The Relationship Between Particulate Matter (PM) Components and Survival Among Cohort Members

Abstract

SAB4-PD-08 Introduction: Several studies have reported statistically significant associations between air pollution, characterized by particulate matter (PM) and/or gaseous pollutant levels, and the survivorship among cohort members. Studies to date have not systematically investigated the associations between specific components of PM and cohort survival. This study considers the relationship between PM, components (metallic ions, organic and elemental carbon, sulfate and nitrate ions), gaseous pollutants, and other indices of environmental nuisance (eg, traffic) and cohort survivorship. Methods: This study makes use of a cohort of 70,000 men who have been followed since 1976. Proportional hazard regression techniques were used to estimate the influence of several external variables, including pollution values, on the survival times of the cohort after adjusting for demographic (age, race), behavioral (smoking history), and prior health status (body mass index, blood pressure) variables among cohort members. Several model specifications were considered, including models with multiple pollution variables. Model results were compared using measures of statistical significance, effect size, and overall model fit as measured by the Akaike Information Criterion. Results: Single pollutant models indicated statistically significant associations between cohort survivorship and elemental carbon (EC), nitrate ion, vanadium, and nickel. Peak ozone and iron were positively associated with cohort survival, but the association was not statistically significant. Two pollutants models showed EC and the nitrate ion retained statistically significant associations with survivorship when they were considered jointly with nickel ion concentrations or with peak ozone. Peak ozone, iron, vanadium, and nickel were statistically significantly associated with survivorship in at least one model with multiple pollutants. When a traffic density variable was introduced into the multiple pollutants models, it appeared to dominate the pollution variables in terms of predicting cohort survivorship and remained statistically significant in many models. Discussion and Conclusions: The results indicate that some components of pollution and PM are much more highly associated with cohort survivorship than others. An indicator of traffic density trumped these variables, however, when they were considered jointly with pollution measures. It is unclear whether this result implies that 1) there is a nonmeasured component of pollution associated with traffic that may explain the observed associations or 2) some nonpollution aspect of traffic is a better predictor of cohort survivorship. This issue is complicated by considerations of differential measurement error among the variables studied.