Environmental predictors of survival in a cohort of U.S. military veterans: A multi-level spatio-temporal analysis stratified by race.

We analyzed racial differences in all-cause mortality rates associated with air pollution in a cohort of military veterans in which 37% of the 70,000 members identified as African-American (black). In this comprehensive analysis, spatial levels comprised individuals, zip-codes, and counties. Temporal levels comprised the 26-y follow-up period (1976-2001) and 4 subperiods. Proportional hazard regression models were used, controlling for individual age, race (white, black), smoking (current, ever), education, height, body-mass index, and systolic and diastolic blood pressure; zipcode-average socioeconomic indicators; and county-average climate. County-level air quality measures included vehicular traffic density as a surrogate for all traffic-related pollutants including noise. The model accounted for nonlinear mortality relationships with age, body-mass index, blood pressure and zip-code racial composition. Relative to whites, more of the black veterans smoked, had slightly higher blood pressure, and lived in predominately black zip-codes that had more poverty than whites. The black veterans lived in counties that had slightly worse ambient air quality and substantially higher levels of vehicular traffic density. We analyzed all-cause mortality associations with county-level average ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide for 1975-81, and subsequent data on particulates by particle size. We also considered sulfate and elemental carbon particles, benzene, SO2, and NOx based on nationwide modeling for 2002. We had no information on indoor air quality or personal exposures; our risk estimates should thus be regarded as characterizing the counties of residence rather than individual exposures of inhabitants. In addition to age, the strongest predictors of veterans' survival were residence in high-poverty zip-codes, smoking, and diastolic blood pressure, to all of which black veterans were less sensitive than whites. Black veterans had significantly lower mortality risks from aging, smoking, and elevated diastolic blood pressure, but larger risks from excessive body-mass index. They were less at risk from living a high-poverty zip-code than whites. We assumed these risk factors to be stable during follow-up and thus applicable to chronic health effects. After controlling for them, the all-cause mortality risk for black veterans was 10% lower than whites. In an effort to reduce random scatter we computed mean risks associated with overlapping groups of similar pollutants. These means were statistically significant for both black and white veterans for traffic-related, gaseous, and NOx-O3 pollutants, for which the overall mean relative risk was 1.076 (1.057-1.090). Grouped mean risks for particulate pollutants, sulfur compounds, and non-traffic pollutants were not significant for either race. Black veterans carried more of the traffic-related risks than whites because of their greater exposures and risk coefficients. PM2.5 risk estimates were negative for black veterans (0.82 [0.75-0.89]) but positive for whites (1.05 [1.005-1.10]) which is consistent with regional differences in overall mortality. The temporal analyses compared mortality rates by follow-up subperiod for the pollutants measured at enrollment. We expected increasing (cumulative) risks for chronic effects and decreasing risks for delayed acute effects, but found no significant trend for either race. We concluded that the higher exposures and mortality risks associated with vehicular traffic posed environmental injustice for the black veterans.

Longitudinal relationships between lung cancer mortality rates, smoking, and ambient air quality: a comprehensive review and analysis.

Lung cancer mortality (LCM) has been associated with smoking and air pollution. This article draws on smoking relationships to clarify air pollution relationships. We analyzed cohort and population-based smoking studies and identified effects of latency and cumulative exposures. We found cogent relationships through longitudinal rather than cross-sectional analysis, thus involving historical data. We also considered passive smoking, occupational exposures, radon, and established carcinogens. We found stable nonsmoking LCM rates during periods of improving ambient air quality. We considered 59 cross-sectional studies of lung cancer and air pollution and found similar statistically significant relative risks for PM2.5, PM10, EC, NO2, SO2, SO2-4, and O3, and minimal risks for benzo(alpha)pyrene and trace metals. Most air pollution studies did not consider latency, exposure duration, or temporal trends; none included estimates of smoking risks implied by their models. We conclude that while LCM studies of smoking, radon, and occupational exposures appropriately considered latency and cumulative exposures; ambient air quality studies have not. Lung cancer has a long memory and exposure histories are required. Effects of cumulative exposures after pollution abatement include substantially reduced risk estimates, delayed health benefits, inability to show accountability for the abatement. However, associations of lung cancer with ambient air pollution cannot be ruled out, especially for historic periods when much higher exposures persisted. Given the major reductions in air quality and smoking habits that have been realized since the Clean Air Act, new studies of LCM and air pollution, including smoking histories and exposure data decades before diagnosis, are needed.HighlightsLifetime cigarette consumption is a good predictor of lung cancer risk.The latent period for lung cancer ranges from about 10 to 30 y.Lung cancer risks for nonsmokers have been stable during the period when ambient air quality improved substantially.Risks of established airborne carcinogens may have been important in the past but not under current conditions.Radon can be important.Air pollution-lung cancer risks should be evaluated jointly with smoking risks but are sensitive to their accuracy.When cumulative exposures to conventional air pollutants are considered rather than concurrent, the applicable risk estimates may decrease by an order of magnitude and abatement benefits will not be readily apparent.Most of the recent cross-sectional studies of lung cancer and regulated air pollutants did not consider latency, duration of exposure, or established carcinogens and their findings have been misinterpreted.Effects of smoking on LCM are now well established as are the spatial distributions of ambient air pollution; however, linkages among the three parameters remain uncertain.

