Background-Chronic exposure to particulate air pollution may accelerate cognitive decline in older adults, although data on this association are limited. Our objective was to examine long-term exposure to particulate matter (PM) air pollution, both coarse ([PM 2.5-10 μm in diameter [PM 2.5-10 ]) and fine (PM <2.5 μm in diameter [PM 2.5 ]), in relation to cognitive decline. Methods-The study population comprised the Nurses' Health Study Cognitive Cohort, which included 19 409 US women aged 70 to 81 years. We used geographic information system-based spatiotemporal smoothing models to estimate recent (1 month) and long-term (7-14 years) exposures to PM 2.5-10 , and PM 2.5 preceding base-line cognitive testing (1995-2001) of participants residing in the contiguous United States. We used generalized estimating equation regression to estimate differences in the rate of cognitive decline across levels of PM 2.5-10 and PM 2.5 exposures. The main outcome measure was cognition, via validated telephone assessments, administered 3 times at approximately 2-year intervals, including tests of general cognition, verbal memory, category fluency, working memory, and attention. Results-Higher levels of long-term exposure to both PM 2.5-10 and PM 2.5 were associated with significantly faster cognitive decline. Two-year decline on a global score was 0.020 (95% CI, −0.032 to −0.008) standard units worse per 10 μg/m 3 increment in PM 2.5-10 exposure and 0.018 (95% CI, −0.035 to −0.002) units worse per 10 μg/m 3 increment in PM 2.5 exposure. These

Objectives:We modelled 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:Modelled 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 modelled traffic particles was associated with a 10% (95% CI 4% to 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, although on a relatively large spatial scale.

Chronic traffic-related particulate air pollution is associated with increased mortality in hospital survivors of acute MI after the second year of survival.

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