Addressing Core Challenges for the Next Generation of Type 2 Translation Research and Systems: The Translation Science to Population Impact (TSci Impact) Framework

A key barrier is limited funding devoted to T2 translation, which reflects missed opportunities for prevention and health promotion in the U.S. As Woolf (2008) emphasizes, program and policy priorities typically are not based on a careful assessment of population needs or consideration of the EBIs most likely to address specific priority needs efficiently and effectively. There are many reasons for the limited investment in evidence-based prevention, ranging from a lack of public awareness to competing resource allocation priorities at federal, state, and local levels, priorities which typically favor treatment over preventive interventions (Catalano et al. 2012). Despite the potential for widespread adoption of effective prevention interventions to reduce costly treatments, only an estimated 2 to 3 % of governmental healthcare spending is directed toward them (Centers for Disease Control and Prevention 1992; Miller et al. 2008; Satcher 2006; Woolf 2008). Further, the majority of NIH funding is devoted to basic “discovery research” or Type 1 translation research.

As Woolf (2008) states, new “breakthrough” interventions may have less potential for saving lives than existing efficacious interventions implemented effectively at scale. The emphasis on the development of new interventions, rather than on research that would guide broad implementation of interventions shown to be effective, suggests that current policy priorities do not sufficiently consider the potential of such interventions to affect public health. It also might reflect an assumption that market forces will drive the dissemination of such interventions. Unfortunately, this has not been the case (see Kerner et al. 2005). Further, infrastructures and systems to support implementation of effective prevention interventions have not been well-developed, absent supportive policies.

Numerous health, social, and economic consequences result from this failure to prioritize T2 translation practice and research. For example, preventing unhealthy behaviors (e.g., poor diet, physical inactivity, tobacco smoking, alcohol and other drug use, mental health problems, school failure, and delinquency) could reduce a significant proportion of the more than 900,000 annual U.S. deaths associated with these behaviors, along with the associated chronic diseases accounting for 75 % of healthcare costs and $1 trillion in lost productivity (CDC, National Center for Chronic Disease Prevention & Health Promotion 2009). Moreover, the estimated annual cost of mental, emotional, and behavioral disorders ranges from $247 to 435 billion (National Research Council and Institute of Medicine 2009a). Despite these economic and health consequences, funding for implementation and evaluation of interventions designed to prevent or reduce unhealthy behaviors and mental, emotional, and behavioral disorders appears to be decreasing (e.g., National Research Council and Institute of Medicine 2009a; Sloboda 2012; also see www.​carnevaleassocia​tes.​com), although derivation of precise funding estimates is hampered by the current monitoring systems (e.g., imprecise categorizing and reporting of NIH and other potentially relevant grants—see National Research Council and Institute of Medicine 2009a). Compounding the problem is the fact that access to such interventions is not uniform across populations, with more disadvantaged populations having less access (Woolf 2007).

An increased emphasis on research to understand how to widely disseminate and implement EBIs could save the lives of children, youth, and adults; reduce health problems; and improve quality of life. It also could substantially reduce healthcare costs, particularly through the use of economic analyses to guide investment shifts from expensive but low-value preventive interventions to ones that are more cost-effective (Woolf et al. 2009). This paper from the Mapping Advances in Prevention Science Task Force of the Society of Prevention Research on T2 Translation Research² summarizes an approach to facilitating widespread application of EBIs.

We define interventions as programs, policies, and practices encompassing intentional actions (whether a singular action or a constellation of actions), designed for an individual, organization, community, region, or system, which are intended to alter health-related behaviors, address risk or protective factors, and improve health-related outcomes (CDC 2007; Rabin et al. 2008). We subscribe to the definition of evidence-based and the standards of evidence described by the Society for Prevention Research (Flay et al. 2004; 2005—also, see Footnote 1).

As stated above, two types of translation research have been identified (Sung et al. 2003; Rabin et al. 2008). Type 1 translation involves the first three phases in the preventive intervention research cycle model (Greenwald 1990; Mrazek and Haggerty 1994): (1) epidemiology (identification of the problem or disorder and review of information to determine its extent); (2) etiology (identification of risk and protective factors for the problem or disorder as potential targets for preventive intervention); and (3) intervention design to reduce risks, enhance protection, and reduce problems, intervention pilot testing, and efficacy trials.³ T2 translation research can be integrated into or coextensive with this third phase, as well as two additional phases—(4) effectiveness trials with well-defined populations and (5) dissemination and implementation research (Sussman et al. 2006).

The ultimate goal of T2 translation research—to enhance public health through widespread use of EBIs—requires an orientation toward broad-spectrum population impact and the systems that support it. This requires coordinated study of effectiveness and efficiency of infrastructures and translation processes or systems, such as methods for preparing individuals and organizations to select and adopt EBIs, quality implementation practices following adoption, and factors that affect sustainability of EBIs across various populations and settings (see Glasgow et al. 2003). To achieve the greatest population impact, we recommend an emphasis on translation during all relevant phases of the research cycle, such that the development and testing of interventions would entail a thorough investigation of context, systems, and other factors that influence pre-adoption processes, adoption, quality implementation, and sustainability of EBIs (Glasgow et al. 2003; Rotheram-Borus and Duan 2003; Sandler et al. 2005), as well as effective collaborations between practitioners and scientists.

