The economic burden of treatment-resistant depression: Cost-of-illness perspective

Introduction

Depression affects more than 264 million people worldwide ( ), making it one of the most prevalent of all medical conditions ( ). The sheer prevalence of depression is troubling, in and of itself. It is a chronic, relapsing illness that causes considerable burden from persisting residual deficits in energy level, motivation, cognitive capacity, and other emotional functions necessary for optimum functioning. It thus comes as no surprise that depression is a major cause of psychosocial dysfunction and impairment ( ; ). This dysfunction increases in a step-wise manner according to depression severity ( ). The World Health Organization (WHO) considers depression, broadly-defined, to be the single largest factor contributing to global disability in disability-adjusted life-years (DALYs)—the sum of life-years lost due to premature death and years lived with disability ( ).

The high prevalence of depression and its adverse effects on day-to-day functioning cause considerable societal burden. Burden-of-disease studies have consistently documented profoundly high economic costs of depressive disorders ( ; ; ; ; ; ). For instance, in 2010, national health care spending in the United States reached nearly $2.6 trillion (or $8383 per person) ( ); and direct and indirect costs associated with diagnosed major depressive disorder (MDD) accounted for a significant proportion of this expenditure. Over a 5-year span, the total economic burden of MDD among US adults (aged 18–64 years) increased by 21.5%—from $173.2 billion per year in 2005 to $210.5 billion per year in 2010 ( ). The mere presence of clinically significant depressive symptoms, short of formally diagnosed MDD (defined as a Center for Epidemiological Studies-Depression (CES-D) scale total score ≥ 16 ( )), has been associated with significantly decreased annual salaries and increased unemployment, as compared with persons lacking clinically significant depressive symptoms ( ). This finding remained significant, even after adjustment for the effects of sociodemographic variables, initial income, type of employment, and smoking status.

The high societal burden and economic impact of diagnosed depression and subthreshold depressive symptoms raises the possibility of even greater incremental costs associated with treatment-resistant illness. That expectation is somewhat intuitive, given the cumulative costs of pharmaceuticals and other failed interventions in treatment-resistant patients ( ). However, as reviewed elsewhere in this volume, treatment resistance in depressed patients is consistently associated with greater symptom burden and proneness to relapses ( ), higher rates of hospitalization and utilization of outpatient services ( ), higher rates of premature mortality ( ; ), higher burden from comorbid mental health and general medical conditions ( ), and greater functional impairment and disproportionately higher rates of early workforce exit ( ; ), compared to those with treatment-responsive depression. Each of these factors brings additional costs to patients, their families, and society. By some estimates, treatment resistance accounts for up to half of the total cost of depression treatment ( )—a disproportionately high percentage of the total cost burden from depression given a 20%–35% prevalence of treatment resistance in depressed patients ( ; ; ; ).

In this chapter, we provide a broad overview of published studies of the cost of illness (COI) associated with treatment-resistant depression, beginning with a general overview of COI study methods. Limitations of existing studies and gaps in the current literature will be highlighted, and future directions in COI research as they pertain to treatment-resistant depression will be discussed.

Economic burden from a cost-of-illness perspective

Overview of the cost-of-illness perspective

As the title implies, this chapter addresses the burden of treatment-resistant depression from a COI perspective. When discussing the economic burden of virtually any illness state, we are concerned with, at minimum, the value (costs and outcomes) of healthcare resources that are produced and consumed. At its simplest level, the goal of COI studies is to identify and measure all such costs that are associated with a particular illness or condition. The endpoint of COI studies is a monetary value—the sum total of the estimated values of all the components of cost burden associated with the given condition within a specified time period. Depending on the size and representativeness of the underlying population studied, COI studies are often thought of as providing estimates of the burden of a given disease to society ( ). In that sense, having accurate knowledge about COI is necessary for understanding the extent of a given health problem in economic terms—information that is needed to gain the attention of policymakers in order to appropriately direct limited healthcare resources ( ). Below, we provide a succinct summary of basic methodological considerations for the COI studies. We will review these in greater detail in the context of treatment-resistant depression. Interested readers are directed to several excellent reviews and commentaries for a more comprehensive coverage of COI methodology ( ; ; ; ; ; ; ; ; ).

Types of costs

To derive relatively unbiased estimates of societal burden using a COI approach, all of the relevant costs of a particular condition or disease must be ascertained. In reality, this can seldom be accomplished. Nevertheless, for purposes of our discussion, the relevant costs that may be considered in COI studies can be broken down into three general groups: direct costs, indirect costs, and intangible costs ( ). Each is briefly defined below.

