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Meta‐Analysis Methods to Estimate the Shape and Uncertainty in the Association Between Long‐Term Exposure to Ambient Fine Particulate Matter and Cause‐Specific Mortality Over the Global Concentration Range

Author

Listed:
  • Hwashin Hyun Shin
  • Aaron J. Cohen
  • C. Arden Pope
  • Majid Ezzati
  • Stephen S. Lim
  • Bryan J. Hubbell
  • Richard T. Burnett

Abstract

Estimates of excess mortality associated with exposure to ambient concentrations of fine particulate matter have been obtained from either a single cohort study or pooling information from a small number of studies. However, standard frequentist methods of pooling are known to underestimate statistical uncertainty in the true risk distribution when the number of studies pooled is small. Alternatively, Bayesian pooling methods using noninformative priors yield unrealistically large amounts of uncertainty in this case. We present a new hybrid frequentist‐bayesian framework for meta‐analysis that incorporates features of both frequentist and Bayesian approaches, yielding estimated uncertainty distributions that are more useful for burden estimation. We also present an example of mortality risk due to long‐term exposure to ambient fine particulate matter obtained from a small number of cohort studies conducted in the United States and Europe. We compare our new risk uncertainty distribution to that obtained by the integrated exposure‐response (IER) model used in the Global Burden of Disease 2010 project for which risk was modeled over the entire global concentration range. We suggest a method to incorporate our new risk uncertainty distribution based on the relatively low concentrations observed in the United States and western Europe into the IER model, thus extending risk estimation to the global concentration range.

Suggested Citation

  • Hwashin Hyun Shin & Aaron J. Cohen & C. Arden Pope & Majid Ezzati & Stephen S. Lim & Bryan J. Hubbell & Richard T. Burnett, 2016. "Meta‐Analysis Methods to Estimate the Shape and Uncertainty in the Association Between Long‐Term Exposure to Ambient Fine Particulate Matter and Cause‐Specific Mortality Over the Global Concentration ," Risk Analysis, John Wiley & Sons, vol. 36(9), pages 1813-1825, September.
  • Handle: RePEc:wly:riskan:v:36:y:2016:i:9:p:1813-1825
    DOI: 10.1111/risa.12421
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    References listed on IDEAS

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    1. Louis Anthony (Tony) Cox, 2013. "Improving Causal Inferences in Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 33(10), pages 1762-1771, October.
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    Cited by:

    1. Zeyu Tang & Jinzhu Jia, 2022. "The Association between the Burden of PM 2.5 -Related Neonatal Preterm Birth and Socio-Demographic Index from 1990 to 2019: A Global Burden Study," IJERPH, MDPI, vol. 19(16), pages 1-20, August.
    2. Bingkui Qiu & Min Zhou & Yang Qiu & Yuxiang Ma & Chaonan Ma & Jiating Tu & Siqi Li, 2021. "An Integration Method for Regional PM 2.5 Pollution Control Optimization Based on Meta-Analysis and Systematic Review," IJERPH, MDPI, vol. 19(1), pages 1-22, December.
    3. Vijay S. Limaye & Wolfgang Schöpp & Markus Amann, 2018. "Applying Integrated Exposure-Response Functions to PM 2.5 Pollution in India," IJERPH, MDPI, vol. 16(1), pages 1-20, December.
    4. John S. Evans, 2016. "Characterizing Uncertainty in Estimates of Mortality Risk from Exposure to Ambient Fine Particulate Matter," Risk Analysis, John Wiley & Sons, vol. 36(9), pages 1748-1750, September.

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