IDEAS home Printed from https://ideas.repec.org/a/spr/envsyd/v44y2024i2d10.1007_s10669-024-09970-1.html
   My bibliography  Save this article

Development of a population attributable risk screening tool to estimate health consequences of consumer product exposure

Author

Listed:
  • Amy B. Rosenstein

    (U.S. Army Corps of Engineers North Atlantic Division)

  • Treye Thomas

    (U.S. Consumer Product Safety Commission)

  • Igor Linkov

    (U.S. Army Engineer Research and Development Center Environmental Laboratory (ERDC-EL))

  • Christopher Cummings

    (U.S. Army Engineer Research and Development Center Environmental Laboratory (ERDC-EL))

  • Kelby Kramer

    (U.S. Army Engineer Research and Development Center Environmental Laboratory (ERDC-EL))

  • Jason Deng

    (Independent Software Engineer)

  • Miriam Pollock

    (U.S. Army Engineer Research and Development Center Environmental Laboratory (ERDC-EL))

  • Jeffrey M. Keisler

    (University of Massachusetts)

Abstract

Chemical exposures from consumer products are a major concern in modern society. To address the need for accurate health risk estimation, particularly for chronic chemical hazards that may be less evident such as those contained in consumer products, this study reports on the development of the Population Attributable Risk Screening Tool (PAR Tool) Version 1.0. The PAR Tool was designed for the use of professional staff at the Consumer Product Safety Commission (CPSC), and assumes some prior knowledge of chemical risk assessment. This tool provides reasonable estimates of potential acute and chronic health impacts and mortality resulting from exposure to chemicals in consumer products. The PAR Tool provides the ability to integrate diverse data sources, including consumer product composition, user exposures, chemical toxicity data, consumer use patterns, and other pertinent factors to estimate the Population Attributable Risk associated with exposures to chemical components in consumer products. By generating PAR estimates on a chemical-by-chemical basis, the tool will assist the CPSC in assessing potential health risks and prioritizing efforts for risk reduction. Output from the tool provides an estimate of the number of specific health outcomes that may occur in the population of users of a specific consumer product. Interim steps produce exposure percentiles that are used along with dose–response data for a selected chemical and exposure pathway to calculate the proportion of the population at risk. The output is an estimated number of cases of an adverse health effect or mortality in a product user population potentially related to exposures to a single chemical in a product. These results are based primarily on user inputs, and the rationale for selection of these inputs is documented. All inputs and calculations within the PAR Tool are traceable and based on accepted practices and resources. The ongoing development of the PAR Tool aims to enhance its functionalities, incorporating features such as uncertainty analysis, increased automation, and user support. For example, future iterations may allow CPSC users to analyze multiple chemicals and health endpoints simultaneously and introduce sensitivity analysis capabilities for exposure and toxicity parameters. Ultimately, the PAR Tool represents a valuable advancement in comprehending product risks related to consumer product exposure, contributing to enhanced consumer protection and improved health outcomes.

Suggested Citation

  • Amy B. Rosenstein & Treye Thomas & Igor Linkov & Christopher Cummings & Kelby Kramer & Jason Deng & Miriam Pollock & Jeffrey M. Keisler, 2024. "Development of a population attributable risk screening tool to estimate health consequences of consumer product exposure," Environment Systems and Decisions, Springer, vol. 44(2), pages 280-292, June.
  • Handle: RePEc:spr:envsyd:v:44:y:2024:i:2:d:10.1007_s10669-024-09970-1
    DOI: 10.1007/s10669-024-09970-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10669-024-09970-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10669-024-09970-1?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Banzhaf, H. Spencer, 2022. "The Value of Statistical Life: A Meta-Analysis of Meta-Analyses," Journal of Benefit-Cost Analysis, Cambridge University Press, vol. 13(2), pages 182-197, July.
    2. 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.
    3. Viscusi, W Kip & Aldy, Joseph E, 2003. "The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World," Journal of Risk and Uncertainty, Springer, vol. 27(1), pages 5-76, August.
    4. repec:reg:rpubli:282 is not listed on IDEAS
    5. Elaine A. Cohen Hubal & David M. Reif & Rachel Slover & Ashley Mullikin & John C. Little, 2020. "Children’s Environmental Health: A Systems Approach for Anticipating Impacts from Chemicals," IJERPH, MDPI, vol. 17(22), pages 1-13, November.
    Full references (including those not matched with items on IDEAS)

