- Open Access
Reanalysis of the association between reduction in long-term PM2.5 concentrations and improved life expectancy
Environmental Health volume 20, Article number: 102 (2021)
Much of the current evidence of associations between long-term PM2.5 and health outcomes relies on national or regional analyses using exposures derived directly from regulatory monitoring data. These findings could be affected by limited spatial coverage of monitoring data, particularly for time periods before spatially extensive monitoring began in the late 1990s. For instance, Pope et al. (2009) showed that between 1980 and 2000 a 10 μg/m3 reduction in PM2.5 was associated with an average 0.61 year (standard error (SE) = 0.20) longer life expectancy. That analysis used 1979–1983 averages of PM2.5 across 51 U.S. Metropolitan Statistical Areas (MSAs) computed from about 130 monitoring sites. Our reanalysis re-examines this association using modeled PM2.5 in order to assess population- or spatially-representative exposure. We hypothesized that modeled PM2.5 with finer spatial resolution provides more accurate health effect estimates compared to limited monitoring data.
We used the same data for life expectancy and confounders, as well as the same analysis models, and investigated the same 211 continental U.S. counties, as Pope et al. (2009). For modeled PM2.5, we relied on a previously-developed point prediction model based on regulatory monitoring data for 1999–2015 and back-extrapolation to 1979. Using this model, we predicted annual average concentrations at centroids of all 72,271 census tracts and 12,501 25-km national grid cells covering the contiguous U.S., to represent population and space, respectively. We averaged these predictions to the county for the two time periods (1979–1983 and 1999–2000), whereas the original analysis used MSA averages given limited monitoring data. Finally, we estimated regression coefficients for PM2.5 reduction on life expectancy improvement over the two periods, adjusting for area-level confounders.
A 10 μg/m3 decrease in modeled PM2.5 based on census tract and national grid predictions was associated with 0.69 (standard error (SE) = 0.31) and 0.81 (0.29) -year increases in life expectancy. These estimates are higher than the estimate of Pope et al. (2009); they also have larger SEs likely because of smaller variability in exposure predictions, a standard property of regression. Two sets of effect estimates, however, had overlapping confidence intervals.
Our approach for estimating population- and spatially-representative PM2.5 concentrations based on census tract and national grid predictions, respectively, provided generally consistent findings to the original findings using limited monitoring data. This finding lends additional support to the evidence that reduced fine particulate matter contributes to extended life expectancy.
Evidence of the association between long-term exposure to particles less than or equal to 2.5 µm in diameter (PM2.5) and adverse health outcomes, first reported in early 1990s [1, 2], has continued to grow [3,4,5,6,7,8,9]. Many of these epidemiological studies were conducted in the U.S., Canada, and Europe, and relied on regulatory monitoring data; however, nationwide and population-focused regulatory monitoring for PM2.5 did not begin until 1999. Lack of available monitoring data affects the time period available to investigate the health effects of PM2.5. The influence could be particularly large for studies evaluating the impact of changing PM2.5 on health over several decades.
The limited historical monitoring data for PM2.5 is more challenging when research considers concentration variability in both space and time. As an example of an effort that was restricted to regions with available monitoring data over a long time period, Pope et al. (2009) investigated the association between the change in PM2.5 from 1980s through 2000s and the change in life expectancy. They concluded that improved life expectancy was attributed to decreased PM2.5 concentrations . This 20-year change in PM2.5 was characterized directly from regulatory monitoring data. For the 2000s, the data came from approximately 1,000 sites in more than 900 major metropolitan areas over the continental U.S. in the Federal Reference Methods (FRM) network . However, for the 1980s they relied on measurements from the Inhalable Particulate Network (IPN) at only about 130 sites which were located in 51 major metropolitan areas [12, 13]. Given these limited monitoring data, PM2.5 exposure was aggregated to the Metropolitan Statistical Area (MSA) and the analysis was restricted to 211 out of more than 3,000 counties. While many cohort studies have improved spatial coverage by estimating exposure at people’s residences from air pollution prediction models, they have been unable to develop predictions in the 1980’s. For instance, a recent study expanded Pope et al.’s analysis to all U.S. counties by using predicted PM2.5 concentrations; however, because of the unavailability of spatially extensive monitoring data before 1999, they focused on the change during 1999–2015 .
