Dynamic risk for first onset of depressive disorders in adolescence: does change matter?
Chronicity
dynamic risk
escalation
first-onset depression
prediction
Journal
Psychological medicine
ISSN: 1469-8978
Titre abrégé: Psychol Med
Pays: England
ID NLM: 1254142
Informations de publication
Date de publication:
04 2023
04 2023
Historique:
medline:
15
6
2023
pubmed:
23
11
2021
entrez:
22
11
2021
Statut:
ppublish
Résumé
Risk factors for depressive disorders (DD) change substantially over time, but the prognostic value of these changes remains unclear. Two basic types of dynamic effects are possible. The 'Risk Escalation hypothesis' posits that worsening of risk levels predicts DD onset above average level of risk factors. Alternatively, the 'Chronic Risk hypothesis' posits that the average level rather than change predicts first-onset DD. We utilized data from the ADEPT project, a cohort of 496 girls (baseline age 13.5-15.5 years) from the community followed for 3 years. Participants underwent five waves of assessments for risk factors and diagnostic interviews for DD. For illustration purposes, we selected 16 well-established dynamic risk factors for adolescent depression, such as depressive and anxiety symptoms, personality traits, clinical traits, and social risk factors. We conducted Cox regression analyses with time-varying covariates to predict first DD onset. Consistently elevated risk factors (i.e. the mean of multiple waves), but not recent escalation, predicted first-onset DD, consistent with the Chronic Risk hypothesis. This hypothesis was supported across all 16 risk factors. Across a range of risk factors, girls who had first-onset DD generally did not experience a sharp increase in risk level shortly before the onset of disorder; rather, for years before onset, they exhibited elevated levels of risk. Our findings suggest that chronicity of risk should be a particular focus in screening high-risk populations to prevent the onset of DDs. In particular, regular monitoring of risk factors in school settings is highly informative.
Sections du résumé
BACKGROUND
Risk factors for depressive disorders (DD) change substantially over time, but the prognostic value of these changes remains unclear. Two basic types of dynamic effects are possible. The 'Risk Escalation hypothesis' posits that worsening of risk levels predicts DD onset above average level of risk factors. Alternatively, the 'Chronic Risk hypothesis' posits that the average level rather than change predicts first-onset DD.
METHODS
We utilized data from the ADEPT project, a cohort of 496 girls (baseline age 13.5-15.5 years) from the community followed for 3 years. Participants underwent five waves of assessments for risk factors and diagnostic interviews for DD. For illustration purposes, we selected 16 well-established dynamic risk factors for adolescent depression, such as depressive and anxiety symptoms, personality traits, clinical traits, and social risk factors. We conducted Cox regression analyses with time-varying covariates to predict first DD onset.
RESULTS
Consistently elevated risk factors (i.e. the mean of multiple waves), but not recent escalation, predicted first-onset DD, consistent with the Chronic Risk hypothesis. This hypothesis was supported across all 16 risk factors.
CONCLUSIONS
Across a range of risk factors, girls who had first-onset DD generally did not experience a sharp increase in risk level shortly before the onset of disorder; rather, for years before onset, they exhibited elevated levels of risk. Our findings suggest that chronicity of risk should be a particular focus in screening high-risk populations to prevent the onset of DDs. In particular, regular monitoring of risk factors in school settings is highly informative.
Identifiants
pubmed: 34802476
doi: 10.1017/S0033291721004190
pii: S0033291721004190
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
2352-2360Subventions
Organisme : NIMH NIH HHS
ID : R01 MH093479
Pays : United States
Organisme : NIMH NIH HHS
ID : R56 MH117116
Pays : United States