Secondary Data Analysis to Examine Long-Term and Cross-Over Effects of Prevention Interventions
Eve E. Reider, Ph.D.
Division of Services and Intervention Research
The goal of this initiative is to encourage research to integrate/harmonize existing data sets from preventive intervention trials implemented early in life to: 1) examine risk and protective factors relevant to later mental health outcomes in childhood, adolescence and young adulthood; and 2) determine whether preventive interventions delivered earlier in life have long-term effects, and cross-over effects (e.g., unanticipated beneficial effects), on important mental health outcomes, including serious mental illness (e.g., depression, anxiety, suicide ideation and behaviors, psychosis behaviors).
There is a growing body of literature reporting on the benefits of preventive interventions for substance abuse and internalizing/externalizing behaviors, delivered in childhood on the long-term outcomes of mental health and reduced substance use. Findings from this research demonstrate that (1) intervening early in development and targeting proximal risk and protective factors can have an impact on a broad array of distal outcomes; and (2) preventive interventions can have unanticipated beneficial effects on outcomes not specifically targeted by the intervention (cross-over effects). Indeed, there is a small body of evidence providing proof of concept that preventive interventions aimed at reducing a number of risk factors for suicide (e.g., substance use, externalizing, and internalizing behavior) can prevent suicidal ideation and behaviors. New technological and analytic approaches for harmonizing/integrating data sets hold potential for increasing statistical power and facilitating the detection of the impact of prevention interventions, in general, and on important subgroups (e.g., sexual minority youth, racial/ethnic minority youth) and in the case of low base rate behaviors (e.g., psychosis, suicide ideation and behaviors).
Theory-based developmental research might focus on harmonizing prevention data sets to: 1) examine interventions that have a similar focus (e.g., prevention of child abuse and neglect, bullying/violence prevention, substance abuse prevention) to better understand their impact on reducing mental health disorders later in life; 2) examine important subgroups (e.g., sexual minority youth, racial/ethnic minority youth) and low base rate behaviors (e.g., psychosis behaviors, suicide ideation and behaviors); 3) examine known risk factors (e.g., child abuse and neglect, being raised in poverty and other adverse circumstances, children in the child welfare and juvenile justice system, etc.); 4) examine the impact of preventive interventions on multiple mental health outcomes, and gain a better understanding of important proximal (short-term) and distal (long-term) mediators; and 5) gain a better understanding of the effects of different levels of intervention (e.g., universal, selected, indicated, tiered) on reducing mental health disorders for different subgroups. This would provide a better understanding of what prevention interventions have an impact on which mental health outcomes, for which populations, and under what conditions.
Information about intervention targets and strategies that are most strongly associated with preventive effects could be used to refine preventive interventions to enhance their potency and efficiency. It would potentially provide evidence for beginning earlier in the life course to intervene on proximal risk factors that are likely to have effects on multiple mental health outcomes later in life (e.g., suicide ideation and behaviors, psychosis behaviors). If interventions prove to be impactful, this information could be used to develop an evidence base for communities to use in their efforts to reduce risk for mental health disorders. Harmonized data sets would be submitted to the NIMH Repository and serve as a resource to the research community.