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Health Effects of Air Pollution: A Statistical Review

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  • Francesca Dominici
  • Lianne Sheppard
  • Merlise Clyde

Abstract

We critically review and compare epidemiological designs and statistical approaches to estimate associations between air pollution and health. More specifically, we aim to address the following questions: 1 Which epidemiological designs and statistical methods are available to estimate associations between air pollution and health? 2 What are the recent methodological advances in the estimation of the health effects of air pollution in time series studies? 3 What are the the main methodological challenges and future research opportunities relevant to regulatory policy? In question 1, we identify strengths and limitations of time series, cohort, case‐crossover and panel sampling designs. In question 2, we focus on time series studies and we review statistical methods for: 1) combining information across multiple locations to estimate overall air pollution effects; 2) estimating the health effects of air pollution taking into account of model uncertainties; 3) investigating the consequences of exposure measurement error in the estimation of the health effects of air pollution; and 4) estimating air pollution‐health exposure‐response curves. Here, we also discuss the extent to which these statistical contributions have addressed key substantive questions. In question 3, within a set of policy‐relevant‐questions, we identify research opportunities and point out current data limitations. Nous faisons une revue critique et une comparaison des modèles épidémiologiques et des approches statistiques pour estimer les associations entre pollution de l'air et santé. Plus précisément, notre but est de traiter les questions suivantes: 1 Quels modèles épidémiologiques et quelles méthodes statistiques sont disponibles pour estimer les associations entre pollution de l'air et santé? Nous identifions les forces et les limites de differents outils: séries temporelles, cohortes, panels 2 Quelles sont les avancées méthodologiques récentes dans l'estimation des effets de la pollution de l'air sur la santé dans les études de séries temporelles? Nous nous concentrons sur les études de séries temporelles et passons en revue les méthodes statistiques pour: 1) combiner les informations de multiples origines pour estimer les effets globaux de la pollution de l'air; 2) estimer les effets sur la santé de la pollution de l'air en tenant compte des incertitudes du modèle; 3) étudier les conséquences de l'erreur de mesure d'exposition dans l'estimation des effets sur la santé de la pollution de l'air; 4) estimer les courbes pollution de l'air‐santé, exposition‐réaction. 3. Quels sont les principaux défis méthodologiques et les futures opportunités de recherche pertinents pour la politique de régulation? Nous identifions les opportunités de recherche et soulignons les limitations actuelles des données

