Measurement error correction for ambient PM exposure using stratified regression calibration: Effects on all-cause mortality.

BACKGROUND

Previous studies on the impact of measurement error for PM were mostly simulation studies, did not control for other pollutants, or used a single regression calibration model to correct for measurement error. However, the relationship between actual and error-prone PM concentration may vary by time and region. We aim to correct the measurement error of PM predictions using stratified regression calibration and investigate how the measurement error biases the association between PM and mortality in the Medicare Cohort.

METHODS

The "gold-standard" measurements of PM were defined as daily monitoring data. We regressed daily monitoring PM on modeled PM using the simple linear regression by strata of season, elevation, census division and time period. Calibrated PM was calculated with stratum-specific calibration parameters β (intercept) and β (slope) for each strata and aggregated to annual level. Associations between calibrated and error-prone annual PM and all-cause mortality among Medicare beneficiaries were estimated with Quasi-Poisson regression models.

RESULTS

Across 208 strata, the median of β and β were 0.62 (25% 0.0.20, 75% 1.06) and 0.93 (25% 0.87, 75% 0.99). From calibrated and error-prone PM data, we estimated that each 10 μg/m increase in PM was respectively associated with 4.9% (95%CI 4.6-5.2) and 4.6% (95%CI 4.4-4.9) increases in the mortality rate among Medicare beneficiaries, conditional on confounders.

CONCLUSIONS

Regression calibration parameters of PM varied by time and region. Using error-prone measures of PM underestimated the association between PM and all-cause mortality. Modern exposure models produce relatively small bias.