1068 VOLUME 113 | NUMBER 8 | August 2005 • Environmental Health Perspectives

Research | Children’s Health

As the prevalence of asthma, particularly

among urban residents, has escalated over

the past three decades (Aligne et al. 2000;

Mannino et al. 1998, 2002), ambient air pollutants, especially ozone and particulate matter (PM), have come under scrutiny as stimuli

of asthma exacerbations. O3 is a potent lung

irritant causing inflammatory changes in the

lung and decreases in lung function (Buchdahl

et al. 2000; McConnell et al. 2002; Mortimer

et al. 2000, 2002). PM with an aerodynamic

diameter of ≤ 10 μm (PM10) and ≤ 2.5 μm

(PM2.5) has been linked to increases in respiratory symptoms, emergency department visits for asthma, and decreases in lung function

(Delfino et al. 2002; McConnell et al. 2003;

Norris et al. 1999; van der Zee et al. 1999; Yu

et al. 2000).

Children with asthma are particularly

at risk for adverse health effects of air pollutants including PM and O3 (Boezen et al.

1999; Delfino et al. 2002; McConnell et al.

1999; Norris et al. 1999; Ostro et al. 2001).

New lines of inquiry suggest that there may be

particularly sensitive subpopulations even

within this group. Because air pollutants are

thought to stimulate a generalized inflammatory reaction in the asthmatic airway, several

investigators have examined whether use of

maintenance anti-inflammatory medication

protects against the adverse effects of air pollutants, with mixed results: Some have reported

protective effects (Delfino et al. 2002; Mortimer

et al. 2000; Peters et al. 1997), whereas others

instead have found that only those children

using maintenance medications show associations between pollutant exposure and respiratory symptoms (Gent et al. 2003). Children

experiencing a respiratory infection—another

potent stimulus of airway inflammation—may

also have increased susceptibility to the effects of

air pollution (Chauhan et al. 2003; Delfino

et al. 2002).

Based on the evidence for adverse health

effects, U.S. National Ambient Air Quality

Standards (NAAQS) were recently revised to

lower the allowable ambient exposure to O3

using a daily 8-hr maximum reference and

to introduce a daily and annual standard for

PM2.5 [U.S. Environmental Protection

Agency (EPA) 1997]. Although there have

been improvements in some indicators of air

pollution, many urban areas have not attained

the new O3 and PM2.5 standards (U.S. EPA

2002). It is important to examine whether

current ambient levels of pollutants at or near

current standards are negatively affecting the

health of sensitive subpopulations.

Detroit, Michigan, offers an ideal setting

for elucidating urban environmental influences

on childhood asthma. Detroit reflects many

demographic trends seen in urban areas around

the country. A high proportion of city residents

have low income and are African American or

Latino. Asthma prevalence among children is

high in Detroit, with 14.3% reporting active

physician-diagnosed asthma and an additional

14.3% with undiagnosed asthma (Joseph et al.

1996). Asthma hospitalization rates for children in Detroit are more than three times the

statewide average (Michigan Department of

Community Health 2002). In addition,

Detroit and surrounding Wayne County have

a long history of elevated air pollution and are

currently in nonattainment of the newer PM2.5

and O3 standards. Detroit is the site of the

busiest U.S.–Canadian border crossing for

truck traffic (3,486,110 trucks/year). Typical

daily traffic volumes for major highways in

Detroit range from 47,000 to 153,000 vehicles

(Michigan Department of Transportation

2001). Nearby point sources for pollutants

include coal-fired utilities, municipal waste

incineration, sewage sludge incineration,

refineries, iron/steel manufacturing, coke ovens,

and chemical plants (Keeler et al. 2002).

We hypothesized that ambient levels of

PM and O3 in Detroit communities would be

associated with fluctuations in lung function

among asthmatic children. We expected that

more severe asthma, as indicated by concurrent

Address correspondence to T.C. Lewis, University of

Michigan Pediatric Pulmonology, L2221 Women’s

Hospital, Box 0212, 1500 East Medical Center Dr.,

Ann Arbor, MI 48109-0212 USA. Telephone: (734)

764-4123. Fax: (734) 936-7635. E-mail: TOBYL@

umich.edu

We acknowledge the contribution of all the partners who have been involved in the Community

Action against Asthma (CAAA) collaborative effort:

University of Michigan Schools of Public Health and

Medicine, the Detroit Department of Health and

Wellness Promotion, the Michigan Department of

Agriculture, Plant and Pest Management Division,

the Henry Ford Health System, and nine communitybased organizations in Detroit: Butzel Family Center,

Community Health and Social Services Center,

Detroiters Working for Environmental Justice,

Detroit Hispanic Development Corporation, Friends

of Parkside, Kettering/Butzel Health Initiative, Latino

Family Services, United Community Housing Coalition, Warren/Conner Development Coalition. We

also thank J. Barres for his efforts in field data collection and laboratory analysis and S. Andersen for her

assistance in preparation of the manuscript.

This work was funded by the National Institute

of Environmental Health Sciences (grants P01-

ES09589-01, R01 ES010688, and K23 ES013242)

and the U.S. Environmental Protection Agency

(grant R826710-01).

The CAAA is affiliated with the Detroit

Community-Academic Urban Research Center (see

www.sph.umich.edu/urc for more information).

The authors declare they have no competing

financial interests.

Received 31 August 2004; accepted 5 May 2005.

Air Pollution–Associated Changes in Lung Function among Asthmatic

Children in Detroit

Toby C. Lewis,1 Thomas G. Robins,2 J. Timothy Dvonch,2 Gerald J. Keeler,2 Fuyuen Y. Yip,2 Graciela B. Mentz,3

Xihong Lin,4 Edith A. Parker,3 Barbara A. Israel,3 Linda Gonzalez,5 and Yolanda Hill6

Department of 1Pediatrics, 2Department of Environmental Health Sciences, 3Department of Health Behavior and Health Education, and 4Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA; 5Detroit Hispanic Development Coalition, Detroit,

Michigan, USA; 6Department of Health and Wellness Promotion, Detroit, Michigan, USA

In a longitudinal cohort study of primary-school–age children with asthma in Detroit, Michigan,

we examined relationships between lung function and ambient levels of particulate matter ≤ 10 μm

and ≤ 2.5 μm in diameter (PM10 and PM2.5) and ozone at varying lag intervals using generalized

estimating equations. Models considered effect modification by maintenance corticosteroid (CS)

use and by the presence of an upper respiratory infection (URI) as recorded in a daily diary among

86 children who participated in six 2-week seasonal assessments from winter 2001 through spring

2002. Participants were predominantly African American from families with low income, and

> 75% were categorized as having persistent asthma. In both single-pollutant and two-pollutant

models, many regressions demonstrated associations between higher exposure to ambient pollutants and poorer lung function (increased diurnal variability and decreased lowest daily values for

forced expiratory volume in 1 sec) among children using CSs but not among those not using CSs,

and among children reporting URI symptoms but not among those who did not report URIs. Our

findings suggest that levels of air pollutants in Detroit, which are above the current National

Ambient Air Quality Standards, adversely affect lung function of susceptible asthmatic children.

