Audience segmentation to disseminate behavioral health evidence to legislators: an empirical clustering analysis.

Publisher: BioMed Central Country of Publication: England NLM ID: 101258411 Publication Model: Electronic Cited Medium: Internet ISSN: 1748-5908 (Electronic) Linking ISSN: 17485908 NLM ISO Abbreviation: Implement Sci Subsets: MEDLINE

Original Publication: [London] : BioMed Central, 2006-

Politics*; Information Dissemination/*methods ; Mental Disorders/*epidemiology ; Mental Disorders/*therapy ; Mental Health Services/*organization & administration; Humans ; Implementation Science ; Information Dissemination/legislation & jurisprudence ; Latent Class Analysis ; Logistic Models ; Mental Disorders/psychology ; Mental Health Services/economics ; Sex Factors ; Substance-Related Disorders/epidemiology ; Substance-Related Disorders/therapy ; United States

Background: Elected officials (e.g., legislators) are an important but understudied population in dissemination research. Audience segmentation is essential in developing dissemination strategies that are tailored for legislators with different characteristics, but sophisticated audience segmentation analyses have not been conducted with this population. An empirical clustering audience segmentation study was conducted to (1) identify behavioral health (i.e., mental health and substance abuse) audience segments among US state legislators, (2) identify legislator characteristics that are predictive of segment membership, and (3) determine whether segment membership is predictive of support for state behavioral health parity laws.
Methods: Latent class analysis (LCA) was used. Data were from a multi-modal (post-mail, e-mail, telephone) survey of state legislators fielded in 2017 (N = 475). Nine variables were included in the LCA (e.g., perceptions of behavioral health treatment effectiveness, mental illness stigma). Binary logistic regression tested associations between legislator characteristics (e.g., political party, gender, ideology) and segment membership. Multi-level logistic regression assessed the predictive validity of segment membership on support for parity laws. A name was developed for each segment that captured its most salient features.
Results: Three audience segments were identified. Budget-oriented skeptics with stigma (47% of legislators) had the least faith in behavioral health treatment effectiveness, had the most mental illness stigma, and were most influenced by budget impact. This segment was predominantly male, Republican, and ideologically conservative. Action-oriented supporters (24%) were most likely to have introduced a behavioral health bill, most likely to identify behavioral health issues as policy priorities, and most influenced by research evidence. This was the most politically and ideologically diverse segment. Passive supporters (29%) had the greatest faith in treatment effectiveness and the least stigma, but were also least likely to have introduced a behavioral health bill. Segment membership was a stronger predictor of support for parity laws than almost all other legislator characteristics.
Conclusions: State legislators are a heterogeneous audience when it comes to behavioral health. There is a need to develop and test behavioral health evidence dissemination strategies that are tailored for legislators in different audience segments. Empirical clustering approaches to audience segmentation are a potentially valuable tool for dissemination science.