In Fig. 1, we present a conceptual framework incorporating key components for advancing T2 translation research—called the Translation Science to Population Impact (TSci Impact) Framework. It characterizes (1) four translation functions, optimally shaped through informational feedback loops across all four phases of translation; (2) the multiple contexts for this work; and (3) necessary infrastructural supports. The framework is grounded in Diffusion of Innovation Theory (Rogers 1995), with its core concept that EBIs diffuse across multiple interrelated stages. The first stage precedes adoption; it entails gaining knowledge about the innovation (among policymakers, practitioners, and the general public), and being persuaded about the innovation’s relative advantages. The second stage involves the decision to adopt the innovation. The next stage is implementation, or putting the innovation into use, and the final stage requires institutionalizing or sustaining its use. Our framework articulates a translation process that occurs across four phases that correspond to many aspects of stages of innovation diffusion, entailing Pre-adoption, Adoption, Implementation, and Sustainability phases. The success of each phase is influenced by wide-ranging factors that warrant systematic investigation (see Table 1). As graphically represented in Fig. 1, an intervention’s research cycle begins with the design and development of preventive interventions. Optimally, wide-ranging interventions targeting all health-compromising behaviors are developed for translation. This starting stage entails application of relevant epidemiology and life course development findings (see National Research Council and Institute of Medicine 2009b, pp. 71–111), along with etiological research that can inform intervention design. Preliminary intervention design is followed by pilot and efficacy testing, including all aspects of Type 1 translation research. The identification of efficacious EBIs sets the stage for addressing key functions of T2 translation research, as described in detail below.

Our framework includes feedback of information among all phases of research, from the later phases of translation to the more formative phases of intervention development, and back again. For example, refinement of an intervention or its infrastructural supports, including strategies for its adoption and for covering anticipated scale-up costs, can be informed by findings from an effectiveness study. As another example, research on intervention participant preferences concerning modifiable implementation strategies during the sustainability phase (e.g., feedback during this phase about program setting and scheduling choices that might enhance its program appeal) could be used subsequently to inform evaluation of implementation quality. In other words, fostering a flow of information between and across the phases of intervention development, testing, and implementation facilitates evaluation and understanding of the factors associated with sustained, quality implementation (see Fig. 1; see Wilcox et al. 2008 for an illustration).

The four phases of translation research occur within multiple contexts, ranging from local communities and organizations to national, state, and county governments that ultimately affect the population impact of EBIs (see Fig. 1 arrows indicating interrelationships between contextual influences and translation functions). Our conceptual framework draws upon work by Proctor and colleagues (2009), who posit that EBI adoption and sustained implementation take place within many contexts at a number of levels. At the community level, stakeholder group, or organizational preferences and attitudes, along with community coalition functioning, can enhance or impede EBI implementation. In addition, the willingness and capacity to implement EBIs may be influenced by organizational factors, such as financial and other incentives, support from leadership, and organizational climate. At higher organizational levels, governmental budget priorities, policies, and practices (at county, state, and national levels), such as those that mandate or restrict funding to the use of EBIs, also affect EBI adoption, implementation, and sustainability. The effectiveness of translation within and across these contexts at various levels is influenced by characteristics of consumer groups, program providers, organizations, communities, government, and service provider systems.

Later discussion summarizes key context-related factors at multiple levels that should be addressed through T2 research. We recognize how infrastructures are influenced by contextual factors represented in the TSci Impact Framework, as well as each translation function. For this reason, subsequent presentation of research questions will be used to illustrate how to address the complex interrelationships between infrastructures and contextual influences, within and across translation phases (also represented by “looping” arrows in Fig. 1). In addition, we will describe how infrastructure development is necessary to foster positive contextual influences on the T2 translation process.

Infrastructure at the pre-adoption and adoption phases includes: (1) systems for consumer and market analyses at the pre-adoption phase; (2) EBI dissemination structures to support adoption decision making; (3) data systems for assessing community or organizational needs; and (4) community-based partnerships engaged in adoption decision making.

As with program adoption, a range of important infrastructural supports for implementation have been identified (see Fig. 2). Here, we focus primarily on the two interrelated elements of EBI-related training and technical assistance, both of which are considered essential in enhancing implementation fidelity and program outcomes (Durlak and DuPre 2008; Dusenbury et al. 2010; Fixsen et al. 2005). We also address the important issue of engaging prospective community participants in community-based EBIs.

Sustainability supports and systems are necessary for long-term maintenance of quality implementation of EBIs, across specific settings and service delivery systems. Key components of related practice infrastructures include continuous training and TA—oriented toward a benchmarked developmental process fostering sustainability and incorporating quality improvement systems for implementers—plus funding or financing structures. In designing these components, the first order of business is improved frameworks for multiphased sustainability infrastructure development.

