Environmental Health Perspectives • VOLUME 110 | NUMBER 10 | October 2002 A 607

Association of Asthma Symptoms with Peak Particulate Air Pollution and

Effect Modification by Anti-inflammatory Medication Use

Ralph J. Delfino,1 Robert S. Zeiger,2,3 James M. Seltzer,2,4 Donald H. Street,5 and Christine E. McLaren1 

1Epidemiology Division, Department of Medicine, University of California, Irvine, Irvine, California, USA; 2Department of Pediatrics,

University of California, San Diego, School of Medicine, La Jolla, California, USA; 3Department of Allergy, Southern California

Permanente Medical Group, San Diego, California, USA; 4Indoor Hygienic Technologies Corporation, Rancho Santa Fe, California, USA; 5Hollistier-Stier Laboratories LLC, Allergy Products, Independence, Oregon, USA

Experimental studies in animals and humans

have shown that exposures to particulate air

pollutants on a time scale of minutes to several

hours can cause adverse respiratory effects.

However, there is no direct experimental evaluation of differences in biologic responses to

particulate air pollution (particulate matter;

PM) from time-varying exposures (with peaks)

versus time-invariant exposures controlling for

cumulative exposure. Biologic responses may

intensify with high peaks in concentrations of

pollutants that overwhelm certain lung defense

mechanisms. In epidemiologic research, daily

peak particle concentrations measured at central outdoor sites may also serve as a better

indicator for personal outdoor exposures during the daytime compared with daylong averages. Most epidemiologic studies have relied

on particle averaging times of 24 hr. There are

few epidemiologic studies examining respiratory effects from peak particle exposures (1,2).

The present epidemiologic study examined

effects of maximum hourly concentrations of

outdoor particulate matter < 10 μm in aerodynamic diameter (PM10) on asthma symptoms

in children to assess the utility of peak versus

daily average exposure data.

Based on our previous findings (1), we

hypothesized that peak hourly exposures to

PM of outdoor origin will be more closely

associated with acute asthmatic symptoms in

some susceptible children than will 24-hr

average exposures. We further hypothesize

that the proinflammatory effects of air pollutants will likely vary across individual asthmatic

children, with greater responses in those with

more severe disease. The severity of asthma

depends largely on the magnitude of underlying pulmonary inflammation (3). Therefore,

subjects taking anti-inflammatory medications

may be protected against proinflammatory

effects of airborne agents and may show smaller

responses to air pollutants, including O3, nitrogen dioxide (NO2), and some particle components. Experimental evidence in asthmatic

patients identifies the proinflammatory effects

of O3 as a major mechanism for adverse effects

of O3 (4,5), and emerging experimental evidence for particulate air pollution is beginning

to characterize the proinflammatory nature of

causal components [e.g., (6)].

In the present article, we again examine

the relationships of daily asthma symptom

severity to 1-hr maximum, 8-hr maximum,

and 24-hr mean PM10 measured March

through April 1996 in Alpine, California.

This panel study included 22 children with

asthma (9–19 years old) living in nonsmoking

households (1,248 person-days). Strengths of

association are compared between the different particle averaging times. In addition,

strengths of association for the relationship of

asthma symptom severity to air pollutants

and aeroallergens are compared between

subgroups of asthmatic children divided into

Address correspondence to R.J. Delfino, Epidemiology

Division, Department of Medicine, College of

Medicine, 224 Irvine Hall, University of California,

Irvine, Irvine, CA 92697-7550 USA. Telephone:

(949) 824-1767. Fax: (949) 824-1343. E-mail:

rdelfino@uci.edu

We appreciate the skilled efforts of our clinic coordinators K. Mostafa of the Southern California

Permanente Medical Group and L. Skific of the

Clinical Research Institute, and the assistance in

recruitment by the Alpine School District. We thank

our research assistants P. Anderson and L. Crane,

and M. Hossain and staff at the San Diego Air

Pollution Control District for providing stationary

site data for air pollutant gases and weather and for

their assistance in placement of the remaining monitors. Most of all, we thank the study participants for

their diligent efforts. 

This project was supported by grant ES06214

from the National Institute of Environmental

Health Sciences (NIEHS). Its contents are solely the

responsibility of the authors and do not necessarily

represent the official views of the NIEHS. 

Received 5 November 2001; accepted 14 March

2002.

Children’s Health Articles

Maxima of hourly data from outdoor monitors may capture adverse effects of outdoor particulate

matter (PM) exposures in asthmatic children better than do 24-hr PM averages, which form the

basis of current regulations in the United States. Also, asthmatic children on anti-inflammatory

medications may be protected against the proinflammatory effects of air pollutants and aeroallergens. We examined strengths of pollutant associations with asthma symptoms between subgroups

of asthmatic children who were on versus not on regularly scheduled anti-inflammatory medications, and tested associations for different particle averaging times. This is a daily panel study of

22 asthmatic children (9–19 years of age) followed March through April 1996 (1,248 persondays). They lived in nonsmoking households in a semirural area of Southern California within the

air inversion mixing zone (range, 1,200–2,100 feet) with transported air pollution from urban

areas of Southern California. The dependent variable derived from diary ordinal scores is episodes

of asthma symptoms that interfered with daily activities. Minimum to 90th-percentile levels of

exposures at the outdoor monitoring site were 12–63 μg/m3 for 1-hr PM < 10 μm in aerodynamic

diameter (PM10); 8–46 μg/m3 for 8-hr PM10; 7–32 μg/m3 for 24-hr PM10; 45–88 ppb for 1-hr

O3; 6–26 ppb for 8-hr NO2; 70–4,714 particles/m3 for 12-hr daytime fungi; and 12–744 particles/m3 for 24-hr pollen. Data were analyzed with generalized estimating equations controlling for

autocorrelation. There was no confounding by weather, day of week, or linear time trend.

Associations were notably stronger in 12 asthmatic children who were not taking anti-inflammatory medications versus 10 subjects who were. Odds ratios (95% confidence intervals) for asthma

episodes in relation to lag 0 minimum to 90th-percentile pollutant changes were, respectively, 1-

hr maximum PM10, 1.92 (1.22–3.02) versus 0.96 (0.25–3.69); 8-hr maximum PM10, 1.68

(0.91–3.09) versus 0.75 (0.18–3.04); 24-hr average PM10, 1.35 (0.82–2.22) versus 0.80

(0.24–2.69); 1-hr maximum O3, 1.28 (0.75–2.17) versus 0.76 (0.24–2.44); 8-hr maximum NO2,

1.91 (1.07–3.39) versus 1.08 (0.30–3.93); 12-hr fungi, 1.89 (1.24–2.89) versus 0.90 (0.35–2.30);

24-hr pollen, 1.90 (0.99–3.67) versus 0.85 (0.18–3.91). Pollutant associations were stronger during respiratory infections in subjects not on anti-inflammatory medications. Although lag 0 1-hr

maximum PM10 showed the strongest association, the most robust associations were for lag 0 and

3-day moving averages (lags 0–2) of 8-hr maximum and 24-hr mean PM10 in sensitivity analyses

testing for thresholds. Most pollutant effects were largely driven by concentrations in the upper

quintile. The divergence of exposure–response relationships by anti-inflammatory medication use

is consistent with experimental data on inflammatory mechanisms of airborne pollutants and

allergens. Key words: asthma, epidemiology, longitudinal data analysis, ozone, panel study, particulate air pollution. Environ Health Perspect 110:A607–A617 (2002). [Online 13 September 2002]

http://ehpnet1.niehs.nih.gov/docs/2002/110pA607-A617delfino/abstract.html
a) those who were taking regularly scheduled

anti-inflammatory medications during the

panel follow-up period and b) those who did

not take anti-inflammatory medications. This

study was conducted in the same region as

the above-cited study (1), a semirural area of

southern California around the small town of

Alpine that lies within the air inversion mixing zone (town elevation, 1,800 ft; range,

1,200–2,100 ft) with transported air pollution from urban areas of southern California.

The “early spring season” of March through

April was chosen because it is the peak pollen

period in southern California. This time

period differs from those of our previous

studies, when low pollen concentrations were

found and no pollen effects were seen (1,7,8).

This provided us the opportunity to examine

differences in response to pollen by subject

medication use and to test putative interactions between O3 and pollen in relation to

asthma outcomes (9,10).