Revisiting the Veterans Cohort Mortality Study: New results and synthesis.

The Veterans Cohort Mortality Study began in 1999 in collaboration with Washington University in St. Louis, comprising ~70,000 male military veterans. We published six research papers on this cohort, considering the dynamics of all-cause mortality as the subjects aged and environmental parameters changed. This paper summarizes those results and presents new results by age group. Pollutants included monitored and modeled criteria pollutants, vehicular traffic density (annual km driven per unit of county land area), and modeled nationwide levels of hazardous species. In addition to spatial relationships, we examined the effects of exposure timing through separate analyses of sequential follow-up and exposure periods from 1976 to 2001. Risks associated with peak ozone decreased with lag between exposure and response, suggesting acute effects. Risks associated with traffic were invariant over time and consistent across five exposure databases. Associations with ozone were also coherent across databases; we found no consistent associations with particulate matter. Epidemiology considers both spatial and temporal relationships; most long-term studies focus on spatial gradients at a given time, thus masking effects of cohort aging and other trends during follow-up. Our new analyses distinguished between these temporal effects by analyzing age deciles for which separate mortality risks had been estimated for nationwide levels of nitrogen oxides (NOx), benzene, and traffic density during four sequential follow-up subperiods, thus providing 40 sets of mortality risk coefficients. We used ordinary least squares regression to define relationships with subject age and follow-up year for the data set of 40 coefficients. We found strong nonlinear relationships between subject age and mortality coefficients for smoking, climate, poverty status, and air pollution; only smoking and climate coefficients changed over time as well. We concluded that these pollutant-mortality relationships reflected differences among the veterans' residential locations rather than changes in their pollution exposures during follow-up. We saw no evidence that cleaner air reduced mortality. Implications: Recent air pollution mortality studies emphasize PM2.5 (particulate matter with an aerodynamic diameter <2.5 µm); we show associations with many other pollutants and a measure of traffic intensity. Control policies should thus be based on multipollutant analyses. We found no reduced risks with improved air quality after distinguishing cohort aging from purely temporal effects; longitudinal studies of accountability must thus account for changes in demography and exposures. Our studies of exposure timing indicate mainly coincident responses and no evidence for cumulative effects typical of smoking; we had no information on personal exposures. We found the strongest risks were associated with high-traffic locations rather than outdoor air quality per se.

Long-term associations of morbidity with air pollution: A catalog and synthesis.