Key words: air pollution, asthma, child, community-based participatory research, Detroit,

lung function, ozone, particulate matter. Environ Health Perspect 113:1068–1075 (2005).

doi:10.1289/ehp.7533 available via http://dx.doi.org/ [Online 6 May 2005]
use of maintenance corticosteroids (CSs),

would confer additional susceptibility to the

effects of air pollution. Additionally, we

expected that co-inflammatory changes associated with a contemporaneous respiratory infection would increase the adverse effects of

higher pollutant levels on measures of lung

function. We were most interested in effects

seen in the first few days after exposure that we

felt were most likely to reflect inflammatory, as

opposed to acute bronchospastic, asthmatic

changes. Because the inflammatory changes

represented by children using inhaled CSs and

those with respiratory infection are likely to be

different, we did not necessarily expect that the

pollution effect lag structures would be the

same for both types of children.

Materials and Methods

Community Action Against Asthma

Partnership

There is a strong history of community-based

participatory research (CBPR) partnerships in

Detroit aimed at addressing asthma and other

health-related issues (Israel et al. 2003). The

study reported here is from Community

Action Against Asthma (CAAA), a project of

the CBPR partnership Michigan Center for

the Environment and Children’s Health. The

CAAA partnership involves a steering committee composed of representatives of communitybased organizations, health service agencies,

an academic institution, and a community

member at large that guided all phases of the

research (see Acknowledgements for list of partners). The steering committee’s role included

the conceptualization of the research questions

and methods, particularly related to interactions with the community, schools, and study

participants; the design of data collection

instruments and processes; the hiring of staff;

and the interpretation and dissemination of

research results (Edgren et al. 2005; Parker

et al. 2003). CAAA involved a longitudinal

epidemiologic cohort study of the effects of

PM and O3 on the respiratory health of children with asthma (present results) and a randomized controlled trial of an intervention

involving home visits by community outreach

workers to assist families in reducing exposure

to indoor asthma triggers (Parker et al. 2003).

These studies were conducted in two communities within Detroit (eastside and southwest)

that demographically have a high proportion of

low-income residents from African-American

and Latino ethnic groups.

Exposure Assessment

Ambient monitoring sites were established on

the rooftop of a representative school in both

southwest and eastside Detroit to assess community-level exposures to PM and O3. Twoweek seasonal measurement campaigns were

conducted for 11 seasons commencing in the

fall (October) of 1999 and ending in the

spring (May) of 2002.

Monitor placement. Many previous studies to assess health effects of air pollutants

have used exposure data from the nearest

available monitor (often existing state or federally mandated monitoring sites). These

often urban-scale monitoring sites, as defined

by the U.S. EPA (1997), are designed to represent exposure to large populations, in a geographic area up to 100 km in diameter. Our

study sought to quantify community-level

exposure within two Detroit communities,

with the possibility that exposures between

the two communities may be different (Keeler

et al. 2002). Although the centroid of southwest Detroit study participants was 15 km

from the centroid of eastside Detroit study

participants, we determined on examination

of the preexisting air monitoring sites in

Detroit that additional community-level sites

would need to be established for the sole

purpose of this study to obtain more accurate

measures of exposure. Each community monitoring site was established near the centroid

of the study participants. Of the 86 children

included in the data analyses for this report

(see “Pulmonary function measures,” below),

82 were located within 5 km of their respective community monitoring location, resulting

in great improvement in exposure estimation

when compared with the preexisting monitoring sites in Detroit or with the geographic

representativeness of exposure estimates for

many previous studies. The remaining four

children were in the eastside Detroit community, 6–7.5 km from the community monitoring site.

U.S. EPA monitoring guidelines for PM

and O3 recommend sampler inlets be placed

between 3 and 15 m above ground level

because of surface reactivity and aerosol resuspension concerns near ground level (U.S.

EPA 1998). Because of the landscape of the

built environment in urban areas, it has been

suggested that rooftop locations (up to four

stories in height) serve as representative locations for pollutant exposure monitoring (Chow

et al. 2002). Using these guidelines, we selected

the community-level monitoring locations on

elementary school rooftops, with sampler

inlets 5–6 m above ground level as previously

described (Keeler et al. 2002).

Field measurements. We performed 24-hr

measurements of PM2.5 and PM10 at each community sampling location using Teflon-coated

aluminum cyclone inlets and filter-pack assemblies (University Research Glassware, Carrboro,

NC) with 2-μm–pore 47-mm Teflon (polytetrafluoroethylene) membrane filters (Pall

Life Sciences, Ann Arbor, MI). Samples were

collected at a flow rate of 16.7 L/min from

0800 hr to 0800 hr, and the total volume of air

sampled was measured with calibrated dry test

meters (Schlumberger, Owenton, KY).

Ambient measurements of O3 and meteorologic variables were also made at each community sampling location. O3 was monitored

continuously and logged as 30-min average

values (Dasibi Environmental, Glendale, CA).

Standard meteorologic variables including

temperature, atmospheric pressure, relative

humidity, wind speed, and wind direction

(R.M. Young Co., Traverse City, MI) were

also recorded in 30-min intervals at each of

the community measurement sites (Keeler

et al. 2002).

Laboratory analyses. All sample handling,

processing, and analysis took place in a class

100 clean laboratory (University of Michigan

Air Quality Laboratory, Ann Arbor, MI)

uniquely suited for ultratrace element analysis

with an emphasis on environmental determinations. All gravimetric determinations

of Teflon filters for PM were made using

a microbalance (Mettler MT-5; Mettler

Toledo, Columbus, OH) in a temperature/

humidity-controlled environment. Standard

protocols included the use of field blanks,

filter-lot blanks, laboratory blanks, replicate

analyses, and externally certified standard

weights for all gravimetric analyses for quality

assurance and quality control purposes. The

detection limit for mass determination, calculated as three times the standard deviation

of seven replicate filter measures, was 5.1 μg.

