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Air Quality, Atmosphere & Health

, Volume 11, Issue 3, pp 245–252 | Cite as

Mortality risk and PM2.5 air pollution in the USA: an analysis of a national prospective cohort

  • C. Arden PopeIII
  • Majid Ezzati
  • John B. Cannon
  • Ryan T. Allen
  • Michael Jerrett
  • Richard T. Burnett
Article
  • 293 Downloads

Abstract

Epidemiologic evidence indicates that exposure to fine particulate matter air pollution (PM2.5) contributes to global burden of disease, primarily because of increased risk of cardiovascular morbidity and mortality. This study evaluates associations between long-term PM2.5 exposure and mortality risk in national, representative cohorts of the US adult population, constructed from public-use National Health Interview Survey (NHIS) data. Two cohorts consisting of 392,807 and 162,373 individuals (without and with individual smoking data) were compiled from public-use NHIS survey data (1986–2001) with mortality linkage through 2011. Cohorts included persons who lived in a metropolitan statistical area (MSA) were 18–84 years of age and had individual risk factor information. Modeled PM2.5 exposures were assigned as MSA-level mean ambient concentration for 1999 through 2008. Mortality hazard ratios (HRs) were estimated using Cox proportional hazard regression models, controlling for age, race, sex, income, marital status, education, body mass index, and smoking status. Estimated HRs for all-cause and cardiovascular mortality, associated with a 10-μg/m3 exposure increment of PM2.5 were 1.06 (1.01–1.11) and 1.34 (1.21–1.48), respectively, in models that controlled for various individual risk factors, including smoking. This study provides evidence that elevated risks of mortality, especially cardiovascular disease mortality, are associated with long-term exposure to PM2.5 air pollution in US nationwide adult cohorts constructed from public-use NHIS data.

Keywords

Air pollution PM2.5 Mortality Cohort study Cardiovascular mortality 

Introduction

There is growing evidence that long-term exposure to ambient fine particulate matter air pollution (suspended particles ≤ 2.5 μm in aerodynamic diameter, PM2.5), even at concentrations common to US urban areas, contributes to increased risk of cardiovascular disease (Adar et al. 2013; Brook et al. 2010; Franklin et al. 2015; Kaufman et al. 2016; Krishnan et al. 2012) and mortality (Hoek et al. 2013; Kioumourtzoglou et al. 2016; Di et al. 2017). Recent assessments of health risk factors that contribute to global burden of disease estimate that exposure to ambient air pollution is the fourth largest contributor to premature mortality worldwide—largely because of the estimated contribution of PM2.5 on cardiovascular disease (GBD 2015 Risk factors collaborators 2016). Given the estimated impact of PM2.5 on human health, there is need for additional evidence from nationally representative cohorts.

Survival analyses of the effects of long-term PM2.5 air pollution exposure have provided primary evidence regarding the impacts of long-term exposure. These studies have been conducted using multiple cohorts from the USA (Dockery et al. 1993; Jerrett et al. 2017; Kioumourtzoglou et al. 2016; Lepeule et al. 2012; Miller et al. 2007; Pope et al. 2002; Puett et al. 2009, 2011; Thurston et al. 2016; Zeger et al. 2008; Di et al. 2017), Europe (Beelen et al. 2014; Carey et al. 2013; Cesaroni et al. 2013), and Canada (Crouse et al. 2012, 2015). A 2013 meta-analysis of studies worldwide indicates that a 10-μg/m3 incremental increase in long-term average ambient PM2.5 concentrations is associated with approximately a 6% increased risk of all-cause mortality and a 15% increased risk of cardiovascular disease mortality (Hoek et al. 2013). Recently a constructed cohort of over 60 million US Medicare beneficiaries from the years 2000–2012 was analyzed (Di et al. 2017). A 10-μg/m3 increase in long-term average ambient PM2.5 concentrations was associated with a 7.3% increase in risk of all-cause mortality. There is heterogeneity in effect estimates across cohorts, and the studies do not generally use cohorts based on representative sampling. Although two key studies have been rigorously and independently reproduced (Dockery et al. 1993; Pope et al. 2002; Krewski et al. 2003), concerns remain regarding independent access to data and lack of opportunities for full reanalysis (Jaffe 2015; Rosenberg et al. 2015).

