Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach

Philip J Batterham,Helen Christensen,Andrew J Mackinnon

doi:10.1186/1471-244x-9-75

Philip J Batterham, Helen Christensen + Show 1 more

Open Access

https://doi.org/10.1186/1471-244x-9-75

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Abstract

BackgroundRelative 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.MethodsThe 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.ResultsThe decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors.ConclusionThe decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Highlights

Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression
Decision tree methodology successfully categorized participants in the PATH cohort into a wide range of depression risk groups, distinguishing subgroups of participants with virtually no risk through to groups with almost 40% risk of having major depressive disorder four years after their status on a raft of risk indicators was ascertained
The decision tree method was useful in assessing the risk of major depressive disorder over four years

Summary

Introduction

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. Depression is a leading cause of disease burden worldwide [1,2], and is the leading risk factor for completed suicide. It frequently leads to substance abuse and lowered work productivity and is a risk factor for physical illnesses such as cardiovascular disease [3]. Little is known about risk factors which predict the incidence, recurrence and chronicity of depression. The analysis of the expected effects of a reduction of these risks over time is rarely investigated, a few papers which break this rule using longitudinal data have begun recently to model risk reduction [4,5]

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