Disparities in diabetes and its complications have persisted, despite increased public health focus on reducing them. Although eliminating diabetes health disparities has been a national goal, inadequate quality of care and poor outcomes are still persistent among racial/ethnic minorities. Many interventions have been developed to reduce diabetes health disparities; however, adoption has been slow as there is a lack of long-term data on the economic and clinical impacts of these interventions. This translational research gap is partly a result of fundamental limitations of current diabetes simulation models to study health disparities. Predicting future diabetes health disparities among racial/ethnic minorities has been difficult as current diabetes simulation models rely on data from the UK Prospective Diabetes Study (UKPDS) and other sources that do not reflect the U.S. diverse population and do not include important conditions associated with diabetes such as dementia and depression. The absence of valuable evidence to support policies addressing diabetes health disparities has led to the misallocation of resources and development of ineffective strategies disproportionately affecting racial/ethnic minorities.
We aim to develop a mathematical model of the relationships between patient risk factors and outcomes using Kaiser Permanente Northern California data, a multi-ethnic, socioeconomically diverse population with diabetes, and then to input national data and published data into the model in order to forecast the long-term implications of efforts to reduce diabetes health disparities
Aim 1: Develop a Simulation Model
To develop a simulation model of diabetes outcomes for white, African American, Latino, South Asian, and Chinese populations.
Aim 2: Forecast impact of changes in risk factors control on future diabetes health disparities
To characterize changes in risk factor control for the U.S. population living with diabetes by racial/ethnic groups based on the National Health and Nutrition Examination Survey (NHANES 2007-2010 and 2013-2016), incorporate these data into the model we develop in Aim 1, and forecast how changes in risk factor control will affect projected diabetes-related health disparities in the future.
Aim 3: Determine the Cost-effectiveness of diabetes health disparities
To conduct a meta-analysis of the costs and effectiveness of published interventions to reduce diabetes health disparities, varying A) in their level of intervention (patient, provider, healthcare organization, community, multi-level, intersectoral) and B) by race/ethnicity and English language proficiency, and to incorporate these findings into the model, in order to determine the cost-effectiveness of interventions to reduce diabetes health disparities.
Principal Investigator: Neda Laiteerapong, MD, MS, University of Chicago
Melissa Franco, MPH, University of Chicago
Jennifer Liu, MPH, Kaiser Permanente Northern California
NIH R01 MD013420-01