This corresponds to a detection limit of

0.2 μg/m3 for a 24-hr sample collected at

16.7 L/min.

For PM, daily PM2.5 and PM10 measurements were the exposure variables used in the

health analysis. For O3, the daily mean O3

concentration and the rolling 8-hr averages

were calculated. With consideration for the

new U.S. EPA standard (U.S. EPA 1997), the

maximum 8-hr average in a 24-hr period was

called the O3 8-hr peak and analyzed as a separate exposure variable.

Recruitment and Enrollment

An asthma screening questionnaire was mailed

and/or hand delivered to parents of 9,627

children, 7–11 years of age, who attended one

of 44 elementary schools in the eastside and

southwest areas of Detroit (Lewis et al. 2004).

Items on the questionnaire included parent

report of their child’s frequency of respiratory

symptoms, presence of physician diagnosis of

asthma, and frequency of doctor-prescribed

asthma medication use. Among the 3,067

returned questionnaires, 708 were eligible for

the study, based on the inclusion criteria that

responses on the screening questionnaire be

consistent with current persistent asthma and

that the address be within the geographic

boundaries of the study. We successfully contacted 510 of those eligible and invited them

Air pollution effects in children with asthma

Environmental Health Perspectives • VOLUME 113 | NUMBER 8 | August 2005 1069
to enroll. Of these, 328 children were

enrolled. Thirty children were lost to followup between obtaining informed consent

and commencing the study; therefore, the

298 children who began the study serve as the

study cohort. There were no significant differences in demographic or asthma characteristics

between the 328 children that enrolled in the

study and the 179 who were contacted but

chose not to enroll, or between the 298 children that began the study and the 30 who

were lost to follow-up before the study began

(Lewis et al. 2004). There were also no demographic differences between the 3,067 who

returned screening questionnaires and the

86 children for whom lung function data are

presented here.

Outcome Assessment

Pulmonary function measures. Each child was

asked to complete three consecutive expiratory maneuvers in the morning and again in

the evening on 14 consecutive days during

each of the 11 seasonal measurement periods

using a hand-held digital lung function monitoring device (AirWatch; iMetrikus Inc.,

Carlsbad, CA). The Airwatch devices are calibrated at the factory and are stated by the

manufacturer to retain calibration for life. We

did not attempt to independently calibrate

the devices.

Peak flow (PF) and forced expiratory

volume in 1 sec (FEV1) were measured.

Although it would have been desirable to

examine additional measures of airway

obstruction, such as mid-volume flows [forced

expiratory flow between 25 and 75% of vital

capacity (FEF25–75)], these were not available

from simple hand-held devices at the time of

the study. During the first five seasons, all

expiratory maneuvers were carried out at

home. Preliminary review of the data raised

concerns about quality of these expiratory

maneuvers. Therefore, for the final six seasons

we switched to using the newly available

AirWatch 2 model, which included error

messages both for quality and length of expiratory maneuver. In addition, all weekday

morning blows during the final six seasons

were performed at school under observation

and coaching by research staff. To maximize

the efficiency of limited staff, this required

restricting participation to those 86 children

who attended a school with at least two other

study participants. Other than being on average 6 months younger, these 86 children did

not differ significantly in demographic characteristics or in intervention group assignment from those who did not continue with

pulmonary function measures in the last six

seasons. These protocol changes produced

substantial improvement in the quality of the

expiratory maneuvers. Results presented here

are based on these final six seasons of data

[winter 2001 (February 10–23), spring 2001

(May 5–18), summer 2001 (July 14–27), fall

2001 (September 22–October 5), winter

2002 (January 18–31), and spring 2002

(May 18–31)].

Lung function parameters of interest were

diurnal variability in these measures, as well as

the lowest value of the day. We defined variability as the difference between morning and

evening value divided by the larger of the two

values for that day. Variability increases during

asthma exacerbation and was expected a priori

to be higher in response to high pollution

exposure. Lowest daily value, measured as percent predicted for sex, age, height, and ethnicity (Hankinson et al. 1999), was defined as the

lower of the morning and evening values

for that day. High pollution exposure was

hypothesized to be associated with reduction

in lowest daily value. Data were analyzed for

days when a valid measurement was obtained

in both morning and evening. A valid measure

was defined as one obtained on an error-free

expiratory maneuver. We excluded extreme

measures, which we felt were more likely to be

attributable to undetected errors in technique

than to be truly representative of the child’s

respiratory health. For FEV1, we defined

extreme as being > 140% or < 30% predicted.

Medication and symptom diary. As part

of the 2-week seasonal measurements, the

child’s primary caregiver completed a medication and symptom diary at the end of each day.

Children were considered to have an upper respiratory tract infection (URI) on any day for

which the caregiver checked “yes” for “Does

your child have a cold, the flu, or other respiratory infection today?” Caregivers also wrote in

the number of times each of the asthma medications were administered that day. Children

were defined as being on a CS if, and only if,

a) at least 7 of the 14 diary days were completed and b) the parent reported use of an

inhaled or oral steroid for ≥ 50% of the days

for which the diary was completed. The assessment of whether or not a child was on a CS

was made for each season independently.

Each participating caregiver was interviewed face to face annually to obtain information about family demographics, the child’s

health status, and the perceived exposure of the

child to tobacco smoke.

Analysis Methods

We examined descriptive statistics and bivariate

analyses of exposures with health outcomes

and then examined multivariable regression

models that included interaction terms between

exposure measures and CS use or, alternatively,

presence of a URI. We used generalized

estimating equations (GEE) (exchangeable

covariance structure), a multivariate analog of

linear regression to account for the withinparticipant correlation of the repeated measures.

GEE was chosen over generalized linear models because of the non-normally distributed

exposure data.

With respect to timing of exposures, we

examined outcomes occurring 1 and 2 days

after the day that exposure was assessed (lag 1

and lag 2). In addition we considered outcomes associated with the average daily exposure 3–5 days before the outcome (lag 3–5).

Covariates in the final models included sex,

home location, annual family income, presence

of one or more smokers in household, race,

season (entered as dummy variables), and parameters to account for intervention group

effect. Home location (eastside vs. southwest)

was determined by ZIP code of residence.

Caregivers were asked to identify their child’s

race, and responses were categorized as African

American or not African American. Annual

family income was asked as a multiple choice

question with 11 response categories ranging

from “< $5,000” to “≥ $80,000.” For regression analysis, these were condensed to four

categories: < $10,000, $10,000–19,999,

Lewis et al.

1070 VOLUME 113 | NUMBER 8 | August 2005 • Environmental Health Perspectives

Table 1. Characteristics of children participating

from winter 2001 through spring 2002 as reported

on baseline caregiver interview (n = 86).

