BioMed Central

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Environmental Health

Research Open Access

Pediatric patient asthma-related emergency department visits and 

admissions in Washington, DC, from 2001–2004, and associations 

with air quality, socio-economic status and age group

Steven M Babin*1, Howard S Burkom1, Rekha S Holtry1, 

Nathaniel R Tabernero1, Lynette D Stokes2,3, John O Davies-Cole2, 

Kerda DeHaan2 and Deitra H Lee2,4

Address: 1Johns Hopkins University, Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, USA, 2Environmental Public 

Health Tracking Program, Bureau of Epidemiology and Health Risk Assessment, District of Columbia Department of Health, 825 North Capitol 

Street NE, 3rd Floor, Washington, DC 20002, USA, 3Currently employed by the Division of Energy Employees Occupational Illness 

Compensation, US Department of Labor, Washington, DC 20210, USA and 4Currently employed by the US Government Accountability Office, 

Washington, DC 20548, USA

Email: Steven M Babin* - steven.babin@jhuapl.edu; Howard S Burkom - howard.burkom@jhuapl.edu; 

Rekha S Holtry - rekha.holtry@jhuapl.edu; Nathaniel R Tabernero - nathaniel.tabernero@jhuapl.edu; 

Lynette D Stokes - stokes.lynette@dol.gov; John O Davies-Cole - john.davies-cole@dc.gov; Kerda DeHaan - kerda.dehaan@dc.gov; 

Deitra H Lee - leedh@gao.gov

* Corresponding author 

Abstract

Background: The District of Columbia (DC) Department of Health, under a grant from the US

Centers for Disease Control and Prevention, established an Environmental Public Health Tracking

Program. As part of this program, the goals of this contextual pilot study are to quantify short-term

associations between daily pediatric emergency department (ED) visits and admissions for asthma

exacerbations with ozone and particulate concentrations, and broader associations with socioeconomic status and age group.

Methods: Data included daily counts of de-identified asthma-related pediatric ED visits for DC

residents and daily ozone and particulate concentrations during 2001–2004. Daily temperature,

mold, and pollen measurements were also obtained. After a cubic spline was applied to control for

long-term seasonal trends in the ED data, a Poisson regression analysis was applied to the time

series of daily counts for selected age groups.

Results: Associations between pediatric asthma ED visits and outdoor ozone concentrations were

significant and strongest for the 5–12 year-old age group, for which a 0.01-ppm increase in ozone

concentration indicated a mean 3.2% increase in daily ED visits and a mean 8.3% increase in daily

ED admissions. However, the 1–4 yr old age group had the highest rate of asthma-related ED visits.

For 1–17 yr olds, the rates of both asthma-related ED visits and admissions increased

logarithmically with the percentage of children living below the poverty threshold, slowing when

this percentage exceeded 30%.

Conclusion: Significant associations were found between ozone concentrations and asthmarelated ED visits, especially for 5–12 year olds. The result that the most significant ozone

Published: 21 March 2007

Environmental Health 2007, 6:9 doi:10.1186/1476-069X-6-9

Received: 10 August 2006

Accepted: 21 March 2007

This article is available from: http://www.ehjournal.net/content/6/1/9

© 2007 Babin et al; licensee BioMed Central Ltd. 

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), 

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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associations were not seen in the age group (1–4 yrs) with the highest rate of asthma-related ED

visits may be related to the clinical difficulty in accurately diagnosing asthma among this age group.

We observed real increases in relative risk of asthma ED visits for children living in higher poverty

zip codes versus other zip codes, as well as similar logarithmic relationships for visits and

admissions, which implies ED over-utilization may not be a factor. These results could suggest

designs for future epidemiological studies that include more information on individual exposures

and other risk factors.

Background

Elevated concentrations of ground-level ozone and particulates have been shown to be associated with increased

incidence of asthma exacerbations [1-3]. Aeroallergens,

such as pollen and mold, may trigger allergy-induced

asthma symptoms. Seasons for various aeroallergens differ by geography, and, in Washington, DC, the following

are the pollen seasons for trees, grasses, and weeds, respectively: February through June, May through August, and

July through October [4]. Also, either very low or very

high ambient temperature may be associated with asthma

exacerbations. Higher temperatures may be associated

with asthma exacerbations because they occur when there

is more sunlight, and sunlight is necessary for emissions

to be converted to ground-level ozone. At the other temperature extreme, studies have suggested that very cold,

dry air acts as an airway irritant, yet primarily results in

exacerbations of exercise-induced asthma [5-7], which is

not a part of this study.