The objective of the current study is to evaluate the associations between long-term exposure to PM2.5 air pollution and risk of mortality in a cohort that includes a reasonably representative sample of the US adult population living in metropolitan areas. Furthermore, the study uses cohort data that are accessible for independent reanalysis and extended analysis. In this study, public-use National Health Interview Survey data and mortality data are linked to metro-level estimates of air pollution. Relative hazards associated with exposure to PM2.5 air pollution after controlling for age, sex, race, income, marital status, education, body mass, and smoking are estimated and reported for all-cause mortality, cardiovascular mortality, and mortality from other causes.

Methods

Study population data

This analysis used data from a sample of adults who participated in the US National Health Interview Survey (NHIS). The NHIS uses a stratified, multi-stage sample design that provides nationally representative sampling of the civilian, noninstitutionalized population of the USA as documented elsewhere (NHIS 2016a). Public-use NHIS survey data for the years 1986 through 2001 (NHIS 2016b) was linked with the NHIS public-use mortality linkage (NHIS 2016c) that provided mortality follow-up through 2011 (see Fig. 1). The analysis was limited to adults who lived in a metropolitan statistical area (MSA), allowing for metro-area assignment of pollution exposure. Two cohorts were created from the NHIS data (see Table 1). Cohort A consisted of 392,807 individuals who lived in an MSA were 18–84 years of age at time of survey and had individual information on age, race, income, marital status, education, and body mass index (BMI). Cohort B consisted of 162,373 individuals (a sub-cohort of Cohort A) that also included information on individual smoking status collected from supplemental NHIS survey data (NHIS 2016d). Smoking data were not available for the years 1986, 1989, and 1996 (see Fig. 1 and Supplemental Tables 1 and 2).
Fig. 1

Construction and design of cohorts, mortality follow-up, and linkage with MSA-level air pollution based on NHIS survey and related data

Table 1

Baseline characteristics in the National Health Interview Survey (NHIS) cohort

Variable

Cohort A

Cohort B

Total surveyed

392,807

162,373

Total deaths

74,647

30,319

 Heart disease

17,173

7070

 Cerebrovascular

4447

1833

 Respiratory

3737

1533

 Influenza/pneumonia

1797

698

 Cancers

19,705

7740

 Other

27,356

11,273

Gender

  

 % Male

46.56

43.59

 % Female

53.44

56.41

Age (range)

42.90 (18–84)

43.70 (18–84)

Race

  

 % White

77.25

76.93

 % Black

15.37

16.03

 % Asian and Pacific Islander

3.69

3.02

 % Other

3.69

4.02

Hispanic origin

  

 % Hispanic

13.36

14.07

 % Non-hispanic

86.64

85.93

PM2.5, μg/m3 (range)

12.59 (7.58–17.33)

12.49 (7.58–17.33)

Income

  

 % $ 0–20,000

19.03

24.44

 % $ 20–40,000

25.00

24.16

 % $ 40–55,000

16.35

16.51

 % $ 55,000+

39.62

34.89

Marital status

  

 % Married

61.66

51.41

 % Divorced

8.41

12.55

 % Separated

2.77

3.93

 % Never married

21.34

23.92

 % Widowed

5.82

8.19

Education

  

 % < High school grad

18.75

18.61

 % High school grad

34.15

31.22

 % Some college

23.08

24.85

 % College grad

14.12

15.21

 % > College grad

9.90

10.11

BMI

  

 % < 20

9.69

8.98

 % 20–25

43.13

41.95

 % 25–30

32.69

32.88

 % 30–35

10.33

11.18

 % > 35

4.16

5.01

Smoking

  

