doi:10.1136/oem.2008.041376 

Occup. Environ. Med. 2009;66;189-197; originally published online 18 Nov 2008; 

 

L Zhang, T Tjoa and D L Gillen 

R J Delfino, S Brummel, J Wu, H Stern, B Ostro, M Lipsett, A Winer, D H Street,

 

wildfires of 2003

hospital admissions to the southern California 

The relationship of respiratory and cardiovascular

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The relationship of respiratory and cardiovascular

hospital admissions to the southern California

wildfires of 2003

R J Delfino,1 S Brummel,2 J Wu,1,3 H Stern,2 B Ostro,4 M Lipsett,5 A Winer,6 D H Street,7

L Zhang,5 T Tjoa,1 D L Gillen2

c Additional information is

published online only at http://

oem.bmj.com/content/vol66/

issue3

1 Department of Epidemiology,

School of Medicine, University of

California, Irvine, California, USA;

2 Department of Statistics,

School of Information and

Computer Science, University of

California, Irvine, California, USA;

3 Program in Public Health,

University of California, Irvine,

California, USA; 4 Air Pollution

Epidemiology Section, California

Office of Environmental Health

Hazard Assessment, Oakland,

California, USA; 5 Exposure

Assessment Section,

Environmental Health

Investigations Branch, California

Department of Health Services,

Oakland, California, USA;

6 Department of Environmental

Health Sciences, School of

Public Health, University of

California, Los Angeles,

California, USA; 7 Independent

consultant, Salem, Oregon, USA

Correspondence to:

Dr Ralph J Delfino, Epidemiology

Department, School of

Medicine, University of

California, Irvine, 100 Theory Dr.,

Suite 100, Irvine, CA 92617-

7555, USA; rdelfino@uci.edu

Accepted 18 August 2008

Published Online First

18 November 2008

ABSTRACT

Objective: There is limited information on the public

health impact of wildfires. The relationship of cardiorespiratory hospital admissions (n = 40 856) to wildfirerelated particulate matter (PM2.5) during catastrophic

wildfires in southern California in October 2003 was

evaluated.

Methods: Zip code level PM2.5 concentrations were

estimated using spatial interpolations from measured

PM2.5, light extinction, meteorological conditions, and

smoke information from MODIS satellite images at 250 m

resolution. Generalised estimating equations for Poisson

data were used to assess the relationship between daily

admissions and PM2.5, adjusted for weather, fungal

spores (associated with asthma), weekend, zip code-level

population and sociodemographics.

Results: Associations of 2-day average PM2.5 with

respiratory admissions were stronger during than before

or after the fires. Average increases of 70 mg/m3 PM2.5

during heavy smoke conditions compared with PM2.5 in

the pre-wildfire period were associated with 34%

increases in asthma admissions. The strongest wildfirerelated PM2.5 associations were for people ages 65–

99 years (10.1% increase per 10 mg/m3 PM2.5, 95% CI

3.0% to 17.8%) and ages 0–4 years (8.3%, 95% CI 2.2%

to 14.9%) followed by ages 20–64 years (4.1%, 95% CI

20.5% to 9.0%). There were no PM2.5–asthma associations in children ages 5–18 years, although their

admission rates significantly increased after the fires. Per

10 mg/m3 wildfire-related PM2.5, acute bronchitis admissions across all ages increased by 9.6% (95% CI 1.8% to

17.9%), chronic obstructive pulmonary disease admissions for ages 20–64 years by 6.9% (95% CI 0.9% to

13.1%), and pneumonia admissions for ages 5–18 years

by 6.4% (95% CI 21.0% to 14.2%). Acute bronchitis and

pneumonia admissions also increased after the fires.

There was limited evidence of a small impact of wildfirerelated PM2.5 on cardiovascular admissions.

Conclusions: Wildfire-related PM2.5 led to increased

respiratory hospital admissions, especially asthma, suggesting that better preventive measures are required to

reduce morbidity among vulnerable populations.

The numbers of wildfires and their duration in the

USA have increased over the past two decades due

to warmer temperatures, earlier snowmelts and

less rainfall, all of which are expected to worsen

because of global warming.1 These phenomena will

likely impact public health. However, although the

adverse effects of urban fine particulate air pollution (PM2.5 or particles with an aerodynamic

diameter of ,2.5 mm) on cardiovascular and

respiratory health have been well documented,2

far fewer studies have evaluated the impacts of

wildfire-generated PM2.5. PM2.5 is the air pollutant

with the greatest increase in concentrations during

fire events,3 followed by particulate matter with an

aerodynamic diameter of ,10 mm (PM10).4 Studies

that have evaluated the impacts of wildfire PM on

hospital admissions, emergency department visits

or clinic visits found associations with respiratory

outcomes.5–11 There is little research on the impact

of wildfire smoke on cardiovascular outcomes; two

studies have found no significant associations.8 9

There have been conflicting reports on wildfire

smoke and total mortality.12 13 Several other studies

have found adverse impacts of wildfire smoke on

respiratory symptoms, medication use and lung

function.10 14–16

We present here the largest study to date

evaluating the relationships of hospital admissions

for cardiorespiratory outcomes to wildfire-associated PM2.5 using data from the catastrophic

wildfires that struck southern California in the

autumn of 2003. We linked PM2.5 concentrations

estimated at the zip code level17 to a populationbased dataset of hospital admissions using spatial

time series analyses of data before, during and after

the fires. Strong, dry winds from inland deserts

fanned flames from nine distinct fires, which

burned nearly three quarters of a million acres

and destroyed approximately 5000 residences and

outbuildings. The wildfires generated large

amounts of dense smoke that covered much of

urban southern California (2003 population of 20.5

million).18 PM2.5 and PM10 concentrations far

exceeded US federal regulatory standards.3 17 The

goal of the present study is to assess the impact of

this large wildfire event on serious morbidity.

