In the United States, chronic disease is the most common cause of disability and death, with cancer, heart disease, and cerebrovascular disease alone accounting for over half of all deaths [1] and consuming over 80% of the healthcare budget in the United States [2]. While community-level programs and policies in prevention and control hold great promise for reducing the burden of chronic disease [3-5], community prevention programs are increasingly pressured to stretch staff and budgets across a broad range of activities and responsibilities, emphasizing the need to direct scarce resources to the most effective programs.

Evidence-based interventions (EBIs) in chronic disease prevention are comprised of increasing numbers of programs and policies that have been research-tested and are ready for dissemination to community and public health practice settings (the Guide to Community Preventive Services [a.k.a., the Community Guide], the Guide to Clinical Preventive Services, Cancer Control PLANET, Research-tested Intervention Programs [6-9]). However, there is reason to believe that the best available evidence is not reaching or being integrated into all practice settings. In a survey of state and local public health practitioners, only 30% of practitioners at the local level had heard of the Community Guide [10], a standard for recommended EBIs in community health [11]. Among state-level practitioners, almost 90% of whom had heard of the Community Guide, a much smaller proportion reported making changes to existing (20%) or new programs (35%) on the basis of Community Guide recommendations [10]. Thus, these data indicate that the challenge for improving uptake of EBIs extends beyond simply spreading awareness to include integration into practice settings and suggest that readiness for EBIs may vary across settings.

Conceptualizing public health organizational readiness for EBIs requires drawing from a broader understanding of how evidence is used in public health practice as well as processes underlying the movement of research evidence into practice settings. Theoretical frameworks that lay the groundwork for these components of readiness include evidence-based public health [12] and the stages of innovation diffusion [13-17]. Evidence-based public health practice has been described as applying the best available, scientifically rigorous, and peer-reviewed evidence (i.e., EBIs), but it also includes using data and information systems systematically, applying program-planning frameworks, engaging the community in assessment and decision making, conducting sound evaluation, and disseminating what is learned to key stakeholders and decision makers [12]. The conceptualization of stages characterizing the process of uptake and integration of EBIs is grounded in Rogers’ theory of Diffusion of Innovations [14,16] and the application of these tenets in program planning and evaluation, most notably, as exemplified by the RE-AIM framework (i.e., reach, effectiveness, awareness, implementation and maintenance) [15,17]. Together, these frameworks and theoretical models can be applied to the development of constructs for characterizing the stages of a public health organization’s readiness for EBIs, from awareness to adoption, implementation, and maintenance.

One of the central challenges for moving EBIs for chronic disease prevention into community and public health practice settings may be the variability in organizational readiness, but the lack of a tool to measure organizational readiness for EBIs currently hinders the ability to test this hypothesis. The aim of this article is to describe the development, measurement properties, and potential uses of a novel survey instrument used to measure stages of organizational readiness for EBIs, designed to be brief and generalizable across chronic disease prevention program areas.

Descriptive statistics of the respondents and their respective organizations and programs are provided in Table 1.

This article describes the initial assessment of the measurement properties of a novel survey instrument to stage organizational readiness for EBIs in community-based chronic disease practice settings. We found support for our theory-based measures of readiness stages using CFA, though some modification was needed before arriving at a final model. Most notably, the awareness scale was split into two separate factors representing community and agency awareness, and the maintenance scale was split into two separate factors representing resource and evaluation maintenance. The mean scale scores followed a hypothesized pattern across types of programs, with the group of grant-funded programs that had received additional support exhibiting higher mean scores, particularly with respect to latter stages.

This staging survey is based on the underlying assumption that moving from one stage to another occurs successively, with strength in earlier stages serving as accumulated capacity to move to the next stage. Our analysis found some support for this, as correlations between adjacent stages were generally stronger than correlations between nonadjacent stages. However, this model may oversimplify the variation that exists in processes involved in moving from one stage to another. It may be that the organizational processes that lead to awareness and initial adoption not only differ from the processes that lead to implementation and maintenance but also that the actual system underlying the process is itself different. For example, it is possible that awareness is driven by a diffusion process, via linked networks of practitioners and organizations, but that implementation and maintenance are driven more by the multilevel systems and structure that drive capacity [38]. In our analysis, while the resource maintenance scale distinguished between programs according to a priori expectation of readiness, it was comprised of items with moderate to low factor loadings, which likely contributed to the moderate alpha for this final scale. It is possible that our scale didn’t fully capture the domains that comprise maintenance. Likewise, the awareness domain, which was split into subscales to achieve better model fit for the measurement instrument as a whole, resulted in two-item subscales, which is considered less than ideal for measurement of latent constructs. Future studies that can afford to increase the number of survey items may benefit from adding items designed to more comprehensively measure the different content domains within each stage.

We found that an organizational climate more favorable to using research evidence was related to all stages of readiness for evidence-based practice, with the caveat that our measure of implementation climate is new and its measurement properties are yet to be examined. This is in line with prior work that has suggested that having an organizational culture favorable to using evidence may moderate the effectiveness of strategies to increase the implementation of evidence-informed policies and programs in public health settings [39]. Organizational factors such as prioritizing use of evidence in practice have been found to predict adoption of evidence-based practice in state health departments [40]. This would suggest that in addition to staging readiness for evidence-based practice, assessing organizational context with respect to the culture and climate toward using evidence will be equally important in determining appropriate implementation strategies in a given setting. It may be that if organizational culture and climate toward using evidence is low, then strategies to enhance culture toward using evidence in general (e.g., engaging leadership) may be considered as a possible starting point for implementation strategies.