A historical PM2.5 prediction model such as the one we previously developed can help overcome this space–time limitation. Our pointwise spatio-temporal prediction model allows estimation of annual average concentrations of PM2.5 at arbitrary point locations in the continental U.S. for 1980–2010; this temporal range includes the years when extensive spatial monitoring data are unavailable . In external validation with PM2.5 data measured before 1999, from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network and the Southern California Children’s Health Study, the model generally performed well, with R2 values over 0.7. These modeled PM2.5 exposures over the 30-year period can provide representative exposure estimates, and thus advance our understanding of the effectiveness of PM2.5 reduction for human health.
Using population-representative exposure estimates from the historical PM2.5 prediction model, we aimed to re-examine the association between PM2.5 reduction and life expectancy increase for 1980–2000. Our hypothesis is that modeled PM2.5 in 1980s at finer spatial resolution provides more accurate estimates of the association with changes in life expectancy compared to limited PM2.5 measurements. Specifically, we replaced PM2.5 MSA averages (which were derived from limited monitoring data) in Pope et al. (2009) with modeled population- and spatially-representative county-average exposure while maintaining the same data for life expectancy and covariates, as well as the health analysis models used in the original analysis. Then, we compared the estimates of association with life expectancy between the new and the original findings.
Historical prediction model for PM2.5
The historical prediction model uses the same PM2.5 prediction model framework as in the Multiethnic Study of Atherosclerosis and Air Pollution (MESA Air) [16,17,18,19]. These MESA Air exposure model predicted 2-week average concentrations at any location in six U.S. metropolitan cities, using monitoring data from two PM2.5 regulatory monitoring networks, the FRM and IMPROVE, and a cohort-focused monitoring campaign. In contrast, the historical prediction model relied only on regulatory monitoring data for 1999–2010 and predicted annual average concentrations in the continental U.S. between 1980 and 2010 including the period before spatially extensive monitoring for PM2.5 began. For this paper, we extended the extrapolation to 1979. Details in the input data and modeling procedure were described elsewhere . In brief, this model consists of three components to characterize temporal and spatial patterns of annual average concentrations of PM2.5: a spatially-varying long-term mean, a spatially-varying temporal trend, and temporally-independent and spatially-dependent spatio-temporal residuals. We estimated the single temporal trend, which approximates linearity, using the data for 1999–2010. Then, to estimate the temporal trend before 1999 we explored various data sources using emissions, meteorology, visibility, and PM2.5 sulfate: our findings suggested that the back-extrapolated trend was also linear. Based on these extensive exploratory analyses, we extrapolated the linear trend to the period prior to 1999. The temporal trend was scaled by a spatially-varying trend coefficient to reflect spatial heterogeneity of the temporal trend, as identified in monitor levels. We characterized the spatially varying long-term mean and trend in a universal kriging framework with dimension-reduced summary predictors. These summary predictors were estimated from more than 800 geographic variables using partial least squares (PLS). These geographic variables computed based on distances and multiple buffers characterize potential pollution sources such as traffic, land use, population, emissions, and vegetation. PLS finds the linear combination of geographic variables which is most correlated with PM2.5 annual averages .
County-average PM2.5 estimation
Using the pointwise historical prediction model, we estimated PM2.5 annual average concentrations in 1979–1983 and 1999–2000 at 1) 72,271 census tract centroids based on the year 2010 census and 2) 12,501 national grid coordinates that cover the contiguous lower 48 states. We obtained census tract boundaries from the National Historical Geographic Information System (www.nhgis.org), and calculated the centroid, as the geometric center, for each of the census tracts using ArcGIS 10.2 Geographic Information System software (Additional file 1: Figure S1). The 12,501 grid coordinates were obtained from a 25-km grid in the continental U.S. Pope et al. (2009) used the PM2.5 concentrations averaged to 51 MSAs for the health analysis of life expectancy estimates in 211 counties. Pope and coauthors computed average PM2.5 concentrations for 1979–1983 with available data measured at about 130 IPN sites , and at more than 500 FRM sites for 1999–2000  (“measured” PM2.5). Using our predicted PM2.5 concentrations at 72,271 census tract centroids and 12,501 national grid coordinates, we averaged PM2.5 to the same 211 counties to be directly comparable with the spatial scale of Pope et al. (2009) life expectancy estimates for our primary analysis (“modeled” PM2.5). As census tract centroids represent populated locations while national grid coordinates represent spatially evenly distributed locations, we treated the county-level averages of those predictions as population- and spatially-representative concentrations, respectively. Then, we also computed the averages across the 51 MSAs to be directly comparable with the spatial scale of their exposure estimates for a sensitivity analysis. We averaged predictions across 4 to 2,343 (median = 56) and 6 to 2,343 (372) census tract centroids to obtain 211 county and 51 MSA averages, respectively.
