Cardiopulmonary Mortality and Fine Particulate Air Pollution by Species and Source in a National U.S. Cohort.

The purpose of this study was to estimate cardiopulmonary mortality associations for long-term exposure to PM2.5 species and sources (i.e., components) within the U.S. National Health Interview Survey cohort. Exposures were estimated through a chemical transport model for six species (i.e., elemental carbon (EC), primary organic aerosols (POA), secondary organic aerosols (SOA), sulfate (SO4), ammonium (NH4), nitrate (NO3)) and five sources of PM2.5 (i.e., vehicles, electricity-generating units (EGU), non-EGU industrial sources, biogenic sources (bio), "other" sources). In single-pollutant models, we found positive, significant (p < 0.05) mortality associations for all components, except POA. After adjusting for remaining PM2.5 (total PM2.5 minus component), we found significant mortality associations for EC (hazard ratio (HR) = 1.36; 95% CI [1.12, 1.64]), SOA (HR = 1.11; 95% CI [1.05, 1.17]), and vehicle sources (HR = 1.06; 95% CI [1.03, 1.10]). HRs for EC, SOA, and vehicle sources were significantly larger in comparison to those for remaining PM2.5 (per unit µg/m3). Our findings suggest that cardiopulmonary mortality associations vary by species and source, with evidence that EC, SOA, and vehicle sources are important contributors to the PM2.5 mortality relationship. With further validation, these findings could facilitate targeted pollution regulations that more efficiently reduce air pollution mortality.

Designing health impact functions to assess marginal changes in outdoor fine particulate matter.

Estimating health benefits from improvements in ambient air quality requires the characterization of the magnitude and shape of the association between marginal changes in exposure and marginal changes in risk, and its uncertainty. Several attempts have been made to do this, each requiring different assumptions. These include the Log-Linear(LL), IntegratedExposure-Response(IER), and GlobalExposureMortalityModel(GEMM). In this paper we develop an improved relative risk model suitable for use in health benefits analysis that incorporates features of existing models while addressing limitations in each model. We model the derivative of the relative risk function within a meta-analytic framework; a quantity directly applicable to benefits analysis, incorporating a Fusion of algebraic functions used in previous models. We assume a constant derivative in concentration over low exposures, like the LL model, a declining derivative over moderate exposures observed in cohort studies, and a derivative declining as the inverse of concentration over high global exposures in a similar manner to the GEMM. The model properties are illustrated with examples of fitting it to data for the six specific causes of death previously examined by the GlobalBurdenofDisease program with ambient fine particulate matter (PM2.5). In a test case analysis assuming a 1% (benefits analysis) or 100% (burden analysis), reduction in country-specific fine particulate matter concentrations, corresponding estimated global attributable deaths using the Fusion model were found to lie between those of the IER and LL models, with the GEMM estimates similar to those based on the LL model.

Greenness, air pollution, and mortality risk: A U.S. cohort study of cancer patients and survivors.

BACKGROUND: Several studies suggest that living in areas of high surrounding greenness may be associated with a lower cardiopulmonary mortality risk. However, associations of greenness with specific causes of death in cancer patients and survivors has not been examined and it is unknown whether this relationship is affected by area levels of fine particulate matter air pollution (PM2.5). This study evaluated associations between greenness and PM2.5 on causes of death in a large, U.S.-based cohort of cancer patients and survivors. METHODS: Surveillance, Epidemiology and End Results (SEER) data were used to generate a cohort of 5,529,005 cancer patients and survivors from 2000 to 2016. Census-tract Normalized Difference Vegetation Index (NDVI) during May-October from 2003 to 2016 was population-weighted to act as a county-level greenness measure. County-level PM2.5 exposure was estimated from annual concentrations averaged from 1999 to 2015. Cox Proportional Hazards models were used to estimate the association between greenness, PM2.5, and cause-specific mortality while controlling for age, sex, race, and other individual and county level variables. FINDINGS: An IQR increase in greenness was associated with a decrease in cancer mortality for cancer patients (Hazard ratio of 0.94, 95% CI: 0.93-0.95), but not for cardiopulmonary mortality (0.98, 95% CI: 0.96-1.00). Inversely, an increase in 10 µg/m3 PM2.5 was associated with increased cardiopulmonary mortality (1.24, 95% CI: 1.19-1.29), but not cancer mortality (0.99, 95% CI: 0.97-1.00). Hazard ratios were robust to inclusion of PM2.5 in models with greenness and vice versa. Although exposure estimates were constant over most stratifications, greenness seemed to benefit individuals diagnosed with high survivability cancers (0.92, 95% CI: 0.90-0.95) more than those with low survivability cancers (0.98. 95% CI: 0.96-0.99). INTERPRETATION: Higher levels of greenness are associated with lower cancer mortality in cancer patients. The evidence suggests minimal confounding between greenness and PM2.5 exposures and risk of mortality.

Shape of BMI-Mortality Risk Associations: Reverse Causality and Heterogeneity in a Representative Cohort of US Adults.

OBJECTIVE: This study examines BMI-mortality associations and evaluates strategies intended to limit reverse causality. Heterogeneity in BMI-mortality risk associations across subgroups and causes of death is explored. METHODS: A cohort of 654,382 adults from the US National Health Interview Survey was constructed. Associations between unit BMI levels and mortality were estimated using Cox proportional hazards models, including and excluding the first 5 years of follow-up, with and without controls for smoking or preexisting conditions, and including and excluding ever-smokers and individuals with preexisting conditions. Stratified analyses by individual characteristics were performed. RESULTS: Addressing reverse causality led to reduced risk of mortality among those with low BMI levels (<18 kg/m2 ). Excluding ever-smokers and individuals with preexisting conditions further led to increased risk among those with high BMI levels (between 33 kg/m2 and >40 kg/m2 ) and lowered the estimated nadir risk from 27 kg/m2 to 23 kg/m2 . After excluding ever-smokers and individuals with preexisting conditions, limiting the analysis to >5 years of follow-up produced no substantive changes. Heterogeneous results were observed across individual characteristics, particularly age and causes of death. CONCLUSIONS: The exclusion of smokers and individuals with preexisting conditions alters the BMI-mortality risk association and results in a somewhat lower range of BMI with minimum mortality risk.