tailieunhanh - Báo cáo y học: " Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach"

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach | BMC Psychiatry BioMed Central Research article Modifiable risk factors predicting major depressive disorder at four year follow-up a decision tree approach Philip J Batterham 1 Helen Christensen1 and Andrew J Mackinnon2 Address 1Centre for Mental Health Research The Australian National University Canberra Australia and 2Orygen Research Centre The University of Melbourne Melbourne Australia Email Philip J Batterham - Helen Christensen - Andrew J Mackinnon - Corresponding author Open Access Published 22 November 2009 Received 5 June 2009 Accepted 22 November 2009 BMC Psychiatry 2009 9 75 doi 1471-244X-9-75 p This article is available from http 1471-244X 9 75 2009 Batterham et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Relative to physical health conditions such as cardiovascular disease little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time using the decision tree method favoured in assessing cardiovascular disease risk. Methods The PATH through Life cohort was used for the study comprising 2 105 20-24 year olds 2 323 40-44 year olds and 2 177 60-64 year olds sampled from the community in the Canberra region Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. Results The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly .