682 volume 119 | number 5 | May 2011 • Environmental Health Perspectives

Research

Declining or comparatively low cognitive 

function in older adults is associated with loss 

of independence in activities of daily living 

(Greiner et al. 1996; McGuire et al. 2006), 

nursing home admission (Gaugler et al. 2007; 

Joray et al. 2004), hospitalization (Chodosh 

et al. 2004), and mortality (Bassuk et al. 2000; 

Nguyen et al. 2003). The burden associated 

with deficits in cognitive function is expected 

to grow as the global population ages (Ferri 

et al. 2005), but few interventions to prevent, 

delay, or halt the progression of cognitive 

decline are currently available. The potential 

relationship between exposure to common 

environmental toxicants and cognition in 

older adults has received relatively little consideration compared with nonenvironmental 

risk factors, despite evidence that many toxicants may be neurotoxic and the modifiable 

nature of many environmental exposures.

Air pollution may exert adverse effects on 

central nervous system (CNS) function. In 

autopsy studies of dogs and people, CalderónGarcidueñas et al. (2002, 2003, 2004, 2008b) 

found evidence of increased levels of inflammatory mediators, β-amyloid deposition, and 

markers of oxidative damage to DNA, as well 

as evidence of blood–brain barrier disruption,

in the brains of those from a city with high levels of air pollution compared with those from a 

city with low levels. Children and dogs residing 

in a highly polluted city were also more likely 

to exhibit white matter hyperintensities in the 

prefrontal cortex than were those residing in a 

city with lower levels (Calderón-Garcidueñas 

et al. 2008a). Although this ecological evidence 

is insufficient to conclude that air pollution is 

the causal agent, other lines of evidence support this claim. In animal studies, exposure to 

ozone has been linked to neuroinflammation, 

lipid peroxidation, and memory deficits (Block 

and Calderón-Garcidueñas 2009; DoradoMartinez et al. 2001), effects that may be mediated by the induction of systemic inflammation. 

Controlled animal experiments demonstrate 

that ultrafine particulate matter (PM), a component of traffic-related air pollution, can 

translocate to the brain via the olfactory nerve 

or systemic circulation (Elder et al. 2006; 

Oberdörster et al. 2004; Peters et al. 2006), 

where it may promote CNS dysfunction. In 

animal studies, exposure to fine or ultrafine 

PM has been associated with CNS inflammation and lipid peroxidation (Campbell et al. 

2005; Kleinman et al. 2008; Zanchi et al. 

2008), neuronal degeneration (Veronesi et al. 

2005), and behavioral changes (Zanchi et al. 

2008). Alternately, the cardiovascular effects of 

air pollution (Brook 2007; Pope and Dockery 

2006) may lead to CNS dysfunction through 

promotion of vascular brain pathology.

One potential manifestation of CNS dysfunction in older adults is cognitive impairment. We hypothesized that exposure to 

black carbon (BC), a marker of traffic-related 

air pollution, would be associated with cognitive function in a cohort of older men, 

because traffic-related air pollution is a major 

source of exposure to ultrafine PM, which 

may induce CNS dysfunction.

Materials and Methods

Study sample. We drew the study sample 

from participants of the U.S. Department 

of Veterans Affairs (VA) Normative Aging 

Study (NAS), an ongoing longitudinal cohort 

study of men established in 1963 (Bell et al. 

1972). Participants in the NAS are invited 

to undergo an in-person examination every 

3 years. In addition to a physical examination and laboratory tests, participants provide 

information on medical history, lifestyle, and 

demographic factors at each study visit and, 

starting in 1993, have been asked to complete 

a battery of cognitive tests. At each visit, participants provided written informed consent 

as approved by the VA Boston Healthcare 

System Institutional Review Board (IRB). 

Address correspondence to M.C. Power, Harvard 

School of Public Health, EOME, Landmark Center, 

3rd Floor East, P.O. Box 15697, Boston, MA 02215 

USA. Telephone: (617) 721-9984. Fax: (617) 384-

8994. E-mail: mcpower@hsph.harvard.edu

Supplemental Material is available online (doi:10.

1289/ehp.1002767 via http://dx.doi.org/).

