Garcia Erika, Picciotto Sally, Costello Sadie, Bradshaw Patrick T, Eisen Ellen A
Environmental Health Sciences Division, School of Public Health, University of California, Berkeley, California, USA.
Epidemiology Division, School of Public Health, University of California, Berkeley, California, USA.
Occup Environ Med. 2017 Mar;74(4):294-300. doi: 10.1136/oemed-2016-104038. Epub 2017 Jan 9.
The healthy worker survivor effect (HWSE) can affect the validity of occupational studies when data are analysed incorrectly. HWSE depends on three underlying conditions: (1) leaving work predicts future exposure, (2) leaving work is associated with disease outcome and (3) prior exposure increases probability of leaving work. If all these conditions are satisfied, then employment status is a time-varying confounder affected by prior exposure, and standard regression will produce bias. We assessed these conditions for cancer outcomes in a cohort of autoworkers exposed to metalworking fluids (MWF).
The cohort includes 31 485 workers followed for cancer incidence from 1985 to 1994. As occupational exposures to straight, soluble and synthetic MWFs are necessarily zero after leaving work, condition (1) is satisfied. Cox models for cancer incidence and for employment termination were used to assess conditions (2) and (3), respectively. Employment termination by select ages was examined to better gauge the presence of condition (2).
The HR for leaving work as a predictor of all cancers combined and prostate cancer was null, but elevated for lung and colorectal cancers among men. Condition (2) was more clearly satisfied for all cancer outcomes when leaving work occurred by age 50. Higher exposures to all three MWF types were associated with increased rates of leaving work (condition (3)), with the exception of straight MWF among women.
We found evidence for the structural conditions underlying HWSE in a cohort of autoworkers. G-methods should be applied to reduce HWSE bias in studies of all cancers presently examined.
当数据分析不正确时，健康工人幸存者效应（HWSE）会影响职业研究的有效性。HWSE取决于三个潜在条件：（1）离开工作岗位预示着未来的暴露情况，（2）离开工作岗位与疾病结局相关，（3）先前的暴露增加了离开工作岗位的可能性。如果所有这些条件都满足，那么就业状况就是一个受先前暴露影响的随时间变化的混杂因素，标准回归会产生偏差。我们在一个接触金属加工液（MWF）的汽车工人队列中评估了这些条件与癌症结局的关系。
该队列包括31485名工人，从1985年至1994年随访癌症发病率。由于离开工作岗位后，直接、可溶和合成MWF的职业暴露必然为零，因此条件（1）得到满足。分别使用癌症发病率的Cox模型和就业终止的Cox模型来评估条件（2）和（3）。检查特定年龄的就业终止情况，以更好地判断条件（2）的存在。
作为所有癌症综合和前列腺癌预测因素的离开工作岗位的风险比为零，但男性中肺癌和结直肠癌的风险比升高。当在50岁时离开工作岗位时，所有癌症结局的条件（2）更明显得到满足。除女性中的直接MWF外，所有三种MWF类型的较高暴露都与离开工作岗位的比率增加相关（条件（3））。
我们在一个汽车工人队列中发现了HWSE潜在结构条件的证据。在目前所研究的所有癌症的研究中，应采用G方法来减少HWSE偏差。
