ArticlePDF Available

The relation between past exposure to fine particulate air pollution and prevalent anxiety: Observational cohort study


Abstract and Figures

To determine whether higher past exposure to particulate air pollution is associated with prevalent high symptoms of anxiety. Observational cohort study. Nurses' Health Study. 71 271 women enrolled in the Nurses' Health Study residing throughout the contiguous United States who had valid estimates on exposure to particulate matter for at least one exposure period of interest and data on anxiety symptoms. Meaningfully high symptoms of anxiety, defined as a score of 6 points or greater on the phobic anxiety subscale of the Crown-Crisp index, administered in 2004. The 71 271 eligible women were aged between 57 and 85 years (mean 70 years) at the time of assessment of anxiety symptoms, with a prevalence of high anxiety symptoms of 15%. Exposure to particulate matter was characterized using estimated average exposure to particulate matter <2.5 μm in diameter (PM2.5) and 2.5 to 10 μm in diameter (PM2.5-10) in the one month, three months, six months, one year, and 15 years prior to assessment of anxiety symptoms, and residential distance to the nearest major road two years prior to assessment. Significantly increased odds of high anxiety symptoms were observed with higher exposure to PM2.5 for multiple averaging periods (for example, odds ratio per 10 µg/m(3) increase in prior one month average PM2.5: 1.12, 95% confidence interval 1.06 to 1.19; in prior 12 month average PM2.5: 1.15, 1.06 to 1.26). Models including multiple exposure windows suggested short term averaging periods were more relevant than long term averaging periods. There was no association between anxiety and exposure to PM2.5-10. Residential proximity to major roads was not related to anxiety symptoms in a dose dependent manner. Exposure to fine particulate matter (PM2.5) was associated with high symptoms of anxiety, with more recent exposures potentially more relevant than more distant exposures. Research evaluating whether reductions in exposure to ambient PM2.5 would reduce the population level burden of clinically relevant symptoms of anxiety is warranted. © Power et al 2015.
Content may be subject to copyright.
open access
the bmj |
2015;350:h1111 | doi: 10.1136/bmj.h1111
1Department of Epidemiology,
Harvard School of Public Health,
Boston MA, USA
2Department of Environmental
Health, Harvard School of Public
Health, Boston MA, USA
3Department of Epidemiology,
Johns Hopkins Bloomberg
School of Public Health,
Baltimore MD, USA
4Channing Division of Network
Medicine, Department of
Medicine at Brigham and
Women’s Hospital and Harvard
Medical School, Boston MA,
5Department of Psychiatry,
Brigham and Women’s Hospital
and Harvard Medical School,
Boston MA, USA
Correspondence to: M C Power
Johns Hopkins Universit y,
Phipps 446D, 600 North Wolfe
Street , Baltimore, MD 21287,
USA melindacpower@gmail.
Additional material is published
online only. To view please visit
the jour nal online ( http://
dx .d oi .o rg /10.113 6/BMJ.h1111)
Cite this a s: BMJ;:h
doi:10.1136/b mj .h1111
Accepted: 5 February 2015
The relation between past exposure to ne particulate air
pollution and prevalent anxiety: observational cohort study
Melinda C Power,1, 2, 3 Marianthi-Anna Kioumourtzoglou,2 Jaime E Hart,2, 4 Olivia I Okereke,1, 4, 5
Francine Laden,1, 2, 4 Marc G Weisskopf1, 2
To determine whether higher past exposure to
particulate air pollution is associated with prevalent
high symptoms of anxiety.
Observational cohort study.
Nurses’ Health Study.
71 271 women enrolled in the Nurses’ Health Study
residing throughout the contiguous United States who
had valid estimates on exposure to particulate matter
for at least one exposure period of interest and data on
anxiety symptoms.
Meaningfully high symptoms of anxiety, dened as a
score of 6 points or greater on the phobic anxiety
subscale of the Crown-Crisp index, administered
The 71 271 eligible women were aged between 57 and
85 years (mean 70 years) at the time of assessment of
anxiety symptoms, with a prevalence of high anxiety
symptoms of 15%. Exposure to particulate matter was
characterized using estimated average exposure to
particulate matter <2.5 μm in diameter (PM2.5) and 2.5
to 10 μm in diameter (PM2.5–10) in the one month, three
months, six months, one year, and 15 years prior to
assessment of anxiety symptoms, and residential
distance to the nearest major road two years prior to
assessment. Signicantly increased odds of high
anxiety symptoms were observed with higher exposure
to PM2.5 for multiple averaging periods (for example,
odds ratio per 10 μg/m3 increase in prior one month
average PM2.5: 1.12, 95% condence interval 1.06 to
1.19; in prior 12 month average PM2.5: 1.15, 1.06 to 1.26).
Models including multiple exposure windows
suggested short term averaging periods were more
relevant than long term averaging periods. There was
no association between anxiety and exposure to
PM2.5–10. Residential proximity to major roads was not
related to anxiety symptoms in a dose dependent
Exposure to ne particulate matter (PM2.5) was
associated with high symptoms of anxiety, with more
recent exposures potentially more relevant than more
distant exposures. Research evaluating whether
reductions in exposure to ambient PM2.5 would reduce
the population level burden of clinically relevant
symptoms of anxiety is warranted.
Anxiety disorders, characterized by disruptive fear,
worry, and related behavioral disturbances such as
avoidance or physical sensations of hyperarousal,1 are
the most common type of psychiatric disorder in the
general population.2 Globally, approximately 16% of
people will have an anxiety disorder in their lifetime
and 11% will have experienced an anxiety disorder in
the past year.2 Anxiety disorders are associated with
reduced productivity and increased psychiatric and
non-psychiatric medical care, absenteeism, and risk of
suicide.3 In 2010, anxiety disorders accounted for
approximately 26.8 million disability adjusted life years
worldwide.4 The monetary cost of anxiety disorders is
also substantial; in the United States, the annual direct
cost of anxiety disorders in the 1990s has been esti-
mated to be $42.3bn (£27.3bn; €37.3bn).5 Women have a
higher prevalence of anxiety disorders than men6 and
the onset for most anxiety disorders is commonly in
adolescence or young adulthood. However, the inci-
dence of anxiety disorders remains substantial in mid-
life, and new cases continue to arise into later life,
especially in the case of generalized anxiety disorder.7
Although numerous pharmacologic and non-pharma-
cologic therapies are available, remission is not always
possible. Many people have persistent symptoms
despite use of rst line treatments.8
Given the substantial personal and societal burden
from anxiety and the problem of treatment resistance, it
is imperative to identify modiable risk factors for anxi-
ety disorders and symptoms. One important environ-
mental exposure that may be related to anxiety is air
pollution. Specically, exposure to particulate matter air
pollution may induce or exacerbate anxiety through
increased oxidative stress and systemic inammation9–1 7
or through promotion or aggravation of chronic dis-
ease.18–3 2 Though there is a small set of studies consider-
ing the association between air pollution and mental
health outcomes,33–44 we are aware of only two small
Toxicological work suggests exposure to particulate air pollution may induce or
exacerbate anxiety through increased oxidative stress and systemic inflammation
While a small but growing body of literature suggests an association between air
pollution and mental health outcomes, including anxiety, data on the relation
between exposure to particulate air pollution and anxiety in humans is lacking
Our study suggests that higher exposure to PM2.5 (particulate matter <2.5 μm in
diameter) especially higher recent exposure, is associated with an increased risk of
high symptoms of anxiety
2doi: 10.1136/bmj.h1111 |
2015;350:h1111 | the bmj
studies that considered anxiety, and neither looked at
total particulate matter. The rst (n=1002) reported that
ozone levels in the prior week were associated with anx-
iety symptoms,33 whereas the second (n=100) reported
that cumulative exposure to airborne manganese was
associated with anxiety symptoms.44 Epidemiologic
research on the relation between exposure to particulate
matter and anxiety is clearly lacking; we evaluated this
association in a large prospective cohort study. Speci-
cally, we hypothesized that higher exposure to particu-
late matter would be associated with a greater risk of
high symptoms of anxiety.
The most biologically relevant period of exposure is
currently unknown. If particulate matter induces anxi-
ety through chronic oxidative stress, inammation, or
induction of chronic disease, long term cumulative
exposure is most likely relevant. If particulate matter
aggravates an existing propensity for anxiety symptoms,
through either aggravation of chronic disease or tran-
sient changes in oxidative stress or inammation, expo-
sures closer to the time of symptom assessment may be
relevant. Therefore, we considered the association
between high anxiety symptoms and exposure to partic-
ulate matter averaged over ve periods prior to the
assessment of anxiety symptoms, specied a priori,
ranging from a measure of long term, cumulative expo-
sure (prior 15 years) to a measure of recent exposure
(prior month).
Study population
The Nurses’ Health Study is a prospective cohort study
of women that began in 1976. A total of 121 701 married
registered nurses, ages 30–55, residing in 11 states, were
originally enrolled; at least 10 participants now reside
in each of the 48 continental states. All participants are
mailed follow-up questionnaires every two years, with
a response rate of greater than 90% for each question-
naire.45 As such, we receive updated information on res-
idential address biennially, and we have geocoded all
home addresses 1986 to 2007 within the contiguous
United States to obtain latitude and longitude, allowing
estimation of exposure to particulate air pollution. The
Crown-Crisp index phobic anxiety scale, one of six
scales from the Crown-Crisp experiential index, is a
measure of anxiety symptom levels and was included in
the 1988 and 2004 questionnaires. As our exposure
data were available from 1988 onward (inclusive), we
used data from the 2004 Crown-Crisp index phobic anx-
iety scale as our outcome measure of anxiety. The
nurses’ provided implied informed consent by comple-
tion and return of each questionnaire.
Residential proximity to roadways
Using geographic information soware (ArcGIS, Version
10.2; Esri, CA), we computed distance from the residen-
tial address of each participant in 2002, up to 500 m,
with a street level geocoding match to the nearest US
census feature class code A1 (limited access to primary
roads with dened exits and divided directions of
travel, that is, interstate highways), A2 (primary major,
non-interstate highways and major roads without
access restrictions), or A3 (smaller, secondary roads,
typically with more than two lanes) road segment.
Distance to a major road is a commonly used proxy
fortrac related exposures, including trac related
airpollution (which typically contains a high proportion
of ultrane particles, those <0.1 μm in diameter).46–48
Weclassied distance to the nearest major road a priori
as <50 m, 50 to <200 m, or 200 m, based on the
observed pattern of particulate concentrations with
increasing distance47–4 9 and the distribution of roadway
proximity in our sample.