Direct costs

Direct costs are incurred when providers diagnose and treat illnesses. These include the costs of hospitalization, outpatient visits, diagnostic tests, pharmaceuticals, psychosocial treatments, preventive care, ancillary services, and other healthcare interventions or services ( ). Direct costs also include those associated with commonly-used nonhealthcare resources such as transportation, informal cares, cost of relocation due to illness, childcare expenses, and related expenditures ( ; ). Direct costs are usually the easiest of the three types of costs to estimate, given the availability of structured billing information or standardized cost data for most healthcare services, tests, and treatments. Therefore, direct costs are the most common types of costs ascertained in COI studies.

Indirect costs

Indirect costs are those related primarily to work productivity losses due to absenteeism (missed days of work due to illness or injury, disability, or discretionary time), presenteeism (reduced productivity when working), and early mortality. There is far less structured or standardized information available on indirect costs for most illnesses. Therefore, researchers infer or impute indirect costs. For example, counted time off due to disability as a missed full day of work in their depression COI study. An outpatient office visit on a working day was assigned the equivalent of a half-day missed (even if the cohort member’s office visit occurred outside of working hours or on breaks), and hospital stays and emergency room visits were counted as a full day of missed work. They multiplied each whole- or half-day missed by the individual’s daily wage to estimate indirect costs due to absenteeism. In that same study ( ), researchers assigned a value of 6.1 times the cost of injury/illness-related absenteeism to calculate the presenteeism costs ( ). As highlighted by this example, indirect cost estimation is often more of an approximation of the likely “true” value than is the estimation of direct costs.

Intangible costs

Intangible costs involve disease-related pain and suffering or loss of functioning, typically assessed by measuring their adverse impacts on quality of life or related metric. Researchers and other stakeholders typically measure the adverse impacts of illnesses on the quality of life or related metrics to calculate the intangible dimensions illness. Estimating the monetary value of intangible costs constitutes a considerable methodological challenge, given the absence of a well-defined market. Measuring effects on quality of life or conducting extensive surveys that assess respondents’ willingness or desire to avoid certain morbidities and what they are willing to pay to do so are often required to derive intangible cost estimates. As such, methods for estimating intangible costs in monetary terms are often more difficult to employ in COI studies than those used for determining direct and indirect costs.

Methodologic considerations in cost-of-illness studies

Data sources

Numerous types of data exist for conducting COI studies. These include patient or caregiver surveys, prospective studies (e.g., prospective cohort studies, clinical trials, etc.), chart review studies, and retrospective cohort studies that utilize large administrative claims databases. Each type of data source has certain advantages and disadvantages. For instance, surveys and prospective studies have the advantage of yielding data that were collected specifically for research purposes, thus providing some assurance of high data quality and completeness. However, statistical power may be limited for some questions due to insufficient enrollment, poor participation, or high attrition; and survey respondents or prospective study participants may not be sufficiently representative of the underlying population of interest.

Retrospective chart review and administrative database studies have the advantages of convenience (due to the lack of need to recruit study cohorts and follow them over time when longitudinal data is required) and high statistical power due to the large sizes of study cohorts that can be created using data that are often already available in a structured form. However, the information in health records or administrative databases was collected for purposes other than for conducting research and are thus subject to misclassification and to limitations related to incomplete or missing information on key study variables ( ).

Exposure groups: People with the disease of interest and those without

To contextualize COI data and increase its interpretability, disease-associated costs are often compared between at least two groups of individuals—those with the disease of interest, and those without the disease of interest—over a defined time period. In the case of treatment-resistant depression, COI comparisons are often made between people with depression that meet a prespecified definition of treatment resistance and those with depression who do not meet the prespecified definition of treatment resistance. Some studies include a second referent group composed of persons with no evidence of depression during the observation period. Researchers can use such an approach to compare raw (unadjusted) costs. However, persons with treatment-resistant depression are much different than persons with treatment-responsive depression, analogous to how people with a disease of interest and those without the disease may differ on a number of baseline characteristics. This difference in baseline characteristics may systematically influence cost differences between groups. As such, investigators will often attempt to balance exposure groups (or adjust cost estimates) on these other factors using matched designs, multivariable modeling approaches, other types of modeling approaches, or a combination of these.

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