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Cost Burden of Treatment Resistance in Patients With Depression

Teresa B. Gibson, PhD; Yonghua Jing, PhD; Ginger Smith Carls, PhD; Edward Kim, MD, MBA; J. Erin Bagalman, MSW; Wayne N. Burton, MD; Quynh-Van Tran, PharmD; Andrei Pikalov, MD, PhD; and Ron Z. Goetzel,

When a clinical staging algorithm for treatment-resistant depression was applied to administrative claims data, higher scores predicted higher future medical costs.

Objective: To develop a claims-based scale for treatment-resistant depression (TRD) and estimate the associated direct cost burden.


Study Design: Retrospective, observational study of patients receiving antidepressant therapy between January 2000 and June 2007 (N = 78,477).


Methods: The Massachusetts General Hospital (MGH) clinical staging method for treatment resistance (assigning points for adequate trials of antidepressant medication, upward dose titration, extended duration, augmentation, and electroconvulsive therapy) was applied to claims data from the MarketScan Research Databases over a 24-month time period. Direct expenditures were measured over a subsequent 12-month period. Patients identified as having TRD (MGH score >3.5) (n = 22,593) were matched to depressed patients without TRD using propensity score methods. Regression models estimated the relationship between TRD and expenditures, controlling for sociodemographics, health plan type, and health status. Similar regression models estimated costs for an antidepressant-only version of the scale (MGH-AD).


Results: Treatment resistance among depressed patients was associated with 40% higher medical care costs (P <.001). The MGH-AD score was associated with an increasing gradient in direct costs. Annual costs for patients with mild TRD (MGH-AD 3.5-4) were $1530 higher than those for non-TRD patients, and costs for patients with complex TRD (MGH-AD >6.5) were $4425 higher than those for non-TRD patients (all P <.001). A 1-point increase in the MGH-AD score was associated with a $590 increase in annual costs (P <.001).


Conclusions: Early identification of TRD patients, using a claims-based algorithm, may support targeted interventions for these patients.


(Am J Manag Care. 2010;16(5):370-377)

A clinical staging algorithm for treatment-resistant depression (TRD) was applied to administrative claims data to measure the cost burden of mild to complex TRD.

  • Patients with TRD incurred 40% higher direct medical costs than patients without TRD.

  •  Higher TRD scores predicted higher future direct medical costs.

  •  This claims-based algorithm can be used by providers and medical plans to find TRD patients at an earlier stage, when clinical interventions and programmatic interventions such as enrollment in disease management programs might improve clinical outcomes and reduce costs.
Major depression is the most common mental health disorder, with a lifetime prevalence of 16.2%.1 Depression is associated with substantial economic costs, including increased direct medical and indirect productivity costs.2 The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study found a remission rate for first-stage treatment (citalopram) of 27.5% or 32.9%, depending on the definition of remission3; at least 67.1% of patients failed to achieve remission in first-stage treatment. The estimated cumulative remission rate for up to 4 stages of treatment in STAR*D was 67%, leaving an estimated 33% of patients without remission.4 Furthermore, each additional unsuccessful course of therapy is associated with lower likelihood of remission5 and higher relapse rates.4

Treatment-resistant depression (TRD) occurs when a patient with unipolar depression fails to respond to adequate antidepressant therapy; however, no consensus has yet emerged around a precise definition of TRD. Most clinical definitions of adequate antidepressant therapy require minimum thresholds of dose, duration, and patient compliance.6 Claims-based definitions of TRD have been based on the number of medication switches, upward dose titrations, and flags for use of specific medication classes, electroconvulsive therapy (ECT), depressionrelated hospitalization, and suicide attempts.2,7-9 Regardless of how TRD was defined, all prior claims-based studies found higher costs among patients with TRD compared with depressed patients without treatment resistance.2,7-9

Treatment resistance is not a dichotomous concept. Although some patients who fail to respond to the first antidepressant trial may respond to the second, others may respond after 3 or 4 trials, and others may require more trials. Several clinical staging algorithms have been developed in an attempt to characterize the type of treatment resistance, from mild to complex.10-12 Thus far, only one such algorithm, the Massachusetts General Hospital (MGH) staging method, has been found to predict nonremission.13

The goal of this study is to develop a claims-based scale for TRD and estimate the associated direct cost burden. A replicable scale based on claims data may be used by providers and payers to identify treatment-resistant patients at an early stage, when targeted interventions have a greater potential to improve clinical outcomes and reduce future medical costs.


We conducted a retrospective, observational study of the direct cost burden of TRD, using administrative claims data for patients with employer-sponsored commercial health insurance coverage. Propensity score weighting techniques were used to match TRD patients to depressed patients without TRD. Multiple regression models controlled for matching variables in order to isolate the effects of TRD and adjunctive therapies on costs.

The study sample was drawn from the 2000-2007 MarketScan Research Databases. Patients were selected from the MarketScan Commercial Claims and Encounters Database, which represents the healthcare experience of enrollees in commercial health insurance plans sponsored by more than 100 large- and medium-sized employers in the United States. The database includes monthly enrollment data, inpatient and outpatient medical claims, and outpatient prescription drug claims. Because the data conform to the Health Insurance Portability and Accountability Act of 1996 confidentiality requirements, the study did not require informed consent or institutional review board approval.