Particulate matter air pollution
We used spatiotemporal prediction models yielding
monthly estimates of exposure to particulate matter
<10 μm (PM10) and <2.5 μm (PM2.5 or ne particulate
matter) in aerodynamic diameter from January 1988
onward at the residential address with at least a zip
code level geocoding match for each participant to
derive multiple exposure metrics for each participant.
These models cover the contiguous United States and
are extensions of previously described models covering
a more limited area.50–52 Data used in these models
included nationwide monitor data, geographic data
(for example, distance to major roadway, population
density, elevation, proportion of urban land use, point
or area source emissions), and meteorological data (for
example, temperature, wind speed, precipitation,
barometric pressure). As nationwide PM2.5 monitor
data were unavailable prior to 1999, our pre-1999 esti-
mates of PM2.5 exposure were derived from a model that
estimates the predicted ratio of PM2.5 to PM10 between
1988 and 1999; to get PM2.5 predictions we combined
the results of this model with estimates from the PM10
model. We derived estimates of exposure to coarse
(PM2. 5–1 0) particulate matter by taking the dierence
between PM10 and PM2.5 estimates. For the current anal-
yses we used these models to derive measures of aver-
age exposure to PM2.5 and PM2.5 –1 0, at the residential
address of each participant for several exposure peri-
ods, including the average exposure between 1 January
1988 and 31 December 2003, and over the 1, 3, 6, and 12
calendar months prior to the participant’s 2004 ques-
tionnaire cycle return date (that is, for a questionnaire
returned in July 2004, we use the average exposure in
June 2004 for the one month averaging period).
Anxiety symptoms
The Crown-Crisp index phobic anxiety scale consists of
eight self rated questions about fearfulness and desire
for avoidance of common situations or environments
(that is, having “unreasonable fear of enclosed spaces”,
being “scared of heights”, disliking “going out alone”,
feeling “panicky in crowds”, feeling “more relaxed
indoors”, feeling “uneasy traveling on buses or trains”)
and tendency to worry (that is, “about getting some
incurable illness”, worrying “unduly when relatives are
late coming home”); total possible index scores range
from 0–16 points, with higher scores indicating more
anxiety.53 We required complete data on all eight items
3the bmj |
2015;350:h1111 | doi: 10.1136/bmj.h1111
to compute a total score. The Crown-Crisp index phobic
anxiety scale has been shown to dierentiate between
people with general anxiety or phobias from those with
other psychiatric conditions and healthy comparison
participants53 54 and has been used in population based
research.55–61 For primary analyses, we dichotomized
Crown-Crisp index phobic anxiety scale scores from
2004 and considered those with a score of 6 points or
more to have high symptoms of anxiety, as prior work
suggests that this cut-o represents a clinically import-
ant threshold.58 61
Covariates included in all models were selected a priori
because they were thought to be potential confounders
or proxies for potential confounders (for example, socio-
economic status) and include calendar month of ques-
tionnaire return (categorical month), educational
attainment (RN, BA, MA, or PhD), husband’s educational
attainment (12 years, 12–16 years, >16 years, not applica-
ble, missing), age, age squared, married or has a partner
(yes/no), employment status (yes/no), physical activity
(<12, 12 to 30, >30 metabolic equivalent task hours per
week), three residential census tract level characteristics
(percent white race/ethnicity, percent of adults without a
high school diploma, and median home value; in
fourths), region of residence (north east, south, midwest,
west), residence within a metropolitan statistical area
(yes/no), and social support62 (low, low-medium,
medium, high social networks). Many covariates were
assessed at multiple cycles; we used the value at the 1988
or closest available questionnaire when considering the
1988–2003 averaging period and the 2002 or closest
available questionnaire in models considering roadway
proximity or particulate matter exposures within the year
prior to the 2004 questionnaire return. With the excep-
tion of month of questionnaire return, we used missing
indicators when covariate data were missing for more
than 2% of our sample and replaced missing data with
median or mode values when covariate data were miss-
ing in less than 2% of our sample.
Primary analysis
For each model we restricted our analytical sample to
people with 2004 Crown-Crisp index phobic anxiety
scale scores and relevant exposure data. We used sepa-
rate logistic regression models to estimate the associa-
tion between each exposure and high anxiety symptoms
(Crown-Crisp index phobic anxiety scale score 6). For
models considering exposure to particulate matter, we
evaluated the shape of the dose-response curve using
penalized splines and report analyses using both hs
of exposure and linear terms. As exposures to particu-
late matter are correlated across averaging periods (see
supplementary table e1), it is challenging to determine
which exposure periods are most relevant when multi-
ple periods appear associated with anxiety. Therefore,
we also considered mutually adjusted models including
either 1988–2003 and past one month or past 12 month
and past one month exposures to particulate matter
parameterized using penalized splines to tackle
whether long term or short term exposures were more
relevant when we observed an association between
anxiety and multiple averaging periods. To avoid the
potential for dierences in the variability of metrics for
exposure to particulate matter across the two averaging
periods to inuence the ndings, we used z score trans-
formations of each of the particulate matter exposures
(that is, one month, 12 months, and 1988–2003) in the
mutually adjusted models.
Sensitivity analyses
We conducted several sensitivity analyses to examine
the robustness of our primary ndings, including use of
alternate categorizations for roadway proximity (<50 m,
50–200 m, and >200 m from A1 or A2 roadways;
<100 m, 100–300 m, and >300 m from either A1, A2, or
A3 roadways or A1 or A2 only roadways; and as a contin-
uous variable for distance from A1, A2, or A3 roadways
using a linear or spline parameterization within the
range of 0 to 500 m); additional adjustment for individ-
ual level covariates oen correlated with anxiety symp-
toms but which were not expected to be confounders,
including physical functioning63 (high, low), self rated
health (excellent or very good, poor to average), num-
ber of major medical comorbidities (3, <3), alcohol
consumption (non-drinker, <3, 3–6, >7 alcoholic drinks
per week), body mass index (normal, overweight,
obese), and smoking status (never, former, current);
restriction to non-movers (to reduce misclassication of
exposure measures given some participants changed
addresses but exact move dates are unknown); restric-
tion to those who returned the questionnaire within
three months of the initial mailing (to reduce misclassi-
cation of short term exposure measures, which are
based on the return date for the questionnaire); restric-
tion to those living in a metropolitan area, dened
using rural-urban commuting codes64 (to reduce poten-
tial confounding by urban versus rural environments);
restriction to non-Hispanic white participants (96.7% of
the sample, to allay concerns about confounding by
race); use of negative binomial regression, which con-
siders Crown-Crisp index phobic anxiety scale scores as
count data and is similar to, but more appropriate and
generally more conservative than Poisson regression
when dealing with over-dispersed count data; and use
of an alternate case denition with improved sensitivity
but less specicity where we considered all people with
a Crown-Crisp index phobic anxiety scale score of 6 or
more and/or self report of use of anti-anxiety or antide-
pressant medications on the 2004 questionnaire to
have high anxiety symptoms.
Eect modication
We used multiplicative interaction terms and likelihood
ratio tests to evaluate evidence for eect modication
by several factors. These were residence within a metro-
politan statistical area (yes/no) and United States cen-
sus region (north east, south, midwest, west), as
particulate matter composition may vary spatially;
prevalent reactive airway disease (chronic obstructive
pulmonary disease or asthma), atrial brillation, heart
4doi: 10.1136/bmj.h1111 |
2015;350:h1111 | the bmj
failure, or multiple major medical conditions at the time
of anxiety assessment (yes/no), as particulate matter
may lead to anxiety through aggravation of symptoms
of common medical conditions; age (over or under 65 at
the time of anxiety assessment), as anxiety incidence
and prevalence change with age; and 1988 Crown-Crisp
index phobic anxiety scale score (0–1, 2–5, 6), given
that high anxiety symptoms may have been present
prior to the 2004 assessment. To limit the number of
tests, we evaluated eect modication only when pri-
mary analyses indicated a main eect, and then only for
the averaging period we judged to have the strongest
association. We made no other adjustments to account
for multiple comparisons. We report 95% condence
intervals and consider a P value <0.05 to be statistically
signicant. All analyses were conducted using SAS,
Version 9.3 or R, Version 3.0.1.
Sample sizes diered across analyses, based on avail-
ability of valid estimates on exposure (n=63 677 for
roadway proximity analyses, n=69 966 for 1988–2003
average analyses of exposure to particulate matter, and
n=71 271 for all other analyses). Among the largest
group (n=71 271), at the time of completion of the
Crown-Crisp index phobic anxiety scale the women in
our sample were on average aged 70 (SD 7, range 57–85)
years, 16% (n=11 320) of them reported current use of
antidepressants and/or anti-anxiety medications, and
the prevalence of high anxiety symptoms (Crown-Crisp
index phobic anxiety scale 6) was 15% (n=10 818) (g 1).
Table 1 shows the socioeconomic characteristics of this
sample. Of the 63 677 with valid estimates of 2002 resi-
dential roadway proximity, distance to the nearest
major road was >200 m for 59.0% (n=37 545), 50–200 m
for 26.4% (n=16 802), and <50 m for 14.7% (n=1120). In
line with temporal trends, mean estimates for exposure
to PM2.5 and PM2 .5 –10 were highest for the 1988–2003 expo-
sure period (Table 2 and supplementary table e1). For
example, the mean (SD) of exposures to PM2.5 –10 particu-
late matter was 9.0 μg/m3 (SD 4.1) in 1988–2003 com-
pared with 7.3 (4.8 μg/m3) for the one month averaging
period. Similarly, the mean (SD) of exposures to PM2.5
particulate matter was 13.8 (2.8 μg/m3) in 1988–2003
Crown-Crisp index score
Participants (%)
Fig  | Distribution of Crown-Crisp index phobic anxiety scale
scores among eligible participants of Nurses’ Health Study
Table  | Socioeconomic characteristics from  or
nearest available Nurses’ Health Study questionnaire
Characteristics No (%) of women (n = )
Educational attainment :
Registered nurse 44 907 (63.0)
Bachelors degree 13 368 (18.8)
Master s degree or PhD 66 07 (9.3)
Missing 6389 (9.0)
Husband’s education:
High school degree or less 24 664 (34.6)
College degree 16 321 (22.9)
P rofessional or gr aduate school
13 978 (19.6)
Not applicable 49 7 7 (7.0 )
Missing 11 331 (15.9)
Marital/partner status:
No current life partner 20 521 (28.8)
Current life partner 49 855 (70.0)
Missing 895 (1.3)
Employment status:
Currently not employed outside
the home
46 617 (65.4)
Currently employed outside the
23 891 (33.5)
Missing 763 (1.1)
Physical activity:
<12 MET S/we ek 32 161 (45.1)
12 to <30 METS/week 27 582 (38.7)
30 METS/week 11 290 (15.8)
Missing 238 (0.3)
Percent of census tr act, white race/ethnicity:
<85% 15 627 (21.9)
85% to <94% 18 858 (26.5)
94% to <97% 15 992 (22.4)
97% 20 789 (29.2)
Missing 5 (0.0)
Percent of census tr act, adult residents without a high school
<5% 8911 (12 .5)
5% to <10% 19 211 (27.0)
10% to <15% 18 037 (25.3)
15% 25 107 (35.2)
Missing 5 (0.0)
Median home value ($):
<95 000 16 934 (23.8)
95 000 to <135 000 18 143 (25.5)
135 000 to <210 000 19 959 (28.0)
210 000 16 139 (22.7)
Missing 96 (0.0)
Region of residence:
North east 35 040 (49.2)
Midwest 12 355 (17.3)
West 10 199 (14.3)
South 13 677 (19.2)
Residence within a metropolitan statistical district:
Yes 64 648 (90.7)
No 662 3 (9.3)
Social support (Berkman-Syme index):
Low 4264 (6.0)
Low-medium 20 148 (28.3)
Medium 10 283 (14.4)
High 28 310 (39.7)
Missing 826 6 (11.6)
$1.00 (£0.65; €0.88).