METHODS

Hospital admission data

Hospital admission data for children and adults

were obtained from the California State Office of

Statewide Health Planning and Development

(OSHPD). Specifically, we analysed 40 856 hospital admissions from the period before the wildfire

episode (1–20 October), the episode period across

southern California (21–30 October) and the

period following the episode (31 October–15

November), for individuals who lived in affected

counties and were diagnosed with the respiratory

and cardiovascular illnesses listed in table 1. Other

variables from OSHPD included in analyses were

age, sex, race, ethnicity, five-digit zip code and

Original article

Occup Environ Med 2009;66:189–197. doi:10.1136/oem.2008.041376 189
admission date. Patient zip code data from OSHPD were

geocoded to zip code centroids and linked to air monitoring data

and U.S. Census 2000 sociodemographic data. Institutional

Review Board approvals were obtained from the California

State Health and Human Services Agency, Committee for the

Protection of Human Subjects, and from the University of

California, Irvine Office of Research Administration.

Analyses were stratified by age groups: paediatric (0–4 and 5–

19 years), adult (20–64 years) and elderly (65–99 years), except

for chronic obstructive pulmonary disease (COPD, 20–64 and

65–99 years) and cardiovascular outcomes (45–99 years).

Census demographic characteristics were missing for 474

admissions due to unmatched zip codes. We also analysed

associations for asthma by gender because of differences in the

age-dependent prevalence of asthma.

Exposures

We estimated daily PM10 and PM2.5 concentrations at a zip

code level from 1 October through 15 November 2003. These

data are presented in more detail in our previous publication.17

To our knowledge, this was the first study that systematically

examined and estimated daily particle concentrations at such a

fine spatial resolution over a relatively large study domain for

this type of application. Spatially-resolved particle mass data

are superior to using only the nearest available monitoring

station data because they are expected to better represent

personal exposures. We used available air pollution data from

governmental network sites to build prediction models.

Missing gravimetric PM concentrations from every 3rd or

6th day measurements or due to the incapacitation of

monitors by the fires were estimated based on (1) temporal

profiles of continuous hourly PM data at co-located or closely

located sites and (2) light extinction from visibility data,

meteorological conditions and smoke information extracted

from moderate resolution imaging spectroradiometer

(MODIS) satellite images at a 250 m resolution. Moderately

strong prediction equations were developed for gravimetric

PM mass at monitoring stations. Light extinction coefficient

and MODIS satellite smoke data were the most important

predictors of those measurements. Measured PM2.5 was more

accurately predicted in regression models compared with PM10

(R2 0.78 vs 0.65, respectively). Therefore, the present analysis

focuses only on PM2.5.

Spatial interpolations of PM2.5 concentrations were performed using inverse distance weighting, kriging or cokriging

methods for the non-fire periods. Since the fire and smoke

created highly heterogeneous pollution surfaces, typical

inverse distance weighting and kriging were not suitable

during the wildfire period. Therefore, polygons were created

based on satellite images to represent each smoke-covered area

under different smoke densities. PM2.5 concentrations in each

smoke-polygon were assigned separately, using measured or

estimated concentrations from the predictive models (as

described above). For each non-fire and fire day, the spatial

PM2.5 surfaces and zip code boundary map were overlaid and

corresponding PM2.5 concentrations were assigned to each zip

code centroid (fig 1).

Measurements of daily airborne fungal spores (see online

supplement) were carried out in another ongoing study in

Riverside County.19 Pollen concentrations were low and

therefore were not included in the analysis. We assumed

that Riverside ambient fungal data reflected region-wide

trends.

Analysis

Outcomes were the total number of admissions for a diagnostic

group within each zip code on each day of the study period. We

hypothesised that associations between the wildfires and

hospital admission rates would primarily be attributable to an

increase in daily zip code-specific levels of PM2.5 resulting from

the fires. However, it is difficult to separate wildfire-generated

PM from other PM sources in this heavily urbanised region. To

this end, we constructed a wildfire indicator representing prewildfire, wildfire and post-wildfire periods, and tested the

interaction between PM2.5 and this indicator. We considered

product terms to be significant at the p,0.1 level. Because dates

of the wildfires varied throughout southern California, dates for

the wildfire period indicator were defined to be county-specific

based on MODIS satellite images of smoke covering any part of

the county’s urban areas (table 2).

The choice of adjustment covariates was motivated by

biological plausibility that the covariate might confound the

relationship between wildfire-related PM2.5 and hospital admissions or an a priori belief that the variable could affect both

PM2.5 and admissions. Meteorological covariates from the

National Climatic Data Center (http://www.ncdc.noaa.gov/

oa/ncdc.html) included relative humidity, temperature and

surface pressure gradient. So-called Santa Ana winds coming

off the inland desert regions to the east (a large negative

pressure gradient) are a strong determinant of wildfire events.

There are few data on the effects of Santa Ana winds on asthma

or other outcomes, but it is anticipated that hot dry desert

winds associated with this weather pattern bring with them

high concentrations of bioaerosols. Therefore, for asthma

admissions, we also included fungal spores as a covariate.

Deuteromycetes (eg, Alternaria) tend to increase during hot, dry

windy periods.20

In addition, we decided a priori that spatial heterogeneity in

census demographic factors at the aggregate zip code level

(age, gender, race and income distributions) could confound

associations. The distributions of each of these potential

confounders were obtained at the zip code level from the 2000

U.S. Census (percentage of non-Caucasians, percentage of

females, median household income and age distributions).

Income was recoded into discrete variables by quartile. To

control for zip code population age distribution, we first

calculated the percentage of individuals in a zip code younger

than 20 years and older than 65 years. Each zip code was then

classified into one of four age categories by cross-classification

of young (proportion of individuals ,20 years old higher than

the median proportion across all zip codes) and old (proportion of individuals.65 years old higher than the median

proportion across all zip codes).

We also tested various functions of time including weekend

versus weekday, day of the week and a smooth of time. In

order to investigate residual confounding by date, we allowed

for a flexible functional form (via smoothing splines, with

degrees of freedom ranging from 1 to 10) (see online

supplement). Controlling for day-of-week trend or the flexible

time-adjusted models showed the PM2.5 associations were

robust with respect to these adjustments. We also tested

various forms of temperature and relative humidity, including

raw continuous scales, smoothed and categorical forms. Those

models exhibiting the best fit with the fewest assumptions for

functional form included weekend versus weekday, and

temperature and relative humidity categorised into quartiles.