Suggested Citation

  • Francesca Dominici & Lianne Sheppard & Merlise Clyde, 2003. "Health Effects of Air Pollution: A Statistical Review," International Statistical Review, International Statistical Institute, vol. 71(2), pages 243-276, August.
    • Handle: RePEc:bla:istatr:v:71:y:2003:i:2:p:243-276
    DOI: 10.1111/j.1751-5823.2003.tb00195.x
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    1. T. Lumley & P. Heagerty, 1999. "Weighted empirical adaptive variance estimators for correlated data regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 459-477, April.
    2. Julia E. Kelsall & Scott L. Zeger & Jonathan M. Samet, 1999. "Frequency Domain Log‐linear Models; Air Pollution and Mortality," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 331-344.
    3. Christian J. Murray & Charles Nelson, 2000. "State-Space Modeling of the Relationship Between Air Quality and Mortality," Working Papers 0017, University of Washington, Department of Economics.
    4. P. J. Everson & C. N. Morris, 2000. "Inference for multivariate normal hierarchical models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 399-412.
    5. Jonathan Wakefield & Ruth Salway, 2001. "A statistical framework for ecological and aggregate studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 119-137.
    6. Katherine A. Guthrie & Lianne Sheppard & Jon Wakefield, 2002. "A Hierarchical Aggregate Data Model with Spatially Correlated Disease Rates," Biometrics, The International Biometric Society, vol. 58(4), pages 898-905, December.
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    2. X. Pautrel, 2008. "Reconsidering the Impact of the Environment on Long-run Growth when Pollution Influences Health and Agents have a Finite-lifetime," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 40(1), pages 37-52, May.
    3. Severine Deguen & Nina Ahlers & Morgane Gilles & Arlette Danzon & Marion Carayol & Denis Zmirou-Navier & Wahida Kihal-Talantikite, 2018. "Using a Clustering Approach to Investigate Socio-Environmental Inequality in Preterm Birth—A Study Conducted at Fine Spatial Scale in Paris (France)," IJERPH, MDPI, vol. 15(9), pages 1-19, August.
    4. Phuong T. Vu & Timothy V. Larson & Adam A. Szpiro, 2020. "Probabilistic predictive principal component analysis for spatially misaligned and high‐dimensional air pollution data with missing observations," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
    5. Peter Guttorp, 2003. "Environmental Statistics—A Personal View," International Statistical Review, International Statistical Institute, vol. 71(2), pages 169-179, August.
    6. Magali Delmas & Maria J. Montes‐Sancho & Jay P. Shimshack, 2010. "Information Disclosure Policies: Evidence From The Electricity Industry," Economic Inquiry, Western Economic Association International, vol. 48(2), pages 483-498, April.
    7. Julie E. Goodman & Catherine Petito Boyce & Sonja N. Sax & Leslie A. Beyer & Robyn L. Prueitt, 2015. "Rethinking Meta‐Analysis: Applications for Air Pollution Data and Beyond," Risk Analysis, John Wiley & Sons, vol. 35(6), pages 1017-1039, June.
    8. Chen, Xie & Mao, Hongzhi & Cheng, Nan & Ma, Ling & Tian, Zhiyong & Luo, Yongqiang & Zhou, Chaohui & Li, Huai & Wang, Qian & Kong, Weiqiang & Fan, Jianhua, 2024. "Climate change impacts on global photovoltaic variability," Applied Energy, Elsevier, vol. 374(C).
    9. Sabel, Clive Eric & Wilson, Jeff Gaines & Kingham, Simon & Tisch, Catherine & Epton, Mike, 2007. "Spatial implications of covariate adjustment on patterns of risk: Respiratory hospital admissions in Christchurch, New Zealand," Social Science & Medicine, Elsevier, vol. 65(1), pages 43-59, July.
    10. Winifred U. Anake & Faith O. Bayode & Hassana O. Jonathan & Conrad A. Omonhinmin & Oluwole A. Odetunmibi & Timothy A. Anake, 2022. "Screening of Plant Species Response and Performance for Green Belt Development: Implications for Semi-Urban Ecosystem Restoration," Sustainability, MDPI, vol. 14(7), pages 1-14, March.
    11. Stefka Fidanova & Petar Zhivkov & Olympia Roeva, 2022. "InterCriteria Analysis Applied on Air Pollution Influence on Morbidity," Mathematics, MDPI, vol. 10(7), pages 1-8, April.
    12. Phuong T. Vu & Adam A. Szpiro & Noah Simon, 2022. "Spatial matrix completion for spatially misaligned and high‐dimensional air pollution data," Environmetrics, John Wiley & Sons, Ltd., vol. 33(4), June.
    13. Qinling Yan & Sanyi Tang & Zhen Jin & Yanni Xiao, 2019. "Identifying Risk Factors Of A(H7N9) Outbreak by Wavelet Analysis and Generalized Estimating Equation," IJERPH, MDPI, vol. 16(8), pages 1-13, April.
    14. Ruth M. Pfeiffer & Mitchell H. Gail, 2023. "Discussion of “A formal causal interpretation of the case‐crossover design” by Zach Shahn, Miguel A. Hernan, and James M. Robins," Biometrics, The International Biometric Society, vol. 79(2), pages 1346-1348, June.
    15. Beatty, Timothy K.M. & Shimshack, Jay P., 2014. "Air pollution and children's respiratory health: A cohort analysis," Journal of Environmental Economics and Management, Elsevier, vol. 67(1), pages 39-57.

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