Mortality-Air Pollution Associations in Low-Exposure Environments (MAPLE): Phase 1.

Abstract

Introduction: Fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter, or PM_2.5) is associated with mortality, but the lower range of relevant concentrations is unknown. Novel satellite-derived estimates of outdoor PM_2.5 concentrations were applied to several large population-based cohorts, and the shape of the relationship with nonaccidental mortality was characterized, with emphasis on the low concentrations (<12 μg/m³) observed throughout Canada.

Methods: Annual satellite-derived estimates of outdoor PM_2.5 concentrations were developed at 1-km² spatial resolution across Canada for 2000-2016 and backcasted to 1981 using remote sensing, chemical transport models, and ground monitoring data. Targeted ground-based measurements were conducted to measure the relationship between columnar aerosol optical depth (AOD) and ground-level PM_2.5. Both existing and targeted ground-based measurements were analyzed to develop improved exposure data sets for subsequent epidemiological analyses.

Residential histories derived from annual tax records were used to estimate PM_2.5 exposures for subjects whose ages ranged from 25 to 90 years. About 8.5 million were from three Canadian Census Health and Environment Cohort (CanCHEC) analytic files and another 540,900 were Canadian Community Health Survey (CCHS) participants. Mortality was linked through the year 2016. Hazard ratios (HR) were estimated with Cox Proportional Hazard models using a 3-year moving average exposure with a 1-year lag, with the year of follow-up as the time axis. All models were stratified by 5-year age groups, sex, and immigrant status. Covariates were based on directed acyclical graphs (DAG), and included contextual variables (airshed, community size, neighborhood dependence, neighborhood deprivation, ethnic concentration, neighborhood instability, and urban form). A second model was examined including the DAG-based covariates as well as all subject-level risk factors (income, education, marital status, indigenous identity, employment status, occupational class, and visible minority status) available in each cohort. Additional subject-level behavioral covariates (fruit and vegetable consumption, leisure exercise frequency, alcohol consumption, smoking, and body mass index [BMI]) were included in the CCHS analysis.

Sensitivity analyses evaluated adjustment for covariates and gaseous copollutants (nitrogen dioxide [NO₂] and ozone [O₃]), as well as exposure time windows and spatial scales. Estimates were evaluated across strata of age, sex, and immigrant status. The shape of the PM_2.5-mortality association was examined by first fitting restricted cubic splines (RCS) with a large number of knots and then fitting the shape-constrained health impact function (SCHIF) to the RCS predictions and their standard errors (SE). This method provides graphical results indicating the RCS predictions, as a nonparametric means of characterizing the concentration-response relationship in detail and the resulting mean SCHIF and accompanying uncertainty as a parametric summary.

Sensitivity analyses were conducted in the CCHS cohort to evaluate the potential influence of unmeasured covariates on air pollution risk estimates. Specifically, survival models with all available risk factors were fit and compared with models that omitted covariates not available in the CanCHEC cohorts. In addition, the PM_2.5 risk estimate in the CanCHEC cohort was indirectly adjusted for multiple individual-level risk factors by estimating the association between PM_2.5 and these covariates within the CCHS.

Results: Satellite-derived PM_2.5 estimates were low and highly correlated with ground monitors. HR estimates (per 10-μg/m³ increase in PM_2.5) were similar for the 1991 (1.041, 95% confidence interval [CI]: 1.016-1.066) and 1996 (1.041, 1.024-1.059) CanCHEC cohorts with a larger estimate observed for the 2001 cohort (1.084, 1.060-1.108). The pooled cohort HR estimate was 1.053 (1.041-1.065). In the CCHS an analogous model indicated a HR of 1.13 (95% CI: 1.06-1.21), which was reduced slightly with the addition of behavioral covariates (1.11, 1.04-1.18). In each of the CanCHEC cohorts, the RCS increased rapidly over lower concentrations, slightly declining between the 25th and 75th percentiles and then increasing beyond the 75th percentile. The steepness of the increase in the RCS over lower concentrations diminished as the cohort start date increased. The SCHIFs displayed a supralinear association in each of the three CanCHEC cohorts and in the CCHS cohort.

In sensitivity analyses conducted with the 2001 CanCHEC, longer moving averages (1, 3, and 8 years) and smaller spatial scales (1 km² vs. 10 km²) of exposure assignment resulted in larger associations between PM_2.5 and mortality. In both the CCHS and CanCHEC analyses, the relationship between nonaccidental mortality and PM_2.5 was attenuated when O₃ or a weighted measure of oxidant gases was included in models. In the CCHS analysis, but not in CanCHEC, PM_2.5 HRs were also attenuated by the inclusion of NO₂. Application of the indirect adjustment and comparisons within the CCHS analysis suggests that missing data on behavioral risk factors for mortality had little impact on the magnitude of PM_2.5-mortality associations. While immigrants displayed improved overall survival compared with those born in Canada, their sensitivity to PM_2.5 was similar to or larger than that for nonimmigrants, with differences between immigrants and nonimmigrants decreasing in the more recent cohorts.

Conclusions: In several large population-based cohorts exposed to low levels of air pollution, consistent associations were observed between PM_2.5 and nonaccidental mortality for concentrations as low as 5 μg/m³. This relationship was supralinear with no apparent threshold or sublinear association.