Parallel to and in coordination with the development of infrastructure for evidence-based practice, successful prevention efforts require the development of infrastructure for practice-oriented research (see Fig. 2 and Table 1). Fundamentally, the infrastructure for prevention-related translation research must entail support for rigorous single-site dissemination, adoption, implementation, and sustainability research projects. Optimally, it also would support multi-site projects, centers, or research networks that combine the interdisciplinary skills of academics across research institutions, and effective data sharing systems. One possibility to be considered is a Multisite Transdisciplinary T2 Research Network supported by the NIH, as was recently presented at a National Institute on Drug Abuse strategic planning meeting (Spoth 2012). Advantages of such a research network have been noted, including: (1) support for T2-related system science grounded in “real world” prevention delivery systems, and also linked with Type 1 translation research; (2) increased capacity to address gaps concerning high-priority research questions; (3) support for innovative study of new social media technologies; (4) support for data sharing projects; and (5) the creation of new scientist training opportunities (also see Greenberg et al. 2009).

Another key need is to develop a workforce of scientists who will focus their research on translation issues. Currently, there are few federally-funded institutional training grants focused on T2 translation issues and very few university-based programs devoted to training in T2 translation research; training often entails application of a “learn as you go” or apprentice model (National Research Council and Institute of Medicine 2009a). Increasing numbers of NIH career development grants have been helpful in this regard (National Research Council and Institute of Medicine 2009a); in addition, CDC-funded Prevention Research Centers are facilitating this work through training in an empowerment evaluation approach designed to integrate evaluation into ongoing service delivery (Cox et al. 2009). Also, some of the NIH Clinical and Translational Science Awards are taking steps to improve T2 translation-related training. To nurture the next generation of T2 translation scientists, training must entail a broad curriculum that includes the basics of prevention research, integrated with community development, health communications, marketing and other business models, and the use of new technologies, placing a strong emphasis on research methods and analytic skill development (both qualitative and quantitative), as described in the Society of Prevention Research document on standards of knowledge for the science of prevention and related training resources (see www.​preventionresear​ch.​org/​advocacy). Finally, it is worth noting that additional workforce development recommendations across all relevant areas of prevention science and public health can be found on the 2009 National Academies report (National Research Council and Institute of Medicine 2009a).

Practitioner–scientist partnerships could serve as primary vehicles for enhanced translation of science into practice. New infrastructures could help to foster practitioner–scientist consortia or partnerships involving broader collaborations among communities, consumers, practitioners, and prevention scientists (Spoth and Greenberg 2005). Common areas of need and interest include how to most effectively create systems-level change, build alliances across institutional settings, and diffuse EBIs with fidelity, while allowing for local input and adaptations that ensure effective, sustained, and institutionalized EBIs (Valentine et al. 2011).

Broadly speaking, T2 research must address questions such as: How do consumer (individuals, organizations) preferences about various EBI features or attributes influence their ultimate intervention choices and demand for EBIs? (See Table 2 for illustrative literature reviews of empirical studies; also see Cunningham et al. 2009; Spoth and Redmond 1993 for examples of consumer preference study and market simulations.) Research also can be used to support adoption of EBIs to fit a new ecology. For example, conducting parent preference research would be advised in adopting a parenting program for use with deployed military families. Another question concerns the most important factors that predispose consumers to make decisions to adopt EBIs. As noted earlier, a market orientation for developing and disseminating interventions may be useful in understanding adoption decisions (Kreuter and Bernhardt 2009; Pentz 2004; Sandler et al. 2005). Future research should examine how marketing principles such as segmentation of the target market, product positioning (i.e., branding, pricing, and promotion), identifying competing needs for limited resources, assessments of consumer and provider preferences, and the packaging of key ingredients into simpler, less intensive interventions might be applied to the improved translation of EBIs.

Studies on effective strategies for disseminating information about EBIs, and how stakeholders ultimately integrate information into adoption decisions, remain major gaps in translation research. First, there is a need to investigate the channels through which different stakeholder groups receive general information about EBIs and their implementation, and the effects of stakeholder networks on information dissemination. Social network methodologies, in particular, may prove useful in understanding how stakeholder networks affect EBI information dissemination (Greenhalgh et al. 2004; Valente 2012). Second, there is a need for research on the role of evidence in early stages of decision making about prevention programs (Tseng 2012). The widespread use of EBIs requires that key stakeholders value evidence-based approaches over other types of interventions. Also, when stakeholders value evidence-based approaches, two questions are what they consider to be “evidence” and how they evaluate the evidence for a given intervention; these questions warrant further investigation (e.g., Granger 2008).

Future research is needed to understand the processes stakeholders use in the selection of EBIs, including evaluation of the types of evidence-related information influencing decision-making processes (see Table 2 for illustrative literature reviews of empirical studies). For example, how do decision makers make use of data on risk and protective factors in the selection of an EBI? What is the effectiveness of various types of decision-making tools (e.g., see the Strategic Prevention Framework, www.​samhsa.​gov/​prevention/​spf.​aspx)? How do groups of stakeholders reach a consensus about adopting an EBI? And how does the adoption decision-making process vary by type of intervention, service system, or community needs?

