title: Flexible modeling of exposure-response relationship between long-term average levels of particulate air pollution and mortality in the American Cancer Society study.
abstract: Accurate estimation of the exposure-response relationship between environmental particulate air pollution and mortality is important from both an etiologic and regulatory perspective. However, little is known about the actual shapes of these exposure-response curves. The objective of this study was to estimate the exposure-response relationships between mortality and long-term average city-specific levels of sulfates and fine particulate matter (PM(2.5)). We reanalyzed the data derived from the American Cancer Society (ACS) Cancer Prevention Study II, a large prospective study conducted in the United States between 1982 and 1989. Exposure to particulate air pollution was assessed prior to entry into the cohort. Mean sulfate concentrations for 1980 were available in 151 cities, and median PM(2.5) levels between 1979 and 1983 were available in 50 cities. Two sampling strategies were employed to reduce the computational burden. The modified case-cohort approach combined a random subcohort of 1200 individuals with an additional 1300 cases (i.e., deaths). The second strategy involved pooling the results of separate analyses of 10 disjoint random subsets, each with about 2200 participants. To assess the independent effect of the particulate levels on all-causes mortality, we relied on flexible, nonparametric survival analytical methods. To eliminate potentially restrictive assumptions underlying the conventional models, we employed a flexible regression spline generalization of the Cox proportional-hazards (PH) model. The regression spline method allowed us to model simultaneously the time-dependent changes in the effect of particulate matter on the hazard and a possibly nonlinear exposure-response relationship. The PH and linearity hypotheses were tested using likelihood ratio tests. In all analyses, we stratified by age and 5-yr age groups and adjusted for the subject's age, lifetime smoking exposure, obesity, and education. For both fine particles (PM(2.5)) and sulfates, there was a statistically significant (at.05 level) departure from the conventional linearity assumption. The adjusted effect of fine particles on mortality indicated a stronger relationship in the lower (up to about 16 microg/m(3)) than in the higher range of their values. Increasing levels of sulfates in the lower range (up to about 12 microg/m(3)) had little impact on mortality, suggesting a possible "no-effect threshold." For body mass index (BMI), the risks were lowest in the middle range and increased for both very obese and very lean individuals. It was concluded that flexible modeling yields new insights about the effect of long-term air pollution on mortality.
authors.affiliation: Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada. michal@michal.ri.mgh.mcgill.ca
authors.forename: Michal
authors.initials: M
authors.lastname: Abrahamowicz
authors.forename: Tom
authors.initials: T
authors.lastname: Schopflocher
authors.forename: Karen
authors.initials: K
authors.lastname: Leffondré
authors.forename: Roxane
authors.initials: R
authors.lastname: du Berger
authors.forename: Daniel
authors.initials: D
authors.lastname: Krewski
mesh_terms: D000393:Air Pollutants / Q000009:adverse effects / Q000032:analysis; D000397:Air Pollution / Q000009:adverse effects*; D000465:Algorithms; D000570:American Cancer Society; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D004784:Environmental Monitoring; D016021:Epidemiologic Studies; D006801:Humans; D009026:Mortality / Q000639:trends*; D017711:Nonlinear Dynamics; D016016:Proportional Hazards Models; D012044:Regression Analysis; D012494:Sampling Studies; D012907:Smoking / Q000401:mortality; D013431:Sulfates
chemical_list: D000393:Air Pollutants; D013431:Sulfates