Methods

Design. The present design is a panel study,

which involves repeated measurements of outcomes and exposures in individuals. The

repeated measurements make it possible to

establish, with some detail, the temporality of

causal associations and to examine acute exposure–response relationships at the level of an

individual subject. Subjects can act as their own

control over time, analogous to a clinical

crossover trial. Panel designs with a large number of repeated measures can be an efficient

way to maximize information derived from a

small number of subjects. Power and precision

can be enhanced because the repeated measures

reduce the variability of the response variable

compared with strictly between-subject comparisons, without reducing the magnitude of

the true exposure–response relationships (11).

Population. The institutional review

boards of the University of California, Irvine,

San Diego State University, and Kaiser

Permanente approved the study protocol.

Informed written consent was obtained from

all subjects and one of their legal guardians.

Recruitment of subjects was done with the

assistance of the Alpine School District nurse

(four grade schools), with referrals from the

Kaiser Permanente Health Plan, Inc., San

Diego Area, Department of Allergy, and with

newspaper advertisements. Monetary incentives were an essential component of both

recruiting and retaining subjects. Subjects

were not blinded to the study but were simply told that it involved the examination of

environmental agents, including outdoor

allergens. Eligibility criteria were as follows:

a) physician-diagnosed asthma with ≥ 1-year

history, including episodic symptoms of

wheezing, cough, and dyspnea; b) a history of

at least several weeks during the warm seasons

(March–October) when the subject required

the use of prescribed asthma medications for

asthma exacerbations apart from respiratory

infections; c) age from 9 to 18 years; d) home

and school addresses in the Alpine or adjacent

areas; and e) no history of smoking by the

subject and no person smoking in the subject’s home. One participant was recruited at

age 18 but turned 19 at the panel follow-up.

Twenty-five asthmatic children agreed to

participate, 16 boys and 9 girls. A 10-year-old

boy and a 17-year-old girl dropped out after

the second week of study and are not retained

for analysis. Remaining subjects were 18

white non-Hispanic Americans, 3 Hispanic

Americans, and 2 Asian Americans. One

white 10-year-old male was asymptomatic

throughout the panel period and therefore

contributed no information to the repeatedmeasures analysis. This subject is not

included in the following analyses involving

22 subjects. Subjects lived an average of 1.9

miles (SD, 0.22) from the central site, ranging from 0.6 to 2.9 miles, except one subject

at 5.45 miles. The panel period was 1 March

through 30 April 1996 (61 days). Subjects

were followed up weekly during the first 2–3

weeks, then biweekly at their home to check

the accuracy of diaries and compliance with

the study protocol and to ask questions of

subjects and resolve problems. Two subjects

started in the second week of March, and all

volunteers completed the full follow-up

through March and April 1996. Missing

symptom score data occurred on 51 persondays (3.8% of total expected follow-up of

1,328 person-days) because subjects had left

the study area all day, and on 29 person-days

because of noncompliance with diary completion (2.2%), leaving 1,248 person-days of

observation.

Classification of allergy among subjects

was based on the presence or absence of positive allergen reactivity as assessed using epicutaneous skin prick tests (SPTs) for house dust

mites, cat, and various pollens and molds

common to the study area (12). Positive SPT

reactivity was defined as a wheal 3 mm greater

than the saline control or having a diameter ≥

50% of a histamine dihydrochloride control.

The SPTs for locally relevant pollens consisted

of standardized allergen extracts of nine trees,

Bermuda grass, a grass mix without Bermuda

grass, and two weed mixes. There were 13

SPTs for fungal spore taxa.

Asthma diary. At the end of each day,

subjects recorded medication use, presence of

respiratory infection or allergic rhinitis symptoms (hay fever), and the maximum level of

asthma symptom severity since the last entry

on the previous evening. Asthma symptoms

served as the main outcome variable in this

article. Our diary questions concerning

asthma symptoms focused on the impact of

the clinical severity of asthma on a subject’s

normal daily activities. Subjects received training in interpreting the scoring system so that it

was meaningful in relation to their activities

and asthma. This is important because of

interindividual differences in the types and

characteristics of symptoms recognized or

experienced by asthmatic patients. The

approach used combines the rating of various

symptoms into one score that is relevant to

the impact of asthma on a subject’s daily 

well-being. Asthma symptoms (cough,

Children’s Health • Delfino et al.

A 608 VOLUME 110 | NUMBER 10 | October 2002 • Environmental Health Perspectives

Table 1. Descriptive statistics for 22 asthmatic subjects, 1 March through 30 April 1996, Alpine, California.

On anti-inflammatory medication

Subject characteristic Yes (n = 10) No (n = 12)

Median age (age range, years) 12.5 (9–17) 15.0 (11–19)

No. males/females 6/4 8/4

No. allergic to fungi or pollen 8 9

No. days at symptom score (%)

0: No asthma symptoms present 282 (49.4) 241 (35.6)

1: Asthma symptoms present but caused no discomfort 160 (28.1) 149 (22.0)

2: Asthma symptoms caused discomfort but did not

interfere with daily activities or sleep 42 (7.4) 178 (26.2)

3: Asthma symptoms interfered somewhat with

daily activities or sleep 74 (13.0) 105 (15.5)

4: Asthma symptoms interfered with most activities (may

have stayed home in bed, returned home early from school,

or called a doctor or nurse for advice) 11 (1.9) 5 (0.7)

5: Asthma symptoms required going to a hospital,

emergency department, or outpatient clinic 1 (0.2) 0 (0)

Missing days (subject range) 40 (0–12) 40 (0–16)

Average symptom score (range) 0.90 (0.05–3.1) 1.24 (0.15–2.4)

No. asthma episodesa

/person-days (%) 86/570 (15) 110/678 (16)

No. subjects with mild persistent or more severe asthma (%)b 2 (20.0) 6 (50.0)

No. subjects with any respiratory infections (%) 5 (50) 6 (50)

No. days with any respiratory infections (%)/subject range 24 (4.2)/0–10 46 (6.8)/0–18

Mean daily as-needed β-agonist inhaler puffs (SD) 1.47 (2.34) 1.01 (1.57)

aDefined as having asthma symptoms that interfered with daily activities (symptom score > 2). bDefined as daily diary

reports of bothersome symptoms or worse more than twice a week throughout the study (16), irrespective of asthma

medication regimen. 
wheeze, sputum production, shortness of

breath, and chest tightness) were rated by the

subjects according to their global severity on a

scale from 0 to 5. Our classification, which we

developed for use in previous studies (1,7,8),

supplants the usual asthma panel study

approach of dichotomizing each individual

symptom into present or absent. Subjects classified their asthma symptom severity across six

levels, as described in Table 1 (see “Results”).

For the purposes of this article, we will say “on

versus not on anti-inflammatory medications”

when comparing those who were taking regularly scheduled anti-inflammatory medications

with those who did not take anti-inflammatory

medications. Rather than using prescribed

medications at baseline, the grouping was ascertained by examining regular daily or near-daily

medication use, which subjects entered into

their diaries by medication name. Subjects and

parents were assisted in labeling medication

entry lines, and any changes were evaluated

during follow-up home visits. Twelve subjects

who were classified as not using anti-inflammatory medications reported no use during the

panel follow-up period. For 10 subjects on regularly scheduled preventive medications, four

used inhaled cromolyn or nedocromil sodium,

and six used inhaled corticosteroids.

Subjects also entered yes or no regarding

whether they had a respiratory infection that

day. Diary instructions below the question

stated: “Were any of the following conditions

present today: a cold, sore throat, fever, doctordiagnosed flu, doctor-diagnosed respiratory

infection (pneumonia, bronchitis, croup,

pharyngitis, laryngitis, tracheitis, middle ear

infection, upper respiratory tract infection, or

sinus infection)?” This question was distinguished from a preceding question on allergy

symptoms that asked the yes or no question

“Did you have symptoms of hay fever today,

which were not due to a cold or flu?”

Instructions clarifying the hay fever question

stated: “Those symptoms should include more

than one of the following: sneezing, runny nose

(including postnasal drip), sinus or nasal congestion, itchy and watery eyes, itchy throat.”

Environmental variables. The PM10 concentrations were measured with a tapered-element oscillating microbalance (TEOM), at a

stationary outdoor monitoring station located

centrally in Alpine and operated by the San

Diego Air Pollution Control District

(SDAPCD). The TEOM is an inertial instrument that measures particle mass in real time

on an exchangeable filter cartridge by monitoring frequency changes of a tapered element

(13). The PM10 data were used as 1-hr averaged data, similar to O3 monitoring, but in

contrast to the standard 24-hr PM10 collected

on filters and weighed on a scale. The U.S.

Environmental Protection Agency has certified

the TEOM for measuring PM10 concentration.

