Since the 1970s, enactment of increasingly stringent air quality controls has led to improvements in ambient air quality in the United States at costs that the U.S. Environmental Protection Agency (EPA) has estimated as high as $25 billion per year.1 However, even with the well-established link between long-term exposure to air pollution and adverse effects on health,2 the extent to which more recent regulatory actions have benefited public health remains in question.
Air pollutant concentrations have been generally decreasing in the United States, with substantial differences in reductions across metropolitan areas. Levels of fine particulate matter air pollution (particulate matter <2.5 µm in aerodynamic diameter, PM2.5) remain relatively high in some areas. In a 2010 study, the EPA estimated that 62 U.S. counties, accounting for 26% of their total study population, had PM2.5 concentrations not in compliance with the National Ambient Air Quality Standards (NAAQS).3
Reductions in particulate matter air pollution are associated with reductions in both cardiopulmonary and overall mortality.2 In the mid-1990s, the Harvard Six Cities Study4 and the American Cancer Society study5 reported associations of cardiopulmonary mortality risk with chronic exposure to fine particulate air pollution while controlling for smoking and other individual risk factors. Reanalysis and extended analyses of these studies have confirmed that fine particulate air pollution is an important independent environmental risk factor for cardiopulmonary disease and mortality.6–12 Additional cohort studies, population-based studies, and short-term time-series studies have also shown associations between reductions in air pollution and reductions in human mortality.13–21 More recently, studies have suggested an association between PM2.5 and life expectancy,22,23 a well-documented and important measure of overall public health.24–26
As our primary analysis, we estimate the association between changes in PM2.5 and in life expectancy in 545 U.S. counties during the period from 2000 to 2007. This period is of particular interest, as the EPA restarted wide collection of PM2.5 data in 1999–2000, after stopping the nationwide PM2.5 monitoring program during the mid-1980s and most of the 1990s. In secondary analyses, we extended to 2007 the data and statistical analysis originally reported by Pope et al23 for the period 1980–2000 and investigated whether the relation reported by Pope et al23 persists in the more recent years.
We constructed and analyzed three data sets to estimate the association between changes in life expectancy and changes in PM2.5 during the period from 2000 to 2007 in 545 counties (data set 1) and to investigate whether the association previously reported by Pope et al23 persists when the data on the same 211 counties are extended to the year 2007 (data sets 2 and 3).
Data set 1 included information on 545 U.S. counties for the years 2000 and 2007. These counties include all counties with available matching PM2.5 data for 2000 and 2007. Additionally, unlike previous studies in which counties were located only in metropolitan areas,23 data set 1 comprises counties in both metropolitan and nonmetropolitan areas. Figure 1 shows the counties in this data set shaded according to life expectancy in 2000 and 2007. Variables in this data set were available at the county level, for both 2000 and 2007, and included life expectancy, PM2.5, per capita income, population, proportions who were high-school graduates, and proportions who were white, black, or Hispanic. Because data on smoking prevalence were not available for all 545 counties, we used age-standardized death rates for lung cancer and chronic obstructive pulmonary disease (COPD) as proxy variables for smoking prevalence.27,28 Death rates were calculated in 5-year age groups and age-standardized for the 2000 U.S. population of adults age 45 years or older. Daily PM2.5 data were obtained from the EPA’s Air Quality System (AQS—http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm). Daily PM2.5 levels for each county were averaged across monitors within that county using a trimmed mean approach; those daily county-level means were further averaged across days to obtain a county-specific yearly PM2.5 average.29
County-level life expectancies were calculated by applying a mixed-effects spatial Poisson model to mortality data from the National Center for Health Statistics (NCHS) and population data from the U.S. Census to obtain robust estimates of the number of deaths in each county.30 These estimated counts were then used to calculate county life expectancies using standard life table techniques, which we discuss in more detail in the eAppendix (Section A, http://links.lww.com/EDE/A630).
