Re: Glyphosate Use and Cancer Incidence in the Agricultural Health Study

Details

• Personal Author:
  Shaffer RM ; Sheppard L
• Description:
  Exposure to glyphosate, a broad-spectrum herbicide, and its consequent health impacts are critically important to understand. Its use and potential to enter the food supply have increased dramatically worldwide over recent decades. The Agricultural Health Study (AHS) is a crucial piece of evidence because there are no other large cohort studies of the potential carcinogenic effects of glyphosate. Thus, the recent AHS results, adding 11 years of follow-up to the previously reported AHS results, have huge potential to improve our understanding of glyphosate toxicity-which in turn informs national and international evaluations that influence policy. We wish to describe a feature of the study analyses that most likely attenuated the effect estimates towards the null. The exposure modeling introduced noise into the exposure estimates because it used a multiple imputation procedure that did not consider the study outcomes. Exposure assessment in the AHS was complete at baseline. The investigators faced a huge challenge because 20 968 individuals, 37% of AHS participants, failed to complete the follow-up questionnaire. The authors used a well-respected approach to fill in missing data with multiple draws of the distribution of the missing exposures conditional on information available from baseline, including demographics, farm characteristics, pesticide use history, and existing medical conditions. They called their procedure multiple imputation, because it has the appearance of the multiple imputation approach described by Rubin. However, the AHS multiple imputation did not consider any of the health outcomes analyzed by Andreotti et al., including non-Hodgkin lymphoma and multiple myeloma. As was elegantly stated in his review, "Regression with missing X's," Little noted that when realizations of the distribution of missing exposures (X1 in his notation) given other covariate data (X2, ..., Xp) are used to estimate regression coefficients, failure to condition on the health outcome (Y) leads to bias. The direction of the bias is attenuation towards no increased risk. Little explained that for an exposure X1, "... then the regression coefficient of X1 is attenuated, because the noise added to the conditional means doesn't account for the partial correlation of X1 and Y given X2, ..., Xp." (p. 1235). Gryparis et al. name this misguided approach to multiple imputation "exposure simulation." We do not know the size of the exposure model residual in the AHS or the magnitude of the resulting bias. However, because the phase II nonrespondent group was large (37%) and the phase II respondent group reported a high prevalence of glyphosate use (52%), there is reason to suspect that the consequence of using this imputation procedure is to meaningfully attenuate the cancer risk estimates. Unfortunately, it is unlikely that the conclusion of Heltshe et al.-"This multiple imputation will allow for bias reduction and improved efficiency in future analyses of the AHS"-is correct. We encourage the AHS investigators to refine their approach and improve our ability to understand the true impacts of pesticide exposures, which-particularly for glyphosate-could have tangible consequences for public health policy. [Description provided by NIOSH]
• Subjects:
  Agriculture Carcinogens Environmental Exposure Herbicides Occupational Health Safety
• Keywords:
  Agricultural Chemicals Biological Monitoring Environmental Exposure Health Effects
• ISSN:
  0027-8874
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 10 ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  214-215
• Volume:
  111
• Issue:
  2
• NIOSHTIC Number:
  nn:20055178
• Citation:
  J Natl Cancer Inst 2019 Feb; 111(2):214-215
• Contact Point Address:
  Lianne Sheppard, PhD, Department of Biostatistics, University of Washington, Box 357232, Seattle, WA 98195-7232
• Email:
  sheppard@uw.edu
• CAS Registry Number:
  Glyphosate (CAS RN 1071-83-6)
• Federal Fiscal Year:
  2019
• Performing Organization:
  University of Washington
• Peer Reviewed:
  False
• Start Date:
  20050701
• Source Full Name:
  Journal of the National Cancer Institute
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:19c75009dbb8f70a1ac3e6a4045b54aceef4d4aaf8078070806e7bac3838d86f2ab2a9e1646a3da37fc52cb013914a53425ecf05278ba6036f36f2f6726684cd
• Download URL:
  https://stacks.cdc.gov/view/cdc/217538/cdc_217538_DS1.pdf
• File Type:
  [PDF - 593.77 KB ]