The TEOM sampler inlet was operated at 16.7

L/min, and the inlet air stream was heated to a

constant 50°C to keep water in the vapor phase.

We did not collect samples for daily gravimetric

mass, particle composition, or size fractions.

Sampling, analysis, and data-processing protocols for PM10 were carried out as part of another

ongoing project at the University of Southern

California (14). Five days of TEOM data were

missing because of equipment malfunction. At

the same central site, outdoor O3 was monitored continuously using ultraviolet photometry, and outdoor NO2 was monitored

continuously using chemiluminescence. Hourly

temperature, relative humidity, and wind speed

and direction were also measured there.

Measurements of aeroallergens were made

using the Burkard 7-day recording volumetric

pollen and fungal spore collector with a sample

flow rate of 10 L/min (Burkard Manufacturing

Co. Ltd., Rickmansworth, Hertfordshire,

England). Placement of the pollen and fungal

spore sampler at the SDAPCD site ensured

no nearby upwind obstructions and was 4 m

above the ground. To prepare the sampler for

pollen and fungal spore collection, Melenex

tape was placed on the mounted drum of the

sampler and coated with silicone solution

evenly applied with an artist’s brush. After 7

days of continuous sample collection, the

tape was cut and mounted onto microscope

slides as 14 12-hr segments from 0900 hr to

2100 hr for daytime counts and 2100 hr to

0900 hr for nighttime counts. The various

fungal spores and pollen grains were then

counted and identified by using a compound

microscope at 500× and 1,000×. The pollen

and fungal spore counts were then converted

into particles per cubic meter of air for each

12-hr time period. One day of data was lost

because of slide damage. Two analytic variables were the total fungal spore and total

pollen concentrations, which were the sum of

the daily concentrations of all identified and

unidentified types of each of these two allergen groups. The airborne exposure variables

in regression models were outdoor ambient

measurements of 1-hr and 8-hr maximum

and 24-hr mean PM10, 1-hr and 8-hr maximum O3 and NO2, and 12-hr daytime mean

and 24-hr daily mean total fungal spore and

total pollen concentrations.

Statistical analysis. We used descriptive

statistics on individual subjects to summarize

the severity of asthma symptoms over time.

We examined temporal trends for daily and

hourly pollutant concentrations and constructed exposure correlation matrices for pollutant and weather variables to assess the

potential for confounding or multicollinearity

in regression analyses.

We used the asthma symptom score to

create a dichotomous response variable

representing the occurrence of clinically

meaningful asthma episodes: “no episode,” no

asthma symptoms, symptoms not bothersome or not interfering with daily activities

(score < 3); versus “episode,” symptoms that

interfered somewhat with daily activities or

worse (score ≥ 3). 

A cut point between a score of 0 and 1

was not informative, probably because symptoms not considered by the subject to be

bothersome (score = 1) may not be clinically

meaningful. A cutoff point between a score of

1 and 2 was examined in regression models.

Regression parameters for this binary variable

versus exposures were consistent with but

smaller than the episode cutoff point.

Therefore, results presented are for the analysis of asthma symptom episodes only.

We performed regression analyses of

effects of air pollutants on binary symptom

scores using generalized estimating equations

(GEEs). The GEE approach can model nonnormal response data that are discrete and correlated (15). The present data are correlated

because repeated daily measurements over

time in each individual constitutes a cluster of

dependent observations. The GEE models

were tested using the logit link in the SAS

(version 8; SAS Institute, Cary, NC) generalized linear model procedure Genmod, which

uses a ridge-stabilized Newton-Raphson algorithm to maximize the log-likelihood function

for the regression parameters. Deviance statistics for GEE models were used to assess the fit

of various models. GEE models for the timevarying predictors (air pollutants) were best

fitted with an autoregressive lag 1 working

correlation matrix. Serial correlation was thus

accounted for to control autocorrelation of

residual errors, a potential source of bias. The

GEE model for medication group alone, a

time-invariant predictor, was best fitted with

an exchangeable correlation. We evaluated the

potential for confounding by temperature, relative humidity, day-of-week trends, linear

time trend across the 61 days, and upper or

lower respiratory infection. This was done

after testing for interaction with the pollutant

or aeroallergen variables. Confounding was

defined as at least a 15% change in the parameter estimate. Two-pollutant and pollutantaeroallergen models were also examined after

testing for interaction. Model regression parameters were multiplied by an increase in the

pollutant of interest from the minimum to the

90th percentile of its distribution. The magnitude of effect is expressed as the symptom

odds ratios (ORs) for this increase to express

what may be among the largest effects for the

study population.

We first performed regression analyses on

all subjects with single pollutants in the

model. We then tested for interaction

between whether a subject was on versus not

on anti-inflammatory medications and each of

Children’s Health • Peak particulate air pollution, asthma, and medication use

Environmental Health Perspectives • VOLUME 110 | NUMBER 10 | October 2002 A 609
the continuous exposure variables. We compared the regression coefficients of subjects on

anti-inflammatory medications with those not

on anti-inflammatory medications to test the

null hypothesis of no difference in regression

coefficients between the two groups. To do

this analysis, we first made a dummy variable

for medication group that is coded 1 for subjects on anti-inflammatory medications and 0

for those not on anti-inflammatory medications. We then used the pollutant variable,

medication group, and a product term

between pollutant variable and medication

group as predictors in the regression equation.

We present disaggregated effects for the two

subgroups from this model.

The analysis focuses on effects of air pollutant concentrations on the day of symptom

reports (lag 0). We also examined effects of air

pollution levels on days before the day of the

diary symptom report to assess the potential

for delayed or cumulative air pollutant health

effects. This was accomplished by regressing

symptoms on pollution levels measured up to

5 days before the day of symptom reporting

(lags 1–5). We then examined moving pollutant averages that combined air pollution levels on current and lag days. The moving

average length was chosen after examining the

distribution of individual day lag effects.

Because pollutants were measured on 5 days

before the start of the study, it was possible to

test lag effects from the first day of follow-up.

However, lags and moving averages for PM10

were missing for up to 5 days after the time

the TEOM was malfunctioning.

Results

Descriptive statistics. Table 1 shows the characteristics of subjects. We compared the two

groups on versus not on anti-inflammatory

medications. Subjects on anti-inflammatory

medications were somewhat younger than

other subjects, and in both groups there were

more boys than girls, which is the typical gender distribution in pediatric asthma. Most

subjects were allergic to pollens or molds.

The overall proportion of days with asthma

episodes was similar between the two medication groups (15–16%). Two subjects on antiinflammatory medications had no episodes,

and one subject not on anti-inflammatory

medications had no episodes. We also used

daily symptom reports to classify a subject’s

asthma severity in a manner consistent with

the National Heart, Lung, and Blood

Institute (NHLBI) symptom-based criteria

(16), irrespective of asthma medication regimen. Subjects with mild persistent or more

severe asthma were defined as having daily

diary reports of bothersome or more severe

symptoms (score > 1) more than twice a week

throughout the study. Remaining subjects

were considered to have mild intermittent

asthma. There were more subjects not taking

anti-inflammatory medications who fitted the

NHLBI classification of persistent asthma.

However, there was no significant difference

in GEE models for medication classification

predicting either symptom scores > 1 or

symptom scores > 2 (p > 0.16). An equal proportion of subjects in the two groups reported

respiratory infections. There was no significant difference in use of as-needed β-agonist

inhalers between anti-inflammatory medication groups (Table 1).

Table 2 describes the exposure concentrations. No days had ozone concentrations >

120 ppb, which is the current U.S. National

Ambient Air Quality Standard (NAAQS)

(17). PM10 levels were very low compared

with urban areas of southern California. The

highest 24-hr mean PM10 was less that a

fourth of the current NAAQS of 150 μg/m3

(17). As expected, the PM10 data show different distributions depending on the averaging

time. The highest 24-hr mean PM10 measurement was 42 μg/m3, compared with the

mean of 1-hr maximum PM10 of 38 μg/m3.

Levels of NO2 were fairly modest, with the

highest 1-hr maximum just reaching the

NAAQS for the annual arithmetic mean (53

ppb) (17). Looking at the overall average by

hour of day, we found the highest PM10

hourly exposures were from 1300 to 2000 hr

(> 22 μg/m3). The highest O3 hourly exposures were from 0100 to 1700 hr (> 50 ppb).

These are times when children are most likely

to be outdoors and playing. Figure 1 shows

time plots of selected pollutant measurements

across the days of study.