The District of Columbia Department of Health (DC

DOH), under a grant from the US Centers for Disease

Control and Prevention (CDC), established an Environmental Public Health Tracking Program (EPHTP) to demonstrate possible relationships between ambient ozone

and particulates and short-term asthma health outcomes,

and to identify areas and populations most likely to be

affected by pollution [8]. Therefore, the goals of this small

pilot study were to determine the degree to which asthma

exacerbations are associated with ozone and particulate

concentrations in the short-term, on the order of days

after presumed exposure, and to identify pediatric populations that may be at increased risk of these health effects.

Asthma exacerbations were determined by daily counts of

pediatric ED visits for asthma-related problems. As will be

described, the spatial variations among pollutant measurements on a given day were rather small. This is consistent with DC being an urban area with little industry and

whose major source of ozone and PM2.5 is the considerable amount of vehicular traffic that permeates the 159

km2 land area of DC. In accordance with the EPHTP goals

mentioned above, we examined other spatial features that

show more significant regional variation, including residence zip code and socio-economic status of the pediatric

population. Some interesting results were thereby

obtained, especially with regard to socio-economic status.

Methods

The concentrations of ozone and particulates of aerodynamic diameter 2.5 μm or less (PM2.5) were obtained

from available measurements at five locations within DC

from October 2001 through September 2004. Ozone

measurements in units of parts per million (ppm) were

made hourly at:

▪ Takoma Elementary School,

▪ River Terrace Elementary School, and

▪ Southeast (SE) end of McMillan Reservoir.

Daily PM2.5 measurements in units of μgm-3 were made

at:

▪ River Terrace Elementary,

▪ National Park Service Office at Haines Point, and

▪ SE end of McMillan Reservoir.

Note that two of the locations above measured both

ozone and PM2.5. For the purposes of our analysis, the

hourly ozone data were converted into 8-hour daily maximum ozone concentrations to conform to non-attainment criteria adopted by the US EPA (see, for example,

[9,10]). While PM2.5 and ozone were each measured at

three different locations within DC, the spatial variation

among measurements for each day during the three-year

period was found to be negligible compared to differences

expected to cause asthma problems. Therefore, we used

the daily mean of the available measurements among

these sites to represent each day's ozone or PM2.5 concentration.

Aeroallergen data were collected at the Walter Reed Army

Medical Center by the US Army Centralized Allergen

Extract Laboratory. While not collected every day, these

data are collected about three days a week and more frequently from spring through early fall. These data covered
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the months of October 2001 through September 2004,

except for December 2002 when no measurements were

taken. These data consisted of daily counts of grass, weed,

and tree pollen and mold spores in units of grains m-3.

Daily temperature data were obtained from the National

Weather Service measurements at Reagan National Airport in DC.

For health outcomes, our data consisted only of daily

counts of asthma-related pediatric emergency department

(ED) visits by DC residents between October 2001 and

September 2004. Asthma-related visits were defined as

those ED visit records in which one of the first three of

nine possible diagnosis fields listed an asthma code

number. These ED record fields contain numbers based

on the International Classification of Diseases Ninth Revision (commonly called ICD-9) codes used by hospital

and physicians' office personnel to report billing information to insurance companies [11]. ICD-9 codes may indicate either symptoms or specific diseases, and those

beginning with 493 indicate an asthma diagnosis. Based

on discussions with medical personnel about ED coding

practices among different DC hospitals, the criterion of an

asthma code in one of the first three fields was chosen as

an indication that an asthma exacerbation was a principal

reason for the visit. The pediatric patients were those with

an age between 1 and 17 years on the ED visit date. Patient

identification was not included in the data except for residence zip code, age at time of visit, date of visit, and

whether or not the patient was admitted. Data were

grouped into these patient age groups: 1–4 years, 5–12

years, 13–17 years, and 1–17 years. Analysis was performed independently on each of these age groups. In

addition to these data, we also obtained pediatric population by zip code and age group for DC according to the

2000 US Census. This US census data also provided for

each zip code the percent of the child population living in

households with income considered to be below the US

poverty level.

