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Coleman NC, Burnett RT, Higbee JD, Lefler JS, Merrill RM, Ezzati M, Marshall JD, Kim SY, Bechle M, Robinson AL, Pope CA. Cancer mortality risk, fine particulate air pollution, and smoking in a large, representative cohort of US adults. Cancer Causes Control 2020; 31:767-776. [PMID: 32462559 DOI: 10.1007/s10552-020-01317-w] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 05/18/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE Air pollution and smoking are associated with various types of mortality, including cancer. The current study utilizes a publicly accessible, nationally representative cohort to explore relationships between fine particulate matter (PM2.5) exposure, smoking, and cancer mortality. METHODS National Health Interview Survey and mortality follow-up data were combined to create a study population of 635,539 individuals surveyed from 1987 to 2014. A sub-cohort of 341,665 never-smokers from the full cohort was also created. Individuals were assigned modeled PM2.5 exposure based on average exposure from 1999 to 2015 at residential census tract. Cox Proportional Hazard models were utilized to estimate hazard ratios for cancer-specific mortality controlling for age, sex, race, smoking status, body mass, income, education, marital status, rural versus urban, region, and survey year. RESULTS The risk of all cancer mortality was adversely associated with PM2.5 (per 10 µg/m3 increase) in the full cohort (hazard ratio [HR] 1.15, 95% confidence interval [CI] 1.08-1.22) and the never-smokers' cohort (HR 1.19, 95% CI 1.06-1.33). PM2.5-morality associations were observed specifically for lung, stomach, colorectal, liver, breast, cervix, and bladder, as well as Hodgkin lymphoma, non-Hodgkin lymphoma, and leukemia. The PM2.5-morality association with lung cancer in never-smokers was statistically significant adjusting for multiple comparisons. Cigarette smoking was statistically associated with mortality for many cancer types. CONCLUSIONS Exposure to PM2.5 air pollution contributes to lung cancer mortality and may be a risk factor for other cancer types. Cigarette smoking has a larger impact on cancer mortality than PM2.5 , but is associated with similar cancer types.
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Affiliation(s)
- Nathan C Coleman
- Department of Economics, Brigham Young University, 142 FOB, Provo, UT, 84602, USA
| | | | - Joshua D Higbee
- Department of Economics, University of Chicago, Chicago, IL, USA
| | - Jacob S Lefler
- Department of Agricultural and Resource Economics, University of California, Berkeley, CA, USA
| | - Ray M Merrill
- Department of Public Health, Brigham Young University, Provo, UT, USA
| | - Majid Ezzati
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College, London, London, UK
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Sun-Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Korea
| | - Matthew Bechle
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Allen L Robinson
- Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA
| | - C Arden Pope
- Department of Economics, Brigham Young University, 142 FOB, Provo, UT, 84602, USA.
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Higbee JD, Lefler JS, Burnett RT, Ezzati M, Marshall JD, Kim SY, Bechle M, Robinson AL, Pope CA. Estimating long-term pollution exposure effects through inverse probability weighting methods with Cox proportional hazards models. Environ Epidemiol 2020; 4:e085. [PMID: 32656485 PMCID: PMC7319228 DOI: 10.1097/ee9.0000000000000085] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 01/19/2020] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Fine particulate matter (PM2.5) is associated with negative health outcomes in both the short and long term. However, the cohort studies that have produced many of the estimates of long-term exposure associations may fail to account for selection bias in pollution exposure as well as covariate imbalance in the study population; therefore, causal modeling techniques may be beneficial. METHODS Twenty-nine years of data from the National Health Interview Survey (NHIS) was compiled and linked to modeled annual average outdoor PM2.5 concentration and restricted-use mortality data. A series of Cox proportional hazards models, adjusted using inverse probability weights, yielded causal risk estimates of long-term exposure to ambient PM2.5 on all-cause and cardiopulmonary mortality. RESULTS Covariate-adjusted estimated relative risks per 10 μg/m3 increase in PM2.5 exposure were estimated to be 1.117 (1.083, 1.152) for all-cause mortality and 1.232 (1.174, 1.292) for cardiopulmonary mortality. Inverse probability weighted Cox models provide relatively consistent and robust estimates similar to those in the unweighted baseline multivariate Cox model, though they have marginally lower point estimates and higher standard errors. CONCLUSIONS These results provide evidence that long-term exposure to PM2.5 contributes to increased mortality risk in US adults and that the estimated effects are generally robust to modeling choices. The size and robustness of estimated associations highlight the importance of clean air as a matter of public health. Estimated confounding due to measured covariates appears minimal in the NHIS cohort, and various distributional assumptions have little bearing on the magnitude or standard errors of estimated causal associations.
