IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v31y2020i8ne2643.html
   My bibliography  Save this article

Quantifying the impact of the modifiable areal unit problem when estimating the health effects of air pollution

Author

Listed:
  • Duncan Lee
  • Chris Robertson
  • Colin Ramsay
  • Kate Pyper

Abstract

Air pollution is a major public health concern, and large numbers of epidemiological studies have been conducted to quantify its impacts. One study design used to quantify these impacts is a spatial areal unit design, which estimates a population‐level association using data on air pollution concentrations and disease incidence that have been spatially aggregated to a set of nonoverlapping areal units. A major criticism of this study design is that the specification of these areal units is arbitrary, and if one changed their boundaries then the aggregated data would change despite the locations of the disease cases and the air pollution surface remaining the same. This is known as the modifiable areal unit problem, and this is the first article to quantify its likely effects in air pollution and health studies. In addition, we derive an aggregate model for these data directly from an idealized individual‐level risk model and show that it provides better estimation than the commonly used ecological model. Our work is motivated by a new study of air pollution and health in Scotland, and we find consistent significant associations between air pollution and respiratory disease but not for circulatory disease.

Suggested Citation

  • Duncan Lee & Chris Robertson & Colin Ramsay & Kate Pyper, 2020. "Quantifying the impact of the modifiable areal unit problem when estimating the health effects of air pollution," Environmetrics, John Wiley & Sons, Ltd., vol. 31(8), December.
    • Handle: RePEc:wly:envmet:v:31:y:2020:i:8:n:e2643
    DOI: 10.1002/env.2643
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.2643
    Download Restriction: no
    File URL: https://libkey.io/10.1002/env.2643?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. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    2. Lee, Duncan, 2013. "CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i13).
    3. A. Lee & A. Szpiro & S.Y. Kim & L. Sheppard, 2015. "Impact of preferential sampling on exposure prediction and health effect inference in the context of air pollution epidemiology," Environmetrics, John Wiley & Sons, Ltd., vol. 26(4), pages 255-267, June.
    4. 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.
    5. World Health Organisation (WHO), 2016. "Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease," Working Papers id:11368, eSocialSciences.
    6. O. Nicolis & M. Díaz & S. K. Sahu & J. C. Marín, 2019. "Bayesian spatiotemporal modeling for estimating short‐term exposure to air pollution in Santiago de Chile," Environmetrics, John Wiley & Sons, Ltd., vol. 30(7), November.
    7. M. Strand & S. Sillau & G. K. Grunwald & N. Rabinovitch, 2015. "Regression calibration with instrumental variables for longitudinal models with interaction terms, and application to air pollution studies," Environmetrics, John Wiley & Sons, Ltd., vol. 26(6), pages 393-405, September.
    8. Sam Clifford & Samantha Low‐Choy & Mandana Mazaheri & Farhad Salimi & Lidia Morawska & Kerrie Mengersen, 2019. "A Bayesian spatiotemporal model of panel design data: Airborne particle number concentration in Brisbane, Australia," Environmetrics, John Wiley & Sons, Ltd., vol. 30(7), November.
    9. O. Gilani & V. J. Berrocal & S. A. Batterman, 2019. "Nonstationary spatiotemporal Bayesian data fusion for pollutants in the near‐road environment," Environmetrics, John Wiley & Sons, Ltd., vol. 30(7), November.
    10. Manfred M. Fischer & Peter Nijkamp (ed.), 2014. "Handbook of Regional Science," Springer Books, Springer, edition 127, number 978-3-642-23430-9, September.
    11. Mehreteab Aregay & Andrew B. Lawson & Christel Faes & Russell S. Kirby & Rachel Carroll & Kevin Watjou, 2018. "Zero‐inflated multiscale models for aggregated small area health data," Environmetrics, John Wiley & Sons, Ltd., vol. 29(1), February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Paulo Canas Rodrigues & Elisabetta Carfagna, 2023. "Data science applied to environmental sciences," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    2. Ying C. MacNab, 2023. "On coregionalized multivariate Gaussian Markov random fields: construction, parameterization, and Bayesian estimation and inference," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 263-293, March.