The full set of adjustment covariates included these variables

plus local pressure gradient, fungal spores (for asthma),

Original article

190 Occup Environ Med 2009;66:189–197. doi:10.1136/oem.2008.041376
county, and zip code-level distributions of median household

income, age, gender and race. Effects of covariates on point

estimates of PM2.5 were small.

Generalised estimating equations for Poisson data21 were

used to estimate the marginal association of daily hospital

admission rates with daily PM2.5 levels and presence of the

wildfires. Log-transformed zip code-specific population estimates were used as the offset (denominator) term in all

models. Age-specific population estimates were used as an

offset term in the analysis of age group-specific outcomes. In

order to obtain asymptotically valid inferences, covariate

estimation was carried out using an independence working

correlation structure in combination with empirical variance

estimates clustering on zip code.22 23 We note that the use of

an independence working correlation structure was motivated

by the desire to obtain consistent parameter estimates in the

presence of time-varying covariates.24

Multiple lag models were considered to investigate associations between PM2.5 and hospital admission rates, including a 7-

day polynomial distributed lag,25 and stratified analyses

considering different lag associations. We found the 2-day

moving average of PM2.5 (average of today and yesterday)

provided the best fitting model that adequately captured the

association between PM2.5 and admissions.

RESULTS

PM exposures

During the wildfires, smoke events dramatically increased

local PM concentrations and created highly heterogeneous

pollution surfaces.17 For reference, the US National Ambient

Air Quality Standard for 24 h average PM2.5 is 35 mg/m3. The

highest 24 h concentrations were >240 mg/m3 at two sites in

San Diego County. Table 2 contains county-level descriptive

statistics for PM2.5. As expected, average PM2.5 concentrations

during the wildfire period increased in all counties. Average

PM levels during the period following the fires were observed

to be lower in all counties relative to the period prior to the

fires. This is because of the onshore flow that brought in the

cool and moist clean air from the Pacific Ocean that helped

end the wildfires.

Table 1 Number of hospital admission by diagnostic* and age groups

Diagnosis Total events

Events with U.S.

Census 2000

defined population{

All respiratory{

Ages 0–4 2158 2143

Ages 5–19 1216 1205

Ages 20–64 8480 8314

Ages 65–99 9456 9357

Total 21 310 21 019

Asthma (ICD-9 493), primary

Ages 0–4 606 600

Ages 5–19 739 733

Ages 20–64 1165 1151

Ages 65–99 543 538

Total 3053 3022

Acute bronchitis and bronchiolitis (ICD-9

466)

Ages 0–4 354 353

Ages 5–19 23 23

Ages 20–64 108 106

Ages 65–99 137 136

Total 622 618

Chronic obstructive pulmonary disease

(ICD-9 491, 492 and 496)

Ages 20–64 927 910

Ages 65–99 1973 1950

Total 2900 2860

Pneumonia (ICD-9 480-87)

Ages 0–4 542 537

Ages 5–19 298 293

Ages 20–64 1721 1686

Ages 65–99 3957 3924

Total 6518 6440

Upper respiratory infections (ICD-9 460–65)

Ages 0–4 522 518

Ages 5–19 77 77

Ages 20–64 108 104

Ages 65–99 47 47

Total 754 746

All cardiovascular1

Ages 45–99 27 486 27 170

Ages 65–99 19 380 19 197

Ischaemic heart disease (ICD-9 410–414)

Ages 45–99 10 448 10 319

Ages 65–99 6491 6430

Cardiac dysrhythmias (ICD-9 426, 427)

Ages 45–99 4051 4004

Ages 65–99 3048 3018

Congestive heart failure (ICD-9 402, 428)

Ages 45–99 6202 6144

Ages 65–99 4750 4712

Cerebrovascular disease and stroke

(ICD-9 430–438)

Ages 45–99 5973 5908

Ages 65–99 4465 4422

*Principal cause of admission was coded by version 9 of the International

Classification of Diseases (ICD-9); {population with available covariates for census

population and census distribution of demographic characteristics used in the

multivariate analysis. This excludes subjects aged >100 years (48 (0.23%) respiratory

and 51 (0.18%) cardiovascular admissions) because 2000 census age categories

needed in the analysis stopped at 99 years; {includes all listed specific respiratory

ICD-9 plus 7463 additional admissions for the following ICD-9 codes: 277 (cystic

fibrosis), 490 (bronchitis NOS), 494 (bronchiectasis), 495 (extrinsic allergic alveolitis),

506 and 508 (other acute/subacute respiratory conditions due to fumes/vapours, or

external agents, not separately analysed because n = 44), 786 (symptoms involving

the respiratory system/other chest symptoms). 1includes all listed specific

cardiovascular ICD-9 codes plus 812 additional admissions for ICD-9 codes 440–459

(diseases of the peripheral circulation).

Figure 1 Interpolated PM2.5 concentrations (mg/m3) at zip code

centroids on 27 October 2003.

Original article

Occup Environ Med 2009;66:189–197. doi:10.1136/oem.2008.041376 191
Spatial time series analysis of hospital admissions

PM2.5 associations: interactions with wildfire period

We found that associations of 2-day lagged average of PM2.5

with admissions for most respiratory outcomes were stronger

during as compared with before or after the wildfires in models

including a product term of wildfire period and PM2.5, but the

interaction was p,0.1 primarily for asthma.

Table 3 shows estimates for the relative change in rates for

admissions in relation to a 10 mg/m3 increase in PM2.5. The table

includes results for age and sex (asthma only) subgroups for the

entire monitored period, and for wildfire periods. In product

term models of PM2.5 by wildfire period, PM2.5 during the

wildfire period was associated with combined respiratory

admissions. Asthma admissions across all ages increased by

4.8% (95% CI 2.1% to 7.6%) in relation to PM2.5 during the

wildfire period, but there was no PM2.5 association before or

after the fires. The strongest wildfire-related PM2.5 associations

with asthma admissions were for the elderly, ages 65–99 years

(10.1% increase), and children ages 0–4 years (8.3%), followed

by adults ages 20–64 years (4.1%). There were no PM2.5

associations in school aged children. Among women ages 20–

64 years, the strongest asthma and PM2.5 association was

during the wildfires, but for men those ages it was after the

wildfires. Among women ages 65–99, the strongest PM2.5

association was after the wildfires, but for men those ages it

was during the wildfires. Fungal spores were also significantly

associated with asthma admissions in the adjusted model that

included PM2.5 (see online supplement).

