Understanding administrative evidence-based practices: Findings from a survey of local health department leaders
Published Date:Jan 2014
Source:Am J Prev Med. 46(1):49-57.
Pubmed Central ID:PMC3982585
Funding:P30 DK092950/DK/NIDDK NIH HHS/United States
U48/DP001903/DP/NCCDPHP CDC HHS/United States
UL1 TR000448/TR/NCATS NIH HHS/United States
There are sparse data showing the extent to which evidence-based public health is occurring among local health departments.
The purpose of the study was to describe the patterns and predictors of administrative evidence-based practices (structures and activities that are associated with performance measures) in a representative sample of local health departments in the United States.
A cross-sectional study of 517 local health department directors was conducted from October through December 2012 (analysis in January through March 2013). The questions on administrative evidence-based practices included 19 items based on a recent literature review (five broad domains: workforce development, leadership, organizational climate and culture, relationships and partnerships, financial processes).
There was a wide range in performance among the 19 individual administrative evidence-based practices, ranging from 35% for access to current information on evidence-based practices to 96% for funding via a variety of sources Among the five domains, values were generally lowest for organizational climate and culture (mean for the domain = 49.9%) and highest for relationships and partnerships (mean for the domain = 77.1%). Variables associated with attaining the highest tertile of administrative evidence-based practices included having a population jurisdiction of 25,000 or larger (adjusted odds ratios (aORs) ranging from 4.4 to 7.5) and state governance structure (aOR=3.1).
This report on the patterns and predictors of administrative evidence-based practices in health departments begins to provide information on gaps and areas for improvement that can be linked with ongoing quality improvement processes.
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