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

INTRODUCTION: Many 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. METHODS: Using 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. RESULTS: Median 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). CONCLUSION: CDCC 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.

Evidence-based public health: a fundamental concept for public health practice.

Despite the many accomplishments of public health, a greater attention to evidence-based approaches is warranted. This article reviews the concepts of evidence-based public health (EBPH), on which formal discourse originated about a decade ago. Key components of EBPH include making decisions on the basis of 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. Three types of evidence have been presented on the causes of diseases and the magnitude of risk factors, the relative impact of specific interventions, and how and under which contextual conditions interventions were implemented. Analytic tools (e.g., systematic reviews, economic evaluation) can be useful in accelerating the uptake of EBPH. Challenges and opportunities (e.g., political issues, training needs) for disseminating EBPH are reviewed. The concepts of EBPH outlined in this article hold promise to better bridge evidence and practice.

Factors affecting evidence-based decision making in local health departments.

BACKGROUND: Data indicating the extent to which evidence-based decision making (EBDM) is used in local health departments (LHDs) are limited. PURPOSE: This study aims to determine use of decision-making processes by New York State LHD leaders and upper-level staff and identify facilitators and barriers to the use of EBDM in LHDs. METHODS: The New York Public Health Practice-Based Research Network implemented a mixed-methods study in 31 LHDs. There were 20 individual interviews; five small-group interviews (two or three participants each); and two focus groups (eight participants each) conducted with people who had decision-making authority. Information was obtained about each person's background and position, decision-making responsibilities, how decisions are made within their LHD, knowledge and experience with EBDM, use of each step of the EBDM process, and barriers and facilitators to EBDM implementation. Data were collected from June to November 2010 and analyzed in 2011. RESULTS: Overall, participants supported EBDM and expressed a desire to increase their department's use of it. Although most people understood the concept, a relatively small number had substantial expertise and experience with its practice. Many indicated that they applied EBDM unevenly. Factors associated with use of EBDM included strong leadership; workforce capacity (number and skills); resources; funding and program mandates; political support; and access to data and program models suitable to community conditions. CONCLUSIONS: EBDM is used inconsistently in LHDs in New York. Despite knowledge and interest among LHD leadership, the LHD capacity, resources, appropriate programming, and other issues serve as impediments to EBDM and optimal implementation of evidence-based strategies.