Letter to editor: Incident depression increases medical utilization in Medicaid patients with hypertension

Response to: Breunig IM, Roffman D, Tevie J, Shaya FT. Incident depression increases medical utilization in Medicaid patients with hypertension. Expert Rev Cardiovasc Ther 2015;13(1):111-118

We read with interest the Original Research article by Breunig et al. [1] and as investigators experienced in using Medicaid datasets for clinical research, we would like to comment on the methodology used by the authors. We are primarily concerned about the difference-in-difference (DID) analytic methods employed in this study and about some of the conclusions drawn.

While interrupted time series analyses employing differencing techniques have increasingly been used in health care to demonstrate the effects of policy or pharmacotherapy changes over time, their applicability to demonstrating clinical/patient changes in this cohort is somewhat problematic. Calculating difference scores in a trend analysis is usually based on having a series of observations over time prior to and after the policy or therapy intervention, with some statisticians recommending at least 25–30 observed time periods over which to calculate the difference scores to establish a trend [2,3]. In this investigation, medical utilization was aggregated for 12 months before and after the first diagnosis of depression among cases, and was compared to the medical utilization in a propensity-matched control group over the same time period. Thus, the research design appears to be a simple pre–post comparison with control group. The preferred method for analyzing pre–post design data is the general linear model framework using the year 1 utilization data prior to the development of depression variable as one main independent covariate, and the depression/control condition variable as the second main covariate predicting year 2 utilization or the difference between year 1 and year 2 utilization as the dependent variable, in order to eliminate systematic bias, regression to the mean and reduce error variance [4–6]. It is not clear from the authors’ description whether year 1 utilization was controlled for in their regression analyses.

Moreover, the DID regression approach is heavily dependent on incorporating various assumptions regarding known and unknown related conditions or variables to maximize its internal validity. Implementation of these assumptions requires additional computational adjustments and sensitivity analyses to ensure proper inference or generalizability. In most clinical research designed to inform clinical practice, these covariates would be added to the general linear model regression equation in a transparent manner, according to the hypothesized relationships among variables, and the effect of each related covariate is demonstrated/presented, not only to validate the clinical/conceptual rationale employed, but also to allow clinicians to compare the case-mix of the research cohort employed to that of their own caseloads, to which the final results will, hopefully, be generalized.

However, in this DID study, the conceptual rationale for the inclusion and definition of these covariates is not provided (e.g., the Charleson Comorbidity Index, or the comorbid conditions included because they were not in the Charleson Index), the computations necessary to define and implement these strategies are not fully explained (i.e., how and why controls for annual changes in schizophrenia, mood or psychotic disorders, or alcohol/drug induced mental illness were implemented), and the effects of these covariates on the various dependent measures of inpatient, outpatient medical or psychiatric utilization in the final regression analyses are never presented. In many state Medicaid systems, only seriously impaired adults are eligible for coverage, for example, those with severe mental illness, HIV/AIDS, end-stage organ disease, substance dependence and so on. These chronic diseases, the antipsychotic/psychotropic, antiretroviral or other medications used to treat them, and street drugs and alcohol can exert substantial and independent effects on the development of depression and the worsening of cardio-metabolic disorders that are not addressed in this study.

Furthermore, the dependent Medicaid utilization variables employed need to be further explicated in order to interpret the findings. In our previous studies, ‘hospital-based outpatient services’ billed through Medicaid typically represent diagnostic assessments or laboratory procedures. A significant increase over time in this dependent measure could indicate that patients with comorbid metabolic conditions were being monitored more closely for worsening indices, and, conceptually, might represent another explanation for incident cardiovascular disease. Since some investigators have found that hypothalamic–pituitary–adrenal signaling disturbance is a critical mediator of the allostatic load reported in mood disorders, we might speculate that being treated for two or more chronic cardio-metabolic conditions contributes to a patient’s cumulative allostatic load and potential for further organ damage through the insidious development of depression as well as cardiovascular disease [7–9]. This process may be the result of the patient’s worsening health status, including hypertension, rather than the cause of it. Although the authors downplay the possibility of worsening health status as being an important factor in their discussion, a complex interplay of factors beyond hypertension and depression cannot be ruled out in this correlational investigation.

Finally, although the statistically significant increase in outpatient physician office visits is highlighted (∼2 visits per year), the significant increase in outpatient psychiatric visits (∼4 visits per year) is not mentioned. Based on these results, it seems very important to credit the primary care physicians for recognizing and referring their patients who develop depressive symptoms to specialty mental health services because the finding indicates that the service system is operating as intended.

Our argument, therefore, is that the statistical analysis procedures employed in clinical research studies should aim for a better fit between the clinical issues/variables being investigated and the clinicians who will be using the results in terms of validity, appropriateness, understandability, interpretability and generalizability.

Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

No writing assistance was utilized in the production of this manuscript.