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Traffic Particles and Occurrence of Acute Myocardial Infarction: a case-control analysis
  1. Cathryn Tonne (cathryn.tonne{at}erg.kcl.ac.uk)
  1. King's College London, United Kingdom
    1. Jeff Yanosky (jyanosky{at}hsph.harvard.edu)
    1. Harvard School of Public Health, United States
      1. Alexandros Gryparis (alexandros{at}post.harvard.edu)
      1. Harvard School of Public Health, United States
        1. Steve Melly (sjmelly{at}hsph.harvard.edu)
        1. Harvard School of Public Health, United States
          1. Murray Mittleman (mmittlem{at}bidmc.harvard.edu)
          1. Harvard School of Public Health, United States
            1. Robert Goldberg (robert.goldberg{at}umassmed.edu)
            1. University of Massachusetts Medical School, United States
              1. Stephanie von Klot (svonklot{at}hsph.harvard.edu)
              1. Harvard School of Public Health, United States
                1. Joel Schwartz (jschwrtz{at}hsph.harvard.edu)
                1. Harvard School of Public Health, United States

                  Abstract

                  Objectives: We modeled exposure to traffic particles using a latent variable approach and investigated whether long-term exposure to traffic particles is associated with an increase in the occurrence of acute myocardial infarction (AMI) using data from a population-based coronary disease registry.

                  Methods: Cases of individually validated AMI were identified between 1995 and 2003 as part of the Worcester Heart Attack Study. Population controls were selected from Massachusetts, USA, resident lists. NO2 and PM2.5 filter absorbance were measured at 36 locations throughout the study area. The air pollution data were used to estimate exposure to traffic particles using a semiparametric latent variable regression model. Conditional logistic models were used to estimate the association between exposure to traffic particles and occurrence of AMI.

                  Results: Modeled exposure to traffic particles was highest near the city of Worcester. Cases of AMI were more exposed to traffic and traffic particles compared to controls. An interquartile range increase in modeled traffic particles was associated with a 10% (95% CI 4%, 16%) increase in the odds of AMI. Accounting for spatial dependence at the census tract, but not block group, scale substantially attenuated this association.

                  Conclusions: These results provide some support for an association between long-term exposure to traffic particles and risk of AMI. The results were sensitive to the scale selected for the analysis of spatial dependence, an issue that requires further investigation. The latent variable model captured variation in exposure, though on a relatively large spatial scale.

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