Life expectancy reanalysis
To compare against results from Pope et al. (2009), we examined the association between PM2.5 reduction and life expectancy increase for 1980–2000 using the same data for life expectancy and covariates in 211 U.S. counties (Additional file 1: Figure S2), and the same seven health analysis models. The only difference from the original analysis was the replacement of MSA-average PM2.5 exposure based on IPN and FRM measurements with our county-average historical predictions. The life expectancy estimates were for two five-year periods (1978–1982 and 1997–2001) and were estimated using mortality statistics from the National Center for Health Statistics and population from the U.S. Census. The detailed procedure for the computation of annual-average life expectancy and area-level characteristics has been described elsewhere .
Using multiple linear regression with the new exposure predictions, we assessed the association between PM2.5 and life expectancy in each time period separately, and for differences between the two periods. We estimated robust standard errors by clustering on MSA. The seven confounder models included progressively expanded sets of county-level socio-demographic variables, proxy variables for smoking prevalence, and smaller subsets of counties as we describe here. Whereas Model 1 only included PM2.5, Models 2 and 3 additionally adjusted for progressively expanded sets of confounders. While Model 2 consisted of average income, total population, and proportions of 5-yr in-migration, high-school graduates, urban residence, black population, and Hispanic population, Model 3 also added two surrogate variables for smoking: mortality rates for lung cancer and chronic obstructive pulmonary disease (COPD). Model 4, as the primary model in our analysis as well as in Pope et al. 2009, included income, population, proportion of black population, and mortality rates for lung cancer and COPD. In addition, confounders used in Models 3 and 4 were applied to the restricted study area with high population. Out of the 211 counties included in the primary analysis, we applied Model 4 to 127 counties with population greater than 100 thousand in 1986 (Model 5) and Models 3 and 4 to the 51 counties that had the largest population in each MSA (Models 6 and 7).
We performed two sensitivity analyses to test the robustness of our results to address the role of spatially-refined exposure and exposure averaging period, respectively. First, we used MSA-average estimates of our census tract or national grid historical predictions instead of county-average estimates. Second, we investigated different exposure time periods, using one or multiple years of PM2.5 averages in the two five-year time periods (1979–1983 and 1999–2004) and compared these to our main findings that were based on the same averaging periods as Pope et al. 2009’s (1979–1983 and 1999–2000). This analysis examined whether our findings were affected by the number of years included in the exposure averages or the selection of specific years in each period.
Measured and modeled PM2.5
Modeled county- and MSA-average PM2.5 estimated by the historical prediction model tended to be lower than measured MSA-averages derived by PM2.5 regulatory monitoring data in both periods. The average concentration of census tract predictions was 17.85 μg/m3 in 1979–1983 and 12.90 μg/m3 in 1999–2000, compared to 20.62 and 14.10 μg/m3 for the measurements; national grid predictions were slightly lower than census tract predictions, with the average concentration of 16.29 and 11.95 μg/m3 in 1979–1983 and 1999–2000, respectively (Table 1). While variability was also lower in modeled PM2.5 than measured PM2.5 in 1979–1983, the more recent 1999–2000 period gave similar variability. Despite the consistent means between MSA and county averages in predictions, variability was slightly larger for county averages than MSA averages. For census tract and national grid predictions, national grid predictions, many of which were estimated at non-residential locations, were more variable than census tract predictions.
