IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v24y2004i6p1673-1681.html

Quantitative Risk Assessment for Developmental Neurotoxic Effects

Author

Listed:
  • Mehdi Razzaghi
  • Ralph Kodell

Abstract

Developmental neurotoxicity concerns the adverse health effects of exogenous agents acting on neurodevelopment. Because human brain development is a delicate process involving many cellular events, the developing fetus is rather susceptible to compounds that can alter the structure and function of the brain. Today, there is clear evidence that early exposure to many neurotoxicants can severely damage the developing nervous system. Although in recent years, there has been much attention given to model development and risk assessment procedures for developmental toxicants, the area of developmental neurotoxicity has been largely ignored. Here, we consider the problem of risk estimation for developmental neurotoxicants from animal bioassay data. Since most responses from developmental neurotoxicity experiments are nonquantal in nature, an adverse health effect will be defined as a response that occurs with very small probability in unexposed animals. Using a two‐stage hierarchical normal dose‐response model, upper confidence limits on the excess risk due to a given level of added exposure are derived. Equivalently, the model is used to obtain lower confidence limits on dose for a small negligible level of risk. Our method is based on the asymptotic distribution of the likelihood ratio statistic (cf. Crump, 1995). An example is used to provide further illustration.

Suggested Citation

  • Mehdi Razzaghi & Ralph Kodell, 2004. "Quantitative Risk Assessment for Developmental Neurotoxic Effects," Risk Analysis, John Wiley & Sons, vol. 24(6), pages 1673-1681, December.
    • Handle: RePEc:wly:riskan:v:24:y:2004:i:6:p:1673-1681
    DOI: 10.1111/j.0272-4332.2004.00558.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.0272-4332.2004.00558.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.0272-4332.2004.00558.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. Ralph L. Kodell & Richard B. Howe & James J. Chen & David W. Gaylor, 1991. "Mathematical Modeling of Reproductive and Developmental Toxic Effects for Quantitative Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 11(4), pages 583-590, December.
    2. Kenny S. Crump, 1995. "Calculation of Benchmark Doses from Continuous Data," Risk Analysis, John Wiley & Sons, vol. 15(1), pages 79-89, February.
    3. Mehdi Razzaghi & Ralph L. Kodell, 2000. "Risk Assessment for Quantitative Responses Using a Mixture Model," Biometrics, The International Biometric Society, vol. 56(2), pages 519-527, June.
    4. Ralph L. Kodell & Ronnie W. West, 1993. "Upper Confidence Limits on Excess Risk for Quantitative Responses," Risk Analysis, John Wiley & Sons, vol. 13(2), pages 177-182, April.
    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. Walter W. Piegorsch, 2010. "Translational benchmark risk analysis," Journal of Risk Research, Taylor & Francis Journals, vol. 13(5), pages 653-667, July.