    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. Ying C. MacNab, 2018. "Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 497-541, September.
    2. Wiki, Jesse & Kingham, Simon & Campbell, Malcolm, 2021. "A geospatial analysis of Type 2 Diabetes Mellitus and the food environment in urban New Zealand," Social Science & Medicine, Elsevier, vol. 288(C).
    3. Ferreira, Marco A.R. & Porter, Erica M. & Franck, Christopher T., 2021. "Fast and scalable computations for Gaussian hierarchical models with intrinsic conditional autoregressive spatial random effects," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
    4. Lan Hu & Yongwan Chun & Daniel A. Griffith, 2020. "Uncovering a positive and negative spatial autocorrelation mixture pattern: a spatial analysis of breast cancer incidences in Broward County, Florida, 2000–2010," Journal of Geographical Systems, Springer, vol. 22(3), pages 291-308, July.
    5. Demba Fofana & E. O. George & Dale Bowman, 2021. "Combining assumptions and graphical network into gene expression data analysis," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-17, December.
    6. Demetris Lamnisos & Nicos Middleton & Nikoletta Kyprianou & Michael A. Talias, 2019. "Geodemographic Area Classification and Association with Mortality: An Ecological Study of Small Areas of Cyprus," IJERPH, MDPI, vol. 16(16), pages 1-13, August.
    7. 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.
    8. Mabel Morales-Otero & Vicente Núñez-Antón, 2021. "Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates," Mathematics, MDPI, vol. 9(3), pages 1-33, January.
    9. Meen Chel Jung & Jaewoo Park & Sunghwan Kim, 2019. "Spatial Relationships between Urban Structures and Air Pollution in Korea," Sustainability, MDPI, vol. 11(2), pages 1-17, January.
    10. Yating Wan & Minya Xu & Hui Huang & Song Xi Chen, 2021. "A spatio‐temporal model for the analysis and prediction of fine particulate matter concentration in Beijing," Environmetrics, John Wiley & Sons, Ltd., vol. 32(1), February.
    11. Jon Wakefield, 2004. "Ecological inference for 2 × 2 tables (with discussion)," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 385-445, July.
    12. Jon Wakefield, 2003. "Sensitivity Analyses for Ecological Regression," Biometrics, The International Biometric Society, vol. 59(1), pages 9-17, March.
    13. KWON, Heeeun & HWANG, Beom Seuk, 2023. "Do Spatial Characteristics Affect Housing Prices in Korea? : Evidence from Bayesian Spatial Models," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 64(2), pages 109-124, December.
    14. Konstantinos Giannakou & Demetris Lamnisos, 2022. "Small-Area Geographic and Socioeconomic Inequalities in Colorectal Cancer in Cyprus," IJERPH, MDPI, vol. 20(1), pages 1-15, December.
    15. Dongkwan Lee & Jean-Michel Guldmann & Choongik Choi, 2019. "Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    16. Nikoline N. Knudsen & Jörg Schullehner & Birgitte Hansen & Lisbeth F. Jørgensen & Søren M. Kristiansen & Denitza D. Voutchkova & Thomas A. Gerds & Per K. Andersen & Kristine Bihrmann & Morten Grønbæk , 2017. "Lithium in Drinking Water and Incidence of Suicide: A Nationwide Individual-Level Cohort Study with 22 Years of Follow-Up," IJERPH, MDPI, vol. 14(6), pages 1-13, June.
    17. Rey, Sergio, 2015. "Bells in Space: The Spatial Dynamics of US Interpersonal and Interregional Income Inequality," MPRA Paper 69482, University Library of Munich, Germany.
    18. Katie Wilson & Jon Wakefield, 2022. "A probabilistic model for analyzing summary birth history data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(11), pages 291-344.
    19. Juan C. Surís-Regueiro & José L. Santiago, 2016. "An Input-Output methodological proposal to quantifying socio economic impacts linked to supply shocks," Working Papers 1603, Universidade de Vigo, Departamento de Economía Aplicada.
    20. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.

    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:wly:envmet:v:31:y:2020:i:8:n:e2643. 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: http://www.interscience.wiley.com/jpages/1180-4009/ .

    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.