Across 51 MSAs, measured versus modeled PM2.5 tended to be more dissimilar as concentration increased. Figure 1 shows MSA-average concentrations of modeled PM2.5 compared to measured PM2.5 in each of the two periods and their differences between the periods. Correlation between MSA averages of modeled and measured PM2.5 was lower in the early period for 1979–1983 (Pearson correlation coefficient = 0.76–0.83) than in the recent period for 1999–2000 (0.93–0.94) (Fig. 1). The difference between the two periods gave poor correlation (0.42–0.44), although both PM2.5 showed a decreasing trend between the two time periods. When county-average predictions were compared to MSA measurements, the pattern of lower correlation in the early period than in the recent period was consistent, with lower correlation coefficients (Pearson correlation coefficient = 0.68–0.70 and 0.88–0.89 for 1979–1983 and 1999–2000, respectively) than those for MSA-average predictions (Additional file 1: Figure S3-S4). Prediction maps of county-average PM2.5 in 1980, 1990, 2000, and 2010 based on census tract predictions also showed the overall decreasing trend in most counties for 1980–2010 (Additional file 1: Figure S5). The concentrations were higher in the eastern region and California’s Central Valley, and the reduction over time was also large in these areas.
The census tract and national grid PM2.5 predictions were highly correlated in both time periods (Pearson correlation coefficient = 0.89–0.96 and 0.86–94 for MSA and county averages, respectively) (Fig. 1, Additional file 1: Figure S3). Compared to measured PM2.5, national grid predictions showed consistently lower concentrations in the full range of concentrations, as opposed to census tract predictions which were lower only at the higher concentrations, as many national grid are non-residential locations.
Life expectancy reanalysis
In unadjusted and adjusted models, the association patterns with measured vs. modeled PM2.5 were consistent. In the cross-sectional analyses, PM2.5 concentrations from monitoring data and historical predictions were negatively associated with life expectancy in each time period, whereas in the change on change analyses, the reduction in PM2.5 was positively associated with the increase in life expectancy (Table 2, Fig. 2, Additional file 1: Table S1). In the unadjusted model, the regression coefficients for modeled PM2.5 were much higher (1.39 and 1.04 for census tract and national grid predictions, respectively) than measured PM2.5 (0.72). Across the four models based on 211 counties, regression coefficients were consistent for measured PM2.5 but dramatically dropped for modeled PM2.5 particularly in Model 3 when lung cancer/COPD mortality was added. In the primary model (Model 4), with county-specific confounders included, a 10 μg/m3 reduction in PM2.5 based on census tract predictions was associated with 0.69 year increase in life expectancy (standard error (SE) = 0.31) (Table 2). This estimated improvement in life expectancy was slightly higher and had a larger SE than the estimate reported by Pope et al. (2009) (0.61 years [SE = 0.20]). National grid prediction gave a higher estimate of 0.81 than census tract prediction, with slightly smaller SE (0.29). In the analyses restricted to the 51 counties with the largest population (Models 6 and 7), estimates corresponding to measured PM2.5 consistently increased, whereas estimates for modeled PM2.5 both decreased and increased. Uncertainty of these estimates increased for modeled PM2.5 due to smaller exposure variability, as the smaller variance of predictor variables results in larger standard errors of regression coefficient estimates.
In the sensitivity analysis that replaced county averages of PM2.5 with MSA averages in order to compare the same exposure averaging scale as Pope et al. (2009), the patterns were generally consistent, but the significance of the association for census tract predictions in the primary model disappeared. Regression coefficients of both census tract and national grid predictions remained higher with larger SEs than those of measured PM2.5 (Additional file 1: Table S2, Additional file 1: Figure S6). The magnitude of estimates increased compared to those of county averages, but their SEs also increased possibly because of reduced variability in MSA-average PM2.5, resulting in statistically non-significant effect estimates. Using different years of exposure for averaging, life expectancy estimates were generally larger when PM2.5 averages included recent years of predictions between 1999 and 2003 (Additional file 1: Figure S7). However, the 95% confidence intervals were wide and overlapped with the original estimates, indicating that they are not statistically different .