We thank S. Melly for his help creating Figure 1 

and J. Weuve for her contribution to the development of this work.

M.C.P. is supported by a grant from the National 

Institute of Environmental Health Sciences (NIEHS; 

T32 ES007069). This study was supported by a grant 

from the U.S. Environmental Protection Agency 

(RD832416 and RD83479801) and by grants from the 

NIEHS (ES00002, ES015172, ES014663, ES005257, 

ES012044, and P01 ES009825). The Cognition and 

Health in Aging Men Project is supported by the 

Clinical Science Research and Development Service, 

U.S. Department of Veterans Affairs, the National 

Institutes of Health (grants R01-AG14345, R01-

AG18436, 5-P42-ES05947, and R01-ES05257), the 

U.S. Department of Agriculture, Agricultural Research 

Service (contract 53-K06-510), and Veterans Affairs 

Merit Review and Research Career Scientist awards 

to A.S. The Veterans Affairs Normative Aging Study 

is supported by the Cooperative Studies Program/

Epidemiology Research and Information Center, U.S. 

Department of Veterans Affairs. 

The views expressed in this article are those of the 

authors and do not necessarily represent the views of 

the U.S. Department of Veterans Affairs.

The authors declare they have no actual or potential 

competing financial interests.

Received 22 July 2010; accepted 20 December 

2010.

Traffic-Related Air Pollution and Cognitive Function in a Cohort of Older Men

Melinda C. Power,1,2 Marc G. Weisskopf,1,2 Stacey E. Alexeeff,1 Brent A. Coull,3 Avron Spiro III,4,5

and Joel Schwartz1,2

1Department of Environmental Health, 2Department of Epidemiology, and 3Department of Biostatistics, Harvard School of Public Health, 

Boston, Massachusetts, USA; 4Veterans Affairs Boston Healthcare System, Boston, Massachusetts, USA; 5Department of Epidemiology, 

Boston University School of Public Health, Boston, Massachusetts, USA

Background: Traffic-related particles induce oxidative stress and may exert adverse effects on 

central nervous system function, which could manifest as cognitive impairment.

Objective: We assessed the association between black carbon (BC), a marker of traffic-related air 

pollution, and cognition in older men.

Methods: A total of 680 men (mean ± SD, 71 ± 7 years of age) from the U.S. Department of 

Veterans Affairs Normative Aging Study completed a battery of seven cognitive tests at least once 

between 1996 and 2007. We assessed long-term exposure to traffic-related air pollution using a validated spatiotemporal land-use regression model for BC.

Results: The association between BC and cognition was nonlinear, and we log-transformed BC 

estimates for all analyses [ln(BC)]. In a multivariable-adjusted model, for each doubling in BC on 

the natural scale, the odds of having a Mini-Mental State Examination (MMSE) score ≤ 25 was 

1.3 times higher [95% confidence interval (CI), 1.1 to 1.6]. In a multivariable-adjusted model for 

global cognitive function, which combined scores from the remaining six tests, a doubling of BC 

was associated with a 0.054 SD lower test score (95% CI, –0.103 to –0.006), an effect size similar 

to that observed with a difference in age of 1.9 years in our data. We found no evidence of heterogeneity by cognitive test. In sensitivity analyses adjusting for past lead exposure, the association with 

MMSE scores was similar (odds ratio = 1.3; 95% CI, 1.1 to 1.7), but the association with global 

cognition was somewhat attenuated (–0.038 per doubling in BC; 95% CI, –0.089 to 0.012).

Conclusions: Ambient traffic-related air pollution was associated with decreased cognitive function in older men.

Key words: aging, black carbon, cognitive dysfunction, epidemiology, particulate matter. Environ 

Health Perspect 119:682–687 (2011). doi:10.1289/ehp.1002767 [Online 20 December 2010]
Traffic-related air pollution and cognition

Environmental Health Perspectives • volume 119 | number 5 | May 2011 683

This study has been approved by the IRBs of 

the participating institutions.

We limited the study sample to participants of the NAS who completed cognitive 

testing between 1996 and 2007 and who lived 

within the range of our BC exposure model. 