We found that there were generally two annual peaks in

the daily ED visits for pediatric asthma exacerbations in

DC: the highest peak in September-November and a secondary peak in March-May. Because annual peaks in

ozone and PM2.5 tend to occur in the summer months,

this discrepancy results from strong seasonal effects on

exacerbations that are associated with other factors [12].

For example, several studies [13-15] have noted increased

asthma morbidity triggered by respiratory viral infections

(e.g., colds) that occur during late summer and early

autumn. While we had no data on respiratory infections,

their known seasonal trending was a motivator for the

selection of knots for the spline to be described subsequently. We had no data on indoor and behavioral factors

related to asthma exacerbations. No day-of-week effects

could be identified in the asthma-related ED daily count

data. Because a focus of the study was on short-term associations of air pollutants (i.e., days) and asthma ED visits

and admissions, it was necessary to control for the above

stronger long-term influences that were not part of the

study. Many investigators have used a natural cubic spline

function to represent the strong seasonal signals as a control for confounders in order to observe short-term

asthma associations with pollutants [16,17]. Once the

long-term trends are removed, the short-term associations

become apparent. Our spline curve was derived from a

best fit to the time series of daily asthma-related ED visits

for DC children of ages 1–17 yrs over a three-year period.

This spline had twelve knots to represent the frequency of

variation of strong seasonal changes over the study

period. Because there were 3 years of data and 4 seasons

per year, 12 knots appeared to be a reasonable choice.

These knots were not evenly spaced, but were selected to

coincide with large long-term changes in the daily ED visits. Once these long-term trends were removed, the residual data (Fig 1) were examined for short-term (on the

order of days) associations with ozone and PM2.5. Note

in Fig. 1 that, while changes in variance remain, the longterm trends have effectively been removed.

While there were a number of choices for an analysis

model, our goal was to quantify short-term associations in

the time series of the ED daily counts with the environmental data. Our health data covered three years and consisted of de-identified, zip-code level, daily counts of

asthma-related ED visits in a pediatric population. A Poisson regression analysis assumes that counts of independent, rare events follow a Poisson distribution and models

the logarithm of daily visit counts as a linear function of

the explanatory variables. The Poisson regression model

adjusts for data skewness by using a log transformation

and is useful for distributions of nonnegative data, and

data for which the variance increases as the mean. Poisson

analysis therefore seemed appropriate for daily counts of

asthma-related ED visits. While other regression models

may also be appropriate, we chose the Poisson regression

for the above reasons and the fact that it had successfully

been used in other studies similar to ours (e.g., [18-20]).

Our database for analysis included date; daily ED visit and

admission counts for each age group (1–4, 5–12, 13–17,

and 1–17 yrs); daily 8-hr maximum ozone concentrations

(based on the mean of the available measurement sites for

that day); daily PM2.5 concentrations (based on the mean

of the available measurement sites for that day); daily

maximum temperature; daily tree, grass, and weed pollen

concentrations; daily mold concentrations; month and

season; and spline curve predictions. Because exerciseinduced asthma diagnosis codes were virtually non-existent in our ED data and are those asthma exacerbations
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associated with cold temperatures, we examined only the

associations with maximum daily temperature. Significant associations in this database were determined using

the statistical analysis tools contained in the Stata [21] statistical software package developed by Stata Corporation

of College Station, TX. Our null hypotheses were that

there were no significant associations of ozone, particulates, aeroallergen, or socioeconomic status with pediatric

asthma-related ED visits.

Results

Same-Day Effects

For ED visits, only a slight association was found with

ozone effects for the 1–17 yrs age group. However, the 5–

12 yrs age group showed a much stronger association

(Table 1), with a 3.2% increase in visits indicated for an

increase of 0.01 ppm in ozone concentration (95% Confidence Interval, or CI, = 1.4–5.0). Significant effects of

tree pollen and ambient temperature were also seen for

the 5–12 yrs age group. For hospital admissions from ED

visits, the analysis was significant for the 1–17 yrs age

group (Table 2), with a 0.01-ppm increase in ozone being

associated with a 4.5% increase in asthma-related ED

admissions (95% CI = 0.6–8.5). The 5–12 yrs age group

showed even greater ozone impacts (Table 2), with a 0.01-

ppm increase in ozone concentration being associated

with an 8.3% increase in asthma-related ED admissions

(95% CI = 2.6–14.4). Weed pollen and ambient temperature also showed significant associations with ED admissions for both the 5–12 yrs and 1–17 yrs age groups.