Characteristic Value

Child age at start of winter 2001a 9.13 ± 1.44

Percent female 43.0

Child ethnicity (%)

African American 77.9

Latino 15.1

Other 7.0

Child location of residence (%)

Eastside 70.1

Southwest 29.9

Caregiver education (%)b

1–8 grade 8.1

9–11 grade 33.7

High school graduate/GED 27.9

Any college 30.2

Household annual income (%)

< $10,000 44.7

$10,000–20,000 32.9

$20,000–40,000 17.6

≥ $40,000 4.7

Caregiver smokes cigarettes (self-report) (%) 31.4

Any household member smokes cigarettes (%) 51.2

Child’s asthma severity (%)

Moderate–severe persistent 50.0

Mild persistent 25.6

Mild intermittent 24.4

Asthma medication use by asthma severity

Persistent (mild severe) [n = 65 (%)]b

CS 15.4

Nonsteroid controllerc 23.1

Short-acting bronchodilatord 30.8

None 30.8

Intermittent [n = 21 (%)]

CS 4.8

Nonsteroid controller 4.8

Short-acting bronchodilator 14.3

None 76.2

aMean ± SD. bPercentages may not add to 100% because

of rounding. cUse of a leukotriene modifier, long-acting

bronchodilator, cromolyn, or theophylline, but no use of a

CS. dUse of a short-acting bronchodilator but no use of

any controller medication.
$20,000–39,000, and ≥ $40,000. Children

were considered to be exposed to tobacco

smoke if caregivers reported one or more cigarette smokers living in the home. To adjust for

an influence of being simultaneously enrolled

in the companion home intervention study,

two indicator variables were included: whether

a child had been randomized to the intervention or to the control groups, and whether the

exposure was occurring before or after the

intervention was complete. An interaction

term between intervention group and time

was also included.

In separate models, interactions of each

exposure variable with CS use or with presence

of URI were also included. This allowed the

pollution effects and the lag structure to differ

with the different types of effect modification

being examined. We did not adjust for relative

humidity and temperature because of the high

degree of collinearity with exposure measurements within several seasons. Adjusting for

season accounts for most of the variability in

temperature and humidity between seasons,

as well as other unmeasured season-specific

covariates (e.g., incidence of respiratory infections in the community).

We analyzed single-pollutant models

examining the effects of PM and O3 independently, as well as two-pollutant models simultaneously including a measure of PM and of

O3. To be able to directly compare the effect

sizes across pollutants, exposures were standardized to the interquartile range for each

specific pollutant. The number of observations on which models were based varies

depending on the pattern of missing data by

pollutant, lag, and season.

Results

Demographic and Asthma

Characteristics of Cohort

Characteristics of the 86 children participating

in the last six seasons of data collection, and

thus contributing to the analyses presented

here, are shown in Table 1. Most participants

were African American, were from the eastside,

and had household annual income < $20,000.

Approximately one-half of the caregivers

reported at least one tobacco smoker in the

household. At baseline, half of the children had

symptoms consistent with moderate-to-severe

persistent asthma. Among the 65 children with

persistent asthma, fewer than one-half were

reported to be taking a controller medication

(steroid or nonsteroidal), and 30% were using

no medication at all. CS use reported on daily

diaries ranged from 13 to 26% of all participants, depending on the season. Frequency of

respiratory infection ranged from a low of 7%

of reported person-days (in spring 2001 and

spring 2002) to a high of 24% of person-days

in winter 2001. CS use and respiratory infection were uncorrelated: Of the > 1,900 persondays of observations entering the regressions,

only 3% were contributed by children reporting both CS use and presence of respiratory

infection, 11% were contributed by children

reporting CS use but not respiratory infection,

and 19% by children reporting respiratory

infection but not CS use.

Description of Ambient Exposure

Measurements

Means across the six seasons of ambient

exposure measures [winter (February) 2001

through spring (May) 2002] by location are

shown in Table 2. The mean concentrations of

PM2.5 were 15.7 and 17.5 μg/m3 measured at

the eastside and southwest sites, respectively

(Table 2). In addition to seasonal variability in

PM [lowest seasonal means occurred in fall

(September) 2001: PM2.5 = 10.6 μg/m3, PM10

= 20.5 μg/m3; highest seasonal means occurred

in spring (May) 2001: PM2.5 = 23.3 μg/m3,

PM10 = 28.4 μg/m3], there was considerable

day-to-day variability in PM measurements

(lowest daily mean PM2.5 = 1.0 μg/m3, PM10 =

2.9 μg/m3; highest daily mean PM2.5 =

56.1 μg/m3, PM10 = 70.9 μg/m3). The

observed levels of pollutants were similar to

values measured at these sites during the first

five seasons of the study, fall 1999 through fall

2000 (Keeler et al. 2002), and the Detroit area

has since been designated in nonattainment for

the annual NAAQS for PM2.5 of 15 μg/m3.

The increased levels of both PM10 and PM2.5

at the southwest site are likely due to a combination of the local proximity of heavy industrial sources (including coal-fired power plants,

refineries, and iron/steel mills) and differences

related to traffic density (e.g., diesel emissions).

As with PM, there was both seasonal variability and day-to-day variability in O3 levels

(lowest daily mean = 5.6 ppb, lowest 8 hr-peak

= 14.8 ppb; highest daily mean = 66.3 ppb,

highest 8-hr peak = 92.0 ppb). Non-negligible

levels of O3 were measured during the winter

2002 (eastside: daily mean = 22.2, 8-hr peak =

24.1; southwest: daily mean = 21.2, 8-hr peak

= 24.2). The interquartile ranges were 12.5

μg/m3, 19.1 μg/m3, 14.5 ppb, and 16.0 ppb

for PM2.5, PM10, daily O3, and 8-hr peak O3,

respectively. Correlations between PM measures and O3 measures were mostly in the

0.5–0.6 range. The intra-PM and intra-O3 correlations were around 0.9 (Table 3).

Description of Lung Function

There were 12,962 error-free observations

available for analysis out of a possible 14,448

(twice-daily measures for 14 days per six seasons for each of the 86 children). We restricted

our analysis to the 10,784 FEV1 observations

between 30 and 140% predicted, which represents approximately 83% of the error-free data.

Population mean lung function values for

FEV1 diurnal variability and lowest daily FEV1

are shown by use of CS medications and by

report of the presence of respiratory infection

in Table 4. These values suggest that, on average, our population experienced a mild to

moderate degree of airway obstruction. The

lack of differences between those reporting and

those not reporting respiratory infection suggests that aggravation of underlying asthma

was not routinely being confused with respiratory infection by the respondents.

