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A Tiered Framework for Risk‐Relevant Characterization and Ranking of Chemical Exposures: Applications to the National Children's Study (NCS)

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  • Panos G. Georgopoulos
  • Christopher J. Brinkerhoff
  • Sastry Isukapalli
  • Michael Dellarco
  • Philip J. Landrigan
  • Paul J. Lioy

Abstract

A challenge for large‐scale environmental health investigations such as the National Children's Study (NCS), is characterizing exposures to multiple, co‐occurring chemical agents with varying spatiotemporal concentrations and consequences modulated by biochemical, physiological, behavioral, socioeconomic, and environmental factors. Such investigations can benefit from systematic retrieval, analysis, and integration of diverse extant information on both contaminant patterns and exposure‐relevant factors. This requires development, evaluation, and deployment of informatics methods that support flexible access and analysis of multiattribute data across multiple spatiotemporal scales. A new “Tiered Exposure Ranking” (TiER) framework, developed to support various aspects of risk‐relevant exposure characterization, is described here, with examples demonstrating its application to the NCS. TiER utilizes advances in informatics computational methods, extant database content and availability, and integrative environmental/exposure/biological modeling to support both “discovery‐driven” and “hypothesis‐driven” analyses. “Tier 1” applications focus on “exposomic” pattern recognition for extracting information from multidimensional data sets, whereas second and higher tier applications utilize mechanistic models to develop risk‐relevant exposure metrics for populations and individuals. In this article, “tier 1” applications of TiER explore identification of potentially causative associations among risk factors, for prioritizing further studies, by considering publicly available demographic/socioeconomic, behavioral, and environmental data in relation to two health endpoints (preterm birth and low birth weight). A “tier 2” application develops estimates of pollutant mixture inhalation exposure indices for NCS counties, formulated to support risk characterization for these endpoints. Applications of TiER demonstrate the feasibility of developing risk‐relevant exposure characterizations for pollutants using extant environmental and demographic/socioeconomic data.

Suggested Citation

  • Panos G. Georgopoulos & Christopher J. Brinkerhoff & Sastry Isukapalli & Michael Dellarco & Philip J. Landrigan & Paul J. Lioy, 2014. "A Tiered Framework for Risk‐Relevant Characterization and Ranking of Chemical Exposures: Applications to the National Children's Study (NCS)," Risk Analysis, John Wiley & Sons, vol. 34(7), pages 1299-1316, July.
    • Handle: RePEc:wly:riskan:v:34:y:2014:i:7:p:1299-1316
    DOI: 10.1111/risa.12165
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    References listed on IDEAS

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    1. S. S. Isukapalli & A. Roy & P. G. Georgopoulos, 2000. "Efficient Sensitivity/Uncertainty Analysis Using the Combined Stochastic Response Surface Method and Automated Differentiation: Application to Environmental and Biological Systems," Risk Analysis, John Wiley & Sons, vol. 20(5), pages 591-602, October.
    2. Resha M. Putzrath & James D. Wilson, 1999. "Fundamentals of Health Risk Assessment. Use, Derivation, Validity and Limitations of Safety Indices," Risk Analysis, John Wiley & Sons, vol. 19(2), pages 231-247, April.
    3. Scott Marshall & Chris Gennings & Linda K. Teuschler & LeAnna G. Stork & Rogelio Tornero‐Velez & Kevin M. Crofton & Glenn E. Rice, 2013. "An Empirical Approach to Sufficient Similarity: Combining Exposure Data and Mixtures Toxicology Data," Risk Analysis, John Wiley & Sons, vol. 33(9), pages 1582-1595, September.
    4. S. S. Isukapalli & A. Roy & P. G. Georgopoulos, 1998. "Stochastic Response Surface Methods (SRSMs) for Uncertainty Propagation: Application to Environmental and Biological Systems," Risk Analysis, John Wiley & Sons, vol. 18(3), pages 351-363, June.
    5. Kevin P. Brand & Mitchell J. Small, 1995. "Updating Uncertainty in an Integrated Risk Assessment: Conceptual Framework and Methods," Risk Analysis, John Wiley & Sons, vol. 15(6), pages 719-729, December.
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    1. Alesia Ferguson & Helena Solo-Gabriele, 2016. "Children’s Exposure to Environmental Contaminants: An Editorial Reflection of Articles in the IJERPH Special Issue Entitled, “Children’s Exposure to Environmental Contaminants”," IJERPH, MDPI, vol. 13(11), pages 1-10, November.

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