Environmental Health Perspectives • VOLUME 108 | NUMBER 5 | May 2000 427

Air Pollution and Daily Hospital Admissions in Metropolitan Los Angeles

William S. Linn,1,2 Yaga Szlachcic,1,3 Henry Gong, Jr.,1,2,3 Patrick L. Kinney,4 and Kiros T. Berhane2

1Rancho Los Amigos National Rehabilitation Center, Downey, California, USA; 2Department of Preventive Medicine, 3Department of

Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA; 4Division of Environmental

Sciences, Joseph A. Mailman School of Public Health, Columbia University, New York, New York, USA

Time–series analyses of daily mortality or

morbidity have shown statistical associations

with air pollution in cities throughout the

world. Physiologic/toxicologic mechanisms

of these phenomena remain unknown, and

time–series analyses have not clearly linked

specific pollutants with specific health

outcomes (1–3); thus, their application to

pollution-control policy decisions remains

controversial (4). Combustion-related particulate matter, the only pollutant common to

virtually all locations of time–series studies,

has been the focus of scientific and regulatory

attention (1,2,4–6). However, recent studies

in a number of North American cities also

associate cardiovascular and/or pulmonary

disease incidence with pollutant gases such as

carbon monoxide, nitrogen dioxide, and/or

ozone (7–12). Where they are not highly correlated, gas and particulate pollutants appear

to have separate statistically and medically

significant influences on cardiopulmonary

morbidity (9,11,12).

The Los Angeles metropolitan area has

been studied relatively little by time–series

analysis, but is a good candidate for study

because of its large diverse population (≈ 14

million); detailed monitoring of air quality

and hospital admissions; mild climate, which

should limit confounding of pollution effects

by weather stresses; and severe but widely

variable air pollution (with maximum levels

of primary pollutant gases, secondary

photochemical oxidant gases, and particulate

pollution occurring at somewhat different

times and places). Powerful tests of pollution

effects should be possible in the entire metropolitan population and in subpopulations

defined geographically, demographically, or

clinically. We hypothesized that regional

and/or seasonal differences in time–series

analysis results in the general population

and/or in particular subgroups, would allow

us to distinguish effects associated with primary pollutants (CO or NO2), photochemical oxidants (O3), or particulate matter more

clearly than has been possible elsewhere. If

so, we could rank these categories of pollution in terms of their public health impact,

and thus provide useful guidance for regulatory policymaking and for future research on

mechanisms. To test this hypothesis, we analyzed daily admission data for 1992–1995

from the South Coast Air Basin (Los

Angeles, Riverside, San Bernardino, and

Orange Counties in California, excluding

mountain and desert regions of the first

three counties) in relation to daily levels of

CO, NO2, O3, and particulate matter ≤ 10

μm in aerodynamic diameter (PM10).

Methods

Data acquisition and management. After its

institutional review board verified confidentiality protection, the California

Office of Statewide Health Planning and

Development (OSHPD) (Sacramento, CA)

provided records of hospital admissions in

the metropolitan counties for 1992–1995

(the only years with adequately comparable

PM10 data). The records included hospital

identifier, date, principal and additional diagnoses as International Classification of Diseases

(ICD; World Health Organization, Geneva)

codes, All-Patient-Refined Diagnosis-Related

Group (APR-DRG; 3M Inc., Murray, UT)—

a broader classification based on Medicare

diagnosis-related groups, sex, age, ethnic

group, and residence zip code. Daily counts

after 21 December 1995 were excluded from

analysis because the records for numerous

patients not discharged until 1996 were

missing, and all 1995 data were excluded

from ethnic-group analyses because of

changes in OSHPD ethnic classifications.

Broad principal-diagnosis categories used in

analyses were cardiovascular (APR-DRG

103–144); cerebrovascular (APR-DRG

14–17 and 22); pulmonary (APR-DRG

75–101); and abdominal—a negative control

category thought to be unrelated to pollution

(APR-DRG 146–207). More-specific principal diagnoses, thought likely to associate with

air pollution on the basis of previous epidemiologic or toxicologic evidence, were also

analyzed: congestive heart failure (CHF)

(APR-DRG 127); myocardial infarction

(APR-DRG 111, 115, and 121); cardiac

arrhythmia (APR-DRG 138); occlusive

stroke (APR-DRG 14); asthma (ICD 493);

and chronic obstructive pulmonary disease

(COPD) (APR-DRG 88). Analyses excluded

patients younger than 30 years of age (with

Address correspondence to W.S. Linn, 51 Medical

Science Building, Rancho Los Amigos National

Rehabilitation Center, 7601 Imperial Highway,

Downey, CA 90242 USA. Telephone: (562) 401-

7561. Fax: (562) 803-6883. E-mail: linn@hsc.usc.edu

We thank the following for donating data and for

help and advice: G. Cox, J. White, H. Lomas, B-M.

Kim, M. Zeldin, S. Prasad, and F. Lurmann. We

thank J. Solomon for programming and data quality

assurance; C. Linn for consultation on hospital

admission data interpretation; S. Terrell for computer

artwork; and K. Clark, V. Valdez, and M. Avila for

administrative assistance. We thank M.P. Jones, M.

Lipsett, and J. Schwartz for advice on data handling

and statistical analysis. 

Major support for this project was provided by the

American Heart Association, Western States Affiliate

(grant 1134-GI1). The Southern California

Environmental Health Sciences Center, funded by

the National Institute of Environmental Health

Sciences (grant 5P30 ES07048-03), provided additional support.

Received 13 July 1999; accepted 1 December

1999.

Articles

We used daily time–series analysis to evaluate associations between ambient carbon monoxide,

nitrogen dioxide, particulate matter ≤ 10 μm in aerodynamic diameter (PM10), or ozone concentrations, and hospital admissions for cardiopulmonary illnesses in metropolitan Los Angeles during

1992–1995. We performed Poisson regressions for the entire patient population and for subgroups

defined by season, region, or personal characteristics, allowing for effects of temporal variation,

weather, and autocorrelation. CO showed the most consistently significant (p < 0.05) relationships

to cardiovascular admissions. A wintertime 25th–75th percentile increase in CO (1.1–2.2 ppm)

predicted an increase of 4% in cardiovascular admissions. NO2, and, to a lesser extent, PM10

tracked CO and showed similar associations with cardiovascular disease, but O3 was negatively or

nonsignificantly associated. No significant demographic differences were found, although

increased cardiovascular effects were suggested in diabetics, in whites and blacks (relative to

Hispanics and Asians), and in persons older than 65 years of age. Pulmonary disease admissions

associated more with NO2 and PM10 than with CO. Pulmonary effects were generally smaller

than cardiovascular effects and were more sensitive to the choice of model. We conclude that in

Los Angeles, atmospheric stagnation with high primary (CO/NO2/PM10) pollution, most common in autumn/winter, increases the risk of hospitalization for cardiopulmonary illness. Summer

photochemical pollution (high O3) apparently presents less risk. Key words: air pollutants, carbon

monoxide, epidemiology, Los Angeles, morbidity, nitrogen dioxide, ozone, particulate matter.

