Effect Modification of Long‐Term Air Pollution Exposures and the Risk of Incident Cardiovascular Disease in US Women
Abstract
Background
Ambient air pollution exposures have been frequently linked to cardiovascular disease (CVD) morbidity and mortality. However, less is known about the populations most susceptible to these adverse effects.
Methods and Results
We assessed the associations of long‐term particulate matter (PM) exposures with incident CVD in a nationwide cohort of 114 537 women in the Nurses’ Health Study, and performed analyses to identify subpopulations at the greatest risk. Residential address level time‐varying monthly exposures to PM2.5, PM10, and PM2.5 to 10 microns in diameter were estimated from spatio‐temporal prediction models. In multivariable models, increases in all size fractions of PM were associated with small, but not statistically significant, increased risks of total CVD, coronary heart disease, and stroke. PM‐associated CVD risks were statistically significantly higher among women with diabetes as compared to those without (P‐for‐interaction <0.0001 for PM10 and PM2.5 and 0.007 for PM2.5 to 10). For each 10 μg/m3 increase in 12‐month average PM2.5, PM2.5 to 10, and PM10, the multivariable adjusted hazard ratios were 1.44 (95% CI: 1.23 to 1.68), 1.17 (95% CI: 1.05 to 1.30), and 1.19 (95% CI: 1.10 to 1.28) among women with diabetes. There were also suggestions of higher risks among older (≥70 years) women, the obese, and those living in the Northeast and South. Smoking status and family history did not consistently modify the association between PM and CVD, and risks were most elevated with exposures in the previous 12 months.
Conclusions
In this nationwide cohort, women with diabetes were identified as the subpopulation most sensitive to the adverse cardiovascular health effects of PM.
Introduction
A large body of evidence has formed implicating long‐term exposures to particulate matter (PM) air pollution with an increased risk of cardiovascular disease (CVD) morbidity and mortality,1, 2 leading to the recognition of air pollution as a major risk factor for these outcomes.3, 4 For example, as part of the Global Burden of Disease 2010 project, ambient PM exposure was estimated to be responsible for 1.5 million ischemic heart disease deaths in 2010 with population‐attributable fractions of 2% to 41%, depending on the country.5 In a recent review of the evidence from long‐term studies, each 10 μg/m3 increase in PM <2.5 microns in aerodynamic diameter (PM2.5) was associated with an 11% (95% CI: 5 to 16) increased risk of cardiovascular mortality.2 Although the majority of studies have observed elevated risks with PM exposures, statistically significant heterogeneity in these estimates has been observed, even within studies.6, 7, 8, 9 Differences in the prevalence of risk factors and different proportions of susceptible subpopulations may underlie this heterogeneity. Therefore, the subpopulations at the greatest risk remain a key question in the literature.
Among women in the Nurses’ Health Study (NHS) living in the Northeastern and Midwestern regions of the United States 1992–2002, we have previously observed an increased risk of incident overall coronary heart disease (CHD), fatal CHD, and nonfatal myocardial infarctions (MI) with increasing long‐term exposures to PM2.5, and evidence of heterogeneity by personal characteristics.10 Our current objectives are to spatially (to the full contiguous United States) and temporally (1989–2006) expand our examination of these associations, to determine whether there is heterogeneity in effect estimates by region of the country or by personal characteristics including cardiovascular risk factors, to compare heterogeneity in effects across PM size fractions, and to assess the impact of these exposures on the risk of incident stroke, in addition to incident CHD.
Methods
Study Population
The NHS is a prospective cohort study of nurses started in 1976. At enrollment, the 121 701 women were 30 to 55 years of age, married, and completed a mailed questionnaire with detailed information on demographics, lifestyle characteristics, and prevalence of a number of diseases. Women were initially enrolled from a selection of 11 states; however, participants now live throughout the contiguous United States. Follow‐up questionnaires are mailed every 2 years to update risk factor and disease status, and response rates have been consistently over 90%. All residential address locations have been geocoded to obtain longitude and latitude. Women were included in the current analyses if they remained alive and free of CVD through 1988, were still responding to questionnaires, and had at least a single address within the contiguous United States during 1988–2006 where air pollution predictions were available. The study protocol was approved by the Brigham and Women's Hospital Institutional Review Board, and consent was implied through the return of the questionnaires.
Outcome Assessment
On the baseline and all subsequent follow‐up questionnaires, participants were asked to report all occurrences of physician diagnosed CVD, specifically, incidence of CHD or stroke. Women (or for fatal cases, next‐of‐kin) were asked to provide consent to review all medical records pertaining to their diagnosis. Cases of fatal CHD (ICD‐8 and ICD‐9 codes 410 to 412, ICD‐10 codes I21 to I22) were classified as definite if confirmed by hospital records or through an autopsy, or if CHD was the most plausible cause of death and there was prior evidence of CHD. Cases were classified as probable if medical records surrounding the death were not available, but CHD was the underlying cause on the death certificate, National Death Index search, or a family member provided supporting information. Nonfatal MI was classified as definite if the criteria of the World Health Organization were met, specifically, symptoms and either electrocardiograph‐detected changes or elevated cardiac‐enzyme concentrations. Cases of nonfatal MIs were designated as probable if an interview or letter confirming hospitalization for the infarction was obtained and medical records were unavailable. Overall incident CHD was determined based on the first occurrence of either nonfatal MI or fatal CHD. Incident strokes were confirmed if characterized by a typical neurological defect of sudden or rapid onset, lasting more than 24 hours and attributable to a cerebrovascular event. Strokes were classified according to the criteria of the National Survey of Stroke as due to ischemia (embolic or thrombotic), hemorrhage (subarachnoid hemorrhage or intracerebral hemorrhage), or unknown cause.11 Nonfatal strokes were designated as probable when the nurse provided additional information by interview or letter, but no medical records were available.