I searched the National Institutes of Health MEDLINE database through January 2017 for long-term studies of morbidity and air pollution and cataloged them with respect to cardiovascular, respiratory, cancer, diabetes, hospitalization, neurological, and pregnancy-birth endpoints. The catalog is presented as an online appendix. Associations with PM2.5 (particulate matter with an aerodynamic diameter <2.5 µm), PM10 (PM with an aerodynamic diameter <10 µm), and nitrogen dioxide (NO2) were evaluated most frequently among the 417 ambient air quality studies identified. Associations with total suspended particles (TSP), carbon, ozone, sulfur, vehicular traffic, radon, and indoor air quality were also reported. I evaluated each study in terms of pollutant significance (yes, no), duration of exposure, and publication date. I found statistically significant pollutant relationships (P < 0.05) in 224 studies; 220 studies indicated adverse effects. Among 795 individual pollutant effect estimates, 396 are statistically significant. Pollutant associations with cardiovascular indicators, lung function, respiratory symptoms, and low birth weight are more likely to be significant than with disease incidence, heart attacks, diabetes, or neurological endpoints. Elemental carbon (EC), traffic, and PM2.5 are most likely to be significant for cardiovascular outcomes; TSP, EC, and ozone (O3) for respiratory outcomes; NO2 for neurological outcomes; and PM10 for birth/pregnancy outcomes. Durations of exposure range from 60 days to 35 yr, but I found no consistent relationships with the likelihood of statistical significance. Respiratory studies began ca. 1975; studies of diabetes, cardiovascular, and neurological effects increased after about 2005. I found 72 studies of occupational air pollution exposures; 40 reported statistically significant adverse health effects, especially for respiratory conditions. I conclude that the aggregate of these studies supports the existence of nonlethal physiological effects of various pollutants, more so for non-life-threatening endpoints and for noncriteria pollutants (TSP, EC, PM2.5 metals). However, most studies were cross-sectional analyses over limited time spans with no consideration of lag or disease latency. Further longitudinal studies are thus needed to investigate the progress of disease incidence in association with air pollution exposure. IMPLICATIONS: Relationships of air pollution with excess mortality are better known than with long-term antecedent morbidity. I cataloged 489 studies of cardiovascular, respiratory, cancer, and neurological effects, diabetes, and birth outcomes with respect to 12 air pollutants. About half of the studies reported statistically significant relationships, more frequently with noncriteria than with criteria pollutants. Indoor and cumulative exposures, coarse or ultrafine particles, and organic carbon were seldom considered. Significant relationships were more likely with less-severe endpoints such as blood pressure, lung function, or respiratory symptoms than with incidence of cancer, chronic obstructive pulmonary disease (COPD), heart failure, or diabetes. Most long-term studies are based on spatial relationships; longitudinal studies are needed to link the progression of pollution-related morbidity to mortality, especially for the cardiovascular system.

A critical review of the ESCAPE project for estimating long-term health effects of air pollution.

The European Study of Cohorts for Air Pollution Effects (ESCAPE) is a13-nation study of long-term health effects of air pollution based on subjects pooled from up to 22 cohorts that were intended for other purposes. Twenty-five papers have been published on associations of various health endpoints with long-term exposures to NOx, NO2, traffic indicators, PM10, PM2.5 and PM constituents including absorbance (elemental carbon). Seven additional ESCAPE papers found moderate correlations (R2=0.3-0.8) between measured air quality and estimates based on land-use regression that were used; personal exposures were not considered. I found no project summaries or comparisons across papers; here I conflate the 25 ESCAPE findings in the context of other recent European epidemiology studies. Because one ESCAPE cohort contributed about half of the subjects, I consider it and the other 18 cohorts separately to compare their contributions to the combined risk estimates. I emphasize PM2.5 and confirm the published hazard ratio of 1.14 (1.04-1.26) per 10µg/m3 for all-cause mortality. The ESCAPE papers found 16 statistically significant (p<0.05) risks among the125 pollutant-endpoint combinations; 4 each for PM2.5 and PM10, 1 for PM absorbance, 5 for NO2, and 2 for traffic. No PM constituent was consistently significant. No significant associations were reported for cardiovascular mortality; low birthrate was significant for all pollutants except PM absorbance. Based on associations with PM2.5, I find large differences between all-cause death estimates and the sum of specific-cause death estimates. Scatterplots of PM2.5 mortality risks by cause show no consistency across the 18 cohorts, ostensibly because of the relatively few subjects. Overall, I find the ESCAPE project inconclusive and I question whether the efforts required to estimate exposures for small cohorts were worthwhile. I suggest that detailed studies of the large cohort using historical exposures and additional cardiovascular risk factors might be productive.

Inferring frail life expectancies in Chicago from daily fluctuations in elderly mortality.