Environ Health Perspect 108:427–434 (2000). [Online 27 March 2000]

http://ehpnet1.niehs.nih.gov/docs/2000/108p427-434linn/abstract.html
exceptions noted in “Results”) and prescheduled admissions.

We obtained air pollution and meteorologic data from the South Coast Air Quality

Management District (SCAQMD; Diamond

Bar, CA) and from the National Weather

Service. These data included hourly PM10

from six SCAQMD stations with continuous

monitors; hourly CO, O3, NO2, temperature,

and relative humidity from those stations plus

others; 24-hr-average PM10 measured every

sixth day by high-volume samplers at or near

each continuous PM10 station; and barometric

pressure and rainfall at the Los Angeles

International Airport. Figure 1 shows monitor

locations. Analyses related daily admission

counts with 24-hr averages of environmental

variables. For O3, maximum hourly concentrations were also analyzed; they correlated

highly with 24-hr averages in all seasons (r

≥ 0.79) and showed similar relationships to

daily morbidity (“Results”). We did not analyze relative humidity because many data were

missing or out of range. Stations differed in

their continuous PM10 monitoring techniques and their relationships of continuous

to high-volume sampler data. On the assumption that high-volume data were more

comparable throughout the basin, we used

season- and station-specific linear regressions

to adjust continuous data to conform with

high-volume data. We defined seasons to

begin 1 January (winter), 1 April (spring), 1

July (summer), and 1 October (autumn).

Geographic differences were investigated

across six regions defined by continuous

PM10 monitoring stations (Figure 1). A

region consisted of all zip codes that had a

majority of their area closest to its station,

except that some western coastal zip codes,

which were separated from their closest station (region 1) by mountains, were assigned

to region 2 to better represent their air quality. Admitted patients were assigned to

regions by their residence zip codes. We

excluded the 6.7% with zip codes missing or

outside the South Coast Air Basin from

regional analyses. We determined pollutant

gas concentrations and temperatures for each

region by averaging across all monitoring stations within it. Missing air monitoring data

(4.4% for PM10, smaller percentages for other

variables) were replaced using analysis of variance with maximum likelihood estimation of

missing values. For PM10, data from all days

in the same season and all stations in the basin

were used in estimation; for other variables,

only stations in the same region were used.

Statistical analyses. We used BMDP

software (SPSS Inc., Chicago) and SAS software (SAS Institute, Inc., Cary, NC) for

statistical analyses. Descriptive statistics and

correlation patterns were examined regionally

and seasonally for admission counts and

atmospheric variables. Further descriptive

analyses were performed to contrast weather

and pollution characteristics between days

with unusually high and unusually low

observed morbidity relative to predicted values accounting for cyclical and secular trends.

Predicted values were from regressions with

indicator variables for the day of the week

and for weekday holidays, with longer term

variation modeled by fitting cubic splines to

successive 28-day intervals of data. We then

compared weather and pollution statistics

between days with high admissions (residual

> 85th percentile) and low admissions (residual < 15th percentile), as well as the immediately preceding days.

Time–series analytical approaches included a) ordinary least squares (OLS) regression

with admission count and atmospheric data

filtered by the Shumway 19-day weighted

moving average procedure (13), with or without an autoregressive component; b) regression of log-transformed daily admission

counts using polynomial distributed lag

models (14,15); and c) Poisson regression

with allowance for overdispersion and autocorrelation, adapted from the analytical strategy of the Particle Epidemiology Evaluation

Project (16) with modifications. In principle,

daily counts are Poisson distributed and

require approach c; however, given the generally large counts with filtering or smoothing, distributions were reasonably near

normal so that other approaches were also

feasible (17). The different approaches yielded similar conclusions when considering

cardiovascular diseases. Polynomial-distributed lag models showed the largest significant effects consistently at lag 0, and effects

beyond lag 1 were nearly always nonsignificant. Therefore, we adopted Poisson regression as the primary analytical tool. Predictors

of daily admission counts included basis

variables of a cubic-spline smooth on time

(which accounted for secular trends and seasonal variation); indicator variables for the

day of the week and for weekday holidays;

indicator variables for hot days (maximum

temperature > 85th percentile for entire

study period), cold days (minimum temperature < 15th percentile), and rain days (> 0.01

inches at the Los Angeles International

Airport); continuous atmospheric variables

(one or more pollutant concentrations,

barometric pressure, and mean temperature);

and an autoregressive term—the residual

admission count at lag 1, determined in a

preliminary regression including all other

predictors. Seasonal variation was more

complex for pulmonary diseases than for

others, probably because the timing and

intensity of winter infectious disease outbreaks varied from year to year. Thus, cubic

splines were determined at 28-day intervals

when smoothing pulmonary disease counts,

and at 4-month intervals otherwise.

Results

Seasonal air quality and hospital admissions.

Table 1 presents seasonal pollution, weather,

and hospital admission statistics for the entire

basin for 1992–1995. Overall means ± SDs

Articles • Linn et al.

428 VOLUME 108 | NUMBER 5 | May 2000 • Environmental Health Perspectives

Figure 1. Air monitoring stations that contributed data to the analyses. Cities identified on the map have

the continuous PM10 monitors that represent the six separate air quality regions analyzed (see text).

Los Angeles County

San Bernardino County

Riverside County

Orange County

Burbank (1)

Azusa (3)

Anaheim (5)

Los Angeles (2)

Long Beach (4)

Riverside (6)

PM10 + gas + temperature monitor

Temperature + gas monitor

Gas monitor

Weather (rain, barometric pressure) monitor
were 1.5 ± 0.8 ppm for CO, 3.4 ± 1.3 parts

per hundred million (pphm) for NO2, 45 ±

18 μg/m3 for PM10, and 2.4 ± 1.2 pphm for

O3. We determined basinwide means from

the six regional values by weighting each

region according to its proportion of cardiovascular plus pulmonary admissions (considered to reflect its proportion of the

population at risk). These levels reflect a

> 80% reduction of CO since the 1960s

(18), and more modest reductions in the

other pollutants. Year-round means and SDs

of daily admissions were 428 ± 76 for cardiovascular, 207 ± 54 for pulmonary, 74 ± 14

for cerebrovascular, and 244 ± 39 for abdominal diseases. Seasonal means for abdominal

diseases (not tabulated) varied < 3%. All disease categories showed marked variation by

day of the week, consistent across seasons.

