Background
When informed by research evidence, legislators’ decisions can radically accelerate the population impact of mental health and substance abuse research (hereafter referred to as behavioral health) [
1‐
6]. For example, evidence about the benefits of insurance regulations (e.g., state parity laws) [
7,
8], effective treatments, preventive interventions, and macro determinants of behavioral health (e.g., structural stigma) [
9,
10] can be translated into action through laws that the 7383 state legislators in the USA have the exclusive authority to enact [
11]. For these reasons, the importance of disseminating behavioral health evidence to legislators has been articulated by actors ranging from behavioral health professionals [
12] to the US National Academies [
13,
14]. Although the rationale for disseminating behavioral health evidence to legislators is clear, there is sparse empirical guidance about how to effectively do it [
6,
15‐
17].
Policy dissemination research—defined as the study of the targeted distribution of evidence to policymakers [
18]—has almost exclusively focused on physical health [
16,
19,
20]. A 2015 review of interventions to increase the use of mental health research in policymaking found few studies, and none focused on legislators [
15]. A review of policy dissemination research funded by the National Institutes of Health between 2007 and 2014 did not identify any projects focused on disseminating behavioral health evidence to legislators or other policymakers in the USA [
16]. Effective policymaker-focused dissemination strategies are presumably different for behavioral health than physical health because stigma towards people with behavioral health conditions is pervasive [
13,
21‐
26] and because willingness to allocate financial resources is lower for behavioral health than physical health services [
27,
28]. Furthermore, while policy dissemination research is a rapidly growing field outside of the USA [
18], these studies have primarily focused on administrative officials who have specialized knowledge about health. Elected policymakers, such as legislators, generally lack such knowledge and thus have distinct dissemination needs [
6]. In the absence of empirical evidence about how behavioral health research might be most effectively disseminated to elected policymakers, dissemination activities are typically based on conjecture about what will work and an assumption about how information might be perceived by policymakers with different characteristics.
As Leeman and colleagues note in their typology of implementation strategies,
audience segmentation research is an essential first step towards understanding how to effectively disseminate evidence to legislators or any target population [
29]. Audience segmentation theory is founded on the premise that a population’s members are heterogeneous in their knowledge and attitudes and that tailored dissemination strategies that reflect these differences are more effective and persuasive than “one-size-fits-all” dissemination [
30]. Routine in the fields of marketing and communication, the practice of audience segmentation entails formative assessment to identify discrete sub-populations (i.e., audience segments) that are similar in their attitudes and behaviors [
31]. Messages that are tailored to audience segments are generally more effective than non-tailored messages [
32,
33]. Despite the importance of audience segmentation, policymakers have typically been treated as a homogenous population in policy dissemination research in the USA [
18] and sophisticated audience segmentation analyses have not been conducted.
There are two main approaches to audience segmentation: demographic separation and empirical clustering [
34]. With demographic separation, a population is divided into audiences on the basis of demographic characteristics (e.g., gender, political party affiliation) with the assumption that there is homogeneity within demographic groups. A more sophisticated approach, empirical clustering, uses statistical techniques (e.g., latent class analysis,
k-means clustering) to identify audience segments based on relationships among relevant variables. Empirical clustering is considered the superior approach [
34] and has been used to identify audience segments among the general public related to issues such as climate change [
35‐
39] and health equity [
40]. To our knowledge, no studies have used an empirical clustering approach to identify audience segments related to behavioral health among the general public or policymakers. More broadly, empirical clustering approaches to audience segmentation have not been widely used in the field of dissemination science.
Study aims and purpose
We conducted an empirical clustering audience segmentation study using latent class analysis (LCA) of data from a survey of US state legislators. The aims were to:
1.
Identify behavioral health audience segments among state legislators
2.
Identify legislator characteristics that are predictive of segment membership
3.
Determine whether segment membership is predictive of support for state behavioral health parity laws
The purpose of the study was to inform the design of dissemination strategies that are tailored for legislators in different audience segments, with ultimate goal of improving evidence-informed behavioral health policymaking among state legislators. It should be emphasized, however, that the use of research evidence in policymaking is extremely complex and that there are myriad barriers to evidence-informed policymaking (e.g., lack of trust in researchers, lack of policy relevant research findings, limited capacity to use research) [
19,
41‐
44]. Audience segmentation, or improved dissemination more broadly, is not a solution to these issues [
45,
46]. Rather, it simply provides an empirical basis for packaging and communicating research evidence in ways that more accurately reflect policymakers’ characteristics.
Discussion
Our results demonstrate that US state legislators are heterogeneous in their knowledge and attitudes about behavioral health and that at least three distinct audience segments exist: budget-oriented skeptics with stigma who have the least faith in behavioral health treatment effectiveness, have the most mental illness stigma, are most influenced by budget impact, and are ideologically conservative; action-oriented supporters who are most likely to have introduced a behavioral health bill, are most likely to identify behavioral health issues as policy priorities, are most influenced by research evidence, and are ideologically diverse; and passive supporters who have the greatest faith in behavioral health treatment effectiveness and the least mental illness stigma, but are also least likely to have introduced a behavioral health bill.
