Methods and initial findings from the Durham Diabetes Coalition: Integrating geospatial health technology and community interventions to reduce death and disability.

OBJECTIVE: The Durham Diabetes Coalition (DDC) was established in response to escalating rates of disability and death related to type 2 diabetes mellitus, particularly among racial/ethnic minorities and persons of low socioeconomic status in Durham County, North Carolina. We describe a community-based demonstration project, informed by a geographic health information system (GHIS), that aims to improve health and healthcare delivery for Durham County residents with diabetes. MATERIALS AND METHODS: A prospective, population-based study is assessing a community intervention that leverages a GHIS to inform community-based diabetes care programs. The GHIS integrates clinical, social, and environmental data to identify, stratify by risk, and assist selection of interventions at the individual, neighborhood, and population levels. RESULTS: The DDC is using a multifaceted approach facilitated by GHIS to identify the specific risk profiles of patients and neighborhoods across Durham County. A total of 22,982 patients with diabetes in Durham County were identified using a computable phenotype. These patients tended to be older, female, African American, and not covered by private health insurance, compared with the 166,041 persons without diabetes. Predictive models inform decision-making to facilitate care and track outcomes. Interventions include: 1) neighborhood interventions to improve the context of care; 2) intensive team-based care for persons in the top decile of risk for death or hospitalization within the coming year; 3) low-intensity telephone coaching to improve adherence to evidence-based treatments; 4) county-wide communication strategies; and 5) systematic quality improvement in clinical care. CONCLUSIONS: To improve health outcomes and reduce costs associated with type 2 diabetes, the DDC is matching resources with the specific needs of individuals and communities based on their risk characteristics.

Framework for Curating and Applying Data Elements within Continuing Use Data: A Case Study from the Durham Diabetes Coalition.

Data within a continuing use context (also known as secondary use) can require translation into the variables necessary for project analysis. We have developed and applied a framework in which: Project objectives inform the curation of data elements. Data elements are rendered into system-readable metadata. Metadata are applied to the source data and used to produce data sets. This process distinguishes between data sets and source data. Data sets contain project-specific variables that are structured for analytic activities. This can differ from source data, which may be stored in a structure dictated by the original source system for data collection, or in a data structure contrary to what is desired for analysis. Data elements mediate this translation, and the process of curation refines their definitions and associated attributes. This framework improves analysis workflow through the application of best practices, consistent processes, and centralized decision-making.