These constraints ensured that both cognitive test data and an estimate of BC exposure 

before cognitive assessment were available for 

each study participant. We also excluded participants if they experienced a stroke before

study start. In addition, we restricted the present study population to those self-reporting 

white race/ethnicity.

Exposure assessment. Estimates of BC 

exposure at the residence of each participant were used as a surrogate for individual 

exposure to traffic-related air pollution. We 

estimated BC exposures using a validated 

spatiotemporal land-use regression model 

that, starting in 1995, provides daily estimates 

of BC concentrations throughout the greater 

Boston, Massachusetts, area, which roughly 

includes the area within Interstate 495; details 

of this model have been published previously 

(Gryparis et al. 2007). Briefly, daily average BC estimates from 83 monitoring sites 

were used to develop a BC prediction model. 

Predictors in the final model include information on meteorological conditions (e.g.,

wind speed), land use (e.g., traffic density), 

daily BC concentrations at a central monitor, 

and other descriptors (e.g., day of the week). 

Based on the training set, the model R2 was 

0.83, whereas the average correlation between 

predicted values and observed BC levels in 

four out-of-sample validation samples was 

0.59. For the present study, addresses with 

predicted daily BC concentrations outside the 

range of the exposure measurements from the 

training set were excluded. To create a metric 

of long-term exposure, we took the average 

of the 365 daily estimate at the participant’s 

residential address prior to the date of the first 

cognitive assessment completed on or after 1 

January 1996, the first date for which computation of a prior 1-year average was possible.

Cognitive testing. Since the onset of cognitive testing, the cognitive battery has been 

revised, reflecting both the addition and subtraction of individual cognitive tests. Seven 

cognitive tests have been administered with 

relative consistency across the span of cognitive testing and were considered in this study:

the Mini-Mental State Examination (MMSE), 

the digit span backward test, a verbal fluency 

task, constructional praxis, immediate recall 

of a 10-word list, delayed recall of a 10-word 

list, and a pattern comparison task. The 

MMSE assesses multiple cognitive domains 

and is widely used as a dementia screening and research instrument (Tombaugh 

and McIntyre 1992); the six additional 

tests assess a variety of domains, including 

attention, memory, executive function, language, and visuomotor ability. All cognitive 

tests were drawn from established cognitive 

test batteries, including the Consortium to 

Establish a Registry for Alzheimer Disease 

(CERAD) battery (Morris et al. 1989), 

the Wechsler Adult Intelligence Scales for 

Adults, Revised (WAIS-R) (Wechsler 1981), 

the Neurobehavioral Evaluation System 2 

(Letz 1991), and the Developmental Test 

of Visual-Motor Integration (Beery and 

Buktenica 1967).

The present study includes cognitive 

data from study visits from 1996 to 2007. 

Therefore, the baseline visit for each study 

participant is defined as the first cognitive 

assessment completed on or after 1 January 

1996. This restriction ensures that we have an 

estimate of BC exposure obtained before the 

cognitive test data used in our analyses. Most 

study participants have completed multiple 

waves of cognitive testing, and all available 

cognitive data obtained between 1996 and 

2007 were used in analyses.

Statistical analysis. Because we observed 

a ceiling effect for MMSE scores, with 

many men achieving the maximum score, 

we created a dichotomous variable for inferior MMSE performance for use in analyses. 

MMSE scores are commonly used to screen 

for dementia (Tombaugh and McIntyre 

1992), but few men in our cohort exhibited 

scores that would typically trigger further 

evaluation (< 24). We therefore chose to label 

scores ≤ 25 as “low” MMSE scores (18% of 

our observations). Cognitive test scores for 

the remaining six tests were z-transformed 

based on the mean and standard deviation of 

the individual test scores at baseline and were 

considered as continuous variables in analyses, 

with positive z-scores indicating better performance for all tests.

