Course trajectories of unipolar depressive disorders identified by latent class growth analysis.
نویسندگان
چکیده
BACKGROUND Current classification of unipolar depression reflects the idea that prognosis is essential. However, do DSM categories of major depressive disorder (MDD), dysthymic disorder (Dysth) and double depression (DD=MDD+Dysth) indeed adequately represent clinically relevant course trajectories of unipolar depression? Our aim was to test DSM categories (MDD, Dysth and DD) in comparison with empirically derived prognostic categories, using a prospectively followed cohort of depressed patients. METHOD A large sample (n=804) of out-patients with unipolar depression were derived from a prospective cohort study, the Netherlands Study of Depression and Anxiety (NESDA). Using latent class growth analysis (LCGA), empirically derived 2-year course trajectories were constructed. These were compared with DSM diagnoses and a wider set of putative predictors for class membership. RESULTS Five course trajectories were identified, ranging from mild severity and rapid remission to high severity and chronic course trajectory. Contrary to expectations, more than 50% of Dysth and DD were allocated to classes with favorable course trajectories, suggesting that current DSM categories do not adequately represent course trajectories. The class with the most favorable course trajectory differed on several characteristics from other classes (younger age, more females, less childhood adversity, less somatic illnesses, lower neuroticism, higher extraversion). Older age, earlier age of onset and lower extraversion predicted poorest course trajectory. CONCLUSIONS MDD, Dysth and DD did not adequately match empirically derived course trajectories for unipolar depression. For the future classification of unipolar depression, it may be wise to retain the larger, heterogeneous category of unipolar depression, adopting cross-cutting dimensions of severity and duration to further characterize patients.
منابع مشابه
Course trajectories of unipolar depressive disorders identified by Latent Class Growth Analysis Chapter 6
Background Current classification of unipolar depression reflects the idea that prognosis is essential. However, do DSM-categories of Major Depressive Disorder (MDD), Dysthymic Disorder and Double Depression indeed adequately represent clinically relevant course trajectories of unipolar depression? Our aim was to test DSM-categories (MDD, Dysthymic Disorder and Double Depression) in comparison ...
متن کاملHeterogeneity in the course of posttraumatic stress disorder: trajectories of symptomatology.
Unconditional and conditional trajectories of posttraumatic stress disorder (PTSD) symptomatology were examined using a sample of U.S. soldiers deployed on a NATO-led peacekeeping mission to Kosovo. Data were collected at 4 time points, ranging from the weeks leading up to deployment to 9-months post deployment. Latent class growth analysis revealed 4 unique symptom trajectories: resilience, re...
متن کاملHeterogeneous Trajectories of Physical and Mental Health in Late Middle Age: Importance of Life-Course Socioeconomic Positions
Drawing on life course and cumulative disadvantage theory, this study examines heterogeneous trajectories of functional limitations and depressive symptoms among late middle-aged individuals. This study used prospective data from 6010 adults, 51 to 64 years old, collected over a 12-year-period from the Health and Retirement Study. Considering the empirical proposition that several physical and ...
متن کاملAdolescent Trajectories of Depressive Symptoms: Codevelopment of Behavioral and Academic Problems.
PURPOSE Increasing evidence suggests the existence of heterogeneity in the development of depressive symptoms during adolescence, but little remains known regarding the implications of this heterogeneity for the development of commonly co-occurring problems. In this study, we derived trajectories of depressive symptoms in adolescents and examined the codevelopment of multiple behavioral and aca...
متن کاملUsing a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction.
Positive and negative affect data are often collected over time in psychiatric care settings, yet no generally accepted means are available to relate these data to useful diagnoses or treatments. Latent class analysis attempts data reduction by classifying subjects into one of K unobserved classes based on observed data. Latent class models have recently been extended to accommodate longitudina...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Psychological medicine
دوره 42 7 شماره
صفحات -
تاریخ انتشار 2012