A key question concerns the need to address why some stakeholders—including policymakers, community leaders, organizational leaders, service providers, and consumers—appear to be more amenable, while others are more resistant to adopting EBIs. A related question concerns what accounts for these differences. For example, what are the incentives for EBI adoption by various stakeholders? Among those organizations that have overcome specific barriers, what strategies have they employed? Adoption of any new practice involves complex organizational behavior changes. Understanding these complexities can aid in designing the necessary tools to support adoption.

Cost is a major adoption consideration for consumers; thus, the availability of funding streams and the methods used to access them is important to investigate. This may begin with an investigation of existing prevention funding in communities and states, as well as models for identifying and accessing additional stable and single-purpose revenue streams. In addition, questions about the economic benefits of many EBIs remain unanswered. More economic analyses (cost-effectiveness, benefit-cost, and cost efficiency) are needed to facilitate decision making about EBIs and to persuade consumers, policymakers, and funders of the overall benefits of investing in prevention programming. Further, better understanding of where and when economic benefits are realized is important, including the specific systems to which costs and benefits accrue, in order to advance strategies for financing EBIs. For example, school districts may invest in an evidence-based obesity or tobacco prevention program, but the benefits accrue to the individuals involved, taxpayers, and the health care system, and not necessarily the school districts that made the original investment. How initial investments can be recouped is an important T2 research question (see Woolf et al. 2009).

In cases where economic analyses have not been conducted, data on the actual costs of implementing a given EBI are essential, and program developers should make it routine to measure variables related to cost to assist consumers in making decisions, with or without an analysis of benefit–cost. There clearly is a need for cost tracking and analysis tools that facilitate reasonably accurate cost projections and are practically feasible, such as cost savings estimators for policymakers (see www.​paxis.​org/​triplep) along with the study of the benefits of their application. User-friendly cost tracking tools would help increase the availability of cost information that is critically important in community and organization administrator decisions about initiation and continued implementation of EBIs.

In considering these research questions and issues, it is noteworthy that Rotheram-Borus and colleagues suggest that the prevention field reorient its approach to EBI diffusion and apply the business model of disruptive innovations. These researchers make the case that following this model is simpler and less expensive, and offers more accessible versions of interventions (comprised of key elements) that would be diffused more broadly and more quickly (Rotheram-Borus et al. 2012). In the future, tests of this approach should be conducted to examine whether streamlined versions of interventions achieve impacts comparable to the more complex versions.

Although previous reviews of the program implementation literature (Dane and Schneider 1998; Domitrovich and Greenberg 2000; Durlak 1998) indicated the absence of significant attention to this issue, recently implementation has become a greater focus of research efforts, and there now is a developing “implementation science” (Dane and Schneider 1998; Durlak 1998; Domitrovich and Greenberg 2000; Fixsen et al. 2005). As a result, there is an increasing knowledge base regarding the wide variety of factors that influence implementation and its quality. The research clearly demonstrates that better quality implementation leads to improved outcomes for children (Durlak et al. 2007). However, many questions about how to improve and maintain quality implementation remain. Three conceptual models that may assist in guiding further research questions include those of the National Implementation Research Network (Fixsen et al. 2005), the school ecological model (Greenberg et al. 2005), and the RE-AIM model (Glasgow et al. 2004). Drawing on these models, specific areas of needed research in this emerging sub-field follow.

Perhaps the most significant challenge to effective translation of EBIs is to sustain implementation efforts over time (Bumbarger and Perkins 2008; Spoth and Greenberg 2011). Frequently in the U.S., innovative public health programming is developed and initially implemented with seed grants from governments or foundations. However, the dilemma of time-limited grant funding is that innovations often are implemented without a clear plan for generating resources to sustain the innovation (Adelman and Taylor 2003; Farquhar 1978; Hallfors et al. 2002; Hallfors and Godette 2002; Mancini and Marek 2004). As noted earlier, the field would benefit from a change in approach whereby practitioner–scientist collaborations work with organizations, service systems, and communities to investigate what structures, strategies, policies, and resources are necessary to create sustainable change. In doing so, it will be important to recognize both the common issues shared by interventions as well as the unique nature and needs of specific intervention models. Such investigations, beginning in the pre-adoption phase, could address the need for developing and testing practical tools to assist agencies, schools, and communities in moving EBIs to a sustainable basis. Yet, at this point, sustainability research has not coalesced into a subfield with a clear research agenda (Scheirer and Dearing 2011).

As the study of sustainability is nascent, numerous T2 research questions should be addressed to guide both policy and practice. One critically important question concerns identification of optimal strategies for organizations, agencies, and communities in effectively using implementation data to support sustainability (see Tibbits et al. 2010). In addition, a key question is what management, organization, training, and TA factors for organizations and communities lead to greater sustainability for specific programs, practices, and policies. Scheirer and Dearing (2011) provide a useful framework for articulating sustainability research questions and formulating a research agenda (also see Table 2). They suggest three key sets of sustainability factors to investigate, namely, intervention characteristics (e.g., specific intervention models costs), organizational supports (e.g., staff buy-in), and community environment (e.g., existence of partnerships). They also emphasize the importance of examining financing mechanisms, along with evaluation of six types of outcomes (continued participant benefits, continued program activities, sustained community-level partnerships, maintained organizational practices, sustained attention to the problem addressed, and program diffusion).