Exposure Assessment
Ambient predictions of PM2.5 and PM <10 μm in aerodynamic diameter (PM10) were available from spatio‐temporal prediction models for all months between January 1988 and June 2006 for each residential address in the contiguous United States.12 The models used monthly average PM2.5 and/or PM10 monitoring data from the US Environmental Protection Agency's Air Quality System, the IMPROVE network, and other sources and the addresses were updated every 2 years.13, 14 Generalized additive mixed models incorporated these measured values with monthly spatial terms, geospatial predictors (including a number of potential sources, meteorology, and topology), and terms for time to create separate PM prediction surfaces for each month and each PM size fraction.12 Since monitoring data on PM2.5 were limited prior to 1999, PM2.5 in the period before 1999 was modeled using data on PM10 and information on the ratio of PM10 to PM2.5 in each location.12 PM2.5–10 was calculated by subtraction of the monthly PM10 and PM2.5 estimates. Cross‐validation results demonstrated that the models had high predictive accuracy (cross‐validation R2 values of 0.59, 0.76, and 0.77 for PM10, pre‐1999 PM2.5, and post‐1999 PM2.5, respectively).12
Potential Confounders and Effect Modifiers
Information on potential confounders and effect modifiers is available every 2 years (every 4 years for diet information) from the follow‐up questionnaires. Therefore, when appropriate, each woman was assigned updated covariate values for each questionnaire cycle. We included a number of risk factors for CVD or predictors of exposure as possible confounders including: age, race, calendar year, season, body mass index (BMI; kg/m2), menopausal status and hormone use, comorbidities (hypertension, hypercholesterolemia, and type 2 diabetes), and family history of MI. Pack‐years of smoking and current smoking status were calculated for each time period from information on lifetime smoking history. Marital status, employment status, and educational attainment of each nurse and, if appropriate, of her spouse/partner were included as measures of individual‐level socioeconomic status. To control for potential regional differences in PM composition and/or disease risk, we controlled for Census region of residence. Census tract level median income and house value were used to adjust for area‐level socioeconomic status. Each potential confounder (or set of confounders) was added to a basic model (adjusted only for age, calendar year, season, and region) 1 at a time to determine the impact on the effect estimates. All confounders were included together in a final multivariable model. We considered a number of a priori potential effect modifiers previously suggested in this cohort and in the wider literature, including region, age, diabetes, family history of MI, BMI, and smoking status.2, 6, 8, 9, 10, 15, 16, 17
Statistical Analyses
Person‐months of follow‐up were calculated from June 1989 through the end of follow‐up, incidence of the specific event of interest, death, or loss to follow‐up, whichever occurred first. Separate time‐varying Cox proportional hazards models were used to assess the association of incident CVD, CHD, or stroke with 12‐month time‐varying averages of each size fraction of PM. Hazard ratios (HRs) and 95% CIs were calculated for each 10 μg/m3 increase, after assessing the linearity of each dose‐response with cubic regression splines.18 The dataset was converted to an Anderson–Gill counting process structure with a record for each month that included the appropriate person‐time, calculated exposure averages, censoring information, and covariates for that month of follow‐up. All models were stratified by age in months and month of study to control for age and temporal effects. To examine effect modification, we calculated stratum‐specific effect estimates and examined the statistical significance of any observed effect modification using likelihood ratio tests. In sensitivity analyses, we performed all analyses from 2000 forward, to determine whether the effect estimates were different in the time period where monitoring data on PM2.5 was directly available for use in our spatio‐temporal prediction models. In the later time period we also conducted sensitivity analyses to examine the impact of longer exposure periods (24‐, 60‐, and 120‐month moving averages). An α level of 0.05 was used to determine statistical significance for main effects, and an α level of 0.0083 (0.05/6 potential modifiers) was used for effect modifiers.
Results
Among 114 537 NHS participants eligible for analysis, 6767 women developed CVD during 1 835 486 person‐years of follow‐up, and 3878 and 3295 incident cases of CHD and stroke, respectively, were confirmed during this time. A total of 96 058 women were available for analyses starting in 2000 (the first year PM2.5 was widely monitored), accounting for 2413 incident CVD cases, 1301 incident CHDs, and 1194 incident strokes during 557 741 years of follow‐up. Characteristics of the population throughout both follow‐up periods are presented in Table 1. During the full follow‐up period, participants were 63.5 (SD=8.5) years old on average, were mainly white, married, and postmenopausal, and were never or former smokers. Characteristics were similar after 2000, with an average participant age of 68.6 (SD=7.3).
| Characteristics, Mean (SD) or % | Follow‐Up Period | |
|---|---|---|
| 1989–2006 | 2000–2006 | |
| N | 114 537 | 96 058 |
| Age, ya | 63.5 (8.5) | 68.6 (7.3) |
| Body mass index, kg/m2 | 25.3 (7.6) | 25.6 (7.5) |
| Body mass index categories | ||
| Underweight/normal | 43 | 41 |
| Overweight | 32 | 33 |
| Obese | 20 | 22 |
| Race | ||
| White | 94 | 94 |
| Black | 2 | 2 |
| Other/multiple races | 6 | 6 |
| Pack‐years of smokingb | 24.0 (21.1) | 23.6 (21.4) |
| Smoking status | ||
| Never | 44 | 45 |
| Former | 42 | 45 |
| Current | 14 | 11 |
| Menopause status | ||
| Premenopausal | 8 | 8 |
| Postmenopausal | 86 | 84 |
| Dubious | 5 | 4 |
| Family history of myocardial infarction | 33 | 32 |
| Diabetes | 8 | 9 |
| Marital status | ||
| Married | 63 | 64 |
| Not married | 37 | 36 |
| Region of residence | ||
| Northeast | 51 | 50 |
| Midwest | 17 | 17 |
| West | 14 | 14 |
| South | 18 | 19 |
| Census tract median income ($1000) | 63.9 (25.0) | 63.4 (24.3) |
| Census tract median home value ($10 000) | 17.2 (12.9) | 17.2 (12.6) |
| 12‐month average exposurea | ||
| PM10 | 22.2 (6.5) | 19.3 (5.4) |
| PM2.5–10 | 8.7 (4.5) | 7.3 (4.1) |
| PM2.5 | 13.4 (3.3) | 12.0 (2.8) |
PM indicates particulate matter.
a
Not age standardized.
b
Among ever smokers only.