We evaluated the association between BC 

and the odds of having a low MMSE score 

using logistic regression with generalized estimating equations and empirical variance estimates to account for repeat measures across 

individuals. For the remaining six tests, which 

yield seven unique scores (the pattern comparison task produces two unique scores), we 

treated each cognitive test score as a repeat 

measure of global cognitive function in a random effects linear mixed model. This model 

included a random intercept for individual to 

account for repeat measures within individuals and a random intercept for cognitive test 

score to account for use of seven different 

cognitive test score types as repeat measures of 

global cognitive function. This analysis provides one hypothesis test for the association 

between BC and global cognitive function. 

We evaluated the presence of heterogeneity 

of BC effect across cognitive test score type 

by adding and testing the significance of a 

random slope for BC effect by cognitive test 

score type. If the slope were to be significant, 

this would indicate that the BC effect is dissimilar across cognitive test score types and 

the global analyses would be inappropriate. 

Final models were multivariable adjusted for 

potential confounders or predictors of cognitive function that are not plausible intermediates for a BC–cognition relationship. 

We obtained data on these covariates from 

information collected at each NAS study visit. 

Final multivariable-adjusted models were 

adjusted for age at cognitive assessment as well 

as information on several variables at baseline: 

education (< 12, 12–16, > 16 years), alcohol 

intake (< 2 drinks/day, ≥ 2 drinks/day), physical activity [< 12, 12 to < 30, ≥ 30 metabolic 

equivalent hours (MET-hr) per week], diabetes (yes/no), dark fish consumption (less than

once a week, equal to or greater than once 

a week), computer experience (yes/no), first 

language (English/not English), percentage of 

the participant’s census tract that is nonwhite, 

percentage of the adult residents in the participant’s census tract with at least a college 

degree, an indicator for whether the cognitive 

data were from the participant’s first cognitive assessment (yes/no), and an indicator for 

whether the participant was a part-time resident of the greater Boston area (yes/no). We 

excluded data from study visits for which we 

lacked covariate information from all analyses 

(1.4% of study visits).

We log-transformed BC estimates for use 

in all analyses after examination of restricted 

cubic splines indicated a log-linear relationship. We additionally explored interactions 

between BC and body mass index (BMI), 

hypertension, smoking status, and diabetes 

using multiplicative interaction terms and 

conducted sensitivity analyses further adjusting our multivariable models for smoking 

and BMI. We used R (version 2.10.1; R 

Development Core Team 2010) to examine 

splines and SAS (version 9.2; SAS Institute 

Inc., Cary, NC) for all other analyses.

Sensitivity analyses: potential confounding 

by lead. Exposure to lead may confound the 

relationship between BC and cognitive function. Traffic-related exposures were a primary 

source of lead exposure in the era of leaded 

gasoline, and it is reasonable to expect that 

men with high current traffic-related air pollution exposures had high past traffic-related 

lead exposures. In addition, cumulative past 

lead exposure may have an adverse impact on 

cognitive function in older adults (Shih et al. 

2006), even at low levels (Weisskopf et al. 

2007; Wright et al. 2003). Thus, we conducted 

sensitivity analyses to determine the influence 

of potential confounding by exposure to lead.

Tibia bone lead concentrations, a biomarker for cumulative past lead exposures (Hu et al. 1998), were available for 
Power et al.

684 volume 119 | number 5 | May 2011 • Environmental Health Perspectives

363 participants (53.4%). Tibia bone lead 

concentrations were imputed for 299 participants (44.0%) using a published prediction 

model developed in the NAS (reduced model) 

(Park et al. 2009). Missing data precluded 

imputation of bone lead concentrations for 

18 participants (2.6%), and we excluded these 

participants from the sensitivity analyses. To 

test the sensitivity of our results to potential 

confounding by exposure to lead, we repeated 

the analyses described above additionally 

adjusting for bone lead concentration and 

imputation status (yes/no).

Results

A total of 680 men met the inclusion criteria 

for this study. Within this group, the number 

of men completing at least one wave of cognitive testing varied across the cognitive tests: 

MMSE (n = 671), digit span backward test 

(n = 663), verbal fluency task (n = 670), constructional praxis (n = 626), immediate recall 

of a 10-word list (n = 669), delayed recall of a 

10-word list (n = 667), and pattern comparison task (n = 645).