 % Never

51.33

 % Current

25.64

 % Former

23.03

PM2.5 air pollution data

Exposures to PM2.5 air pollution were assigned as estimated average ambient concentrations for the 10-year time period from 1999 through 2008 at the MSA of residence as follows: monthly PM2.5 estimates for each US FIPS code from 1999 to 2008 were estimated using traffic indicators, land-use regression, and Bayesian Maximum Entropy interpolation of land-use regression space-time residuals, as documented elsewhere (Beckerman et al. 2013; Jerrett et al. 2017). The model was predictive of ground-level ambient concentrations (cross-validation R 2 = 0.79). County-level averages were estimated using population-weighted averages (using 2000 census populations) from census tract estimates averaged for all months of the period 1999–2008. MSA-level PM2.5 concentrations were then estimated using population-weighted county averages across the primary counties that comprise a given MSA. Correspondence between MSAs and counties was not entirely constant throughout the study period, and counties that comprised each MSA were adjusted accordingly based on US Census Bureau historical statistical area delineations (United States Census Bureau 2016). MSAs, as defined for the NHIS survey years and the accompanying estimated PM2.5 concentrations, are presented in Supplemental Table 2.

Harmonizing key analysis variables

Although some of the key variables included in this analysis (such as age and sex) were reported and/or formatted reasonably consistently across the NHIS survey years, other variables required some harmonization across the study years. For the 1986–1996 NHIS survey years, BMI was not directly available but was calculated using height and weight variables. For the 1997–2001 NHIS survey years, height and weight variables were not available and BMI was only available from the supplemental surveys that provided the smoking data (resulting in the size of Cohort A being reduced to approximately the size of Cohort B). Marital status variables were harmonized to include married, divorced, separated, never married, and widowed. Education variables were harmonized to include several categories: less than high school graduate, high school graduate, some college, college graduate, and more than college graduate. Smoking status variables were harmonized to include never smoker, current smoker, and former smoker. Race was harmonized across survey years to include White, Black, Asian and Pacific Islander, and other.

Harmonizing variables for family income required adjustment for inflation over time. Further, for the years 1986–1996, the NHIS survey allowed for 27 possible income categories (with the highest at $50,000 and over) and for 1997–2001, it allowed for 11 different categories (with the highest at $75,000 and over). To allow for approximately equivalent income categories, the mean income for each income category (or lower bound for the highest income brackets) was calculated and then adjusted for inflation using a base year of 2001 and the consumer price index. These values were then categorized into bins of $0–20,000; $20,000–40,000; $40,000–55,000; and $55,000 or over, providing approximately comparable bins of inflation-adjusted (2001) dollars.

Statistical analysis

Adjusted mortality hazard ratios (HRs) were estimated using the Cox proportional hazards regression model. Survival times, in years, were calculated from the NHIS survey year until the year of death or the end of follow-up (2011). Survival times of those still alive at the end of follow-up were censored; in analysis of cause-specific mortality, if death occurred for another cause, survival times were censored at year of death. The models were stratified by sex, race (White, Black, Asian and Pacific Islander, and other), and 1-year age categories, allowing each combination of categories to have its own baseline hazard. The models included the PM2.5 exposure estimates as a continuous variable, and the HRs per 10 μg/m3 increase in PM2.5 are reported. For Cohort A, the models also included the variables for different levels of income, marital status, education levels, and different ranges of BMI. For Cohort B, smoking status variables were also included in the model. Models were estimated for different cause-of-death groupings as provided in the mortality linkage. Deaths occurring prior to 1999 were coded using the ninth revision of the International Statistical Classification of Diseases, Injuries, and Causes of Death (ICD-9), and deaths from 1999 on were coded using the tenth revision of the International Statistical Classification of Diseases, Injuries, and Causes of Death (ICD-10). Deaths coded under ICD-9 guidelines were recoded into comparable ICD-10 based cause-of-death groups. The cause-of-death groupings used in this analysis included all-cause, cardiovascular (I00–I09, I11, I13, I20–I51), cerebrovascular disease (I60–I69), chronic lower respiratory diseases (J40–J47), influenza and pneumonia (J09–J18), cancer (C00–C97), and all other.

To further explore the sensitivity of the results with regard to controlling for smoking, models were estimated for cohort B both with and without controlling for smoking status. Additionally, models were estimated after separating the data by various subgroups including the following: age (age ≥ 65 and age < 65), smoking status, gender, race (White and Non-white), and education (high school graduate or less and some college or more). Formal tests for heterogeneity of the PM2.5-mortality association across the subgroups were conducted by introducing interaction variables to the models. Finally, to explore the shape of the concentration-response relationship between PM2.5 and mortality risk, a class of concentration-response models that allowed for various shapes including linear, log-linear, threshold, and flexible variations on sigmoidal shapes were estimated using an approach that has been documented and illustrated elsewhere (Nasari et al. 2016).

