IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v25y2005i4p927-935.html
   My bibliography  Save this article

Developing Policy in the Face of Scientific Uncertainty: Interpreting 0.3 μT or 0.4 μT Cutpoints from EMF Epidemiologic Studies

Author

Listed:
  • Leeka Kheifets
  • Jack D. Sahl
  • Riti Shimkhada
  • Mike H. Repacholi

Abstract

There has been considerable scientific effort to understand the potential link between exposures to power‐frequency electric and magnetic fields (EMF) and the occurrence of cancer and other diseases. The combination of widespread exposures, established biological effects from acute, high‐level exposures, and the possibility of leukemia in children from low‐level, chronic exposures has made it both necessary and difficult to develop consistent public health policies. In this article we review the basis of both numeric standards and precautionary‐based approaches. While we believe that policies regarding EMF should indeed be precautionary, this does not require or imply adoption of numeric exposure standards. We argue that cutpoints from epidemiologic studies, which are arbitrarily chosen, should not be used as the basis for making exposure limits due to a number of uncertainties. Establishment of arbitrary numeric exposure limits undermines the value of both the science‐based numeric EMF exposure standards for acute exposures and precautionary approaches. The World Health Organization's draft Precautionary Framework provides guidance for establishing appropriate public health policies for power‐frequency EMF.

Suggested Citation

  • Leeka Kheifets & Jack D. Sahl & Riti Shimkhada & Mike H. Repacholi, 2005. "Developing Policy in the Face of Scientific Uncertainty: Interpreting 0.3 μT or 0.4 μT Cutpoints from EMF Epidemiologic Studies," Risk Analysis, John Wiley & Sons, vol. 25(4), pages 927-935, August.
    • Handle: RePEc:wly:riskan:v:25:y:2005:i:4:p:927-935
    DOI: 10.1111/j.1539-6924.2005.00635.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1539-6924.2005.00635.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1539-6924.2005.00635.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sander Greenland, 2005. "Multiple‐bias modelling for analysis of observational data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 267-306, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. L. Kheifets & J. Sahl & R. Shimkhada & M. Repacholi, 2007. "Examining Science to Develop Policy," Risk Analysis, John Wiley & Sons, vol. 27(2), pages 289-290, April.
    2. Sander Greenland & Leeka Kheifets, 2006. "Leukemia Attributable to Residential Magnetic Fields: Results from Analyses Allowing for Study Biases," Risk Analysis, John Wiley & Sons, vol. 26(2), pages 471-482, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lawrence C. McCandless & Sylvia Richardson & Nicky Best, 2012. "Adjustment for Missing Confounders Using External Validation Data and Propensity Scores," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 40-51, March.
    2. Julian P. T. Higgins & Simon G. Thompson & David J. Spiegelhalter, 2009. "A re‐evaluation of random‐effects meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 137-159, January.
    3. Douglas E. Schaubel & Guanghui Wei, 2011. "Double Inverse-Weighted Estimation of Cumulative Treatment Effects Under Nonproportional Hazards and Dependent Censoring," Biometrics, The International Biometric Society, vol. 67(1), pages 29-38, March.
    4. Carlos Díaz-Venegas, 2014. "Identifying the Confounders of Marginalization and Mortality in Mexico, 2003–2007," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(2), pages 851-875, September.
    5. A. Goubar & A. E. Ades & D. De Angelis & C. A. McGarrigle & C. H. Mercer & P. A. Tookey & K. Fenton & O. N. Gill, 2008. "Estimates of human immunodeficiency virus prevalence and proportion diagnosed based on Bayesian multiparameter synthesis of surveillance data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 541-580, June.
    6. McCandless Lawrence C., 2012. "Meta-Analysis of Observational Studies with Unmeasured Confounders," The International Journal of Biostatistics, De Gruyter, vol. 8(2), pages 1-31, January.
    7. Maria Gheorghe & Susan Picavet & Monique Verschuren & Werner B. F. Brouwer & Pieter H. M. Baal, 2017. "Health losses at the end of life: a Bayesian mixed beta regression approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 723-749, June.
    8. K. M. Rhodes & J. Savović & R. Elbers & H. E. Jones & J. P. T. Higgins & J. A. C. Sterne & N. J. Welton & R. M. Turner, 2020. "Adjusting trial results for biases in meta‐analysis: combining data‐based evidence on bias with detailed trial assessment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 193-209, January.
    9. Andrew Gelman & Christian Hennig, 2017. "Beyond subjective and objective in statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 967-1033, October.
    10. Rebecca M. Turner & David J. Spiegelhalter & Gordon C. S. Smith & Simon G. Thompson, 2009. "Bias modelling in evidence synthesis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 21-47, January.
    11. S. Dias & N. J. Welton & V. C. C. Marinho & G. Salanti & J. P. T. Higgins & A. E. Ades, 2010. "Estimation and adjustment of bias in randomized evidence by using mixed treatment comparison meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(3), pages 613-629, July.
    12. Qi Zhou & Yoo-Mi Chin & James D. Stamey & Joon Jin Song, 2020. "Bayesian sensitivity analysis to unmeasured confounding for misclassified data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 577-596, December.
    13. de Luna, Xavier & Lundin, Mathias, 2009. "Sensitivity analysis of the unconfoundedness assumption in observational studies," Working Paper Series 2009:12, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    14. Leah Comment & Brent A. Coull & Corwin Zigler & Linda Valeri, 2022. "Bayesian data fusion: Probabilistic sensitivity analysis for unmeasured confounding using informative priors based on secondary data," Biometrics, The International Biometric Society, vol. 78(2), pages 730-741, June.
    15. N. J. Welton & A. E. Ades & J. B. Carlin & D. G. Altman & J. A. C. Sterne, 2009. "Models for potentially biased evidence in meta‐analysis using empirically based priors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 119-136, January.
    16. Enrique A. Navarro-Camba & Jaume Segura-García & Claudio Gomez-Perretta, 2018. "Exposure to 50 Hz Magnetic Fields in Homes and Areas Surrounding Urban Transformer Stations in Silla (Spain): Environmental Impact Assessment," Sustainability, MDPI, vol. 10(8), pages 1-11, July.
    17. P. Gustafson & L. C. McCandless & A. R. Levy & S. Richardson, 2010. "Simplified Bayesian Sensitivity Analysis for Mismeasured and Unobserved Confounders," Biometrics, The International Biometric Society, vol. 66(4), pages 1129-1137, December.
    18. Graham Scotland & Stirling Bryan, 2017. "Why Do Health Economists Promote Technology Adoption Rather Than the Search for Efficiency? A Proposal for a Change in Our Approach to Economic Evaluation in Health Care," Medical Decision Making, , vol. 37(2), pages 139-147, February.
    19. N. J. Cooper & A. J. Sutton & A. E. Ades & S. Paisley & D. R. Jones, 2007. "Use of evidence in economic decision models: practical issues and methodological challenges," Health Economics, John Wiley & Sons, Ltd., vol. 16(12), pages 1277-1286.
    20. Desiree C Wilks & Stephen J Sharp & Ulf Ekelund & Simon G Thompson & Adrian P Mander & Rebecca M Turner & Susan A Jebb & Anna Karin Lindroos, 2011. "Objectively Measured Physical Activity and Fat Mass in Children: A Bias-Adjusted Meta-Analysis of Prospective Studies," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-8, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:riskan:v:25:y:2005:i:4:p:927-935. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1539-6924 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.