BACKGROUND: Traditional diagnostic criteria for depression and anxiety fail to account for symptom severity. We previously evaluated a severity-based classification system of mood and anxiety symptoms. This study examines whether those severity groups are predictive of differences in health care utilization.
METHODS: We used a cohort design to compare the health care utilization of 1232 subjects classified into 4 groups according to symptom severity. Health care billing data were evaluated for each subject for a 15-month period around the index visit. Multiple linear regression models were used to examine relative contributions of individual variables to differences in health care utilization. Analysis of variance procedures were used to compare charges among the severity groups after adjusting for demographic and medical comorbidity variables.
RESULTS: After adjustment, significant differences in health care utilization between groups were seen in all but 3 of the 15 months studied. Also, after adjustment, the presence of a mood or anxiety disorder influenced utilization for only a 6-month period. At 9 to12 months, subjects in the high-severity group showed a more than twofold difference in adjusted charges compared with the low-severity group ($225.36 vs $94.37).
CONCLUSIONS: Our severity-based classification predicts statistically and clinically significant differences in health care utilization over most of a 15-month period. Differences in utilization persist even after adjustment for medical comorbidity and significant demographic covariates. Our work lends additional evidence that beyond screening for the presence of mood and anxiety disorders, it is important to assess symptom severity in primary care patients. Further study directed toward developing effective methods of identifying patients with high levels of mood and anxiety symptom severity could result in significant cost savings.
Mental health problems in the primary care setting have received a great deal of attention over the past 20 years. Much of the interest and study has focused on depressive disorders, which have been shown to be common in primary care.1-7 Studies have demonstrated that while depressive disorders result in significant morbidity,8,9 they are often underrecognized by primary care physicians.10-12 Consequently, instruments have been developed to assist primary care physicians in the screening and identification of patients who meet standard Diagnostic and Statistical Manual13 (DSM) criteria for depressive disorders.6,7,14,15
This underrecognition and the development of screening tools have fostered the creation of a screen-detect-treat-improve strategy. This strategy is embodied in the National Institute of Mental Health/Agency for Health Care Policy and Research guidelines for the detection and treatment of depression in primary care.16 The underlying assumption is that primary care patients who meet criteria for depression are at risk for significant morbidity and mortality, and may significantly increase costs to the health care system.17 Unfortunately, early clinical trials utilizing this screen-detect-treat-improve strategy have shown little success in improving outcomes.18-21 One explanation for this may be that screening on the basis of DSM22 criteria alone does not identify those patients with the highest morbidity and those most likely to benefit from intervention.
In a previous study, we described a mathematical approach to classifying patients with mood and anxiety symptoms in primary care.23 This approach grouped patients according to the self-reported severity of 15 mood and anxiety symptoms. These groupings did not show much agreement with the diagnosis of DSM-III-R criteria-based mood or anxiety disorders, but did as well or better than DSM-III-R criteria at predicting differences in health-related quality of life (HRQOL). This follow-up study sought to determine if these severity-based groups were also useful in predicting differences in health care utilization over time. If severity-related groupings are proved predictive of utilization differences, our study would lend additional evidence to support the routine assessment of mood and anxiety symptom severity before, or even instead of, screening for mental health disorders.
Methods
Sample And Procedures
For this study we used a secondary analysis of data collected as part of a study of alcohol screening methods in primary care funded by the National Institute on Alcohol Abuse and Alcoholism. Subjects were adult primary care patients presenting for nonurgent care to the Family Practice Center of the University of Texas Medical Branch (UTMB) in Galveston, Texas. They were enrolled over 15 months, beginning in October 1993. The sampling strategy called for an oversampling of women, African Americans, and Mexican Americans. Full details of the sampling strategy are available elsewhere.24 Institutional Review Board approval was obtained from UTMB before the initial study and before the subsequent sampling of charge data.
Primary Measures
Mood and Anxiety Symptoms and Severity-Based Clusters. We developed a severity measure of mood and anxiety symptoms for the primary alcohol screening study. We asked patients to rate the frequency of their symptoms using a 2-week time frame. Response options included “none of the time,” “a little of the time,” “some of the time,” “most of the time,” and “all of the time.” Questions from the symptom measure are presented in Table 1.