Association of Exposure and Lung

Function

Single-pollutant models. Regression models

expressing the association between ambient pollutant exposure and lung function for children

on CSs are shown in Table 5. Results for children not on CSs are not presented, because no

statistically significant relationships were identified at the p = 0.05 level. Associations for children reporting presence of URI symptoms are

shown in Table 5. Only one statistically significant relationship was observed for children who

did not report respiratory infection symptoms

(described in “Additional analyses,” below).

All associations with p-values < 0.2 were in

the expected direction (increased pollutant

associated with increased FEV1 diurnal variability and decreased lowest daily value), indicating consistency across the models examined.

For children on maintenance CSs, PM10 and

Air pollution effects in children with asthma

Environmental Health Perspectives • VOLUME 113 | NUMBER 8 | August 2005 1071

Table 2. Ambient pollutant and meteorologic measurements (mean ± SD) in two Detroit communities averaged across six seasons (winter 2001 through spring 2002).

Measurement Eastside Southwest Interquartile range

PM2.5 daily mean (μg/m3

) 15.7 ± 10.6 17.5 ± 12.2 12.5

PM10 daily mean (μg/m3) 23.0 ± 13.5 28.2 ± 16.1 19.1

O3 daily meana (ppb) 27.6 ± 12.5 26.5 ± 9.8 14.5

O3 peak 8-hr meana (ppb) 40.4 ± 18.2 41.4 ± 18.6 16.0

Temperature (°C) 11.5 ± 9.8 11.7 ± 9.8

Relative humidity (%) 72.2 ± 14.1 73.1 ± 14.7

aO3 was not measured during winter 2001.

Table 3. Pearson correlation matrix of pollutant

measures made in two locations (eastside and

southwest) during six seasons (winter 2001 through

spring 2002).

Daily mean O3

Pollutant PM10 O3 8-hr peak

PM2.5 daily mean 0.93 0.57 0.53

PM10 daily mean 0.59 0.57

O3 daily mean 0.87
8-hr peak O3 were both associated with poorer

lung function 2 days after exposure. For children reporting symptoms of respiratory infection, both PM2.5 and PM10 were associated

with poorer lung function 3–5 days after exposure. O3, particularly the 8-hr peak concentration, was associated with poorer lung function,

predominantly at lag 1 and lag 2. In general,

the estimated effect sizes were modest and were

in similar ranges for PM and for O3.

Two-pollutant models. In models including either of the PM measurements simultaneously with daily mean O3, all associations with

p-values < 0.1 were in the expected direction

(Tables 6 and 7). The pattern of these associations differed from that in the single-exposure

models, most likely owing to differences in

missing data patterns across these analyses. The

two-pollutant models suggest that PM and O3

independently affect lung function, even after

adjustment was made for the effect of the other

pollutant. Among children taking CSs (Table

6), the combination of PM2.5 and daily O3 was

more likely to have a significant effect in the

longer lags, whereas the effects of PM10 and

daily O3 were seen in both shorter and longer

lags. A similar pattern was also seen among

children reporting respiratory infection on the

day of lung function assessment (Table 7).

Some of the largest and most significant effect

estimates were seen for the models including

PM10 and O3 among those children with respiratory infections.

Additional analyses. Regressions including

either of the PM measures simultaneously

with 8-hr peak O3 also showed associations

with lung function in the expected direction,

although these were statistically significant

slightly less often than the presented PM/daily

O3 models.

We examined identical models using PF

as an alternative health outcome. Data cleaning for PF was the same as for FEV1, except

that, based on the distribution of the data, a

slightly narrower range of values were considered valid (values between 30 and 120% predicted). As with FEV1 models, PF models

showed multiple statistically significant associations between pollutants and respiratory outcomes, with all but one of these associations

seen among children who either used CSs or

who reported URIs. In general, the pattern of

significant associations was similar in the PF

and the FEV1 models. There were several

instances where an exposure would be significantly associated with worsening lung function in the FEV1 model and in the PF model

would have a point estimate in the expected

direction of worse lung function, but where

the p-value was > 0.05, implying that FEV1

was a more sensitive indicator than PF for

pollution effects on lung function.

The vast majority of analyses for children

not on CSs or not reporting a URI did not

show significant associations (data not shown).

Of the 144 models examined (associations

between PM2.5, PM10, daily O3, and 8-hr peak

O3 exposures singly or jointly with FEV1 or PF

diurnal variability or lowest daily value), only

four models showed significant associations

between pollutants and lung function in the

groups not on CSs or not reporting URIs.

These exceptions are described here. Among

children without URIs, a) the single-pollutant

model examining the association between

PM10 and diurnal variability in PF at lag 1

showed an odds ratio (OR) of 1.72 [95% confidence interval (CI), 0.38 to 3.06; p = 0.012],

whereas among children with URIs, OR =

1.83 (95% CI, –4.26 to 7.91; p = 0.56); and

b) the two-pollutant model of PM10 and daily

O3 exposure on diurnal variation of FEV1 at

lag 1 showed an OR for O3 exposure of 3.27

(95% CI, 0.30 to 6.23; p = 0.03; OR for PM10

was not significant), contrasted with children

with URIs (OR for O3 exposure = 9.53; 95%

CI, 5.82 to 13.47; p < 0.001; OR for PM10

was not significant). For children not on CSs,

a) the two-pollutant model evaluating the joint

effects of PM2.5 and daily O3 exposure on

diurnal variation of FEV1 at lag 3–5 days

showed an OR for PM2.5 exposure of 2.21

(95% CI, 0.26 to 4.16; p = 0.03; OR for O3

exposure was not significant), contrasted with

children on CSs (OR for PM2.5 exposure =

2.70; 95% CI, 1.0 to 4.40; p = 0.002; OR for

O3 exposure was not significant); and b) the

Lewis et al.

1072 VOLUME 113 | NUMBER 8 | August 2005 • Environmental Health Perspectives

Table 4. Distribution of valid FEV1 diurnal variabilitya and lowest daily FEV1

a values (mean ± SD) over the

six seasonal assessment periods by at-risk status determined by seasonal diary (winter 2001 through

spring 2002).

Subgroup of children Person-days (n) Variability FEV1 Lowest daily FEV1

On CSsa 393 15.8 ± 14.2 78.1 ± 17.5

Not on CSs 1,545 15.0 ± 11.6 71.7 ± 18.9

Reporting URIa 231 14.9 ± 12.3 74.0 ± 19.0

Not reporting URI 1,481 15.7 ± 12.3 71.5 ± 19.4

aSee “Materials and Methods” for definitions.

