Assessment of healthy worker survivor bias among middle-aged manufacturing employees in Korea

Abstract Background Healthy Worker Survivor Bias (HWSB) describes a selection bias where those continuously employed tend to exhibit superior health outcomes compared to their unemployed counterparts. This bias challenges for adjustments due to its time-dependent nature. This study aims to evaluate the extent of HWSB related to changes in employment status among middle-aged manufacturing workers using comprehensive national data from Korea. Methods The study analyzed data from the National Health Insurance Service, targeting individuals aged 40-49 who maintained consistent insurance coverage-either as manufacturing employees or non-employees (self-employed or unemployed)-from 2008 to 2010. The observation period spanned from January 2011 to December 2022, with all-cause mortality as the primary outcome. We employed landmark analysis to quantify HWSB related to employment status by comparing age-standardized mortality ratios between employees and the general population from the initial assessment to the last landmark period (up to 7 years), calculating 95% confidence intervals (CI) using Poisson distribution. Results The cohort included 4,621,983 participants (726,616 manufacturing employees and 3,895,367 (84.3%) non-employees). Throughout an average follow-up of 10.9 years (43.1% male; median age of 44), there were 116,418 deaths (11,530 (1.6%) among manufacturing employees and 104,888 (2.7%) among non-employees). Analysis revealed a consistent linear increase in HWSB over time. Notably, the bias was more pronounced among females (7-year HWSB-ES [95% CI]: 0.27 [0.17-0.37]) compared to males (7-year HWSB-ES [95% CI]: 0.18 [0.14-0.22]). Conclusions This study quantifies the extent of HWSB among middle-aged manufacturing workers in Korea, highlighting its significant impact, notably in females. These findings are vital for occupational health research, emphasizing the necessity to consider HWSB to prevent the underestimation of health risks associated with hazardous exposures. Key messages • Findings suggest HWSB is more pronounced in female manufacturing workers, indicating the need for targeted occupational health interventions. • The study underscores the importance of accounting for HWSB in occupational health studies to avoid underestimating health risks associated with workplace exposures.

Authors

Institutions

Publication Details

Journal
European Journal of Public Health
Published
2024-10-28
DOI
https://doi.org/10.1093/eurpub/ckae144.1837
Citations
1
Primary Topic
Occupational Health and Safety Research
Type
article
Field-Weighted Citation Impact
0.45
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessment of healthy worker survivor bias among middle-aged manufacturing employees in Korea

H Park, Joo‐Heon Yoon, Bo Hyon Yun, J Sim et al.
1 citations
European Journal of Public Health
Occupational Health and Safety Research
0.45
article

Assessment of healthy worker survivor bias among middle-aged manufacturing employees in Korea

H Park, Joo‐Heon Yoon, Bo Hyon Yun, J Sim, Jiyoung Oh, J Lee
article en
1 citations

Abstract

Abstract Background Healthy Worker Survivor Bias (HWSB) describes a selection bias where those continuously employed tend to exhibit superior health outcomes compared to their unemployed counterparts. This bias challenges for adjustments due to its time-dependent nature. This study aims to evaluate the extent of HWSB related to changes in employment status among middle-aged manufacturing workers using comprehensive national data from Korea. Methods The study analyzed data from the National Health Insurance Service, targeting individuals aged 40-49 who maintained consistent insurance coverage-either as manufacturing employees or non-employees (self-employed or unemployed)-from 2008 to 2010. The observation period spanned from January 2011 to December 2022, with all-cause mortality as the primary outcome. We employed landmark analysis to quantify HWSB related to employment status by comparing age-standardized mortality ratios between employees and the general population from the initial assessment to the last landmark period (up to 7 years), calculating 95% confidence intervals (CI) using Poisson distribution. Results The cohort included 4,621,983 participants (726,616 manufacturing employees and 3,895,367 (84.3%) non-employees). Throughout an average follow-up of 10.9 years (43.1% male; median age of 44), there were 116,418 deaths (11,530 (1.6%) among manufacturing employees and 104,888 (2.7%) among non-employees). Analysis revealed a consistent linear increase in HWSB over time. Notably, the bias was more pronounced among females (7-year HWSB-ES [95% CI]: 0.27 [0.17-0.37]) compared to males (7-year HWSB-ES [95% CI]: 0.18 [0.14-0.22]). Conclusions This study quantifies the extent of HWSB among middle-aged manufacturing workers in Korea, highlighting its significant impact, notably in females. These findings are vital for occupational health research, emphasizing the necessity to consider HWSB to prevent the underestimation of health risks associated with hazardous exposures. Key messages • Findings suggest HWSB is more pronounced in female manufacturing workers, indicating the need for targeted occupational health interventions. • The study underscores the importance of accounting for HWSB in occupational health studies to avoid underestimating health risks associated with workplace exposures.

European Journal of Public HealthVol. 34(Supplement_3)
Yonsei University (KR), Yonsei University Health System (KR)
Openalex Percentile: Top 35%
Occupational Health and Safety Research
0.45
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.