5the bmj |
2015;350:h1111 | doi: 10.1136/bmj.h1111
compared with 12.7 (4.2 μg/m3) for the one month aver-
aging period.
Residential proximity to roadways
Nurses who lived 50 to 200 m from the nearest major
road were more likely to have increased Crown-Crisp
index phobic anxiety scale scores than those living
>200 m away (adjusted odds ratio 1.06, 95% con-
dence interval 1.01 to 1.12; P=0.03). However, there was
no evidence of a dose-response pattern, as those living
within 50 m of the nearest major road did not have
increased odds (adjusted odds ratio 1.01, 0.95 to 1.08;
P=0.74). Findings of all sensitivity analyses were simi-
lar or more uniformly null (see supplementary table e2
and gure e1).
Particulate matter
We observed associations between higher PM2.5 and
high anxiety across several averaging periods. Given
evidence for slightly non-linear dose-response patterns
in some averaging periods (see supplementary gure
e2), we report associations with both hs of exposure
(g 2) and per 10 μg/m3 increase in exposure (table 2).
Notably, while associations were similar across 1, 3, 6,
and 12 month averaging periods, associations for the
1988–2003 averaging period were weaker than for the
shorter averaging periods. All sensitivity analyses were
reasonably consistent with our primary models (see
supplementary tables e4 to e10). Mutually adjusted
models suggest that these associations were primarily
driven by an association between anxiety and shorter
averaging periods (g 3). There was little evidence to
support an association between high anxiety and expo-
sure to PM2. 5–1 0 in either our primary (see supplemen-
tary table e3 and gure e3) or our sensitivity analyses
(see supplementary tables e4 to e10). We did not
observe signicant eect modication of the associa-
tion with one month PM2.5 by any of the proposed vari-
ables (all likelihood ratio test P>0.16).
Table  | Odds ratio (% condence interval) for  g/m increase in exposure to PM.
over multiple averaging periods and high symptoms of anxiety in participants of Nurses’
Health Study
Period Mean (SD) PM. (g/m) Odds ratio* (% CI) P value
1 month 12.74 (4.18) 1.12 (1.0 6 to 1.19) 0.0001
3 months 12.13 (3.40 ) 1.13 (1.0 6 to 1.2 1) 0.0004
6 months 11.59 (2. 77) 1.14 (1.05 t o 1.23) 0.002
12 months 11.3 8 (2.6 0) 1.15 (1.06 to 1. 25) 0.001
198 8–2 0 03 13. 75 (2. 82) 1.09 (1.0 1 to 1.18) 0.03
PM2.5=particulate matter <2.5 μm in diameter.
*Adjuste d for month of ques tionnaire retur n, nurse’s educ ation, husband ’s education, age, age squared,
whethe r the nurse has a partne r, employment sta tus, physical activ ity, percent of re sidential censu s tract that
is white, percent of r esidential cens us tract adult s who lack a high sc hool educatio n, median home val ue of
residential census trac t, geographic regio n, residence wit hin a metropoli tan statistical area , and social
Fihs of PM
2.5 exposure
1 month
Odds ratio (95% CI)
h (ref)
3 months
Odds ratio (95% CI)
6 months
Odds ratio (95% CI)
12 months
Odds ratio (95% CI)
Odds ratio (95% CI)
Fig  | Adjusted* odds ratios (% condence intervals) between particulate matter <. m in diameter (PM.)
considered using hs of exposure, over multiple averaging periods and high symptoms of anxiety in Nurses’ Health
Study. *Adjusted for month of questionnaire return, nurse’s education, husband’s education, age, age squared, whether
the nurse has a partner, employment status, physical activity, percent of residential census tract that is white, percent of
residential census tract adults who lack a high school education, median home value of residential census tract,
geographic region, residence within a metropolitan statistical area, and social support
6doi: 10.1136/bmj.h1111 |
2015;350:h1111 | the bmj
Our data support an association between exposure to
particulate matter of <2.5 μm in diameter (PM2.5) but not
2.5 to 10 μm in diameter (PM2.5 –1 0) or proximity to road-
ways, and high symptoms of anxiety. The association
between PM2.5 and high anxiety seems primarily driven
by a relation with shorter term average exposures to
PM2.5. There is little evidence to suggest dierences in
this association by demographic, geographic, or health
related characteristics.
Limitations and strengths of this study
Our study has some limitations. We were unable to con-
sider the clinical diagnosis of specic anxiety disorders.
However, prior epidemiologic work suggests that Crown-
Crisp index scores are a valid16 17 and clinically relevant
measure, as they are associated with accelerated
aging,58 ischemic heart disease,56 and sudden cardiac
death.55 59 65 We were unable to consider the association
between anxiety and uctuations in PM2.5 over periods
of less than one month, or short term uctuations in
ultrane particulate matter (residential distance to a
major road as a proxy measure is necessarily a longer
term indicator). It is possible that the association we
observed with PM2.5 is attributable, in whole or in part,
to a correlation between PM2.5 and another exposure.
Ambient ozone and noise are unlikely, although still
possible, candidates given relatively weak correlations
with PM2.5.66 68 Similarly, we cannot preclude a contribu-
tion of other pollutants that share sources with PM2.5 (for
example, nitrogen dioxide, sulphur dioxide).67 Our mod-
els provide predictions of exposure at each participant’s
residential address. Given lack of information on the
activity pattern of each participant, this could lead to bias
due to misclassication. Nevertheless, any such bias is
expected to be towards the null69 and so would not
account for the observed associations with PM2.5. Fur-
thermore, as environmental regulations set acceptable
exposure limits based on outdoor measures, we believe
that this exposure is most relevant from the public
health perspective. Our study considered only women; it
is possible that our results may not be generalizable to
men. Similarly, the women in our study were relatively
old. Given that advancing age is related to lower physio-
logic reserve,70 it is possible that our results would not
generalize to younger age groups.
Our study also has several strengths. While dis-
cussed previously as a limitation, our focus on anxiety
symptoms is also a strength. Our data suggest a short
term, potentially reversible relation between exposures
to particulate matter and severity of anxiety symptoms,
which may not have been identied if we had focused
exclusively on anxiety disorders. Although the relevant
exposure period was unknown, we considered multi-
ple averaging periods of exposure; this ultimately sug-
gested that short term exposure to PM2.5 may be the
PM2.5, z score prior 1 month (μg/m3)
Panel B
Panel A
Odds ratio
-4 -2 0 2 46
PM2.5, z score 1988-2003 (μg/m3
-4 -2 0 2
PM2.5, z score prior 1 month (μg/m3)
-4 -2 0246
PM2.5, z score prior 12 months (μg/m3
-4 -2 024
Odds ratio
Fig  | Odds ratio (% condence interval) exposure to particulate matter <. m in diameter (PM.) on high symptoms
of anxiety in Nurses’ Health Study when multiple averaging periods are included in same model, parameterized using
splines. Results of a model including both z transformed prior  month and z transformed prior one month PM.
exposure (panel A), and including both the z transformed prior  years (–) and z transformed prior one month
PM. exposure (panel B). The th centile was chosen as the reference level for the corresponding exposure of interest.
Pvalues indicate strength of evidence for an association (versus no association) for exposure to particulate matter, over
its entire range, on high anxiety
7the bmj |
2015;350:h1111 | doi: 10.1136/bmj.h1111
most relevant exposure. We had access to a large
prospective cohort, allowing adequate power to detect
modest but meaningful associations. Attrition was
small and any potential selection bias due to informa-
tive drop-out would be expected to be a downward
bias, and so our estimate of an adverse association
with PM2.5 may be an underestimate. We were able to
adjust for many socioeconomic and sociodemographic
factors, which we thought to be the strongest potential
confounders. Our results were robust to multiple sensi-
tivity analyses.
Comparison to other studies and discussion of
potential mechanism
To our knowledge this is the rst study to consider the
association between exposure to particulate matter and
anxiety. However, our ndings are consistent with two
prior studies of other air pollutants and anxiety,33 44 as
well as work suggesting associations between air pollu-
tion and other related, but distinct, mental health out-
comes, including depressive symptoms,34 35 psychiatric
emergency,36–38 emergency room visits for depression or
suicide,39–42 and reported suicide.43
Exposure to particulate matter could induce or
exacerbate anxiety through increased oxidative stress
and inammation or through inducing or aggravating
major medical conditions. Inammation and oxida-
tive stress have been hypothesized to contribute to the
incidence and severity of anxiety.9 10 Several toxico-
logical studies have shown that oxidative stress7 1–75 or
systemic inflammation75 76 induces anxiety-like
behaviors in mice and rats. These results are consis-
tent with cross sectional associations between anxi-
ety symptoms and inflammatory markers in
people,77–7 9 as well as epidemiologic ndings linking
C reactive protein, an inammatory marker, to gener-
alized anxiety disorder in patients with stable coro-
nary heart disease.80 Inhaled particulate matter may
therefore contribute to anxiety through induction of
systemic11–1 7 or brain based8 1–8 3 oxidative stress and
inammation. Alternatively, anxiety may occur as a
result of a respiratory or cardiac medical condition.