Results

Summary information regarding the characteristics for Cohort A and Cohort B is presented in Table 1. The size of each cohort relative to the total number of individuals sampled with mortality status is presented in Supplemental Table 1. Table 2 presents estimated HRs (and 95% confidence intervals) associated with a 10-μg/m3 elevation in PM2.5, different levels of income, marital status, education, BMI, and (for Cohort B) smoking status. These results are presented for Cohorts A and B and for all-cause and cardiovascular disease mortality. For all-cause mortality, the estimated HRs associated with a 10-μg/m3 elevation in PM2.5 were 1.038 (95% CI 1.005–1.071) and 1.056 (1.005–1.110) for Cohorts A and B, respectively. The HRs for cardiovascular mortality were much higher, at 1.272 (95% CI 1.192–1.357) and 1.337 (1.209–1.479) for Cohorts A and B, respectively. Also, as reported in Table 2, mortality risks declined with higher levels of income; mortality risks were lower for those who were married; mortality risks were lower for those with higher levels of education; mortality risks were lower for those with BMI levels from approximately 20–30; and mortality risks were much higher for smokers and somewhat higher for ever smokers versus never smokers.
Table 2

Comparison of pollution and demographic-variable hazard ratios according to model and cause of death

Variable

All causes of death

Heart disease

Model A

Model B

Model A

Model B

PM2.5, 10 μg/m3

1.038

(1.005–1.071)

1.056

(1.005–1.110)

1.272

(1.192–1.357)

1.337

(1.209–1.479)

Income, inflation adjusted to 2001

 $0–20,000

1

 

1

 

1

 

1

 

 $20–40,000

0.845

(0.828–0.863)

0.837

(0.811–0.863)

0.834

(0.801–0.868)

0.813

(0.764–0.866)

 $40–55,000

0.761

(0.741–0.781)

0.750

(0.720–0.782)

0.779

(0.738–0.822)

0.737

(0.677–0.803)

 $55,000+

0.647

(0.631–0.663)

0.640

(0.614–0.666)

0.636

(0.604–0.669)

0.613

(0.563–0.668)

Marital status

 Married

1

 

1

 

1

 

1

 

 Divorced

1.226

(1.194–1.260)

1.136

(1.094–1.180)

1.219

(1.152–1.291)

1.155

(1.066–1.251)

 Separated

1.201

(1.146–1.260)

1.125

(1.052–1.202)

1.273

(1.152–1.407)

1.162

(1.011–1.337)

 Never married

1.265

(1.229–1.302)

1.234

(1.182–1.288)

1.373

(1.292–1.458)

1.431

(1.312–1.561)

 Widowed

1.112

(1.086–1.138)

1.083

(1.046–1.121)

1.164

(1.114–1.218)

1.129

(1.056–1.208)

Education

 < High school grad

1

 

1

 

1

 

1

 

 High school grad

0.894

(0.877–0.911)

0.931

(0.903–0.959)

0.865

(0.833–0.898)

0.883

(0.831–0.937)

 Some college

0.846

(0.826–0.866)

0.898

(0.866–0.932)

0.822

(0.783–0.863)

0.840

(0.779–0.905)

 College grad

0.690

(0.669–0.712)

0.766

(0.729–0.805)

0.699

(0.656–0.745)

0.747

(0.675–0.827)

 > College grad

0.632

(0.611–0.654)

0.756

(0.715–0.798)

0.632

(0.589–0.679)

0.704

(0.629–0.789)

BMI

        

 < 20

1.487

(1.444–1.531)

1.415

(1.351–1.483)

1.274

(1.192–1.362)

1.216

(1.097–1.348)

 20–25

1

 

1

 

1

 

1

 

 25–30

0.937

(0.921–0.954)

0.972

(0.945–0.999)

1.019

(0.983–1.056)

1.063

(1.004–1.124)

 30–35

1.121

(1.094–1.149)