The wildfires led to notably higher particle concentrations, so

that a 10 mg/m3 increase in PM2.5 used for effect estimates in

table 3 represents only a small part of that increase. The overall

population-weighted concentrations of predicted 24 h PM2.5 at

the zip code level were 90 mg/m3 and 75 mg/m3

, under heavy and

light smoke conditions, respectively, in contrast to concentrations of 20 mg/m3 during the non-fire period.17 Therefore, we

rescaled effect estimates to represent the wildfire-related

increases in PM2.5. A 55 mg/m3 increase in PM2.5 during light

smoke and a 70 mg/m3 increase in PM2.5 during heavy smoke

conditions are predicted to lead to an adjusted 26% and 34%

increase in asthma admissions for all ages, respectively.

For combined ages, acute bronchitis admissions increased

more in relation to 10 mg/m3 PM2.5 during the wildfires (9.6%),

but there was no association before or after the fires. In

subgroup analyses, this association was still evident in children

ages 0–4 years and the elderly.

COPD admissions for people ages 20–64 years significantly

increased by 6.8% from 10 mg/m3 PM2.5 during the wildfires, but

there was no association before or after the fires. The COPD

increase with PM2.5 during the fires was smaller for subjects

ages 65–99 years (3.1%).

PM2.5 was also associated with increased overall pneumonia

admissions, both before (4.5%) and during the fires (2.8%). This

was consistent across ages, except children ages 5–19 years

showed an association only during the wildfires. There were no

associations of PM2.5 with admissions for upper respiratory

infections (not shown).

There was a small relative increase in admission rates for

total cardiovascular outcomes in people ages 45–99 years in

relation to PM2.5 during the fires. There were suggestions of a

small increase in admissions for congestive heart failure in

relation to PM2.5 during the wildfires (p,0.1 compared with

the pre-wildfire period), and an even smaller increase in

admissions for ischaemic heart disease, but for both outcomes,

the 95% confidence intervals crossed 1.0. PM2.5 was inversely

associated with cardiac dysrhythmia admissions across all

periods. Admissions for cerebrovascular disease and stroke

were positively associated with PM2.5 (1.9%) across all

periods.

Associations with wildfire period

In this analysis of the wildfire indicator variable, the prewildfire period is the referent time. Models were adjusted for

the same covariates as PM2.5 models, and are shown unadjusted

and adjusted for PM2.5 (table 4). Generally, there was little

change in point estimates adjusting for PM2.5. There were

significantly increased risks for all respiratory hospital admissions after the fires compared with the pre-fire period.

Admissions increased for all ages by 17% (p,0.001), and in

age groups 5–19 years by 37% (p,0.008) and 65–99 years by

15% (p,0.004). Unexpected decreased risks of respiratory

admissions were found during the fires compared with the

pre-fire period in 0–4 year olds and elderly adults.

The period following the fires was associated with a 26%

increase in the rate of asthma admissions for all ages. Asthma

admissions were also increased during the fires among those

aged 5–19 years (25%) and 20–64 years (27%), but associations

for both groups were stronger after the fires (56% and 36%,

respectively).

Increased risk of asthma admissions for the period during the

wildfires was stronger in females ages 5–19 years (49%, p,0.02)

than males (11%, p = 0.5) and in females ages 20–64 years (41%,

p,0.001) than males (27.6%, p = 0.7) (not shown). Increased

risk of asthma admissions for the period after the wildfires was

also stronger in females ages 5–19 years (81%, p,0.01) than

males (39%, p,0.11) and in females ages 20–64 years (47%,

p,0.02) than males (12%, p = 0.7).

Table 2 County-level mean particulate matter (PM2.5) levels,* Southern California, 1 October–15 November 2003

Daily PM2.5 levels (mg/m3)

County

Los Angeles Orange Riverside San Bernardino San Diego Ventura

Before fires

Dates 01/10–23/10 01/10–23/10 01/10–20/10 01/10–20/10 01/10–24/10 01/10–22/10

Concentration (SD) 27.2 (12.4) 23.3 (9.6) 32.7 (14.7) 35.7 (16.6) 18.5 (6.7) 18.4 (8.3)

During fires

Dates 24/10–29/10 24/10–28/10 21/10–29/10 21/10–30/10 25/10–30/10 23/10–30/10

Concentration (SD) 54.1 (21) 64.3 (26.5) 42.1 (25.5) 45.3 (28.7) 76.1 (66.6) 50.1 (50.5)

After fires

Dates 30/10–15/11 29/10–15/11 30/10–15/11 31/10–15/11 31/10–15/11 31/10–15/11

Concentration (SD) 15.9 (5.5) 15.5 (10.2) 16.9 (8.6) 18.4 (8.3) 14.2 (7.2) 12.9 (4.3)

*PM2.5 concentrations are calculated with equal weighting per zip code.

Original article

192 Occup Environ Med 2009;66:189–197. doi:10.1136/oem.2008.041376
Admissions for acute bronchitis and bronchiolitis for combined ages were increased by 48% after the fires. The association

for the post-fire period was seen in both ages 0–4 years (51%)

and ages 20–64 years (137%). Pneumonia admissions for ages 0–

4, 20–64 and 65–99 years were 46%, 30% and 27% higher during

the period after the fires, respectively.

There was a 6.1% increased risk of combined cardiovascular

admissions (p,0.05), and an 11.3% increased risk of congestive

heart failure admissions after the fires (p,0.06). However, risk

of cardiovascular admissions was lower during the fires by 4.4%.

