Long-term exposure to air pollution from road traffic and cardiovascular disease with a focus on exposure modeling

Abstract

Air pollution is an important environmental health factor contributing to the burden of disease. From a public health point of view cardiovascular effects of long-term exposure are predominant, primarily coronary events and stroke. However, sub-types of disease have not been well investigated and few studies have been conducted in areas with lower air pollution levels. The role of timing of exposure is also unclear.

In epidemiological studies different types of models are used to estimate exposure of study participants. It is therefore important to understand if modeled levels are similar for different model types. Furthermore, there is a need to develop better modeling techniques, and it has been proposed to combine models into so called hybrid models.

The aim of this thesis was to investigate the relation between individual long-term air pollution exposure from road traffic and the risk of coronary events and stroke in an area with comparatively low exposure levels, while considering timing of exposure. Furthermore a comparison of dispersion modeling (DM) and land use regression (LUR) was done in several study areas and a hybrid model based on DM and LUR was developed for Stockholm.

From four cohorts in Stockholm County, 20070 individuals were followed for an average of 12 years. Information on covariates was available from questionnaires and interviews from the time of recruitment. Air pollution exposure from traffic was assessed at residential addresses during follow-up using dispersion modeled levels of nitrogen oxides (NOx), as a marker of exhaust emissions, and particles with an aerodynamic diameter of <10 μm (PM10), as a marker of road dust. A suggestive association between road traffic exposure at the recruitment address and cardiovascular disease incidence was seen. For NOx the hazard ratio for stroke and coronary events per 20μg/m3 was 1.16 (0.83 -1.61) and 1.02 (0.82-1.27), respectively. Corresponding hazard ratios for PM10 were 1.14 (0.68-1.90) and 1.14 (0.87- 1.49), respectively, per 10μg/m3. Results did not appear to be modified by covariates, disease sub-types or exposure time windows.

LUR models and DMs were compared in 4 to 13 European study areas depending on the pollutant. At study addresses, the median Pearson correlation (range) for annual mean concentrations of NO2, PM10 and PM2.5 were: 0.75 (0.19–0.89), 0.39 (0.23–0.66) and 0.29 (0.22–0.81). A hybrid model was developed for Stockholm for 93 bi-weekly NOx observations using DM estimates, LUR variables, stationary monitoring and individual meteorological factors. The hybrid model explained NOx levels at monitoring stations better (R2 =89%) than the LUR and DM models (R2 =58% and R2 =68%, respectively).

In conclusion, our results suggest an elevated risk of coronary events and stroke related to traffic air pollution exposures in Stockholm County, however, no modification by time window of exposure could be detected. On average, estimates from LUR and DMs correlate well for NO2 but less so for particulates. To combine DM and LUR seems promising for increasing the quality of the exposure assessment.