Socioeconomic and demographic variables were obtained from the U.S. Census and the American Community Survey except per capita income, which was obtained from the Bureau of Economic Analysis. All yearly income variables were adjusted for inflation with 2000 as the base year. Age-standardized death rates for lung cancer and COPD were calculated using mortality data from NCHS using death rates for 2005 to serve as a proxy for 2007 (NCHS data for 2007 was not readily available). Last, data on smoking prevalence (proportion of the population who are current smokers) were available from the Behavioral Risk Factor Surveillance System in both 2000 and 2007 for 383 of the 545 counties.
Data set 2 included data for the year 1980 and the year 2000 for the same 211 U.S. counties included in the 51 metropolitan statistical areas (MSAs) previously analyzed by Pope and colleagues.23 This data set is identical to that mentioned in the study by Pope et al,23 where it is described in more detail.
Data set 3 extended data set 2 to 2007. All data were available at the county level except for PM2.5, which for the year 1980 was available only at the MSA level and for the year 2007 was available at the county level for only 113 of the 211 counties originally included in the study by Pope et al.23 Thus, for the year 2007, we assigned the same PM2.5 values to all the counties that shared an MSA, consistent with the previous analysis.23 Details and results pertaining to data sets 2 and 3 are summarized in the eAppendix (Section B1, http://links.lww.com/EDE/A630).
Cross-sectional and first-difference linear regression models were fitted to all three data sets. Specifically, we regressed life expectancy versus PM2.5 levels across counties separately for the years 1980 (data set 2), 2000 (data sets 1 and 2), and 2007 (data sets 1 and 3). We then regressed changes in life expectancy over the years 2000–2007 (data sets 1 and 3), 1980–2000 (data set 2), and 1980–2007 (data set 3) versus changes in PM2.5 over those same periods adjusted for changes in the socioeconomic, demographic, and proxy smoking variables outlined above. Additionally for our largest data set (data set 1: 545 counties, 2000–2007), we performed several stratified and weighted analyses. More specifically, we estimated the effect of changes in PM2.5 on life expectancy in models stratified by (1) percentage of the population with an urban residence in 2000; (2) population density in 2000; (3) land area in 2000; (4) PM2.5 levels in 2000; (5) 5-year in-migration in 2000; and (6) change in average yearly temperature over the entire period. These stratified analyses allowed us to examine whether PM2.5 effects on life expectancy were different in counties with particular demographic or weather characteristics. The sensitivity of our results to model specification was further assessed by fitting models weighted by (1) total population; (2) year 2000 population density; and (3) inverse land area. We included direct measures of the change in prevalence of smoking for the subgroup of counties with matching data on smoking prevalence (383 of 545), and fit separate models for men and women to determine whether effects differed by sex. To account for the correlation owing to clustering of counties in the same MSA, robust clustered standard errors were calculated for all models.23,31 Specifically, the variance of the vector of estimated regression coefficients, βest, is given by: Var(βest) = (XTX)−1 (XTVestX) (XTX)−1, where Vest is a block-diagonal matrix with nonzero blocks V0, j = (yj − µest, j)(yj − µest, j)T, where j indexes the MSAs. βest is equal to the ordinary least squares estimator. Models were estimated using either REGRESS in STATA version 11.0, lm() in R version 2.11.1, or PROC SURVEYREG in SAS, version 9.2.
We report the results of our primary analysis, which estimated the cross-sectional relation between life expectancy and PM2.5, and between changes in life expectancy and changes in PM2.5, for the period from 2000 to 2007 in 545 U.S. counties (data set 1). Results of the secondary analyses of the counties studied by Pope et al23 using data sets 2 and 3 are shown in the eAppendix (Section B; eTables 1a, b and 2a, b, http://links.lww.com/EDE/A630). Table 1 lists the summary statistics for the variables in data set 1. In 2000, 189 of the 545 counties had a PM2.5 level greater than the current 3-year NAAQS level of 15 µg/m3; by 2007, only 48 of those 189 were not in compliance with the NAAQS. On average, PM2.5 levels decreased at a rate of 0.22 µg/m3 per year, a rate 33% lower than that observed in the 211 counties analyzed for the period 1980–2000 (0.33 µg/m3 per year).23
Figure 2A, 2B shows life expectancies plotted against PM2.5 levels for the years 2000 and 2007. Consistent with Pope et al,23 cross-sectional regression models showed a negative association between life expectancy and PM2.5 in both years. Details are summarized in the eAppendix (Section C, http://links.lww.com/EDE/A630).
