Ways COVID‐19 may impact unrelated pharmacoepidemiologic research using routinely collected data

I read with interest the review by Pottegård et al1 discussing considerations for pharmacoepidemiologic studies aiming to address the ongoing novel human coronavirus (COVID-19) pandemic. The authors raise a variety of excellent points about how the various data sets we routinely use can help researchers study treatments relevant to the outbreak. With 2020 coming to a close, I feel it is time for pharmacoepidemiologists to address another looming question related to real-world evidence: how can we incorporate routinely collected data from 2020 and 2021 into analyses of safety and efficacy for drugs and medical treatments unrelated to COVID-19?

Authors

Institutions

Publication Details

Journal
UNC Libraries
Published
2024-08-30
DOI
https://doi.org/10.17615/3qb8-v676
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Ways COVID‐19 may impact unrelated pharmacoepidemiologic research using routinely collected data

Michael Webster‐Clark
UNC Libraries
Advanced Causal Inference Techniques
article

Ways COVID‐19 may impact unrelated pharmacoepidemiologic research using routinely collected data

Michael Webster‐Clark
article en

Abstract

I read with interest the review by Pottegård et al1 discussing considerations for pharmacoepidemiologic studies aiming to address the ongoing novel human coronavirus (COVID-19) pandemic. The authors raise a variety of excellent points about how the various data sets we routinely use can help researchers study treatments relevant to the outbreak. With 2020 coming to a close, I feel it is time for pharmacoepidemiologists to address another looming question related to real-world evidence: how can we incorporate routinely collected data from 2020 and 2021 into analyses of safety and efficacy for drugs and medical treatments unrelated to COVID-19?

UNC Libraries
University of North Carolina at Chapel Hill (US)
Openalex Percentile: Top 85%
Advanced Causal Inference Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.