Relative to Monday admissions, Sunday

admissions averaged 64% for cardiovascular,

70% for pulmonary, 76% for cerebrovascular, and 67% for abdominal diseases. 

Table 2 shows pairwise correlations of

basinwide average daily pollutant concentrations, mean temperature, and barometric

pressure within each season. NO2 showed

high positive correlations (r ≥ 0.8) with CO

in all seasons, and correlations nearly as high

with PM10. O3 was positively correlated with

all three other pollutants only in the spring,

and most strongly with PM10. O3 showed a

weaker positive relationship to PM10 in the

summer and a negative relationship to CO

and NO2 in the winter. Higher mean temperatures were associated with higher pollutant levels in all seasons, with the exception

of CO in the autumn. Barometric pressure

showed varying relationships with pollutants.

Expressing the data as residuals from cubicspline smoothing brought about no marked

changes in these correlations, except that in

autumn the positive relationship between O3

and temperature became nonsignificant.

Regional measurements and basinwide averages correlated strongly for every pollutant in

every season (r > 0.7), except for PM10 in

the summer (r = 0.5–0.6 for some regions).

Different regions’ measurements of a given

pollutant also correlated positively in all seasons. Southern coastal region 4 and eastern

inland region 6 contrasted most sharply,

with r-values between 0.3 and 0.7. In light

of this generally similar behavior of air quality in different regions, time–series analyses

focused on the entire basin, and regional

comparisons were limited to regions 4 and 6.

Figure 2 shows average concentrations

by region and season for CO, PM10, and

O3. The mean and variance of CO decreased

markedly in the spring and summer in all

regions. Autumn and winter CO were highest in the southern coastal region 4. PM10

was highest in the summer and autumn,

particularly in the eastern inland region 6, but

seasonal variation was less for PM10 than for

CO. O3 was highest in the spring and summer, particularly in inland regions 3 and 6.

Contrast of atmospheric conditions

between days with high and low admission

counts. Table 3 summarizes significant (p

< 0.05) differences in basinwide weather and

pollution statistics between days with unusually high and unusually low admission counts

(residuals from cubic-spline smoothing) in a

particular broad disease category. Highadmission days (and/or immediately preceding days) tended to have relatively warm dry

weather. Primary pollution (CO and NO2)

was significantly elevated on winter, spring, or

autumn days with high cardiovascular admissions; spring and summer days with high pulmonary admissions; and spring and autumn

days with high cerebrovascular admissions.

Elevated PM10 tended to accompany elevated

primary pollutants on days with high cardiovascular or pulmonary admissions; PM10 also

was associated with high abdominal admissions in the spring. O3 was increased (along

with the other pollutants) on days with high

pulmonary admissions in spring and summer, the seasons of the highest mean O3 concentrations (Table 1). By contrast, O3 was

decreased on days with high cardiovascular

admissions in the winter, when O3 was generally low and negatively correlated with the

other pollutants. 

Table 4 shows mean weather and pollution conditions on days of high and low cardiovascular admissions in the winter and

summer for the contrasting southern coastal

region 4 and eastern inland region 6. In the

summer, pollution (except for CO) and heat

were markedly greater in region 6, but there

were no clear pollution or temperature differences between high- and low-admission days

in either region. In the winter, CO was

markedly higher in region 4, and other

regional differences were modest. In region 4,

winter high-admission days had significantly

higher temperature, barometric pressure, CO,

NO2, and PM10, and significantly lower

probability of rain, than low-admission days.

In region 6, these tendencies were less obvious,

but CO and NO2 were significantly elevated

on the days preceding high-admission days. In

similar analyses of pulmonary diseases (not

tabulated), we found only a few significant

associations with high admissions: high

same-day PM10 in region 4 in the winter,

Articles • Daily pollution and morbidity in Los Angeles

Environmental Health Perspectives • VOLUME 108 | NUMBER 5 | May 2000 429

Table 1. Aira and hospital admission statistics for

the entire South Coast Air Basin, by season.

Variable

(units) Season Mean ± SD Min Max

CO Winter 1.7 ± 0.8 0.5 5.3

(ppm) Spring 1.0 ± 0.3 0.4 2.2

Summer 1.2 ± 0.4 0.3 2.7

Autumn 2.1 ± 0.8 0.6 4.3

NO2 Winter 3.4 ± 1.3 1.1 9.1

(pphm) Spring 2.8 ± 0.9 1.1 6.1

Summer 3.4 ± 1.0 0.7 6.7

Autumn 4.1 ± 1.4 1.6 8.4

PM10 Winter 37 ± 19 5 115

(μg/m3) Spring 42 ± 12 14 83

Summer 49 ± 10 14 78

Autumn 54 ± 22 15 132

O3 Winter 1.4 ± 0.7 0.2 4.4

(pphm) Spring 3.2 ± 1.0 0.9 7.0

Summer 3.3 ± 0.8 0.4 6.3

Autumn 1.5 ± 0.9 0.1 4.7

Temperature Winter 14.8 ± 2.7 8.4 23.2

(mean, °C) Spring 19.2 ± 3.0 9.5 29.2

Summer 23.9 ± 2.3 19.0 31.2

Autumn 16.4 ± 3.7 8.7 26.7

Rain Winter 28

(% of days) Spring 4

Summer 1

Autumn 10

Cardiovascular Winter 450 ± 77 300 607

adm/day Spring 428 ± 76 277 586

Summer 406 ± 70 239 559

Autumn 428 ± 76 273 610

Pulmonary Winter 241 ± 54 117 574

adm/day Spring 196 ± 34 118 329

Summer 172 ± 27 107 256

Autumn 220 ± 63 115 595

Cerebrovascular Winter 77 ± 14 44 126

adm/day Spring 74 ± 14 43 116

Summer 72 ± 14 41 114

Autumn 75 ± 14 45 117

Abbreviations: adm, admissions; max, maximum; min,

minimum; pphm, parts per hundred million.

aPollutant concentrations are averaged across six

regions, each weighted according to its proportion of

cardiovascular plus pulmonary admissions.

