Landmark Study Released from Stanford Medical School Reveals First Ever Predictive Model for Uncontrolled Type 2 Diabetes Mellitus Related to Social Determinants

SAN FRANCISCO, Oct. 29, 2019 /PRNewswire/ -- Dr. Sanjay Basu and Dr. Rajiv Narayanaswamy, in conjunction with Stanford Medical School, recently released an article in an August issue of Medical Care that addresses social determinant health (SDH) factors in the formation of uncontrolled type 2 diabetes mellitus (Hemoglobin A1c greater than 9%). The research is based on terabytes of area level data and claims data on over 1 million patients with Type 2 Diabetes Mellitus. The study focuses within the United States and explores the exact social determinants that help lead to the formation of uncontrolled T2DM.  This is the first such study to offer a prediction model based on SDH factors at the census tract level.  This study found the average risk for this individual across the United States would equal a 17.8% predicted probability that their diabetes would be uncontrolled.  The study has far reaching implications for future care and T2DM medical coverage. The full article may be viewed and accessed HERE.

"This is a major, peer-reviewed study that sheds light on an important aspect of T2DM causality, and addressing them has the potential to save the US Healthcare system billions," said Rajiv Narayanaswamy. "Social determinants are often the key to both prediction and prevention, and this research highlights the importance of assessing SDH at the census tract level to more accurately predict the risk of uncontrolled T2DM of a given population. For hospitals entering into contract negotiations with payers, having this data is crucial. Furthermore, the model fundamentally changes basic strategic approaches to patient access programs across the country, and will have significant influence on how and where medications are marketed in the future."

Social Determinants as Health Predictors

SDH factors are broadly defined as a wide array of conditions that can deeply affect both health conditions and health outcomes. These include, but are not limited to: early life conditions, stress, work, social support, addictions, gender, race, social exclusion, food and food insecurities. Predictive models for disease based on SDH factors are valuable tools used by public health organization across the nation.

From the study: "Social determinants of health at the area level are well-understood to influence health outcomes and health disparities, including for type 2 diabetes mellitus. SDH at the area level that may influence T2DM outcomes include the density of fast food or healthier food outlets, safe spaces for physical activity, and resources such as food pantries that offer more nutrient-dense than calorie-dense food" … "A predictive model developed through a machine learning approach may assist health care organizations to identify which area-level SDH data to monitor for prediction of diabetes control…"

About Rajiv Narayanaswamy, M.B.B.S., M.S., M.B.A.

Dr. Rajiv Narayanaswamy is an internationally trained medical doctor and management consultant. Rajiv focuses on national healthcare initiatives and has led national delivery system reform programs, public health initiatives, and is considered a leading expert on social determinants of health. His work on SDH analytics is widely recognized. He currently works for KPMG LLP as a Management Consultant.

About Sanjay Basu, M.D., Ph.D.

Dr. Basu is an Assistant Professor of Medicine at Stanford and a primary care physician and epidemiologist. Dr. Basu regularly conducts research on health and social policies to reduce morbidity and mortality from cardiovascular disease and type II diabetes, both in the United States and abroad. Dr. Basu's approach interweaves the fields of computer science, econometrics and large-scale data analysis.

Media Contact:

Name, Title: Rajiv Narayanaswamy
Phone: 510-579-3657
Email: [email protected]

SOURCE Rajiv Narayanaswamy and Sanjay Basu