Study Population

Depression was identified using the International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes (296.2x, 296.3x, 300.4, 309.0, 311.xx). Adult patients (age 18-64 years) were selected if they had at least 2 claims with a diagnosis of depression and at least 1 prescription fill for an antidepressant medication between January 1, 2000, and June 30, 2007. Continuous enrollment was required throughout a 24-month identification period following the first observed prescription fill for an antidepressant medication. Consistent with prior studies, patients were excluded from the study if they had any claim with a diagnosis of dementia (290.xx), schizophrenia (295.xx), delusional disorder (297.xx), other nonorganic psychoses (298.xx), pervasive development disorder (299.xx), mental retardation (317.xx-319.xx), other cerebral degenerations (331.xx), Parkinson’s disease (332.xx), senility without mention of psychosis (787.xx), or manic depression or bipolar disorder (296.0, 296.1, 296.4, 296.5, 296.7, 296.80, 296.81, 296.89).7,8 A total of 106,139 patients met the initial selection criteria. The analysis of costs required an additional 12 months of continuous enrollment following the 24-month identification period; 78,477 patients met this criterion.


In order to determine whether the TRD algorithm predicted future costs, the 36-month study period was divided into 2 parts. Variables used to define treatment resistance and match patients were captured during a 24-month identification period following the first antidepressant prescription fill. Costs were measured during the subsequent 12-month follow-up period.

MGH, MGH-AD, and Definition of TRD

Using 24 months of medical claims following the first observed antidepressant prescription fill, 2 claims-based versions of the MGH TRD staging method were created.12 The full version of the MGH scale assigned 1 point for each adequate antidepressant trial (ie, 2 or more fills for the same antidepressant medication), half a point for each optimization strategy, and 3 points for any use of ECT. Optimization strategies included an extended duration (at least 3 fills), an upward titration in dose, and augmentation with an atypical antipsychotic, a mood stabilizer, or a stimulant.

The antidepressant-only version of the MGH score (MGH-AD) excluded ECT and the augmentation measures. ECT was excluded from the MGH-AD because it is not an early indicator of potential treatment resistance; rather, the use of ECT indicates that multiple prior treatment strategies have failed. Augmentation measures were excluded from the MGH-AD so that the effects of augmentation could be modeled independently. Accordingly, 3 dichotomous variables were created to indicate any use of atypical antipsychotics, mood stabilizers, or stimulants. (Detailed instructions on how to create the MGH-AD are available in a Technical eAppendix at

An MGH score exceeding 3 was established as a TRD threshold, consistent with the work of Berman and colleagues (2007)14 and Marcus and colleagues (2008),15 who used entry criteria that included a minimum of 2 antidepressant regimen failures prior to randomization to receive either adjunctive placebo or adjunctive aripiprazole in a clinical trial of inadequate responders to standard antidepressant therapy. An MGH score of 3 is the equivalent of 2 adequate antidepressant trials with 1 optimization strategy each (although other combinations are possible). Any additional optimization strategy, or an adequate trial of a third antidepressant, would increase the MGH score and meet the threshold for TRD.

Explanatory/Matching Variables

Explanatory variables (measured during the 24-month identification period) included age, sex, US Census region, urban residence, health plan type, and health status. Health plan type included indemnity plans, exclusive provider organization/ point-of-service plans, preferred provider organizations, health maintenance organizations, and capitated point-of-service plans. Health status was measured using the Deyo version of the Charlson Comorbidity Index, a numeric scale based on the presence or absence of 19 conditions (eg, diabetes, heart disease), each assigned a weight.16 A count of the number of Psychiatric Diagnosis Groups (eg, organic mental disorders, substance user disorders), which are not included in the Charlson Comorbidity Index, ranged from 1 to 12.17 The year of the first observed antidepressant prescription claim also was retained to control for trends in diagnosis, treatment, and medical spending.

Direct Costs

The primary outcomes for this study were direct (medical) expenditures summed in the 12 months following the 2-year identification period. Medical expenditures included spending for hospitalizations, emergency department visits, outpatient services, and outpatient prescription drugs. Expenditures summarized patient out-of-pocket payments, health plan payments, and any additional third-party payers (eg, coordination of benefits). All dollar metrics were adjusted to 2006 values, using the medical component of the Consumer Price Index.18

Statistical Analysis and Propensity Score Matching

Patients with TRD were matched one-to-one with non-TRD depressed patients by estimating a propensity score for each patient with depression using a logistic regression model of the probability of having TRD, controlling for sociodemographic variables, plan type, health status, and the year the index date occurred (time). Depressed patients with TRD were matched with non-TRD depressed patients using caliper matching.19

After matching, multivariate generalized linear models were used to adjust dollar comparisons for differences in sociodemographics, plan type, health status, and time. To estimate the independent cost effects of each augmentation strategy, the MGH-AD was used as a measure of TRD and indicator variables for treatment with atypical antipsychotics, mood stabilizers, or stimulants were included in the models. Separate models were estimated for the MGH-AD as a continuous variable and as a categorical variable. For use as a categorical variable, the MGH-AD was collapsed into 5 values (0-3, 3.5- 4, 4.5-5, 5.5-6, 6.5+). As a sensitivity analysis, models with a single indicator variable for TRD were estimated. All models controlled for age, sex, region, type of health plan, number of Psychiatric Diagnosis Groups, Charlson Comorbidity Index score, and year of index date.

Exponential conditional mean regression models were estimated for medical spending.20,21 The percentage difference was calculated as exp(coefficient) − 1 (eg, 21.4% = exp(0.194) − 1). Incremental costs were estimated in dollars. For models using the categorical MGH-AD, the incremental cost is obtained by comparing costs for patients at each level of the MGH-AD to what their costs would have been if they were in the non-TRD (MGH-AD <3.5) category. For models using the continuous MGH-AD score, the incremental cost represents the potential savings (in dollars) per enrollee with depression from reducing the MGH-AD score by 1 point. For the adjunctive therapies, the incremental cost represents the additional cost of the adjunctive therapy for patients on an adjunctive therapy.


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