Investigation of a severity-based classification of mood and anxiety symptoms in primary care patients

Abstract

Background: Current Diagnostic and Statistical Manual of Mental Disorders (DSM) classifications describe spectrums of symptoms that define mood and anxiety disorders. These DSM classifications have been applied to primary care populations to establish the frequency of these disorders in primary care. DSM classifications, however, might not adequately describe the underlying or natural groupings of mood and anxiety symptoms in primary care. This study explores common clusters of mood and anxiety symptoms and their severity while exploring the degree of cluster congruency with current DSM classification schemes. We also evaluate how well the groupings derived from these different classifying methods explain differences in patients' health-related quality of life.

Methods: Study design was cross-sectional, using a sample of 1333 adult primary care patients attending a university-based family medicine clinic. We applied cluster analysis to responses on a 15-item instrument measuring symptoms of mood and anxiety and their severity. We used the PRIME-MD to determine the presence of DSM-III-R disorders. The SF-36 Health Survey was used to assess health-related quality of life.

Results: Cluster analysis produced four groups of patients different from groupings based on the DSM. These four groups differed from each other on sociodemographic indicators, health-related quality of life, and frequency of DSM disorders. Cluster membership was associated in three of four clusters with a clinically significant and progressive decrease in mental and physical health functioning as measured by the SF-36 Health Survey. This decline was independent of the presence of a DSM diagnosis.

Conclusions: A primary care classification scheme for mood and anxiety symptoms that includes severity appears to provide more useful information than traditional DSM classifications of disorders.