Component Associations of the Healthy Worker Survivor Bias in Medical Radiation Workers

Our findings demonstrate that sex-specific differences in healthy worker survivor bias were primarily driven by the association between prior exposure and employment status. To improve bias mitigation in occupational cohort studies, sex-specific components should be incorporated.

Authors

Institutions

Publication Details

Journal
American Journal of Industrial Medicine
Published
2025-04-25
DOI
https://doi.org/10.1002/ajim.23727
Primary Topic
Air Quality and Health Impacts
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Component Associations of the Healthy Worker Survivor Bias in Medical Radiation Workers

Jae‐Ho Jeong, Young Min Kim
American Journal of Industrial Medicine
Air Quality and Health Impacts
article

Component Associations of the Healthy Worker Survivor Bias in Medical Radiation Workers

Jae‐Ho Jeong, Young Min Kim
article en

Abstract

Our findings demonstrate that sex-specific differences in healthy worker survivor bias were primarily driven by the association between prior exposure and employment status. To improve bias mitigation in occupational cohort studies, sex-specific components should be incorporated.

American Journal of Industrial MedicineVol. 68(6)
Korea University (KR), Kyungpook National University (KR)
National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 90%
Air Quality and Health Impacts
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.