Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 247 - Causal Inference and Statistical Learning of Intervention and Policy Effects
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
Sponsor: Health Policy Statistics Section
Abstract #318598
Title: A Propensity-Matched Study of Obesity and Diabetes Risk: A COVID-19 Perspective
Author(s): Claire Nurse* and Janet Rosenbaum
Companies: SUNY Downstate School of Public Health and SUNY Downstate School of Public Health
Keywords: Obesity; COVID-19; Type-2 Diabetes; Propensity score

The COVID-19 pandemic spurred widespread weight gain, suggesting that type-2 diabetes may also increase. However, past research finding an association between obesity and type 2 diabetes is subject to potential confounding on socioeconomic and other variables. Using data from the nationally representative longitudinal Midlife in the US study, we evaluated the association between obesity and diabetes 18 years later using robust regression methods. To reduce potential confounding, we used nearest-neighbor propensity score matching on demographics (age, race, gender), socioeconomic status (educational attainment, household income), and health risks (physical activity, smoking status) to match adults with obesity (n=450) and without obesity (n=617), which resulted in covariate balance assessed by standardized differences. In Poisson regression in the matched sample, individuals with obesity had 2.3 times the risk of diabetes in 18 years than those without obesity (ARR , 95% confidence interval (1.62, 3.50)). COVID-19 pandemic recovery must include population-based obesity interventions (e.g., optimizing agricultural subsidies to target obesity) and type-2 diabetes screening.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2021 program