The HRs for each outcome for a 10 μg/m3 increase in each size fraction of PM are presented in Table 2 for both follow‐up periods. In basic models adjusted for age, time period, region, and season, small increases in the risk of incident CVD, CHD, and stroke were observed for all size fractions of PM exposure. These increases only reached statistical significance for the association of PM10 with total CVD, and for PM2.5–10 with all 3 outcomes in the full period of follow‐up. In multivariable models, the results were generally attenuated, and none reached statistical significance. Individual and area‐level socioeconomic status appeared to be the most important confounders, and both attenuated the effect estimates for all size fractions. HRs tended to be more elevated for PM2.5 in analyses with follow‐up starting after 2000, while for PM2.5 to 10 HRs were higher in the full period of follow‐up.
| Outcome | Cases | Person‐Years | PM10 | PM2.5–10 | PM2.5 | |||
|---|---|---|---|---|---|---|---|---|
| Basica | Multivariableb | Basica | Multivariableb | Basica | Multivariableb | |||
| 1989–2006 (N=114 537) | ||||||||
| Incident CVD | 6767 | 1 835 486 | 1.05 (1.01–1.10)c | 1.02 (0.97–1.06) | 1.11 (1.03–1.19)c | 1.03 (0.96–1.11) | 1.04 (0.96–1.13) | 1.02 (0.94–1.10) |
| Incident CHD | 3878 | 1 835 486 | 1.06 (0.99–1.12) | 1.01 (0.95–1.07) | 1.12 (1.02–1.23)c | 1.03 (0.93–1.13) | 1.02 (0.92–1.14) | 0.99 (0.88–1.10) |
| Incident stroke | 3295 | 1 835 486 | 1.05 (0.99–1.12) | 1.03 (0.97–1.10) | 1.11 (1.00–1.22)c | 1.05 (0.95–1.16) | 1.03 (0.92–1.16) | 1.03 (0.92–1.15) |
| 2000–2006 (N=96 058) | ||||||||
| Incident CVD | 2413 | 557 741 | 1.07 (0.98–1.16) | 1.05 (0.96–1.14) | 1.07 (0.95–1.21) | 1.02 (0.90–1.16) | 1.11 (0.96–1.28) | 1.11 (0.96–1.29) |
| Incident CHD | 1301 | 557 741 | 1.06 (0.94–1.19) | 1.02 (0.91–1.15) | 1.08 (0.91–1.28) | 1.01 (0.85–1.19) | 1.08 (0.88–1.32) | 1.05 (0.86–1.29) |
| Incident stroke | 1194 | 557 741 | 1.07 (0.96–1.21) | 1.07 (0.95–1.20) | 1.07 (0.90–1.27) | 1.03 (0.87–1.23) | 1.14 (0.93–1.40) | 1.18 (0.96–1.45) |
BMI indicates body mass index; CHD, coronary heart disease; CVD, cardiovascular disease; HRs, hazard ratios; MI, myocardial infarction; PM, particulate matter; SES, socioeconomic status.
a
Adjusted for age, time period, region, and season.
b
Additionally adjusted for race, smoking status and pack‐years, BMI, menopausal status and hormone use, hypercholesterolemia, hypertension, diabetes, family history of MI, and individual‐ and area‐level SES.
c
P‐value <0.05.
The 12‐, 24‐, 60‐, and 120‐month averages were highly correlated (Table 3) within each size fraction. Within each averaging period, PM2.5 and PM2.5–10 were weakly correlated (r≈0.2), PM2.5 and PM10 were moderately correlated (r≈0.6), and PM10 and PM2.5–10 were strongly correlated (r≈0.8). For CVD, averaging periods >12 months were associated with attenuations in risk for each of the size fractions; the HRs for each 10 μg/m3 increase in PM2.5 were 1.13, 1.08, 1.04, and 1.02 for the 12‐, 24‐, 60‐, and 120‐month exposure windows (Figure). This pattern of attenuations was similar for analyses of stroke, but was less dramatic for analyses of total CHD.
| 12‐Month | 24‐Month | 60‐Month | 120‐Month | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PM10 | PM2.5–10 | PM2.5 | PM10 | PM2.5–10 | PM2.5 | PM10 | PM2.5–10 | PM2.5 | PM10 | PM2.5–10 | PM2.5 | |
| 12‐month | ||||||||||||
| PM10 | 1.00 | 0.86 | 0.67 | 0.99 | 0.85 | 0.67 | 0.96 | 0.84 | 0.65 | 0.93 | 0.85 | 0.61 |
| PM2.5–10 | 1.00 | 0.20 | 0.85 | 0.99 | 0.21 | 0.82 | 0.97 | 0.21 | 0.77 | 0.93 | 0.18 | |
| PM2.5 | 1.00 | 0.65 | 0.20 | 0.98 | 0.65 | 0.21 | 0.95 | 0.67 | 0.28 | 0.91 | ||
| 24‐month | ||||||||||||
| PM10 | 1.00 | 0.86 | 0.68 | 0.98 | 0.86 | 0.66 | 0.95 | 0.87 | 0.62 | |||
| PM2.5–10 | 1.00 | 0.22 | 0.84 | 0.98 | 0.22 | 0.78 | 0.95 | 0.19 | ||||
| PM2.5 | 1.00 | 0.68 | 0.24 | 0.97 | 0.70 | 0.30 | 0.93 | |||||
| 60‐month | ||||||||||||
| PM10 | 1.00 | 0.87 | 0.70 | 0.98 | 0.88 | 0.66 | ||||||
| PM2.5–10 | 1.00 | 0.24 | 0.82 | 0.98 | 0.21 | |||||||
| PM2.5 | 1.00 | 0.72 | 0.30 | 0.97 | ||||||||
| 120‐month | ||||||||||||
| PM10 | 1.00 | 0.87 | 0.72 | |||||||||
| PM2.5–10 | 1.00 | 0.28 | ||||||||||
| PM2.5 | 1.00 | |||||||||||
PM indicates particulate matter.