Table 5. Associations of ambient pollutant concentrations with lung function of children with asthma: single-pollutant models.a

Daily mean

PM2.5 PM10 O3 O3 daily 8-hr peak

Lung functionb Coefficientc 95% CI p-Value Coefficient 95% CI p-Value Coefficient 95% CI p-Value Coefficient 95% CI p-Value

Among children reporting use of maintenance CSsd

Diurnal variability FEV1

Lag 1e 1.61 –0.50 to 3.72 0.14 1.53 –0.85 to 3.90 0.21 –0.41 –3.02 to 2.19 0.76 1.75 –0.20 to 3.70 0.08

Lag 2e 2.96 –1.74 to 7.66 0.22 5.32 0.32 to 10.33 0.04 –0.73 –3.21 to 1.75 0.56 3.19 0.29 to 6.08 0.03

Lag 3–5e 1.37 –1.49 to 4.22 0.35 1.46 –2.21 to 5.13 0.43 –1.86 –4.86 to 1.14 0.22 –0.03 –0.28 to 0.22 0.82

Lowest daily value FEV1

Lag 1e –2.23 –6.99 to 2.53 0.36 –0.28 –2.34 to 1.77 0.79 –0.28 –4.94 to 4.39 0.91 –1.0 –5.68 to 3.68 0.68

Lag 2e –0.21 –4.09 to 3.68 0.92 –2.21 –3.97 to –0.46 0.01 0.21 –3.06 to 3.48 0.90 –3.95 –6.78 to –1.12 0.006

Lag 3–5e –0.76 –5.00 to 3.49 0.73 –2.58 –7.65 to 2.49 0.32 –1.05 –7.68 to 5.58 0.76 0.07 –0.28 to 0.41 0.70

Among children reporting presence of URI on day of lung function assessment

Diurnal variability FEV1

Lag 1e 2.00 –2.64 to 6.64 0.40 3.51 –4.52 to 11.55 0.39 4.08 –1.78 to 9.94 0.17 5.79 1.74 to 9.85 0.005

Lag 2e 0.35 –5.90 to 6.60 0.91 1.12 –4.62 to 6.86 0.70 7.62 –0.49 to 15.73 0.07 4.74 0.46 to 9.02 0.03

Lag 3–5e 2.51 0.06 to 4.95 0.05 3.90 0.34 to 7.47 0.03 1.47 –7.73 to 10.67 0.75 0.27 0.01 to 0.53 0.04

Lowest daily value FEV1

Lag 1e –1.21 –5.62 to 3.21 0.59 –2.72 –9.47 to 4.03 0.43 –2.65 –6.16 to 0.87 0.14 –3.00 –5.16 to –0.84 0.007

Lag 2e –0.10 –4.36 to 4.16 0.96 –0.24 –5.10 to 4.63 0.92 –4.36 –8.26 to –0.47 0.03 –2.64 –5.45 to 0.18 0.07

Lag 3–5e –2.88 –5.46 to –0.30 0.03 –4.48 –8.36 to –0.60 0.02 –1.01 –3.98 to 1.96 0.50 –0.03 –0.18 to 0.12 0.70

aEach coefficient is an estimate of percent change in lung function shown and is derived from a separate linear regression model using GEE. Covariates in each model: sex, home location,

annual family income, presence of one or more smokers in household, race, season, randomization assignment for the intervention, and interaction between time and this randomization

assignment. bAssessment of a child’s lung function based on error-free expiratory maneuvers. cThe regression coefficient is the estimated change in lung function associated with an

increase of one interquartile range in the ambient pollutant concentration. dRegressions pertain to those children reporting use of inhaled and/or oral CSs at least 50% of days in a given

season on diary. eNumber of days between measurement of ambient pollutant concentration and lung function; lag 3–5 is based on the mean of pollutant concentrations on those days.
two-pollutant model of PM10 and daily O3 on

diurnal variation of FEV1 at lag 3–5 days

showed an OR for PM10 exposure of 2.92

(95% CI, 0.74 to 5.11; p = 0.009; OR for O3

exposure not significant), contrasted with children using CSs (OR for PM10 exposure 3.30;

95% CI, 0.58 to 6.02; p = 0.02; OR for O3

exposure not significant).

Discussion

PM2.5 and 8-hr O3 concentrations in Detroit

measured in this study are close to or exceed

the updated NAAQS standards, whereas levels of PM10 are well within current standards.

Our findings in single- and two-pollutant

regression models strongly suggest that these

levels of air pollutants were associated with

adverse effects on pulmonary function among

at-risk children with asthma. Among the subgroup of asthmatic children using maintenance CSs, single-pollutant models suggest

lag 2 effects for PM10 and 8-hr peak O3. The

two-pollutant models revealed effects more

broadly across various lags for children on

steroids. For children reporting respiratory

infection, the single-pollutant models showed

PM effects in later lags and a striking O3

effect, particularly when examining the 8-hr

peak concentration. In several of these twopollutant models, PM and O3 measures

simultaneously showed statistically significant

associations with poorer lung function,

strongly suggesting the presence of independent effects. Moreover, the fact that the same

single- and two-pollutant models for children

not on CSs and, separately, children without

a current URI showed essentially no significant associations between any pollutant measures and any lung function measures greatly

decreases the likelihood that some kind of

unrecognized systematic bias is responsible

for the positive findings among those on CSs

or with URIs. On the whole, the observed

effects, although mostly modest in absolute

terms, appear quite statistically robust.

Strengths of our study include multiple

assessments of air pollution and lung function

across seasons; the use of FEV1 as an outcome

measure; an ability to examine two susceptible subgroups—children on maintenance CSs

and children reporting respiratory infections;

an ability to examine simultaneous effects of

O3 and suspended particles; and a CBPR

approach that contributed to high retention

of participants across seasons.

Several limitations of this study need to be

considered. The data presented here are based

on 86 children from an original group of

510 eligible children with persistent asthma

who were successfully contacted, raising the

potential for selection biases affecting exposure–health outcome associations. It appears

unlikely that such biases are substantively operative given that a) demographic and disease

status of the 86 children for whom data are

analyzed here were very similar to those of the

other 212 children who began the study; b) the

basis on which the 86 were chosen (i.e., having

at least one other participating child present in

the same school) is very unlikely to be associated with the magnitude of the child’s health

response to air pollutants; and c) in that, in

this longitudinal design, each child essentially

serves as his or her own control, different distributions of unmeasured covariates among the

86 compared with the other children are substantially less likely to affect exposure–health

outcome associations.