Daily ED Visit Residuals Figure 1

Daily ED Visit Residuals. Daily time series plot of the residuals of daily pediatric asthma ED visits for 1–17 yr olds (observed 

minus the spline predicted).
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Other age groups did not show significant impacts from

ozone, and no significant associations of PM2.5 were seen

over this three-year period.

Lagged Day Effects

We also looked for associations of ozone and PM2.5 concentrations with ED visits and admissions up to 4 days following anomalies in de-trended pollutant levels. These

lagged effects were examined in separate models and are

shown in Table 3. For ED visits, the 1–17 yrs age group

showed significant associations only with grass pollen. An

increase in grass pollen of 10 grains m-3 was associated

with a 2.6% increase in asthma-related ED visits (95% CI

= 0.3–5). The 5–12 yrs age group showed significant

lagged associations with ozone, tree pollen, and grass pollen. A 0.01 ppm increase in ozone was associated with a

1.9% increase (95% CI = 0.2–3.7) after 1 day, a 2.3%

increase (95% CI = 0.6–4.1) after 2 days, a 2.8% increase

(95% CI = 1.1–4.6) after 3 days, and a 3.3% increase (95%

CI = 1.6–5.1) after 4 days, for asthma-related ED visits.

A 100 grains m-3 increase in tree pollen was associated

with a 1.2% increase (95% CI = 0.4–2.1) after 1 day, a

1.5% increase (95% CI = 0.7–2.4) after 2 days, a 1.1%

increase (95% CI = 0.3–2) after 3 days, and a 1.1%

increase (95% CI = 0.2–2) after 4 days, for asthma-related

ED visits for 5–12 yr olds. Grass pollen also showed a progressively increasing effect when lagged from 1 to 3 days,

and the association diminished for greater lags. Specifically, a 10 grains m-3 increase in grass pollen was associated with a 3.5% increase (95% CI = 0.1–7.1) after 1 day,

a 4.3% increase (0.9–7.9) after 2 days, a 5.5% increase

(95% CI = 2.2–9) after 3 days, and a 3.1 % increase (95%

CI = -0.5–6.8) after 4 days, for asthma-related ED visits.

Covariate Interactions

We analyzed the data for interactions among ozone,

PM2.5, and different types of pollen and mold spore

counts. However, no significant interactions were identified, so these results are not shown. This result may be due

to the small number of high pollen days in the 36 months

of data.

Socioeconomic Status

As mentioned earlier, one of the goals of the EPHTP is to

identify areas and populations that may be at greater risk

of health effects of pollution [8]. Because the ozone and

PM2.5 concentrations did not vary appreciably by location within DC, we compared the geospatial information

of residence in zip codes of low socioeconomic status with

other zip codes. The asthma-related ED visit rate for each

zip code was determined by dividing the number of

asthma-related ED visits from a zip code by the pediatric

population of that zip code. When this ED visit rate was

plotted against the percentage of children living in households with income below poverty level, a logarithmic relationship was observed (Fig. 2). Similar results were

obtained when ED admission rate was used instead of

visit rate. Note that there is a strong, nearly linear relationship as the percent below poverty rises to nearly 5%.

Above 5%, the relationship becomes curvilinear. Above

about 30%, the relationship becomes almost linear again

but with a very slow rise. Based on our literature search,

we believe this is the first time such a logarithmic relationship has been observed between asthma-related ED visit

or admission rates and pediatric socio-economic status.

These logarithmic correlations are very high (R2 close to

unity), indicating that this relationship is highly significant. Fig. 2 shows both asthma-related ED visit and

admission rates increased with the percentage of the pediatric population below poverty level, suggesting that overutilization of hospital ED for routine primary care of

asthma exacerbation does not explain the positive slope

of these curves. Together, the curves in Fig. 2 suggest a real

increased risk of acute asthma exacerbations as the percent

of children below poverty level increases.

Based on the curves in Fig. 2, we defined "high child poverty" zip codes as those for which the US census data indicated that at least 30% of children under 18 years old lived

in households below the poverty level. Based on the 2000

US census, ten DC zip codes met this criterion. The map

in Fig. 3 indicates these ten zip codes with cross-hatching.