No statistically significant differences in effects were observed across regions in the full period of follow‐up (Table 4); however, there was a suggestion of effect modification by region after 2000 (Table 5), with higher HRs observed in the Northeast and South. Multivariable models stratified by selected cardiovascular risk factors are presented in Table 6 for the full follow‐up period. For all 3 outcomes, HRs were higher among women with diabetes as compared to those without diabetes, and the test for interaction was statistically significant for the majority of the end points. Among women with diabetes, during the full period of follow‐up, the HR of incident total CVD for a 10 μg/m3 increase in PM2.5 was 1.44 (95% CI: 1.23 to 1.68), for PM2.5–10 was 1.17 (95% CI: 1.05 to 1.30), and for PM10 was 1.19 (95% CI: 1.10 to 1.28). The HRs were higher for stroke (1.66 [95% CI: 1.31 to 2.10] for PM2.5, 1.18 [95% CI: 1.01 to 1.38] for PM2.5–10, and 1.23 [95% CI: 1.10 to 1.38] for PM10) than for CHD among these women. Effect modification was not consistently detected for the other effect modifiers (age, family history of MI, BMI, smoking status) across size fractions. The patterns were similar in post‐2000 analyses (Table 7).
| Outcome | Cases | Person‐Years | PM10 | PM2.5–10 | PM2.5 |
|---|---|---|---|---|---|
| Incident CVD | |||||
| Northeast | 3368 | 942 058 | 1.06 (0.98–1.14) | 1.16 (1.00–1.34) | 1.06 (0.93–1.21) |
| Midwest | 1160 | 317 068 | 0.99 (0.89–1.10) | 0.99 (0.83–1.18) | 0.98 (0.80–1.21) |
| West | 961 | 254 336 | 1.01 (0.95–1.07) | 1.03 (0.94–1.13) | 0.99 (0.87–1.12) |
| South | 1278 | 322 024 | 0.95 (0.81–1.11) | 0.92 (0.77–1.10) | 1.02 (0.83–1.27) |
| P‐for‐interaction | 0.51 | 0.22 | 0.87 | ||
| Incident CHD | |||||
| Northeast | 1996 | 942 058 | 1.03 (0.93–1.14) | 1.11 (0.92–1.35) | 1.01 (0.85–1.20) |
| Midwest | 659 | 317 068 | 1.02 (0.88–1.18) | 1.06 (0.84–1.34) | 1.00 (0.76–1.31) |
| West | 492 | 254 336 | 0.99 (0.91–1.08) | 0.99 (0.87–1.13) | 0.99 (0.83–1.17) |
| South | 731 | 322 024 | 0.97 (0.79–1.20) | 1.02 (0.81–1.30) | 0.92 (0.69–1.22) |
| P‐for‐interaction | 0.92 | 0.79 | 0.96 | ||
| Incident stroke | |||||
| Northeast | 1569 | 942 058 | 1.06 (0.95–1.19) | 1.16 (0.93–1.44) | 1.06 (0.87–1.28) |
| Midwest | 576 | 317 068 | 0.98 (0.84–1.14) | 0.93 (0.72–1.20) | 1.03 (0.76–1.38) |
| West | 526 | 254 336 | 1.03 (0.95–1.12) | 1.09 (0.96–1.23) | 0.99 (0.83–1.17) |
| South | 624 | 322 024 | 0.97 (0.77–1.22) | 0.89 (0.68–1.15) | 1.12 (0.83–1.52) |
| P‐for‐interaction | 0.80 | 0.27 | 0.89 | ||
Adjusted for age, time period, season, race, smoking status and pack‐years, BMI, menopausal status and hormone use, hypercholesterolemia, hypertension, diabetes, family history of MI, and individual‐ and area‐level SES. BMI indicates body mass index; CHD, coronary heart disease; CVD, cardiovascular disease; HRs, hazard ratios; MI, myocardial infarction; PM, particulate matter; SES, socioeconomic status.
| Outcome | Cases | Person‐Years | PM10 | PM2.5–10 | PM2.5 |
|---|---|---|---|---|---|
| Incident CVD | |||||
| Northeast | 1189 | 278 905 | 1.30 (1.10–1.52) | 1.47 (1.09–1.99) | 1.44 (1.12–1.86) |
| Midwest | 402 | 93 823 | 1.00 (0.80–1.25) | 0.91 (0.65–1.29) | 1.15 (0.74–1.78) |
| West | 335 | 78 560 | 0.95 (0.84–1.06) | 0.97 (0.81–1.15) | 0.85 (0.67–1.07) |
| South | 487 | 106 454 | 1.10 (0.82–1.48) | 0.93 (0.69–1.24) | 1.34 (0.91–1.98) |
| P‐for‐interaction | 0.02 | 0.08 | 0.02 | ||
| Incident CHD | |||||
| Northeast | 665 | 278 905 | 1.29 (1.04–1.60) | 1.53 (1.02–2.28) | 1.39 (0.99–1.96) |
| Midwest | 212 | 93 823 | 1.07 (0.78–1.45) | 1.05 (0.66–1.67) | 1.17 (0.64–2.15) |
| West | 166 | 78 560 | 0.87 (0.73–1.03) | 0.88 (0.68–1.13) | 0.73 (0.52–1.02) |
| South | 258 | 106 454 | 1.07 (0.71–1.61) | 0.92 (0.62–1.38) | 1.30 (0.76–2.22) |
| P‐for‐interaction | 0.05 | 0.15 | 0.05 | ||
| Incident stroke | |||||
| Northeast | 568 | 278 905 | 1.23 (0.98–1.56) | 1.27 (0.81–1.97) | 1.42 (0.98–2.05) |
| Midwest | 205 | 93 823 | 0.94 (0.68–1.28) | 0.78 (0.48–1.27) | 1.15 (0.62–2.13) |
| West | 178 | 78 560 | 1.03 (0.88–1.20) | 1.06 (0.85–1.34) | 0.99 (0.73–1.35) |
| South | 243 | 106 454 | 1.19 (0.79–1.81) | 0.97 (0.64–1.46) | 1.44 (0.83–2.50) |
| P‐for‐interaction | 0.45 | 0.52 | 0.44 | ||
Adjusted for age, time period, season, race, smoking status and pack‐years, BMI, menopausal status and hormone use, hypercholesterolemia, hypertension, diabetes, family history of MI, and individual‐ and area‐level SES. BMI indicates body mass index; CHD, coronary heart disease; CVD, cardiovascular disease; HRs, hazard ratios; MI, myocardial infarction; PM, particulate matter; SES, socioeconomic status.