As discussed earlier, the sustainability process has recognizable developmental phases, suggesting key questions that fit within the Scheirer and Dearing framework. For example: how does the nature of ongoing TA change once a community matures in its use of an EBI or service delivery model? What strategies can be used with organizational leadership to nurture champions for long-term prevention implementation, especially in cases where the greatest benefits may not be realized for years (e.g., early childhood interventions)? Concerning sustainability support structures, it is critically important to clarify what types of national, regional, or state diffusion networks and TA systems can most effectively support sustainability for various types of EBIs and service delivery systems. Moreover, it would be helpful to understand how findings on cost–benefit effectiveness can be best used to alter policymaking and influence sustainable funding.

The above discussion of the T2 phases has referenced policy-specific research questions. Relevant policies operate at all levels highlighted in Fig. 1 (national, state, county, and community organizational) and represent potentially powerful contextual effects to investigate. A good illustration of the powerful role of regulatory and other policy change is the case of reduced tobacco use in this country and the related reductions in morbidity and mortality (e.g., clean indoor air policy, taxes on tobacco products, regulation of advertising, and illegal sales contributed to this outcome; see Biglan and Taylor 2000). These types of policy efforts could be applied to other health-related behaviors, and they warrant further research (e.g., Biglan and Taylor 2000; National Research Council and Institute of Medicine 2009a). Because the relevant policy is very broad (Briar-Lawson and Drews 2000; Midgley 2000), further specifying policy-related research questions lies beyond the scope of this article. Nonetheless, the systematic study of policies affecting T2 translation is recommended (see Table 2). Research might examine, for example, how community- or county-level policies, such as mandated set-aside funds from local taxes, can be aligned with federal-level policies and funding streams to support the purchase of EBI materials, program delivery, implementer training, and evaluation support on an ongoing basis at the local level (Pentz 2000, 2004, 2007). In addition, research is needed on relevant processes of local prevention policy enactment (Pentz et al. 2004).

To conduct a broad T2 research agenda, multiple methodologies are needed, including naturalistic/descriptive studies of the longer-term outcomes of EBIs, experimental trials that vary important features of system development, policy, management, training, and financing, and adaptive design studies. In the next section, we discuss important design and methods considerations relevant to T2 translation research.

There is scientific debate about the types of study designs that best suit this emerging field of T2 translation research. Clearly, the designs and scientific strategies will depend on the classes of research questions that are being addressed. Some researchers have questioned whether it is appropriate to rely primarily on randomized controlled trials to build a knowledge base for T2 translation research. Often labeled as the “gold standard” in medicine and public health, the traditional randomized controlled trial typically is used for identifying efficacious interventions that demonstrate impact under ideal conditions (National Research Council and Institute of Medicine 2009a). Other researchers have suggested that designs should be more suitable to the study of interventions under real-world conditions (Brown et al. 2009; Glasgow et al. 2004, 2006). Yet others have suggested that randomized trials have very little place in T2 research (Berwick 2008). While the field is sorting out the most useful designs to be used, it is clear that there is a need to address some major limitations in the ability of traditional randomized trials to examine fundamental questions related to implementation (National Research Council and Institute of Medicine 2009a; West et al. 2008).

In this context, it is important to note the distinction between the goals of strengthening clinical or public health evidence and that of improving the processes involved in delivering or better translating health-related interventions (programs, policies, and practices). Strengthening clinical evidence often involves demonstrating efficacy or effectiveness of one program versus another through a randomized effectiveness trial or natural experiment. Another way that programs can be improved is through adaptation, and designs are being explored that accommodate such program changes in the course of conducting a series of randomized trials (Brown et al. 2009). In addition, improving the processes involved in delivering a program often uses quality improvement methodology. This entails paying careful attention to the development of monitoring and feedback systems at the point of service or program provision (e.g., hospital infection control; Chan et al. 2011), the level where training and supervision occur (e.g., improving teachers’ delivery of a prevention program in schools; Poduska et al. 2009), or the level of organizational or community leadership (e.g. school-wide implementation of a prevention program; Bradshaw et al. 2008). As another example, the Veteran’s Administration’s QUERI system (Demakis et al. 2000; Fortney et al. 2012) uses a systemic quality improvement program that could be adapted for many of the prevention delivery systems that are described in this paper.

Standard randomized trials often are difficult to conduct when there are complex behavioral- or system-level interventions, and they often provide insufficient information about mechanisms of change and the role of contexts (Berwick 2008). Flay et al., adaptive time-series designs for evaluating complex multicomponent interventions in neighborhoods and communities, unpublished have delineated adaptive designs that can be especially useful in evaluating very complex, multi-component interventions. Importantly, they have recommended a hierarchical decision-making approach to research designs, a process that begins with consideration of randomization but then also allows for alternative designs when such is not feasible.

