# Confounding and the healthy worker survivor effect in studies of medical radiation workers: a systematic review of methodological approaches

**Authors:** Eun Jung Park, Kyoungyeol Yuk, Jaeho Jeong, Won Jin Lee

PMC · DOI: 10.4178/epih.e2026009 · 2026-02-04

## TL;DR

This paper reviews how studies on medical radiation workers address confounding and the healthy worker survivor effect, finding that the latter is rarely considered.

## Contribution

The paper systematically reviews methodological approaches to address confounding and the HWSE in radiation worker studies.

## Key findings

- Sixteen high-quality studies were identified, all using regression to control for confounding.
- Only 18.8% of studies adjusted for the healthy worker survivor effect by employment characteristics.
- No studies used advanced methods like g-methods to address the HWSE.

## Abstract

Confounding and the healthy worker survivor effect (HWSE) represent major methodological challenges in epidemiology, particularly in studies of low-dose exposures, where effect sizes are small and risk estimates can be readily distorted by bias. This systematic review aimed to summarize the methods used to adjust for confounding and the HWSE in studies of medical radiation workers. We systematically searched PubMed and Embase for studies of medical radiation workers from inception through June 30, 2025. Studies reporting excess risk estimates for any health outcomes associated with occupational radiation exposure were included. Study selection followed the PECO (Population, Exposure, Comparator, Outcome) criteria, and data were synthesized descriptively. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was registered in PROSPERO (CRD42024589851). Sixteen eligible studies from 3 countries were identified, all of which were rated as high quality. To control for confounding, regression was used in all studies, followed by stratification (62.5%) and restriction (18.8%). Age, sex, and birth year were adjusted for in all models, with smoking being the next most frequently controlled variable. To mitigate the HWSE, only a single approach, adjustment for employment characteristics, was identified, and it was applied in 3 studies (18.8%). No other approaches, including restriction or g-methods, were employed. Although confounding is generally addressed using conventional analytical approaches, the HWSE has rarely been considered in studies of medical radiation workers. More comprehensive strategies that explicitly account for the HWSE are needed to improve the validity of risk estimates, particularly in low-dose occupational studies.

## Full-text entities

- **Diseases:** Breast cancer (MESH:D001943), asthma (MESH:D001249), ischemic heart disease (MESH:D017202), macular degeneration (MESH:D008268), circulatory diseases (MESH:D012769), ocular diseases (MESH:D005128), cancer (MESH:D009369), diabetes (MESH:D003920), HWSE (MESH:D000067329), glaucoma (MESH:D005901), cataract (MESH:D002386)
- **Chemicals:** alcohol (MESH:D000438), UVB (-)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Figures

2 figures with captions in the complete paper: https://tomesphere.com/paper/PMC13033441/full.md

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Source: https://tomesphere.com/paper/PMC13033441