Precision Strategies as a Timely and Unifying Framework for Ongoing Prevention Science Advances

Abstract

The purpose of this Special Issue is to encourage prevention scientists to take advantage of the recently actuated precision medicine movement to promote research toward determining what works for whom and under what conditions, also termed treatment matching (Collins and Varmus 2015). This strategy is not new to medicine, behavioral health, or prevention science. Nevertheless, the orchestrated proclamation by President Obama and Director of NIH, Francis Collins, M.D. of the Precision Medicine Initiative (PMI) with its $215M and 1 million participant prospective natural history study reinvigorated these efforts at the NIH and among treatment developers and healthcare providers (Collins and Varmus 2015; PMWC 2018; www.whitehouse.gov/precision-medicine). The 1 million participant prospective study has come to fruition in the form of the ALL OF US Research Program (NIH 2019; Sankar and Parker 2017) which emphasizes genetics, environmental factors, social influences, and lifestyle and is thus highly consistent with underlying theories of prevention science (Meagher et al. 2017). Past medical and behavioral clinical trials directed toward treatment matching have yielded mixed results, but overall such studies have played important roles in advancing their respective fields (Broekhuizen et al. 2012; Project MATCH Research Group 1998; Strecher 1999). August and Gewirtz (2018) discuss recent progress in prevention science targeting internalizing and externalizing outcomes (including substance use) but concluded that the literature Boffered few clues as to alternative interventions that might be effective for those who fail to benefit [from existing interventions]^ (pp. 1–2). Generally speaking, attempts at intervention matching in prevention science have not yielded sizable improvements over other prevention programs, but there are exceptions (Broekhuizen et al. 2012; O’LearyBarrett et al. 2016; Strecher 1999). The more common contribution of a study to precision prevention is the discovery of a subpopulation for which an intervention either lacks or provides especially large efficacy as opposed to identification of the optimal treatments for multiple subpopulations (Cho et al. 2016; Glenn et al. 2018; Howe 2018). Moreover, for the purpose of advancing precision prevention science, discoveries about subpopulations for which an intervention fails to work or yields iatrogenic effects can be among the most cited and important studies in terms of experimental insight into the underlying mechanisms of etiology and prevention (Dishion et al. 1999; Tryon 2018). To this end, there are myriad secondary analyses of existing datasets with potential to contribute important insights to prevention medicine.