1.155

(1.112–1.200)

1.283

(1.222–1.347)

1.321

(1.224–1.425)

 > 35

1.533

(1.481–1.587)

1.595

(1.512–1.682)

1.847

(1.725–1.978)

1.902

(1.712–2.113)

Smoking

 Never

  

1

   

1

 

 Current

  

2.233

(2.165–2.303)

  

2.036

(1.909–2.172)

 Former

  

1.344

(1.305–1.384)

  

1.259

(1.188–1.334)

Figure 2 presents estimated HRs (and 95% CIs) for all-cause and cardiovascular mortality for Cohort A (triangles) and Cohort B (circles) without controlling for smoking (White) and controlling for smoking (Black) estimated for the full cohorts and separating the data by age, smoking status, gender, race, and education. For the combined cohorts, the illustrated HRs displayed in Fig. 2 are the same as reported in Table 2. For Cohort B, the HR is similar with or without controlling for smoking status. Figure 2 illustrates only limited heterogeneity across cohort subgroups. The estimated HRs were not significantly different across older versus younger subjects in either cohort (p > 0.25). Estimated HRs were somewhat larger for never smokers versus current or former smokers, but the difference was not statistically significant (p ~ 0.10 for all-cause mortality and p > 0.25 for cardiovascular mortality). For all-cause mortality in Cohort A, the HR for males was significantly larger than for females (p < 0.05), but no significant differences between male and female subjects were observed for Cohort B with control for smoking status or for cardiovascular mortality in either cohort. Although the HRs were generally larger for Non-whites than for Whites, these differences were not statistically significant (p > 0.10). HRs were somewhat larger for those more educated than less educated, and these differences were statistically significant for all-cause mortality (p < 0.05) but not cardiovascular mortality (p > 0.10). When the HRs were estimated separately for each of the NHIS survey years (1986–2001) (results not shown), the HRs for PM2.5 were mostly greater than 1 and were occasionally statistically significant (for cardiovascular disease), but treating each year as a separate cohort resulted in minimal statistical power.
Fig. 2

HRs (and 95% CIs) for all-cause and cardiovascular mortality for Cohort A (triangles) and Cohort B (circles) without controlling for smoking (White) and controlling for smoking (Black) estimated for the full-combined cohorts and separating the data for older (age ≥ 65) versus younger (age < 65), for smoking status (current, former, and never), for male versus female, for race (White versus Non-white), and for education (high school graduate or less versus some college or more)

Table 3 presents estimated HRs (and 95% confidence intervals) associated with a 10-μg/m3 elevation in PM2.5 for the different cause-of-death groupings analyzed. PM2.5 was statistically significantly associated with elevated mortality risks for all-cause, cardiovascular, and influenza and pneumonia mortality, but not with cerebrovascular, chronic lower respiratory, cancer, or other mortality.
Table 3

Comparison of hazard ratios among various causes of death for 10 μ/m3 increase in PM2.5

Cause of death

Cohort A

Cohort B

All

1.038 (1.005–1.071)

1.056 (1.005–1.110)

Heart

1.272 (1.192–1.357)

1.337 (1.209–1.479)

Cerebrovascular

1.026 (0.905–1.164)

1.099 (0.904–1.336)

Respiratory

0.962 (0.840–1.102)

1.021 (0.825–1.262)

Influenza/pneumonia

1.528 (1.256–1.860)

1.461 (1.064–2.005)

Cancer

1.018 (0.959–1.081)

1.040 (0.945–1.144)

Other

0.912 (0.867–0.960)

0.895 (0.826–0.970)

Figure 3 illustrates the shape of the concentration-response relationship between PM2.5 and mortality risk for cardiovascular mortality using Cohort B, controlling for all covariates including smoking status and using the flexible modeling approach (Nasari et al. 2016). The optimal non-linear model is shown as the solid line with 95% uncertainty bounds (shaded area). This non-linear model is slightly sigmoidal and provided a somewhat improved fit to the data (log-likelihood value − 67,892.31) versus the traditional linear model (log-likelihood value − 67,900.74). Similar, somewhat sigmoidal shapes were observed when estimated for all-cause and cardiovascular mortality using both cohorts A and B.
Fig. 3