A relative increase in cerebrovascular disease and stroke

admissions during the wildfires may have been attributable to

Table 3 Relative rate of asthma admissions in relation to a 10 mg/m3 increase in 2-day moving average particulate matter (PM2.5)

Hospital admissions

outcome

All periods

RR (95% CI)*

Pre-wildfire period

RR (95% CI)

Wildfire period

RR (95% CI) p Value{

Post-wildfire period

RR (95% CI) p Value

All respiratory

All ages 1.009 (0.999 to 1.018) 1.022 (1.004 to 1.040) 1.028 (1.014 to 1.041) 0.639 0.999 (0.968 to 1.031) 0.198

Ages 0–4 0.994 (0.967 to 1.021) 0.982 (0.921 to 1.046) 1.045 (1.010 to 1.082) 0.103 0.894 (0.807 to 0.991) 0.126

Ages 5–19 1.014 (0.983 to 1.046) 1.026 (0.946 to 1.113) 1.027 (0.984 to 1.076) 0.990 0.958 (0.852 to 1.077) 0.354

Ages 20–64 1.015 (1.002 to 1.029) 1.036 (1.007 to 1.066) 1.024 (1.005 to 1.044) 0.534 1.007 (0.960 to 1.056) 0.315

Ages 65–99 1.009 (0.996 to 1.022) 1.022 (0.994 to 1.050) 1.030 (1.011 to 1.049) 0.649 1.024 (0.976 to 1.074) 0.932

Asthma

All ages

Males and females 1.022 (1.001 to 1.042) 0.998 (0.949 to 1.050) 1.048 (1.021 to 1.076) 0.097 0.986 (0.910 to 1.068) 0.792

Males 1.010 (0.980 to 1.040) 1.021 (0.944 to 1.106) 1.031 (0.990 to 1.073) 0.848 1.063 (0.948 to 1.192) 0.553

Females 1.029 (1.001 to 1.058) 0.979 (0.913 to 1.050) 1.059 (1.022 to 1.097) 0.056 0.928 (0.829 to 1.037) 0.412

Ages 0–4

Males and females 0.996 (0.947 to 1.048) 0.924 (0.824 to 1.035) 1.083 (1.021 to 1.149) 0.017 0.924 (0.767 to 1.113) 0.999

Males 1.018 (0.963 to 1.076) 0.942 (0.815 to 1.089) 1.086 (1.016 to 1.162) 0.101 1.057 (0.839 to 1.332) 0.380

Females 0.937 (0.845 to 1.040) 0.880 (0.706 to 1.099) 1.073 (0.965 to 1.194) 0.116 0.699 (0.515 to 0.949) 0.214

Ages 5–19

Males and females 1.006 (0.966 to 1.048) 1.045 (0.936 to 1.167) 0.999 (0.935 to 1.068) 0.492 0.918 (0.788 to 1.069) 0.198

Males 0.991 (0.935 to 1.051) 1.034 (0.892 to 1.198) 0.969 (0.883 to 1.064) 0.462 0.979 (0.806 to 1.189) 0.671

Females 1.026 (0.964 to 1.092) 1.065 (0.901 to 1.260) 1.033 (0.943 to 1.132) 0.768 0.831 (0.640 to 1.079) 0.136

Ages 20–64

Males and females 1.043 (1.012 to 1.076) 1.037 (0.957 to 1.123) 1.041 (0.995 to 1.090) 0.931 1.000 (0.882 to 1.132) 0.624

Males 1.013 (0.954 to 1.077) 1.159 (0.996 to 1.349) 0.939 (0.837 to 1.053) 0.026 1.275 (1.020 to 1.595) 0.486

Females 1.052 (1.015 to 1.090) 0.995 (0.904 to 1.096) 1.064 (1.014 to 1.116) 0.247 0.908 (0.780 to 1.056) 0.310

Ages 65–99

Males and females 1.027 (0.974 to 1.082) 0.951 (0.849 to 1.064) 1.101 (1.030 to 1.178) 0.032 1.168 (0.967 to 1.412) 0.072

Males 1.046 (0.957 to 1.142) 0.948 (0.804 to 1.116) 1.185 (1.077 to 1.305) 0.029 0.902 (0.629 to 1.294) 0.804

Females 1.018 (0.958 to 1.081) 0.947 (0.813 to 1.102) 1.065 (0.977 to 1.162) 0.195 1.263 (1.024 to 1.557) 0.032

Acute bronchitis and

bronchiolitis

All ages 1.044 (0.990 to 1.102) 1.001 (0.890 to 1.126) 1.096 (1.018 to 1.179) 0.223 1.031 (0.870 to 1.222) 0.779

Ages 0–4 1.017 (0.949 to 1.089) 0.987 (0.847 to 1.149) 1.092 (0.997 to 1.195) 0.276 0.910 (0.700 to 1.183) 0.588

Ages 5–19 No convergence

Ages 20–64 1.039 (0.912 to 1.183) 1.001 (0.792 to 1.266) 1.044 (0.872 to 1.252) 0.778 1.259 (0.921 to 1.722) 0.275

Ages 65–99 1.134 (1.039 to 1.238) 1.073 (0.764 to 1.505) 1.143 (1.032 to 1.265) 0.730 1.190 (0.865 to 1.638) 0.652

Chronic obstructive

pulmonary disease

Ages 20–99 1.018 (0.994 to 1.042) 1.007 (0.958 to 1.058) 1.038 (1.004 to 1.075) 0.320 1.024 (0.943 to 1.112) 0.728

Ages 20–64 1.022 (0.980 to 1.066) 0.995 (0.916 to 1.081) 1.068 (1.009 to 1.131) 0.161 1.015 (0.893 to 1.153) 0.781

Ages 65–99 1.019 (0.992 to 1.048) 1.014 (0.955 to 1.077) 1.031 (0.990 to 1.074) 0.660 1.023 (0.928 to 1.128) 0.878

Pneumonia

All ages 1.009 (0.994 to 1.024) 1.045 (1.012 to 1.078) 1.028 (1.007 to 1.050) 0.420 0.980 (0.927 to 1.035) 0.045

Ages 0–4 0.995 (0.944 to 1.049) 1.048 (0.931 to 1.180) 1.018 (0.948 to 1.092) 0.691 0.823 (0.649 to 1.044) 0.089

Ages 5–19 1.030 (0.966 to 1.098) 1.017 (0.882 to 1.172) 1.064 (0.990 to 1.142) 0.586 1.017 (0.767 to 1.349) 0.998

