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Organizational Supports for Research Evidence Use in State Public Health Agencies

A Latent Class Analysis

Hu, Hengrui, MS; Allen, Peg, PhD, MPH; Yan, Yan, MD, PhD; Reis, Rodrigo S., PhD, MSc; Jacob, Rebekah R., MSW, MPH; Brownson, Ross C., PhD

Journal of Public Health Management and Practice: May 30, 2018 - Volume Publish Ahead of Print - Issue - p
doi: 10.1097/PHH.0000000000000821
Research Full Report: PDF Only

Objective: Use of research evidence in public health decision making can be affected by organizational supports. Study objectives are to identify patterns of organizational supports and explore associations with research evidence use for job tasks among public health practitioners.

Design: In this longitudinal study, we used latent class analysis to identify organizational support patterns, followed by mixed logistic regression analysis to quantify associations with research evidence use.

Setting: The setting included 12 state public health department chronic disease prevention units and their external partnering organizations involved in chronic disease prevention.

Participants: Chronic disease prevention staff from 12 US state public health departments and partnering organizations completed self-report surveys at 2 time points, in 2014 and 2016 (N = 872).

Main Outcome Measures: Latent class analysis was employed to identify subgroups of survey participants with distinct patterns of perceived organizational supports. Two classify-analyze approaches (maximum probability assignment and multiple pseudo-class draws) were used in 2017 to investigate the association between latent class membership and research evidence use.

Results: The optimal model identified 4 latent classes, labeled as “unsupportive workplace,” “low agency leadership support,” “high agency leadership support,” and “supportive workplace.” With maximum probability assignment, participants in “high agency leadership support” (odds ratio = 2.08; 95% CI, 1.35-3.23) and “supportive workplace” (odds ratio = 1.74; 95% CI, 1.10-2.74) were more likely to use research evidence in job tasks than “unsupportive workplace.” The multiple pseudo-class draws produced comparable results with odds ratio = 2.09 (95% CI, 1.31-3.30) for “high agency leadership support” and odds ratio = 1.74 (95% CI, 1.07-2.82) for “supportive workplace.”

Conclusions: Findings suggest that leadership support may be a crucial element of organizational supports to encourage research evidence use. Organizational supports such as supervisory expectations, access to evidence, and participatory decision-making may need leadership support as well to improve research evidence use in public health job tasks.

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Division of Biostatistics (Mr Hu and Dr Yan) and Division of Public Health Sciences, Department of Surgery (Drs Yan and Brownson), Washington University School of Medicine, and Prevention Research Center, Brown School (Mr Hu and Ms Jacob and Drs Allen, Reis, and Brownson), Washington University in St Louis, St Louis, Missouri.

Correspondence: Peg Allen, PhD, MPH, Prevention Research Center, Brown School, Washington University in St Louis, One Brookings Dr, Campus Box 1196, St Louis, MO 63130 (

This work was supported by the National Cancer Institute of the National Institutes of Health (NIH) [R01CA160327]. The research presented in this article is that of the authors and does not reflect the official policy of the NIH. The authors appreciate the collaboration of the 12 state health department chronic disease directors and staff and partner survey participation. The input of consultants Leslie Best and Ellen Jones throughout the project is appreciated, as is the survey development input from Jenine Harris, Sonia Sequeira, and consultants Elizabeth A. Baker, Maureen Dobbins, Jon F. Kerner, and Katherine A. Stamatakis. Adriano Akira Ferreira Hino analyzed the test-retest reliability data. Lindsay Elliott, Alicia Manteiga, Amanda Burgess, Anna deRuyter, and Meenakshi Lakshman helped with data collection and reports.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (

The authors have no conflicts of interest.

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