This study re-analyzed the change in life expectancy associated with the reduction in PM2.5 in the continental U.S. from 1980 to 2010 using modeled exposure estimates. We compared these to the original findings, which were affected by the limited availability of regulatory monitoring data, particularly in 1980s. We hypothesized that predicted exposures, derived from a historical prediction model that was able to extend the spatial and temporal coverage of PM2.5 estimates, can provide more accurate health effect estimates and in turn better inform whether decades of improved air quality improved life expectancy. Our estimated improvement in life expectancy attributed to PM2.5 reduction was largely consistent with the original analysis estimate.
There have been further studies that attempted to confirm the initial findings in Pope et al. (2009). These studies focused on the assessment of fine-spatial scale exposure at the county level rather than the MSA level and the expansion of counties from 211 to the more than 3,000 counties in the contiguous U.S. [14, 23]. However, their evaluation was limited to the current century and did not incorporate data back in the 1980s and 1990s when major regulatory actions were established and a dramatic change in PM2.5 occurred. Using PM2.5 regulatory monitoring data in 545 counties for 2000–2007, Correia et al. (2011) found 0.35 year increase in life expectancy for 10 μg/m3 reduction in PM2.5; these estimates were similar in Bannett et al. (2019) that used predicted PM2.5 in 3,082 counties for 1999–2015. For the recent study periods with a slower decline in PM2.5 and lower concentrations overall, their estimates of life expectancy improvement were lower compared to ours of 0.67 years. Relying on our validated historical modeling approach, we were able to estimate the change during this critical time period.
The primary life expectancy estimate in our reanalysis using modeled PM2.5 was slightly higher and had larger uncertainty than the estimate in the original analysis, although estimates and confidence intervals from both analyses are consistent with increased PM2.5 being associated with lower life expectancy. The larger uncertainty can be attributed to reduced variability of our population-representative PM2.5 predictions. Reasons for their reduced variability include averaging over 70,000 census tract centroids and the degree of smoothing that is typical of prediction models. Smoothing in prediction may play a significant role particularly in the 1980s when spatially extensive monitoring data did not exist. The larger variability of measured PM2.5 could be due to some monitoring sites located at extremely polluted and/or unpolluted locations as well as those IPN sites that operated in only a few seasons. Our previous study showed that 102 IPN sites for 1979–1982 decreased to 16 for 1980–1981, when the site inclusion criteria for computing representative annual average concentrations of PM2.5 were applied . Changes over time in locations of monitoring sites and different sampling schedules among monitors might also have affected the variability in the originally reported PM2.5 difference. We note that exposure measurement error driven by these spatial and/or temporal misalignments could lead to biases in either direction, as well as incorrect standard errors of health effect estimates [24,25,26]. In addition, when we replaced our county-average predictions with MSA-average predictions in our sensitivity analysis to match to the exposure averaging scale of the original analysis, standard errors of estimates increased and the primary model estimate became statistically non-significant. As opposed to limited monitoring data in 1980s, our prediction model allowed us to estimate county averages, resulting in good alignment of the exposure and the outcome and increased exposure variability.
By expanding the temporal and spatial scales of PM2.5 to cover the entire continental U.S. back to 1980, our study provides a mechanism for future high-quality policy-relevant analyses of PM2.5 health impacts. Mortality and morbidity data are often available much earlier than the establishment of the extensive spatial monitoring of PM2.5. Our PM2.5 estimates linked to administrative health data allow the assessment of the health benefits achieved from the reduction of PM2.5 over time to be evaluated . In addition, areas with no nearby regulatory monitoring sites have been shown to have different demographic characteristics than areas represented by monitors . It may be inadequate to rely on simpler area averages computed directly from regulatory monitoring sites to capture the differences in susceptibility depending on the sociodemographic subgroup. For example, only 567 (18%) of the 3,109 counties in the continental U.S. in 2000 had at least one regulatory monitor with sufficient daily measurements to provide representative annual averages (Additional file 1: Figure S8). The median number of monitoring sites per MSA based on the 2000 Census was 1 (interquartile-range: 1 – 3), with median area and population per monitoring site of 2,232 km2 and 198,306 people.
The higher and less uncertain estimates of the PM2.5 effect on life expectancy when PM2.5 was obtained from national grid-based predictions, in comparison to census tract-based predictions, could be due to exposure measurement error because grid locations do not adequately represent locations where people live. National grid coordinates possibly include many non-residential locations with low concentrations in addition to residential locations, whereas most census tract centroids are likely to fall in residential areas and better represent residential exposure. On average, the grid-based estimates underestimated area-average PM2.5 concentrations compared to the census tract-based estimates.