| Outcome | Cases | Person‐Years | PM10 | PM2.5–10 | PM2.5 |
|---|---|---|---|---|---|
| Incident CVD | |||||
| <70 years old | 3429 | 1 392 830 | 0.99 (0.93–1.05) | 1.00 (0.92–1.09) | 0.97 (0.87–1.07) |
| ≥70 years old | 3338 | 442 656 | 1.06 (1.00–1.13) | 1.07 (0.98–1.17) | 1.08 (0.97–1.22) |
| P‐for‐interaction | 0.07 | 0.17 | 0.13 | ||
| Women with diabetes | 1512 | 138 940 | 1.19 (1.10–1.28)a | 1.17 (1.05–1.30)a | 1.44 (1.23–1.68)a |
| Women without diabetes | 5255 | 1 696 546 | 0.98 (0.94–1.03)a | 0.99 (0.93–1.06)a | 0.94 (0.86–1.03)a |
| P‐for‐interaction | <0.0001a | 0.007a | <0.0001a | ||
| Family history | 2819 | 612 206 | 1.05 (0.99–1.11) | 1.06 (0.97–1.15) | 1.09 (0.97–1.22) |
| No family history | 3948 | 1 223 280 | 1.01 (0.96–1.06) | 1.01 (0.94–1.09) | 1.00 (0.90–1.10) |
| P‐for‐interaction | 0.24 | 0.43 | 0.23 | ||
| Underweight/normal | 2685 | 791 374 | 0.99 (0.93–1.05) | 0.98 (0.90–1.06) | 0.99 (0.88–1.12) |
| Overweight | 2127 | 583 357 | 1.04 (0.97–1.11) | 1.05 (0.95–1.15) | 1.04 (0.91–1.19) |
| Obese | 1737 | 376 203 | 1.09 (1.02–1.18) | 1.13 (1.02–1.25) | 1.12 (0.97–1.30) |
| P‐for‐interaction | 0.09 | 0.10 | 0.41 | ||
| Never smokers | 2459 | 803 757 | 1.01 (0.95–1.07) | 1.02 (0.93–1.11) | 0.99 (0.88–1.12) |
| Former smokers | 2877 | 765 086 | 1.05 (0.99–1.12) | 1.06 (0.97–1.15) | 1.09 (0.97–1.22) |
| Current smokers | 1422 | 262 982 | 1.01 (0.92–1.09) | 1.00 (0.89–1.12) | 1.00 (0.86–1.17) |
| P‐for‐interaction | 0.47 | 0.70 | 0.47 | ||
| Incident CHD | |||||
| <70 years old | 2038 | 1 392 830 | 0.95 (0.88–1.02)a | 0.96 (0.85–1.08) | 0.89 (0.77–1.02) |
| ≥70 years old | 1840 | 442 656 | 1.09 (1.00–1.19)a | 1.11 (0.99–1.25) | 1.13 (0.97–1.33) |
| P‐for‐interaction | 0.007a | 0.04 | 0.02 | ||
| Women with diabetes | 1027 | 138 940 | 1.12 (1.02–1.23)a | 1.14 (1.00–1.30)a | 1.20 (0.99–1.46) |
| Women without diabetes | 2851 | 1 696 546 | 0.95 (0.89–1.01)a | 0.92 (0.84–1.01)a | 0.94 (0.84–1.06) |
| P‐for‐interaction | 0.003a | 0.008a | 0.03 | ||
| Family history | 1691 | 612 206 | 1.04 (0.96–1.12) | 1.02 (0.92–1.14) | 1.10 (0.95–1.28) |
| No family history | 2187 | 1 223 280 | 0.95 (0.89–1.02) | 0.94 (0.85–1.04) | 0.93 (0.82–1.07) |
| P‐for‐interaction | 0.09 | 0.23 | 0.09 | ||
| Underweight/normal | 1444 | 791 374 | 0.95 (0.87–1.03) | 0.91 (0.81–1.03) | 0.97 (0.82–1.13) |
| Overweight | 1196 | 583 357 | 1.01 (0.92–1.11) | 1.02 (0.90–1.16) | 1.01 (0.84–1.21) |
| Obese | 1078 | 376 203 | 1.04 (0.94–1.14) | 1.04 (0.91–1.19) | 1.08 (0.89–1.30) |
| P‐for‐interaction | 0.31 | 0.28 | 0.68 | ||
| Never smokers | 1309 | 803 757 | 1.00 (0.92–1.09) | 1.01 (0.90–1.14) | 0.97 (0.82–1.14) |
| Former smokers | 1689 | 765 086 | 1.00 (0.92–1.08) | 0.96 (0.85–1.07) | 1.06 (0.91–1.24) |
| Current smokers | 876 | 262 982 | 0.97 (0.87–1.08) | 0.96 (0.82–1.12) | 0.96 (0.78–1.17) |
| P‐for‐interaction | 0.90 | 0.77 | 0.61 | ||
| Incident stroke | |||||
| <70 years old | 1552 | 1 392 830 | 1.02 (0.94–1.11) | 1.04 (0.92–1.18) | 1.02 (0.88–1.19) |
| ≥70 years old | 1743 | 442 656 | 1.04 (0.96–1.13) | 1.06 (0.94–1.20) | 1.04 (0.88–1.21) |
| P‐for‐interaction | 0.76 | 0.77 | 0.89 | ||
| Women with diabetes | 659 | 138 940 | 1.23 (1.10–1.38)a | 1.18 (1.01–1.38) | 1.66 (1.31–2.10)a |
| Women without diabetes | 2636 | 1 696 546 | 1.03 (0.96–1.09)a | 1.08 (0.99–1.18) | 0.94 (0.83–1.07)a |
| P‐for‐interaction | 0.004a | 0.33 | <0.0001a | ||
| Family history | 1316 | 612 206 | 1.08 (0.99–1.17) | 1.13 (1.00–1.26) | 1.04 (0.88–1.23) |
