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    A trajectory-based approach to understand the factors associated with persistent depressive symptoms in primary care
    Gunn, J ; Elliott, P ; Densley, K ; Middleton, A ; Ambresin, G ; Dowrick, C ; Herrman, H ; Hegarty, K ; Gilchrist, G ; Griffiths, F (ELSEVIER, 2013-06-01)
    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. LIMITATIONS: 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.
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    Self-Rated Health and Long-Term Prognosis of Depression
    Ambresin, G ; Chondros, P ; Dowrick, C ; Herrman, H ; Gunn, JM (ANNALS FAMILY MEDICINE, 2014-01-01)
    PURPOSE: Indicators of prognosis should be considered to fully inform clinical decision making in the treatment of depression. This study examines whether self-rated health predicts long-term depression outcomes in primary care. METHODS: Our analysis was based on the first 5 years of a prospective 10-year cohort study underway since January 2005 conducted in 30 randomly selected Australian primary care practices. Participants were 789 adult patients with a history of depressive symptoms. Main outcome measures include risks, risk differences, and risk ratios of major depressive syndrome (MDS) on the Patient Health Questionnaire. RESULTS: Retention rates during the 5 years were 660 (84%), 586 (74%), 560 (71%), 533 (68%), and 517 (66%). At baseline, MDS was present in 27% (95% CI, 23%-30%). Cross-sectional analysis of baseline data showed participants reporting poor or fair self-rated health had greater odds of chronic illness, MDS, and lower socioeconomic status than those reporting good to excellent self-rated health. For participants rating their health as poor to fair compared with those rating it good to excellent, risk ratios of MDS were 2.10 (95% CI, 1.60-2.76), 2.38 (95% CI, 1.77-3.20), 2.22 (95% CI, 1.70-2.89), 1.73 (95% CI, 1.30-2.28), and 2.15 (95% CI, 1.59-2.90) at 1, 2, 3, 4, and 5 years, after accounting for missing data using multiple imputation. After adjusting for age, sex, multimorbidity, and depression status and severity, self-rated health remained a predictor of MDS up to 5 years. CONCLUSIONS: Self-rated health offers family physicians an efficient and simple way to identify patients at risk of poor long-term depression outcomes and to inform therapeutic decision making.