Reduced lung function, reactive airway diseases such
as asthma and chronic obstructive pulmonary dis-
ease, atrial brillation, and congestive heart failure
are associated with an increased prevalence of anxi-
ety symptoms or disorders.18–2 3 These associations are
likely mediated by fear and misinterpretation of
symptoms, although an impact of the stress of dealing
with major medical conditions or a purely physiolog-
ical reaction to oxygenation changes associated with
dysfunctional breathing and/or heart function may
also contribute.84–89 As particulate matter has been
linked to multiple medical conditions and aggrava-
tion of symptoms,24–32 particulate air pollution may
also contribute to anxiety through this alternative
mechanism. While our ndings are consistent with
the oxidative stress/inflammatory mechanistic
hypothesis, our data do not support the hypothesis
that particles promote anxiety through induction or
aggravation of medical conditions, as there was no
dierence in the association by whether or not people
had major medical comorbidities. The reported asso-
ciation with PM2.5, but not PM2. 5–1 0 may be related to
size related dierences in toxicity, which are likely a
function of dierences in lung penetrability, surface
area, and composition by particle size.90–94
Anxiety is a common and costly disorder. Our data
support an association between exposure to PM2.5, a
common environmental exposure, and high symp-
toms of anxiety. If conrmed, our ndings may have
policy and clinical implications, as it is possible that
reductions in exposure to PM2.5, through changes to
regulations or individual behavior, may help reduce
anxiety symptoms. Future work directly evaluating
this possibility is warranted.
We thank Peter James for his help with the Rural Urban Commuting
Area codes.
Contributors: All authors made substantial contributions to the
conception and design (MCP, MGW, MAK, JEH, OIO, and FL), acquisition
of the data (FL, JEH), or analysis and interpretation (MCP, MGW, MAK,
JEH, OIO, and FL). MCP draed the article and all other authors revised
it critically for important intellectual content. MCP is guarantor. All
authors had full access to all of the data in the study and can take
responsibility for the integrity of the data and the accuracy of the data
Funding: This work was supported by grants from the National
Institute of Environmental Health Sciences (R21 ES019982, R01
ES017017). MCP was supported by a training grant from the
National Institute of Aging (T32 AG027668). The funding agencies
had no role in the design and conduct of the study; collection,
management, analysis, and interpretation of the data; preparation,
review, or approval of the manuscript; or decision to submit the
manuscript for publication. The authors were independent of the
study funders.
Competing interests: All authors have completed the ICMJE uniform
disclosure form at (available on
request from the corresponding author) and declare: no support from
any organization for the submitted work; no nancial relationships
with any organizations that might have an interest in the submitted
work in the previous three years; no other relationships or activities
that could appear to have influenced the submitted work.
Ethical approval: This study was approved by the institutional review
board of the Brigham and Women’s Hospital and the human subjects
committee of the Harvard School of Public Health.
Data sharing: The statistical code is available from the corresponding
author at
Transparency: The lead author (MCP) arms that the manuscript is
an honest, accurate, and transparent account of the study being
reported; that no important aspects of the study have been omitted;
and that any discrepancies from the study as planned (and, if relevant,
registered) have been explained.
This is an Open Acces s article dist ributed in accordance with the
Creati ve Commons Attr ibution Non Comm ercial (CC BY-NC 4.0) license,
which per mits others to distribute, remix, adapt, build upon this wor k
non-commercially, and licens e their derivat ive works on di erent
terms, provided the orig inal work is proper ly cited and the use is
non-commercial. See:
by - nc /4 .0 /.
1 American Psychiatric Association. Diagnostic and statistical manual of
mental disorders, h edition (DSM-V). APA, 2013.
2 Kessler RC, Aguilar-Gaxiola S, Alonso J, Chatterji S, Lee S, Ormel J, et al.
The global burden of mental disorders: an update from the WHO
World Mental Health (WMH) surveys. Epidemiol Psichiatr Soc
3 Lepine JP. The epidemiology of anxiety disorders: prevalence and
societal costs. J Clin Psychiatry 2002;63:4–8.
4 Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE,
et al. Global burden of disease attributable to mental and substance
use disorders: ndings from the global burden of disease study 2010.
Lancet 2013;382:1575–86.
8doi: 10.1136/bmj.h1111 |
2015;350:h1111 | the bmj
5 Greenberg PE, Sisitsky T, Kessler RC, Finkelstein SN, Berndt ER,
Davidson JR, et al. The economic burden of anxiety disorders in the
1990s. J Clin Psychiatry 1999;60:427–35.
6 McLean CP, Asnaani A, Litz BT, Hofmann SG. Gender dierences in
anxiety disorders: prevalence, course of illness, comorbidity and
burden of illness. J Psychiatr Res 2011;45:1027–35.
7 Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE.
Lifetime prevalence and age-of-onset distributions of DSM-IV
disorders in the national comorbidity survey replication. Arch Gen
Psychiatry 2005;62:593–602.
8 Lanouette NM, Stein MB. Advances in the management of
treatment-resistant anxiety disorders. Focus (Am Psychiatr Publ)
9 Salim S, Chugh G, Asghar M. Inflammation in anxiety. Adv Protein
Chem Struct Biol 2012;88:1–25.
10 Hou R, Baldwin DS. A neuroimmunological perspective on anxiety
disorders. Hum Psychopharmacol 2012;27:6–14.
11 Sørensen M, Daneshvar B, Hansen M, Dragsted LO, Hertel O, Knudsen L,
et al. Personal PM2.5 exposure and markers of oxidative stress in
blood. Environ Health Perspect 2003;111:161–5.
12 Dagher Z, Garçon G, Billet S, Gosset P, Ledoux F, Courcot D, et al.
Activation of dierent pathways of apoptosis by air pollution
particulate matter (PM2.5) in human epithelial lung cells (l132) in
culture. Toxicology 2006;225:12–24.
13 Kim JY, Mukherjee S, Ngo L, Christiani DC. Urinary 8-hydroxy-2’-
deoxyguanosine as a biomarker of oxidative DNA damage in workers
exposed to ne particulates. Environ Health Perspect
14 Hovatta I, Tennant RS, Helton R, Marr RA, Singer O, Redwine JM, et al.
Glyoxalase 1 and glutathione reductase 1 regulate anxiety in mice.
Nature 2005;438:662–6.
15 Brook RD, Rajagopalan S, Pope CA, Brook JR, Bhatnagar A, Diez-Roux AV,
et al. Particulate matter air pollution and cardiovascular disease: an
update to the scientic statement from the American Heart
Association. Circulation 2010;121:2331–78.
16 Dubowsky SD, Suh H, Schwartz J, Coull BA, Gold DR. Diabetes, obesity,
and hypertension may enhance associations between air pollution
and markers of systemic inflammation. Environ Health Perspect
17 Bind MA, Baccarelli A, Zanobetti A, Tarantini L, Suh H, Vokonas P, et al.
Air pollution and markers of coagulation, inflammation, and
endothelial function: associations and epigene-environment
interactions in an elderly cohort. Epidemiology 2012;23:332–40.
18 Spitzer C, Gläser S, Grabe HJ, Ewert R, Barnow S, Felix SB, et al. Mental
health problems, obstructive lung disease and lung function: ndings
from the general population. J Psychosom Res 2011;71:174–9.
19 Scott KM, Von Kor M, Ormel J, Zhang M-y, Bruaerts R, Alonso J, et al.
Mental disorders among adults with asthma: Results from the world
mental health survey. Gen Hosp Psychiatry 2007;29:123–33.
20 Brenes GA. Anxiety and chronic obstructive pulmonary disease:
Prevalence, impact, and treatment. Psychosom Med
21 Yohannes AM, Willgoss TG, Baldwin RC, Connolly MJ. Depression and
anxiety in chronic heart failure and chronic obstructive pulmonary
disease: Prevalence, relevance, clinical implications and management
principles. Int J Geriatr Psychiatry 2010;25:1209–21.
22 Thrall G, Lip GYH, Carroll D, Lane D. Depression, anxiety, and quality of
life in patients with atrial brillation. CHEST 2007;132:1259–64.
23 Lane DA, Langman CM, Lip GYH, Nouwen A. Illness perceptions,
aective response, and health-related quality of life in patients with
atrial brillation. J Psychosom Res 2009;66:203–10.
24 Ling SH, van Eeden SF. Particulate matter air pollution exposure: Role
in the development and exacerbation of chronic obstructive
pulmonary disease. Int J Chron Obstruct Pulmon Dis 2009;4:233–43.
25 Faustini A, Stafoggia M, Cappai G, Forastiere F. Short-term eects of
air pollution in a cohort of patients with chronic obstructive
pulmonary disease. Epidemiology 2012;23:861–79.
26 Delno RJ. Epidemiologic evidence for asthma and exposure to air
toxics: Linkages between occupational, indoor, and community air
pollution research. Environ Health Perspect 2002;110:573.
27 Künzli N, Bridevaux P-O, Liu L-JS, Garcia-Esteban R, Schindler C,
Gerbase MW, et al. Trac-related air pollution correlates with
adult-onset asthma among never-smokers. Thorax 2009;64:664–70.
28 Link MS, Luttmann-Gibson H, Schwartz J, Mittleman MA, Wessler B,
Gold DR, et al. Acute exposure to air pollution triggers atrial
brillation. J Am Coll Cardiol 2013;62:816–25.
29 Pope CA. Review: Epidemiological basis for particulate air pollution
health standards. Aerosol Sci Technol 2000;32:4–14.
30 Rich DQ, Mittleman MA, Link MS, Schwartz J, Luttmann-Gibson H,
Catalano PJ, et al. Increased risk of paroxysmal atrial brillation
episodes associated with acute increases in ambient air pollution.
Environ Health Perspect 2006;114:120–3.
31 Kesavachandran C, Pangtey BS, Bihari V, Fareed M, Pathak MK,
Srivastava AK, et al. Particulate matter concentration in ambient air
and its eects on lung functions among residents in the national
capital region, India. Environ Monit Assess 2013;185:1265–72.
32 Shah ASV, Langrish JP, Nair H, McAllister DA, Hunter AL, Donaldson K,
et al. Global association of air pollution and heart failure: A systematic
review and meta-analysis. Lancet;382:1039–48.
33 Evans GW, Colome SD, Shearer DF. Psychological reactions to air
pollution. Environ Res 1988;45:1–15.
34 Lim YH, Kim H, Kim JH, Bae S, Park HY, Hong YC. Air pollution and
symptoms of depression in elderly adults. Environ Health Perspect
35 Jacobs SV, Evans GW, Catalano R, Dooley D. Air pollution and
depressive symptomatology: Exploratory analyses of intervening
psychosocial factors. Popul Environ 1984;7:260–72.
36 Briere J, Downes A, Spensley J. Summer in the city: Urban weather
conditions and psychiatric emergency-room visits. J Abnorm Psychol
37 Rotton J, Frey J. Psychological costs of air pollution: Atmospheric
conditions, seasonal trends, and psychiatric emergencies. Popul
Environ 1984;7:3–16.
