Regression calibration with instrumental variables for longitudinal models with interaction terms, and application to air pollution studies

Measurement error correction for ambient PM2.5 exposure using stratified regression calibration: Effects on all-cause mortality

BACKGROUND

Previous studies on the impact of measurement error for PM2.5 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 PM2.5 concentration may vary by time and region. We aim to correct the measurement error of PM2.5 predictions using stratified regression calibration and investigate how the measurement error biases the association between PM2.5 and mortality in the Medicare Cohort.

METHODS

The "gold-standard" measurements of PM2.5 were defined as daily monitoring data. We regressed daily monitoring PM2.5 on modeled PM2.5 using the simple linear regression by strata of season, elevation, census division and time period. Calibrated PM2.5 was calculated with stratum-specific calibration parameters β0 (intercept) and β1 (slope) for each strata and aggregated to annual level. Associations between calibrated and error-prone annual PM2.5 and all-cause mortality among Medicare beneficiaries were estimated with Quasi-Poisson regression models.

RESULTS

Across 208 strata, the median of β0 and β1 were 0.62 (25% 0.0.20, 75% 1.06) and 0.93 (25% 0.87, 75% 0.99). From calibrated and error-prone PM2.5 data, we estimated that each 10 μg/m3 increase in PM2.5 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 PM2.5 varied by time and region. Using error-prone measures of PM2.5 underestimated the association between PM2.5 and all-cause mortality. Modern exposure models produce relatively small bias.

Health effects of concurrent ambient and tobacco smoke-derived particle exposures at low concentrations in children with asthma

Exposure to particulate matter less than 2.5 microns from either ambient pollution (AMB-PM_2.5) or secondhand smoke (SHS-PM_2.5) have been associated with asthma worsening, but there is little information on effects and relative potency with concurrent exposures. We studied health effects of concurrent exposures to AMB-PM_2.5 and SHS-PM_2.5 over a 6-year period in schoolchildren with asthma. Regression calibration with instrumental variables (RCIV) was utilized to estimate effects of personal exposure to low-level SHS and AMB-PM_2.5 on daily albuterol usage and urinary leukotriene E4 (uLTE₄; a biomarker of asthma-related inflammation) using urine cotinine and concentrations from fixed and personal pollution monitors. Each IQR increase in SHS-PM_2.5 exposure was associated with a 6.7% increase (95% CI: 1.0-12.8%) in uLTE₄ on the same day and 9.4% increase (95% CI: -2.6 to 22.7%) in albuterol use the next day, when children were co-exposed to mean levels of AMB-PM_2.5. The dose-response relationship between health outcomes and one pollutant was higher at lower levels of the other pollutant. For example, at lower levels of predicted SHS-PM_2.5 exposure, increases in health outcomes per IQR increase in AMB-PM_2.5 ranged between 2 and 5%, but were negligible at higher SHS-PM_2.5 levels. Comparing at equivalent co-exposure levels, SHS-PM_2.5 was 1.6 times more potent than AMB-PM_2.5 for uLTE₄ (95% CI: 1.1-2.3); estimates for albuterol usage were similar but less significant. Effects at mean co-exposure levels were closer [SHS to AMB-PM_2.5 potency ratio = 1.2 (95% CI: 0.9-1.5) for uLTE₄ and 1.2 (95% CI: 0.7-1.9) for albuterol usage]. In summary, concurrent exposure to relatively low levels of SHS and AMB-PM_2.5 were associated with health outcomes in asthmatic schoolchildren. Dose responses varied with changes in the relative amounts of each pollutant; SHS-PM_2.5 was observed to be more potent than AMB-PM_2.5 when co-exposure levels were equivalent.

Incorporating Measurement Error from Modeled Air Pollution Exposures into Epidemiological Analyses

PURPOSE OF REVIEW

Outdoor air pollution exposures used in epidemiological studies are commonly predicted from spatiotemporal models incorporating limited measurements, temporal factors, geographic information system variables, and/or satellite data. Measurement error in these exposure estimates leads to imprecise estimation of health effects and their standard errors. We reviewed methods for measurement error correction that have been applied in epidemiological studies that use model-derived air pollution data.

RECENT FINDINGS

We identified seven cohort studies and one panel study that have employed measurement error correction methods. These methods included regression calibration, risk set regression calibration, regression calibration with instrumental variables, the simulation extrapolation approach (SIMEX), and methods under the non-parametric or parameter bootstrap. Corrections resulted in small increases in the absolute magnitude of the health effect estimate and its standard error under most scenarios. Limited application of measurement error correction methods in air pollution studies may be attributed to the absence of exposure validation data and the methodological complexity of the proposed methods. Future epidemiological studies should consider in their design phase the requirements for the measurement error correction method to be later applied, while methodological advances are needed under the multi-pollutants setting.