The exposure correlation matrix is shown

in Table 3. Variables were approximately

normally distributed, so Pearson correlation

coefficients were computed. Because of photochemical oxidation processes, both O3 and

PM10 are often jointly higher on hotter and

dryer days, which explain the correlations

among the pollutant and weather variables.

Pollen was also moderately correlated with O3

and PM10 because pollination of many native

plants is greatest on hot, dry days. In our region

of study, the months of March and April studied represent the period of greatest pollination.

Fungal concentrations were significantly but

weakly correlated with the pollutants. PM10

was moderately correlated with NO2, showing

that upwind combustion sources from urban

areas of southern California (largely traffic

related) are important.

Regression analysis. Single-pollutant

regression models. Symptoms were not associated with day of week, linear trend, temperature, or relative humidity, and these variables

did not confound exposures. Models testing

lag days for aeroallergens, for PM10, and for

the gaseous pollutants were not significant in

regression models fitted with each exposure

alone, including 1–5 days before the day of

symptom reports (lags 1–5). The largest and

most robust effects were found for exposures

on the same day (lag 0) of the subject’s asthma

symptom report. However, findings in a

regression model fitted with a product term

for anti-inflammatory medication by exposure

showed lag effects for the group not on antiinflammatory medications. Effects were suggested for PM10 at lags 1 and 2 (p < 0.1), with

robust associations for the 3-day moving average of PM10 lags 0–2. Therefore, for simplicity, models are presented for lag 0 and the

3-day moving average of PM10 for each of the

daily averaging times (maximum 1-hr, maximum 8-hr, and 24-hr mean). Lag 0 effects of

12-hr daytime total fungi were slightly greater

than 24-hr average total fungi, whereas effects

of 24-hr total pollen were slightly greater than

12-hr daytime total pollen. Therefore, for

simplicity, models are presented for 12-hr

total fungi and 24-hr total pollen.

There was no association between symptoms and medication group in a GEE model

Children’s Health • Delfino et al.

A 610 VOLUME 110 | NUMBER 10 | October 2002 • Environmental Health Perspectives

Table 2. Daily air pollution and weather measurements, 1 March through 30 April 1996, Alpine, California.a

No.

Exposure and averaging time observations Mean (SD) Min/max 90th percentile

PM10 1-hr max (μg/m3)

b 56 38 (15) 12/69 63

PM10 8-hr max (μg/m3) 56 28 (12) 8/57 46

PM10 24-hr mean (μg/m3) 56 20 (9) 7/42 32

O3 1-hr max (ppb) 61 69 (16) 45/108 88

O3 8-hr max (ppb) 61 60 (12) 39/97 75

NO2 1-hr max (ppb) 61 24 (10) 8/53 42

NO2 8-hr max (ppb) 61 15 (7) 6/34 26

Fungi 12-hr daytime mean (particles/m3)

c 60 3,132 (1,647) 70/8,147 4,714

Fungi 24-hr mean (particles/m3) 60 2,973 (1,542) 757/7,975 5,380

Pollen 12-hr daytime mean (particles/m3) 60 427 (314) 17/1,310 875

Pollen 24-hr mean (particles/m3

) 60 345 (292) 12/1,257 744

Temperature 1-hr max (°F) 61 71 (10) 50/91 85

Relative humidity 24-hr mean (%) 61 59 (24) 12/97 86

Abbreviations: max, maximum; min, minimum.

aMeasurements were taken at the stationary outdoor monitoring site of the SDAPCD. bFive days are missing because of

TEOM equipment malfunction. c12-hr levels for fungi and pollen are for the sampling period of 0900 to 2100 hr; 24-hr levels

for fungi and pollen are from 2100 hr of the previous day to 2100 hr of the current day. One day is missing because of slide

damage.
that included only a medication indicator

variable: OR = 1.06; 95% confidence interval

(CI) = 0.25–4.43. However, we found significant interactions between exposures (air pollutants or aeroallergens) and classification of

subjects by regular use versus nonuse of antiinflammatory medications. Therefore, we

present GEE models without the product

term of medication group by pollutant concentration (model 1) to serve as a comparison

for relationships disaggregated by medication

use (Table 4, model 2). GEE results for the

models with each pollutant alone (model 1)

show that the lower 95% confidence limits

fall below 1.00 for relationships between

symptoms and pollutants or aeroallergens.

Nevertheless, several models suggest that risk

of an asthma symptom episode is greater with

elevations in exposures, including 1-hr maximum PM10, 3-day moving averages of all

PM10 daily averaging times, NO2, and the

aeroallergens. Exposure–response relationships in model 2 are presented separately for

the two medication groups in Table 4. For all

pollutants and aeroallergens, ORs were larger

for subjects not taking anti-inflammatory

medications. A divergence in effects is suggested for all models, with several showing

significant between-group product terms (p <

0.05). In summary, results in Table 4 show

that only the group of children not on antiinflammatory medications had positive associations between asthma symptoms and air

pollutants or aeroallergens. For all exposures

except O3 in this group, the lower 95% CIs are

either above 1.00 or do not fall far below 1.00.

Therefore, models below focus on this group to

test effects of multiple exposures and to test

sensitivity to high concentrations. Subgroup

models for these subjects showed narrower confidence limits for every exposure. For instance,

asthma symptoms were more clearly associated

with O3 (OR, 1.45; 95% CI, 0.97–2.11), and

the lower 95% confidence limits for 8-hr PM10

and 24-hr pollen were above 1.00.

Symptoms were not associated with day of

week, linear trend, temperature, or relative

humidity in either model 1 or model 2.

Inclusion of temperature in the models led to

instability in all variance estimates, suggesting

problems of multicollinearity, but did not

confound air pollution variables. Deviance

was not substantially changed (deviance difference < 4.0) when adding day of week, linear

trend, temperature, or relative humidity to

models with the exposure alone. Symptoms

were associated with respiratory infections in

both groups: for the group not on anti-inflammatory medications, OR = 2.80 (95% CI,

0.92–8.49), and for the group on antiinflammatory medications, OR = 1.93 (95%

CI, 1.01–3.71). In exposure models including

respiratory infections, parameter estimates

shown in Table 4 increased in all models for

the group not on anti-inflammatory medications. However, we found significant interactions between exposures and respiratory

infections, making it more informative to present the magnitude of increase in the ORs for

exposure given the presence versus absence of

a respiratory infection among subjects not on

anti-inflammatory medications (Table 5).

ORs were around three times greater or more

during respiratory infections than at other

times for all exposures except total pollen

(OR, 0.95). Pollutant exposures were not

associated with risk of respiratory infections in

GEE models, suggesting that risk of infection

onset was independent of the exposures, but

the probability of an asthmatic response to

respiratory infection was enhanced on high air

pollution days.

Children’s Health • Peak particulate air pollution, asthma, and medication use

Environmental Health Perspectives • VOLUME 110 | NUMBER 10 | October 2002 A 611

Figure 1. Time plot of air pollutant exposures, (A) PM10 and (B) 03 and NO2, Alpine, California, panel study,

1 March through 30 April 1996. 

1 March 1996 15 March 1996 29 March 1996 12 April 1996 26 April 1996

1 March 1996 15 March 1996 29 March 1996 12 April 1996 26 April 1996

Date

Date

80

70

60

50

40

30

20

10

0

A

B

120

100

80

60

40

20

0

PM10 (μg/m3) O3 and NO2 (ppb)

1-hr maximum PM10

24-hr mean PM10

1-hr maximum O3

1-hr maximum NO2

Table 3. Air pollution and weather correlation matrix,a 1 March through 30 April 1996, Alpine, California.

