Answers and Questions: A Star*D Report

As the United States roils with controversy over the best ways to provide health care for the greatest number of people while containing costs at sustainable levels, good quantitative data seem to have lost importance relative to horror stories about “death committees” and the like. Therefore, this seems to be a propitious time for the publication of “The Impact of non‐clinical factors on care use for patients with depression: A STAR*D report”[1].

The authors of this article have mined the treasure‐trove of data from the Star*D [Sequenced Treatment Alternatives to Relieve Depression] study to ask if the under‐utilization of mental health care by certain types of patients can be reduced by enrollment in a program of diligently delivered measurement‐based care. This article shows that “yes we can”—that enrollment in STAR*D significantly reduced the impact of so‐called predisposing factors (such as male gender, Hispanic or African‐American race, and low educational levels) on the rates of mental health care utilization. Simply showing that these predisposing factors are not immutable barriers to accessing mental health care is, in and of itself, an important finding. It is interesting to note that enrollment in STAR*D did not reduce the influence of these same predisposing factors on the utilization of general (nonmental health) medical care; clearly, something other than a general improvement in participants’ faith in doctors or “medicine” is at work here.

The authors conclude, “data suggest diligent, measurement‐based mental health programs may reduce race, gender, and education disparities in the use of needed mental health care”. To evaluate this conclusion, however, it is necessary to consider also several elements of the STAR*D program that had nothing to do with the actual medication algorithms. Participants were: (a) enrolled in a study, which included clinically trained research coordinators [CRCs] who met with patients at every visit (b), conducted a comprehensive assessment of depressive symptoms and medication side effects (c), and discussed the importance and clinical implications of those symptoms with the patient (d). All medications were provided free of charge (e), and any nonpublic service psychotherapy or medication visits not covered by the patient's insurance were paid for by the study (f). The STAR*D program provided the psychiatrist with specific yet flexible protocols for treatment options based on symptom and side effect measurements (the elements specifically cited by the authors), and also a training module and the opportunity for consultations as required.

A reasonably good case could be made for the possible positive effects of each of the first six elements listed above. Compared to base rates before enrollment, simply being enrolled in the study (a) significantly increased the likelihood that patients would seek mental heath care. Sometimes referred to as the “Hawthorne effect,” or the “observer effect,” this reflects the concept that the act of observation or measurement, may in itself, have an effect on the phenomenon being studied. The assessment process and its explication by the CRCs (c,d) combines nonspecific factors such as increased face time with a mental health professional with elements that resemble psycho‐education, an intervention that has been shown to improve outcome in patients with bipolar disorder [2]. In an age of increasingly impersonal and time pressured office visits, it is not difficult to imagine that groups who feel slighted, ignored, or disrespected by the predominant culture in general, and perhaps the medical profession in particular, would be responsive to the increased concern and interest shown by the CRCs (b), even independently of the importance of specific psycho‐educational content. The authors note that enrollment in STAR*D reduced the gap in mental health care use rates between insured and uninsured participants. This suggests that the provision of medication and treatment without charge [except for usual copayments by the privately insured], (f), may have also contributed to the weakened impact of at least some predisposing factors, given that minorities and the poorly educated are likely to be overrepresented in the ‘uninsured’ group.

In conclusion, this important and scholarly article provides strong evidence that the total package of STAR*D interventions made a significant and salutary contribution toward minimizing the degree to which predisposing factors including race, gender, and ethnicity contribute to gaps in utilization of mental health care. What the article does not do is clarify the extent to which each of several disparate elements in the package (including but not limited to the “diligent and measurement‐based” aspects) contributed to the total effect. The authors acknowledge this limitation, and call for further research to further explicate the role of each element. Only following such additional research will it be practical to incorporate those changes necessary to our “business as usual,” which have the highest probability of improving mental health care delivery. Even prior to further study, clinicians may consider already integrating those elements that can most easily and inexpensively be added to current practice: more precise and quantitative symptom assessment, and more patient education regarding symptoms, side effects, medications and treatment options.

As with much of the best research, this article presents us not only with answers, but also with a plethora of new and important questions. After investing enormous amounts of time, effort, and financial resources in the development of relatively effective treatments for psychiatric illness, it seems only proper and fitting to devote additional resources to good research that will help clarify which interventions most increase the likelihood that these good treatments will reach all the people who need them most.