Assessing Organizational Supports for Evidence-Based Decision Making in Local Public Health Departments in the United States: Development and Psychometric Properties of a New Measure. Issue 5 (September 2019)

• Record Type: Journal Article
• Authors: Mazzucca, Stephanie; Parks, Renee G.; Tabak, Rachel G.; Allen, Peg; Dobbins, Maureen; Stamatakis, Katherine A.; Brownson, Ross C.
• Is Part Of: Journal of public health management and practice. Volume 25:Issue 5(2019)
• Publication Date: 2019-09
• Subjects: confirmatory factor analysis -- evidence-based decision making -- measurement -- organization -- public health; Public health administration -- United States -- Periodicals; 253.6
• DOI: 10.1097/PHH.0000000000000952
• Languages: English
• ISSNs: 1078-4659

Abstract

Abstract : Context: Fostering evidence-based decision making (EBDM) within local public health departments and among local health department (LHD) practitioners is crucial for the successful translation of research into public health practice to prevent and control chronic disease. Objective: The purpose of this study was to identify organizational supports for EBDM within LHDs and determine psychometric properties of a measure of organizational supports for EBDM in LHDs. Design: Cross-sectional, observation study. Setting: Local public health departments in the United States. Participants: Local health department practitioners (N = 376) across the United States participated in the study. Main Outcome Measures: Local health department practitioners completed a survey containing 27 items about organizational supports for EBDM. Most items were adapted from previously developed surveys, and input from researchers and practitioners guided survey development. Confirmatory factor analysis was used to test and refine the psychometric properties of the measure. Results: The final solution included 6 factors of 22 items: awareness of EBDM (3 items), capacity for EBDM (7 items), resources availability (3 items), evaluation capacity (3 items), EBDM climate cultivation (3 items), and partnerships to support EBDM (3 items). This factor solution achieved acceptable fit (eg, Comparative Fit Index = 0.965). Logistic regression models showed positive relationships between the 6 factors and the number of evidence-based interventions delivered. Conclusions: This study identified important organizational supports for EBDM within LHDs. Results of this study can be used to understand and enhance organizational processes and structures to support EBDM to improve LHD performance and population health. Strong measures are important for understanding how LHDs support EBDM, evaluating interventions to improve LHD capacity, and to guide programmatic and policy efforts within LHDs. Abstract : Supplemental Digital Content is Available in the Text.