Figure 2C, 2D show changes in life expectancy plotted against changes in PM2.5 levels for 2000–2007. We also plotted the estimated regression lines under Models 1 and 3 of Table 2.
Table 2 summarizes estimated regression coefficients for the association between the changes in PM2.5 and changes in life expectancy for 545 counties for the years 2000–2007 for selected regression models. When controlling for changes in all available socioeconomic and demographic variables as well as smoking prevalence proxy variables (model 3), a 10 µg/m3 decrease in PM2.5 was associated with an estimated mean increase in life expectancy of 0.35 years (SE = 0.16 years, P = 0.033). The estimated effect of PM2.5 on life expectancy was consistent across models adjusting for various patterns of potentially confounding variables (eg, models 2–4). Models 5–9 in Table 2 show the results for select stratified and weighted regressions. In counties with a population density >200 people per square mile, a 10 µg/m3 decrease in PM2.5 was associated with an increased life expectancy of 0.72 (0.22 years, P < 0.01; model 6), when compared with −0.31 years (0.22 years, P = 0.165) in counties with <200 people per square mile (P difference < 0.01). In counties with proportion of urban residences >90%, a 10 µg/m3 decrease in PM2.5 was associated with an increased life expectancy of 0.95 (0.31, P < 0.01; model 7), when compared with −0.16 (0.16 years, P = 0.299) in counties with <90% urban residences (P difference < 0.01).
When we reestimated model 3 of Table 2 using the square root of population density as the weight (model 8), the estimated effect of a 10 µg/m3 reduction of PM2.5 on life expectancy was more than double that observed in our unweighted analysis (0.74 [0.24] vs. 0.35 [0.16]). When that same model was weighted by the inverse of county land area (model 9), the effect was nearly triple that of the unweighted analysis (0.96 [0.27]). Additional details regarding stratified and weighted analyses are provided in eTables 3 and 4 of the eAppendix (http://links.lww.com/EDE/A630).
We conducted similar analyses for the data sets of 211 counties for the period from 1980 to 2007 and from 2000 to 2007, the results of which are presented in eTable 2a and b of the eAppendix (http://links.lww.com/EDE/A630), respectively. Results for the period from 1980 to 2000 were identical to those reported by Pope et al.23
Figure 3 summarizes the point estimate and 95% confidence interval for the effect of a 10 µg/m3 decrease in PM2.5 on life expectancy for a select unweighted and unstratified regression model in each data set/time period. Models fitted using data sets 2 and 3 (left) controlled for changes in income, population, proportion of the population that is black, lung cancer death rate, and COPD death rate, corresponding to model 4 in eTable 2a and b (http://links.lww.com/EDE/A630). Models fitted using data set 1 controlled for all available variables and corresponded to model 3 in Table 2. These estimates were fairly consistent, though estimates corresponding to the counties from Pope et al23 for the period 2000–2007 appeared slightly larger than those from other analyses.
In the analyses stratified by sex, the estimated effect of a 10 µg/m3 reduction in PM2.5 for the covariate pattern corresponding to model 3 of Table 2 was an additional 0.59 (0.17) years of life expectancy for women and 0.08 (0.20) years for men (P difference = 0.027). Differences by sex were also observed in stratified and weighted models, although with less precision. Sex differences were smaller in the most urban counties (urban rate > 90%). Similar results were observed for the period from 1980 to 2000 in data set 2. (Sex-specific results are presented in eTable 5, http://links.lww.com/EDE/A630.)
Effect estimates were not highly sensitive to the inclusion of the estimated change in smoking prevalence. Table 3 summarizes the results for the inclusion/exclusion of the smoking prevalence variable across several models. For example, when model 3 in Table 2 was reestimated for the 383 counties with matching smoking prevalence data, a reduction of 10 µg/m3 was associated with an increase in life expectancy of 0.49 (0.19) years without including change in smoking prevalence in the model, and 0.47 (0.19) years when including those changes. Similar results for smoking were observed in our stratified and weighted models, as well as in our models for men and women separately.
