Straight Metalworking Fluids and All-Cause and Cardiovascular Mortality Analyzed by Using G-Estimation of an Accelerated Failure Time Model With Quantitative Exposure: Methods and Interpretations

Am J Epidemiol. 2016 Apr 1;183(7):680-8. doi: 10.1093/aje/kwv232. Epub 2016 Mar 10.

Abstract

Straight metalworking fluids have been linked to cardiovascular mortality in analyses using binary exposure metrics, accounting for healthy worker survivor bias by using g-estimation of accelerated failure time models. A cohort of 38,666 Michigan autoworkers was followed (1941-1994) for mortality from all causes and ischemic heart disease. The structural model chosen here, using continuous exposure, assumes that increasing exposure from 0 to 1 mg/m(3) in any single year would decrease survival time by a fixed amount. Under that assumption, banning the fluids would have saved an estimated total of 8,468 (slope-based 95% confidence interval: 2,262, 28,563) person-years of life in this cohort. On average, 3.04 (slope-based 95% confidence interval: 0.02, 25.98) years of life could have been saved for each exposed worker who died from ischemic heart disease. Estimates were sensitive to both model specification for predicting exposure (multinomial or logistic regression) and characterization of exposure as binary or continuous in the structural model. Our results provide evidence supporting the hypothesis of a detrimental relationship between straight metalworking fluids and mortality, particularly from ischemic heart disease, as well as an instructive example of the challenges in obtaining and interpreting results from accelerated failure time models using a continuous exposure in the presence of competing risks.

Keywords: cardiovascular outcomes; epidemiologic methods; healthy worker effect; mortality; occupational exposures; particulate matter.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Cardiovascular Diseases / mortality*
  • Female
  • Humans
  • Male
  • Manufacturing Industry / statistics & numerical data
  • Michigan / epidemiology
  • Models, Theoretical
  • Occupational Exposure / statistics & numerical data*
  • Time Factors