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Do socioeconomic characteristics modify the short term association between air pollution and mortality? Evidence from a zonal time series in Hamilton, Canada
  1. M Jerrett1,
  2. R T Burnett2,
  3. J Brook3,
  4. P Kanaroglou1,
  5. C Giovis1,
  6. N Finkelstein1,
  7. B Hutchison4
  1. 1School of Geography and Geology and McMaster Institute of Environment and Health, McMaster University, Hamilton, Ontario, Canada
  2. 2Health Canada
  3. 3Environment Canada
  4. 4Centre for Health Economics and Policy Analysis and Department of Clinical Epidemiology and Biostatistics, McMaster University
  1. Correspondence to:
 Professor M Jerrett
 Biostatistics Division, Department of Preventive Medicine and Department of Geography, University of Southern California, 1540 Alcazar Street, CHP-220, Los Angeles, CA 90089-9011, USA;


Study objective: To assess the short term association between air pollution and mortality in different zones of an industrial city. An intra-urban study design is used to test the hypothesis that socioeconomic characteristics modify the acute health effects of ambient air pollution exposure.

Design: The City of Hamilton, Canada, was divided into five zones based on proximity to fixed site air pollution monitors. Within each zone, daily counts of non-trauma mortality and air pollution estimates were combined. Generalised linear models (GLMs) were used to test mortality associations with sulphur dioxide (SO2) and with particulate air pollution measured by the coefficient of haze (CoH).

Main results: Increased mortality was associated with air pollution exposure in a citywide model and in intra-urban zones with lower socioeconomic characteristics. Low educational attainment and high manufacturing employment in the zones significantly and positively modified the acute mortality effects of air pollution exposure.

Discussion: Three possible explanations are proposed for the observed effect modification by education and manufacturing: (1) those in manufacturing receive higher workplace exposures that combine with ambient exposures to produce larger health effects; (2) persons with lower education are less mobile and experience less exposure measurement error, which reduces bias toward the null; or (3) manufacturing and education proxy for many social variables representing material deprivation, and poor material conditions increase susceptibility to health risks from air pollution.

  • GLM, generalised linear model
  • CoH, coefficient of haze
  • SO2, sulphur dioxide
  • NT, non-trauma
  • AIC, Akaike information criterion
  • CT, census tract
  • MPC, mean percentage change
  • air pollution
  • mortality
  • acute effects
  • socioeconomic factors
  • GIS

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Supplementary materials

  • .

    Web-only Appendix

    Full results for models using both the regional mean of pollution and the zonal means.

    Table A1 Full results for the downtown zone: estimated at the local mean of pollution
    Table A2 Results for the industrial zone: estimated at the local mean of pollution
    Table A3 Results for the east end zone: estimated at the local mean of pollution
    Table A4 Results for the south mountain zone: estimated at the local mean of pollution
    Table A5 Results for the west end zone: estimated at the local mean of pollution

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