The colors correspond to the asthma-related ED visit rates

Table 1: Risk Factor Associations with ED Visits for Ages 5–12 yrs

Risk Factor Increase Risk Factor Mean % Change in Daily Visits 

per Risk Factor Increase

T-test p-value

0.01 ppm Ozone 3.2 (1.4, 5) 0.000

1 μg m-3 PM2.5 -0.2 (-0.6, 0.2) 0.331

1000 spores m-3 Mold Count -0.2 (-1.1, 0.7) 0.707

10 grains m-3 Weed Pollen -2.9 (-6.8, 1.1) 0.148

100 grains m-3 Tree Pollen 1.8 (0.9, 2.6) 0.000

10 grains m-3 Grass Pollen 1.6 (-2, 5.4) 0.378

1°F Temperature 0.2 (0.1, 0.4) 0.007

The numbers in parenthesis represent the lower and upper 95% confidence limits for the percent change in visits.
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in the legend. Note that these higher-poverty zip codes

tend to have higher ED visit rates than other zip codes.

It is important to emphasize the contextual and ecologic

nature of this study. Given the patient's residence zip

code, we knew neither which patients within a zip code

were in households classified as below the poverty level,

nor which patients had sufficient exposure to outdoor air

pollutants. With these stipulations, we estimated zip

code-level relative risks of acute asthma exacerbation for

pediatric residents of high child-poverty zip codes versus

those from all DC zip codes. For each relative risk estimate, the numerator was the count of daily ED visits from

each residence zip code, and the denominator was a US

Census population estimate for the year 2000 for that zip

code. Both numerator and denominator were age-group

specific (e.g., counts of only 5–12 yr olds used in both

numerator and denominator). Table 4 presents these relative risk estimates with upper and lower 95 % confidence

limits showing that all estimates are statistically significant. These confidence intervals were calculated using the

method of Katz et al [22]. These aggregated calculations

support the hypothesis that children of each age group

have increased relative risk of requiring an ED visit for

asthma exacerbation if their residence is in a high-poverty

zip code. The second part of Table 4 presents relative risk

estimates in which the numerators were daily counts of

only those ED visits that resulted in hospital admission.

These tables suggest that, once a child visits an ED for an

asthma exacerbation, there is an increased risk of that

child needing admission if that child's residence is in a

high child poverty zip code. These results are consistent

with the curves seen in Fig. 2. Therefore, our results appear

to corroborate the hypothesis that, relative to DC children

in general, children from high poverty zip codes visit the

hospital ED with more severe asthma symptoms, making

admission more likely.

Discussion

We performed Poisson regression analyses of time series

of daily counts of pediatric asthma-related hospital ED

visits and admissions to seek associations with environmental risk factors. The dataset covered the period from

October 1, 2001, through September 30, 2004, and

included only DC residents. Separate analyses were done

to analyze environmental effects on more serious cases by

limiting the subset of counts to those for which the visits

resulted in hospital admissions. The risk factors included

in the analysis were daily concentrations of ozone, PM2.5,

and other environmental factors such as pollen.

During the study period, we found no statistically significant effect of PM2.5 on pediatric asthma-related ED visits

or admissions. This result is likely related to the relatively

Table 2: Risk Factor Associations with ED Admissions for Ages 1–17 yrs and for Ages 5–12 yrs

Risk Factor Unit Increase Risk Factor Mean % Change in Daily Visits 

per Risk Factor Unit Increase

T-test p-value

0.01 ppm Ozone 4.5 (0.6, 8.5) 0.023

1 μg m-3 PM2.5 0.2 (-0.6, 1.1) 0.548

1000 spores m-3 Mold Count 1.1 (-0.8, 3) 0.250

10 grains m-3 Weed Pollen 7.7 (0.7, 15.2) 0.029

100 grains m-3 Tree Pollen 1.5 (-0.3, 3.3) 0.105

10 grains m-3 Grass Pollen 0.7 (-7.4, 9.5) 0.874

1°F Temperature 0.5 (0.1, 0.8) 0.018

Risk Factor Unit Increase Risk Factor Mean % Change in Daily Visits 

per Risk Factor Unit Increase

T-test p-value

0.01 ppm Ozone 8.3 (2.6, 14.4) 0.004

1 μg m-3 PM2.5 -0.4 (-1.6, 0.8) 0.479

1000 spores m-3 Mold Count 2 (-0.6, 4.6) 0.140

10 grains m-3 Weed Pollen 10 (0.5, 20.5) 0.039

100 grains m-3 Tree Pollen 1.8 (-0.8, 4.4) 0.173

10 grains m-3 Grass Pollen 1.8 (-9.4, 14.5) 0.764

1°F Temperature 0.9 (0.4, 1.5) 0.001

The numbers in parenthesis represent the lower and upper 95% confidence limits for the percent change in admissions. The upper table is for ages 