Membership in these latent audience segments had much higher predictive validity for support of state behavioral health parity laws than non-latent legislator characteristics (e.g., political party, education level). These findings underscore the importance of, and provide an empirical foundation for, developing and testing behavioral health dissemination strategies that are tailored to these different audience segments of legislators.
Implications for tailored dissemination strategies
Budget-oriented skeptics with stigma are a priority population to target given that they are the largest segment of legislators (47%) and have the highest levels of mental illness stigma and the least faith in behavioral health treatment effectiveness. As these are likely the political actors who contribute to structural stigma through policies that restrict opportunities for people with mental illness [
9,
10], there is an urgent need to disseminate information that reduces stigma among these legislators. Effective communications interventions to reduce stigma among the general public exist [
13,
22,
66], and research is needed to understand how these interventions might be tailored for legislators in this segment. These dissemination efforts should be tailored to the conservative characteristics of this segment (e.g., 73.6% Republican, 66.1% socially conservative). Dissemination might be most effective if information is framed in ways that resonate with a conservative worldview [
67,
68] and if messages originate from sources that conservative legislators perceive as credible. Post hoc analyses (Additional file
2) revealed that legislative staff and state behavioral health agencies were the primary sources that legislators in this segment turned to for behavioral health research when making policy decisions. Thus, these sources might be important intermediaries to target in dissemination efforts.
Less than 20% of legislators in the budget-oriented skeptics with stigma strongly agreed that behavioral health treatments were effective, compared to over 70% of legislators in the other two segments. As Watson and Corrigan note, legislators’ perceptions of behavioral health treatment effectiveness often represent concerns about wasting finite resources [
5]. This is consistent with our findings that legislators in this segment were much more influenced by budget impact when making behavioral health policy decisions than any other group and were by far the most fiscally conservative (78.3%). Thus, messages targeting this segment might emphasize the costs of unaddressed behavioral health problems and the potential return on investment for prevention and treatment [
14,
69‐
71]. However, it should be noted that socially conservative ideology, in addition to fiscally conservative ideology, was a strong predictor of segment membership in the fully adjusted model. This suggests that fiscal concerns are not the only core attribute of this segment. Attitudes towards people with mental illness (e.g., perceptions of the extent to which their problems are the result of individual versus structural issues) [
5,
26,
72] and other characteristics often associated with people with mental illness (e.g., low social class, minority race/ethnicity) [
73] potentially play an important role and should be considered in dissemination strategies.
Although action-oriented supporters was the smallest segment of legislators (24%), it is promising that this segment is characterized by prioritizing behavioral health issues, introducing behavioral health bills, and being strongly influenced by evidence when making behavioral policy decisions. It is also encouraging that this was the most politically and ideological diverse segment (e.g., 51.5% Democrat, 45.4% Republican). These findings are consistent with a 2012 survey of state legislators that found bipartisan support for behavioral health issues and that legislators who prioritized behavioral health issues were more influenced by research evidence than legislators’ who did not prioritize these issues [
58]. Taken together, these findings suggest that dissemination efforts targeting legislators in the action-oriented supporters segment should include concrete information about the science supporting evidence-based policy options to address behavioral health issues. Post hoc analyses (Additional file
2) showed that legislators in this segment identified behavioral health advocacy organizations as their primary source of behavioral health research. Thus, these organizations should be a target for the dissemination of this information. Passive supporters might be the lowest priority population to target given their exceptionally high faith in behavioral health treatment effectiveness and relatively low levels of mental illness stigma. Although legislators in this segment are least likely to have introduced behavioral health bills, they are similarly influenced by research evidence as action-oriented supporters when deciding whether to support behavioral health bill. Research should assess whether legislators in this segment respond similarly to dissemination strategies that are tailored for legislators in the action-oriented supporters segment.
Implications for knowledge translation
The current study focused on generating information to enhance the precision of dissemination efforts that
push research evidence to legislators. Our results, however, also have potential implications for knowledge translation efforts more broadly [
74]. Specifically, there could be implications for efforts that facilitate the
pull of research by legislators in the budget-oriented skeptics with stigma segment and
exchange efforts that foster relationships between behavioral health researchers and legislators in this segment.
In terms of
pull efforts, evidence clearinghouses could include economic evaluation data given that considerations related to budget impact were of high importance to legislators in this segment. The Washington State Institute for Public Policy’s Benefit-Costs Results clearinghouse is one model that could be adapted in states with conservative legislatures [
75]. In terms of implications for
exchange efforts, interventions that foster positive relationships between behavioral health researchers and legislators in the budget-oriented skeptics with stigma category could increase trust in researchers and potentially increase research use. Post hoc analyses (Additional file
2) showed that only 18.3% of legislators in this segment identified universities as a primary source of behavioral health research, a proportion significantly lower than the other two segments. This is consistent with the results of a 2012 state legislator survey which found that social and fiscal conservative ideology—strong predictors of membership in the budget-oriented skeptics with stigma—was inversely associated with the extent to which universities were perceived as reliable sources of research [
76]. Trusted intermediary organizations such as the American Legislative Exchange Council—an ideologically conservative legislator assistance organization—could help broker these relationships. A knowledge exchange intervention that recently demonstrated success with federal legislators in the USA is a model that could be adapted [
77].