| No family history | 1979 | 1 223 280 | 1.06 (0.99–1.14) | 1.08 (0.98–1.20) | 1.06 (0.92–1.22) |
| P‐for‐interaction | 0.79 | 0.60 | 0.83 | ||
| Underweight/normal | 1389 | 791 374 | 1.04 (0.96–1.13) | 1.07 (0.96–1.20) | 1.00 (0.85–1.18) |
| Overweight | 1073 | 583 357 | 1.05 (0.95–1.15) | 1.06 (0.93–1.21) | 1.05 (0.87–1.26) |
| Obese | 772 | 376 203 | 1.15 (1.03–1.28) | 1.23 (1.06–1.42) | 1.15 (0.92–1.43) |
| P‐for‐interaction | 0.27 | 0.26 | 0.61 | ||
| Never smokers | 1266 | 803 757 | 1.02 (0.93–1.11) | 1.02 (0.91–1.16) | 1.02 (0.86–1.21) |
| Former smokers | 1406 | 765 086 | 1.11 (1.02–1.21) | 1.18 (1.05–1.32) | 1.08 (0.91–1.27) |
| Current smokers | 616 | 262 982 | 1.05 (0.93–1.18) | 1.06 (0.89–1.26) | 1.05 (0.83–1.34) |
| P‐for‐interaction | 0.31 | 0.20 | 0.89 | ||
Adjusted for age, time period, region, season, race, smoking status and pack‐years, BMI, menopausal status and hormone use, hypercholesterolemia, hypertension, diabetes, family history of MI, and individual‐ and area‐level SES as appropriate. BMI indicates body mass index; CHD, coronary heart disease; CVD, cardiovascular disease; HRs, hazard ratios; MI, myocardial infarction; PM, particulate matter; SES, socioeconomic status.
a
P‐for‐interaction below our α level for statistical significance of 0.0083.
| Outcome | Cases | Person‐Years | PM10 | PM2.5–10 | PM2.5 |
|---|---|---|---|---|---|
| Multivariable | Multivariable | Multivariable | |||
| Incident CVD | |||||
| <70 years old | 672 | 320 469 | 1.07 (0.93–1.24) | 1.08 (0.89–1.32) | 1.08 (0.82–1.42) |
| ≥70 years old | 1741 | 237 266 | 1.04 (0.94–1.14) | 1.00 (0.87–1.15) | 1.13 (0.95–1.34) |
| P‐for‐interaction | 0.70 | 0.48 | 0.78 | ||
| Women with diabetes | 479 | 50 349 | 1.19 (1.02–1.39) | 1.15 (0.94–1.40) | 1.46 (1.06–2.01) |
| Women without diabetes | 1934 | 507 392 | 1.00 (0.92–1.09) | 0.98 (0.88–1.09) | 1.05 (0.90–1.23) |
| P‐for‐interaction | 0.05 | 0.17 | 0.07 | ||
| Family history | 972 | 179 039 | 1.01 (0.90–1.13) | 0.97 (0.83–1.13) | 1.09 (0.87–1.37) |
| No family history | 1441 | 378 702 | 1.06 (0.96–1.16) | 1.04 (0.92–1.17) | 1.13 (0.94–1.36) |
| P‐for‐interaction | 0.53 | 0.51 | 0.81 | ||
| Underweight/normal | 1008 | 229 716 | 1.03 (0.92–1.15) | 0.98 (0.85–1.14) | 1.16 (0.93–1.43) |
| Overweight | 764 | 182 467 | 1.06 (0.94–1.21) | 1.03 (0.87–1.22) | 1.18 (0.92–1.52) |
| Obese | 587 | 122 863 | 1.02 (0.88–1.19) | 1.02 (0.84–1.25) | 1.04 (0.78–1.40) |
| P‐for‐interaction | 0.90 | 0.90 | 0.81 | ||
| Never smokers | 952 | 248 739 | 1.09 (0.98–1.22) | 1.12 (0.98–1.29) | 1.08 (0.87–1.35) |
| Former smokers | 1119 | 248 456 | 1.04 (0.93–1.16) | 0.97 (0.84–1.13) | 1.23 (0.99–1.51) |
| Current smokers | 340 | 59 419 | 0.83 (0.67–1.04) | 0.76 (0.56–1.03) | 0.91 (0.62–1.33) |
| P‐for‐interaction | 0.10 | 0.05 | 0.38 | ||
| Incident CHD | |||||
| <70 years old | 394 | 320 471 | 1.07 (0.88–1.31) | 1.14 (0.88–1.48) | 0.96 (0.66–1.39) |
| ≥70 years old | 907 | 237 270 | 1.00 (0.87–1.14) | 0.95 (0.79–1.16) | 1.09 (0.86–1.38) |
| P‐for‐interaction | 0.53 | 0.22 | 0.57 | ||
| Women with diabetes | 309 | 50 349 | 1.09 (0.89–1.33) | 1.03 (0.78–1.34) | 1.31 (0.88–1.96) |
| Women without diabetes | 992 | 507 392 | 0.96 (0.85–1.08) | 0.94 (0.80–1.10) | 0.99 (0.79–1.24) |
| P‐for‐interaction | 0.29 | 0.57 | 0.23 | ||
| Family history | 552 | 179 039 | 1.02 (0.87–1.19) | 0.95 (0.77–1.17) | 1.21 (0.90–1.63) |
| No family history | 749 | 378 702 | 0.97 (0.85–1.11) | 0.97 (0.81–1.15) | 0.96 (0.74–1.24) |
| P‐for‐interaction | 0.62 | 0.89 | 0.24 | ||