1–17 yrs and the lower table is for ages 5–12 yrs.
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low levels of PM2.5 during our study period (daily PM2.5

never reached Code Red levels). While other studies have

shown significant associations between particulates and

pediatric ED asthma admissions [23], Fusco et al [24] also

found no significant relationship between pediatric

asthma and particulates. These different results may be

related to different particulate size distributions and concentrations among different locations, different types of

sources at those locations (e.g., smokestack industries,

diesel vs. gasoline vehicular traffic), and different periods

of time.

The 8-hour daily maximum ozone concentrations reached

Code Red levels on only five days during the study period.

We found some significant associations of ozone and tree

pollen on counts of ED visits and admissions for the 1–17

yrs age group, and the largest associations were found on

the 5–12 yrs age group. We also calculated significant but

smaller associations of ED visits with grass pollen and

temperature. Significant ozone associations were seen in

the 5–12 yrs age group even when the daily ED visit data

were lagged by up to 4 days from the ozone data. Interestingly, regression using grass pollen data did not show a

significant same-day effect, but for daily ED visits, this

effect became significant for a one-day lag and became

stronger as the lag increased to three days before dropping

to non-significant levels after three days. Tobias et al [25]

noted similar lags of 2 to 3 days, depending on the type of

pollen.

The most significant effects of ozone on asthma-related

ED visits and admissions were found in the 5–12 yrs age

group. Compared to adults, children have a higher alveolar ventilation relative to body mass, as well as a higher

peripheral airways resistance, resulting in children having

a greater risk of adverse ventilatory effects. As children

grow, respiratory rate decreases from about 22 breaths per

minute at age 4 to around 14 breaths per minute by age

16 years [26], thereby approaching normal rates for

adults. Because the 5–12 yrs age group represents schoolage children, it is plausible that this includes a significant

number of children who spend a larger amount of time

outside than the 1–4 yrs age group. If so, then the 5–12

year old children would be at greater risk for effects of outdoor pollutants. Significant associations found in 5–12

yrs age group for tree and weed pollen concentrations also

suggest that this age group may be at greater risk for outdoor exposures triggering reactive airway conditions due

to greater time spent outdoors. Ziska et al. [27] observed

that ragweed plants in urban locations, where carbon

Table 3: Lagged Day Associations of ED Visits with Ozone and Pollen.

Visits 1–17 yrs

Ozone Tree Pollen Grass Pollen

Lag (days) Mean % Change in Daily 

Visits per Unit

T-test p-value Mean % Change in Daily 

Visits per Unit

T-test p-value Mean % Change in Daily 

Visits per Unit

T-test p-value

0 1 (-0.1, 2.1) 0.078 0.5 (0, 1.1) 0.068 0.7 (-1.8, 3.1) 0.602

1 0.1 (-1, 1.2) 0.879 0.4 (-0.2, 1) 0.202 1.9 (-0.4, 4.3) 0.112

2 -0.2 (-1.3, 0.9) 0.724 0.5 (-0.1, 1.1) 0.105 2.3 (-0.1, 4.7) 0.056

3 0.3 (-0.8, 1.5) 0.572 0.2 (-0.4, 0.8) 0.492 2.6 (0.3, 5) 0.030

4 0.2 (-0.9, 1.3) 0.754 0.2 (-0.4, 0.9) 0.441 -0.4 (-2.9, 2.3) 0.787

Visits 5–12 yrs

Ozone Tree Pollen Grass Pollen

Lag (days) Mean % Change in Daily 

Visits per Unit

T-test p-value Mean % Change in Daily 

Visits per Unit

T-test p-value Mean % Change in Daily 

Visits per Unit

T-test p-value

0 3.2 (1.4, 5) 0.000 1.8 (0.9, 2.6) 0.0001 1.6 (-2, 5.4) 0.378

1 1.9 (0.2, 3.7) 0.030 1.2 (0.4, 2.1) 0.005 3.5 (0.1, 7.1) 0.046

2 2.3 (0.6, 4.1) 0.009 1.5 (0.7, 2.4) 0.0002 4.3 (0.9, 7.9) 0.013

3 2.8 (1.1, 4.6) 0.002 1.1 (0.3, 2) 0.01 5.5 (2.2, 9) 0.001

4 3.3 (1.6, 5.1) 0.000 1.1 (0.2, 2) 0.016 3.1 (-0.5, 6.8) 0.088

The top and bottom tables are for the 1–17 yr old and 5–12 yr old age groups, respectively. The mean % change in daily visits in these tables is per 

