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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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).
    7. Manfred M. Fischer & Peter Nijkamp (ed.), 2014. "Handbook of Regional Science," Springer Books, Springer, edition 127, number 978-3-642-23430-9, December.
    8. 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.
    9. 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.
    10. 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.
    11. World Health Organisation (WHO), 2016. "Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease," Working Papers id:11368, eSocialSciences.
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