Data on air pollution and life expectancy from 545 U.S. counties in 2000 and 2007 show that recent declines in PM2.5 to relatively low levels continue to prolong life expectancy in the United States. These benefits are largest among the most urban and densely populated counties. These associations were estimated controlling for socioeconomic and demographic variables as well proxy variables for and direct measures of smoking prevalence.
In previous studies, a 10 µg/m3 decrease in PM2.5 has been associated with gains from 0.42 to 1.51 years of life expectancy.22,23 Here, a decrease of 10 µg/m3 in PM2.5 was associated with an increase in life expectancy of 0.35 (0.16) years for 545 counties for the period from 2000 to 2007. An increase in life expectancy of 0.56 (0.19) years was estimated for the same 211 counties included in the analysis of Pope et al23 but extended to the period 1980–2007. The estimated effect in those 211 counties from 2000 to 2007 was equal to 1.00 (0.32). Stratified and weighted analyses within the 545 counties from 2000 to 2007 yielded larger estimates between 0.72 (0.22) and 1.12 (0.32) years—broadly in agreement with those previously reported.
From 2000 to 2007, the average increase in life expectancy across the counties in this study was 0.84 years, and the average decrease in PM2.5 in those same counties was 1.56 µg/m3. Although PM2.5 reductions presumably account for some of the improvements in life expectancy over this period, it is only one of many contributing factors. Other factors may include improvements in the prevention and control of the chronic diseases of adulthood, particularly cardiovascular diseases (CVD) and stroke,32,33 and changes in the risk factors associated with them, including medical advances, declines in smoking, and decreases in blood pressure and cholesterol.33 Considering the well-established link between air pollution and CVD mortality,5,7,8 and changes in other CVD risk factors, issues of multicausality and competing risk make it difficult to quantify exactly the changes in life expectancy attributable to reductions in PM2.5. However, if we consider one of our more conservative effect estimates (model 3, Table 2), the 1.56 µg/m3 reduction in PM2.5 accounts for about 0.055 years (1.56 × 0.035) of additional life expectancy or roughly 7% of the increase in life expectancy. Using the estimate from our most urban counties (model 7, Table 2), the increase in life expectancy attributable to the average reduction in PM2.5 was 0.148 years (1.56 × 0.095) or as much as 18% of the total increase.
An interesting aspect of this study was how pronounced the PM2.5 effect was for the original 211 counties from 2000 to 2007. Considering that they were originally selected simply on the availability of matching pollution data, what is special about these counties that results in larger estimates of the effect of PM2.5 on life expectancy? The stratified and weighted analyses suggest plausible explanations. For instance, the 211 counties were all in metropolitan areas, and the stratified analyses suggest that the effect of PM2.5 on life expectancy is greatest in the most urban counties. One possible reason is that the composition of PM2.5 is different in urban areas34 causing PM2.5 to have a larger health impact. Another possibility is the “nonmetropolitan mortality penalty”—the recent phenomenon in which mortality rates are higher in rural when compared with urban areas.35 Although it is not clear why the mortality gap between metro and nonmetro areas has widened, some hypotheses include greater improvements in standards of care in metro areas, changes in uninsurance rates, changes in disease incidence, and changes in health behaviors.35 These, however, would be valid explanations only if they occurred at different rates in metropolitan areas when compared with rural areas. If so, then perhaps failure to include variables that captured one or more of these differences could explain the different estimates of the effect of PM2.5 on life expectancy.
Alternatively, metropolitan areas are more densely populated than nonmetro areas. Our models that stratified by population density showed that the effect of PM2.5 on life expectancy is greatest in the most densely populated study areas (those with a population density of at least 200 people per square mile)—possibly suggesting a role for differential exposure misclassification. That is, in densely populated areas, it is more likely that any two people from the same area are exposed to the same level of PM2.5 with perhaps less exposure misclassification. This possibility was supported in our models weighted by the square root of population density and the inverse of land area, which placed more weight on the most densely populated counties and the smallest counties. In these models, the effect of a 10 µg/m3 decrease in PM2.5 on life expectancy was much larger than the equivalent unweighted analysis.
