A novel dataset of predictors of mortality for older Veterans living with type II diabetes
Avi U. Vaidya,
Gabriel A. Benavidez,
Julia C. Prentice,
David C. Mohr,
Paul R. Conlin,
Kevin N. Griffith
Affiliations
Avi U. Vaidya
Department of Health Policy, Vanderbilt University Medical Center, Nashville, TN, USA
Gabriel A. Benavidez
Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA
Julia C. Prentice
Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA; Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, Boston, MA, USA
David C. Mohr
Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, Boston, MA, USA; Department of Health Law, Policy and Management, Boston University School of Public Health, Boston, MA, USA
Paul R. Conlin
VA Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
Kevin N. Griffith
Department of Health Policy, Vanderbilt University Medical Center, Nashville, TN, USA; Partnered Evidence-Based Policy Resource Center, VA Boston Healthcare System, Boston, MA, USA; Corresponding author at: Department of Health Policy, Vanderbilt University Medical Center, Nashville, TN, USA.
The dataset summarized in this article includes a nationwide prevalence sample of U.S. military Veterans who were aged 65 years or older, dually enrolled in the Veterans Health Administration and traditional Medicare and had a previous diagnosis of diabetes (diabetes mellitus) as of December 2005 (N = 275,190) [1]. Our data were originally used to develop and validate prognostic indices of 5- and 10-year mortality among older Veterans with diabetes. We include various potential predictors including demographics (e.g., sex, age, marital status, and VA priority group), healthcare utilization (e.g., # of outpatient visits, # days of inpatient stays), medication history, and major comorbidities. This novel dataset provides researchers with an opportunity to study the associations between a large variety of individual-level risk factors and longevity for patients living with diabetes.
