title: A trajectory-based approach to understand the factors associated with persistent depressive symptoms in primary care.
abstract: BACKGROUND
  Depression screening in primary care yields high numbers. Knowledge of how depressive symptoms change over time is limited, making decisions about type, intensity, frequency and length of treatment and follow-up difficult. This study is aimed to identify depressive symptom trajectories and associated socio-demographic, co-morbidity, health service use and treatment factors to inform clinical care.
  
  
  METHODS
  789 people scoring 16 or more on the CES-D recruited from 30 randomly selected Australian family practices. Depressive symptoms are measured using PHQ-9 at 3, 6, 9 and 12 months.
  
  
  RESULTS
  Growth mixture modelling identified a five-class trajectory model as the best fitting (lowest Bayesian Information Criterion): three groups were static (mild (n=532), moderate (n=138) and severe (n=69)) and two were dynamic (decreasing severity (n=32) and increasing severity (n=18)). The mild symptom trajectory was the most common (n=532). The severe symptom trajectory group (n=69) differed significantly from the mild symptom trajectory group on most variables. The severe and moderate groups were characterised by high levels of disadvantage, abuse, morbidity and disability. Decreasing and increasing severity trajectory classes were similar on most variables.
  
  
  CONCLUSIONS
  Adult only cohort, self-report measures.
  
  
  CONCLUSIONS
  Most symptom trajectories remained static, suggesting that depression, as it presents in primary care, is not always an episodic disorder. The findings indicate future directions for building prognostic models to distinguish those who are likely to have a mild course from those who are likely to follow more severe trajectories. Determining appropriate clinical responses based upon a likely depression course requires further research.
authors: General Practice and Primary Health Care Academic Centre, The University of Melbourne, 200 Berkeley Street, Carlton, VIC 3053, Australia. j.gunn@unimelb.edu.au
authors: Jane
authors: J
authors: Gunn
authors: Peter
authors: P
authors: Elliott
authors: Konstancja
authors: K
authors: Densley
authors: Aves
authors: A
authors: Middleton
authors: Gilles
authors: G
authors: Ambresin
authors: Christopher
authors: C
authors: Dowrick
authors: Helen
authors: H
authors: Herrman
authors: Kelsey
authors: K
authors: Hegarty
authors: Gail
authors: G
authors: Gilchrist
authors: Frances
authors: F
authors: Griffiths
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001315:Australia; D001499:Bayes Theorem; D002908:Chronic Disease; D015331:Cohort Studies; D003863:Depression / Q000175:diagnosis* / Q000523:psychology; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D011320:Primary Health Care / Q000379:methods*; D012307:Risk Factors; D012720:Severity of Illness Index; D055815:Young Adult