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Activity Number: 386 - SPEED: Statistics in Epidemiology Part 1
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #323570
Title: A Difference-in-Difference Design for Studying Post-COVID Conditions
Author(s): Lei Qian* and Lina S Sy and Sara Y Tartof
Companies: Kaiser Permanente Southern California and Kaiser Permanente Southern California and Kaiser Permanente Southern California
Keywords: DID design; long COVID conditions; EHR; confounding adjustment; post-COVID conditions
Abstract:

Comprehensive longitudinal electronic health records are useful for studying post-COVID conditions (PCC). However, due to the heterogeneity of patients with and without COVID-19, it is challenging to find an appropriate comparison group. Further, dramatic decreases in healthcare utilization during the COVID-19 pandemic could cause strong secular confounding. Therefore, we proposed a difference-in-difference design to compare the change in utilization for select PCC outcomes in the COVID-19 test positive population with the change in PCC outcomes in a matched COVID-19 test negative population during a 6-month follow-up period, before and after the start of the COVID-19 pandemic. Comparing healthcare utilization within the same individuals during two time periods (2019 and 2020) controlled for variables such as demographics, health status, and healthcare seeking behavior. Comparing to a COVID-19 test negative group during the same period adjusted for secular confounding as well. We applied this approach to 255,718 test positive and test negative patients from 8 integrated healthcare organizations across the US to evaluate the excess healthcare utilization associated with PCC.


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

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