Ages 20–64 1.008 (0.982 to 1.035) 1.041 (0.982 to 1.104) 1.032 (0.994 to 1.072) 0.823 1.013 (0.913 to 1.124) 0.633

Ages 65–99 1.011 (0.993 to 1.030) 1.050 (1.006 to 1.097) 1.029 (1.002 to 1.057) 0.445 0.985 (0.920 to 1.055) 0.127

All cardiovascular 0.996 (0.989 to 1.003) 0.992 (0.976 to 1.009) 1.008 (0.999 to 1.018) 0.104 0.991 (0.964 to 1.019) 0.955

Ischaemic heart disease 0.991 (0.980 to 1.003) 0.990 (0.963 to 1.017) 1.007 (0.990 to 1.024) 0.313 0.989 (0.950 to 1.030) 0.976

Congestive heart failure 0.989 (0.974 to 1.004) 0.978 (0.942 to 1.015) 1.016 (0.993 to 1.039) 0.096 0.969 (0.914 to 1.027) 0.791

Cardiac dysrhythmia 0.980 (0.962 to 0.998) 0.979 (0.935 to 1.025) 0.989 (0.961 to 1.017) 0.721 0.976 (0.912 to 1.044) 0.934

Cerebrovascular disease

and stroke

1.019 (1.004 to 1.035) 1.015 (0.980 to 1.052) 1.016 (0.997 to 1.036) 0.971 1.044 (0.987 to 1.104) 0.379

*Rate ratio and 95% confidence interval per 10 mg/m3 increase in 2-day moving average PM2.5, adjusted for fungal spore counts (asthma only), race, gender, county, median

income, weekend, relative humidity, temperature, age and pressure gradient. RR6100 is the percentage increase in hospital admissions. Estimates for the three strata are

derived from the product term models, while estimates for the full period are from a model without interaction terms; {the product term p value for the difference with the prefire period.

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Occup Environ Med 2009;66:189–197. doi:10.1136/oem.2008.041376 193
a cross-period effect of PM2.5 (table 3) because this period

association was confounded in the model adjusting for PM2.5.

DISCUSSION

This is the first study to systematically examine and estimate

the impacts on hospital admissions from wildfire-related PM2.5

at such a fine spatial resolution (zip codes) over a large urban

region. During the wildfire period, smoke events dramatically

increased PM2.5 compared to the preceding non-fire period. The

wildfires and associated PM2.5 were significantly associated

with hospital admissions for respiratory illnesses, especially

asthma, but also acute bronchitis and COPD. The impact on

cardiovascular admissions was weaker.

Although product terms between PM2.5 and the wildfire

period indicator were not significant at the p,0.1 level in many

models, we still observed a trend of stronger associations for

PM2.5 with respiratory admissions during the wildfire period.

Some models showed increased admissions in relation to PM2.5

before the wildfires, possibly due to the relatively high

concentration of urban PM seen during this hot period

(table 2). Some models also showed increased admissions in

relation to PM2.5 after the wildfires, despite much lower PM2.5

concentrations. This may have been attributable to notable

increases in respiratory admissions seen then, possibly due to a

delayed impact of wildfire smoke.

Models with the wildfire period indicator support this

possibility and suggest that some effects of wildfires are not

entirely explained by PM2.5 exposures. Results yielded inconsistencies for respiratory and cardiovascular admissions when

comparing product term models for PM2.5 by period to models

using the period indicator alone. There were nominal associations of daily PM2.5 during the wildfires with cardiovascular

admissions, but the period indicator showed associations only

after the wildfires. Non-asthma respiratory admission rates

were also most strongly increased after the wildfires ended

compared with the pre-fire period, while the PM2.5 association

was generally strongest during the wildfires. We also found the

period following the wildfires was significantly associated with

higher overall asthma admission rates. These associations were

stronger among females. Asthma admissions were increased

during the fires as well, but evident only among females ages 5–

19 and ages 20–64. Possible reasons for stronger associations

Table 4 Relative rate of respiratory admissions in relation to wildfire period

Hospital admissions

outcome n*

Pre-wildfire

period

(referent)

Wildfire period RR (95% CI){ Post-wildfire period RR (95% CI)

Unadjusted for PM2.5 Adjusted for PM2.5 Unadjusted for PM2.5 Adjusted for PM2.5

All respiratory

All ages 21 019 1.00 0.961 (0.916 to 1.008) 0.903 (0.850 to 0.960) 1.143 (1.072 to 1.219) 1.173 (1.097 to 1.253)

Ages 0–4 2143 1.00 0.865 (0.757 to 0.989) 0.842 (0.717 to 0.988) 1.152 (0.957 to 1.388) 1.162 (0.954 to 1.415)

Ages 5–19 1205 1.00 1.098 (0.910 to 1.324) 1.087 (0.863 to 1.370) 1.373 (1.089 to 1.732) 1.467 (1.142 to 1.883)

Ages 20–64 8314 1.00 0.991 (0.922 to 1.066) 0.923 (0.843 to 1.012) 1.074 (0.971 to 1.188) 1.104 (0.992 to 1.228)

Ages 65–99 9357 1.00 0.932 (0.867 to 1.003) 0.874 (0.795 to 0.959) 1.147 (1.045 to 1.259) 1.193 (1.084 to 1.313)

Asthma

All ages 3022 1.00 1.088 (0.965 to 1.227) 0.992 (0.856 to 1.149) 1.264 (1.085 to 1.473) 1.336 (1.134 to 1.573)

Ages 0–4 600 1.00 0.806 (0.632 to 1.029) 0.714 (0.515 to 0.990) 1.092 (0.759 to 1.572) 1.133 (0.777 to 1.654)

Ages 5–19 733 1.00 1.254 (0.999 to 1.575) 1.282 (0.958 to 1.716) 1.564 (1.160 to 2.109) 1.629 (1.184 to 2.243)

Ages 20–64 1151 1.00 1.273 (1.067 to 1.518) 1.221 (0.979 to 1.524) 1.362 (1.043 to 1.779) 1.486 (1.111 to 1.987)

Ages 65–99 538 1.00 0.869 (0.657 to 1.151) 0.645 (0.450 to 0.925) 0.924 (0.606 to 1.408) 1.005 (0.650 to 1.552)