Susceptible sub-populations with existing disease have exhibited stronger relationships between air quality and mortality in time-series studies, but their associated life expectancies have largely been overlooked. Murray and Nelson developed a new time-series model that estimated a small unobserved (frail) sub-population and their resulting life expectancies in Philadelphia, including environment relationships. As a further example in a different geographic area, we used this model with 1987-2000 daily mortality data in Chicago and found a stable frail population at risk of ∼900 persons with a mean life expectancy of ∼11 days; fewer than two daily deaths were associated with air pollution. We considered daily concentrations of CO, NO2, O3, PM10 and SO2, and found PM10 and O3 to have stronger associations with frail mortality. Our estimates of life expectancy and air pollution and temperature relationships are similar to those found in other studies that used different methods. Temperature was more important than air pollution during the 1995 heat wave, when mortality risks increased dramatically after 2 d exposure and life expectancies decreased to 3-5 d. Modeling this event separately had substantial effects on lagged mortality--air pollution relationships and the population at risk. The premises of the Murray-Nelson model were supported by simultaneously considering an additional subgroup of non-frail individuals; they contributed only ∼1% of total elderly deaths. We conclude that frail life expectancies estimated by the Murray-Nelson model are robust, and that under these conditions non-frail persons have little risk of acute mortality, with or without contributions from air pollution.

A new time-series methodology for estimating relationships between elderly frailty, remaining life expectancy, and ambient air quality.

BACKGROUND: Many publications estimate short-term air pollution-mortality risks, but few estimate the associated changes in life-expectancies. OBJECTIVE AND METHODS: We present a new methodology for analyzing time series of health effects, in which prior frailty is assumed to precede short-term elderly nontraumatic mortality. The model is based on a subpopulation of frail individuals whose entries and exits (deaths) are functions of daily and lagged environmental conditions: ambient temperature/season, airborne particles, and ozone. This frail susceptible population is unknown; its fluctuations cannot be observed but are estimated using maximum-likelihood methods with the Kalman filter. We used an existing 14-y set of daily data to illustrate the model and then tested the assumption of prior frailty with a new generalized model that estimates the portion of the daily death count allocated to nonfrail individuals. RESULTS: In this demonstration dataset, new entries into the high-risk pool are associated with lower ambient temperatures and higher concentrations of particulate matter and ozone. Accounting for these effects on antecedent frailty reduces this at-risk population, yielding frail life expectancies of 5-7 days. Associations between environmental factors and entries to the at-risk pool are about twice as strong as for mortality. Nonfrail elderly deaths are seen to make only small contributions. CONCLUSIONS: This new model predicts a small short-lived frail population-at-risk that is stable over a wide range of environmental conditions. The predicted effects of pollution on new entries and deaths are robust and consistent with conventional morbidity/mortality times-series studies. We recommend model verification using other suitable datasets.

Revisiting a population-dynamic model of air pollution and daily mortality of the elderly in Philadelphia.

Epidemiological studies find that elderly, susceptible, and previously impaired individuals are more sensitive to transient air pollution exposures than healthy persons. However, any associated changes in life expectancy remain largely unresolved. Murray and Nelson published a model of daily mortality and air pollution that addresses mortality displacement or harvesting by directly considering population dynamics on the basis of the assumption that a period of illness or frailty precedes most elderly deaths. The underlying concept is that a person's response to an environmental exposure also depends on his/her physiological ability to withstand stress at that time. They used Kalman filtering to estimate an unobservable quantity--the size of the frail subpopulation from which elderly (ages > or = 65 yr) nontraumatic deaths are assumed to derive. They found a small subpopulation, relatively robust to environmental variations over 14 yr, with remaining life expectancies of 8-31 days in this frail status. Here, this model and dataset are expanded to examine the ramifications in more detail (including seasonality), to consider peak ozone as an additional pollutant, and to consider remaining life expectancies of the this frail subpopulation on a daily basis. Previous studies of mortality displacement and of Philadelphia mortality-air-pollution associations are also summarized in general, and agreement with the Murray-Nelson model was found, thus supporting its validity. The estimated additional mortality associated with a given environmental exposure persists for a few days at most but is not always compensated by subsequent mortality deficits. It is concluded that the pollution-associated mortality increases of a few percent in this dataset are consistent with losses of remaining life expectancy of up to a few days. It is also recommended that a more complex population-dynamic model be implemented to examine the extent to which previous short-term environmental exposures and seasonal trends may also influence morbidity and thus entry into the frail at-risk subpopulation.