Despite noted limitations in the applicability of randomized trials, we anticipate that the use of randomization in innovative ways will make major contributions, especially in the formative phases of T2 translation science. Indeed, Flay proposed the use of randomized assignment in implementation research one quarter century ago as part of the overall scientific approach for moving prevention science into practice (Flay 1986). Designs need to take into account which phase or phases of translation are being studied, ranging from pre-adoption through sustainability. In each of these phases, there are opportunities to use random assignment to implementation or dissemination condition and timing of an implementation or dissemination strategy (Hawkins et al. 2008; Spoth et al. 2007b). Currently, there is a considerable number of randomized implementation trials that are testing alternative strategies for implementing EBIs (Haggerty et al. 2007; Landsverk et al. 2011, 2012). Thus, standard randomized controlled trials with modifications may be particularly useful, both from a scientific point of view and from the perspective of community and institutional partners in implementation or dissemination research (Brown et al. 2012).

One particular concern is that the use of a control condition results in withholding of an EBI to a portion of the subjects, which is often unacceptable to some organizations and communities. There are alternatives to the traditional “control group,” such as a “roll-out” design where groups or communities are randomized to the timing of when they initiate a standard or innovative implementation or dissemination strategy (Brown et al. 2008, 2009, 2006; Chamberlain et al. 2010; Landsverk et al. 2012). In roll-out designs, all communities receive an intervention; it is the timing that is randomly determined. Compared to the traditional two time point wait-listed design, the multiple time point roll-out (also called the dynamic wait-listed design) has higher statistical power and is often logistically easier to manage (Brown et al. 2006). The fact that communities are assigned at random times also decreases the potential for bias over multiple baseline designs and improves acceptability to practitioners and community partners (Biglan et al. 2000).

In addition to the roll-out designs, other important RCT-related designs for future T2 translation research include those embedded in multiphase optimization strategies (MOST–Collins et al. 2005) and sequential multiple assignment randomized trials (SMART–Murphy 2003). Drawing upon engineering principles focused on enhancing efficiencies, MOST includes methods for identifying active components of interventions and optimal intervention doses, and then applies fully crossed or fractional factorial RCT designs to assess the relative performance of intervention components or dose levels. SMART is a RCT design that can be used to address the best sequencing of various intervention components and to clarify which types of tailoring are optimal. An illustration of the application of both types of designs to the development and testing of eHealth interventions can be found in Collins et al. (2007).

Mixed, multiple methods may be particularly important for T2 translation; mixed methods that combine qualitative and quantitative approaches have great potential. An example of this mixed methods approach is the CAL-OH implementation study. It evaluates the efficiency of two alternative implementation strategies aimed at California and Ohio counties’ social service systems in implementation of an evidence-based program for foster care. It uses quantitative methods in the context of a true randomized design, whereby 52 counties were first matched, then randomly assigned to one of two implementation strategies (Chamberlain et al. 2008). Comparisons of implementation strategies were conducted, to assess how fast they progressed through stages of implementation, as well as the quantity of service delivery. This study also examined how social network connections among service system leaders affected adoption. These network connections were evaluated through survey methodology as well as through ethnographic interviews. The combination of these data, when analyzed using mixed methods, revealed a much richer network structure than that available with each separate method (Palinkas et al. 2012).

Another important methodological approach, called evaluability assessments for practice settings, facilitates assessment of the appropriateness of conducting more conventional and rigorous evaluations before the fact (Leviton et al. 2010). These assessments can serve several purposes, including rapid feedback on implementation quality, examination of feasibility and acceptability of EBIs, and the evaluation of the appropriateness and feasibility of conventional methods for evaluating intervention impacts.

Finally, methods of “systems science,” such as social network analysis (Valente 2010; 2012) systems engineering (Czaja and Nair 2006; Czaja et al. 2003), process control (Rivera et al. 2007), and ethnographic methods (Palinkas et al. 2011) almost certainly will play important roles in the study of the complex interactions within and across implementation systems, as will a new generation of computationally intensive methods (Landsverk et al. 2012).

Whether focusing on the application of emerging designs or alternative applications of research methods, innovative ways of integrating T2 translation research into EBI delivery systems as part of the ongoing service delivery process is essential.

Currently, there is limited consensus on the measures or methods (observations, provider self-reports, participant reports) that provide the most reliable and valid assessments of implementation fidelity and adaptation. Further evaluation of current tools and development and testing of new methods is warranted, including clarification of which dimensions of fidelity (e.g., adherence, dosage, and participant responsiveness) are most important to measure for a particular intervention and how these dimensions actually affect program outcomes (see Berkel et al. 2011).

Few studies have evaluated the degree to which or the process by which schools, agencies, community teams, and organizations monitor or measure their prevention activities. In general, some of the tools used in prevention research trials, such as conducting observations of program delivery with trained independent observers or videotaping and later coding program sessions, are not practical, effective, or efficient for use in community settings. Thus, alternative user-friendly instruments and methodologies should be explored in order to increase local monitoring of EBIs (Berkel et al. 2011; Fagan et al. 2011).