Repeated measurements of lung function

in the community setting are notoriously difficult to obtain accurately. This was corroborated by our own experience with the first

Air pollution effects in children with asthma

Environmental Health Perspectives • VOLUME 113 | NUMBER 8 | August 2005 1073

Table 6. Associations of ambient pollutant concentrations with lung function among children with asthma

reporting use of maintenance CSs: two-pollutant models.a,b

PM2.5 daily mean O3 daily mean

Lung functionc Coefficientd 95% CI p-Value Coefficient 95% CI p-Value

Effect of concurrent exposure to both PM2.5 and O3

Diurnal variability FEV1

Lag 1e 0.99 –5.64 to 7.62 0.77 1.27 –3.58 to 6.11 0.61

Lag 2e 4.62 –4.31 to 13.54 0.31 3.51 –3.79 to 10.81 0.35

Lag 3–5e 2.70 1.0 to 4.40 0.002 3.76 0.27 to 7.26 0.04

Lowest daily value FEV1

Lag 1e 3.36 –3.92 to 10.63 0.37 –2.53 –9.78 to 4.71 0.49

Lag 2e 0.88 –8.69 to 10.46 0.86 –0.13 –8.09 to 7.83 0.98

Lag 3–5e –2.78 –4.87 to –0.70 0.009 –2.81 –9.02 to 3.41 0.38

Effect of concurrent exposure to PM10 and O3

Diurnal variability FEV1

Lag 1e 2.94 –1.07 to 6.96 0.15 5.32 1.82 to 8.82 0.003

Lag 2e 13.73 8.23 to 19.23 < 0.001 5.55 1.93 to 9.17 0.003

Lag 3–5e 3.30 0.58 to 6.02 0.02 –1.63 –6.97 to 3.72 0.55

Lowest daily value FEV1

Lag 1e –6.25 –11.15 to –1.36 0.01 –2.33 –4.85 to 0.02 0.07

Lag 2e –5.97 –11.06 to –0.87 0.02 –9.92 –13.28 to –6.56 < 0.001

Lag 3–5e 1.98 –0.38 to 4.33 0.10 –4.56 –7.92 to –1.20 0.008

aEach coefficient is an estimate of percent change in lung function shown and is derived from a separate linear regression model using GEE. Covariates in each model: sex, home location, annual family income, presence of one or more

smokers in household, race, season, randomization assignment for the intervention, and interaction between time and

this randomization assignment. bRegressions pertain to those children reporting use of inhaled and/or oral CSs at least

50% of days in a given season on diary. cAssessment of a child’s lung function based on error-free expiratory maneuvers. dThe regression coefficient is the estimated change in lung function associated with an increase of one interquartile

range in the ambient pollutant concentration. eNumber of days between measurement of ambient pollutant concentration

and lung function. Lag 3–5 is based on the mean of pollutant concentrations on those days.

Table 7. Associations of ambient pollutant concentrations with lung function among children with asthma

reporting symptoms of URI: two-pollutant models.a,b

PM2.5 daily mean O3 daily mean

Lung functionc Coefficientd 95% CI p-Value Coefficient 95% CI p-Value

Effect of concurrent exposure to both PM2.5 and O3

Diurnal variability FEV1

Lag 1e 3.99 –2.76 to 10.74 0.25 4.69 –0.72 to 10.09 0.09

Lag 2e 4.10 –1.41 to 9.60 0.15 6.51 –1.96 to 14.98 0.13

Lag 3–5e 3.81 –1.83 to 9.45 0.19 3.52 –1.27 to 8.30 0.15

Lowest daily value FEV1

Lag 1e –0.74 –4.14 to 2.65 0.67 –2.82 –6.34 to 0.70 0.12

Lag 2e –1.67 –5.09 to 1.75 0.34 –3.99 –7.54 to –0.44 0.03

Lag 3–5e –2.78 –4.79 to –0.77 0.007 –2.16 –14.59 to 10.28 0.73

Effect of concurrent exposure to PM10 and O3

Diurnal variability FEV1

Lag 1e 3.21 –1.28 to 7.71 0.16 9.53 5.58 to 13.47 < 0.001

Lag 2e 5.40 –0.82 to 11.62 0.09 7.66 –0.50 to 15.83 0.07

Lag 3–5e 6.27 0.07 to 12.47 0.05 2.53 –8.40 to 13.45 0.65

Lowest daily value FEV1

Lag 1e –13.11 –21.59 to –4.62 0.003 –4.41 –7.81 to –1.00 0.01

Lag 2e –3.32 –6.83 to 0.18 0.06 –5.22 –8.29 to –2.16 0.001

Lag 3–5e –3.17 –5.82 to –0.51 0.02 1.97 –2.56 to 6.51 0.39

aEach coefficient is an estimate of percent change in lung function shown, and is derived from a separate linear regression model using GEE. Covariates in each model: sex , home location, annual family income, presence of one or more

smokers in household, race, season, randomization assignment for the intervention, and interaction between time and

this randomization assignment. bRegressions pertain to those children reporting URI on the day of lung function assessment. cAssessment of a child’s lung function based on error-free expiratory maneuvers. dThe regression coefficient is the

estimated change in lung function associated with an increase of one interquartile range in the ambient pollutant concentration. eNumber of days between measurement of ambient pollutant concentration and lung function. Lag 3–5 is

based on the mean of pollutant concentrations on those days.
Airwatch model and protocol, and prompted

our switch to the updated Airwatch 2 model

and change in protocol to allow supervision

and coaching of a large portion of the lung

function data collection. These changes substantially improved the reproducibility of PF

and FEV1 measurements based on visual

inspection of the data and supported by a

reduction in the within-child intra-half-day

coefficient of variation (0.202 vs. 0.102 for PF,

and 0.238 vs. 0.114 for FEV1). During the

final data collection period (May 2002), we

performed an exercise to validate the Airwatch

data. We compared morning FEV1 values

obtained by an experienced respiratory therapist at the child’s school using a standard

spirometer (Renaissance II; Puritan Bennett,

Pleasanton, CA) to those error-free FEV1 values obtained the same morning with Airwatch

while the child was being coached in the usual

manner by study staff for a convenience sample

of 37 children. After removal of two outliers

with clearly invalid technique, the mean difference (spirometry value – Airwatch value) was

–0.14 L. A t-test revealed that this value is not

statistically different than zero. Lung function

values obtained by spirometry were in general

quite reproducible (mean difference between

two best blows, 0.19 L). This reproducibility

measure was unassociated with pollution levels

of either PM or O3, so there is no indication

that ambient pollution levels differentially

affected the quality of expiratory maneuvers.