One limitation of this study is that we restricted the life expectancy analysis study period to 1980–2000 to facilitate comparison with the results in the original analysis. Future analyses could leverage updated life expectancy data to investigate other time periods including more recent time periods and ascertain whether the results are consistent. Furthermore, the two estimates of PM2.5 reduction from monitored vs. modeled PM2.5 on the improvement in life expectancy are likely to have different measurement error impacts. Future studies also should quantify these differences.
Using modeled PM2.5 concentrations expanded spatially and temporally to overcome limited coverage of monitoring data, we replicated Pope et al.’s life expectancy analysis, work that has been influential in environmental policy and regulatory accountability. Our analysis confirmed the previous findings of the contribution of improved air quality to improved life expectancy.
Availability of data and materials
The datasets analyzed in this study are available from the corresponding author SY Kim (email@example.com) after coauthors’ approval of the request.
Chronic obstructive pulmonary disease
Federal Reference Methods
Inhalable Particulate Network
Interagency Monitoring of Protected Visual Environments
Metropolitan Statistical Area
- MESA Air:
Multi-ethnic Study of Atherosclerosis and Air Pollution
Partial least squares
- PM2.5 :
Particulate matter with a diameter equal to or less than 2.5 μm
Dockery DW, Pope CA 3rd, Xu X, Spengler JD, Ware JH, Fay ME, Ferris BG, Speizer FA. An association between air pollution and mortality in Six U.S. Cities N Engl J Med. 1993;329:1753–9.
Pope CA 3rd, Thun MJ, Namboodiri MM, Dockery DW, Evans JS, Speizer FE, Heath JCW. Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults. Am J Respir Crit Care Med. 1995;151:669–74.
Beelen R, Raaschou-Nielsen O, Stafoggia M, Andersen ZJ, Weinmayr G, Hoffmann B, et al. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project. Lancet. 2014;383(9919):785–95.
Crouse DL, Peters PA, van Donkelaar A, Goldberg MS, Villeneuve PJ, Brion O, et al. Risk of nonaccidental and cardiovascular mortality in relation to long-term exposure to low concentrations of fine particulate matter: a Canadian national-level cohort study. Environ Health Perspect. 2012;120(5):708–14.
Di Q, Wang Y, Zanobetti A, Wang Y, Koutrakis P, Choirat C, Dominici F, Schwartz JD. Air pollution and mortality in the medicare population. N Engl J Med. 2017;376(26):2513–22.
Kaufman JD, Adar SD, Barr RG, Budoff M, Burke GL, Curl CL, et al. Association between air pollution and coronary artery calcification within six metropolitan areas in the USA (the Multi-Ethnic Study of Atherosclerosis and Air Pollution): a longitudinal cohort study. Lancet. 2016;388(10045):696–704.
Krewski D, Burnett RT, Goldberg MS, Hoover K, Siemiatycki J, Jerrett M, et al. Reanalysis of the Harvard six cities study and the American Cancer Society Study of particulate air pollution and mortality: special report. Cambridge: Health Effects Institute; 2000.
Laden F, Schwartz J, Speizer FE, Dockery DW. Reduction in fine particulate air pollution and mortality extended follow-up of the Harvard six cities study. Am J Respir Crit Care Med. 2006;173:667–72.
Pope CA 3rd, Burnett RT, Thurston GD, Thun MJ, Calle EE, Krewski D, et al. Cardiovascular mortality and long-term exposure to particulate air pollution: epidemiological evidence of general pathophysiological pathways of disease. Circulation. 2004;109:71–7.
3rd Pope CA, Ezzati M, Dockery DW. Fine-particulate air pollution and life expectancy in the United States. N Engl J Med. 2009;360(4):376–86.
U.S. EPA. Inhalable particulate network operations and quality assurance manual. Research Triangle Park: U.S. Environmental Protection Agency; 1980.
U.S. EPA. Inhalable particulate network report: operation and data summary (Report No. EPA 600/S4–84–088). Research Triangle Park: U.S. Environmental Protection Agency; 1985.