Table 2. Pairwise correlation coefficients (r) for

atmospheric factors expressed as basinwide 24-

hr averages,a by season.*

Factor Season NO2 PM10 O3 Tmean BPa

CO Winter 0.89 0.78 -0.43 0.22 0.43

Spring 0.92 0.54 0.29 0.38 0.15

Summer 0.94 0.72 0.03 0.51 -0.17

Autumn 0.84 0.58 -0.36 -0.08 0.38

NO2 Winter – 0.88 -0.23 0.38 0.38

Spring – 0.67 0.35 0.53 0.03

Summer – 0.80 0.11 0.51 -0.21

Autumn – 0.80 -0.00 0.28 0.12

PM10 Winter – – -0.01 0.37 0.39

Spring – – 0.63 0.64 -0.18

Summer – – 0.40 0.44 -0.30

Autumn – – 0.28 0.40 -0.03

O3 Winter – – – 0.33 -0.11

Spring – – – 0.57 -0.23

Summer – – – 0.18 -0.05

Autumn – – – 0.62 -0.42

Tmean Winter – – – – 0.13

Spring – – – – -0.34

Summer – – – – -0.20

Autumn – – – – -0.39

Abbreviations: BP, barometric pressure; T, temperature. 

aBP measured only at the Los Angeles International

Airport; other variables averaged from measurements in

all six regions, weighted according to regions’ proportions of total cardiovascular plus pulmonary admissions.

*p < 0.05 for r > 0.10; p < 0.01 for r > 0.13.
high previous-day CO and low previous-day

O3 in region 6 in the winter, and high previous-day NO2 and O3 in region 6 in the

summer.

Analyses of admission counts in broad

disease categories. Table 5 presents the results

from single-pollutant autoregressive Poisson

models, including all of the time and weather

predictors mentioned in “Methods,” relating

daily average concentrations with same-day

hospital admission counts over the entire

basin for each broad disease category.

Admissions of patients under 30 years of age

are not included. To a good approximation,

the coefficients represent proportionate

increases in admission counts expected from

unit increases in pollutant concentrations.

Primary pollution, as represented by CO

and NO2, showed the most consistent associations with cardiovascular-disease admissions;

they were significantly related in year-round

analyses and in single-season analyses except

for the spring. PM10 showed a similar pattern

of relationships, but was nonsignificant in

the summer as well as the spring. The

cardiovascular disease/primary pollution relationship was not very sensitive to inclusion or

exclusion of weather or other pollutant variables in the models. In the winter, the

interquartile range of CO concentrations

was 1.1 to 2.2 ppm, and the corresponding

predicted increase in cardiovascular admissions was 4.2%, which represented approximately 20 extra admissions/day.

Pulmonary-disease admissions were

significantly related to NO2 or PM10 in

year-round and winter analyses, and also to

NO2 or CO in autumn. Cerebrovasculardisease admissions were significantly related

only to CO or NO2 only in the spring.

Abdominal-disease admissions were significantly related only to NO2, and only in the

year-round analysis.

O3 showed either negative or nonsignificant positive relationships with cardiovascular,

pulmonary, cerebrovascular, and abdominal

disease admissions in year-round and singleseason analyses. The same was true in OLS

regressions of Shumway-filtered data (not

tabulated). Alternative analyses intended to

give additional weight to high-O3 conditions,

by expressing exposure in terms of daily maxima or in exceedances of an assumed threshold,

or by restricting the analysis to the three highO3 inland regions, still showed no significant

positive associations. In Poisson models

excluding mean temperature and barometric

pressure, daily mean O3 showed significant

positive associations with pulmonary diseases

in the spring and year-round.

Table 6 illustrates the sensitivity of

results to the choice of regression procedure

and model for the cardiovascular disease/CO

relationship in the winter and the pulmonary

disease/O3 relationship in the spring. Across

a broad range of models with and without

weather and other pollutants as predictors,

Articles • Linn et al.

430 VOLUME 108 | NUMBER 5 | May 2000 • Environmental Health Perspectives

Table 4. Atmospheric differences between days of high and low cardiovascular admissionsa in most contrasting regions/seasons.

Variable Admissions Region 4 (Long Beach) Region 6 (Riverside)

(units) (n) Winter Summer Winter Summer

CO (ppm) Low 2.1 1.0 1.1 1.1

High 2.9* 1.1 1.3** 1.2

NO2 (pphm) Low 3.4 2.8 2.5 3.8

High 4.4* 2.9 2.9** 4.0

PM10 (μg/m3) Low 27.6 38.0 39.1 84.6

High 42.8* 36.9 48.5 83.5

O3 (pphm) Low 1.4 2.8 1.9 4.4

High 1.4 2.9 1.8 4.5

Thigh (°C) Low 17.9 25.6 19.2 33.7

High 20.1* 25.6 19.6 33.9

Tlow (°C) Low 10.6 17.7 8.9 17.1

High 10.4 17.2 9.2 17.0

BPb (mbar) Low 1,016.2 1,012.9 1,017.3 1,012.5

High 1,018.5* 1,013.0 1,016.4 1,013.5*

Rainb (% of days) Low 38 0 26 0

High 12* 0 22 2

aSee Table 3 for definition of high- and low-admission days. bMeasured at the Los Angeles International Airport, closer to

region 4 than region 6. Other measurements made within the indicated region. *Significant (p < 0.05) differences.

**Although this difference did not reach significance, the difference between days immediately preceding high- and

low-admission days was significant (p < 0.05). 

Table 3. Atmospheric variables showing significant (p < 0.05) differences between days with high and low

hospital admissions in the entire basin, by season.a

Disease category Season CO NO2 PM10 O3 Thigh Tlow BP Rain

Cardiovascular Winter + + + – + + –

Spring + + + b –

Summer +

Autumn + + +

Pulmonaryc Spring + + + + + –

Summer + + + + + +

Autumn +

Cerebrovascular Winter b

Spring + + b +

Autumn b ++–

Abdominald Spring + –

Abbreviations: BP, barometric pressure; T, temperature. 

aHigh- and low-admission days (> 85th and < 15th percentiles, respectively) are determined by residuals from regressions accounting for temporal effects (see text). A significant increase in atmospheric variables on high-admission days

relative to low-admission days (and/or the immediately preceding days) is indicated by +; a significant decrease by –.

bThe change in atmospheric measurement from the preceding day was significantly more positive on high- than lowadmission days, although values measured on high and low days were not significantly different. cFor pulmonary diseases in winter, high-admission days’ increases in NO2, PM10, and high temperature approached significance (p < 0.10). dFor abdominal diseases in winter, high-admission days’ increases in CO approached significance (p < 0.10). 

