IDEAS home Printed from https://ideas.repec.org/p/bep/jhubio/1041.html
   My bibliography  Save this paper

Seasonal Analyses of Air Pollution and Mortality in 100 U.S. Cities

Author

Listed:
  • Roger Peng

    (Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics)

  • Francesca Dominici

    (Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics)

  • Roberto Pastor-Barriuso

    (Department of Epidemiology & Biostatistics, Escuela Nacional de Sanidad)

  • Scott Zeger

    (Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics)

  • Jonathan Samet

    (Johns Hopkins Bloomberg School of Public Health, Department of Epidemiology)

Abstract

Time series models relating short-term changes in air pollution levels to daily mortality counts typically assume that the effects of air pollution on the log relative rate of mortality do not vary with time. However, these short-term effects might plausibly vary by season. Changes in the sources of air pollution and meteorology can result in changes in characteristics of the air pollution mixture across seasons. The authors develop Bayesian semi-parametric hierarchical models for estimating time-varying effects of pollution on mortality in multi-site time series studies. The methods are applied to the updated National Morbidity and Mortality Air Pollution Study database for the period 1987--2000, which includes data for 100 U.S. cities. At the national level, a 10 micro-gram/m3 increase in PM(10) at lag 1 is associated with a 0.15 (95% posterior interval: -0.08, 0.39),0.14 (-0.14, 0.42), 0.36 (0.11, 0.61), and 0.14 (-0.06, 0.34) percent increase in mortality for winter, spring, summer, and fall, respectively. An analysis by geographical regions finds a strong seasonal pattern in the northeast (with a peak in summer) and little seasonal variation in the southern regions of the country. These results provide useful information for understanding particle toxicity and guiding future analyses of particle constituent data.

Suggested Citation

  • Roger Peng & Francesca Dominici & Roberto Pastor-Barriuso & Scott Zeger & Jonathan Samet, 2004. "Seasonal Analyses of Air Pollution and Mortality in 100 U.S. Cities," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1041, Berkeley Electronic Press.
    • Handle: RePEc:bep:jhubio:1041
    Note: oai:bepress.com:jhubiostat-1041
    as

    Download full text from publisher

    File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1041&context=jhubiostat
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dominici F. & Daniels M. & Zeger S. L. & Samet J. M., 2002. "Air Pollution and Mortality: Estimating Regional and National Dose-Response Relationships," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 100-111, March.
    2. 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.
    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. Roger Peng & Leah Welty & Aidan McDermott, 2004. "The National Morbidity, Mortality, and Air Pollution Study Database in R," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1044, Berkeley Electronic Press.
    2. Svajone Bekesiene & Ieva Meidute-Kavaliauskiene, 2022. "Artificial Neural Networks for Modelling and Predicting Urban Air Pollutants: Case of Lithuania," Sustainability, MDPI, vol. 14(4), pages 1-24, February.
    3. 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.
    4. G. Brooke Anderson & Keith W. Oleson & Bryan Jones & Roger D. Peng, 2018. "Classifying heatwaves: developing health-based models to predict high-mortality versus moderate United States heatwaves," Climatic Change, Springer, vol. 146(3), pages 439-453, February.
    5. Lang, Corey, 2015. "The dynamics of house price responsiveness and locational sorting: Evidence from air quality changes," Regional Science and Urban Economics, Elsevier, vol. 52(C), pages 71-82.
    6. Berger, James O. & Sun, Dongchu & Song, Chengyuan, 2020. "An objective prior for hyperparameters in normal hierarchical models," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    7. Shr, Yau-Huo & Hsu, Wen & Hwang, Bing-Fang & Jung, Chau-Ren, 2023. "Air quality and risky behaviors on roads," Journal of Environmental Economics and Management, Elsevier, vol. 118(C).
    8. Howard H. Chang & Jingwen Zhou & Montserrat Fuentes, 2010. "Impact of Climate Change on Ambient Ozone Level and Mortality in Southeastern United States," IJERPH, MDPI, vol. 7(7), pages 1-15, July.
    9. Joshua Graff Zivin & Matthew Neidell, 2013. "Environment, Health, and Human Capital," Journal of Economic Literature, American Economic Association, vol. 51(3), pages 689-730, September.
    10. Auffhammer, Maximilian & Bento, Antonio M. & Lowe, Scott E., 2009. "Measuring the effects of the Clean Air Act Amendments on ambient PM10 concentrations: The critical importance of a spatially disaggregated analysis," Journal of Environmental Economics and Management, Elsevier, vol. 58(1), pages 15-26, July.
    11. repec:jss:jstsof:26:i07 is not listed on IDEAS
    12. Botts, Carsten H. & Daniels, Michael J., 2008. "A flexible approach to Bayesian multiple curve fitting," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5100-5120, August.
    13. Michelle Bell & Jonathan Samet & Francesca Dominici, 2004. "Ozone and Mortality: A Meta-Analysis of Time-Series Studies and Comparison to a Multi-City Study (The National Morbidity, Mortality, and Air Pollution Study)," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1057, Berkeley Electronic Press.
    14. Figueroa-Zúñiga, Jorge I. & Arellano-Valle, Reinaldo B. & Ferrari, Silvia L.P., 2013. "Mixed beta regression: A Bayesian perspective," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 137-147.
    15. Corey Lang, 2012. "The Dynamics of House Price Capitalization and Locational Sorting: Evidence from Air Quality Changes," Working Papers 12-22, Center for Economic Studies, U.S. Census Bureau.
    16. Roger D. Peng & Francesca Dominici & Thomas A. Louis, 2006. "Model choice in time series studies of air pollution and mortality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(2), pages 179-203, March.
    17. Zhang, Zhenhua & Zhang, Guoxing & Su, Bin, 2022. "The spatial impacts of air pollution and socio-economic status on public health: Empirical evidence from China," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    18. Hwashin Hyun Shin & Dave Stieb & Rick Burnett & Glen Takahara & Barry Jessiman, 2012. "Tracking National and Regional Spatial‐Temporal Mortality Risk Associated with NO2 Concentrations in Canada: A Bayesian Hierarchical Two‐Level Model," Risk Analysis, John Wiley & Sons, vol. 32(3), pages 513-530, March.
    19. Yi Zhang & Tao Shi & Ai-Jun Wang & Qi Huang, 2022. "Air Pollution, Health Shocks and Labor Mobility," IJERPH, MDPI, vol. 19(3), pages 1-21, January.
    20. Seyed Mohammad Javad Razmi & Tayyebe Azodi, 2018. "The Comparative Effect of Air Pollution Caused by Greenhouse Gases Emissions on the Health of Men and Women in the Upper Middle-Income Countries," Modern Applied Science, Canadian Center of Science and Education, vol. 12(8), pages 1-19, August.
    21. Jaeeun Yu & Jinsu Park & Taeryon Choi & Masahiro Hashizume & Yoonhee Kim & Yasushi Honda & Yeonseung Chung, 2021. "Nonparametric Bayesian Functional Meta-Regression: Applications in Environmental Epidemiology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(1), pages 45-70, March.

    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:bep:jhubio:1041. 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: Christopher F. Baum (email available below). General contact details of provider: http://www.bepress.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.