Estimated concentration-response relationship between PM2.5 and mortality risk for cardiovascular mortality using Cohort B controlling for all covariates including smoking status and using the flexible modeling approach

Discussion

This study observed that long-term exposure to PM2.5 air pollution was associated with elevated risks of mortality in nationwide cohorts constructed using representative samples of the US adult population living in metropolitan areas. Given the substantially elevated risks for cardiovascular deaths and the large number of cardiovascular deaths, it is clear that the PM2.5-associated increased risk for all-cause mortality was driven largely by cardiovascular disease deaths. PM2.5 was not associated with elevated risk of chronic lower respiratory disease deaths but was associated with excess risk for influenza and pneumonia. These results are similar to findings that were observed from the American Cancer Society cohort (Pope et al. 2004) where PM2.5 was not associated with chronic obstructive pulmonary disease but most strongly associated with cardiovascular disease and also associated with influenza and pneumonia. The estimated PM2.5 effect for all-cause mortality from Cohort B, which included control for all covariates including smoking status, of a 5.6% increase in mortality risk per 10 μg/m3 elevation in PM2.5, is similar to estimates based on meta-analyses (Hoek et al. 2013). The estimated PM2.5 effect estimate for cardiovascular of a 33.7% increase in mortality risk per 10 μg/m3 elevation in PM2.5, however, is larger than average estimates based on meta-analyses (Hoek et al. 2013) but smaller than estimates from the Women’s Health Initiative study (Miller et al. 2007). Cause-of-death groupings for cardiovascular deaths are not entirely consistent across various studies.

This analysis has several notable strengths. It uses data from large, nationwide, well-characterized samples of US adults. Given that the cohorts are compiled from public-use NHIS data, these data are fully accessible for independent compilation, reanalysis, and extended analysis. This analysis controlled for multiple key individual risk factors including, age, sex, race, income, marital status, education, body mass, and smoking. The analysis evaluated the shape of the concentration-response function using a recently developed flexible modeling approach (Nasari et al. 2016). While the results do not provide a definitive shape of the concentration-response relationship, there was some evidence that a slightly sigmoidal shape fit the data better than linear. Furthermore, the shape of the concentration-response function is similar to that reported using data from the American Cancer Society, Cancer Prevention II cohort (Nasari et al. 2016).

This study also has limitations. Only individuals from the NHIS survey who resided in a MSA could be assigned metro-level PM2.5 exposure, reducing the cohort size and restricting the analysis to MSA-level PM2.5 exposures. Limited data on smoking status further restricted the number of individuals in Cohort B that allowed for adequate control of smoking. Because some of the key survey questions changed over the years, harmonization of the variables was required, resulting in possible lack of specificity (especially for income). PM2.5 exposure was estimated for a 10-year time period—1999 (the first year of broad-based PM2.5 monitoring in the USA) through 2008—resulting in lack of perfect temporal matching of exposure. The analysis was restricted to cause-of-death groupings provided by public-use data. This resulted in the inability to explore different or more finely defined cause-of-death groups and specifically the inability to evaluate lung-cancer effects.

The results from this study are largely consistent with and contribute to the growing body of evidence that long-term exposure to ambient PM2.5, even at concentrations common to US urban areas, contributes to increased risk of cardiovascular disease and mortality. The results are uniquely based on publically available and reasonably representative national cohort data.

Notes

Funding

This study was supported in part by grants from the National Institute of Environmental Health Sciences (NIH ES019217), US Environmental Protection Agency Center for Air, Climate, and Energy Solutions (CACES) (EPA Grant Number R835873), and the Mary Lou Fulton Professorship at Brigham Young University.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

Supplementary material

11869_2017_535_MOESM1_ESM.docx (37 kb)
ESM 1 (DOCX 36 kb)

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of EconomicsBrigham Young UniversityProvoUSA
  2. 2.MRC-PHE Centre for Environment and Health, School of Public HealthImperial College LondonLondonUK
  3. 3.Environmental Health Sciences, UCLA Fielding School of Public HealthUniversity of California Los AngelesLos AngelesUSA
  4. 4.Environmental Health Directorate, Health CanadaOttawaCanada

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