Demographic Representativeness of the RADx-Up Cohort Compared to the U.S. Census Data

2618 

Contributed Abstract 

Poster 

Xue Yu (1), Meri Varkila (2), Nivetha Subramanian (2), Julie Parsonnet (2), Shuchi Anand (1), Maria Montez-Rath (1), Glenn Chertow (1)

(1) Stanford University, N/A, (2) Stanford Univeristy, N/A

Meri Varkila  
Stanford Univeristy

Nivetha Subramanian  
Stanford Univeristy

Julie Parsonnet  
Stanford Univeristy

Shuchi Anand  
Stanford University

Maria Montez-Rath  
Stanford University

Glenn Chertow  
Stanford University

Xue Yu  
Stanford University

Xue Yu  
Stanford University

Wastewater surveillance is a promising tool for tracking COVID-19,but its effectiveness in underserved populations-who may experience disproportionately severe illness-has not been fully established. The NIH-funded RADx-UP program, comprising 144 projects across the U.S., aims to expand COVID-19 testing accessibility, particularly in hard-hit areas. The utility of wastewater surveillance in underserved communities can be assessed by comparing its findings with screening data from RADx-UP, which includes both symptomatic and asymptomatic individuals. However, this assessment requires RADx-UP to be representative of the U.S. population, a criterion that can be evaluated through generalizability analysis. In this study, we report participant characteristics and compute a generalizability score, which quantifies how well a study sample reflects a target population of interest based on demographics and clinical characteristics, at the Federal Information Processing Standard (FIPS) geography code level. With over 350,000 participants in 1005 FIPS, we anticipate major metropolitan areas have sufficient data for generalizable estimates, especially in underserved counties.

causal inference study|propensity score model|multiple imputation| | |

Section on Statistics in Epidemiology

Causal Inference