Figure 2. Air quality in six regions (see Figure 1),

1992–1995. Bar = seasonal mean; flag = seasonal

SD.

5

4

3

2

1

0

CO (ppm) 140

120

100

80

60

40

20

0

Winter Spring Summer Autumn

Winter Spring Summer Autumn

PM10 (μg/m3) O3 (pphm)

Winter Spring Summer Autumn

6

5

4

3

2

1

0

A

B

C

Region 1 Region 4

Region 2 Region 5

Region 3 Region 6
the estimated winter CO effect was always

significant and was reasonably consistent in

size. None of the other pollutants’ effects was

significant when included in a model with

CO. The spring O3 effect on pulmonary

admissions was significant when O3 was the

only atmospheric factor in the model,

predicting a 1.5% increase in admissions for a

1-pphm increase in daily mean O3 concentration. However, the O3 effect was nonsignificant if the model included weather and/or

other pollutant variables. Interpretation of

these findings is complicated by collinearity

and by possibly different characteristics of

exposure measurement error for different pollutants (19). Nevertheless, it seems clear that

in the winter, CO was the analyzed atmospheric factor that was most closely linked

with excess cardiovascular morbidity. In the

spring, O3 was the pollutant most closely

associated with excess pulmonary morbidity;

however, morbidity was still more closely

associated with warm temperatures, and all

four pollutants tended to rise with temperature, making interpretation difficult.

Because diabetes mellitus is an important

risk factor for cardiovascular disease, we reanalyzed cardiovascular admissions separately

for diabetics (all of those with ICD code 250

entered among four additional diagnoses in

the record; approximately 20% of all

patients) and for others, using the autoregressive Poisson model. In year-round analyses,

the slope ± SE was 0.039 ± 0.006 for diabetics as compared to 0.031 ± 0.004 for others.

Year-round analyses of NO2 and PM10

effects showed similar modest slope increases

for diabetics, as did single-season analyses.

None of the slope differences between diabetics and others was statistically significant.

Analyses of cardiovascular disease admission counts by age, sex, and ethnicity. Table

7 presents results from single-pollutant

autoregressive Poisson models applied to cardiovascular admission counts in three age

strata (30–64, 65–74, and ≥ 75 years of age)

separately for men and women. Results for

O3 (not tabulated) were never statistically significant. CO effects were near-significant for

women 30–64 years of age and sig-nificant in

all other age–sex groups in year-round analyses and in one or more seasonal analyses.

Effect sizes increased with age similarly in

both sexes, but age-related differences were

not significant. NO2 effects showed a similar

pattern of significance, but with less suggestion of age dependence. PM10 effects were

also significant year-round and/or in one

season for all groups except men ≥ 75 years

of age. 

Table 8 presents the results from single-pollutant autoregressive Poisson models applied to

cardiovascular admission counts for adults ≥ 30

years of age in four ethnic categories—white

(non-Hispanic), black, Hispanic, and other.

The other category includes people of AsianPacific ancestry (the large majority), Native

Americans, and others not classifiable in the

first three groups. O3 effects (not tabulated)

were never significant. Regression coefficients,

though not significantly different, suggested

meaningful ethnic differences in exposure–response relationships. CO, NO2, and

PM10 effects were significant in whites in yearround, winter, and autumn analyses. In

blacks, CO and NO2 effects were significant

year-round (also in the winter for CO) and

were similar to these effects in whites. CO and

NO2 effects in Hispanics were significant yearround but were smaller than these effects in

whites and blacks. The remaining (other)

category, with a relatively small number of

admissions, showed consistently small and

nonsignificant regression slopes.

Analyses of admission counts for more

specific diagnoses. Table 9 presents results in

adults ≥ 30 years of age from singlepollutant autoregressive Poisson models

relating basinwide daily average pollutant

concentrations with same-day admission

counts. Occlusive strokes showed the most

consistent positive relationships to pollution:

significant associations with O3 in the summer only; and with CO, NO2, and PM10

year-round and in at least two single-season

analyses. Asthma, COPD, and CHF were

Articles • Daily pollution and morbidity in Los Angeles

Environmental Health Perspectives • VOLUME 108 | NUMBER 5 | May 2000 431

Table 5. Poisson regression coefficients (SEs): hospital admissions in broad disease categories in the

entire metropolitan area, versus same-day pollution levels.a

Pollutant,

units Season Cardiovascular Pulmonary Cerebrovascular Abdominal

CO All 0.032 (0.003)* 0.007 (0.005) 0.009 (0.007) 0.003 (0.004)

(ppm) Winter 0.038 (0.006)* 0.016 (0.009) -0.008 (0.014) 0.006 (0.008)

Spring 0.010 (0.015) 0.014 (0.024) 0.107 (0.033)* -0.007 (0.019)

Summer 0.035 (0.014)* 0.020 (0.021) 0.030 (0.033) 0.021 (0.018)

Autumn 0.027 (0.006)* 0.020 (0.008)* 0.008 (0.012) 0.006 (0.007)

NO2 All 0.014 (0.002)* 0.007 (0.003)* 0.004 (0.004) 0.004 (0.002)*

(pphm) Winter 0.016 (0.004)* 0.011 (0.005)* -0.013 (0.007) 0.002 (0.005)

Spring 0.001 (0.006) 0.007 (0.010) 0.042 (0.012)* -0.004 (0.007)

Summer 0.011 (0.005)* 0.004 (0.008) 0.009 (0.012) 0.008 (0.006)

Autumn 0.014 (0.003)* 0.012 (0.004)* 0.007 (0.006) 0.007 (0.004)

PM10 All 0.00064 (0.00012)* 0.00057 (0.00018)* 0.00006 (0.00025) 0.00017 (0.00014)

(μg/m3) Winter 0.00095 (0.00024)* 0.00081 (0.00032)* -0.00021 (0.00052) 0.00013 (0.00030)

Spring -0.00031 (0.00037) 0.00010 (0.00061) 0.00126 (0.00083) 0.00039 (0.00047)

Summer 0.00039 (0.00041) 0.00061 (0.00061) 0.00127 (0.00096) 0.00068 (0.00052)

Autumn 0.00065 (0.00020)* 0.00078 (0.00029) -0.00004 (0.00039) 0.00008 (0.00022)