Table 1 summarizes the baseline characteristics of the cohort. The mean ± SD age of 

the study sample at baseline was 71 ± 7 years 

(range, 51–97 years), and most men had at 

least some college or graduate-level education (70.1%). On average, study participants 

completed 2.14 cognitive assessments (range,

1–5). On the natural scale, 1-year average 

BC exposure estimates ranged from 0.03 to 

1.77 μg/m3 (mean ± SD, 0.58 ± 0.28 μg/m3) 

and exhibited a skewed distribution. Figure 1 

illustrates the substantial spatial variability of 

BC over the study area in the year 1995.

Because the association between cognitive test scores and BC exposure estimates 

appeared log-linear, we log-transformed BC 

for use in all analyses and report associations 

for a doubling in BC concentration on the 

natural scale, or approximately a 0.69 unit 

change in ln(BC). BC exposure was significantly associated with the risk of having a 

low MMSE score [multivariable-adjusted 

odds ratio (OR) = 1.3 for a doubling in BC 

concentration; 95% confidence interval 

(CI), 1.1–1.6; Table 2]. In the analysis of 

Table 1. Baseline characteristics of the cohort 

(n = 680).

Characteristic n (%)

BC concntration 

(mean ± SD)

Age (years)

50–59 48 (7.1) 0.550 ± 0.288

60–69 284 (41.8) 0.574 ± 0.277

70–79 273 (40.1) 0.583 ± 0.267

80–89 72 (10.6) 0.605 ± 0.333

90–99 3 (0.4) 0.466 ± 0.285

Education (years)

< 12 203 (29.9) 0.629 ± 0.304

12–16 338 (49.7) 0.572 ± 0.276

> 16 139 (20.4) 0.521 ± 0.239

First language

English 585 (86.0) 0.578 ± 0.282

Not English/bilingual 95 (14.0) 0.581 ± 0.266

Computer experience

Yes 277 (40.7) 0.542 ± 0.255

No 403 (59.3) 0.604 ± 0.294

Physical activity (MET-hr/week)

< 12 385 (56.6) 0.574 ± 0.286

12 to < 30 178 (26.2) 0.602 ± 0.293

≥ 30 117 (17.2) 0.558 ± 0.237

Alcohol (drinks/day)

≥ 2 166 (24.4) 0.573 ± 0.242

< 2 514 (75.6) 0.581 ± 0.291

Diabetes

Yes 106 (15.6) 0.567 ± 0.254

No 574 (84.4) 0.581 ± 0.285

Consumed dark fish (times/week)

≥ 1 100 (14.7) 0.566 ± 0.283

< 1 580 (85.3) 0.581 ± 0.280

Nonwhite (% of census tract)

< 5% 268 (39.4) 0.521 ± 0.298

5 to < 10% 188 (27.6) 0.541 ± 0.249

 ≥ 10% 224 (32.9) 0.679 ± 0.255

≥ 25 years of age with at least a college degree (% of 

census tract)

< 30% 214 (31.5) 0.626 ± 0.301

30 to < 50% 281 (41.3) 0.540 ± 0.277

≥ 50% 185 (27.2) 0.583 ± 0.250

Smoking

Never 192 (28.2) 0.557 ± 0.255

Former 453 (66.6) 0.584 ± 0.294

Current 35 (5.1) 0.634 ± 0.208

BMI (kg/m2

)

< 25 152 (22.4) 0.587 ± 0.262

≥ 25 528 (77.6) 0.576 ± 0.285

Hypertension

Yes 454 (66.8) 0.587 ± 0.288

No 226 (33.2) 0.562 ± 0.264

Tibia bone lead

Measured 363 (53.4) 0.599 ± 0.286

Imputed 299 (44.0) 0.559 ± 0.271

Missing 18 (2.6) 0.507 ± 0.289 Figure 1. Predicted 1-year average BC exposures (μg/m3

) for the greater Boston area in 1995.