    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. Bishop, Kelly C. & Kuminoff, Nicolai V. & Mathes, Sophie M. & Murphy, Alvin D., 2024. "The marginal cost of mortality risk reduction: Evidence from housing markets," Journal of Urban Economics, Elsevier, vol. 139(C).
    2. Cardoso, Diego S. & Dahis, Ricardo, 2024. "Calculating the economic value of non-marginal mortality risk reductions," Economics Letters, Elsevier, vol. 237(C).
    3. Zubova, Ekaterina, 2022. "Value of statistical life in Russia: Estimates based on panel microdata for 2010–2020," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 45-64.
    4. Peter Benczur & Virmantas Kvedaras & Nadir Preziosi, 2023. "Health-adjusted income: complementing GDP to reflect the valuation of life expectancy," JRC Research Reports JRC134152, Joint Research Centre.
    5. Khalil, Umair, 2017. "Do more guns lead to more crime? Understanding the role of illegal firearms," Journal of Economic Behavior & Organization, Elsevier, vol. 133(C), pages 342-361.
    6. Glenn Jenkins & Chun-Yan Kuo & Arnold C. Harberger, 2011. "Cost-Benefit Analysis for Investment Decisions: Chapter 15 (Cost-Effectiveness and Cost-Utility Analysis)," Development Discussion Papers 2011-15, JDI Executive Programs.
    7. Muhammad Rafiq & Mir Kalan Shah, 2010. "The Value of Reduced Risk of Injury and Deaths in Pakistan—Using Actual and Perceived Risk Estimates," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 49(4), pages 823-837.
    8. Alexander Cheung & Joseph Marchand & Patricia Mark, 2022. "Loss of Life and Labor Productivity: The Canadian Opioid Crisis," The ANNALS of the American Academy of Political and Social Science, , vol. 703(1), pages 303-323, September.
    9. Marcela Parada-Contzen & Andrés Riquelme-Won & Felipe Vasquez-Lavin, 2013. "The value of a statistical life in Chile," Empirical Economics, Springer, vol. 45(3), pages 1073-1087, December.
    10. Min Gong & David Krantz & Elke Weber, 2014. "Why Chinese discount future financial and environmental gains but not losses more than Americans," Journal of Risk and Uncertainty, Springer, vol. 49(2), pages 103-124, October.
    11. Ryan Edwards, 2013. "The cost of uncertain life span," Journal of Population Economics, Springer;European Society for Population Economics, vol. 26(4), pages 1485-1522, October.
    12. James K. Hammitt, 2020. "Valuing mortality risk in the time of COVID-19," Journal of Risk and Uncertainty, Springer, vol. 61(2), pages 129-154, October.
    13. Sandra Schaffner & Hannes Spengler, 2005. "Der Einfluss unbeobachteter Heterogenität auf kompensatorische Lohndifferentiale und den Wert eines statistischen Lebens: eine mikroökonometrische Parallelanalyse mit IABS und SOEP," Discussion Papers of DIW Berlin 539, DIW Berlin, German Institute for Economic Research.
    14. Moreno Gigi & van Eijndhoven Emma & Benner Jennifer & Sullivan Jeffrey, 2017. "The Long-Term Impact of Price Controls in Medicare Part D," Forum for Health Economics & Policy, De Gruyter, vol. 20(2), pages 1-56, December.
    15. French, Michael T. & Gumus, Gulcin & Homer, Jenny F., 2009. "Public policies and motorcycle safety," Journal of Health Economics, Elsevier, vol. 28(4), pages 831-838, July.
    16. James J. Heckman, 2015. "Introduction to A Theory of the Allocation of Time by Gary Becker," Economic Journal, Royal Economic Society, vol. 0(583), pages 403-409, March.
    17. Kuhn, Michael & Frankovic, Ivan & Wrzaczek, Stefan, 2017. "Medical Progress, Demand for Health Care, and Economic Performance," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168249, Verein für Socialpolitik / German Economic Association.
    18. Hippolyte d’Albis & Emmanuel Thibault, 2018. "Ambiguous life expectancy and the demand for annuities," Theory and Decision, Springer, vol. 85(3), pages 303-319, October.
    19. Nicholas Z Muller & Akshaya Jha, 2017. "Does environmental policy affect scaling laws between population and pollution? Evidence from American metropolitan areas," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-15, August.
    20. Gopal K. Basak & Chandramauli Chakraborty & Pranab Kumar Das, 2021. "Optimal Lockdown Strategy in a Pandemic: An Exploratory Analysis for Covid-19," Papers 2109.02512, arXiv.org.

    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:spr:envsyd:v:44:y:2024:i:2:d:10.1007_s10669-024-09970-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.