O3 All -0.007 (0.003)** 0.008 (0.004) 0.003 (0.005) 0.003 (0.003)

(pphm) Winter -0.021 (0.008)** -0.006 (0.010) 0.028 (0.016) -0.012 (0.010)

Spring 0.003 (0.005) 0.011 (0.008) 0.011 (0.011) 0.000 (0.006)

Summer 0.001 (0.005) 0.006 (0.007) 0.007 (0.011) 0.011 (0.006)

Autumn -0.003 (0.007) 0.009 (0.011) -0.003 (0.014) 0.000 (0.008)

aRegression analyses used 24-hr average measurements of pollutants and same-day admission counts for patients ≥ 30

years of age throughout the South Coast Air Basin. Example interpretation: the coefficient 0.038 relating winter cardiovascular admissions to CO indicates that admissions increase by a factor of e0.038, i.e., by 3.9%, with a 1-ppm increase in

CO concentration, after allowing for the effects of time and weather on admission rates. *Significant in expected direction, p < 0.05. **Significant in the ”wrong” direction, p < 0.05. 

Table 6. Sensitivity of key results to choice of regression procedure and model.

Procedure (additional predictors) Slope (SE) Relative riska

Cardiovascular admissions versus CO, winter

Poisson autoregressive (time) 0.044 (0.005)* 1.050

Poisson autoregressive (time, weather) 0.038 (0.006)* 1.043

Poisson autoregressive (time, weather, O3) 0.036 (0.007)* 1.040

Poisson autoregressive (all above + PM10) 0.033 (0.011)* 1.037

Poisson autoregressive (all above + NO2) 0.047 (0.013)* 1.053

PDLb (time, weather) 0.044 (0.008)* 1.050

OLS autoregressive (time, weather) 19.6 (3.1)* 1.048

Pulmonary admissions versus O3, spring

Poisson autoregressive (time) 0.012 (0.004)* 1.015

Poisson autoregressive (time, weather) 0.003 (0.005) 1.003

Poisson autoregressive (time, weather, CO) 0.002 (0.005) 1.002

Poisson autoregressive (all above + PM10) 0.008 (0.006) 1.009

Poisson autoregressive (all above + NO2) 0.008 (0.006) 1.009

PDLb (time, weather) 0.003 (0.011) 1.004

OLS autoregressive (time, weather) 1.58 (1.79) 1.010

aPredicted relative risk at the 75th percentile concentration of pollutant, versus the 25th percentile. bCubic polynomial

distributed lag model, lag 0 slope given, lags 1–3 slopes nonsignificant. *Significant in expected direction, p < 0.05.
significantly associated with CO and NO2

year-round and in one or more single-season

analyses. Myocardial infarction was associated with CO and NO2, and arrhythmias with

CO, in year-round analyses only.

We also analyzed asthma admissions for

patients 0–29 years of age. In year-round

analyses, slopes ± SEs were 0.036 ±

0.016/ppm CO, 0.024 ± 0.008/pphm NO2,

and 0.0011 ± 0.0006/μg/m3 PM10—all significant (p < 0.05) and appreciably larger

than the slopes in adults > 30 years of age.

O3 effects were nonsignificant. Most of the

admitted patients in this youngest group

were children: the mean age was 7.

Relationships of cardiovascular disease

admissions to CO or PM10 in the two most

contrasting regions. Table 10 presents

comparative statistics for cardiovascular

admissions, CO, and PM10 in southern

coastal region 4 and eastern inland region 6.

The two regions showed reasonably similar

daily admission counts and similar positive

correlations of daily CO and PM10 levels,

but markedly different concentration ranges

(Figure 2). By OLS regression allowing for

autocorrelation, a wintertime 1-ppm rise in

CO predicted a 9-μg/m3 rise in PM10 in

region 4, but a 25 μg/m3 rise in region 6. In

single-pollutant autoregressive Poisson models, region 4 showed highly significant relationships between PM10 and admissions,

year-round and in the winter, despite its low

PM10. In region 6, despite its high PM10,

regression slopes were significantly lower

than in region 4, and were not significantly

different from zero. Admissions showed a

more plausible relationship with CO across

the two regions, with highly significant positive slopes in region 4 and modestly lower

non-significant slopes in region 6, consistent

with its generally lower and less variable CO

concentrations.

Discussion

Limitations; recommendations for future

research. Problems with this and other

time–series studies include exposure misclassification, response misclassification, and

model misspecification. Exposure misclassification occurs when the monitored environmental factors are not the ones responsible

for health effects, when monitoring errors are

appreciable and differ by time and location,

when monitoring station data poorly represent background air near patients’ homes,

when personal microenvironments differ

from background, or when exposures that

precipitate hospital admissions occur away

from home. Future expansion of the monitoring program, at least for particulate pollution, should provide better background concentration estimates, allowing more powerful

tests for regional differences in effects. New

personal monitoring studies, designed to elucidate longitudinal relationships between

background and personal exposures, might

help to disentangle the effects of particulate

pollutants and covarying gases (e.g., CO and

NO2). Small panel studies have suggested

that personal particulate exposures track

background concentrations closely in healthy

older adults and children in The Netherlands

(20,21), but not in older adults with COPD

in Los Angeles (22). To our knowledge, no

longitudinal studies of personal CO exposure

have been reported. 

Response misclassification can result

from errors in diagnosis or in medical record

coding. Reviews suggest that 15–20% of

assigned ICD codes are inaccurate (23,24).

Inaccuracies should increase random errors

in specific disease counts and reduce the statistical significance of disease/pollution relationships, but should not introduce bias

unless coding inaccuracies covary with pollution. Misdiagnoses are hard to evaluate, but

are undoubtedly important, given the complexities of disease processes and the fuzzy

boundaries between diagnoses. Wrong diagnoses or codes would likely shift patients to

different specific disease counts within the

same broad category, and thus should have

relatively little effect on broad-category

analyses. In any event, we have had only limited success in finding specific pollutant–disease relationships with mechanistic or public-health implications. Future studies focusing on precise diagnoses and accounting for

other risk factors (e.g., additional diagnoses

and particular demographic characteristics)

might be more successful.