38 Strahilevitz M, Strahilevitz A, Miller JE. Air pollutants and the admission
rate of psychiatric patients. Am J Psychiatry 1979;136:205–7.
39 Szyszkowicz M, Willey JB, Grafstein E, Rowe BH, Colman I. Air pollution
and emergency department visits for suicide attempts in Vancouver,
Canada. Environ Health Insights 2010;4:79–86.
40 Szyszkowicz M, Rowe BH, Colman I. Air pollution and daily emergency
department visits for depression. Int J Occup Med Environ Health
41 Szyszkowicz M. Air pollution and emergency department visits for
depression in Edmonton, Canada. Int J Occup Med Environ Health
42 Kim C, Jung SH, Kang DR, Kim HC, Moon KT, Hur NW, et al. Ambient
particulate matter as a risk factor for suicide. Am J Psychiatry
43 Biermann T, Stilianakis N, Bleich S, Thürauf N, Kornhuber J, Reulbach U.
The hypothesis of an impact of ozone on the occurrence of completed
and attempted suicides. Med Hypotheses 2009;72:338–41.
44 Bowler RM, Harris M, Gocheva V, Wilson K, Kim Y, Davis SI, et al.
Anxiety aecting parkinsonian outcome and motor eciency in adults
of an Ohio community with environmental airborne manganese
exposure. Int J Hyg Environ Health 2012;215:393–405.
45 Colditz GA, Manson JE, Hankinson SE. The Nurses’ Health Study:
20-year contribution to the understanding of health among women.
JWomens Health 1997;6:49–62.
46 Ran U, Schikowski T, Sugiri D, Krutmann J, Kramer U. Long-term
exposure to trac-related particulate matter impairs cognitive
function in the elderly. Environ Res 2009;109:1004–11.
47 Zhu Y, Hinds WC, Kim S, Sioutas C. Concentration and size distribution
of ultrane particles near a major highway. J Air Waste Manag Assoc
48 Sioutas C, Delno RJ, Singh M. Exposure assessment for atmospheric
ultrane particles (UFPS) and implications in epidemiologic research.
Environ Health Perspect 2005;113:947–55.
49 Karner AA, Eisinger DS, Niemeier DA. Near-roadway air quality:
Synthesizing the ndings from real-world data. Environ Sci Technol
50 Yanosky JD, Paciorek CJ, Suh HH. Predicting chronic ne and coarse
particulate exposures using spatiotemporal models for the
northeastern and midwestern United States. Environ Health Perspect
51 Yanosky JD, Paciorek CJ, Schwartz J, Laden F, Puett R, Suh HH.
Spatio-temporal modeling of chronic PM10 exposure for the Nurses’
Health Study. Atmos Environ 2008;42:4047–62.
52 Yanosky J, Paciorek C, Laden F, Hart J, Puett R, Liao D, et al.
Spatio-temporal modeling of particulate air pollution in the
conterminous united states using geographic and meteorological
predictors. Environ Health 2014;13:63.
53 Crown S, Crisp AH. A short clinical diagnostic self-rating scale for
psychoneurotic patients. Br J Psychiatry 1966;112:917–23.
54 Mavissakalian M, Michelson L. The Middlesex Hospital Questionnaire:
A validity study with american psychiatric patients. Br J Psychiatry
55 Albert CM, Chae CU, Rexrode KM, Manson JE, Kawachi I. Phobic
anxiety and risk of coronary heart disease and sudden cardiac death
among women. Circulation 2005;111:480–7.
56 Haines AP, Imeson JD, Meade TW. Phobic anxiety and ischaemic heart
disease. Br Med J (Clin Res Ed) 1987;295:297–9.
57 Kawachi I, Sparrow D, Vokonas PS, Weiss ST. Decreased heart rate
variability in men with phobic anxiety (data from the Normative Aging
Study). Am J Cardiol 1995;75:882–5.
58 Okereke OI, Prescott J, Wong JY, Han J, Rexrode KM, De Vivo I. High
phobic anxiety is related to lower leukocyte telomere length in
women. PLoS One 2012;7:e40516.
59 Watkins LL, Blumenthal JA, Babyak MA, Davidson JR, McCants CB, Jr.,
O’Connor C, et al. Phobic anxiety and increased risk of mortality in
coronary heart disease. Psychosom Med 2010;72:664–71.
60 Weisskopf MG, Chen H, Schwarzschild MA, Kawachi I, Ascherio A.
Prospective study of phobic anxiety and risk of parkinson’s disease.
Mov Disord 2003;18:646–51.
No commercial reuse: See rights and reprints Subscribe:
61 McGrath M, Kawachi I, Ascherio A, Colditz GA, Hunter DJ, De Vivo I.
Association between catechol-o-methyltransferase and phobic
anxiety. Am J Psychiatry 2004;161:1703–5.
62 Berkman LF, Syme SL. Social networks, host resistance, and
mortality:A nine-year follow-up study of Alameda County residents.
Am J Epidemiol 1979;109:186–204.
63 Ware JE, Jr., Sherbourne CD. The MOS 36-item short-form health
survey (SF-36). I. Conceptual framework and item selection.
64 United States Department of Agriculture. Documentation: 2010
rural-urban commuting area (RUCA) codes. June 2, 2014. http://www.
65 Kawachi I, Colditz GA, Ascherio A, Rimm EB, Giovannucci E, Stampfer MJ,
et al. Prospective study of phobic anxiety and risk of coronary heart
disease in men. Circulation 1994;89:1992–7.
66 Bell ML, Kim JY, Dominici F. Potential confounding of particulate matter
on the short-term association between ozone and mortality in multisite
time-series studies. Environ Health Perspect 2007;115:1591–5.
67 Brunekreef B, Holgate ST. Air pollution and health. Lancet
68 Babisch W, Wolf K, Petz M, Heinrich J, Cyrys J, Peters A. Associations
between trac noise, particulate air pollution, hypertension, and
isolated systolic hypertension in adults: The KORA Study. Environ
Health Perspect 2014;122:492–8.
69 Kioumourtzoglou MA, Spiegelman D, Szpiro AA, Sheppard L, Kaufman
JD, Yanosky JD, et al. Exposure measurement error in PM2.5 health
eects studies: A pooled analysis of eight personal exposure
validation studies. Environ Health 2014;13:2.
70 Buchner DM, Wagner EH. Preventing frail health. Clin Geriatr Med
71 Ng F, Berk M, Dean O, Bush AI. Oxidative stress in psychiatric
disorders: Evidence base and therapeutic implications. Int J
Neuropsychopharmacol 2008;11:851–76.
72 de Oliveira MR, Silvestrin RB, Mello e Souza T, Moreira JCF. Oxidative
stress in the hippocampus, anxiety-like behavior and decreased
locomotory and exploratory activity of adult rats: Eects of sub acute
vitamin A supplementation at therapeutic doses. Neurotoxicology
73 Salim S, Sarraj N, Taneja M, Saha K, Tejada-Simon MV, Chugh G.
Moderate treadmill exercise prevents oxidative stress-induced
anxiety-like behavior in rats. Behav Brain Res 2010;208:545–52.
74 Salim S, Asghar M, Chugh G, Taneja M, Xia Z, Saha K. Oxidative stress:
A potential recipe for anxiety, hypertension and insulin resistance.
Brain Res 2010;1359:178–85.
75 Patki G, Solanki N, Atrooz F, Allam F, Salim S. Depression, anxiety-like
behavior and memory impairment are associated with increased
oxidative stress and inflammation in a rat model of social stress. Brain
Res 2013;1539:73–86.
76 Engler H, Doenlen R, Engler A, Riether C, Prager G, Niemi M-B, et al.
Acute amygdaloid response to systemic inflammation. Brain Behav
Immun 2011;25:1384–92.
77 Pitsavos C, Panagiotakos DB, Papageorgiou C, Tsetsekou E, Soldatos C,
Stefanadis C. Anxiety in relation to inflammation and coagulation
markers, among healthy adults: The Attica Study. Atherosclerosis
78 Liukkonen T, Räsänen P, Jokelainen J, Leinonen M, Järvelin MR,
Meyer-Rochow VB, et al. The association between anxiety and
c-reactive protein (CRP) levels: Results from the Northern Finland
1966 Birth Cohort Study. Eur Psychiatry 2011;26:363–9.
79 Vogelzangs N, Beekman ATF, de Jonge P, Penninx BWJH. Anxiety
disorders and inflammation in a large adult cohort. Transl Psychiatry
80 Bankier B, Barajas J, Martinez-Rumayor A, Januzzi JL. Association
between c-reactive protein and generalized anxiety disorder in stable
coronary heart disease patients. Eur Heart J 2008;29:2212–7.
81 Campbell A, Oldham M, Becaria A, Bondy SC, Meacher D, Sioutas C,
etal. Particulate matter in polluted air may increase biomarkers of
inflammation in mouse brain. Neurotoxicology 2005;26:133–40.
82 Calderon-Garciduenas L, Maronpot RR, Torres-Jardon R, Henriquez-
Roldan C, Schoonhoven R, Acuna-Ayala H, et al. DNA damage in nasal
and brain tissues of canines exposed to air pollutants is associated
with evidence of chronic brain inflammation and neurodegeneration.
Toxicol Pathol 2003;31:524–38.
83 MohanKumar SM, Campbell A, Block M, Veronesi B. Particulate
matter, oxidative stress and neurotoxicity. Neurotoxicology
84 Mikkelsen RL, Middelboe T, Pisinger C, Stage KB. Anxiety and
depression in patients with chronic obstructive pulmonary disease
(COPD). A review. Nord J Psychiatry 2004;58:65–70.
85 Livermore N, Sharpe L, McKenzie D. Panic attacks and panic disorder
in chronic obstructive pulmonary disease: A cognitive behavioral
perspective. Respir Med 2010;104:1246–53.
86 Griez E, Schruers K. Experimental pathophysiology of panic.
JPsychosom Res 1998;45:493–503.
87 Gehi AK, Sears S, Goli N, Walker TJ, Chung E, Schwartz J, et al.
Psychopathology and symptoms of atrial brillation: Implications for
therapy. J Cardiovasc Electrophysiol 2012;23:473–8.
88 Katon W, Lin EHB, Kroenke K. The association of depression and
anxiety with medical symptom burden in patients with chronic
medical illness. Gen Hosp Psychiatry 2007;29:147–55.
89 Roy-Byrne PP, Davidson KW, Kessler RC, Asmundson GJG, Goodwin
RD, Kubzansky L, et al. Anxiety disorders and comorbid medical
illness. Gen Hosp Psychiatry 2008;30:208–25.
