Challenges in merging Medicaid and Medicare databases to obtain healthcare costs for dual-eligible beneficiaries: using diabetes as an example.

Dual-eligible Medicaid-Medicare beneficiaries represent a group of people who are in the lowest income bracket in the US, have numerous co-morbidities and place a heavy financial burden on the US healthcare system. As cost-effectiveness analyses are used to inform national policy decisions and to determine the value of implemented chronic disease control programmes, it is imperative that complete and valid determination of healthcare utilization and costs can be obtained from existing state and federal databases. Differences and inconsistencies between the Medicaid and Medicare databases have presented significant challenges when extracting accurate data for dual-eligible beneficiaries. To describe the challenges inherent in merging Medicaid and Medicare claims databases and to present a protocol that would allow successful linkage between these two disparate databases. Healthcare claims and costs were extracted from both Medicaid and Medicare databases for King County, Seattle, WA, USA. Three Medicaid files were linked to eight Medicare files for unique dual-eligible beneficiaries with type 2 diabetes mellitus. Although major differences were identified in how variables and claims were defined in each database, our method enabled us to link these two different databases to compile a complete and accurate assessment of healthcare use and costs for dual-eligible beneficiaries with a costly chronic condition. For example, of the 1759 dual-eligible beneficiaries with diabetes, the average cost of healthcare was USD 15,981 per capita, with an average of 76 claims per person per year. The resulting merged database provides a virtually complete documentation of both utilization and costs of medical care for a population who receives coverage from two different programmes. By identifying differences and implementing our linkage protocol, the merged database serves as a foundation for a broad array of analyses on healthcare use and costs for effectiveness research.

Hospitals collaborate to decrease surgical site infections.

BACKGROUND: Despite a large body of evidence describing care processes known to reduce the incidence of surgical site infections, many are underutilized in practice. METHODS: Fifty-six hospitals volunteered to redesign their systems as part of the National Surgical Infection Prevention Collaborative, a 1-year demonstration project sponsored by the Centers for Medicare & Medicaid Services. Each facility selected quality improvement objectives for a select group of surgical procedures and reported monthly clinical process measure data. RESULTS: Forty-four hospitals reported data on 35,543 surgical cases. Hospitals improved in measures related to appropriate antimicrobial agent selection, timing, and duration; normothermia; oxygenation; euglycemia; and appropriate hair removal. The infection rate decreased 27%, from 2.3% to 1.7% in the first versus last 3 months. CONCLUSIONS: The Collaborative demonstrated improvement in processes known to be associated with reduced risk of surgical site infections. Quality improvement organizations can be effective resources for quality improvement in the surgical arena.

Root biomass allocation in the world's upland forests.

Because the world's forests play a major role in regulating nutrient and carbon cycles, there is much interest in estimating their biomass. Estimates of aboveground biomass based on well-established methods are relatively abundant; estimates of root biomass based on standard methods are much less common. The goal of this work was to determine if a reliable method to estimate root biomass density for forests could be developed based on existing data from the literature. The forestry literature containing root biomass measurements was reviewed and summarized and relationships between both root biomass density (Mg ha-1) and root:shoot ratios (R/S) as dependent variables and various edaphic and climatic independent variables, singly and in combination, were statistically tested. None of the tested independent variables of aboveground biomass density, latitude, temperature, precipitation, temperature:precipitation ratios, tree type, soil texture, and age had important explanatory value for R/S. However, linear regression analysis showed that aboveground biomass density, age, and latitudinal category were the most important predictors of root biomass density, and together explained 84% of the variation. A comparison of root biomass density estimates based on our equations with those based on use of generalized R/S ratios for forests in the United States indicated that our method tended to produce estimates that were about 20% higher.