Abstract

BackgroundLong-term exposure to particulate matter less than 2.5 μm in diameter (PM2.5) has been consistently associated with risk of all-cause mortality. The methods used to assess exposure, such as area averages, nearest monitor values, land use regressions, and spatio-temporal models in these studies are subject to measurement error. However, to date, no study has attempted to incorporate adjustment for measurement error into a long-term study of the effects of air pollution on mortality.MethodsWe followed 108,767 members of the Nurses’ Health Study (NHS) 2000–2006 and identified all deaths. Biennial mailed questionnaires provided a detailed residential address history and updated information on potential confounders. Time-varying average PM2.5 in the previous 12-months was assigned based on residential address and was predicted from either spatio-temporal prediction models or as concentrations measured at the nearest USEPA monitor. Information on the relationships of personal exposure to PM2.5 of ambient origin with spatio-temporal predicted and nearest monitor PM2.5 was available from five previous validation studies. Time-varying Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95 percent confidence intervals (95%CI) for each 10 μg/m3 increase in PM2.5. Risk-set regression calibration was used to adjust estimates for measurement error.ResultsIncreasing exposure to PM2.5 was associated with an increased risk of mortality, and results were similar regardless of the method chosen for exposure assessment. Specifically, the multivariable adjusted HRs for each 10 μg/m3 increase in 12-month average PM2.5 from spatio-temporal prediction models were 1.13 (95%CI:1.05, 1.22) and 1.12 (95%CI:1.05, 1.21) for concentrations at the nearest EPA monitoring location. Adjustment for measurement error increased the magnitude of the HRs 4-10% and led to wider CIs (HR = 1.18; 95%CI: 1.02, 1.36 for each 10 μg/m3 increase in PM2.5 from the spatio-temporal models and HR = 1.22; 95%CI: 1.02, 1.45 from the nearest monitor estimates).ConclusionsThese findings support the large body of literature on the adverse effects of PM2.5, and suggest that adjustment for measurement error be considered in future studies where possible.

Highlights

  • Long-term exposure to particulate matter less than 2.5 μm in diameter (PM2.5) has been consistently associated with risk of all-cause mortality

  • As an improvement over previous methods that applied the same calibration factor to all participants and time periods, the regression calibration approach (RRC) method recalibrates the measurement error model for monthly PM2.5 exposure for each risk set observed in the main study by its counterpart in the validation study, and the 12-month average personal PM2.5 of ambient origin is constructed from the monthly PM2.5 exposures estimated by the risk set-specific exposure measurement error models

  • Because the average number of people per household in a Census tract accounted for the betweencity heterogeneity observed in the risk set calibration factors for the nearest EPA Air Quality System (AQS) monitor in the validation study [30], we included interaction terms of the number of people per household and PM2.5 in the measurement error models for this exposure

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Summary

Introduction

Long-term exposure to particulate matter less than 2.5 μm in diameter (PM2.5) has been consistently associated with risk of all-cause mortality. As noted in that review, the assessment of exposure has varied across studies from city level measures to central ambient monitoring locations to complex land-use regression or other spatial exposure models, all of which have the potential to induce substantial measurement error. These measurement errors have been shown to be both classical, leading to attenuation of the exposureresponse association, as well as Berkson, leading to increases in the width of the confidence intervals, resulting in overall biased results [15,16,17,18,19]. This new method makes it possible to utilize regression calibration in longterm studies of chronic exposure to air pollution

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