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- A comparison of meta-analysis results with and without adjustment for the healthy worker effect: cancer mortality among workers in the semiconductor industry
- Sung-Ho Hwang, Moon-Young Park, Won Jin Lee, Inho Park, Kimyong Hong, Donguk Park, Kyoung-Mu Lee
- Epidemiol Health. 2021;43:e2021057. Published online September 8, 2021
- DOI: https://doi.org/10.4178/epih.e2021057
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Abstract
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AbstractOBJECTIVES
This study compared the results of meta-analysis with and without adjustment for the healthy worker effect on the association between working in the semiconductor industry and cancer mortality.METHODSSix studies that reported standardized mortality ratios (SMRs) for cancers were selected for meta-analysis. Using a random-effects model, the SMR results from each study were combined for all cancers and leukemias to estimate the summary SMRs (95% confidence interval, CI). To adjust for the healthy worker effect, the relative standardized mortality ratio (rSMR=SMR<sub>x</sub>/SMR<sub>not x</sub>) were calculated using observed and expected counts for the specific cause of interest (i.e., all cancers and leukemias) and the observed and expected counts for all other causes of mortality. Then, the rSMR results were combined to estimate the summary rSMRs (95% CIs).RESULTSThe SMRs for all causes of mortality among semiconductor industry workers ranged from 0.25 to 0.80, which reflects a significant healthy worker effect. A remarkable difference was found between the summary SMRs and the summary rSMRs. The summary SMR for all cancers was 0.70 (95% CI, 0.63 to 0.79) whereas the summary rSMR was 1.38 (95% CI, 1.20 to 1.59). The summary SMR for leukemia was 0.88 (95% CI, 0.72 to 1.07), and the summary rSMR was 1.88 (95% CI, 1.20 to 2.95).CONCLUSIONSOur results suggest that adjustment for the healthy worker effect (i.e., rSMR) may be useful in meta-analyses of cohort studies reporting SMRs. -
SummaryKorean summary
본 연구에서는 반도체 근로자의 암사망률을 일반 인구와 비교하여 표준화사망비(SMR)를 제시한 연구를 대상으로 한 메타분석에서, summary SMR값(95% CI)과 건강근로자효과에 대해 보정한 rSMR을 산출한 후 종합한 summary rSMR값(95% CI)을 비교하였다. 모든 암의 경우 summary SMR=0.70 (0.63-0.79), summary rSMR=1.38(1.20-1.59)로 나타났으며, 백혈병의 경우 summary SMR=0.88 (0.72-1.07), summary rSMR=1.88(1.20-2.95)로 나타났다. 본 연구결과는 SMR 연구를 종합하는 메타분석 연구에서 rSMR과 같은 건강근로자효과에 대해 보정하는 방법론을 적용할 수 있음을 보여주는 사례이다.Key Message
The relevance of epidemiological research reporting the standardized mortality ratios (SMRs) for workers in the semiconductor industry is often limited by the healthy worker effect. One of the simple correction methods in the relative standardized mortality ratio (rSMR). We observed significant rightward shift when the summary rSMRs were compared with the summary rSMRs, which suggest that adjustment for the healthy worker effect (i.e., rSMR) may be useful in meta-analyses of cohort studies reporting SMRs. -
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- Health risks, emergency preparedness and Norwegian-Russian cooperation on Svalbard. A systematic review
Turid Austin Wæhler, Tor Ingebrigtsen
International Journal of Circumpolar Health.2022;[Epub] CrossRef
- Health risks, emergency preparedness and Norwegian-Russian cooperation on Svalbard. A systematic review
Original Article
- A study on Statistical Method for Controlling the Effect of Intermediate Events: Application to the Control of the Healthy Worker Effect.
- Chung Mo Nam, Jinheum Kim, Dae Ryong Kang, Yeon Soon Ahn, Hoo Yeon Lee, Dae Hee Lee
- Korean J Epidemiol. 2002;24(1):7-16.
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- 16 Download
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AbstractPURPOSE
The healthy worker effect is an important issue in occupational epidemiology. This study was conducted to propose a new method to test the relation between exposure and mortality in the presence of the healthy worker effect.METHODSIn this study, the healthy worker hire effect was assumed to operate as a confounding variable of health status at the beginning of employment and healthy worker survival effect as a confounding and intermediate variable of employment status. In addition, the proposed method reflects the length bias sampling caused by changing of an employment status. Simulation studies were also carried out to compare the proposed method with Cox's time dependent covariates models .RESULTSThe theoretical development of the healthy worker survival effect is based on the result that an observation with change of an employment status requires that the survival time without intermediate event exceeds the waiting time for the intermediate event. According to our simulation studies, both the proposed method and Cox's time dependent covariates model which includes the change of employment status as time dependent covariates seem to be satisfactory at 5% significance level. However, Cox's time dependent covariates models without or with the change of employment status as time fixed covariate are unsatisfactory. The proposed test is superior in power to tests based on Cox's model.CONCLUSIONSThe healthy worker effect may not be controlled by classical Cox's proportional hazards models. The proposed method performed well in the presence of healthy worker effect in terms of level and power -
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