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Affiliation(s)
- Joshua D. Higbee
- Department of Economics, University of Chicago, Chicago, Illinois
| | - Jacob S. Lefler
- Department of Agricultural and Resource Economics, University of California – Berkeley, Berkeley, California
| | | | - Majid Ezzati
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington
| | - Sun-Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Korea
| | - Matthew Bechle
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington
| | - Allen L. Robinson
- Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - C. Arden Pope
- Department of Economics, Brigham Young University, Provo, Utah
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Kim SY, Bechle M, Hankey S, Sheppard L, Szpiro AA, Marshall JD. Concentrations of criteria pollutants in the contiguous U.S., 1979 - 2015: Role of prediction model parsimony in integrated empirical geographic regression. PLoS One 2020; 15:e0228535. [PMID: 32069301 PMCID: PMC7028280 DOI: 10.1371/journal.pone.0228535] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Accepted: 01/17/2020] [Indexed: 12/20/2022] Open
Abstract
National-scale empirical models for air pollution can include hundreds of geographic variables. The impact of model parsimony (i.e., how model performance differs for a large versus small number of covariates) has not been systematically explored. We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979–2015; (2) explore systematically the impact on model performance of the number of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-average concentrations from regulatory monitoring data for PM10, PM2.5, NO2, SO2, CO, and ozone at all monitoring sites for 1979–2015. We also use ~350 geographic characteristics at each location including measures of traffic, land use, land cover, and satellite-based estimates of air pollution. We then develop IEG models, employing universal kriging and summary factors estimated by partial least squares (PLS) of geographic variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited number of variables selected from the full set by forward selection, and (3) all variables. We evaluate model performance using 10-fold cross-validation (CV) using conventional and spatially-clustered test data. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R2 conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). Concentration estimates for all Census Blocks reveal generally decreasing concentrations over several decades with local heterogeneity. Our findings suggest that national prediction models can be built by empirically selecting only a small number of important variables to provide robust concentration estimates. Model estimates are freely available online.
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Affiliation(s)
- Sun-Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Korea
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, United States of America
- * E-mail:
| | - Matthew Bechle
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States of America
| | - Steve Hankey
- School of Public and International Affairs, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States of America
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, United States of America
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Adam A. Szpiro
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States of America
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Lefler JS, Higbee JD, Burnett RT, Ezzati M, Coleman NC, Mann DD, Marshall JD, Bechle M, Wang Y, Robinson AL, Arden Pope C. Air pollution and mortality in a large, representative U.S. cohort: multiple-pollutant analyses, and spatial and temporal decompositions. Environ Health 2019; 18:101. [PMID: 31752939 PMCID: PMC6873509 DOI: 10.1186/s12940-019-0544-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 11/07/2019] [Indexed: 05/15/2023]
Abstract
BACKGROUND Cohort studies have documented associations between fine particulate matter air pollution (PM2.5) and mortality risk. However, there remains uncertainty regarding the contribution of co-pollutants and the stability of pollution-mortality associations in models that include multiple air pollutants. Furthermore, it is unclear whether the PM2.5-mortality relationship varies spatially, when exposures are decomposed according to scale of spatial variability, or temporally, when effect estimates are allowed to change between years. METHODS A cohort of 635,539 individuals was compiled using public National Health Interview Survey (NHIS) data from 1987 to 2014 and linked with mortality follow-up through 2015. Modelled air pollution exposure estimates for PM2.5, other criteria air pollutants, and spatial decompositions (< 1 km, 1-10 km, 10-100 km, > 100 km) of PM2.5 were assigned at the census-tract level. The NHIS samples were also divided into yearly cohorts for temporally-decomposed analyses. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) in regression models that included up to six criteria pollutants; four spatial decompositions of PM2.5; and two- and five-year lagged mean PM2.5 exposures in the temporally-decomposed cohorts. Meta-analytic fixed-effect estimates were calculated using results from temporally-decomposed analyses and compared with time-independent results using 17- and 28-year exposure windows. RESULTS In multiple-pollutant analyses, PM2.5 demonstrated the most robust pollutant-mortality association. Coarse fraction particulate matter (PM2.5-10) and sulfur dioxide (SO2) were also associated with excess mortality risk. The PM2.5-mortality association was observed across all four spatial scales of PM2.5, with higher but less precisely estimated HRs observed for local (< 1 km) and neighborhood (1-10 km) variations. In temporally-decomposed analyses, the PM2.5-mortality HRs were stable across yearly cohorts. The meta-analytic HR using two-year lagged PM2.5 equaled 1.10 (95% CI 1.07, 1.13) per 10 μg/m3. Comparable results were observed in time-independent analyses using a 17-year (HR 1.13, CI 1.09, 1.16) or 28-year (HR 1.09, CI 1.07, 1.12) exposure window. CONCLUSIONS Long-term exposures to PM2.5, PM2.5-10, and SO2 were associated with increased risk of all-cause and cardiopulmonary mortality. Each spatial decomposition of PM2.5 was associated with mortality risk, and PM2.5-mortality associations were consistent over time.
