IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v71y2022i1p148-174.html
   My bibliography  Save this article

Daily mortality/morbidity and air quality: Using multivariate time series with seasonally varying covariances

Author

Listed:
  • Guowen Huang
  • Patrick E. Brown
  • Sze Hang Fu
  • Hwashin Hyun Shin

Abstract

We study the associations between daily mortality and short‐term variations in the ambient concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in four cities in Canada. First, a novel multivariate time series model within Bayesian framework is proposed for exposure assessment, where the response is a mixture of Gamma and Half‐Cauchy distributions and the correlations between pollutants vary seasonally. A case‐crossover design and conditional logistic regression model is used to relate exposure to disease data for each city, which then are combined to obtain a global estimate of exposure health effects allowing exposure uncertainty. The results suggest that every 10 ppb increase in O3 is associated with a 3.88% (95% credible interval [CI], 2.5%, 5.18%) increase in all‐cause mortality, a 5.04% (2.84%, 7.43%) increase in circulatory mortality, a 7.87% (2.4%, 12.9%) increase in respiratory mortality, a 0.76% (0.19%, 1.35%) increase in all‐cause morbidity and a 6.6% (0.58%, 12.7%) increase in respiratory morbidity. Similarly, every 10 ppb increase in NO2 is associated with a 2.13% (0.42%, 3.87%) increase in circulatory morbidity. The health impacts of PM2.5 are not found to be present once other pollutants are accounted for.

Suggested Citation

  • Guowen Huang & Patrick E. Brown & Sze Hang Fu & Hwashin Hyun Shin, 2022. "Daily mortality/morbidity and air quality: Using multivariate time series with seasonally varying covariances," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 148-174, January.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:1:p:148-174
    DOI: 10.1111/rssc.12525
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssc.12525
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssc.12525?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lewandowski, Daniel & Kurowicka, Dorota & Joe, Harry, 2009. "Generating random correlation matrices based on vines and extended onion method," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1989-2001, October.
    2. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    3. Duncan Lee & Gavin Shaddick, 2010. "Spatial Modeling of Air Pollution in Studies of Its Short-Term Health Effects," Biometrics, The International Biometric Society, vol. 66(4), pages 1238-1246, December.
    4. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    5. Francesco Finazzi & E. Marian Scott & Alessandro Fassò, 2013. "A model-based framework for air quality indices and population risk evaluation, with an application to the analysis of Scottish air quality data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(2), pages 287-308, March.
    6. Montserrat Fuentes & Adrian E. Raftery, 2005. "Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models," Biometrics, The International Biometric Society, vol. 61(1), pages 36-45, March.
    7. Gavin Shaddick & Jon Wakefield, 2002. "Modelling daily multivariate pollutant data at multiple sites," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(3), pages 351-372, July.
    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. Yi Liu & Gavin Shaddick & James V. Zidek, 2017. "Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 559-581, December.
    2. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    3. Matthias Breuer & Harm H. Schütt, 2023. "Accounting for uncertainty: an application of Bayesian methods to accruals models," Review of Accounting Studies, Springer, vol. 28(2), pages 726-768, June.
    4. Xin Fang & Bo Fang & Chunfang Wang & Tian Xia & Matteo Bottai & Fang Fang & Yang Cao, 2019. "Comparison of Frequentist and Bayesian Generalized Additive Models for Assessing the Association between Daily Exposure to Fine Particles and Respiratory Mortality: A Simulation Study," IJERPH, MDPI, vol. 16(5), pages 1-20, March.
    5. C. Forlani & S. Bhatt & M. Cameletti & E. Krainski & M. Blangiardo, 2020. "A joint Bayesian space–time model to integrate spatially misaligned air pollution data in R‐INLA," Environmetrics, John Wiley & Sons, Ltd., vol. 31(8), December.
    6. Radka Jersakova & James Lomax & James Hetherington & Brieuc Lehmann & George Nicholson & Mark Briers & Chris Holmes, 2022. "Bayesian imputation of COVID‐19 positive test counts for nowcasting under reporting lag," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 834-860, August.
    7. Aaron Osgood‐Zimmerman & Jon Wakefield, 2023. "A Statistical Review of Template Model Builder: A Flexible Tool for Spatial Modelling," International Statistical Review, International Statistical Institute, vol. 91(2), pages 318-342, August.
    8. Esther Ulitzsch & Steffi Pohl & Lale Khorramdel & Ulf Kroehne & Matthias Davier, 2022. "A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 593-619, June.
    9. Linda S. L. Tan, 2021. "Use of model reparametrization to improve variational Bayes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(1), pages 30-57, February.
    10. Gael M. Martin & David T. Frazier & Christian P. Robert, 2021. "Approximating Bayes in the 21st Century," Monash Econometrics and Business Statistics Working Papers 24/21, Monash University, Department of Econometrics and Business Statistics.
    11. Stephen R. Martin & Philippe Rast, 2022. "The Reliability Factor: Modeling Individual Reliability with Multiple Items from a Single Assessment," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1318-1342, December.
    12. Louis Charlot, 2021. "Bayesian hierarchical analysis of a multifaceted program against extreme poverty," Papers 2109.06759, arXiv.org.
    13. David L. Miller & Richard Glennie & Andrew E. Seaton, 2020. "Understanding the Stochastic Partial Differential Equation Approach to Smoothing," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(1), pages 1-16, March.
    14. Murphy, James, 2020. "Explanatory Item Response Models for Dyadic Data from Multiple Groups," SocArXiv sx9um, Center for Open Science.
    15. Gabriel Riutort-Mayol & Virgilio Gómez-Rubio & José Luis Lerma & Julio M. del Hoyo-Meléndez, 2020. "Correlated Functional Models with Derivative Information for Modeling Microfading Spectrometry Data on Rock Art Paintings," Mathematics, MDPI, vol. 8(12), pages 1-25, December.
    16. Medina-Olivares, Victor & Calabrese, Raffaella & Crook, Jonathan & Lindgren, Finn, 2023. "Joint models for longitudinal and discrete survival data in credit scoring," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1457-1473.
    17. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    18. Dellaportas, Petros & Titsias, Michalis K. & Petrova, Katerina & Plataniotis, Anastasios, 2023. "Scalable inference for a full multivariate stochastic volatility model," Journal of Econometrics, Elsevier, vol. 232(2), pages 501-520.
    19. I. Gede Nyoman Mindra Jaya & Henk Folmer, 2022. "Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease," Journal of Geographical Systems, Springer, vol. 24(4), pages 527-581, October.
    20. Alex Stringer & Patrick Brown & Jamie Stafford, 2021. "Approximate Bayesian inference for case‐crossover models," Biometrics, The International Biometric Society, vol. 77(3), pages 785-795, September.

    More about this item

    Statistics

    Access and download statistics

    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:bla:jorssc:v:71:y:2022:i:1:p:148-174. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.