Medicare utilization, screening, and costs among participants in the Southeastern Diabetes Initiative: A population-based evaluation.

Aims: Type 2 diabetes mellitus imposes significant burdens on patients and health care systems. Population-level interventions are being implemented to reach large numbers of patients at risk of or diagnosed with diabetes. We describe a population-based evaluation of the Southeastern Diabetes Initiative (SEDI) from the perspective of a payer, the Centers for Medicare & Medicaid Services (CMS).

Using electronic health record data for substance use Screening, Brief Intervention, and Referral to Treatment among adults with type 2 diabetes: Design of a National Drug Abuse Treatment Clinical Trials Network study.

Background: The Affordable Care Act encourages healthcare systems to integrate behavioral and medical healthcare, as well as to employ electronic health records (EHRs) for health information exchange and quality improvement. Pragmatic research paradigms that employ EHRs in research are needed to produce clinical evidence in real-world medical settings for informing learning healthcare systems. Adults with comorbid diabetes and substance use disorders (SUDs) tend to use costly inpatient treatments; however, there is a lack of empirical data on implementing behavioral healthcare to reduce health risk in adults with high-risk diabetes.

Substance use and mental diagnoses among adults with and without type 2 diabetes: Results from electronic health records data.

Background: Comorbid diabetes and substance use diagnoses (SUD) represent a hazardous combination, both in terms of healthcare cost and morbidity. To date, there is limited information about the association of SUD and related mental disorders with type 2 diabetes mellitus (T2DM).

Methods: We examined the associations between T2DM and multiple psychiatric diagnosis categories, with a focus on SUD and related psychiatric comorbidities among adults with T2DM.

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.

Improving population representation through geographic health information systems: mapping the MURDOCK study.

This paper highlights methods for using geospatial analysis to assess, enhance, and improve recruitment efforts to ensure representativeness in study populations. We apply these methods to the Measurement to Understand Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) study, a longitudinal population health study focused on the city of Kannapolis and Cabarrus County, NC. Although efforts have been made to recruit a participant registry that is representative of the 18 ZIP code catchment region inclusive of Cabarrus County and Kannapolis, bias in such recruitment is inevitable.

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.

Missing signposts on the roadmap to quality: a call to improve medication adherence indicators in data collection for population research.

Purpose: Poor adherence to prescribed medicines is associated with increased rates of poor outcomes, including hospitalization, serious adverse events, and death, and is also associated with increased healthcare costs. However, current approaches to evaluation of medication adherence using real-world electronic health records (EHRs) or claims data may miss critical opportunities for data capture and fall short in modeling and representing the full complexity of the healthcare environment. We sought to explore a framework for understanding and improving data capture for medication adherence in a population-based intervention in four U.

The Measurement to Understand Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) Study Community Registry and Biorepository.

Current understanding of chronic diseases is based on crude clinical characterization, imaging studies, and laboratory testing that has evolved over decades. The Measurement to Understand Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) Study is a multi-tiered, longitudinal study designed to enable classification of chronic diseases using clinically annotated biospecimen collections, -omic technologies, electronic health records, and standard epidemiological methods. We expect that detailed molecular classification will improve mechanistic understanding of chronic diseases, augmenting discovery and testing of new treatments, and allowing refined selection of prevention and treatment strategies.

The MURDOCK Study: a long-term initiative for disease reclassification through advanced biomarker discovery and integration with electronic health records.

Background: Facing critically low return per dollar invested on clinical research and clinical care, the American biomedical enterprise is in need of a significant transformation. A confluence of high-throughput "omic" technologies and increasing adoption of the electronic health record has fueled excitement for a new paradigm for biomedical research and practice. The ability to simultaneously measure thousands of molecular variables and assess their relationships with clinical data collected during the course of care could enable reclassification of disease not only by gross phenotypic observation but according to underlying molecular mechanism and influence of social determinants.

Multiple switching in Medicaid managed care: a proportional hazards model.

This case study (n=41,969) aims to discover managerially useful predictors of multiple switching among HMOs in a Medicaid managed care population observed over 33 months. Cox's proportional hazards model was used to analyze eligibility data for the entire population, claims data for Medicaid services received during six months prior to HMO enrollment (sample n=2,474) and telephone interviews (sample n=656). Each analytic stage involved four comparisons: (1) enrollees with one switch compared with enrollees with no switches; (2) enrollees with multiple switches compared with those having no switches; (3) in relation to making the first switch, enrollees with multiple switches compared with those having one switch; and (4) in relation to making the second switch, multiple compared with those having one switch.