine differences between early and late onset PD in various determinants. Admixture analysis was used to determine the best fitting model of age at onset distribution in PD. Data was collected from 511 individuals (ages 18-65) with PD diagnoses, who participated in the Netherlands Study of Depression and Anxiety (NESDA). DSM-IV comorbidities and various measures of childhood adversities, suicidal behavior, anxiety and depressive symptoms were assessed. The best fitting cut-off score between early and late age at onset groups was 27 years (early age at onset",

keywords = "Panic disorder, Age at onset, Risk factors, DIAGNOSTIC INTERVIEW CIDI, CHILDHOOD LIFE EVENTS, OF-ONSET, ANXIETY DISORDERS, TREATMENT RESPONSE, COMORBIDITY, DEPRESSION, RELIABILITY, OUTPATIENTS, INVENTORY",

author = "Lee Tibi and {van Oppen}, Patricia and Aderka, {Idan M.} and {van Balkom}, {Anton J. L. M.} and Batelaan, {Neeltje M.} and Philip Spinhoven and Penninx, {Brenda W.} and Anholt, {Gideon E.}",

year = "2013",

month = dec,

doi = "10.1016/j.jpsychires.2013.09.001",

language = "English",

volume = "47",

pages = "1870--1875",

journal = "Journal of Psychiatric Research",

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number = "12",

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T1 - Examining determinants of early and late age at onset in panic disorder

T2 - An admixture analysis

AU - Tibi, Lee

AU - van Oppen, Patricia

AU - Aderka, Idan M.

AU - van Balkom, Anton J. L. M.

AU - Batelaan, Neeltje M.

AU - Spinhoven, Philip

AU - Penninx, Brenda W.

AU - Anholt, Gideon E.

PY - 2013/12

Y1 - 2013/12

N2 - Past research demonstrated that age at onset might account for different clinical and etiological characteristics in panic disorder (PD). However, prior research relied on arbitrary choices of age cut-offs. Using a data-driven validated method, this study aimed to examine differences between early and late onset PD in various determinants. Admixture analysis was used to determine the best fitting model of age at onset distribution in PD. Data was collected from 511 individuals (ages 18-65) with PD diagnoses, who participated in the Netherlands Study of Depression and Anxiety (NESDA). DSM-IV comorbidities and various measures of childhood adversities, suicidal behavior, anxiety and depressive symptoms were assessed. The best fitting cut-off score between early and late age at onset groups was 27 years (early age at onset

AB - Past research demonstrated that age at onset might account for different clinical and etiological characteristics in panic disorder (PD). However, prior research relied on arbitrary choices of age cut-offs. Using a data-driven validated method, this study aimed to examine differences between early and late onset PD in various determinants. Admixture analysis was used to determine the best fitting model of age at onset distribution in PD. Data was collected from 511 individuals (ages 18-65) with PD diagnoses, who participated in the Netherlands Study of Depression and Anxiety (NESDA). DSM-IV comorbidities and various measures of childhood adversities, suicidal behavior, anxiety and depressive symptoms were assessed. The best fitting cut-off score between early and late age at onset groups was 27 years (early age at onset

KW - Panic disorder

KW - Age at onset

KW - Risk factors

KW - DIAGNOSTIC INTERVIEW CIDI

KW - CHILDHOOD LIFE EVENTS

KW - OF-ONSET

KW - ANXIETY DISORDERS

KW - TREATMENT RESPONSE

KW - COMORBIDITY

KW - DEPRESSION

KW - RELIABILITY

KW - OUTPATIENTS

KW - INVENTORY

U2 - 10.1016/j.jpsychires.2013.09.001

DO - 10.1016/j.jpsychires.2013.09.001

M3 - Article

SN - 0022-3956

VL - 47

SP - 1870

EP - 1875

JO - Journal of Psychiatric Research

JF - Journal of Psychiatric Research

IS - 12

ER -

Examining determinants of early and late age at onset in panic disorder: An admixture analysis

Abstract

Keywords

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