unit 0.01 ppm increase in ozone, 100 grains m-3 increase in tree pollen, and 10 grains m-3 increase in grass pollen.
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dioxide levels are high, produced more pollen than ragweed plants in rural areas where amounts of carbon dioxide were low. Our result that weed pollen was significantly

associated with ED admissions may be related to the fact

that ragweed is the primary component of weed pollen

and is known to produce huge amounts of pollen that are

considered among the most highly allergenic of all pollen.

When averaged over the 36 months of this study, the

annual rates of asthma-related ED visits (visits divided by

the year 2000 DC census for that age group) were 0.070,

0.033, 0.022, and 0.029 for the age groups 1–4 yrs, 5–12

yrs, 13–17 yrs, and 1–17 yrs, respectively. It is noteworthy

that the most significant associations of asthma exacerbations with outdoor environmental factors were not seen in

the age group with the highest rate of asthma-related ED

visits, the 1–4 yr olds. This may be related to less time

spent outdoors and to the clinical difficulty in accurately

diagnosing asthma in this age group. Because of the developmental factors mentioned above, children of ages 1–4

yrs may experience significant wheezing and shortness of

breath due to allergens or other factors, but these effects

often wane as the child develops. Therefore, there is some

debate whether these children should have been diagnosed with asthma as opposed to a more temporary reactive airway condition. It is thus possible that diagnoses of

asthma in this age group may not represent its true incidence.

An even more interesting finding resulted when we compared pediatric populations from zip codes in which more

than 30% of the children lived in households with

income classified as below the poverty level (defined as

"high child poverty zip codes") with all other zip codes

ED Visit and Admission Rates for Ages 1–17 Yr Figure 2 s vs Percent below Poverty Level in Zip Code

ED Visit and Admission Rates for Ages 1–17 Yrs vs Percent below Poverty Level in Zip Code. Upper plot shows 

the asthma-related ED admit rate and the lower plot shows the asthma-related ED visit rate for children ages 1–17 years versus the percent of children living below poverty levels for zip codes within DC.
Environmental Health 2007, 6:9 http://www.ehjournal.net/content/6/1/9

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within DC. Fig. 2 reveals a significant logarithmic relationship between the percentage of children living in

below-poverty-level households and both asthma-related

ED visits and admissions, with each symbol representing

a zip code. This figure also shows that the ratio of ED

admission to visit rates tends to increase as the percent zip

code population below poverty increases. At and above

about 30%, the rate of increase in both asthma-related ED

visit and admission rates levels off. Increased rates for

both asthma-related ED visits and admissions were seen

in residents of high child poverty zip codes compared

with other zip codes. Table 4 suggests a real increased relative risk of acute asthma exacerbations that require hospital admission among the pediatric residents of highpoverty zip codes. Possible explanations for this observation include postponing primary care until symptoms

become severe, discrepancies in outdoor air exposure

(e.g., lack of air conditioning), discrepancies in indoor air

quality [28], and discrepancies in health care access and

availability [29-31]. For example, Levy et al [29] found

that only 36% of asthmatic children in three public housing developments in Boston had been prescribed any

daily controller medication.

We summarize the limitations of this study. The available

data restrict it to a contextual study involving measured

outdoor risk factors for a population with a spatial resolution no finer than the zip code level. Krieger et al [32] have

shown that inhomogeneities in socio-economic status

within a zip code may have statistical impacts that are significant for some health outcomes, such as cancer incidence, and not for others, such as cancer mortality. Our

Asthma-related ED Visit Rates by Zip Code for FY2001-2003 Figure 3

Asthma-related ED Visit Rates by Zip Code for FY2001-2003. Asthma-related ED visit rates averaged over fiscal years 

2001-2003 (October 2001-September 2004) for ages 1-17 years for zip codes within DC. The hatched zip codes are those 

for which the 2000 US census determined that 30% or more of the children lived below poverty levels. The grey zips are 

those for which there is little or no pediatric population.
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asthma data did not include census tract information.