| Underweight/normal | 496 | 229 716 | 0.98 (0.83–1.15) | 0.97 (0.78–1.20) | 0.99 (0.73–1.35) |
| Overweight | 411 | 182 467 | 0.99 (0.83–1.19) | 0.92 (0.73–1.18) | 1.15 (0.81–1.62) |
| Obese | 351 | 122 863 | 1.01 (0.83–1.23) | 0.96 (0.74–1.26) | 1.13 (0.77–1.65) |
| P‐for‐interaction | 0.97 | 0.96 | 0.79 | ||
| Never smokers | 478 | 248 739 | 1.10 (0.94–1.28) | 1.15 (0.95–1.40) | 1.03 (0.75–1.42) |
| Former smokers | 620 | 248 456 | 0.93 (0.79–1.08) | 0.81 (0.66–1.01) | 1.15 (0.86–1.52) |
| Current smokers | 202 | 59 419 | 0.90 (0.68–1.19) | 0.89 (0.61–1.28) | 0.88 (0.53–1.45) |
| P‐for‐interaction | 0.23 | 0.05 | 0.65 | ||
| Incident stroke | |||||
| <70 years old | 300 | 320 471 | 1.08 (0.87–1.34) | 1.02 (0.75–1.38) | 1.27 (0.84–1.91) |
| ≥70 years old | 894 | 237 270 | 1.07 (0.94–1.22) | 1.04 (0.86–1.25) | 1.16 (0.92–1.47) |
| P‐for‐interaction | 0.92 | 0.89 | 0.72 | ||
| Women with diabetes | 194 | 50 349 | 1.31 (1.05–1.65) | 1.29 (0.97–1.73) | 1.64 (1.00–2.69) |
| Women without diabetes | 1000 | 507 392 | 1.04 (0.93–1.16) | 1.01 (0.87–1.17) | 1.13 (0.91–1.40) |
| P‐for‐interaction | 0.06 | 0.13 | 0.17 | ||
| Family history | 457 | 179 039 | 0.98 (0.83–1.16) | 0.99 (0.79–1.23) | 0.95 (0.68–1.32) |
| No family history | 737 | 378 702 | 1.14 (1.01–1.29) | 1.10 (0.93–1.29) | 1.37 (1.07–1.76) |
| P‐for‐interaction | 0.15 | 0.45 | 0.08 | ||
| Underweight/normal | 542 | 229 716 | 1.06 (0.92–1.23) | 0.98 (0.80–1.19) | 1.33 (1.00–1.78) |
| Overweight | 387 | 182 467 | 1.15 (0.97–1.37) | 1.15 (0.93–1.44) | 1.24 (0.87–1.76) |
| Obese | 254 | 122 863 | 1.02 (0.81–1.28) | 1.06 (0.79–1.43) | 0.93 (0.60–1.46) |
| P‐for‐interaction | 0.66 | 0.54 | 0.42 | ||
| Never smokers | 494 | 248 739 | 1.10 (0.94–1.27) | 1.10 (0.90–1.33) | 1.16 (0.85–1.58) |
| Former smokers | 549 | 248 456 | 1.15 (0.99–1.33) | 1.12 (0.93–1.36) | 1.32 (0.98–1.77) |
| Current smokers | 150 | 59 419 | 0.75 (0.54–1.06) | 0.62 (0.38–1.00) | 0.93 (0.52–1.65) |
| P‐for‐interaction | 0.08 | 0.07 | 0.55 | ||
Adjusted for age, time period, region, season, race, smoking status and pack‐years, BMI, menopausal status and hormone use, hypercholesterolemia, hypertension, diabetes, family history of MI, and individual‐ and area‐level SES as appropriate. BMI indicates body mass index; CHD, coronary heart disease; CVD, cardiovascular disease; HRs, hazard ratios; MI, myocardial infarction; PM, particulate matter; SES, socioeconomic status.
Discussion
In this nationwide prospective study, exposures to PM were associated with small, but nonstatistically significant, elevations in incident CVD, CHD, and stroke in this cohort of US women. However, there were consistently statistically significant elevations in risk for almost all PM size fractions and outcomes among women with diabetes. There were also suggestions of effect modification by region of residence, smoking status, obesity, and age, but they were not consistent across outcomes, size fractions, and time periods.
Overall, we observed larger HRs in models restricted to the later time periods, especially for PM2.5. This may be due to effect modification by age, as there was a suggestion of higher HRs among older participants in the full follow‐up period. These findings may also be due to improvements in our prediction models after 2000. Reductions in measurement error in these models would be expected to lead to increases in the HRs. However, there was overlap in the 95% CIs for the 2 time periods, so it is also possible that our findings of higher risks in the later time period are due to chance.
