Surveillance

Precision Public Health

Precision Surveillance of Hypertension, Obesity and Diabetes for Healthy Aging

The Precision SHODHA project aims to provide state and local governments with actionable small areal insights on hypertension, obesity and diabetes care in USA and India. The proposed project involves harmonizing datasets from national surveys and administrative sources, use small-area estimation techniques and decision sciences frameworks.

This is ongoing work with Dr. Mohammed K. Ali and Dr. Nikhil Tandon.

We collaborate with Emory’s Department of Biostatistics (Dr. Emily Peterson) and Georgia Tech’s School of Industrial and Systems Engineering (Dr. Gian Gabriel Garcia).

Erica Kocher is working with a team from Georgia Tech to understand state-level cost effectiveness in achievement of WHO Diabetes Compact targets in India.

Jiali Guo is using electronic health records from USA to develop care continua.

Thomas Hsiao is working with some cool kiosk data to develop county estimates of hypertension and overweight/obesity prevalence for USA.

Estimates of Hypertension and Diabetes Care Continuum in India from NFHS-5, 2019-21.

Publications

  1. Varghese 2023 JAMA Internal Medicine on Diabetes in India
  2. Varghese 2023 JAMA Network Open on Hypertension in India
  3. Varghese 2023 The Lancet Diabetes & Endocrinology on Advancing diabetes surveillance

Getting Involved

These projects are ideal for doctoral students who are interested in domestic and global health surveillance.

  1. Regional and socio-demographic disparities in cardiometabolic disease care continuum
  2. Cost effectiveness of intervening on different steps of disease care continuum
  3. Prevalence of cardiometabolic disease subphenotypes
  4. Population achievement of personalized targets for glycemic and blood pressure control in India
  5. Care continuum among youth-onset diabetes in India
  6. Data visualization tools for continuum of care in India and USA
  7. Data fusion of real-world data, surveys and kiosk data for spatial statistics

Pre-requisites

  1. Proficiency in R
  2. Required Coursework: GH 523: Quantitative Methods or equivalent training in survey data analysis, EPI 568 Bias Analysis
  3. Ideal Coursework: EPI 563 Concepts and Applications in Spatial Epidemiology or equivalent training in spatial analysis