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432 VOLUME 108 | NUMBER 5 | May 2000 • Environmental Health Perspectives

Table 7. Poisson regression coefficients (SEs): cardiovascular disease admissions in the entire metropolitan area versus same-day pollution levels, by age and sex.a

Sex, age in years Year-round Significant (p < 0.05) positive 

[count]b Pollutantc coefficient coefficients for separate seasons

Male 30–64 CO 0.014 (0.007)* Winter 0.040 (0.013), autumn 0.025 (0.013)

[99 ± 21] NO2 0.007 (0.004) Winter 0.017 (0.008), autumn 0.016 (0.007)

PM10 0.0003 (0.0003) Winter 0.0016 (0.0005)

Male 65–74 CO 0.037 (0.009)* Autumn 0.045 (0.016)

[61 ± 14] NO2 0.014 (0.005)* Autumn 0.024 (0.009)

PM10 0.0008 (0.0003)* Autumn 0.0013 (0.0006)

Male 75+ CO 0.040 (0.009)* Summer 0.068 (0.034), autumn 0.042 (0.015)

[59 ± 13] NO2 0.013 (0.005)* Autumn 0.020 (0.008)

PM10 0.0003 (0.0003) None

Female 30–64 CO 0.017 (0.009) None

[68 ± 15] NO2 0.015 (0.004)* Winter 0.018 (0.008)

PM10 0.0007 (0.0003)* None

Female 65–74 CO 0.033 (0.009)* Winter 0.043 (0.015)

[56 ± 12] NO2 0.014 (0.005)* Winter 0.017 (0.008)

PM10 0.0002 (0.0003) Winter 0.0012 (0.0006)

Female 75+ CO 0.040 (0.007)* Winter 0.047 (0.013), autumn 0.025 (0.011)

[88 ± 17] NO2 0.014 (0.004)* Winter 0.021 (0.008)

PM10 0.0005 (0.0003) Winter 0.0012 (0.0005)

aThese results are for 1992–1994 only. See footnote to Table 5 for explanation of regression procedure and coefficients.

bAnnual mean daily admission count ± SD, for patients ≥ 30 years of age. cO3 results not tabulated; none was significantly

positive. *Year-round relationship significant, p < 0.05.

Table 8. Poisson regression coefficients (SEs): cardiovascular disease admissions in the entire metropolitan area versus same-day pollution levels, by ethnic category.a

Category Year-round Significant (p < 0.05) positive 

[count]b Pollutantc coefficient coefficients for separate seasons

White CO 0.034 (0.005)* Winter 0.038 (0.008), autumn 0 .036 (0.008)

[290 ± 53] NO2 0.014 (0.003)* Winter 0.018 (0.005), autumn 0.017 (0.005)

PM10 0.0006 (0.0002)* Winter 0.0011 (0.0003), autumn 0.0007 (0.0003)

Black CO 0.031 (0.010)* Winter 0.042 (0.017)

[49 ± 11] NO2 0.014 (0.006)* None

PM10 0.0003 (0.0004) None

Hispanic CO 0.019 (0.009)* None

[63 ± 13] NO2 0.010 (0.005)* None

PM10 0.0005 (0.0003) None

Other CO 0.001 (0.013) None

[29 ± 8] NO2 0.002 (0.007) None

PM10 -0.0004 (0.0005) None

aThese results are for 1992–1994 only. See footnote to Table 5 for explanation of regression procedure and coefficients.

bAnnual mean daily admission count ± SD, for patients ≥ 30 years of age. cO3 results not tabulated; none was significantly

positive. *Year-round relationship significant, p < 0.05. 
Limitations of our primary analytical

model include the use of only one pollutant

at a time and possibly incomplete accounting for weather and temporal influences.

Because estimated CO effects were similar in

various single- and multipollutant models

that accounted for seasonal and weekly

cycles, more complete modeling of weather

or temporal effects should not change the

conclusions concerning CO. By contrast,

estimates of O3 effects were highly sensitive

to inclusion or exclusion of weather and other

pollutant variables. If pollutants not in the

model affected admissions, the likely result

would be to overestimate effects of the modeled pollutant and underestimate total effects

of pollution (12). Thus, effects we associated

with CO might be at least partly due to

covarying gases (e.g., oxides of nitrogen) or to

particulate substances. Similarly, incomplete

accounting for lagged effects would likely

result in overestimated effects of very recent

exposure, but underestimated cumulative

effects of recent and earlier exposures (14).

Conclusion 

In general, our results from metropolitan

Los Angeles appear consistent with reports

from elsewhere (7–12) that day-to-day

increases in urban CO and/or PM10 and/or

NO2 are associated with meaningful increases in cardiovascular illnesses. We found only

a few equivocally positive relationships

between cardiopulmonary morbidity and

O3, in situations when other pollutants and

heat stress increased along with O3. This is

surprising, in light of severe O3 pollution in

Los Angeles, obvious acute respiratory effects

of O3 in animal and human exposure studies

(2), and recent observations of O3-related

hospital admissions in Toronto, Canada,

where O3 levels are lower than in Los

Angeles (12). O3 has been linked to mortality in Los Angeles (13), although PM10

might explain that association (25). On the

other hand, a recent time–series study of

asthma admissions in central and western

Los Angeles (26) generally supports our

findings, showing associations with PM10

but not with O3. The tendency of O3 concentrations to decrease indoors, where most

people spend most of their time (27), might

attenuate morbidity/O3 relationships, but

would not likely do so in Los Angeles more

than in Toronto. In any event, our results

suggest that the excess risk of hospitalization in

Los Angeles is greater on high-primary-pollution days than on high-O3 days. The greatest

risk of pollution-related hospital admissions

apparently occurs on autumn/winter days with

weak Santa Ana weather conditions, when air

incursion from the desert approximately counterbalances that from the ocean, resulting in

maximal atmospheric stagnation.

We could not distinguish clearly among

CO-, NO2- and particle-associated effects.

