Editorial Comment: What Influences the Use of Administrative Evidence-Based Practices in Local Health Departments?

In 2012, Frontiers published an article by Allen et al. about identifying administrative and management practices that make up an evidence-based local health department.1 They recommended that local health departments (LHDs) consider using such practices to implement sustained evidence-based policies, programs, and interventions. Strategies that should be given ‘high priority’ for implementation were highlighted. My accompanying editorial2 acknowledged the value of this practical advice to LHDs in optimizing their performance and achieving desired health outcomes

Evidence-based Decision Making to Improve Public Health Practice

Despite the many accomplishments of public health, greater attention on evidence-based approaches is warranted. This article reviews the concepts of evidence-based public health (EBPH), on which formal discourse originated about 15 years ago. Key components of EBPH include: making decisions based on the best available scientific evidence, using data and information systems systematically, applying program planning frameworks, engaging the community in decision making, conducting sound evaluation, and disseminating what is learned. Core competencies for EBPH are emerging, including not only technical skills but also attention to administrative practices in public health agencies. To better bridge evidence and practice, the concepts of EBPH outlined in this article should be carried out in their entirety

Evidence-Based Decision Making in Local Health Departments

Evidence-based decision making (EBDM) represents an important strategy to increase efficacy and efficiency of public health programs and practice. There is insufficient information on the application of EBDM among local health departments (LHDs). This qualitative study examined use of EBDM in New York State (NYS) LHDs and factors facilitating and impeding its adoption through interviews and focus groups with 47 LHD commissioners, health directors, and other upper-level staff. Findings suggest variability in application of EBDM in NYS LHDs. A number of internal factors (e.g., staff capacity, organizational culture) and external factors (e.g., policy environment, appropriate and replicable evidence-based models) contribute to its uneven use, even within a single LHD

Toward optimal implementation of cancer prevention and control programs in public health: A study protocol on mis-implementation

Abstract Background Much of the cancer burden in the USA is preventable, through application of existing knowledge. State-level funders and public health practitioners are in ideal positions to affect programs and policies related to cancer control. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. Greater attention to mis-implementation should lead to use of effective interventions and more efficient expenditure of resources, which in the long term, will lead to more positive cancer outcomes. Methods This is a three-phase study that takes a comprehensive approach, leading to the elucidation of tactics for addressing mis-implementation. Phase 1: We assess the extent to which mis-implementation is occurring among state cancer control programs in public health. This initial phase will involve a survey of 800 practitioners representing all states. The programs represented will span the full continuum of cancer control, from primary prevention to survivorship. Phase 2: Using data from phase 1 to identify organizations in which mis-implementation is particularly high or low, the team will conduct eight comparative case studies to get a richer understanding of mis-implementation and to understand contextual differences. These case studies will highlight lessons learned about mis-implementation and identify hypothesized drivers. Phase 3: Agent-based modeling will be used to identify dynamic interactions between individual capacity, organizational capacity, use of evidence, funding, and external factors driving mis-implementation. The team will then translate and disseminate findings from phases 1 to 3 to practitioners and practice-related stakeholders to support the reduction of mis-implementation. Discussion This study is innovative and significant because it will (1) be the first to refine and further develop reliable and valid measures of mis-implementation of public health programs; (2) bring together a strong, transdisciplinary team with significant expertise in practice-based research; (3) use agent-based modeling to address cancer control implementation; and (4) use a participatory, evidence-based, stakeholder-driven approach that will identify key leverage points for addressing mis-implementation among state public health programs. This research is expected to provide replicable computational simulation models that can identify leverage points and public health system dynamics to reduce mis-implementation in cancer control and may be of interest to other health areas

Costs of Chronic Diseases at the State Level: The Chronic Disease Cost Calculator

IntroductionMany studies have estimated national chronic disease costs, but state-level estimates are limited. The Centers for Disease Control and Prevention developed the Chronic Disease Cost Calculator (CDCC), which estimates state-level costs for arthritis, asthma, cancer, congestive heart failure, coronary heart disease, hypertension, stroke, other heart diseases, depression, and diabetes.MethodsUsing publicly available and restricted secondary data from multiple national data sets from 2004 through 2008, disease-attributable annual per-person medical and absenteeism costs were estimated. Total state medical and absenteeism costs were derived by multiplying per person costs from regressions by the number of people in the state treated for each disease. Medical costs were estimated for all payers and separately for Medicaid, Medicare, and private insurers. Projected medical costs for all payers (2010 through 2020) were calculated using medical costs and projected state population counts.ResultsMedian state-specific medical costs ranged from $410 million (asthma) to$1.8 billion (diabetes); median absenteeism costs ranged from $5 million (congestive heart failure) to$217 million (arthritis).ConclusionCDCC provides methodologically rigorous chronic disease cost estimates. These estimates highlight possible areas of cost savings achievable through targeted prevention efforts or research into new interventions and treatments