Depending on the homogeneity of DC zip codes for socioeconomic status, analysis at the census tract level may be

more informative that at the zip code level to which this

study was constrained. Another limitation was that the

period of our study was only 36 months, while datasets in

some similar studies cover much longer time intervals.

The spatial analysis of environmental effects was limited

in resolution because ozone and PM2.5 data were available from only three stations within DC during the study

period. The fact that there were only five Code Red ozone

days and no Code Red PM2.5 days during this study suggests limited opportunities to observe relationships

between these pollutants and asthma-related ED visits,

although asthma-related ED visits may also be associated

with conditions below Code Red levels. Despite these limitations, the results of this study, corroborative of similar

studies noted above, may be used for hypotheses and

designs of more comprehensive, individual-based epidemiological studies.

Conclusion

We conclude that asthmatic exacerbations, as measured

by hospital ED visits and admissions, have significant

short-term associations with measured ozone concentrations, especially among 5–12 year olds. The finding that

the most significant associations were not seen in the age

group with the highest number of asthma-related ED visits, the 1–4 yr olds, may be a combined result of this

youngest age group's relatively reduced exposure to outdoor air, especially on high-risk days, and the clinical difficulty in accurately diagnosing asthma in this group. We

also found an interesting logarithmic relationship

between asthma-related ED visit and admission rates and

the percentage of children living below the poverty level

within a zip code (Fig. 2). Together, the curves in Fig. 2

suggest that overuse of ED facilities does not explain the

increased visits by asthmatic children in high-poverty zip

codes and that there appears to be a real increased relative

risk of acute asthma exacerbations that require hospital

admission among these children. Therefore, the results

observed among age and poverty groups above may suggest designs for future studies on efforts to offer health

education and services such as asthma maintenance therapy to families in high poverty areas.

Competing interests

The author(s) declare that they have no competing interests.

Authors' contributions

SMB participated in the study design and analyses, drafted

the manuscript, and provided support as a meteorologist

and physician. HSB designed and developed the statistical

analyses. RSH coordinated implementation of the study

and contributed her expertise in respiratory therapy and

public health issues such as socioeconomic status. NRT

participated in the spatial analysis and provided geographic information systems analyses. LDS acquired federal funding and collaborated in the study conception,

design, and implementation, as well as provided support

for understanding the ED data. JODC collaborated in the

study conception and implementation. KD provided support for study design and relevant public health issues

Table 4: Relative Risk and Conditional Relative Risk for Asthma-related ED Visits and Admissions for High Poverty vs Other Zip 

Codes

Age Group (yrs) Outcome Relative Risk for asthma-related ED visits/

admissions for high poverty vs others

1–17 visits 1.45 (1.39 – 1.51)

admissions 1.75 (1.51 – 2.03)

1–4 visits 1.4 (1.32 – 1.48)

admissions 1.73 (1.35 – 2.21)

5–12 visits 1.38 (1.3 – 1.47)

admissions 1.77 (1.42 – 2.2)

13–17 visits 1.71 (1.54 – 1.9)

admissions 1.73 (1.19 – 2.52)

Age Group (yrs) Given an asthma-related ED visit, the Relative Risk of asthma- related ED visits being admitted:: 

high poverty vs others

1–17 1.2 (1.05 – 1.37)

1–4 1.23 (1 – 1.52)

5–12 1.18 (0.98 – 1.43)

13–17 1.17 (0.85 – 1.62)

The numbers in parenthesis represent the lower and upper 95 % confidence limits for the percent change in visits and admissions.
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Environmental Health 2007, 6:9 http://www.ehjournal.net/content/6/1/9

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including socioeconomic status. DHL collaborated in the

study conception and design. All authors read and

approved the final manuscript.

Acknowledgements

The authors would like to express their sincere appreciation to Susan 

Kosisky and her staff at the United States Army Centralized Allergen 

Extract Laboratory, Department of Allergy-Immunology, Walter Reed 

Medical Center, for providing aeroallergen data. This research is sponsored 

by the District of Columbia Department of Health under contract POHC2006-C-0100. The views and conclusions contained in this document are 

those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the United States 

Government.

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