A survey tool for measuring evidence-based decision making capacity in public health agencies
1 Prevention Research Center in St. Louis, Brown School, Washington University in St. Louis, St. Louis, MO, USA
2 Bureau of Health Promotion, Kansas Department of Health and Environment, Topeka, KS, USA
3 Office of Preventive Health, Mississippi State Department of Health, Jackson, MS, USA
4 Office of Tobacco Control, Mississippi State Department of Health, Jackson, MS, USA
5 School of Health Related Professions, University of Mississippi Medical Center, and National Association of Chronic Disease Directors, Jackson, MS, USA
6 Active Living KC, Kansas City Health Department, Kansas City, MO, USA
7 Prevention Research Center in St. Louis, Saint Louis University School of Public Health, St. Louis, MO, USA
8 Division of Health Behavior Research, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
9 Division of Public Health Sciences, Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
10 George Warren Brown School of Social Work, Division of Public Health Sciences, School of Medicine, Washington University in St. Louis, Kingshighway Building 660 S. Euclid Campus, Box 8109, St. Louis, MO 63110, USA
BMC Health Services Research 2012, 12:57 doi:10.1186/1472-6963-12-57Published: 9 March 2012
While increasing attention is placed on using evidence-based decision making (EBDM) to improve public health, there is little research assessing the current EBDM capacity of the public health workforce. Public health agencies serve a wide range of populations with varying levels of resources. Our survey tool allows an individual agency to collect data that reflects its unique workforce.
Health department leaders and academic researchers collaboratively developed and conducted cross-sectional surveys in Kansas and Mississippi (USA) to assess EBDM capacity. Surveys were delivered to state- and local-level practitioners and community partners working in chronic disease control and prevention. The core component of the surveys was adopted from a previously tested instrument and measured gaps (importance versus availability) in competencies for EBDM in chronic disease. Other survey questions addressed expectations and incentives for using EBDM, self-efficacy in three EBDM skills, and estimates of EBDM within the agency.
In both states, participants identified communication with policymakers, use of economic evaluation, and translation of research to practice as top competency gaps. Self-efficacy in developing evidence-based chronic disease control programs was lower than in finding or using data. Public health practitioners estimated that approximately two-thirds of programs in their agency were evidence-based. Mississippi participants indicated that health department leaders' expectations for the use of EBDM was approximately twice that of co-workers' expectations and that the use of EBDM could be increased with training and leadership prioritization.
The assessment of EBDM capacity in Kansas and Mississippi built upon previous nationwide findings to identify top gaps in core competencies for EBDM in chronic disease and to estimate a percentage of programs in U.S. health departments that are evidence-based. The survey can serve as a valuable tool for other health departments and non-governmental organizations to assess EBDM capacity within their own workforce and to assist in the identification of approaches that will enhance the uptake of EBDM processes in public health programming and policymaking. Localized survey findings can provide direction for focusing workforce training programs and can indicate the types of incentives and policies that could affect the culture of EBDM in the workplace.