8-hr max 1-hr max 8-hr max 24-hr mean 1-hr max 8-hr max 12-hr daytime 24-hr

O3 PM10 PM10 PM10 NO2 NO2 fungib pollenb Max temp 24-hr mean RH

1-hr max O3 0.95# 0.68# 0.72# 0.74# 0.31* 0.36** 0.30* 0.56# 0.76# –0.49#

8-hr max O3 0.62# 0.65# 0.71# 0.25 0.26* 0.26* 0.57# 0.76# –0.52#

1-hr max PM10 0.93# 0.84# 0.49# 0.55# 0.24 0.47# 0.69# –0.34*

8-hr max PM10 0.95# 0.48# 0.55# 0.28* 0.45# 0.66# –0.23

24-hr PM10 0.37** 0.44# 0.21 0.47# 0.64# –0.17

1-hr max NO2 0.91# 0.29* 0.27* 0.33** –0.28*

8-hr max NO2 0.32* 0.28* 0.36** –0.28*

12-hr fungi 0.29* 0.12 –0.07

24-hr pollen 0.68# –0.50#

Max temp –0.75**

Abbreviations: max, maximum; RH, relative humidity; temp, termperature. 

aPearson correlation coefficients (p-value); n is 61 for O3, NO2, and weather, 60 for fungi and pollen, and 56 for PM10 observations. bCorrelations for 24-hr fungi and 12-hr pollen were similar. *p < 0.05; **p < 0.01; #

p < 0.001.
Multiple exposure regression models. For

the group not on anti-inflammatory medications, we then tested multivariate models

involving multiple exposures. The aim was to

explore possible copollutant confounding and

interactions. This information could indicate

underlying effects of an air pollutant mixture

not fully captured by any one criteria air pollutant. Regressions of two air pollutants generally led to decreases in both regression

parameters, likely the result of multicollinearity. For example, the model including both

8-hr PM10 and 8-hr NO2 showed ORs

(95% CIs) of 1.19 (0.75–1.88) and 1.50

(0.80–2.82), respectively. Differences in the

fit of single-pollutant versus two-pollutant

models were not significant. However, there

were significant positive multiplicative interactions between the pollutants, which make

interpretation of the simple joint regression

models difficult. The interaction was significant between 1-hr maximum PM10 and 8-hr

maximum NO2 (p < 0.01). Figure 2 shows

this pollutant interaction in relation to the

probability of a symptom response on the vertical axis (scaled to the log odds). The mesh

plot was smoothed using a Loess transform

function. For example, if 1-hr maximum

PM10 was 69 μg/m3 and 8-hr maximum NO2

was at 31 ppb, the predicted probability of an

asthma symptom response is 33%. This can

be compared with an overall average probability during the study of 17% (105 asthma

episodes/622 person-days of PM10 and NO2

observations) in the 12 subjects not on antiinflammatory medications.

Regression models including O3 and any

one of the other pollutant variables led to a

greater decrease in the O3 parameter than in

the copollutant parameter (e.g., 1-hr maximum NO2, OR decreased from 1.93 to 1.81,

whereas 1-hr maximum O3, OR decreased

from 1.43 to 1.23). Differences in the fit of

single-pollutant versus this two-pollutant

model were not significant. There was a dramatic decrease in the O3 regression parameter

when regressed with PM10, which did not

change from the model with PM10 alone [e.g.,

1-hr maximum PM10, OR = 1.73 (95% CI,

1.03–2.92); 1-hr maximum O3, OR = 0.99

(95% CI, 0.56–1.77)]. There were no significant product terms of PM10 or NO2 with O3;

however, there was a suggestion of a positive

interaction between O3 and NO2 (p = 0.12).

The interaction is shown in Figure 3 in relation to the probability of a symptom response

on the vertical axis (scaled to the log odds).

Models including pollutants and aeroallergens. We also tested multivariate models

including pollutants and aeroallergens for the

group not on anti-inflammatory medications.

Joint regression models were tested for pollutants and total pollen in nine pollen-allergic

subjects and for pollutants and total fungi in

eight fungus-allergic subjects. Only one of the

pollen-allergic subjects was not in the fungusallergic group. Effect magnitudes for aeroallergens were greater in the allergic group than in

all 12 subjects not on anti-inflammatory medications, but differences were small (~15%).

There were no significant multiplicative interactions between the aeroallergens and pollutants. Including both aeroallergens and air

pollutants in the same model generally led to a

decrease in regression parameters for both

exposures. In the case of regressions of total

pollen with O3, or with any of the 3-day moving averages of PM10, all standard errors were

inflated by over two times, suggesting multicollinearity. The aeroallergens did not confound the product term for O3 and NO2 or

Children’s Health • Delfino et al.

A 612 VOLUME 110 | NUMBER 10 | October 2002 • Environmental Health Perspectives

Table 4. Effect modification by anti-inflammatory medication use on the relationship of asthma symptomsa in children to increases in PM10, O3, NO2, and aeroallergen concentrations, Alpine, California panel study, 1 March through 30 April 1996.

OR (95% CI) per increase to 90th percentile of exposureb

Pollutant and Model 2c

aeroallergen variables Exposure concentrationd Model 1e On medicationf Not on medicationg p-Valueh

1-hr max PM10 lag 0 51 μg/m3 1.41 (0.87–2.30) 0.96 (0.25–3.69) 1.92 (1.22–3.02)** 0.13

8-hr max PM10 lag 0 38 μg/m3 1.19 (0.74–1.94) 0.75 (0.18–3.04) 1.68 (0.91–3.09) 0.05

24-hr mean PM10 lag 0 25 μg/m3 1.08 (0.73–1.61) 0.80 (0.24–2.69) 1.35 (0.82–2.22) 0.15

3-day moving average 1-hr max PM10 51 μg/m3 1.45 (0.76–2.76) 1.01 (0.14–7.02) 1.92 (0.99–3.71) 0.33

3-day moving average 8-hr max PM10 38 μg/m3 1.32 (0.76–2.29) 0.82 (0.17–3.94) 1.89 (1.10–3.24)* 0.11

3-day moving average 24-hr mean PM10 25 μg/m3 1.22 (0.84–1.77) 0.75 (0.26–2.14) 1.75 (1.15–2.68)** 0.008

1-hr max O3 lag 0 43 ppb 1.02 (0.71–1.47) 0.76 (0.24–2.44) 1.28 (0.75–2.17) 0.11

8-hr max O3 lag 0 36 ppb 0.94 (0.64–1.40) 0.73 (0.20–2.67) 1.14 (0.62–2.09) 0.21

1-hr max NO2 lag 0 34 ppb 1.40 (0.80–2.47) 0.90 (0.17–4.78) 1.95 (0.88–4.32) 0.08

8-hr max NO2 lag 0 20 ppb 1.49 (0.95–2.33) 1.08 (0.30–3.93) 1.91 (1.07–3.39)* 0.12

12-hr fungi lag 0 4,644 particles/m3 1.37 (0.95–1.97) 0.90 (0.35–2.30) 1.89 (1.24–2.89)** 0.005

24-hr pollen lag 0 732 particles/m3 1.37 (0.84–2.21) 0.85 (0.18–3.91) 1.90 (0.99–3.67) 0.07

Max, maximum. 

aThe asthma symptom severity score was dichotomized to a) no symptoms or symptoms not bothersome or not interfering with daily activities, versus b) symptoms interfering with daily

activities. bGEE models involve 56 days for PM10, 61 days for O3 and NO2, and 60 days for aeroallergens; lag 0 concentrations are from the same day as the symptom reports. cPollutant,

indicator for medication group, and interaction term for pollutant by medication group. d90th percentile minus the minimum. ePollutant alone. f

On anti-inflammatory medications; six subjects on inhaled corticosteroids and four subjects on cromolyn or nedocromil; total of 570 person-days of O3 and NO2 observations, 524 person-days of PM10 observations, and 560 person-days of aeroallergen observations. gNot on anti-inflammatory medications; twelve subjects with 678 person-days of O3 and NO2 observations, 622 person-days of PM10 observations,

and 666 person-days of aeroallergen observations. hFor between-group product term in model 2. *p < 0.05; **p < 0.01.

Table 5. ORs for risk of asthma symptomsa from a 90th percentile increase in pollutants or aeroallergens in

those who report a respiratory infection compared with those who do not have respiratory infections. 

Pollutant and aeroallergen variables Air pollutant levelb OR (95% CI)c

1-hr max PM10 lag 0 51 μg/m3 4.88 (1.31–18.2)

8-hr max PM10 lag 0 38 μg/m3 6.78 (1.38–33.3)

24-hr mean PM10 lag 0 25 μg/m3 4.68 (0.71–30.7)

3-day moving average 1-hr max PM10 51 μg/m3 11.1 (1.10–112)

3-day moving average 8-hr max PM10 38 μg/m3 10.1 (1.42–72.0)

3-day moving average 24-hr mean PM10 25 μg/m3 2.67 (0.60–11.8)

1-hr max O3 lag 0 43 ppb 3.27 (1.00–10.7)

8-hr max O3 lag 0 36 ppb 2.72 (0.67–11.0)

1-hr max NO2 lag 0 34 ppb 3.46 (0.45–26.6)

8-hr max NO2 lag 0 20 ppb 6.72 (1.73–26.1)

12-hr fungi lag 0 4,644 particles/m3 6.38 (1.03–39.6)

24-hr pollen lag 0 732 particles/m3 0.95 (0.16–5.74)

Max, maximum. Effect modification of respiratory infection in children not on anti-inflammatory medications, Alpine,

California, panel study, 1 March through 30 April 1996.

aThe asthma symptom severity score was dichotomized to a) no symptoms or symptoms not bothersome or not interfering

with daily activities, versus b) symptoms interfering with daily activities. b90th percentile minus the minimum. cPer

increase to 90th percentile concentration of pollutant or aeroallergen given the presence vs. absence of a respiratory

infection from GEE models including the pollutant or aeroallergen, an indicator variable for respiratory infection and a

product term between them. Models involve 12 subjects with 678 person-days of O3 and NO2 observations, 622 persondays of PM10 observations, and 666 person-days of aeroallergen observations.
the product term for PM10 and NO2

described above.

