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Combining evidence on air pollution and daily mortality from the 20 largest US cities: a hierarchical modelling strategy

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  • Francesca Dominici
  • Jonathan M. Samet
  • Scott L. Zeger

Abstract

Reports over the last decade of association between levels of particles in outdoor air and daily mortality counts have raised concern that air pollution shortens life, even at concentrations within current regulatory limits. Criticisms of these reports have focused on the statistical techniques that are used to estimate the pollution–mortality relationship and the inconsistency in findings between cities. We have developed analytical methods that address these concerns and combine evidence from multiple locations to gain a unified analysis of the data. The paper presents log‐linear regression analyses of daily time series data from the largest 20 US cities and introduces hierarchical regression models for combining estimates of the pollution–mortality relationship across cities. We illustrate this method by focusing on mortality effects of PM10 (particulate matter less than 10 μm in aerodynamic diameter) and by performing univariate and bivariate analyses with PM10 and ozone (O3) level. In the first stage of the hierarchical model, we estimate the relative mortality rate associated with PM10 for each of the 20 cities by using semiparametric log‐linear models. The second stage of the model describes between‐city variation in the true relative rates as a function of selected city‐specific covariates. We also fit two variations of a spatial model with the goal of exploring the spatial correlation of the pollutant‐specific coefficients among cities. Finally, to explore the results of considering the two pollutants jointly, we fit and compare univariate and bivariate models. All posterior distributions from the second stage are estimated by using Markov chain Monte Carlo techniques. In univariate analyses using concurrent day pollution values to predict mortality, we find that an increase of 10 μg m‐3 in PM10 on average in the USA is associated with a 0.48% increase in mortality (95% interval: 0.05, 0.92). With adjustment for the O3 level the PM10‐coefficient is slightly higher. The results are largely insensitive to the specific choice of vague but proper prior distribution. The models and estimation methods are general and can be used for any number of locations and pollutant measurements and have potential applications to other environmental agents.

Suggested Citation

  • Francesca Dominici & Jonathan M. Samet & Scott L. Zeger, 2000. "Combining evidence on air pollution and daily mortality from the 20 largest US cities: a hierarchical modelling strategy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 263-302.
  • Handle: RePEc:bla:jorssa:v:163:y:2000:i:3:p:263-302
    DOI: 10.1111/1467-985X.00170
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    File URL: https://doi.org/10.1111/1467-985X.00170
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    Cited by:

    1. Shi Li & Bhramar Mukherjee & Stuart Batterman & Malay Ghosh, 2013. "Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference," Biometrics, The International Biometric Society, vol. 69(4), pages 925-936, December.
    2. Peng, Roger, 2008. "Caching and Distributing Statistical Analyses in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 26(i07).
    3. Cutter, W. Bowman & Neidell, Matthew, 2009. "Voluntary information programs and environmental regulation: Evidence from 'Spare the Air'," Journal of Environmental Economics and Management, Elsevier, vol. 58(3), pages 253-265, November.
    4. 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.
    5. Duncan Lee & Tereza Neocleous, 2010. "Bayesian quantile regression for count data with application to environmental epidemiology," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 905-920, November.
    6. 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.
    7. Koop, Gary & Tole, Lise, 2004. "Measuring the health effects of air pollution: to what extent can we really say that people are dying from bad air?," Journal of Environmental Economics and Management, Elsevier, vol. 47(1), pages 30-54, January.
    8. 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.
    9. Wong, Heung & Shao, Quanxi & Ip, Wai-cheung, 2013. "Modeling respiratory illnesses with change point: A lesson from the SARS epidemic in Hong Kong," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 589-599.
    10. Burr, Wesley S. & Shin, Hwashin H. & Takahara, Glen, 2019. "Synthetically lagged models," Statistics & Probability Letters, Elsevier, vol. 144(C), pages 37-43.
    11. Yi Liu & Gavin Shaddick & James V. Zidek, 0. "Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 0, pages 1-23.
    12. Enrico Moretti & Matthew Neidell, 2011. "Pollution, Health, and Avoidance Behavior: Evidence from the Ports of Los Angeles," Journal of Human Resources, University of Wisconsin Press, vol. 46(1), pages 154-175.
    13. Gillian A. Lancaster & Mick Green & Steven Lane, 2006. "Reducing bias in ecological studies: an evaluation of different methodologies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 681-700, October.
    14. Laura F. Boehm Vock & Brian J. Reich & Montserrat Fuentes & Francesca Dominici, 2015. "Spatial variable selection methods for investigating acute health effects of fine particulate matter components," Biometrics, The International Biometric Society, vol. 71(1), pages 167-177, March.
    15. Chi Wang & Giovanni Parmigiani & Francesca Dominici, 2012. "Bayesian Effect Estimation Accounting for Adjustment Uncertainty," Biometrics, The International Biometric Society, vol. 68(3), pages 661-671, September.
    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. Duncan Lee & Claire Ferguson & E. Marian Scott, 2011. "Constructing representative air quality indicators with measures of uncertainty," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 109-126, January.
    18. 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.
    19. Marta Blangiardo & Monica Pirani & Lauren Kanapka & Anna Hansell & Gary Fuller, 2019. "A hierarchical modelling approach to assess multi pollutant effects in time-series studies," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-16, March.
    20. 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.

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