Acute bronchitis/

bronchiolitis

All ages 618 1.00 1.143 (0.878 to 1.490) 0.959 (0.696 to 1.321) 1.482 (1.042 to 2.109) 1.580 (1.089 to 2.291)

Ages 0–4 353 1.00 1.128 (0.819 to 1.555) 0.899 (0.607 to 1.333) 1.520 (0.947 to 2.440) 1.547 (0.954 to 2.507)

Ages 5–19 23 1.00

Ages 20–64 106 1.00 1.350 (0.688 to 2.648) 1.320 (0.608 to 2.863) 2.454 (1.068 to 5.640) 2.515 (1.055 to 5.998)

Ages 65–99 136 1.00 1.166 (0.643 to 2.115) 0.934 (0.422 to 2.066) 0.911 (0.428 to 1.942) 0.997 (0.439 to 2.262)

Chronic obstructive

pulmonary disease

Ages 20–99 2860 1.00 0.988 (0.875 to 1.115) 0.913 (0.779 to 1.069) 1.043 (0.885 to 1.228) 1.064 (0.897 to 1.262)

Ages 20–64 910 1.00 0.967 (0.779 to 1.201) 0.873 (0.660 to 1.156) 1.175 (0.862 to 1.601) 1.311 (0.954 to 1.802)

Ages 65–99 1950 1.00 1.002 (0.869 to 1.156) 0.926 (0.767 to 1.117) 0.985 (0.811 to 1.196) 0.981 (0.798 to 1.206)

Pneumonia

All ages 6440 1.00 0.943 (0.868 to 1.025) 0.888 (0.799 to 0.986) 1.294 (1.158 to 1.446) 1.318 (1.174 to 1.479)

Ages 0–4 537 1.00 0.938 (0.705 to 1.247) 0.951 (0.678 to 1.333) 1.458 (0.974 to 2.182) 1.374 (0.885 to 2.133)

Ages 5–19 293 1.00 0.891 (0.604 to 1.312) 0.830 (0.541 to 1.272) 0.960 (0.588 to 1.569) 0.969 (0.578 to 1.624)

Ages 20–64 1686 1.00 0.927 (0.795 to 1.081) 0.837 (0.690 to 1.016) 1.314 (1.064 to 1.622) 1.300 (1.047 to 1.615)

Ages 65–99 3924 1.00 0.959 (0.861 to 1.068) 0.899 (0.782 to 1.033) 1.277 (1.102 to 1.481) 1.331 (1.142 to 1.552)

Unadjusted for PM2.5 Adjusted for PM2.5 Unadjusted for PM2.5 Adjusted for PM2.5

All cardiovascular{ 27 170 1.00 0.958 (0.920 to 0.997) 0.947 (0.902 to 0.994) 1.061 (1.006 to 1.119) 1.053 (0.994 to 1.114)

Ischaemic heart disease 10319 1.00 0.913 (0.852 to 0.978) 0.905 (0.832 to 0.985) 1.029 (0.943 to 1.123) 1.029 (0.936 to 1.131)

Congestive heart failure 6144 1.00 0.891 (0.817 to 0.972) 0.911 (0.819 to 1.014) 1.113 (0.997 to 1.242) 1.105 (0.982 to 1.244)

Cardiac dysrhythmia 4004 1.00 0.968 (0.874 to 1.072) 0.964 (0.851 to 1.093) 1.089 (0.949 to 1.251) 1.057 (0.914 to 1.223)

Cerebrovascular disease

and stroke

5908 1.00 1.066 (0.981 to 1.159) 1.017 (0.922 to 1.123) 1.013 (0.907 to 1.132) 1.013 (0.902 to 1.138)

*Number of hospital admissions for zip codes with defined populations; {adjusted for race, gender, county, median income, weekend, relative humidity, temperature, age and

pressure gradient; {cardiovascular admissions were for subjects ages 45–99 years.

PM2.5, particulate matter.

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194 Occup Environ Med 2009;66:189–197. doi:10.1136/oem.2008.041376
among females include the differential impact of hormones and

the menstrual cycle, airway function and structure, atopy and

perception of symptoms.26

Although there was no association of asthma admissions

with PM2.5 in young people ages 5–19 years, the periods during

and after the wildfires were significantly associated with

increased admissions in this group. We speculate this may be

attributable to unmeasured volatile (non-particulate) toxic air

pollutants, including those associated with the more than 5000

buildings that burned. Alternatively, factors associated with the

fires, such as psychosocial stress, could have led to effects that

were independent of PM2.5.

Associations with the post-wildfire period and wildfirerelated PM2.5 were also found for acute bronchitis and

bronchiolitis, and pneumonia. This is the first report of wildfire

associations with admissions for acute bronchitis and bronchiolitis, and pneumonia.

We also found a significantly increased risk of admissions for

total cardiovascular outcomes and congestive heart failure after

the fires. It is possible that systemic inflammation increases

more strongly in relation to sustained multiday exposures to air

pollutants than with acute single day exposures, as recently

shown in our panel study of subjects with coronary artery

disease.27 Analyses of the London ‘‘killer smog’’ of 1952,28 and

recent analyses of particulate air pollution in Dublin, Ireland,29

suggest that there may be delayed effects for weeks to months.

The post-fire increases in cardiorespiratory admissions may be

attributed to the following:

c 1) People may delay deciding to go to hospital until

symptoms become too severe30;

c 2) Cumulative biological effects of wildfire PM may

culminate in severe symptoms many days after the initial

cardiorespiratory impact. For example, most subjects with

asthma show a progressive clinical and functional deterioration that takes place over hours to weeks31;

c 3) Sustained effects of wildfire PM may lead to susceptibility

to, or increased severity of, later respiratory infections,

possibly through alterations in immune function or respiratory clearance mechanisms.