This survey instrument may be grouped with other types of organizational assessments in the dissemination and implementation literature [41], most relevant being assessments of culture and climate [42] and readiness for change [43], although some distinctions are worth noting. Other organizational assessments include domains that do not have a clear sequenced relationship, while by definition, our framework’s stages are sequentially dependent on each other. While our assessment of readiness for EBIs in chronic disease prevention settings may be similar in orientation to instruments that measure readiness for change, our survey is more specific to organizational readiness for EBIs anchored in particular chronic disease program areas rather than the more global orientation of readiness for change. As Weiner [44] notes [43], the question of whether our instrument is measuring “readiness” or “determinants of readiness” may be important to consider when determining appropriate applications.

Another key point of departure for our instrument is that we were targeting public health chronic disease prevention settings versus clinical healthcare settings. The emphasis in public health settings is to deliver interventions using a community-based approach rather than an individual, patient-oriented approach, which is reflected in the structure and function of the organization, from program level on up [45]. These types of interventions tend to be carried out in teams within the organization, and often extend to community partners. Our instrument reflects this, as items comprising each stage include some assessment of agency staff, agency leadership, and the broader community (which could include individual community members as well as partners). Previous scales used to assess stages of EBI (i.e., “innovation”) diffusion in public health organizations have been substantially longer than the one created for this project (e.g., Steckler et al.[21], which had 125+ items).

This paper has a number of strengths and limitations worth noting. To our knowledge, our survey represents the first of its kind to be used in community and public health chronic disease prevention settings. The instrument is brief (less than six minutes average completion time in our sample) and easy to complete, as evidenced by the response rate, which suggests the feasibility of data collection for longitudinal assessments; our response rate was in line with previous work among local health officials (73% in Brownson et al.) [10]. The limited time and attention of chronic disease program staff to devote to a survey is no doubt shared across professional settings; this serves as a demonstration of the utility and validity of a brief instrument and may bolster efforts in other areas. We used a rigorous confirmatory analytic approach to confirm which items belonged to which content domains or scales. Additional testing is required to determine whether this measure of organizational readiness for EBIs is indeed predictive of actual adoption and implementation of EBIs. Our final model resulted in six scales, two of which (community and agency awareness) had only two items each, which is considered less than ideal for measurement of latent constructs. The final alphas for our scales were generally acceptable, especially considering the brevity of the scales, with the exception of the relatively low alpha for the resource maintenance scale. This suggests room for improvement in future versions of the instrument measuring these domains. We did not have sufficient sample size in this study to confirm our final model with a hold-out sample or to test for item invariance across multiple subgroups (e.g., program type, urban vs. rural setting). It is possible that readiness for evidence-based practice may vary by type of practice setting and that different measures may be needed. In many cases there was one respondent representing an organization; it is yet unclear how many respondents it takes in a particular setting to precisely measure these issues and track changes over time. Future work could explore conceptual and analytical issues around level of measurement and clustering with multilevel data collection and analysis.

Another important consideration in interpreting and using this instrument is the basis by which evidence-based practice was assessed. For the current survey, we define evidence-based practice as “an evidence-based intervention, evidence-based policy, or evidence-based clinical care…that integrates science-based interventions with community preferences to improve the health of populations.” Examples of sources of evidence-based interventions provided to respondents in the body of the survey were based on systematic research reviews (i.e., Community Guide, Cochrane). There may be additional information about community and organizational context, as well as knowledge derived from professional experience (practice-based evidence), that also comprise elements of evidence applied in practice settings [46] that were not explicitly addressed.

The lack of adequate measurement tools hinders progress in dissemination and implementation research [47]. To help fill this void, these results describe the reliability and measurement properties of a theory-based tool; the short, user-friendly instrument may be useful to researchers and practitioners seeking to assess organizational readiness for evidenced-based practice across a variety of chronic disease prevention programs and settings. Although the field is young, it is likely that intervention strategies to enhance dissemination and implementation progress will need to be tailored to stage of readiness. This parallels the recognized need in clinical settings to generate more evidence to guide the choice of implementation strategies based on the expectation of relative effectiveness given the characteristics of a particular setting [48].

There are many potential applications of a survey instrument such as this, in addition to tailoring intervention strategies to stage of readiness. For example, the survey could be used to provide a gateway to initiating contact with practice settings and as a basis from which to seek additional, in-depth information using more qualitative approaches. In this light, this staging survey may be placed in the spectrum of participatory approaches [49], in which assessment of organizational readiness may serve as one part in the process of integrating knowledge about the practice setting by including input from practitioners into developing strategies to increase the use of evidence in that setting. There are numerous analytical applications of the quantitative measures of readiness stage, including examination of the relationship between stages and measures of program success and how this may differ across program areas. Finally, the scales derived from this survey instrument may also be considered as part of a spectrum of implementation outcomes [50] and could be explored as markers of success of an implementation strategy (i.e., evaluate shift from low implementation to high implementation/maintenance).

‘The authors declare that they have no competing interests’.

KAS carried out the CFA analysis, supervised all other data analysis, participated in survey development, and drafted the manuscript. AM supervised the CFA analysis and contributed to writing the manuscript. CF participated in data collection and management, conducted the test-retest reliability analysis, and contributed to writing the manuscript. EB participated in data collection and survey development and contributed to writing the manuscript. MD coordinated survey development, assisted with data collection and management, and contributed to writing the manuscript. DAL participated in the study design and survey development and contributed to writing the manuscript. RCB led the study design and survey development and contributed to writing the manuscript. All authors read and approved the final manuscript.