90 Araujo JA, Nel AE. Particulate matter and atherosclerosis: Role of
particle size, composition and oxidative stress. Part Fibre Toxicol
91 Diociaiuti M, Balduzzi M, De Berardis B, Cattani G, Stacchini G,
Ziemacki G, et al. The two PM2.5 (ne) and PM2.5–10 (coarse)
fractions: Evidence of dierent biological activity. Environ Res
92 Kelly FJ. Oxidative stress: Its role in air pollution and adverse health
eects. Occup Environ Med 2003;60:612–6.
93 Li N, Sioutas C, Cho A, Schmitz D, Misra C, Sempf J, et al. Ultrane
particulate pollutants induce oxidative stress and mitochondrial
damage. Environ Health Perspect 2003;111:455–60.
94 Pakkanen TA, Loukkola K, Korhonen CH, Aurela M, Mäkelä T, Hillamo RE,
et al. Sources and chemical composition of atmospheric ne and
coarse particles in the Helsinki area. Atmos Environ
© BMJ Publishing Group Ltd 2015
... Many studies (e.g., Oudin et al. 2016, Roberts et al. 2019, Bakolis et al. 2020, Heo et al. 2021, use both measures of PM and NOx to establish if the effect on mental health varies according to the pollutant. Other pollutants studied include black carbon (Power et al. 2015) and Ozone (Bakolis et al. 2020). In addition to objective measures of air pollution, some studies have explored the effect of perceived air pollution on mental health (e.g., , Signoretta et al. 2019). ...
... The effect of air pollution on mental health differs according to the period of exposure. Power et al. (2015) found a stronger association between short-term exposure to PM2.5 and symptoms of anxiety compared to long-term exposure to PM2.5. However, Liu et al. (2020) found no evidence that stress was directly influenced by real-time PM2.5 exposure in young adults (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25) in Plovdiv, ...
... An unexpected result showed that higher levels of air pollution result in a reduced probability of poor mental health. This is contrary to previous epidemiological research (e.g., Power et al. 2015, Oudin et al. 2016, Roberts et al. 2019, Signoretta et al. 2019. One possible explanation could be that the selected air pollution variable may be capturing aspects of unexplained social connectivity. ...
To improve causality, this thesis used the counterfactual framework to develop two novel and statistically robust approaches to analyse the effect of urban greenspace on mental health. The first approach was a cross-sectional assessment that used statistical matching in addition to regression modelling to establish the effect of local public greenspace on a person’s mental health for those with and without a private garden. The second approach used longitudinal data in a Before-After Control Intervention study design to establish the effect of the change in different greenspace characteristics on mental health when a person moved between urban areas. Both these approaches were applied to the British Household Panel Survey – a nationally representative survey of Great Britain containing individual-level information on mental health and the socio-economic confounders of mental health. Findings from the first approach suggested that the effect of access to private greenspace on mental health outweighs the beneficial effects of access to public greenspace. Specifically, having a private domestic garden substantially reduced the maximum probability of poor mental health for men and women, regardless of their access to local public greenspace. The second approach highlighted the importance of greenspace quality and proximity for mental health. Bird species richness and distance to nearest greenspace, proxy measures for greenspace quality and proximity respectively, provided the most inference when modelling the effect of change in greenspace characteristics on mental health. Comparatively, measures of greenspace quantity and recognised standards and guidelines of greenspace access provided less inference than a model that did not include a measure of greenspace. Given these results, greenspace quality, proximity and access to private gardens should be a priority for future policies to improve the status of both urban greenspace and mental health in Great Britain.
... Numerous studies have documented that exposure to environmental factors (e.g., air quality, temperature, noise) is significantly related to various human diseases and premature deaths. For instance, air pollution is associated with morbidity and mortality due to cardiovascular diseases [3], lung cancers [3], diabetes [4], Alzheimer's diseases [5,6], depression [7], and anxiety [8]. Meanwhile, noise exposure can damage the human cardiovascular system, autonomic nervous system, endocrine system, and neurocognitive functioning [9]. ...
... In addition, some other common pollutants are related to particles, including black carbon (BC) and particle-bounded PAHs (p-PAHs). Particle exposure has been associated with various human diseases, including cardiovascular diseases, lung cancers, diabetes, Alzheimer's, depression [7], and anxiety [3][4][5][6]8]. The IHME estimates indicate that particle pollution resulted in about 6.5 million deaths and 209.6 million DALYs worldwide in 2019, representing the second largest risk factor for the global burden of diseases [1]. ...
Full-text available
Recent advances in sensor technology have facilitated the development and use of personalized sensors in monitoring environmental factors and the associated health effects. No studies have reviewed the research advancement in examining population-based health responses to environmental exposure via portable sensors/instruments. This study aims to review studies that use portable sensors to measure environmental factors and health responses while exploring the environmental effects on health. With a thorough literature review using two major English databases (Web of Science and PubMed), 24 eligible studies were included and analyzed out of 16,751 total records. The 24 studies include 5 on physical factors, 19 on chemical factors, and none on biological factors. The results show that particles were the most considered environmental factor among all of the physical, chemical, and biological factors, followed by total volatile organic compounds and carbon monoxide. Heart rate and heart rate variability were the most considered health indicators among all cardiopulmonary outcomes, followed by respiratory function. The studies mostly had a sample size of fewer than 100 participants and a study period of less than a week due to the challenges in accessing low-cost, small, and light wearable sensors. This review guides future sensor-based environmental health studies on project design and sensor selection.
... Higher temperatures and noise levels attenuated the increase in total, eye and constitutional symptoms, while anxiety enhanced constitutional symptoms. Known relationships between air pollution exposure and temperature [52][53][54][55][56], noise [57,58] and anxiety [59][60][61] in epidemiological literature are mixed and vary by endpoints. For example, air pollution acts synergistically with and directly on temperature and anxiety respectively in some studies [52,55,60], but not others [59,61]. ...
... Known relationships between air pollution exposure and temperature [52][53][54][55][56], noise [57,58] and anxiety [59][60][61] in epidemiological literature are mixed and vary by endpoints. For example, air pollution acts synergistically with and directly on temperature and anxiety respectively in some studies [52,55,60], but not others [59,61]. Notably, these studies investigated ambient (not perceived) temperature and noise, and only explored anxiety as a direct effect of air pollution. ...
Full-text available
Background Traffic-related air pollution (TRAP) exposure causes adverse effects on wellbeing and quality of life, which can be studied non-invasively using self-reported symptoms. However, little is known about the effects of different TRAP concentrations on symptoms following controlled exposures, where acute responses can be studied with limited confounding. We investigated the concentration–response relationship between diesel exhaust (DE) exposure, as a model TRAP, and self-reported symptoms. Methods We recruited 17 healthy non-smokers into a double-blind crossover study where they were exposed to filtered air (FA) and DE standardized to 20, 50, 150 µg/m³ PM2.5 for 4 h, with a ≥ 4-week washout between exposures. Immediately before, and at 4 h and 24 h from the beginning of the exposure, we administered visual analog scale (VAS) questionnaires and grouped responses into chest, constitutional, eye, neurological, and nasal categories. Additionally, we assessed how the symptom response was related to exposure perception and airway function. Results An increase in DE concentration raised total (β ± standard error = 0.05 ± 0.03, P = 0.04), constitutional (0.01 ± 0.01, P = 0.03) and eye (0.02 ± 0.01, P = 0.05) symptoms at 4 h, modified by perception of temperature, noise, and anxiety. These symptoms were also correlated with airway inflammation. Compared to FA, symptoms were significantly increased at 150 µg/m³ for the total (8.45 ± 3.92, P = 0.04) and eye (3.18 ± 1.55, P = 0.05) categories, with trends towards higher values in the constitutional (1.49 ± 0.86, P = 0.09) and nasal (1.71 ± 0.96, P = 0.08) categories. Conclusion DE exposure induced a concentration-dependent increase in symptoms, primarily in the eyes and body, that was modified by environmental perception. These observations emphasize the inflammatory and sensory effects of TRAP, with a potential threshold below 150 µg/m³ PM2.5. We demonstrate VAS questionnaires as a useful tool for health monitoring and provide insight into the TRAP concentration–response at exposure levels relevant to public health policy.
... Less attention has focused on the effects of smoke on mental health and well-being. Exploring this relationship is even more pertinent considering the growing body of research documenting the negative effects of air pollution on mental health, including depression, anxiety, suicide, and psychological distress [29,30,31,32,33,34,35]. Understanding these effects of wildfire smoke is crucial as the world enters a time in which wildfire smoke events can spread hundreds of miles past the immediate burn for prolonged periods of time and the interconnection between the physical and mental well-being effects of smoke is increasingly recognized [36]. ...
Full-text available
Background Smoke from wildfires is a growing public health risk due to the enormous amount of smoke-related pollution that is produced and can travel thousands of kilometers from its source. While many studies have documented the physical health harms of wildfire smoke, less is known about the effects on mental health and well-being. Understanding the effects of wildfire smoke on mental health and well-being is crucial as the world enters a time in which wildfire smoke events become more frequent and severe. We conducted a scoping review of the existing information on wildfire smoke’s impact on mental health and well-being and developed a model for understanding the pathways in which wildfire smoke may contribute to mental health distress. Methods We conducted searches using PubMed, Medline, Embase, Google, Scopus, and ProQuest for 1990–2022. These searches yielded 200 articles. Sixteen publications met inclusion criteria following screening and eligibility assessment. Three more publications from the bibliographies of these articles were included for a total of 19 publications. Results Our review suggests that exposure to wildfire smoke may have mental health impacts, particularly in episodes of chronic and persistent smoke events, but the evidence is inconsistent and limited. Qualitative studies disclose a wider range of impacts across multiple mental health and well-being domains. The potential pathways connecting wildfire smoke with mental health and well-being operate at multiple interacting levels including individual, social and community networks, living and working conditions, and ecological levels. Conclusions Priorities for future research include: 1) applying more rigorous methods; 2) differentiating between mental illness and emotional well-being; 3) studying chronic, persistent or repeated smoke events; 4) identifying the contextual factors that set the stage for mental health and well-being effects, and 5) identifying the causal processes that link wildfire smoke to mental health and well-being effects. The pathways model can serve as a basis for further research and knowledge synthesis on this topic. Also, it helps public health, community mental health, and emergency management practitioners mitigate the mental health and well-being harms of wildfire smoke.
