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Exposure estimates based on our validated land-use regression model are more accurate than estimates based on ambient monitoring, which are often used for cohorts of this size.
We linked individual-level data on self-reported symptoms and diseases (and confounding variables) to residential exposure estimates from a validated land-use regression model and accounted for spatial clustering of respondents.
Exposure to BC was estimated from a validated land-use regression model that incorporated temporal effects and space-time interactions and was assigned at the individual level (Gryparis et al. 2007).
A major advantage of the present study is the use of a validated land-use regression model to characterize the individual-level differences in exposures instead of classifying exposure based on measurements at the nearest monitor, land-use regression models without BC measurements, or a weighted form of distance to roadway.
In order to predict local BC level, we used a validated spatial temporal land use regression model to predict 24-hr measures of traffic exposure data (BC) at > 80 locations in the Boston area.
Individual-level estimates of residential BC concentrations were predicted from a validated spatiotemporal land-use regression model.
Exposure to BC up to 4 weeks prior was predicted from a validated spatiotemporal land-use regression model.
We assessed long-term exposure to traffic-related air pollution using a validated spatiotemporal land-use regression model for BC.
We predicted individual-level estimates of residential BC concentrations from a validated spatiotemporal land-use regression model.
We estimated local BC levels using a validated spatiotemporal land-use regression model, derived using ambient and indoor monitor data.
Individual-level data on numerous self-reported medical conditions and confounding variables were linked to exposure estimates at residential addresses using a validated national land-use regression model.
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