0.02–0.07

0.08–0.19

0.20–0.28

0.29–0.35

0.36–0.41

0.42–0.48

0.49–0.55

0.56–0.61

0.62–0.82

0.83–1.55 

Limited access highway

Town boundary
Traffic-related air pollution and cognition

Environmental Health Perspectives • volume 119 | number 5 | May 2011 685

global cognitive function, which combined 

the remaining seven cognitive test scores, a 

doubling in BC was significantly associated 

with a decrement of 0.054 SD in cognitive test score in our multivariable-adjusted 

model (95% CI, –0.103 to –0.006; Table 

2), an effect similar to a 1.9-year difference in age in our data. We found no evidence of heterogeneity of results across tests 

[p-value = 0.25]. See Supplemental Material, 

Tables 1 and 2 (doi:10.1289/ehp.1002767)]

for the full multivariable-adjusted models. 

In our sensitivity analyses, we additionally 

adjusted for smoking and BMI, and our 

results were materially unchanged.

We found no evidence of interactions 

between BC and diabetes, smoking, BMI or 

hypertension on MMSE scores (p-values for 

all interactions > 0.1). Our exploration of 

potential interactions between BC and these 

factors on global cognitive function, in models that combined the remaining seven test 

scores, suggested that the adverse effects of 

BC were concentrated in overweight and 

obese individuals (p-value for interaction = 

0.10) and in ever smokers (p-value for interaction = 0.07), but we found no evidence for 

effect modification by hypertension or diabetes (p-values for interactions > 0.1).

Among participants with measured tibia 

bone lead concentrations, the mean ± SD bone 

lead concentration was 19.2 ± 11.9 μg lead/g 

bone mineral; among those with imputed 

bone lead concentrations, it was 16.1 ± 7.0 μg 

lead/g bone mineral. In the sensitivity analysis 

that adjusted for estimates of cumulative lead 

exposure, we observed that the multivariableadjusted association between BC and low 

MMSE scores was almost identical (OR = 1.3 

per doubling in BC; 95% CI, 1.1–1.7). The 

association between BC and global cognitive 

function was somewhat attenuated after we 

adjusted for lead in our multivariable-adjusted 

model (–0.038 per doubling in BC; 95% CI,

–0.089 to 0.012).

Discussion

The results of this study suggest an adverse 

effect of traffic-related air pollution on global 

cognitive function in older men. Confounding 

by lead cannot fully account for these findings. 

When we explored the potential for effect 

modification, our results suggest that the effect 

of traffic-related air pollution on cognition 

may be greater in smokers or overweight and 

obese individuals. Because these conditions are 

proinflammatory, this is biologically plausible 

but requires further confirmation.

To our knowledge, this is only the second study to consider the association between 

air pollution and cognitive function in older 

adults. Ranft et al. (2009) considered the 

association between two markers of air pollution, a measure of PM with an aerodynamic 

diameter ≤ 10 μm (PM10) and distance to a 

major road, and cognitive function in 399 

women 68–79 years of age who had lived 

at the same address for 20 years. Residence 

within 50 m of a major road, but not PM10, 

was significantly associated with a decrement 

in the total score of the CERAD-Plus test 

battery, the Stroop test, and a test of olfactory function in multivariable-adjusted models. In addition, residence within 50 m of 

a major road was adversely associated with 

performance on all subtests of the CERAD 

battery, although few individual associations 

achieved statistical significance. Our findings 

are consistent with these, because BC, a traffic 

particle, is strongly associated with distance to 

major road and weakly associated with PM10.

A handful of studies have evaluated the 

association between air pollution and cognitive function in younger populations, and, collectively, they also suggest an adverse effect. In 

a study of adults 20–59 years of age, Chen and 

Schwartz (2009) reported that estimates of 

exposure to ozone, but not PM10, were associated with inferior performance on two of 

three neurobehavioral tests in fully adjusted 

models. Calderón-Garcidueñas et al. (2008a) 

found the performance age on several subtests of the Wechsler Intellegence Scale for 

Children, Revised (WISC-R) to be significantly behind chronological age in children 

residing in cities with high levels of air pollution but not in those residing in cities with low 

levels. In a study that compared neurobehavioral test scores across students from two primary schools in the same city in China, Wang 

et al. (2009) found that students who attended 

the school with high levels of traffic-related 

air pollution were more likely to exhibit poor 

performance than were those who attended 

the school with low levels. Franco Suglia et al. 