Another interesting finding was the difference in the effect of changes in PM2.5 on men and women. Findings in the literature regarding the effects of air pollution by sex for long-term exposure have been mixed. Studies using the American Cancer Society and Harvard Six Cities cohorts show no significant difference in pollution-related mortality between men and women.4–8,10 Studies using a Medicare cohort have reported different effects by age and region, but did not stratify by sex.28,36,37 In a study using the Adventist Health cohort, Chen et al38 reported a large effect of PM2.5 on fatal coronary heart disease in women but no association in men. Similarly, in separate studies, Lipsett et al,39 using a cohort of women (California Teachers’ Study), reported associations between particulate matter and cardiovascular mortality, while Puett et al,40 using a cohort of men (Male Health Professionals), found no association with all-cause mortality or fatal coronary heart disease. For our main analysis using all 545 counties, we find a larger effect of PM2.5 on women, suggesting that reductions in PM2.5 are more beneficial to gains in life expectancy for women. Models fitted using data for the period from 1980 to 2000 as mentioned in Pope et al23 showed similar results. Future studies should investigate more thoroughly the possibility of different PM2.5-mortality associations for men versus women.
One factor that appeared to play no role in the PM2.5 and life expectancy relation, however, was baseline PM2.5 level. This is in agreement with the findings by Pope et al,23 and implies that, although we may see differences across levels of population density, urban rate, and land area, this is not attributable to these areas having a higher or lower baseline PM2.5 level. Furthermore, this finding suggests that there is no clear threshold below which further reductions in PM2.5 levels provide no benefit (eAppendix, eTable 3, http://links.lww.com/EDE/A630). The fact that our results were not sensitive to the inclusion of direct measures of change in smoking prevalence suggests that the estimated gains in life expectancy for a 10 µg/m3 reduction in PM2.5 are not a result of confounding attributable to changes in smoking prevalence.
Unlike previous cross-sectional analyses,16,17 we were able to estimate the association between county-specific temporal changes in PM2.5 levels and county-specific temporal changes on life expectancy adjusted by temporal changes in several potential confounding factors. By looking at within-county temporal changes, we reduce the potential bias owing to unmeasured confounding. Furthermore, by estimating clustered robust standard errors at the MSA level, we took a conservative approach in accounting for potential spatial correlation between neighboring counties.
Our analysis has the strengths of using some of the largest available data sets, and applying relatively simple analyses. Additionally, we improved on the original analysis by constructing a data set with PM2.5 measured at the county level, in contrast to the more coarse MSA-level readings used in previous studies.7,23
The analysis is limited, however, in its ability to control for all potential unmeasured confounding. Additionally, in comparing selected years, we do not fully exploit potentially informative data between those years. Furthermore, sophisticated analyses of the U.S. Medicare population by Greven et al36 did not observe associations between “local” trends in PM2.5 levels and “local” trends in mortality in 814 zip code level locations in the United States for the period from 2000 to 2006. “Local” trends were defined as the difference between monitor-specific trends and national trends. The Medicare cohorts, however, consisted only of people age 65 years and older, whereas our life expectancy calculations integrate over all ages. Also, other studies using Medicare-based cohorts have found significant associations between PM2.5 and overall mortality.28,37 Future studies are needed to investigate whether these differences among studies are attributable to differences in statistical models, data sources, or populations studied.
It is also worth considering whether life expectancy was the most appropriate outcome to consider in our model. Because life expectancies are calculated from age-specific mortality rates, perhaps a model with age-specific mortality rates as the outcome would be more appropriate, allowing the age groups most affected by PM2.5 exposure to be pinpointed precisely.
In summary, our study reports strong evidence of an association between recent further reductions in fine particulate air pollution and improvements in life expectancy in the United States, especially in densely populated urban areas.
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