Our multivariable adjusted results in the full cohort for the full period of follow‐up are generally lower than the majority of studies in the literature, including a previous analysis in the NHS cohort.10, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 In models among women in the Northeastern and Midwestern regions of the United States, we previously observed multivariable adjusted HRs for CHD incidence of 1.10 (95% CI: 0.94 to 1.29), 1.11 (95% CI: 0.79 to 1.55), and 1.04 (95% CI: 0.82 to 1.32) for each 10 μg/m3 increase in PM10, PM2.5, and PM2.5–10, respectively.10, 15 However, our findings for incident CVD in the later time period are comparable to the equivalent meta‐estimate of 10.6% (95% CI 5.4, 16.0) for CVD and PM2.5 from all studies of long‐term exposures published though January 2013.2 The results of studies published since then have been more mixed. In the European Study of Cohorts for Air Pollution Effects (ESCAPE), each 5 μg/m3 increase in PM2.5 was associated with a HR=0.99 (95% CI: 0.91 to 1.08) for overall CVD deaths, with similar findings for ischemic heart disease and MI deaths, and for exposures to PM10 and PM2.5–10.7 They did observe non–statistically significantly increased risks of cerebrovascular death of ≈20% with all size fractions of PM. In related analyses of the incidence of acute coronary events, HRs of 1.13 (95% CI: 0.98 to 1.30) per 5 μg/m3 increase in PM2.5, 1.12 (95% CI: 1.01 to 1.25) per 10 μg/m3 increase in PM10, and 1.06 (95% CI: 0.98 to 1.15) per 5 μg/m3 increase in PM2.5–10 were observed.8 In an analysis of the American Cancer Society Cancer Prevention Study II (ACS) cohort, each 10 μg/m3 increase in PM2.5 was associated with a HR=1.12 (95% CI: 1.10 to 1.15) for CVD mortality, with similar effect sizes for ischemic heart disease and cerebrovascular mortality.16
Previously in the NHS, we observed suggestions of effect modification of our PM2.5 results by a number of cardiovascular risk factors, although none of the modifiers reached statistical significance (defined as P<0.05). In analyses of PM2.5, higher HRs for total CHD were observed among women with diabetes, those with a family history of MI, individuals with hypercholesterolemia or hypertension, former and never smokers, and the obese.15 A few recent long‐term studies have systematically assessed effect modification to identify susceptible subpopulations with mixed results. In both ESCAPE analyses, no effect modification was observed by smoking status, BMI, or hypertension; diabetes was not considered as an effect modifier.7, 8 Recent results from the Agricultural Health Study showed elevated risks of CVD mortality for increasing PM2.5 exposures only among male participants.9 Effect modification by BMI was observed, with the highest risks among men with a BMI ≥26.5 kg/m2. In the ACS, no statistically significant effect modification was observed by baseline report of diabetes, hypertension or existing heart disease.16 A recent review of effect modification in studies of the effects of short‐term PM exposures found strong evidence of effect modification by age, with higher risks among older individuals, weak or limited evidence of effect modification by sex and socioeconomic status, and no effect modification by race.17 Short‐term studies have also examined effect modification by diabetes, and most have suggested that individuals with diabetes are a susceptible subpopulation.3, 4, 29, 30, 31, 32, 33, 34 Therefore, although there is consistent evidence that individuals with diabetes are particularly vulnerable to the cardiovascular effects of acute exposures to air pollution, our study is one of the first to demonstrate high risks of CVD among individuals with diabetes with long‐term exposures to PM.
Differences in susceptibility, especially among individuals with diabetes, have also been reported in studies designed to elucidate the biological mechanisms, typically inflammation, underlying the adverse health effects of PM. The most comparable studies, in terms of the populations studied and the PM2.5 exposure averaging times used, have focused on the effects of PM2.5 on C‐reactive protein (CRP). In the longitudinal Study of Women's Health Across the Nation, associations between PM2.5 averaged over the previous 12 months and CRP were strongest among women with diabetes, with a 72.1% increase (95% CI: 2.9 to 187.8) in CRP for each 10 μg/m3 increase in PM2.5 in the full study population and a 301% increase (no confidence interval provided) in CRP among the oldest participants with diabetes (aged 50.5 to 55 years of age at the start of the study).35 In the German Heinz Nixdorf Recall Study, increases in annual average PM2.5 were associated with increases in CRP in only male participants. However, each 3.91 μg/m3 increase in PM2.5 was associated with an 82.6% increase (95% CI: 1.1 to 230.3) in CRP among women with diabetes, which was the largest increase observed among a number of subgroups examined.36
Few studies have examined the impacts of different exposure averaging windows. In our previous work in the NHS, our findings for all size fractions were generally consistent for exposure averaging times of 12 to 48 months.10, 15 The HRs were most consistent in analyses of PM10 and PM2.5 to 10. This is in contrast to our current findings that show lower HRs for the 60‐ and 120‐month exposure averaging times compared to the 12‐ and 24‐month averages. However, the high correlations in exposures across time windows make it difficult to determine whether these differences are solely due to chance or are related to true differences in risk.
The limitations of this study should be noted. The NHS predominantly comprises middle‐aged and elderly white women who at one time were nurses. Therefore, our results may not be generalizable to men or to more racially or socioeconomically diverse populations. Although we used a sophisticated spatio‐temporal model to estimate ambient PM concentrations at the home addresses of each participant, measurement error may still be present. We do not have information on time‐activity patterns, or housing characteristics that may influence the infiltration of ambient PM. Additionally, the lack of available monitoring information on PM2.5 prior to 1999 and the need to estimate PM2.5 to 10 concentrations by subtraction for the full time period could also lead to exposure misclassification. Although we had time‐varying information on a number of confounders, residual confounding is still always a concern even though validation studies suggest accurate reporting in this group.37 The issue of residual confounding is also raised by our observation of confounding by socioeconomic status, both individual and area level; our inability to control for small‐scale spatial autocorrelation; or spatial patterns in our selected confounders and effect modifiers, including the potentially strong spatial patterns of diabetes prevalence in the United States.
This study also has notable strengths, including extensive follow‐up, a large number of well‐validated cases of CVD, as well as close to 20 years of monthly ambient PM predictions. Compared to a number of studies with only mortality data, we had the ability to examine incident cases. Finally, we have time‐varying information available on a variety of confounders and effect modifiers of interest, which allowed us to evaluate the importance of effect modification independent of key cardiovascular risk factors.
In conclusion, in this nationwide population of middle‐aged and older women, exposures to PM were associated with small, but non–statistically significant increases in the risk of incident cardiovascular disease. This risk was elevated and statistically significant among women with diabetes. There also were suggestions of effect modification by age and region; however, these were not consistent across PM size fractions, time periods of follow‐up, or outcomes. Overall, this study adds to the literature on chronic exposures to air pollution with cardiovascular outcomes, and adds to the limited body of knowledge identifying especially susceptible populations.
Sources of Funding
This work was supported by NIH grants R01 ES017017, UM1 CA186107, R01 HL034594, and R01 HL088521 and American Heart Association Science Development Grant #13SDG14580030.
Disclosures
None.
Acknowledgments
We would like to thank the participants and staff of the Nurses’ Health Study for their valuable contributions.
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© 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
History
Received: 11 August 2015
Accepted: 22 September 2015
Published online: 25 November 2015
Published in print: December 2015
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(J Am Heart Assoc. 2015;4:e002301 https://doi.org/10.1161/JAHA.115.002301)
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Funding Information
NIH: R01 ES017017, UM1 CA186107, R01 HL034594, R01 HL088521
American Heart Association Science Development Grant: 13SDG14580030
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