(2008) found that higher residence-based 

estimates of average lifetime exposure to BC 

were associated with lower cognitive test scores 

in children 8–11 years of age. Similarly, in 

4- to 5-year-old children from the INMA 

(Environment and Childhood) study, Freire 

et al. (2010) observed that higher residencebased estimates of nitric dioxide were associated with lower scores on all subscales of a 

standardized version of the McCarthy Scales of 

Children’s Abilities, although only one of these 

associations achieved statistical significance.

Several biological mechanisms have been 

proposed to explain how traffic-related air 

pollution may have an adverse effect on 

CNS function. First, ultrafine particles, such 

as those found in diesel exhaust, are small 

enough to pass through the air–blood barrier 

of the lung, enter the systemic circulation, 

and translocate to other body tissues, including the brain (Oberdörster et al. 2004; Peters 

et al. 2006), and may also translocate directly 

to the brain via the olfactory nerve (CalderónGarcidueñas et al. 2003; Elder et al. 2006). 

The presence of such particles in the brain is 

associated with neuroinflammation and oxidative stress (Calderón-Garcidueñas et al. 2003; 

Elder et al. 2006). Second, traffic-related PM 

may exert indirect effects on CNS function 

through effects on cardiovascular health. 

Exposure to PM is associated with a variety of 

cardiovascular end points (Brook 2007; Pope 

and Dockery 2006), including hypertension 

(Auchincloss et al. 2008; Brook 2007) and 

atherosclerosis (Künzli et al. 2005, 2010), as 

well as markers of increased cardiovascular 

risk, including progression of atherosclerotic 

plaques (Floyd et al. 2009; Künzli et al. 2010; 

Sun et al. 2005) and homocysteine (Baccarelli 

et al. 2007; Park et al. 2008). Several of these 

factors are also associated with worse cognitive 

function (Komulainen et al. 2007; Qiu et al. 

2005; Sander et al. 2010; Vidal et al. 2008), 

which likely reflects the link between cognitive impairment and vascular brain pathology.

The present study has several strengths. The 

BC exposure model enabled us to report individual estimates of long-term exposure to traffic-related air pollution. In our study, we were 

also able to control for socioeconomic status 

(SES) using both personal and residence-based 

characteristics, which should limit residual confounding by this factor. Furthermore, we were 

able to address the potential for confounding 

by past exposure to lead, another environmental pollutant that may affect cognitive function 

in older adults, using a combination of measured and imputed biomarkers for cumulative 

past lead exposure. Although correlated with 

traffic-related air pollution, exposure to lead 

cannot completely account for the observed 

associations. Finally, our results are based on a 

substantial number of observations, given that, 

on average, each of our 680 participants completed 2.14 cognitive testing sessions.

Table 2. Adjusted associations for a doubling in BC concentration on the natural scale and cognitive test 

score(s).

Effect per doubling in BC concentration (95% CI)

Analysis Model 1: age adjusted

Model 2: age and 

education adjusted

Model 3: multivariable 

adjusteda

Low MMSE score OR 1.4 (1.1 to 1.6) 1.3 (1.0 to 1.5) 1.3 (1.1 to 1.6)

Global analysis estimate –0.073 (–0.122 to –0.023) –0.052 (–0.100 to –0.004) –0.054 (–0.103 to –0.006)

aAdjusted for age, education, first language, computer experience, physical activity, alcohol consumption, diabetes, 

dark fish consumption, percentage of residential census tract that is nonwhite, percentage of residential census tract 

adults with a college degree, indicator for first cognitive assessment, and indicator for part-time resident.
Power et al.