To help guard against the potential for

spurious associations due to inaccuracies in

lung function measures, we opted to exclude

very high (> 140% predicted) or very low

(< 30% predicted) lung function values, which

were expected to be most likely a result of suboptimal exhalation technique. If this datacleaning step inadvertently excluded some

valid data, the effect would be to blunt the

true variability in lung function, most likely

biasing findings toward the null hypothesis.

The data set used for regression models was

limited by the decision to use only FEV1 measures from the revised protocol in place during

the last six seasons and from some limits on the

exposure data collected (i.e., PM exposures

were determined only on days on which lung

function was measured, and O3 measurements

were made only during one winter). These

restrictions should not introduce any systematic bias, and most of those models with

modest effect estimates size of 2–3% reached

statistical significance at the p = 0.05 level,

indicating retention of reasonable power.

Another potential limitation was our

reliance on caregiver report for information

about asthma severity, medication use, and

presence of URI symptoms. There was no

independent method to verify these reports.

We also did not attempt to learn what medications the child’s physician had actually

prescribed; rather, we focused on what the

child was actually taking through daily diary

reports. Although there may be some misclassification of steroid use, our ability to detect a

difference in the effect of pollutants among

those we classified as steroid users compared

with nonusers reassures us that our methods

and definitions were able to separate the children into clinically meaningful groups. It is

also possible that unmeasured characteristics

of the children or their home environment

may have influenced their responsiveness to

pollutants that were not included in our models. However, beyond the changes attributable

to the household intervention (which were

included in our models), these characteristics

were unlikely to vary much within the same

individual and therefore were unlikely to confound the observed relationships.

The pattern of association within each

group was not always consistent across lags or

between the single- and two-pollutant models,

a common finding in the literature (Delfino

et al. 2002; Gent et al. 2003; van der Zee et al.

1999). Factors that may contribute to this phenomenon here include the following: a) The

set of observations on which regressions were

based varied somewhat among models because

each model used all available data for the contributing variables; b) some within-season correlations between PM and O3 (particularly in

the summer months) were higher than the

general correlations, which may have influenced standard error calculations; and c) our

data-cleaning steps may have been too conservative, potentially obscuring true relationships.

Because of practical field measurement

conditions, we were limited to examination of

PF and FEV1. Although FEV1 is a more sensitive indicator of lower airway function than is

PF, measures of small airway function, such as

FEF25–75, could have been even more sensitive

and might have been able to detect more consistent relationships. We examined two different parameters of the lung function measures

(diurnal variability and lowest daily value) to

capture slightly different potential influences

on airways. Diurnal variability in PF has been

shown to correlate with symptoms (Gern et al.

1994) and responsiveness to methacholine

(Gibson et al. 1995; Valletta et al. 1995), and

to be more sensitive to changes in the environment than the mean absolute value of PF

(Valletta et al. 1995). We extrapolated that a

similar phenomenon might be likely with

FEV1. Diurnal variability and lowest daily

value were expected to track together, but not

necessarily to be concordant (Brand et al.

1997; Valletta et al. 1995), which was what

we observed. We feel that the weight of the

evidence—specifically, the presence of many

statistically significant associations of pollution

and lung function among children taking

CSs regularly or children reporting respiratory

infection versus none or few significant associations among children lacking these characteristics—combined with the generally consistent

pattern of effect estimates in the expected

direction across models, supports the existence

of a true underlying relationship.

The relationship of routine asthma medication use to observed health effects of air pollution is complex, because it sometimes appears

to be a marker for disease severity and at other

times appears to protect against the adverse

effects of pollution. An assessment of the effect

of sulfur dioxide on mild asthmatic patients in

the Czech Republic in 1991–1992 showed an

inverse association between air pollutant levels

and PF for children on medication (either theophylline or beta-agonists; no children were on

CSs) but not for children not on medication

(Peters et al. 1997). Another study (Gent et al.

2003) found consistent effects of O3 and PM

on respiratory symptoms among those children

using maintenance medication for asthma but

not among medication nonusers. These authors

concluded that asthma medication serves as a

proxy for disease severity in their analyses. In

studies that have compared groups of asthmatic

children with similar frequencies of symptoms

at baseline, positive associations between exposure to O3, PM10, and nitrogen dioxide and

symptoms were present only among those not

taking anti-inflammatory medications (CSs or

cromolyn) (Delfino et al. 1998, 2002). A mixed

picture was seen by Mortimer et al. (2000):

Among children who were full-term or normal

birth weight, children on cromolyn were more

symptomatic in response to O3 exposure than

were those who were on no medication or were

on beta-agonists, methylxanthines, or steroids;

but among preterm or low-birth-weight children, those who did not use any medications

were more symptomatic than those on any type

of medication. In our cohort, CS use appears to

act as a proxy for disease severity. It appears

possible that there is subclinical airway inflammation or preexisting airway remodeling that is

“unmasked” by exposure to higher air pollutant

levels, resulting in greater pollution-related

effects on lung function among children

on CSs.

Our investigation is consistent with the few

other studies that have examined the relationship between pollution exposure and asthma

exacerbation associated with viral illness. Tarlo

et al. (2001) reported that asthma exacerbations with diary-reported colds were associated

with higher levels of sulfur dioxide and nitrogen oxides compared with exacerbations without colds. Chauhan et al. (2003) found that

participants exposed to high levels of NO2 in

the week before the onset of polymerase-chainreaction confirmed virally induced exacerbation had worse symptom scores and lower PF

than did participants exposed to low levels of

NO2 before their virally induced exacerbations.

Lewis et al.

1074 VOLUME 113 | NUMBER 8 | August 2005 • Environmental Health Perspectives
Air pollution effects in children with asthma

Environmental Health Perspectives • VOLUME 113 | NUMBER 8 | August 2005 1075

Potential mechanisms by which pollutants may

increase susceptibility to viral infection include

disruption of mucociliary clearance (Ehrlich

1980; Schlesinger and Driscoll 1987), impairment of cellular immunity (Zwick et al. 1991),

or release of inflammatory mediators (Rusznak

et al. 1996).

In summary, in our population of predominantly African-American and Latino children

living in the economically stressed city of

Detroit, children with asthma who were CS

dependent or who had URIs were adversely

affected by current levels of ambient air pollution. Our results emphasize the continued

need for enforcement of existing standards as

well as the importance of considering susceptible subgroups within the population when formulating new standards. CBPR partnerships

can play a critical role in this policy process.

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CORRECTION

The “Demographic and Asthma Characteristics of Cohort” section of “Results” in the

manuscript originally published online was

incorrect in describing less than two-thirds

and 15% of the cohort; it has been corrected

here to fewer than one-half and 30%.