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Original research
Estimating impacts of reducing acrylonitrile exposure on lung cancer mortality in an occupational cohort with the parametric g-formula
  1. Alexander Keil1,
  2. Gregory Haber2,
  3. Barry Graubard3,
  4. Patricia A Stewart4,
  5. Debra Silverman1,
  6. Stella Koutros1
  1. 1 Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
  2. 2 Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
  3. 3 Biostatistics Branch, Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
  4. 4 Stewart Exposure Assessments, LLC, Arlington, Virginia, USA
  1. Correspondence to Dr Alexander Keil, Occupational and Environmental Epidemiology Branch, National Cancer Institute Division of Cancer Epidemiology and Genetics, Bethesda, Maryland, USA; keilap{at}nih.gov

Abstract

Objectives To inform the potential human carcinogenicity of acrylonitrile, we estimate associations between acrylonitrile exposures and lung cancer mortality in US workers with the objectives of (1) assessing potential for healthy worker survivor bias and (2) adjusting for this bias while assessing the expected lung cancer mortality under different hypothetical occupational exposure limits on acrylonitrile exposure using the parametric g-formula.

Methods We used data from a cohort of 25 460 workers at facilities making or using acrylonitrile in the USA. We estimated HRs to quantify associations between employment and lung cancer mortality, and exposure and leaving employment. Using the parametric g-formula, we estimated cumulative lung cancer mortality at hypothetical limits on acrylonitrile exposure.

Results Recent and current employment was associated with lung cancer, and exposure was associated with leaving employment, indicating potential for healthy worker survivor bias. Relative to no intervention, reducing the historical exposure under limits of 2.0, 1.0 and 0.45 parts per million would have been expected to reduce lung cancer mortality by age 90 by 4.46 (95% CI 0.78 to 8.15), 5.03 (95% CI 0.96 to 9.11) and 6.45 (95% CI 2.35 to 10.58) deaths per 1000 workers, respectively. A larger lung cancer mortality reduction would be expected under elimination of exposure: 7.21 (95% CI 2.72 to 11.70) deaths per 1000 workers.

Conclusions Healthy worker survivor bias likely led to underestimation of excess risk. Our results corroborate previous study findings of an excess hazard of lung cancer among the highest exposed workers.

  • Epidemiology
  • Occupational Health
  • Statistics
  • Volatile Organic Compounds
  • Longitudinal studies

Data availability statement

Data are available on reasonable request. Data can be made available after approval of NCI data transfer agreement. Mortality data cannot be transferred and can only be requested directly from NCHS via the National Death Index approval process. After the time of publication, data can be made available subject to these approvals. Contact SK, KoutrosS@nih.gov for more information.

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Data availability statement

Data are available on reasonable request. Data can be made available after approval of NCI data transfer agreement. Mortality data cannot be transferred and can only be requested directly from NCHS via the National Death Index approval process. After the time of publication, data can be made available subject to these approvals. Contact SK, KoutrosS@nih.gov for more information.

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Footnotes

  • X @PronouncedKeil

  • Contributors Substantial contributions to the conception or design of the work were made by all authors. Responsibilities include: acquisition (SK and PAS), analysis (AK) or interpretation of data (all) for the work drafting (AK)/revising (all) for intellectual content. Final approval of the version to be published (all): agreement to be accountable (all). AK is responsible for the overall content as guarantor.

  • Funding Intramural Research Program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics (ZIA CP010120).

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.