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Exposure Misclassification and Threshold Concentrations in Time Series Analyses of Air Pollution Health Effects

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  • M. Brauer
  • J. Brumm
  • S. Vedal
  • A. J. Petkau

Abstract

Linear, no‐threshold relationships are typically reported for time series studies of air pollution and mortality. Since regulatory standards and economic valuations typically assume some threshold level, we evaluated the fundamental question of the impact of exposure misclassification on the persistence of underlying personal‐level thresholds when personal data are aggregated to the population level in the assessment of exposure‐response relationships. As an example, we measured personal exposures to two particle metrics, PM2.5 and sulfate (SO42−), for a sample of lung disease patients and compared these with exposures estimated from ambient measurements. Previous work has shown that ambient:personal correlations for PM2.5 are much lower than for SO42−, suggesting that ambient PM2.5 measurements misclassify exposures to PM2.5. We then developed a method by which the measured:estimated exposure relationships for these patients were used to simulate personal exposures for a larger population and then to estimate individual‐level mortality risks under different threshold assumptions. These individual risks were combined to obtain the population risk of death, thereby exhibiting the prominence (and the value) of the threshold in the relationship between risk and estimated exposure. Our results indicated that for poorly classified exposures (PM2.5 in this example) population‐level thresholds were apparent at lower ambient concentrations than specified common personal thresholds, while for well‐classified exposures (e.g., SO42−), the apparent thresholds were similar to these underlying personal thresholds. These results demonstrate that surrogate metrics that are not highly correlated with personal exposures obscure the presence of thresholds in epidemiological studies of larger populations, while exposure indicators that are highly correlated with personal exposures can accurately reflect underlying personal thresholds.

Suggested Citation

  • M. Brauer & J. Brumm & S. Vedal & A. J. Petkau, 2002. "Exposure Misclassification and Threshold Concentrations in Time Series Analyses of Air Pollution Health Effects," Risk Analysis, John Wiley & Sons, vol. 22(6), pages 1183-1193, December.
    • Handle: RePEc:wly:riskan:v:22:y:2002:i:6:p:1183-1193
    DOI: 10.1111/1539-6924.00282
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    References listed on IDEAS

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    1. Sabit Cakmak & Richard T. Burnett & Daniel Krewski, 1999. "Methods for Detecting and Estimating Population Threshold Concentrations for Air Pollution‐Related Mortality with Exposure Measurement Error," Risk Analysis, John Wiley & Sons, vol. 19(3), pages 487-496, June.
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    Cited by:

    1. Garrett Glasgow & Bharat Ramkrishnan & Anne E Smith, 2022. "A simulation-based assessment of the ability to detect thresholds in chronic risk concentration-response functions in the presence of exposure measurement error," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-17, March.
    2. Anne E. Smith, 2015. "Response to Commentary by Fann et al. on “Enhancing the Characterization of Epistemic Uncertainties in PM2.5 Risk Analyses”," Risk Analysis, John Wiley & Sons, vol. 35(3), pages 381-384, March.
    3. Yuheng Wu & Lin Zhang & Jilong Wang & Yi Mou, 2021. "Communicating Air Quality Index Information: Effects of Different Styles on Individuals’ Risk Perception and Precaution Intention," IJERPH, MDPI, vol. 18(19), pages 1-15, October.
    4. Suresh H. Moolgavkar & Ellen T. Chang & Heather N. Watson & Edmund C. Lau, 2018. "An Assessment of the Cox Proportional Hazards Regression Model for Epidemiologic Studies," Risk Analysis, John Wiley & Sons, vol. 38(4), pages 777-794, April.
    5. Anne E. Smith, 2016. "Inconsistencies in Risk Analyses for Ambient Air Pollutant Regulations," Risk Analysis, John Wiley & Sons, vol. 36(9), pages 1737-1744, September.
    6. Anne E. Smith, 2018. "Setting Air Quality Standards for PM2.5: A Role for Subjective Uncertainty in NAAQS Quantitative Risk Assessments?," Risk Analysis, John Wiley & Sons, vol. 38(11), pages 2318-2339, November.

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