Sensitivity analysis. Effects at low levels

of particulate air pollution and NO2 in the

present study may be surprising, whereas

there is some expectation of effects for O3

given that the 1-hr maximum approached the

NAAQS (17) (120 ppb) on 3 days (94–108

ppb). We did not identify any statistical outliers in pollutant distributions (> 3 SD above

the mean). However, it is possible that only

the highest concentrations drove associations.

Therefore, models for subjects not on antiinflammatory medications were further tested

by progressively dropping the upper 5% of

pollutant concentrations (2–3 days) to test for

a “threshold effect.” We found that dropping

the upper 5% of 1-hr maximum PM10 (2

days at 69 μg/m3) led to a marked reduction

in the OR from 1.72 to 1.27, but there was

little change in ORs for 8-hr maximum PM10

and 24-hr mean PM10. There were no

decreases in parameters for the 3-day moving

averages of PM10. Dropping the upper 10%

led to a further reduction in the OR for 1-hr

maximum PM10 to 0.97, but not for 8-hr

maximum PM10 or 24-hr mean PM10. Again,

the 3-day moving average PM10 effects

remained above an OR of 1.5. No reductions

in effect magnitudes were found after dropping the upper 15% of 8-hr maximum PM10

or 24-hr mean PM10. Dropping the upper

15% of 3-day moving average PM10 variables

notably dropped the effect of the 1-hr maximum to an OR of 1.11, but not the 3-day 8-

hr maximum or the 3-day 24-hr mean.

Dropping the upper 5% also led to a

marked reduction in the OR for 1-hr maximum

O3 from 1.43 to 1.19 and for 8-hr maximum

NO2 from 1.73 to 1.17, and similar reductions

were seen for other averaging times. We found

no further reductions in ORs after dropping the

upper 10% of O3 and NO2. After we dropped

the upper 15%, the OR for NO2 dropped to

near unity, but O3 showed an increase. After we

dropped the upper 10% of PM10, NO2, and

O3, pollutant interactions noted above were still

significant between PM10 and NO2 and were

borderline significant (p < 0.1) between NO2

and O3. All of the pollutant interactions were

nonsignificant after dropping the upper 15%.

Dropping the upper 5% of fungal spores

did not reduce its effect (OR, 2.03), and the

same was found for total pollen (OR, 1.87).

Further dropping the upper 10% of fungal

spores markedly reduced the OR to 1.37, but

the reduction was less for total pollen (OR,

1.57). Dropping the upper 15% of fungi

eliminated any association (OR, 1.18; p =

0.7). However, dropping the upper 15% of

total pollen did not reduce the effect magnitude (OR, 1.82).

Further exclusions below the 15th percentile led to additional reductions in most

ORs and wide confidence intervals below the

fifth quintile. Exceptions to this were found

for the 3-day moving average of 24-hr mean

PM10 and total pollen, which showed associations for the fourth compared with the lowest

quintile [PM10 OR = 1.93 (95% CI,

1.34–2.79); pollen OR = 1.50 (95% CI,

1.04–2.14)] but not the third quintile.

Discussion

Overview of findings. These findings confirm

the acute adverse effects of air pollutants on

asthmatic symptoms and suggest that the irritant potential of PM10 at inversion layer elevations is greater than expected based upon

mass concentration. Our findings also point

to the potential relevance of peak PM10 exposures to acute respiratory effects. Magnitudes

of association for current-day PM10 showed

modest graded differences of 1-hr maximum

> 8-hr maximum > 24-hr mean PM10.

However, confidence intervals overlapped

markedly, and findings for the 1-hr maximum were sensitive to removal of the two

highest days at 69 μg/m3.

Our strongest lag day associations for

PM10 were found with lag 0 (the current

day’s exposure) and a 3-day moving average.

Given that the time coarse of lung function

and symptom changes can be on the order of

minutes to hours for early- to late-phase asthmatic reactions (18), asthma is expected to be

acutely related to airborne exposure on the

day of an exacerbation (lag 0). We found

slightly stronger associations with a 3-day

moving average than with lag 0 of 24-hr

mean PM10, suggesting some cumulative or

delayed effects as well. The lag 0 and multiday moving average findings are consistent

with results of our previous asthma panel

study during fall 1995 in the same region (1).

Sixteen of 22 subjects in the present study

were subjects in this previous study.

Associations of asthma symptoms with

PM10, NO2, O3, fungi, and pollen were

largely isolated to asthmatic children not taking anti-inflammatory medications, with significant or near-significant differences in

regression slopes from subjects who were taking anti-inflammatory medications (Table 4).

This is consistent with a similar divergence of

association by anti-inflammatory medication

use found in our earlier asthma panel (1). The

present study also showed a further enhancement of symptom responses to pollutant exposures among subjects not on anti-inflammatory

medication during respiratory infections.

Asthma symptoms were more clearly associated with O3 in a subgroup model including

only subjects not on anti-inflammatory medications. Positive associations between asthma

outcomes and O3 have been consistently

Children’s Health • Peak particulate air pollution, asthma, and medication use

Environmental Health Perspectives • VOLUME 110 | NUMBER 10 | October 2002 A 613

Figure 2. Predicted probability of an asthma symptom response and interaction between PM10 and NO2 in

asthmatics not on anti-inflammatory medications. The probability of a symptom response is on the vertical axis

and is scaled to the log odds from the GEE model. The plot was smoothed using a Loess transform function.

0.50

0.27

0.12

0.05

10

15

20

25

30

35

10

20

30

40

50

60

70

8-hr max NO2 (ppb)

1-hr max PM10 (μg/m3)

Predicted probability of symptom response
reported in the literature (19) and in two of

our previous studies in the same region (1,7),

but not in another study in Alpine (8).

Asthma symptoms were associated with

combined elevations of NO2 and O3. This

suggests either a pollutant interaction or that

underlying effects of an air pollutant mixture

on days with joint elevations are not fully captured by any one of these criteria air pollutants.

In two-pollutant models, there was a loss of O3

association in joint regressions with PM10, but

this may have been caused by collinearity

effects. Significant interactions were also found

between PM10 and NO2. Again, this suggests

that the product term represented either a pollutant interaction or underlying effects of an

air pollutant mixture. Outdoor fungal and

pollen particles were associated with asthma

symptoms as well, but there were no interactions with any air pollutant.

Results of testing for a threshold suggest

that effects from lag 0 1-hr maximum PM10,

O3, and NO2 were attributable primarily to

2–3 days of elevated concentrations for each

variable. For these days, one day showed both

higher PM10 and NO2 levels, and one day

showed both higher PM10 and O3 levels.

These were all warm, dry days with maximum temperatures from 74°F to 91°F and

minimum relative humidity from 11% to

26%. Winds were 2–3 mph, all out of the

west (urban areas). More days, including the

upper 15th percentile, were driving associations for lag 0 8-hr maximum PM10 and 24-

hr mean PM10, as well as the 3-day moving

averages of 8-hr average PM10 (down to 38

μg/m3). Effects were found down to the

fourth quintile for the 3-day moving average

of 24-hr average PM10 (20–23 μg/m3) and to

the fourth quintile of lag 0 pollen 345–590

particles/m3. Fungal spores had no effect

below the 90th percentile. Our aim in this

sensitivity analysis was to examine whether

there was an apparent threshold in the models. As stated above, higher concentrations

were not statistical outliers in pollutant distributions. Results after discarding valid concentrations at the upper end of the pollutant

distribution were exploratory in nature. The

sensitivity analysis identified the need for both

a greater amount of data to model the shape

of the exposure–response curve and better

modeling strategies to determine that shape.

Limitations. The external validity of differences in results by subjects on versus not

on anti-inflammatory medications is limited

by small numbers of subjects (10 vs. 12,

respectively). The major limitation in the present study is reliance on outdoor central site

exposures to represent individual exposures.