As part of a fundamental restructuring of the U.S. healthcare system, the Affordable Care Act created the National Prevention, Health Promotion, and Public Health Council (National Prevention Council), chaired by the U.S. Surgeon General. The National Prevention Council consists of the heads of 17 departments, agencies, and offices from the federal government collaborating to develop a conceptual framework and strategic plans for improving the health of Americans through disease prevention and health promotion. In June 2011, the Council released the nation’s first comprehensive strategy for disease prevention and health promotion (National Prevention Health, Promotion, and Public Health Council 2011) called the National Prevention Strategy. Each of four strategic directions and seven priority areas articulated by the Strategy states a number of recommended actions in clear alignment with those indicated in the TSci Impact Framework (Fig. 1). Several of the related recommendations in the National Prevention Strategy dovetail with those we have suggested above, including the dissemination of community-based interventions that address health inequities, especially in inner-city neighborhoods and rural areas, the development, testing, and implementation of effective strategies to engage underserved populations, and the organization of representative, multi-sector community partnerships. However, expansion of research and the widespread adoption of research findings will not occur without more effective collaborations between researchers and the advocacy community. These efforts and similar international ones, such as the WHO Healthy Cities project (http://​www.​euro.​who.​int/​en/​what-we-do/​health-topics/​environment-and-health/​urban-health/​activities/​healthy-cities), demonstrate a growing coordination of governmental, community, and advocacy efforts focusing on prevention with a broad translation mandate.

The type of federal agency coordination illustrated by the National Prevention Council illustrates one way to move toward a common conceptual framework for prevention across federal agencies. A key aspect of a common framework concerns the risk and protective factors shared by the diverse problems addressed by various agencies. Obesity, inactivity, substance misuse, poor academic outcomes, and delinquent and criminal behavior are predicted by shared factors, as well as distinct risk and protective factors (Catalano et al. 2011). Clear evidence indicates that the same EBIs can positively affect seemingly disparate health-related behaviors and outcomes because these interventions target shared risk and protective factors (National Research Council and Institute of Medicine 2009a). In addition, there is evidence that EBIs designed to prevent mental, emotional, and behavioral disorders among children can have positive impacts on more distal outcomes such as academic achievement (e.g., Durlak et al. 2007).

Thus, there is growing attention to the need for federal and other governmental agencies to move from functioning in funding silos to forming multisectoral, multiagency partnerships that recognize both the agency-specific (particularly NIH) and the cross-agency outcome benefits achieved through collaboration. Agencies must focus more attention on the construction of readily explainable frameworks for the many health outcomes that are important across the federal stakeholders, including an understanding of why the agencies would jointly engage in prevention, how risk and protective factors cluster and cross over their interest areas, and how to measure and increase the translational impact of interventions targeting youth across outcomes of interest.

In this context, the authors recommend that the National Council on Prevention, Health Promotion, and Public Health, along with its nationally representative advisory group, facilitate the organization of a White House Office on the Translation of Evidence-Based Prevention, the primary functions of which would entail coordinating activities concerning the establishment of national goals for advancing translational practice and research (to address the needs articulated in this article, for example), along with monitoring progress toward these goals. Other activities of this office could parallel those summarized for a White House Office-led strategic coordinating group in the 2009 National Research Council and Institute of Medicine report on prevention of mental, emotional, and behavioral disorders (pp. 318–382). In that connection, one possibility would be that the Office could monitor the health and well-being of the nation’s children and youth, using data from this monitoring to construct an index of well-being. This index could show state-by-state results, as well as those of the nation overall, and how children’s and youth’s well-being is changing over time. It would be important that this Office be structured in a way that assured a non-partisan approach, similar to that of the Office of Management and Business, with its politically broad coalition of support, and that it work closely with bipartisan congressional caucuses that focus on dissemination and prevention issues.

One way to support T2 prevention translation across federal, state, and local levels is to form a better alignment across these levels in reporting requirements for grants and other funded programs. Varied requirements for assessing and reporting intervention outcomes constitute a major impediment to conducting T2 prevention research and pooling data among communities. The expansion of federal interagency workgroups and greater attention by oversight agencies (e.g., the White House National Office of Drug Control Policy) could facilitate efforts to coordinate data collection and reporting practices. The most recent National Drug Control Strategy emphasizes, “Preventing drug use before it begins is a cost-effective, common-sense way to build safe and healthy communities” (Office of National Drug Control Policy 2011). Toward this end, it highlights the need to develop community-focused prevention systems and increase collaboration among federal and state agencies to implement evidence-based prevention initiatives, including alignment of reporting requirements and fostering collaborations between public health and public safety organizations.

Another consideration for facilitating better T2 translation across federal, state, and local levels concerns adequate attention to TA needs, particularly concerning program outcome research and evaluation. Federal initiatives are raising public awareness that health problems can be prevented and are helping to support implementation of a continuum of programs supported by the best available evidence. However, financial support alone for the purchase of EBIs does not ensure that the interventions will be implemented as intended or that desired outcomes will emerge. Thus, federal initiatives in support of EBIs must include training, TA, and continuous evaluation to determine optimal conditions for achieving anticipated health impacts. Evaluation research should be a part of all large, federal, programmatic initiatives in order to increase our understanding of the factors that ensure successful selection, adoption, implementation, and sustainability of prevention programs (see Office of Management and Budget Memos M-12-13, 14, May 18, 2012; Haskins and Baron 2011). Concomitant with such initiatives, there should be an aligned focus on addressing the types of T2 research questions delineated earlier, particularly questions about continued effectiveness of EBIs once implemented on a wide scale. The Maternal, Infant, and Early Childhood Home Visiting Program under the Affordable Care Act is an example of a collaboration with a comprehensive view to programmatic and evaluation TA needs, whereby the Health Research Services Administration leads the programmatic TA efforts and the Administration for Children and Families (ACF) manages the systematic review of evidence (http://​homvee.​acf.​hhs.​gov/​), the national evaluation, and the set-aside funding for tribal communities, which includes programmatic and evaluation TA.