... To eliminate bias of hypnotic misuse by physicians or patient requests, we excluded those using hypnotics without an initial diagnosis of insomnia, which accounted for 24.57% of the original Taipei residents study subjects above 18 years old. To reduce confounding of air pollutantcaused anxiety and depressive symptoms or aggravating symptoms of any underlying chronic illness that may cause secondary insomnia, we adjusted major chronic diseases as well 5,43 . The cases with complete educational information were younger and comprised more males than the cases with missing educational information. ...
Full-text available
Ambient air pollution was known to cause central nervous system diseases and depressive symptoms. In this study, we examined the associations between air pollution exposure and the prevalence of insomnia in Taipei City of Taiwan. We applied the health information system of electrical medical records of Taipei City Hospital to collect a total of 5108 study subjects (insomniacs N = 912 and non-insomniacs N = 4196) over 18 years old from the family medicine and internal medicine outpatients of six branches of Taipei City Hospital. These patients were grouped into insomniacs and non-insomniacs following the primary insomnia diagnosis (ICD9:780.52, 780.54, 307.41, 307.42, ICD10: G47.00, G47.01, G47.09, F51.01, F51.09) and the prescription times of anxiolytics and hypnotics. We estimated one-year average concentrations of PM2.5, ozone, and NOx before the first date of insomnia diagnosis and the last date of outpatient visit for insomniacs and non-insomniacs, respectively, by using the data of nearest air quality monitoring stations relative to study subjects’ residential addresses. Logistic regression analysis was employed to examine the independent effects of air pollution concentrations on the risk of insomnia. One-year average PM2.5, ozone, and NOx levels for insomniacs was significantly higher than those of non-insomniacs. After adjusting for confounding factors, increase each 1(μg/m³) in one-year average PM2.5 showed a statistically significant association with insomnia (the odds ratio 1.610, 95% CI [1.562,1.660]). As to multi pollutants, one-year average PM2.5 (1.624, [1.570, 1.681] and ozone (1.198, [1.094, 1.311]) exposure showed a significant association with insomnia. Subgroup analysis revealed that the influence of PM2.5 and ozone on insomnia have significant risks in people with major chronic disease. This study demonstrated a positive association between PM2.5 and ozone exposure and the prevalence of hypnotic-treated insomnia. Especially, the people with major chronic diseases were with obvious effect of PM2.5 and ozone on risk of insomnia.
... The psychological effects of air pollution were not only associated with life satisfaction and subjective well-being (Dolan and Laffan, 2016), but also correlated with people's mood (Lim et al., 2012). Emotional reactions may be the most sensitive among the possible adverse effects of air pollution (Power et al., 2015). Weather factors can directly affect individual behavior and psychological processes or indirectly affect individual behavior. ...
Full-text available
Based on the data of Shanghai and Shenzhen A-share listed companies from 2015 to 2019, this paper studies the influence of air quality on the R&D investment of listed companies from the perspectives of investor sentiment and government concern. It is found that, on the whole, air quality has a significant inhibitory effect on R&D investment. Air quality significantly promotes investor sentiment, which serves as a path to further restrain the R&D investment of listed companies. Air pollution is an effective concern for the government and serves as a veil between air quality and R&D investment. Furthermore, this paper analyzes the heterogeneity of enterprises from the aspects of regional technology complexity, property right nature, whether it is a polluting enterprise or not, and whether it is a key regulated enterprise or not, and comes to relevant conclusions. This paper expands the research on air quality and enterprise R&D investment decision-making, which helps to clarify and improve the transmission mechanism and implementation effect of environmental protection policies.
... Children and old people are particularly vulnerable to pollution [17]. Air pollution is not only associated with physical health harm, but also negative emotion and mental health problems, such as anxiety [18]. Exposure to air pollution affects the brain function, which can trigger behavioral changes [19,20]. ...
Full-text available
Background Local environmental factors are associated with health and healthcare-seeking behaviors. However, there is a paucity in the literature documenting the link between air pollution and healthcare-seeking behaviors. This study aimed to address the gap in the literature through a cross-sectional study of domestic migrants in China. Methods Data were extracted from the 2017 China Migrants Dynamic Survey (n = 10,051) and linked to the official air pollution indicators measured by particulate matter (PM2.5 and PM10) and air quality index (AQI) in the residential municipalities (n = 310) of the study participants over the survey period. Probit regression models were established to determine the association between air pollution and refraining from visiting health facilities after adjustment for variations in the predisposing, enabling and needs factors. Thermal inversion intensity was adopted as an instrumental variable to overcome potential endogeneity. Results One unit (µg/m³) increase in monthly average PM2.5 was associated with 1.8% increase in the probability of refraining from visiting health facilities. The direction and significance of the link remained unchanged when PM2.5 was replaced by AQI or PM10. Higher probability of refraining from visiting health facilities was also associated with overwork (β = 0.066, p = 0.041) and good self-related health (β = 0.171, p = 0.006); whereas, lower probability of refraining from visiting health facilities was associated with short-distance (inter-county) migration (β=-0.085, p = 0.048), exposure to health education (β=-0.142, p < 0.001), a high sense of local belonging (β=-0.082, p = 0.018), and having hypertension/diabetes (β=-0.169, p = 0.005). Conclusion Air pollution is a significant predictor of refraining from visiting health facilities in domestic migrants in China.
... From a clinical perspective, air pollutants, particularly PM10, significant influence the probability of suffering dementia. More specifically, PM10 could induce dementia directly through induction of systemic or brain-based oxidative stress and inflammation (Power et al. 2015). Several literatures discover that air pollutants, especially PM10, cause systemic or brain-base oxidative stress and inflammation (Calderon-Garciduenas et al. 2003), which significantly damage cytokine signaling. ...
Full-text available
Dementia has been cited as a critical public health risk in the contemporary world, while few empirical researchers try to reveal the casual relationship between air pollutant concentrations (APCs) and dementia, especially given the increasing prevalence of air pollution on a global scale. Accordingly, this paper tries to infer the causal relationship between APCs and dementia. The 59,605 valid data was compiled through a combination of the statistic from the China Family Panel Study, China Environmental Statistics Yearbook, World Meteorological Association and China National Bureau of Statistics. The RD design of this study utilizes the discontinuous variation in APCs and dementia as one crosses the Huai River boundary, which is an arbitrary heating policy that causes the significant difference in APCs between the north and south of China. We used stata17.0 to analyze the data. The results of the RD regression indicated that a 100 μ g/m³ rise in APCs led to an increase of 42.4% in the hazard ratio of suffering dementia (Coeff=-0.58, SD= 0.23, P < 0.05). Meanwhile, heterogeneous models revealed that the hazard ratio of suffering dementia by APCs was more significant in the older compared to younger (coeff= 1.35 vs coeff= 1.55, P < 0.05), male compared to female (coeff= 1.62 vs coeff= 0.71, P < 0.05), smoking compared to non-smoke (coeff= 2.12 vs coeff= 0.93, P < 0.05), and thin groups compared to medium and obesity (coeff= 2.05 vs coeff= 1.22, coeff= 1.28, P < 0.05). In addition, the O3 and SO2 were the air pollutants with the highest (coeff= 1.54, P < 0.05) and lowest effects (coeff= 0.81, P < 0.05) on the hazard ratio of suffering dementia among the five APCs, respectively. And the robustness of the results was ensured by changing the RD bandwidth, polynomial order. The results indicated that APCs significantly induced the hazard ratio of suffering dementia of Chinese residents, which provides empirical evidence in supporting the Chinese government to invest more in combating air pollution and ensure the public health of Chinese residents.
Background and Aim Few studies have reported the association between air pollution exposure with different dimensions of depression. We aimed to explore this association across different dimensions of depressive symptoms in a large population. Methods Data from the enrollment phase of the French CONSTANCES cohort (2012-2020) were analyzed cross-sectionally. Annual concentrations of particulate matter with a diameter <2.5 µm (PM2.5), black carbon (BC), and nitrogen dioxide (NO2) from the land-use regression models were assigned to the residential addresses of participants. Total depressive symptoms and its four dimensions (depressed affect, disturbed interpersonal relations, low positive affect, somatic complaints) were measured using Centre of Epidemiologic Studies Depression questionnaire (CES-D). We reported results of negative binomial regression models (reported as Incidence Rate Ratio (IRR) and 95% confidence interval (CI) for an interquartile range (IQR) increase in exposure), for each pollutant separately. Stratified analyses were performed by sex, income, family status, education, and neighborhood deprivation. Results The study included 123,754 participants (mean age, 46.50±13.61 years; 52.4% women). The mean concentration of PM2.5, BC and NO2 were 17.14 µg/m³ (IQR=4.89), 1.82 10⁻⁵/m (IQR=0.88) and 26.58 µg/m³ (IQR=17.41) respectively. Exposures to PM2.5, BC and NO2 were significantly associated with a higher CES-D total (IRR= 1.022; 95% CI=1.002: 1.042, IRR = 1.027; 95% CI=1.013: 1.040, and IRR = 1.029; 95% CI=1.015: 1.042 respectively), and with depressed affect, and somatic complaints. For all pollutants, a higher estimate was observed for depressed affect. We found stronger adverse associations for men, lower-income participants, low and middle education groups, those living in highly deprived areas, and single participants. Conclusion Our finding could assist the exploration of the etiological pathway of air pollution on depression and also considering primary prevention strategies in the areas with air pollution.
Full-text available
Accumulating data suggest that air pollution increases the risk of internalizing psychopathology, including anxiety and depressive disorders. Moreover, the link between air pollution and poor mental health may relate to neurostructural and neurofunctional changes. We systematically reviewed the MEDLINE database in September 2021 for original articles reporting effects of air pollution on 1) internalizing symptoms and behaviors (anxiety or depression) and 2) frontolimbic brain regions (i.e., hippocampus, amygdala, prefrontal cortex). One hundred and eleven articles on mental health (76% human, 24% animals) and 92 on brain structure and function (11% human, 86% animals) were identified. For literature search 1, the most common pollutants examined were PM2.5 (64.9%), NO2 (37.8%), and PM10 (33.3%). For literature search 2, the most common pollutants examined were PM2.5 (32.6%), O3 (26.1%) and Diesel Exhaust Particles (DEP) (26.1%). The majority of studies (73%) reported higher internalizing symptoms and behaviors with higher air pollution exposure. Air pollution was consistently associated (95% of articles reported significant findings) with neurostructural and neurofunctional effects (e.g., increased inflammation and oxidative stress, changes to neurotransmitters and neuromodulators and their metabolites) within multiple brain regions (24% of articles), or within the hippocampus (66%), PFC (7%), and amygdala (1%). For both literature searches, the most studied exposure time frames were adulthood (48% and 59% for literature searches 1 and 2, respectively) and the prenatal period (26% and 27% for literature searches 1 and 2, respectively). Forty-three percent and 29% of studies assessed more than one exposure window in literature search 1 and 2, respectively. The extant literature suggests that air pollution is associated with increased depressive and anxiety symptoms and behaviors, and alterations in brain regions implicated in risk of psychopathology. However, there are several gaps in the literature, including: limited studies examining the neural consequences of air pollution in humans. Further, a comprehensive developmental approach is needed to examine windows of susceptibility to exposure and track the emergence of psychopathology following air pollution exposure.
