[Summary] Response to Kazdin: Rebooting for Whom? Portfolios, Technology, and Personalized Intervention (Shoham and Insel 2011)
Relying on accessibility may be unhelpful unless you can target exactly what specifically works for whom. His main idea: Attribute x Treatment Interaction (ATI) paradigm.
Relying on mass distribution/accessibility of small-effect sizes may be unhelpful relative to the impact on targeting exactly what specific mechanisms work for what populations/issues.
His main idea: Attribute x Treatment Interaction (ATI) paradigm is basically “what works for whom”? What characteristics “interact” with (or moderate) treatments to impact outcomes?
A brief summary
We focus here on an underdeveloped theme in Kazdin and Blase’s essay—that bending the curve of mental illness will require better knowledge of for whom simplified intervention and prevention strategies will suffice and for whom more intensive intervention is necessary. Such “for whom” questions deserve a central place on the national research agenda as we move toward individualized or personalized health care. In the absence of such knowledge, we risk treatment decisions guided by accessibility to resources rather than patient needs—the very problem Kazdin and Blase aim to solve.
Access is cool, but small effect sizes may hide the potential harm on those it does not help
Although reaching more people is a laudable aim, it is not clear whether this by itself will reduce the burden of mental ill- ness, much less offset the small effect sizes of simplified, scaled- down interventions such a portfolio approach would likely entail.
More treatment does not necessarily mean less burden, especially if the treatment is insufficient or inappropriate.
Basic diagnosis-intervention has not been super effective, it’s important to get more specific with the ATI paradigm (what works for whom)
As the cornerstone of personalized intervention, research on prospective treatment moderators (what works for whom) nec- essarily cuts across a wide range of case and treatment charac- teristics. The basic question in this Attribute × Treatment Interaction (ATI) paradigm is which cases characteristics moderate (interact with) which treatment conditions to predict clinical outcomes. Because the most conspicuous case-level moderators—psychiatric diagnosis—have not proved terribly useful for guiding psychosocial intervention, the search for meaningful moderators has recently expanded to include such diverse factors as current and historical problem severity, cog- nitive processes, and characteristics of the family social envi- ronment.
One way to discover the most promising moderators is his RDoC with the NIMH requiring research based on neurobiological mechanisms
Where can we expect to discover the most promising mod- erators? The horizon includes several promising developments we think are worth mentioning. One is the Research Domain Criteria (RDoC) project at the National Institute of Mental Health (NIMH; Insel & Cuthbert, 2009; Sanislow et al., 2011), which is attempting to ground the patient attribute (A) side of the ATI equation in underlying neurobiological dimensions of psychopathology. Given the high variability in pathophysiol- ogy among patients diagnosed with the same disorder (as determined by the Diagnostic and Statistical Manual of Mental Disorders), variability in treatment response among patients similarly classified is not surprising. The science- based, bottom-up RDoC approach to mental disorders aims to establish validity in ways that may ultimately align better with treatment response.
Without ATI it’s shooting in the dark
without better understanding of who benefits from which prevention strategies, we risk shooting in the dark and hitting targets indiscriminately, which could be costly and even iatrogenic.
Mental health disorders have clear developmental trajectories and treatments sadly don’t begin until an average of 11 years after onset
Like physical illnesses, most mental disorders have a clear developmental trajectory. It is disconcerting in this respect that treatment for mental disorders begins on average 11 years after problem onset (Wang et al., 2005).
Personalized and preemptive interventions major focus for NIMH
This approach of personalized and preemptive interven- tions is a major focus of the NIMH Strategic Plan (www .nimh.nih.gov/about/strategic-planning-reports/index.shtml).
we have launched a broad effort on biomarkers that could serve as moderators or predictors of response. One such study, EMBARC (which stands for Establishing Moderators/ Mediators for a Biosignature of Antidepressant Response in Clinical Care), is combining genomics, imaging, quantitative EEG, and cognitive measures to develop a profile or biosigna- ture of antidepressant response. In another effort, the Study to Assess Risk and Resilience in Soldiers, we are looking for pre- dictors of posttraumatic stress disorder and depression in sol- diers. And in another, we are following younger siblings of children with autism to identify the earliest signs of this disor- der. We hope that the identification of such risk factors will translate into treatment moderators, thus leading to better tar- geted interventions.
Technology solutions may be harmful and not well-received by patients
Although technology could prove a game changer, it may also have some unintended consequences. As commentators like Abraham Verghese (2011) have pointed out, the com- plaints we hear from patients, family, and friends are rarely about the dearth of technology but about its excesse
Kazdin’s idea to scale specific mechanisms may not work better than treatment as usual
Apart from the problem of abbreviated (if more accessible) interventions sacrificing essential mechanisms of change, we worry that pared-down portfolio interventions gaining prema- ture adoption in community settings will yield effects no larger than those for “treatment as usual,” which are very small. The e-Health picture may well improve as additional efficacy and effectiveness data accumulate, but in our view the “which treat- ment for whom” question will not soon go away.
Technology is a tool not an answer, still need to know what works for whom
At the same time, we would caution that technology is a tool, not an answer: With a better understanding of how and for whom technology- assisted treatments work (see Amir, Taylor, & Donohue, in press, for a promising example of this), mental health profes- sionals should be in a better position to personalize psychoso- cial intervention and ultimately reduce the burden of mental illness.