Implications for dissemination science and future research
Our study demonstrates the utility of empirical clustering approaches to audience segmentation in policy dissemination research. Compared to prior studies that used demographic separation approaches to identify audience segments of legislators [
58,
59,
65,
78], our empirical clustering approach produced a more nuanced understanding of how evidence about a specific issue (i.e., behavioral health) might be most effectively packaged for different types of legislators. Although our study was focused on legislators in the USA, dissemination studies targeting administrative (i.e., not elected) policymakers, such as those being conducted in Australia [
79] and Canada [
80], might consider how empirical clustering could be used to identify audience segments within government agencies.
The value of identifying audience segments hinges upon the extent to which dissemination strategies that are tailored for these segments are more effective than non-tailored strategies. Narratives (i.e., stories about people) are a medium that can be integrated into dissemination materials, tailored for different audience segments, and manipulated by researchers to test dissemination effects. Narratives are important in policymaking processes because they are engaging and evocative, can humanize abstract problems, and illustrate how contextual factors (that can often be modified by policies) affect individuals [
81,
82]. Two experiments have tested the effects of narrative-focused dissemination materials on support for evidence-based policies among state legislators (one study focused on cancer [
65], one focused on obesity [
50]), and numerous studies have tested the effects of narratives about behavioral health issues on policy support among the general public [
26,
83‐
85].
For example, a recent public opinion experiment by McGinty et al. [
26] tested the effects of narratives that framed issues related to people with mental illness in different ways. The study found that narratives that emphasized systematic barriers to mental health treatment were more effective at increasing public willingness to pay additional taxes to improve the mental health system than a narrative about successful treatment-and-recovery. As McGinty et al. note (p. 212), future research should build on these studies and test the effects of such narratives on policymakers as opposed to the general public. By identifying behavioral health audience segments of state legislators, the current study could inform the tailoring and enhance the precision of policymaker-targeted narratives about behavioral health issues.
Measuring the effects of dissemination strategies on policymakers, particularly legislators, can be challenging [
18,
86]. Proximal measures of the effectiveness of tailored versus non-tailored dissemination materials could include perceptions of the materials (e.g., perceived likelihood of using the information, clarity, and relevance) and support for evidence-based policies that are the focus of the dissemination materials. Such outcomes have been previously assessed among state legislators via brief surveys that accompany materials [
50,
87,
88].
More distal measures of effectiveness could include legislators’ research use and policymaking behaviors. Information on these outcomes could be obtained via unobtrusive measures such as legislative voting [
89‐
91], committee hearings [
92,
93], the content of bills and other legislative documents [
94], public statements [
95], and social media behaviors [
96]. Structured interviews that assess the use of research evidence in policymaking, such as the Staff Assessment of enGagement with Evidence instrument [
97,
98], could also be adapted for state legislators to examine differences between those who receive tailored versus non-tailored dissemination materials.
Limitations
Our study has five main limitations. First, while our response rate of 16.4% is reasonable for state legislators [
48] and higher than response rates of recent legislator surveys [
49‐
51], it is sub-optimal by typical health services research standards. Demographic information about non-respondents allowed us to determinate that respondents were significantly different than non-respondents in terms of their political party affiliation, gender, and geographic region and to develop and apply non-response weights to adjust for these differences. This increases our confidence that results are not biased by non-response issues.
Second, our stigma measures were focused on mental illness and not able to assess stigma towards people with substance use disorders. This distinction could be important because evidence suggests that the public holds has more negative and stigmatizing attitudes towards people with substance use disorders than mental illness [
22,
24].
Third, our survey questions were broadly focused on behavioral health issues and we did not explicitly anchor questions to adult or child populations. While some of our questions implied that we were asking about adults (e.g., willingness to work closely with someone who has serious mental illness), it is not clear whether respondents were thinking of adults or children, or both, when answering questions. Perceptions of adults’ behavioral health issues, and policy solutions to address them, are often different than perceptions of children’s mental health issues [
22,
99‐
102], and different audience segments might exist for children’s behavioral health.
Fourth, our study was limited to elected policymakers in the legislative branch of government at the state-level in the USA and results are not necessarily generalizable to elected policymakers at different levels of government, those outside the USA, or administrative policymakers in executive branches of government.
Fifth, a limitation of LCA is the risk of misclassifying of individuals, particularly those whose posterior segment membership probabilities are far from 0 or 1. In our analysis, mean posterior probabilities for segment membership ranged from 0.91 to 0.94 and mean probabilities for segment non-membership ranged from 0.03 to 0.06, which is encouraging [
103].
Acknowledgements
We thank the National Conference of State Legislatures for providing feedback on the survey instrument, SSRS for data collection, the legislators who participated in cognitive interviews to test the survey instrument, and the legislators who took the time to complete the survey.