    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. Tao, Jian & Shi, Ning-Zhong & Lee, S.-Y.Sik-Yum, 2004. "Drug risk assessment with determining the number of sub-populations under finite mixture normal models," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 661-676, July.
    2. Liu, Rui-Yin & Tao, Jian & Shi, Ning-Zhong & He, Xuming, 2011. "Bayesian analysis of the patterns of biological susceptibility via reversible jump MCMC sampling," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1498-1508, March.
    3. Mirjam Moerbeek & Aldert H. Piersma & Wout Slob, 2004. "A Comparison of Three Methods for Calculating Confidence Intervals for the Benchmark Dose," Risk Analysis, John Wiley & Sons, vol. 24(1), pages 31-40, February.
    4. Mehdi Razzaghi & Ralph L. Kodell, 2000. "Risk Assessment for Quantitative Responses Using a Mixture Model," Biometrics, The International Biometric Society, vol. 56(2), pages 519-527, June.
    5. Yasushi Suwazono & Kouichi Sakata & Mitsuhiro Oishi & Yasushi Okubo & Mirei Dochi & Etsuko Kobayashi & Teruhiko Kido & Koji Nogawa, 2007. "Estimation of Benchmark Dose as the Threshold Amount of Alcohol Consumption for Blood Pressure in Japanese Workers," Risk Analysis, John Wiley & Sons, vol. 27(6), pages 1487-1495, December.
    6. Walter W. Piegorsch, 2010. "Translational benchmark risk analysis," Journal of Risk Research, Taylor & Francis Journals, vol. 13(5), pages 653-667, July.
    7. Signe M. Jensen & Felix M. Kluxen & Christian Ritz, 2019. "A Review of Recent Advances in Benchmark Dose Methodology," Risk Analysis, John Wiley & Sons, vol. 39(10), pages 2295-2315, October.
    8. Esben Budtz-Jørgensen & Niels Keiding & Philippe Grandjean, 2001. "Benchmark Dose Calculation from Epidemiological Data," Biometrics, The International Biometric Society, vol. 57(3), pages 698-706, September.
    9. Meredith M. Regan & Paul J. Catalano, 1999. "Likelihood Models for Clustered Binary and Continuous Out comes: Application to Developmental Toxicology," Biometrics, The International Biometric Society, vol. 55(3), pages 760-768, September.
    10. Kan Shao & Jeffrey S. Gift, 2014. "Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 101-120, January.
    11. Salomon J. Sand & Dietrich Von Rosen & Agneta Falk Filipsson, 2003. "Benchmark Calculations in Risk Assessment Using Continuous Dose‐Response Information: The Influence of Variance and the Determination of a Cut‐Off Value," Risk Analysis, John Wiley & Sons, vol. 23(5), pages 1059-1068, October.
    12. Mirjana Glisovic‐Bensa & Walter W. Piegorsch & Edward J. Bedrick, 2024. "Bayesian benchmark dose risk assessment with mixed‐factor quantal data," Environmetrics, John Wiley & Sons, Ltd., vol. 35(5), August.
    13. Keiko Kubo & Kazuhiro Nogawa & Teruhiko Kido & Muneko Nishijo & Hideaki Nakagawa & Yasushi Suwazono, 2017. "Estimation of Benchmark Dose of Lifetime Cadmium Intake for Adverse Renal Effects Using Hybrid Approach in Inhabitants of an Environmentally Exposed River Basin in Japan," Risk Analysis, John Wiley & Sons, vol. 37(1), pages 20-26, January.
    14. David W. Gaylor & William Slikker, 2004. "Role of the Standard Deviation in the Estimation of Benchmark Doses with Continuous Data," Risk Analysis, John Wiley & Sons, vol. 24(6), pages 1683-1687, December.
    15. Harvey J. Clewell & Gregory A. Lawrence & Donald B. Calne & Kenny S. Crump, 2003. "Determination of an Occupational Exposure Guideline for Manganese Using the Benchmark Method," Risk Analysis, John Wiley & Sons, vol. 23(5), pages 1031-1046, October.
    16. Jingyu Liu & Walter W. Piegorsch & A. Grant Schissler & Susan L. Cutter, 2018. "Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 803-823, June.
    17. R. Webster West & Ralph L. Kodell, 1999. "A Comparison of Methods of Benchmark‐Dose Estimation for Continuous Response Data," Risk Analysis, John Wiley & Sons, vol. 19(3), pages 453-459, June.
    18. Matthew W. Wheeler & Jose Cortiñas Abrahantes & Marc Aerts & Jeffery S. Gift & Jerry Allen Davis, 2022. "Continuous model averaging for benchmark dose analysis: Averaging over distributional forms," Environmetrics, John Wiley & Sons, Ltd., vol. 33(5), August.
    19. Alfred K. Mbah & Ibrahim Hamisu & Eknath Naik & Hamisu M. Salihu, 2014. "Estimating Benchmark Exposure for Air Particulate Matter Using Latent Class Models," Risk Analysis, John Wiley & Sons, vol. 34(11), pages 2053-2062, November.
    20. Walter W. Piegorsch & Susan L. Cutter & Frank Hardisty, 2007. "Benchmark Analysis for Quantifying Urban Vulnerability to Terrorist Incidents," Risk Analysis, John Wiley & Sons, vol. 27(6), pages 1411-1425, December.

    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:24:y:2004:i:6:p:1673-1681. 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.