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

Abstract

ObjectiveThe 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 MethodsA 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.ResultsThe 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.ConclusionsTo 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.

Substance use disorders and medical comorbidities among high-need, high-risk patients with diabetes

“…Duke University received funds from the Centers for Medicare and Medicaid Services (2012–2016) and the Bristol-Myers Squibb Foundation to augment existing standard of care for adults with type 2 diabetes in community-based medical settings to improve diabetes management (Spratt et al, 2015). Guided by a framework of the spectrum of health and strategies for improvement (Fielding and Teutsch, 2011), an electronic health record (EHR)-based medical risk algorithm was developed using the Duke University Health System EHR data to predict risk for a serious outcome (hospital/ED admission or death) in the subsequent year among adults with type 2 diabetes, which was used to guide the risk-stratified intervention and allocation of available resources (Spratt et al, 2015).…”

“…Guided by a framework of the spectrum of health and strategies for improvement (Fielding and Teutsch, 2011), an electronic health record (EHR)-based medical risk algorithm was developed using the Duke University Health System EHR data to predict risk for a serious outcome (hospital/ED admission or death) in the subsequent year among adults with type 2 diabetes, which was used to guide the risk-stratified intervention and allocation of available resources (Spratt et al, 2015). The Duke Medicine Enterprise Data Warehouse (EDW) stores the EHR data generated in the healthcare delivery of available patients in the Duke University Health System, including three hospitals and over 200 affiliated primary care and specialty clinics (Horvath et al, 2014).…”

“…The risk algorithm was developed initially to predict serious outcomes in 2011 based on the EHR data in 2010, and it was validated by EHR data from 2012 (Spratt et al, 2015). By applying this algorithm to patients’ EHR data, the risk scores for a serious outcome (hospital/ED admission or death) in the coming year were used to identify adults with type 2 diabetes for targeting risk-stratified interventions.…”

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

“…GIS was used to explore gaps in access to care and selfmanagement resources and to direct resources into areas of need. 34 Placebased health disparities is emerging as an important area of research that can inform future policy. 3,35 Social media data hold the promise of linking social context to health/well-being and behavioral change.…”

Barriers to care for patients with diabetes in Durham, North Carolina, why are we withholding life-sustaining medications from the patients who need them the most?

“…The Durham Diabetes Coalition (DDC) was established to reduce death, disability and disparity from diabetes in Durham County, NC, using individual and community approaches guided by geospatial and secondary data analysis of the epidemic [11]. As a part of the clinical services provided by the DDC, home visits are arranged for patients who are at high risk for adverse diabetes outcomes over the next year.…”