Predictors of a Medical-Offset Effect Among Patients Receiving Antidepressant Therapy

Several studies have shown that patients receiving treatment for mental disorders often manifest reduced utilization of general medical services. This phenomenon is sometimes referred to as the “medical-offset effect” because the savings in medical care costs may be sufficient to outweigh the costs of mental health treatment (1). The offset effect is generally held to derive from two related phenomena: 1) patients with untreated mental disorders frequently present with physical symptoms and persistent complaints that resolve with appropriate mental health treatment (2), and 2) physical disorders may contribute to emotional distress, which in turn may exacerbate patients' symptoms or delay recovery (3). An offset effect has not been consistently observed in studies of this phenomenon, however (4, 5). This may be because some patient characteristics and/or interventions may be more or less likely to be associated with a medical-offset effect than others. In this study, we examined potential predictors of a medical-offset effect.

METHOD

Sample Selection

Study subjects were identified by using the computerized claims-processing system of a large private health insurer in New England with a membership of approximately 1.8 million persons. We scanned all paid pharmacy claims between July 1, 1991, and June 30, 1993, to identify each plan member who received a prescription for a selective serotonin reuptake inhibitor (SSRI) or a tricyclic antidepressant. To ensure that each patient was being treated for depression, we excluded those who did not have at least one provider encounter within the 6 months before the earliest-observed prescription (“index date”) with a listed diagnosis of depression (ICD-9-CM 296.2, 296.3, 300.4, 309.0, 309.1, or 311). We further excluded patients who had received any antidepressants—including SSRIs, tricyclic antidepressants, and monoamine oxidase inhibitors—in the 6 months preceding the index date to ensure that each subject was initiating a new course of therapy. Finally, to ensure that the claims histories were complete, we excluded all patients who were eligible for Medicare benefits anytime during the study period or who had not been continuously enrolled in the plan during the 1-year periods before and after the index date.

Cost Measures

For each patient, we tabulated the costs of medical services provided in 1) physicians' offices, 2) hospital outpatient clinics, 3) hospital inpatient units, and 4) all of these settings combined. Costs were calculated by summing patient copayments and amounts reimbursed by the insurer for services rendered during the 1-year periods before and after the index date (pretreatment and follow-up, respectively). Since our interest was in changes in the costs of medical services only (i.e., exclusive of mental health treatment), all encounters in which a diagnosis of a mental disorder (ICD-9-CM 290–319) was rendered or in which the specialty of the provider or institution was mental health were excluded from the cost calculations.

For each cost measure, we calculated the change between the 1-year pretreatment and follow-up periods (i.e., as follow-up cost minus pretreatment cost). To control for the effect of inflation, we used the medical care component of the consumer price index to deflate costs in the follow-up period to levels prevailing in the pretreatment period. We then stratified patients according to the direction and magnitude of the change in costs between these periods. The subjects for whom costs increased were first distinguished from the subjects for whom costs decreased, and then, within each of these groups, the subjects for whom the change in costs exceeded the median increase or decrease, respectively, were distinguished from those for whom the change was less than this amount. All subjects for whom costs did not change were excluded from analyses of that measure. This method resulted in four ordered patient strata for each cost measure, subjects who had 1) large reductions in costs, 2) small reductions in costs, 3) small increases in costs, and 4) large increases in costs.

Analyses

For each cost measure, a variety of factors were compared across the four patient strata, including patient demographic characteristics, the prevalence of selected diseases that have been identified as important comorbid conditions for depression management (on the basis of guidelines for depression diagnosis and management from the Agency for Health Care and Policy Research [6]), various patterns of antidepressant use that have been reported to be associated with changes in the costs of mental health services among patients with depression (7) (i.e., early discontinuation, switching or augmentation, upward titration, partial compliance, 6-month stability), the type of medication received (SSRI versus tricyclic antidepressant), and the numbers of visits to mental health providers.

We used techniques of ordinal regression analysis to estimate odds ratios and 95% confidence intervals for each of these factors within a multivariate context (8). For each cost measure, a regression model was estimated that included the patient stratification variable as the dependent variable (equal to 1, 2, 3, or 4, corresponding to the four ordered strata described in the preceding section) and all of the factors of interest as explanatory variables. For binary factors (e.g., presence or absence of diabetes), the resulting odds ratios depict the relative likelihood of being in lower-ordered versus higher-ordered strata in the presence of the factor of interest, for any given bifurcation of patients (i.e., stratum 1 versus all higher-ordered ones, strata 1 and 2 versus all higher-ordered ones, etc.) (8). For continuous factors (e.g., patient age), the odds ratios depict the relative likelihood associated with a one-unit increase (e.g., 1 more year of age) of being in lower-ordered versus higher-ordered strata (8). We used the odds ratios to assess the relative likelihood of experiencing a large reduction in cost in the presence of the factor of interest.

RESULTS

A total of 1,661 patients met all of the criteria for study entry; 69.7% were female, and the mean age was 42.3 years (SD=11.8). Table 1 shows how the patients broke out into the four strata for each cost measure, and it contrasts the mean costs of medical services in the 1-year pretreatment and follow-up periods, also providing mean changes in costs between these periods. For the total cost measure, patients with large cost reductions had nearly $11,000 less in medical care costs on average between the 1-year pretreatment and follow-up periods, compared to a mean reduction of $471 for the subjects with small cost reductions, and there were average increases of $386 and $7,527, respectively, in the strata with small and large cost increases.

Odds ratios and 95% confidence intervals for the factors of interest are displayed in table 2. Patients with cancer were 39%–81% more likely to experience large reductions in medical care costs, depending on the measure examined. The odds ratios for anxiety, coronary heart disease, chronic fatigue syndrome, and a minimum of 6 months of antidepressant use were significant for the costs of office-based physician services. Patients with these characteristics were 52%, 72%, 112%, and 74%, respectively, more likely to experience large reductions in medical care costs. Patients who received an SSRI as initial therapy were 31% less likely to experience large decreases in the costs of hospital inpatient services. Patient age and the number of visits to psychiatrists and other mental health providers were not predictive of changes in medical care costs, as their respective odds ratios were at or near unity for all cost measures.

DISCUSSION

The purpose of this study was to identify predictors of large reductions in the costs of medical care services (consistent with a so-called “medical-offset effect”) among patients treated for depression. Our findings suggest that patients with selected comorbid conditions (e.g., coronary heart disease, cancer, chronic fatigue syndrome, anxiety) and those who use antidepressants for at least 6 months may manifest a cost offset, especially as regards the use of physician services. Curiously, factors previously thought to be associated with an offset effect, including patient age and receipt of mental health counseling, were found not to be associated with changes in medical care costs.

We emphasize that the statistical associations that we observed should not be construed as causal in nature. One cannot conclude, on the basis of our research, that use of antidepressants by patients with any of the characteristics identified in this study will definitely result in a cost offset. Our study only points to factors that may be associated with a medical-offset effect for patients receiving antidepressant therapy; those for which a sound clinical rationale may exist should be examined in studies using more rigorous research designs.

With the rise of disease management initiatives, there has been heightened interest in identifying the characteristics of patients for whom timely and appropriate administration of antidepressant therapy yields savings in the costs of medical services. Our study sheds light on some factors that may be predictive of a medical-offset effect. Additional research in a more rigorous framework is warranted to corroborate our findings and assess the strengths of the underlying relationships.

TABLE 1

Enlarge table

TABLE 2

Enlarge table