Original Research

Does the Severity of Mood and Anxiety Symptoms Predict Health Care Utilization?

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References

Charge Differences By Mood And Anxiety Disorder Criteria

Table 3 presents mean charges for patients who did and did not meet DSM-III-R criteria for any mood or anxiety disorder according to the PRIME-MD. Again, a trend of diminishing charges over time was seen for all patients. For each period studied, patients who met criteria for either a mood or anxiety disorder had nearly twice the charges of patients who did not meet criteria for these disorders. Significant differences were seen between groups for each period.

Mood Or Anxiety Disorder And Symptom Severity

No significant interactions were seen between the presence of a clinically diagnosed mood or anxiety disorder and symptom severity with respect to our charge data when examined using analysis of covariance procedures. This lack of interaction persisted across all periods for which we obtained charge data. Again this lends support to the idea that mood and anxiety symptom severity operates independently from the presence of a diagnosed disorder.

Regression Analyses

The results of stepwise multiple regression analyses are seen in Table 4. Medical comorbidity and income entered each regression model. Age was a significant factor influencing charges in 3 of the 5 periods. The influence of symptom severity on utilization showed decreasing levels of significance over time. The variable that tracked the presence of a mood or anxiety disorder entered the regression models for only the 2 periods encompassing the first 6 months after the index visit. This indicates that whether a subject met DSM-III-R criteria did not significantly influence utilization beyond the 6 months immediately following the index visit. The variance in total charges explained by each model was consistently approximately 8% to 11%, except for the initial period, where the model explained 15% of the charge variance. Regression diagnostics confirmed that the independent variables were not collinear.

Adjustment Of Mean Charges For Significant Covariates

Table 5 displays mean charges across symptom severity groups after adjusting for covariates that entered our regression models. While the magnitude of charges was reduced somewhat compared with the unadjusted values, the relative charge differences between symptom severity groups were nearly the same as in the unadjusted means. As expected, significant differences existed in the mean charges between severity groups for all periods, except the 6 to 9 months after the index visit. There was an almost fourfold reduction in the mean charges for the high-severity group over the entire study period; however, even at 9 to 12 months, patients in this group showed an average of 2 times the charges of those in the low-severity group.

Discussion

In this study we sought to expand our initial study of differences between patients with varying levels of mood and anxiety symptoms by examining differences in health care utilization. We also sought to determine whether any utilization differences would persist over time. Because of the availability of charge data in the UTMB health system, we used charge data as a surrogate measure for health care utilization.

Limitations

Our study has limitations that should be understood before we address potential implications. The findings are limited by being a retrospective secondary analysis of data. Our original study was not specifically designed to address the questions we have raised here. Also, the subjects were recruited from a single primary care site, which may limit the generalizability of the results. However, the high quality of the initial sampling and our ability to adjust for potential sociodemographic and medical confounders may balance these limitations.

Total charges from a single system were used as an indirect measure of health care utilization. Although it is possible that patients may have accessed health care outside of the UTMB system during the study period, our setting of Galveston Island represents a relatively closed health care environment, with UTMB being the dominant care provider. One confirmation of this fact is the 14.3% rate of attrition from our sample. While this rate may seem high, it should be remembered that our study was purely observational, with no direct contact between the investigators and subjects after the initial index visit. We were unable to obtain information on third-party payers from our billing data. We could not therefore adjust for potential variations in charges based on these differences.

A final limitation stems from the initial sampling design, which enrolled subjects who were presenting for health care and measured their symptom severity at a single point. The relative impact of mental health symptom severity on utilization in our study may be different from that of subjects who were not actively presenting for care. But our sampling design, which enrolled only patients with prior appointments for nonurgent care, and our adjustment for medical comorbidity should have helped alleviate this issue. However, it is very likely that the progressive decrease seen in both the charges and in the ability of our regression models to explain charge variances is due to this limitation. In future studies, measuring mental health symptom severity at multiple points over time might provide a way of understanding the relationship of our findings to patients presenting for medical care.

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