CO showed the strongest statistical relationships with most indices of morbidity even in

the regions and seasons with the highest and

widest ranging PM10. NO2 tracked CO closely enough that CO-associated effects might

reasonably be attributed to NO2 and/or

another oxide of nitrogen. Weaker statistical

relationships of illness to PM10 might have

resulted from less accurate exposure assessment even if PM10 were inherently more

toxic (19). Too little is known about the relationships between the ambient background

and personal exposures to judge which pollutants are most subject to exposure misclassification. Even if PM10/morbidity associations

were entirely explainable by CO/morbidity

associations, some particulate species closely

associated with CO might be the active

agent(s). Alternatively, our findings might

reflect separate effects of CO and some

Articles • Daily pollution and morbidity in Los Angeles

Environmental Health Perspectives • VOLUME 108 | NUMBER 5 | May 2000 433

Table 9. Poisson regression coefficients (SEs): hospital admissions in more specific disease categories in

the entire metropolitan area, versus same-day pollution levels.a

Disease Year-round Significant (p < 0.05) positive 

[count]b Pollutant coefficient coefficients for separate seasons

Myocardial infarction CO 0.040 (0.009)* None

[47 ± 11] NO2 0.011 (0.005)* None

PM10 0.0006 (0.0003) None

O3 -0.007 (0.007) None

Congestive heart failure CO 0.025 (0.009)* Summer 0.074 (0.038)

[49 ± 11] NO2 0.010 (0.005)* Winter 0.019 (0.009)

PM10 0.0004 (0.0003) None

O3 -0.001 (0.007) None

Cardiac arrhythmia CO 0.023 (0.009)* None

[50 ± 10] NO2 0.006 (0.005) None

PM10 0.0002 (0.0003) None

O3 -0.001 (0.007) None

Occlusive stroke CO 0.044 (0.009)* Winter 0.036 (0.017), summer 0.091 (0.039), 

[45 ± 10] autumn 0.032 (0.015)

NO2 0.020 (0.005)* Winter 0.027 (0.010), autumn 0.021 (.008)

PM10 0.0013 (0.0003)* Winter 0.0024 (0.0006), autumn 0.0012 (0.0005)

O3 0.007 (0.007) Summer 0.025 (0.012)

Asthmac CO 0.028 (0.010)* Winter 0.045 (0.017), autumn 0.039 (0.016)

[38 ± 9] NO2 0.014 (0.005)* Winter 0.028 (0.010), autumn 0.019 (0.008)

PM10 0.0003 (0.0004) None

O3 -0.001 (0.008) None

COPD CO 0.019 (0.007)* Winter 0.035 (0.012), autumn 0.029 (0.011)

[89 ± 19] NO2 0.008 (0.004)* Autumn 0.016 (0.006)

PM10 0.0003 (0.0002) None

O3 -0.007 (0.005) None

aSee footnote to Table 5 for explanation of regression procedure and coefficients. bAnnual mean daily admission count ±

SD for patients ≥ 30 years of age. cSee text concerning asthma in patients < 30 years of age. *Year-round relationship

significant, p < 0.05.

Table 10. Daily cardiovascular (CV) admissions, CO, and PM10: statistics for the two most contrasting

regions.

Region 4 Region 6

Statistic Season (Long Beach) (Riverside)

CV admission count interquartile range All 48–67 43–57

Winter 52–71 44–59

CO interquartile range (ppm) All 0.93–2.40 0.81–1.58

Winter 1.33–3.17 0.74–1.70

PM10 interquartile range (μg/m3) All 28–45 44–86

Winter 20–43 24–61

Correlation, CO vs. PM10 All 0.56* 0.55*

Winter 0.70* 0.75*

Correlation, CO vs. PM10 residualsa All 0.63* 0.69*

Winter 0.72* 0.76*

Regression slope, CV vs. COb All 0.022 (0.005)* 0.012 (0.008) 

Winter 0.027 (0.008)* 0.017 (0.015)

Regression slope, CV vs. PM10b All 0.0012 (0.0003)* 0.0001 (0.0002)

Winter 0.0018 (0.0006)* 0.0003 (0.0004)

aObserved values minus values predicted by day of the week and cubic-spline smooth of longer term trends. bFrom single-pollutant autoregressive Poisson models including temporal and weather effects. *Significant, p < 0.05.
component of PM10, as suggested by the

Schwartz (9) Tucson, Arizona, study. One

argument against CO effects per se is that

typical ambient background CO concentrations are below normal bloodstream concentrations of metabolically produced CO (3).

Even on most high-CO days in Los Angeles,

inhaling the background concentration

should reduce the blood’s oxygen-carrying

capacity by < 1%. However, CO concentrations near sources (e.g., heavy traffic) exceed

background levels and may cause appreciable

cardiovascular stress (9,10). If both are driven

by atmospheric stagnation, these higher

microenvironmental concentrations should

track background levels. Thus, a low range of

monitored background CO does not necessarily rule out an effect of CO on cardiovascular morbidity.

The observed association of all pulmonary diseases with PM10 or NO2 more

than CO, and of all cardiovascular diseases

with CO more than PM10 (Table 5), appears

consistent with the well-known properties of

CO as a circulatory toxicant without direct

effects on the lungs, and of some particulate

species as respiratory irritants. The association of occlusive strokes with all four tested

pollutants appears consistent with the

hypothesis of Seaton et al. (28) that urban

pollution provokes alveolar inflammation,

releasing mediators which increase blood

coagulability. A previous finding of increased

plasma viscosity during a primary pollution

episode in Germany (29) also supports that

hypothesis. By our data, we cannot test

Seaton et al.’s (28) attribution of the inflammatory effect to ultrafine particles. 

We found possibly meaningful demographic differences in morbidity/pollution

relationships, although none of them attained

statistical significance. Persons ≥ 65 years of

age and diabetics showed somewhat increased

cardiovascular disease effects as compared to

others without those risk factors, but men did

not appear to be more at risk than women of

similar age. Persons younger than 30 years of

age showed the largest pollution-related

effects on asthma. Although air pollution

health risks are believed to fall disproportionately on ethnic minorities (30), whites usually

showed the largest pollution-related effects on

cardiovascular disease. Effect sizes in blacks,

the minority group generally at greatest risk

for cardiovascular disease, were similar to

those in whites, whereas effect sizes were

generally smaller in Hispanics and undetectable in the other (predominantly Asian)

ethnic category. Definitive interpretation

would require evaluation of ethnic differences in exposure, susceptibility, and access

to hospitals. On average, O3 exposures in the

basin appear higher for whites than for blacks

or Asian/Pacific Islanders (31). Differences in

other exposures apparently have not been

studied formally, but the high-primary-pollution regions 2 and 4 have high proportions of

minority residents. Thus, smaller effects in

some minorities (if real) probably are not

explained by less exposure.

The relatively nonspecific pattern of

diagnoses suggests that excess patients

admitted to hospitals on high-pollution

days in metropolitan Los Angeles are individuals with preexisting problems which

make them highly vulnerable to any extra

stresses on their oxygen delivery systems,

including unfavorable changes in the air

environment. If so, generalized efforts to

preserve cardiopulmonary health should

help to prevent (or at least to postpone) pollution-associated illnesses. Our findings suggest that control of primary pollutants is

more important to public health than control of O3, which in any case depends on

control of primary pollutants.

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434 VOLUME 108 | NUMBER 5 | May 2000 • Environmental Health Perspectives