The magnitude of exposure misclassification is

expected to be greatest for the gaseous pollutants. In the case of O3, this is because of low

indoor-to-outdoor concentration ratios. We

previously conducted an exposure assessment

study for two seasons (spring and fall 1994) in

Alpine using personal passive O3 badges. We

found relatively low correlations between personal (1,175 samples) and outdoor central site

O3 concentrations even after incorporating

time–activity, traffic, and spatial data in

microenvironmental models (adjusted R2 =

0.22 and 0.19 in the two seasons) (20). The

present study lacked personal exposure measurements, although we aim to provide predictive PM10 data for this and earlier studies

using data from our ongoing investigations in

Alpine with personal real-time particle measurements from nephelometers (21).

The present measurements of particle

exposures (PM10) were additionally limited

by an inability to separate fine (PM2.5) from

coarse (PM2.5–10) particle fractions. Fine particles have been found to be a stronger predictor of asthma-related responses than are

coarse particles in the Harvard Six Cities

Diary Study (22). This difference is expected

because the deposition fraction in the lower

respiratory tract and alveoli of the lungs is

greater for fine particles and because toxic

irritants in air pollution formed from combustion and photochemical processes are generally found in the fine particle fraction

(SO4

2–, NO3–, H+, metals, and organic compounds). Nevertheless, coarse particle components, including crustal elements and some

bioaerosols, may also be important respiratory

irritants for asthmatic patients. An additional

weakness in the PM exposure metric is that

TEOM measurements of PM10 could have

potentially underestimated concentrations at

key times because of the expected volatilization of important semivolatile particle components as they cross the 50°C heating

element (23).

Particle effects by hourly averaging time.

Our findings of modestly stronger effects for

shorter PM10 averaging times are tempered

by the fact that confidence intervals overlapped markedly and stronger effects of the 1-

hr maximum were driven by 2 of 56 observed

days whereas at least 8 days were driving associations for 8-hr maximum PM10 and 24-hr

mean PM10. Also, there was no difference in

magnitude of association for the 3-day moving averages of the different PM10 variables,

and only the 8-hr and 24-hr PM10 3-day

moving averages maintained their effect magnitudes after dropping up to 15% of the

highest values.

In our previous Fall 1995 panel study (1),

analyses in all 24 children showed statistically

significant risks of asthma symptoms from

increases in daily 1-hr and 8-hr maximum

Children’s Health • Delfino et al.

A 614 VOLUME 110 | NUMBER 10 | October 2002 • Environmental Health Perspectives

Figure 3. Predicted probability of an asthma symptom response and interaction between O3 and NO2 in asthmatics not on anti-inflammatory medications. The probability of a symptom response is on the vertical axis

and is scaled to the log odds from the GEE model. The plot was smoothed using a Loess transform function.

0.50

0.27

0.12

0.05 10

20

30

50

60

70

1-hr max NO2 (ppb)

1-hr max O3 (ppb)

Predicted probability of symptom response

40

50

80

90

100
PM10. Effects were smaller and nonsignificant

for the 24-hr PM10 average. However, subgroup analyses showed this difference only in

10 more symptomatic asthmatic subjects,

only one of whom was on an anti-inflammatory medication. Among less symptomatic

subjects, particle effects were generally weaker

for the 1-hr compared with the 8-hr maximum or 24-hr average. Dropping the upper

10% of observations did not alter these previous findings. One other asthma panel study

has examined hourly maximum PM10 measured using β-attenuation monitors. Ostro et

al. (2) reported results of an asthma panel

study of 138 African-American children living in Los Angeles and Pasadena, California.

Diary reports of wheezing, cough, and dyspnea were associated somewhat more strongly

with a 17-μg/m3 increase in 24-hr PM10

(ORs, 1.04–1.25) than with a 31-μg/m3

increase in 1-hr PM10 (ORs, 1.02–1.07). No

results were presented for 8-hr maxima.

A reasonable biologic rationale for the

hypothesis that maximum hourly particle

exposures can induce asthmatic reactions is

that changes in personal particle exposure concentrations over the course of a day will alter

the dose of particles in the lung in a timedependent manner. Therefore, it is expected

that biologic responses may intensify with high

peak excursions that overwhelm lung defense

mechanisms (e.g., mucociliary transport, and

neutralization or metabolism of toxic substances). However, clinical data on the relevance of particle averaging time to respiratory

responses is inadequate to support or refute

this rationale. A more defensible rationale for

epidemiologic research is that shorter averaging

times can be used as better surrogates for population exposures than daylong averages. Peak

particle excursions could occur during the daytime when children are outdoors and physically active, thus leading to higher pulmonary

doses. The peak concentrations of PM10 in the

present study occurred between 1300 and

2000 hr. The finding that the most robust particle effects were for the 8-hr maximum exposures over the current and prior 2 days could

be the result of maximal personal exposures

and doses occurring around the same 8-hr

time period. One-hour peaks may be more

influenced by local point sources near the

monitoring station that are not representative

of regional exposures, thus explaining weaker

associations with asthma symptoms for multiday 1-hr than for 8-hr maximum PM10 in the

present study as well as in our previous study

(1). Sustained high peaks during the day may

partly explain epidemiologic associations with

24-hr average PM10 at levels below the

NAAQS of 150 μg/m3 (24).

Effect modification by anti-inflammatory

medication use. Our findings support the view

that proinflammatory components of PM are

causally related to acute asthma exacerbations.

In our earlier study, subjects on inhaled antiinflammatory medications during follow-up

(six on corticosteroids, one on cromolyn)

experienced fewer symptoms. To evaluate

effect modification by medication use, we

therefore compared them with seven other

subjects not on anti-inflammatory medications but with a similar frequency of asthma

symptom reports (1). The OR (95% CI) in

the previous report for a 36-μg/m3 increase

(minimum to 90th percentile) in the 5-day

moving average of 8-hr maximum PM10 was

9.66 (95% CI, 2.80–33.2) among subjects

not on anti-inflammatory medications, compared with 2.96 (95% CI, 0.32–27.0) among

subjects on anti-inflammatory medications.

The OR for a 58-ppb increase (minimum to

90th percentile) in the 1-hr maximum O3 was

4.14 (95% CI, 1.71–10.0) among subjects

not on anti-inflammatory medications, compared with 1.20 (95% CI, 0.46–3.15) among

subjects on anti-inflammatory medications.

Heterogeneity in effects of other pollutant

variables and fungal spores was similar. Air

pollution levels were higher then (mean 8-hr

PM10, 43 ± 12 μg/m3; mean O3, 90 ± 18

ppb), possibly explaining stronger associations

than in the present study (Table 4).

Several other panel studies have examined the importance of medication use to air

pollutant associations (2,25–27). Two of

these studies showed stronger associations

between asthma outcomes and air pollutants

among subjects taking any asthma medication versus subjects not taking asthma medication, but they did not separate subjects by

anti-inflammatory medication use (25,26).

Mortimer et al. (27) reported results of a

series of 2-week asthma panels in 846 innercity children. They compared effects on

asthma outcomes by outdoor O3 levels across

medication groups based on baseline data for

prescribed medication. Associations between

incidence of symptoms and an increase of 15

ppb in O3 were largest among those prescribed cromolyn but not steroids (OR, 1.46;

95% CI, 1.06–2.01) followed by nonsignificant ORs for those prescribed β-agonists or

xanthines only (1.18), steroids (1.08), and no

medication (1.04). The percent change in

peak expiratory flow (PEF) was also greatest

among those prescribed cromolyn but not

steroids (OR, –1.27%; 95% CI = –2.47to

–0.06) followed by nonsignificant PEF

changes of around –0.5% for the other

groups. Ostro et al. (2) reported results of an

asthma panel study of 138 African-American

children living in Los Angeles and Pasadena,

California. Diary reports of daily and newonset presence or absence of wheezing,

cough, and dyspnea were associated with 

3-day lagged 24-hr average PM10 and

Alternaria spores. For an interquartile

increase in 24-hr PM10 of 17 μg/m3, the OR

ranged from 1.10 to 1.30 in subjects with

anti-inflammatory prescriptions and from

1.09 to 1.26 in subjects without such medications. For an interquartile increase in

Alternaria of 20 spores/m3, the OR ranged

from 1.05 to 1.19 in subjects with antiinflammatory prescriptions and from 0.98 to

1.10 in subjects without such medications.

In summary, there is little consistency

between the present study and the few other

epidemiologic studies examining medication

subgroups, possibly because of methodologic

differences in the stratification of groups.