Air pollution and survival within the Washington University-EPRI veterans cohort: risks based on modeled estimates of ambient levels of hazardous and criteria air pollutants.

For this paper, we considered relationships between mortality, vehicular traffic density, and ambient levels of 12 hazardous air pollutants, elemental carbon (EC), oxides of nitrogen (NOx), sulfur dioxide (SO2), and sulfate (SO4(2-)). These pollutant species were selected as markers for specific types of emission sources, including vehicular traffic, coal combustion, smelters, and metal-working industries. Pollutant exposures were estimated using emissions inventories and atmospheric dispersion models. We analyzed associations between county ambient levels of these pollutants and survival patterns among approximately 70,000 U.S. male veterans by mortality period (1976-2001 and subsets), type of exposure model, and traffic density level. We found significant associations between all-cause mortality and traffic-related air quality indicators and with traffic density per se, with stronger associations for benzene, formaldehyde, diesel particulate, NOx, and EC. The maximum effect on mortality for all cohort subjects during the 26-yr follow-up period is approximately 10%, but most of the pollution-related deaths in this cohort occurred in the higher-traffic counties, where excess risks approach 20%. However, mortality associations with diesel particulates are similar in high- and low-traffic counties. Sensitivity analyses show risks decreasing slightly over time and minor differences between linear and logarithmic exposure models. Two-pollutant models show stronger risks associated with specific traffic-related pollutants than with traffic density per se, although traffic density retains statistical significance in most cases. We conclude that tailpipe emissions of both gases and particles are among the most significant and robust predictors of mortality in this cohort and that most of those associations have weakened over time. However, we have not evaluated possible contributions from road dust or traffic noise. Stratification by traffic density level suggests the presence of response thresholds, especially for gaseous pollutants. Because of their wider distributions of estimated exposures, risk estimates based on emissions and atmospheric dispersion models tend to be more precise than those based on local ambient measurements.

On exposure and response relationships for health effects associated with exposure to vehicular traffic.

This work examines various metrics and models that have been used to estimate long-term health effects of exposure to vehicular traffic. Such health impacts may include effects of air pollution due to emissions of combustion products and from vehicle or roadway wear, of noise, stress, or from socioeconomic effects associated with preferred residential locations. Both categorical and continuous exposure metrics are considered, typically for distances between residences and roadways, or for traffic density or intensity. It appears that continuous measures of exposure tend to yield lower risk estimates that are also more precise than categorical measures based on arbitrary criteria. The selection of appropriate exposure increments to characterize relative risks is also important in comparing pollutants and other agents. Confounding and surrogate variables are also important issues, since studies of traffic proximity or density cannot identify the specific agents related to traffic exposures that might be responsible for the various health endpoints that have been implicated. Studies based on ambient air quality measurements are necessarily restricted to species for which data are available, some of which may be serving as markers for the actual agents of harm. Studies based on modeled air quality are limited by the accuracy of mobile source emission inventories, which may not include poorly maintained (high emitting) vehicles. Additional exposure modeling errors may result from precision limitations of geocoding methods. Studies of the health effects of traffic are progressing from establishing the existence of relationships to describing them in more detail, but effective remedies or control strategies have generally not yet been proposed in the context of these epidemiological studies. Resolution of these dose-response uncertainties is important for the development of effective public health strategies for the future.

Air pollution, blood pressure, and their long-term associations with mortality.

This article addresses the importance of blood pressure as a covariate in studies of long-term associations between air quality and mortality. We focus on a cohort of about 50,000 U.S. veterans who had been diagnosed as hypertensive at some time and whose survival rates were predicted by blood pressure (BP) and ambient air quality, among other factors. The relationship between BP and air quality is considered by reviewing the literature, by deleting variables from the proportional hazards regression model, and by stratifying the cohort by diastolic blood pressure (DBP) level. The literature review shows BP to be an important predictor of survival and finds small transient associations between air quality and BP that may be either positive or negative. The regression model sensitivity runs showed that the associations with air pollution are robust to the deletion of the BP variables, for the entire cohort. For stratified regressions, the confidence intervals for the air pollution-mortality associations overlap for the two DBP groups. We conclude that associations between mortality and air quality are not mediated through blood pressure, nor vice versa.