Full-text available
Background Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations. Methods We developed generalized additive mixed models that describe regional and small-scale spatial and temporal gradients (and corresponding uncertainties) in monthly mass concentrations of fine (PM2.5), inhalable (PM10), and coarse mode particle mass (PM2.5–10) for the conterminous United States (U.S.). These models expand our previously developed models for the Northeastern and Midwestern U.S. by virtue of their larger spatial domain, their inclusion of an additional 5 years of PM data to develop predictions through 2007, and their use of refined geographic covariates for population density and point-source PM emissions. Covariate selection and model validation were performed using 10-fold cross-validation (CV). Results The PM2.5 models had high predictive accuracy (CV R2=0.77 for both 1988–1998 and 1999–2007). While model performance remained strong, the predictive ability of models for PM10 (CV R2=0.58 for both 1988–1998 and 1999–2007) and PM2.5–10 (CV R2=0.46 and 0.52 for 1988–1998 and 1999–2007, respectively) was somewhat lower. Regional variation was found in the effects of geographic and meteorological covariates. Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region (CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999–2007). Conclusions Our models provide estimates of monthly-average outdoor concentrations of PM2.5, PM10, and PM2.5–10 with high spatial resolution and low bias. Thus, these models are suitable for estimating chronic exposures of populations living in the conterminous U.S. from 1988 to 2007.
Full-text available
Studies on the association between traffic noise and cardiovascular diseases rarely considered air pollution as a covariate in the analyses. Isolated systolic hypertension has not yet been in the focus of epidemiological noise research. The association between traffic noise (road and rail) and the prevalence of hypertension was assessed in two study populations with a total of 4,166 participants aged 25-74 years. Traffic noise (weighted day-night average noise level LDN) at the facade of the dwellings was derived from noise maps. Annual average PM2.5 mass concentrations at residential addresses were estimated by land-use regression. Hypertension was assessed by blood pressure readings, self-reported doctor diagnosed hypertension, and antihypertensive drug intake. In the Greater Augsburg study population, traffic noise and air pollution were not associated with hypertension. In the City of Augsburg population (n = 1,893), where the exposure assessment was more detailed, the adjusted odds ratio (OR) for a 10-dB(A) increase in noise was 1.16 (95% CI: 1.00, 1.35), and 1.11 (95% CI: 0.94, 1.30) after additional adjustment for PM2.5. The adjusted OR for a 1-μg/m(3) increase in PM2.5 was 1.15 (95% CI: 1.02, 1.30), and 1.11 (95% CI: 0.98, 1.27) after additional adjustment for noise. For isolated systolic hypertension, the fully adjusted OR for noise was 1.43 (95% CI: 1.10, 1.86) and for PM2.5 was 1.08 (95% CI: 0.87, 1.34). Traffic noise and PM2.5 were both associated with a higher prevalence of hypertension. Mutually adjusted associations with hypertension were positive but no longer statistically significant.
Full-text available
Exposure measurement error is a concern in long-term PM2.5 health studies using ambient concentrations as exposures. We assessed error magnitude by estimating calibration coefficients as the association between personal PM2.5 exposures from validation studies and typically available surrogate exposures. Daily personal and ambient PM2.5 and when available sulfate, measurements were compiled from nine cities, over 2 to 12 days. True exposure was defined as personal exposure to PM2.5 of ambient origin. Since PM2.5 of ambient origin could only be determined for five cities, personal exposure to total PM2.5 was also considered. Surrogate exposures were estimated as ambient PM2.5 at the nearest monitor or predicted outside subjects' homes. We estimated calibration coefficients by regressing true on surrogate exposures in random effects models. When monthly-averaged personal PM2.5 of ambient origin was used as the true exposure, calibration coefficients equaled 0.31 (95%CI:0.14, 0.47) for nearest monitor and 0.54 (95%CI:0.42, 0.65) for outdoor home predictions. Between-city heterogeneity was not found for outdoor home PM2.5 for either true exposure. Heterogeneity was significant for nearest monitor PM2.5 for both true exposures, but not after adjusting for city-average motor vehicle number for total personal PM2.5. Calibration coefficients were <1, consistent with previously reported chronic health risks using nearest monitor exposures being under-estimated when ambient concentrations are the exposure of interest. Calibration coefficients were closer to 1 for outdoor home predictions, likely reflecting less spatial error. Further research is needed to determine how our findings can be incorporated in future health studies.
The Nurses' Health Study was designed as a prospective follow-up study to examine relations between contraception and breast cancer. With follow-up questionnaires mailed every 2 years, investigators have added extensive details of lifestyle practices. The study, currently in its 20th year, has maintained high follow-up with >90% of participants responding to each of the follow-up cycles since 1988. The relations between use of hormones, diet, exercise, and other lifestyle practices have been related to the development of a wide range of chronic illnesses among women. This review describes the methods used to follow up the study participants and summarizes the major findings that have been described over the first 20 years of the study. We highlight additional areas added to the study in recent years to address emerging issues in women's health. Special emphasis is placed on the recent findings from the study, including relations between weight gain and heart disease, diabetes, and mortality, the lack of relation between calcium and osteoporotic fractures, and the positive relation between postmenopausal use of hormones and risk of breast cancer.
Anxiety disorders are among the most common and disabling mental illnesses. While effective treatments exist, many patients—possibly even 50–60%—remain symptomatic despite first-line treatments. With the exception of obsessive compulsive disorder (OCD), there are generally no universal definitions of treatment resistance and many treatments (both pharmacologic and non-pharmacologic) have not been tested specifically in refractory cases. This article reviews the evidence for possible medication, psychotherapy, brain stimulation, and neurosurgical approaches—including some promising novel treatments—for managing treatment-resistant anxiety.
Objective The purpose of this study was to determine how health-related quality of life (HRQoL), depression, and anxiety change over the first 12 months following diagnosis of atrial fibrillation (AF). In addition, we also aimed to investigate whether illness perceptions and beliefs about medication at the time of diagnosis are associated with HRQoL and affective response over time. Methods Seventy patients [mean (S.D.) age of 71.4 (9.1) years; 45 (64.3%) were men] with ‘lone’ AF completed the Beck Depression Inventory Short Form (BDI-SF-13), State–Trait Anxiety Inventory (STAI), Perceived Stress Scale (PSS), Short-Form Medical Outcomes Survey (SF-36), Illness Perception Questionnaire, and Beliefs about Medication Questionnaire at baseline and the BDI-SF-13, STAI, PSS, and SF-36 at 6 and 12 months after diagnosis of AF. Results Lone AF patients reported few depressive symptoms, while anxiety symptoms predominated, with a prevalence of elevated state anxiety (STAI-S ≥40) of 38.5%, 30.9%, and 35.7% at baseline and at 6 and 12 months, respectively. There were no significant differences in the levels of depression and mean levels of state and trait anxiety, perceived stress, and HRQoL (except for an increase in energy and decline in general health perception) over time. Baseline state and trait anxiety afforded the best prediction of state anxiety trajectory over 12 months (42% and 5%, respectively). The number of symptoms patients perceived as attributable to AF and specific concerns relating to their medication, at baseline, were independent predictors of physical health trajectories over 12 months after adjustment for age, gender, and AF type (P=.01) and together accounted for 15% of the variance in the slope. Conclusion Anxiety appears to be the main affective response to diagnosis of AF in a cohort of patients without other associated comorbidities. Patients' perceptions of their symptoms and concerns about the necessity of medication at diagnosis should be specifically addressed as part of their medical management.
Errors in Byline, Author Affiliations, and Acknowledgment. In the Original Article titled “Lifetime Prevalence and Age-of-Onset Distributions of DSM-IV Disorders in the National Comorbidity Survey Replication,” published in the June issue of the ARCHIVES (2005;62:593-602), an author’s name was inadvertently omitted from the byline and author affiliations footnote on page 592, and another author’s affiliation was listed incorrectly. The byline should have appeared as follows: “Ronald C. Kessler, PhD; Patricia Berglund, MBA; Olga Demler, MA, MS; Robert Jin, MA; Kathleen R. Merikangas, PhD; Ellen E. Walters, MS.” The author affiliations footnote should have appeared as follows: “Author Affiliations: Department of Health Care Policy, Harvard Medical School, Boston, Mass (Dr Kessler; Mss Demler and Walters; and Mr Jin); Institute for Social Research, University of Michigan, Ann Arbor (Ms Berglund); and Section on Developmental Genetic Epidemiology, National Institute of Mental Health, Rockville, Md (Dr Merikangas).” On page 601, the first sentence of the acknowledgment should have appeared as follows: “The authors appreciate the helpful comments of William Eaton, PhD, and Michael Von Korff, ScD.” Online versions of this article on the Archives of General Psychiatry Web site were corrected on June 10, 2005.
In the present study, we have examined the behavioral and biochemical effect of induction of psychological stress using a modified version of the resident-intruder model for social stress (social defeat). At the end of the social defeat protocol, body weights, food and water intake were recorded, depression and anxiety-like behaviors as well as learning-memory function was examined. Biochemical analysis including oxidative stress measurement, inflammatory markers and other molecular parameters, critical to behavioral effects were examined. We observed a significant decrease in the body weight in the socially defeated rats as compared to the controls. Furthermore, social defeat increased anxiety-like behavior and caused memory impairment in rats (P<0.05). Socially defeated rats made significantly more errors in long term memory tests (P<0.05) as compared to control rats. Furthermore, brain extracellular signal-regulated kinase-1/2 (ERK1/2), and an inflammatory marker, interleukin (IL)-6 were activated (P<0.05), while the protein levels of glyoxalase (GLO)-1, glutathione reductase (GSR)-1, calcium/calmodulin-dependent protein kinase type (CAMK)-IV, cAMP-response-element-binding protein (CREB) and brain-derived neurotrophic factor (BDNF) were significantly less (P<0.05) in the hippocampus, but not in the prefrontal cortex and amygdala of socially defeated rats, when compared to control rats. We suggest that social defeat stress alters ERK1/2, IL-6, GLO1, GSR1, CAMKIV, CREB, and BDNF levels in specific brain areas, leading to oxidative stress-induced anxiety-depression-like behaviors and as well as memory impairment in rats.