J Health Serv Res Policy. 2002 Oct;7(4):239-44. (PMID: 12425783)
Adm Policy Ment Health. 2017 Mar;44(2):160-163. (PMID: 27418342)
Implement Sci. 2017 Nov 3;12(1):125. (PMID: 29100551)
Soc Psychiatry Psychiatr Epidemiol. 2017 Mar;52(3):249-258. (PMID: 28144713)
Pharmacoeconomics. 2016 Sep;34(9):863-87. (PMID: 27002518)
Implement Sci. 2017 Jun 26;12(1):81. (PMID: 28651613)
Health Res Policy Syst. 2017 Apr 26;15(1):35. (PMID: 28446185)
Prev Sci. 2018 Feb;19(2):260-270. (PMID: 28849362)
Health Commun. 2016;31(2):242-55. (PMID: 26086340)
Am J Public Health. 1999 Sep;89(9):1328-33. (PMID: 10474548)
Am J Public Health. 2017 Oct;107(10):1601-1603. (PMID: 28817318)
Psychiatr Serv. 2017 May 1;68(5):462-469. (PMID: 28045350)
Psychiatr Serv. 2003 Apr;54(4):501-7. (PMID: 12663837)
J Gen Intern Med. 2018 Jul;33(7):999-1001. (PMID: 29761264)
Psychiatr Serv. 2005 May;56(5):557-63. (PMID: 15872164)
Health Commun. 2015;30(2):154-63. (PMID: 25470440)
Inquiry. 2017 Jan 1;54:46958017705465. (PMID: 28452251)
Am J Public Health. 2011 Sep;101(9):1620-6. (PMID: 21778500)
Health Res Policy Syst. 2015 Apr 26;13:22. (PMID: 25928693)
Am J Psychiatry. 2013 May;170(5):494-501. (PMID: 23511486)
Healthc Policy. 2011 Nov;7(2):83-98. (PMID: 23115572)
Prev Med. 2012 Nov;55(5):427-9. (PMID: 22995372)
PLoS One. 2013;8(4):e60158. (PMID: 23577088)
Am J Public Health. 2014 Oct;104(10):1894-900. (PMID: 25122015)
Psychiatr Serv. 2007 May;58(5):632-5. (PMID: 17463343)
JAMA Psychiatry. 2018 Feb 1;75(2):119-120. (PMID: 29261839)
Am J Prev Med. 2006 Feb;30(2):164-72. (PMID: 16459216)
Psychiatr Serv. 2014 Apr 1;65(4):490-7. (PMID: 24430508)
Psychol Bull. 2007 Jul;133(4):673-93. (PMID: 17592961)
Psychiatr Serv. 2014 Oct;65(10):1265-8. (PMID: 25270496)
Implement Sci. 2016 Jan 04;11:1. (PMID: 26727969)
BMJ Open. 2014 Jul 01;4(7):e005293. (PMID: 24989620)
Am J Prev Med. 2015 Jun;48(6):755-66. (PMID: 25998926)
PLoS One. 2011;6(7):e21704. (PMID: 21818262)
J Health Polit Policy Law. 2018 Apr 1;43(2):185-228. (PMID: 29630706)
Psychiatr Serv. 2014 Oct;65(10):1269-72. (PMID: 25270497)
Health Res Policy Syst. 2018 May 31;16(1):47. (PMID: 29855328)
Am J Public Health. 2009 Dec;99(12):2123-7. (PMID: 19833993)
Child Dev. 2011 Jan-Feb;82(1):17-32. (PMID: 21291426)
Soc Sci Med. 2015 Feb;126:73-85. (PMID: 25528557)
J Am Acad Child Adolesc Psychiatry. 2008 Mar;47(3):339-49. (PMID: 18216729)
Med Care. 2016 Oct;54(10):901-6. (PMID: 27623005)
Psychiatr Serv. 2016 Dec 1;67(12):1355-1361. (PMID: 27364817)
Implement Sci. 2008 May 16;3:26. (PMID: 18485219)
Prev Sci. 2013 Apr;14(2):157-68. (PMID: 21318625)
Annu Rev Psychol. 2009;60:307-37. (PMID: 19035826)
Psychiatr Serv. 2018 May 1;69(5):587-589. (PMID: 29385960)
Bull World Health Organ. 2006 Aug;84(8):620-8. (PMID: 16917649)
J Health Commun. 1996 Jul-Sep;1(3):267-83. (PMID: 10947364)
J Health Polit Policy Law. 2016 Oct;41(5):873-916. (PMID: 27256811)
Am Psychol. 2016 Nov;71(8):742-751. (PMID: 27977256)
Health Aff (Millwood). 2016 Nov 1;35(11):1982-1990. (PMID: 27834237)
Am J Prev Med. 2015 Jun;48(6):767-70. (PMID: 25998927)
Psychiatr Serv. 2007 May;58(5):613-8. (PMID: 17463340)
Psychiatr Serv. 2018 Feb 1;69(2):136-146. (PMID: 28967320)
J Natl Cancer Inst. 2011 Feb 16;103(4):306-16. (PMID: 21212381)
Qual Life Res. 2003 May;12(3):229-38. (PMID: 12769135)
Am J Manag Care. 2017 Jul 1;23(7):e238-e244. (PMID: 28850791)
Health Aff (Millwood). 1992 Fall;11(3):186-96. (PMID: 1398442)
Psychiatr Serv. 2012 Apr;63(4):319-24. (PMID: 22388473)
Psychiatr Serv. 2015 Aug 1;66(8):783-97. (PMID: 25828881)
Health Res Policy Syst. 2011 Jun 24;9:29. (PMID: 21702956)
Arch Gen Psychiatry. 2009 Feb;66(2):128-33. (PMID: 19188534)
Lancet Psychiatry. 2016 May;3(5):415-24. (PMID: 27083119)
Psychiatr Serv. 2015 Aug 1;66(8):769. (PMID: 26234412)
Psychiatr Serv. 2015 Oct;66(10):1101-4. (PMID: 26129997)
Cancer Causes Control. 2016 Aug;27(8):1035-41. (PMID: 27299656)
BMC Health Serv Res. 2014 Jan 03;14:2. (PMID: 24383766)
Implement Sci. 2017 Sep 20;12(1):116. (PMID: 28931436)
Health Res Policy Syst. 2014 Jul 14;12:34. (PMID: 25023520)
Prev Chronic Dis. 2017 Feb 02;14:E10. (PMID: 28152363)
J Clin Child Adolesc Psychol. 2014;43(2):145-57. (PMID: 24460518)
PLoS One. 2011 Mar 10;6(3):e17571. (PMID: 21423743)

R21 MH111806 United States MH NIMH NIH HHS; R25 MH080916 United States MH NIMH NIH HHS; R21MH111806 United States MH NIMH NIH HHS; R25MH080916 United States MH NIMH NIH HHS

Keywords: Audience segmentation; Dissemination; Latent class analysis; Policymaker; State legislators; United States

Date Created: 20180921 Date Completed: 20190422 Latest Revision: 20190422

20250114

PMC6148796

10.1186/s13012-018-0816-8

30231934