Some contemporary federal initiatives have placed a priority on supporting science-based approaches through encouraging or mandating the use of EBIs. Of critical importance to advancing the field, funding models also have been devised to allow for the braiding or integration of evaluation research into programmatic initiatives for the purposes of continually monitoring the effects of programs once they are implemented in communities. For example, in 2010 the DHHS Office of Adolescent Health, in collaboration with the Assistant Secretary for Planning and Evaluation and ACF, announced the availability of funds to support the implementation of evidence-based programs to reduce teenage pregnancy. Funded communities are expected to evaluate the implementation fidelity of the strategy and its effectiveness in reducing teen pregnancy rates and associated sexual outcomes.

For braided funding to work optimally, however, many conditions must be met. Not only should multiple federal agencies arrive at a common view about research and service goals of the federal program, but the reviewers of the applications to these funding opportunities need to do so as well. With T2 translation applying cross-disciplinary approaches, it is critically important that reviewers consider the appropriateness of a full range of methodologies that can be applied to such programs, including qualitative, quantitative, and simulation modeling, as described above. Further, projects supported through braided funding may assist with implementation monitoring or outcome evaluation but fall short of the kind of integration of translation research into programmatic initiatives contemplated by the TSci Impact Framework, an approach that is intended to create the opportunity to answer a broad range of translation research questions.

Considering limitations in current braided funding efforts, it is critical to develop additional federal and state funding models that can support coordinated, comprehensive prevention approaches in communities. Given the scale of the work to be accomplished, such funding models will likely require resource contributions from multiple agencies, as well as braiding funding from both service and research-supporting agencies. Funding models should explicitly integrate state-of-the-art methodologies for conducting T2 research, including randomized controlled trials or rigorous adaptive designs. Funding models could include consideration of: (1) braiding grant funding and TA from one federal agency for community-based implementation of EBIs and local evaluation, and contract funding from another federal agency for cross-site research on shared health outcomes; and (2) collaboration among federal and state agencies, in which state agencies fund grants to community-based organizations for program implementation and the federal agencies fund partnership grants to universities for collaboration on community-based participatory T2 research in the same communities. Ideally, these approaches are integrated with private–public partnerships that channel additional funding streams into the mix.

As noted in the section on infrastructural challenges, one of the most difficult barriers to surmount is limited funding and financing strategies and structures. The Annie E. Casey Foundation (www.​aecf.​org) is developing a series of documents describing financing projects, strategies, and structures that provide some excellent examples of state-level initiatives to strategically change investment in EBIs (Langford et al. 2012). Langford and colleagues define financing structures as mechanisms for prioritizing, coordinating, and expending dollars on services and programs (e.g., budget structures such as set-asides, or braided funding). Changed investment occurs either through redirection (shifting funds from lower-priority services or programs to higher-priority EBIs) or reinvestment (shifting funding from higher to lower-cost services and then reinvesting the savings, particularly to reach more people in targeted populations with EBIs). At this point, most of these illustrative initiatives focus primarily on a single service system (e.g., juvenile justice).

Following these examples, we recommend that each state organize “Prevention Translation Financing Teams” to support priority prevention goals through state EBI-delivery systems. The purpose of these teams would be to develop a strategic plan for financing population impact-oriented EBIs within each state. The teams’ composition could follow guidelines used by successful initiatives in states such as Maryland, as illustrated by Langford et al. (2012), to assure inclusion of all appropriate stakeholders for broad EBI implementation in state delivery systems. Eventually, these teams could network to form a learning community, possibly including coordination with the Coalition for Evidence-Based Policy (see www.​coalition4eviden​ce.​org).

The National Prevention Strategy provides a conceptual frame, along with potential objectives and priorities for state strategic plans for financing EBIs. It would be especially helpful to focus on scalable EBIs with existing, proven delivery systems, combining general population or universal EBIs with those that are more targeted, particularly those that address prevalent and costly behavioral health problems. Practical guidelines for developing such a strategic plan will be more readily available through efforts like those of the Annie E. Casey Foundation.

Overall, federal and state governments should take a stronger lead in promoting widespread EBI adoption, effective implementation, and sustainability, particularly through prevention delivery systems that foster collaborations and networking among practitioners and scientists. The Coalition for Evidence-Based Policy is greatly fostering progress on this front by facilitating legislative and policy change that supports broader implementation of highly effective programs, particularly through its tiered initiatives that fund evidence-based social programs (see Haskins and Baron 2011; www.​coalition4eviden​ce.​org). Importantly, this federal-level effort requires development of an infrastructure for national prevention systems to support widespread implementation of EBIs in communities nationwide, to increase the prevalence of healthy, nurturing environments (Biglan et al. 2012).