One major difference is the assessment of

medication used at baseline rather than during the days of panel follow-up. If a subject is

not taking an anti-inflammatory medication

during follow-up, putting that subject in the

medication category is exposure misclassification. When outcome data are analyzed as

repeated measures, capturing repeated clinical determinants is critically important.

It is biologically plausible that an asthmatic individual taking anti-inflammatory

medications would be less susceptible to air

pollution health effects that act through

proinflammatory mechanisms. Experimental

evidence in asthmatic patients identifies the

proinflammatory effects of O3 as a major

mechanism for adverse effects of O3 in asthmatic populations (4,5). The proinflammatory nature of PM components is beginning

to be clarified, but the large number of

organic and inorganic components makes

this a major task. Studies examining the

proinflammatory effects of the polycyclic

aromatic hydrocarbon fraction of diesel

exhaust particles serve as an important example [reviewed in Nel et al. (28)]. The proinflammatory effects of NO2 have also been

described [reviewed in Bascom et al. (19) and

Chauhan et al. (29)]. Four experimental

studies of mild atopic asthmatic adults have

shown an enhancement of airway responses

to allergens after exposure to 260–400 ppb

NO2 (30–33). There is also some evidence

that NO2 exposure leads to persistent neutrophilic infiltration in human airways (34)

and to increased histamine release in histocultured human nasal mucosa (35). The

inflammatory mechanisms of pollen and fungal allergens in allergic respiratory disease

have also been described (36–38). Our finding of a divergence of association in the medication groups for aeroallergens is not

unexpected and demonstrates the value of

stratifying panel data.

Effect modification by respiratory infections. Our finding that positive associations

between asthma symptom severity and air

pollution is greater during respiratory infections is supported by experimental data.

Rodent models have shown reduced particle

Children’s Health • Peak particulate air pollution, asthma, and medication use

Environmental Health Perspectives • VOLUME 110 | NUMBER 10 | October 2002 A 615
clearance, increased susceptibility to microbeinduced death, and adverse pulmonary

responses when pulmonary infections are

concurrent with inhalation of PM, including

diesel particles and metal-rich particles

(39–41). There is also some experimental evidence that NO2 exposure leads to an alteration of lymphocyte subsets and diminution

of macrophage inactivation of respiratory

viruses (29). These effects could enhance the

severity of the infectious illness, which in turn

could lower the threshold of the asthmatic

response to inhaled pollutants. A recent epidemiologic study by Linaker et al. (42) of

114 asthmatic children using weekly averaged

NO2 levels from personal passive diffusion

samplers showed an increased risk of asthma

episodes after respiratory infections that

occurred when NO2 concentrations were

higher. Some other coherent data can be

found in aggregate time-series data showing

associations of increased levels of PM and/or

NO2 with a) hospitalization for pneumonia

(43), b) hospitalization for respiratory infections in aggregate (44), c) increased physician

visits for croup in children (45), d) mortality

from pneumonia (46,47), and e) mortality

from respiratory infections in aggregate (48).

The prevailing view here is that air pollution

may have enhanced the severity of ongoing

infectious illness, leading to hospitalization or

mortality.

Effects of NO2. The respiratory health

effects of NO2 on asthma are not entirely

clear, and there are inconsistencies in the

experimental literature. Some studies have

shown alterations in lung function, airway

responsiveness, or symptoms, whereas others

have not, even at high concentrations

[reviewed in Bascom et al. (19)]. In addition

to the study by Linaker et al. (42) discussed

above, another panel study of 30 asthmatic

children found that PEF throughout the day

was inversely associated with outdoor home

NO2 levels, whereas only morning PEF was

inversely associated with indoor bedroom

NO2 levels (49). Several epidemiologic timeseries studies have shown an increase in risk

of asthma hospital admissions or emergency

department visits with increases in outdoor

NO2 levels (44,50–55), but many more have

reported either no results or results that were

nonsignificant for NO2. This inconsistency

may be because of exposure misclassification

due to the high spatial variability of NO2,

which is strongly influenced by local traffic

density (56). The site of the monitoring station in the present study was generally

upwind (east) of a two-lane highway accessing a small residential district (343 vehicles/

day) (57) and was 0.19 mile away and 270

feet higher than an interstate freeway.

Although it is possible that on some days

local traffic could have influenced NO2 levels,

most of the NO2 measured at the monitoring

station and in the semirural study area is

likely transported along with other copollutants from urban areas of San Diego County.

This means that local temporal influences on

the central site are not likely to be major limitations to the use of NO2 measurements in

the present study.

Pollutant interactions. We found statistical interactions between PM10 and NO2 and

between O3 and NO2. Copollutant models,

however, generally resulted in multicollinearity problems, making interpretation of interaction terms problematic. It is possible that

joint elevations in two air pollutant types

were acting as an indicator for more causal

particle- and gas-phase components at the

inversion elevation zone of the study region,

but we have no supporting aerometric data.

Most other epidemiologic research to date has

also not been able to attribute the proportion

of adverse respiratory effects to any single air

pollutant, which is not inconsistent with the

view that a mix of air pollutants may be to

some extent acting synergistically (58). A simple explanation is that at some jointly high

concentrations, the human lung may not be

able handle the total pollutant burden contained in the inspired air. Despite a general

lack of statistical interaction in observational

studies, unmeasured joint effects have been

proposed as one explanation for greater relative effects of O3 on lung function in epidemiologic studies compared with clinical

studies (59).

Effects of fungi and pollen. Associations

between aeroallergens and symptoms were

more robust for total pollen than for total

fungi in the sensitivity analysis. March and

April, the season of study, are the peak pollination months for the native plants in the

area of study, where native dry-land brush

and canyon trees predominate around the residential areas where subjects lived. One of the

original aims of the study was to test the

putative interaction between O3 and pollen

that has been suggested in experimental studies (9,10). However, there were no pollutant–aeroallergen interactions, which is

consistent with our previous studies in southern California in other months of the year

with lower pollen counts but equally high

fungal spore counts (1,7,8). Although effect

magnitudes of the air pollutants were in some

cases reduced when regressed with the aeroallergens, this was not the case when interaction terms for PM10 and NO2 or for O3 and

NO2 were included in the models. This suggests that causal components of the pollutant

mix were acting independently of aeroallergens. Our previous asthma panel studies did

not find confounding of air pollutant associations by outdoor pollen or fungi (1,7,8).

Several time-series investigations have found

associations between asthma hospital admissions and both outdoor air pollutants (particles, NO2, or O3) and pollen or fungi, but

none has found any confounding between

aeroallergens and these pollutants (54,60,61).

In two studies, there was evidence that SO2,

NO2, and O3 enhanced the effect of grass

pollen on asthma admissions (54,60),

although there was a negative interaction

between O3 and tree pollen in one of these

studies (54). The general inconsistency

between epidemiologic and experimental data

on the enhancement of allergen-induced respiratory responses by air pollutants may be

explained by a lack of temporally resolved data

to account for hourly exposures and a lack of

spatially resolved data to account for personal

exposures to pollutants and aeroallergens.

Conclusions

We have found associations of asthma symptoms in schoolchildren with air pollutants

and aeroallergens. A few observed days drove

stronger associations of symptoms with 1-hr

maximum PM10, but overall, longer averaging times showed more robust associations.

The enhancement of exposure–response relationships in subjects not taking anti-inflammatory medications is consistent with

experimental data on the inflammatory

mechanisms of pollutants and allergens. Our

findings are clinically relevant in that the

symptom response was defined as symptoms

that subjects perceived to have interfered with

regular daily activities. We have no information to confirm whether these symptom

responses stemmed from enhancements of

pulmonary inflammation, although significant interactions of pollutants with respiratory infections suggest that.

Pollutant associations were found at relatively low concentrations and were largely

driven by the top exposure quintiles.

Ultrafine particles and secondary air pollutant

gases and organic aerosols above the base of

the temperature inversion layer could be

important in these associations found at low

mass concentrations of PM10. Could some of

these unmeasured pollutants have been the

underlying causal agents? Further investigations addressing this question may yield clues.

Our view is that the next phase of epidemiologic research is to more accurately

quantify both respiratory responses and airborne exposures in asthmatic children. This

includes a) the use of biomarkers of response

that better capture underlying inflammatory

processes in asthma, b) the use of better spatially and temporally resolved data that take

into account personal time–place–activity

patterns and hourly exposures, and c) measurements of pollutant components suggested

in experimental data to be causally related to

irritant and immune responses.

Children’s Health • Delfino et al.

A 616 VOLUME 110 | NUMBER 10 | October 2002 • Environmental Health Perspectives
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