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Affiliation(s)
- Jacob S. Lefler
- Department of Agricultural and Resource Economics, University of California, Berkeley, CA 94720 USA
| | | | | | - Majid Ezzati
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | | | - Dalton D. Mann
- Department of Economics, Brigham Young University, Provo, UT USA
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA USA
| | - Matthew Bechle
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA USA
| | - Yuzhou Wang
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA USA
| | - Allen L. Robinson
- Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA USA
| | - C. Arden Pope
- Department of Economics, Brigham Young University, Provo, UT USA
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Pope CA, Lefler JS, Ezzati M, Higbee JD, Marshall JD, Kim SY, Bechle M, Gilliat KS, Vernon SE, Robinson AL, Burnett RT. Erratum: "Mortality Risk and Fine Particulate Air Pollution in a Large, Representative Cohort of U.S. Adults". Environ Health Perspect 2019; 127:99002. [PMID: 31559854 PMCID: PMC6791534 DOI: 10.1289/ehp6182] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 09/17/2019] [Indexed: 05/24/2023]
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Pope CA, Lefler JS, Ezzati M, Higbee JD, Marshall JD, Kim SY, Bechle M, Gilliat KS, Vernon SE, Robinson AL, Burnett RT. Mortality Risk and Fine Particulate Air Pollution in a Large, Representative Cohort of U.S. Adults. Environ Health Perspect 2019; 127:77007. [PMID: 31339350 PMCID: PMC6792459 DOI: 10.1289/ehp4438] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 05/09/2019] [Accepted: 05/22/2019] [Indexed: 05/17/2023]
Abstract
BACKGROUND Evidence indicates that air pollution contributes to cardiopulmonary mortality. There is ongoing debate regarding the size and shape of the pollution–mortality exposure–response relationship. There are also growing appeals for estimates of pollution–mortality relationships that use public data and are based on large, representative study cohorts. OBJECTIVES Our goal was to evaluate fine particulate matter air pollution ([Formula: see text]) and mortality using a large cohort that is representative of the U.S. population and is based on public data. Additional objectives included exploring model sensitivity, evaluating relative effects across selected subgroups, and assessing the shape of the [Formula: see text]–mortality relationship. METHODS National Health Interview Surveys (1986–2014), with mortality linkage through 2015, were used to create a cohort of 1,599,329 U.S. adults and a subcohort with information on smoking and body mass index (BMI) of 635,539 adults. Data were linked with modeled ambient [Formula: see text] at the census-tract level. Cox proportional hazards models were used to estimate [Formula: see text]–mortality hazard ratios for all-cause and specific causes of death while controlling for individual risk factors and regional and urban versus rural differences. Sensitivity and subgroup analyses were conducted and the shape of the [Formula: see text]–mortality relationship was explored. RESULTS Estimated mortality hazard ratios, per [Formula: see text] long-term exposure to [Formula: see text], were 1.12 (95% CI: 1.08, 1.15) for all-cause mortality, 1.23 (95% CI: 1.17, 1.29) for cardiopulmonary mortality, and 1.12 (95% CI: 1.00, 1.26) for lung cancer mortality. In general, [Formula: see text]–mortality associations were consistently positive for all-cause and cardiopulmonary mortality across key modeling choices and across subgroups of sex, age, race-ethnicity, income, education levels, and geographic regions. DISCUSSION This large, nationwide, representative cohort of U.S. adults provides robust evidence that long-term [Formula: see text] exposure contributes to cardiopulmonary mortality risk. The ubiquitous and involuntary nature of exposures and the broadly observed effects across subpopulations underscore the public health importance of breathing clean air. https://doi.org/10.1289/EHP4438.
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Affiliation(s)
- C. Arden Pope
- Department of Economics, Brigham Young University, Provo, Utah, USA
| | - Jacob S. Lefler
- Department of Economics, Brigham Young University, Provo, Utah, USA
| | - Majid Ezzati
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Joshua D. Higbee
- Department of Economics, Brigham Young University, Provo, Utah, USA
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA
| | - Sun-Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Korea
| | - Matthew Bechle
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA
| | - Kurtis S. Gilliat
- Center for the Economics of Human Development, University of Chicago, Chicago, Illinois, USA
| | | | - Allen L. Robinson
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
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