686 volume 119 | number 5 | May 2011 • Environmental Health Perspectives

Several potential sources of bias must be 

considered when interpreting our results. The 

use of exposure estimates based on residential 

address may misclassify personal exposure levels. However, the lack of significant occupational or commuting exposure in this largely 

retired cohort should mean that residencebased exposure estimates are an excellent 

measure of personal exposures over the past 

few years. Because 87% of our participants 

have lived at the same address for at least 

5 years before their baseline cognitive assessment (mean ± SD duration, 18 ± 12 years) 

and because people are likely to choose similar neighborhoods when relocating, misclassification due to participant relocation is also 

expected to be minimal. In addition, occasional exposures encountered elsewhere are 

unlikely to be correlated with cognitive function and so would result in nondifferential 

misclassification of exposure, which would 

be expected to attenuate our effect estimates 

(Gryparis et al. 2009). Similarly, nondifferential misclassification of cognitive function 

is expected when assessing cognitive function and would make it harder to detect a 

true effect, although our use of global analyses helps to reduce the impact of noise by 

using multiple measures to capture underlying global cognitive function. We observed 

statistically significant associations between 

BC and cognitive function, when measured 

as the risk of a “low” MMSE score and as 

overall performance on our battery of cognitive tests, despite the presence of misclassification, suggesting that the true association 

may be even stronger than what we observed. 

Although residual confounding cannot be 

excluded, we do not believe confounding 

to be a major source of bias in the present 

study given the availability of information 

on potential confounders and the use of several different, related variables to account for 

SES. Moreover, we do not believe confounding by regional pollutants, which may have 

an independent effect on cognition, to be a 

major source of bias. Correlation between 

BC and regional pollutants may be induced 

through similar spatial or temporal patterns. 

However, traffic-related air pollution exhibits significant spatial variation not shared by 

more regional pollutants [e.g., ozone or particulate matter with an aerodynamic diameter 

< 2.5 μm (PM2.5)], and temporal correlation 

is quite weak between our 1-year residencebased BC estimates (which have varying start 

dates depending on the timing of the baseline 

cognitive assessment) and the 1-year average 

of ozone concentrations at four central monitors in the Boston area (r = –0.03) or 1-year 

average PM2.5 concentrations at a central site 

monitor in Boston (r = 0.16) calculated over 

the same time periods. Incomplete follow-up 

may induce bias in the present study but is 

unlikely to account for the observed associations. In our study, and generally in studies 

of older adults, participants who are lost to 

follow-up exhibit lower cognitive test scores 

than do those who complete follow-up (Euser 

et al. 2008). Because air pollution has been 

related to cardiovascular morbidity and mortality (Brook 2007; Pope and Dockery 2006) 

and because we observed lower BC estimates 

in participants with three or more follow-up 

visits, we expect that a greater loss of those 

with both lower cognitive test scores and 

higher BC estimates to lead to an underestimate of the association. Thus, the true association may be stronger than that observed.

One additional limitation lies in our 

inability to attribute our findings to a particular traffic-related exposure. We have estimated 

exposure to BC particles for each participant. 

However, traffic-related air pollution is a complex mixture of gases and particles, and BC is 

correlated with other components of the mixture. Although existing evidence suggests that 

PM, specifically ultrafine PM, is a likely agent 

for adverse effects to the CNS (CalderónGarcidueñas et al. 2003; Elder et al. 2006; 

Oberdörster et al. 2004; Peters et al. 2006), 

other components may be important. In 

addition, measures of traffic-related air pollution are often correlated with other environmental exposures. Although we were able 

to address the potential for lead to account 

for the observed associations, other exposures 

must be considered, including noise. Elevated 

noise levels have been linked to metrics of 

cognitive function in children in a handful 

of studies (Clark et al. 2006; Stansfeld et al. 

2005), and chronic occupational exposure 

to noise is associated with neurophysiological measures related to attention in adults 

(Gomes et al. 1999). Given the high correlation between measures of traffic-related air 

pollution and noise (Allen et al. 2009; Davies 

et al. 2009), it is possible that the associations observed for one exposure are attributable to the other. Further research to evaluate 

the relative contribution of particular trafficrelated pollutants or other exposures to the 

observed association is warranted.

Conclusions

Our findings suggest that traffic-related air 

pollution may have an adverse effect on cognition in older men. This is the first study to 

find an association between traffic-related air 

pollution and cognition in older men, and 

only the second to consider the relationship 

in older adults. Given the ubiquitous nature 

of the exposure, if traffic-related air pollution 

is causally related to cognitive impairment in 

older adults, implementation of interventions 

to reduce exposure, including establishment 

of more stringent emissions standards, would 

be expected to have substantial benefits.

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