U.S. EPA. Air quality criteria for particulate matter (Report No. EPA 600/P-99/002aF-bF): Volume 1. Washington, DC: U.S. Environmental Protection Agency; 2004.
Bennett JE, Tamura-Wicks H, Parks RM, Burnett RT, 3rd Pope CA, Bechle MJ, Marshall JD, Danaei G, Ezzati M. Particulate matter air pollution and national and county life expectancy loss in the USA: a spatiotemporal analysis. PLoS Med. 2019;16(7):e1002856.
Kim SY, Olives C, Sheppard L, Sampson PD, Larson TV, Keller JP, et al. Historical prediction modeling approach for estimating long-term concentrations of PM2.5 in cohort studies before the 1999 implementation of widespread monitoring. Environ Health Perspect. 2017;125:38–46.
Keller JP, Olives C, Kim SY, Sheppard L, Sampson PD, Szpiro AA, et al. A unified spatiotemporal modeling approach for prediction of multiple air pollutants in the multi-ethnic study of atherosclerosis and air pollution. Environ Health Perspect. 2015;123(4):301–9.
Lindstrom J, Szpiro AA, Sampson PD, Oron AP, Richards M, Larson TV, Sheppard L. A flexible spatio-temporal model for air pollution with spatial and spatio-temporal covariates. Environ Ecol Stat. 2014;21:411–33.
Sampson PD, Szpiro AA, Sheppard L, Lindström J, Kaufman JD. Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data. Atmos Environ. 2011;45:6593–606.
Szpiro AA, Sampson PD, Sheppard L, Lumley T, Adar S, Kaufman JD. Predicting intra-urban variation in air pollution concentrations with complex spatio-temporal dependencies. Environmetrics. 2010;21:606–31.
Sampson PD, Richards M, Szpiro AA, Bergen S, Sheppard L, Larson TV, et al. A regionalized national universal kriging model using partial least squares regression for estimating annual PM2.5 concentrations in epidemiology. Atmos Environ. 2013;75:383–92.
Ezzati M, Friedman AB, Kulkarni SC, Murray CJ. The reversal of fortunes: trends in county mortality and cross-county mortality disparities in the United States. PLoS Med. 2008;5(4):e66.
Schenker N, Gentleman JF. On judging the significance of differences by examining the overlap between confidence intervals. Am Stat. 2001;55:182–6.
Correia AW, 3rd Pope CA, Dockery DW, Wang Y, Ezzati M, Dominici F. Effect of air pollution control on life expectancy in the United States: an analysis of 545 U.S. counties for the period from 2000 to 2007. Epidemiol. 2013;24(1):23–31.
Szpiro AA, Sheppard L, Lumley T. Efficient measurement error correction with spatially misaligned data. Biostatistics. 2011;12(4):610–23.
Szipro AA, Paciorek CJ, Sheppard L. Does more accurate exposure prediction necessarily improve health effect estimates? Epidemiology. 2011;22(5):680–5.
Szipro AA, Paciorek CJ. Measurement error in two-stage analyses, with application to air pollution epidemiology. Environmetrics. 2013;24:501–17.
Fann N, Kim SY, Olives C, Sheppard L. Estimated changes in life expectancy and adult mortality resulting from declining PM2.5 exposures in the contiguous United States: 1980–2010. Environ Health Perspect. 2017;125(9):097003.
Bravo MA, Fuentes M, Zhang Y, Burr MJ, Bell ML. Comparison of exposure estimation methods for air pollutants: ambient monitoring data and regional air quality simulation. Environ Res. 2012;116:1–10.
Although this publication was developed under research assistance agreements awarded by the U.S. Environmental Protection Agency (EPA), it has not been formally reviewed by the EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of EPA; the EPA does not endorse any products or commercial services mentioned in this publication.
This work was supported by the U.S. Environmental Protection Agency [RD83479601, RD83587301, RD831697]; the National Institute of Environmental Health Sciences [NIA R01ES026187]; the National Research Foundation of Korea [2018R1A2B6004608]; the National Cancer Center of South Korea [NCC-2110570]; and the Korean Foundation for Cancer Research [2020-A-2].
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
. Estimates of life expectancy associated with a 10 μg/m3 increase in long-term average PM2.5 concentrations by the two time periods (1979-1983 and 1999-2000), adjusted for socioeconomic, demographic, and proxy indicators for prevalence of smoking across 211 U.S. counties, using PM2.5 concentrations based on Metropolitan Statistical Area (MSA) averages of Inhalable Particulate Network (IPN) or Federal Reference Method (FRM) monitoring data vs. county averages of predictions at census tract centroids and national grid coordinates estimated by the historical prediction model. Table S2. Estimates of the increase in life expectancy associated with a reduction in PM2.5 of 10 μg/m3 over approximately 20 years between 1979-1983 and 1999-2000, adjusted for socioeconomic, demographic, and proxy indicators for prevalence of smoking in 211 U.S. counties, using Metropolitan Statistical Area (MSA) average PM2.5 concentrations based on measurements from regulatory monitoring networks (Inhalable Particulate Network (IPN) or Federal Reference Method (FRM)) vs. predictions at census tract centroids and national grid coordinates estimated by the historical prediction model. Figure S1. Maps of 3,109 counties in the continental U.S., and 25-km grid coordinates, regulatory monitoring sites, and census tract centroids in Los Angeles county in 2010. Figure S2. Map of the 211 U.S. counties included in the life expectancy analysis in Pope et al. 2009. Figure S3. Scatter plots of PM2.5 concentrations using Metropolitan Statistical Area (MSA) averages based on measurements from regulatory monitoring networks (Inhalable Particulate Network (IPN) or Federal Reference Method (FRM)) vs. county averages based on predictions at census tract centroids (CT) and national grid coordinates (NG) estimated by the historical prediction model by 1980s (1979-1983), 2000s (1999-2000), and 1980s-2000s (1979-1983 minus 1999-2000) (green and red lines for the identity and best-fitted lines, respectively). Figure S4. Scatter plots of county-level annual averages of PM2.5 (μg/m3) predictions at census tract centroids estimated by the historical exposure prediction model against Metropolitan Statistical Area (MSA) averages of PM2.5 measurements from regulatory monitoring networks (Inhalable Particulate Network (IPN) or Federal Reference Method (FRM)) by 1980s (1979-1983), 2000s (1999-2000), and 1980s-2000s (1979-1983 minus 1999-2000) (same plots to those in the first column of Figure S3 but with colored dots indicating the rank of the concentration in 1980s). Figure S5. Maps of county-level annual-average PM2.5 concentrations (μg/m3) in 1980, 1990, 2000, and 2010 based on census tract centroid predictions estimated by the historical prediction model. Figure S6. Scatter plots of PM2.5 (μg/m3) and life expectancy for 1980-2000 between 1979-1983 and 1999-2000 across 211 U.S. counties using Metropolitan Statistical Area (MSA) average PM2.5 concentrations based on measurements from regulatory monitoring networks (Inhalable Particulate Network (IPN) or Federal Reference Method (FRM)) vs. predictions at census tract centroids (CT) and national grid coordinates (NG) estimated by the historical prediction model by 1990s, 2000s, and 1980s-2000s. Figure S7. Effect estimates and 95% confidence intervals of the life expectancy increase for a PM2.5 reduction of 10 μg/m3 between 1980s and 2000s across 211 U.S. counties, adjusted for socioeconomic, demographic, and proxy indicators for prevalence of smoking in four health analysis models, by different years of PM2.5 predictions at census tract centroids estimated by the historical prediction model (black closed circles indicating effect estimates of PM2.5 reduction based on IPN and FRM measurements between 1979-1983 and 1999-2000 in Pope et al. (2009); black open circles based on predictions for the same averaging periods as Pope et al. (2009) in our re-analysis; all the other symbols and colors based on predictions using different single start and end years). Figure S8. Map of 567 counties where there is at least one regulatory monitoring site after applying the minimum inclusion criteria for computing annual averages in 2000.
About this article
Cite this article
Kim, SY., Pope, A.C., Marshall, J.D. et al. Reanalysis of the association between reduction in long-term PM2.5 concentrations and improved life expectancy. Environ Health 20, 102 (2021). https://doi.org/10.1186/s12940-021-00785-0
- Fine particulate matter
- Life expectancy
- Long-term exposure
- Measured exposure
- Modeled exposure