The strongest evidence for delayed effects in our study was

the post-fire increase in asthma admissions combined with the

association between asthma admission and PM2.5 during the

wildfires. However, given past annual trends (see online

supplement), it is possible that asthma admissions following

the wildfire period would have increased at this time of year

anyway. This also applies to the post-fire increases in

admissions for acute bronchitis and bronchiolitis, and pneumonia. Other limitations are that the period analysis does not have

the temporal resolution of the daily time series analysis of

PM2.5. Therefore, differences in results of these analyses could

result due to imprecision in the estimate for the nonquantitative indicator variable. Furthermore, power may be

limited for specific outcomes subdivided by gender and age,

which would apply to several nominally significant associations

we found.

Our results for respiratory admissions are consistent with

two other studies of the 2003 southern California wildfires

using other less severe outcomes and focusing on particular

regions, including emergency department visits in San Diego

county11 32 and respiratory symptoms in 16 towns in southern

California.16 Kunzli et al16 reported results for school children in

an ongoing cohort study who were potentially affected by the

wildfires. They found parental self-reports of the smell of fire

smoke indoors were associated with reported asthma attacks,

wheezing, cough, bronchitis, colds, upper respiratory symptoms, medication usage and physician visits. Authors also

analysed the impacts of between-community differences in

PM10 using data from our study.17 Changes in PM10 were

associated with upper respiratory symptoms, cough and

unspecified medication use.

Several investigations of wildfires have identified people with

asthma as an especially sensitive subpopulation, using analyses

of emergency department visits in California mountain counties

during wildfires in 1987,6 emergency department visits in eight

Florida hospitals during wildfires in 1998,5 and hospital

admissions during the 1997 Indonesian wildfires.9 A report

from Australia examining smoke from bushfires and asthma

emergency department visits found no association.33

Other time series studies have shown associations of asthma

hospital admissions with urban air pollution.34 However, the

period of observation in our investigation is far shorter than

most time series investigations, and thus statistical power is

lower. Despite this, we found strong associations between

PM2.5 and hospital admissions. We attribute this to the large

increase in wildfire-related PM, and the spatial time series

approach, which likely reduced exposure error compared with

the typical use of widely-dispersed regional PM data.

Nevertheless, we are still limited by aggregate (not personal)

exposure data.

This is the first report of associations of wildfire-related PM2.5

with admissions for acute bronchitis and bronchiolitis, and for

pneumonia. Our results showing increased COPD admissions in

relation to PM2.5 during the wildfires are consistent with a

study of increased COPD hospital admissions during the 1997

Indonesian wildfires,9 increased COPD emergency department

visits during the 1987 wildfires in California mountain

counties,6 and respiratory symptoms in a panel of 21 patients

with COPD associated with a forest fire near Denver, Colorado

in June 2002.35

Total cardiovascular and congestive heart failure admissions

increased only in the period following the wildfires. However,

there was a small relative increase in admission rates for total

cardiovascular outcomes in relation to PM2.5 during the fires.

Cerebrovascular disease and stroke were significantly increased

in relation to PM2.5 across the entire study period. Unexpected

findings were the inverse associations for cardiac dysrhythmias

and PM2.5 across the whole period. While urban particles

generally have been associated with a variety of adverse

cardiovascular outcomes,2 including stroke,36 there is little

research investigating the effects of smoke from wildfires or

wood combustion on circulatory disease.4 Our results can only

be compared to null associations for cardiovascular hospital

admissions during the 1997 Indonesian wildfires.9 Moore et al8

found that, although there was an excess of respiratory

complaints, physician visits for cardiovascular illnesses in

regions of British Columbia, Canada were not associated with

wildfires.

The mechanisms explaining our findings for wildfire smoke

are likely somewhat similar to those found for pollutant

components from fossil fuel combustion. Evidence is mounting

that urban air pollution triggers oxidative stress and inflammation.2 A study of people exposed to forest fire smoke in

Indonesia in 1997 showed increased circulating levels of

interleukin-1b and interleukin-6 during the smoke period.37 An

experimental study of subjects exposed to clean air versus wood

smoke in a chamber showed increased airway inflammatory

responses (exhaled alveolar NO) and evidence of increased

oxidative stress (malonadehyde in breath condensates).38 An in

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Occup Environ Med 2009;66:189–197. doi:10.1136/oem.2008.041376 195
vitro study using mouse alveolar macrophages tested the effects

of size-segregated PM from transported wildfire smoke collected

in Helsinki, Finland.39 Investigators showed that although the

transported particles induced less cytokine production per unit

mass compared with urban particles, they found enhanced

inflammatory and cytotoxic activities per cubic meter of air due

to the increased particulate mass concentration in the accumulation mode size range (0.1–2.5 mm in diameter). This might

explain our finding of a larger asthma association per 10 mg/m3

PM2.5 during the wildfires as compared with the pre-wildfire

period as simply due to the considerably higher concentrations

rather than higher toxicity of wildfire smoke.

It is also possible that unmeasured volatile and semivolatile

organic compound components are important in the effects of

wildfire smoke, but such data are rarely available. In the present

study, these include toxic gases emitted from synthetic

materials in the approximately 5000 residences and outbuildings

that burned.

Conclusions

We conclude the catastrophic wildfires that struck southern

California in October of 2003 led to significantly increased

hospital admissions for respiratory illnesses, especially asthma.

Southern California experienced a second similar wildfire

disaster in October 2007, yielding the two largest wildfire

disasters in California’s history within this recent 4-year period.

A concern is that growing impacts of global warming on

wildfire risk will continue to impact public health in similar

regions across the globe.1

Given there were significant morbidity impacts associated

with wildfire-related PM2.5, we recommend that in addition to

advisories to avoid outdoor activities that increase exposure

during wildfires, preventive measures need to be taken where

possible to reduce exacerbations of asthma. This may include

the early use of anti-inflammatory medications at the first sign

of increasing asthma symptoms. All of the health impacts

identified in this study occurred in the face of numerous

advisories by public health agencies and the media to avoid

outdoor activities and to use air conditioning. Additional

preventive measures in susceptible people including those with

persistent asthma, such as the use of indoor air filters,10 40 should

be considered and then systematically evaluated in future

wildfires.

Acknowledgements: We thank Joe Cassmassi and others at the South Coast Air

Quality Management District for assistance with the meteorological and air pollutant

data.

Funding: This study was funded by the South Coast Air Quality Management District

contract no. 04182, and the National Institutes of Health, National Institute of

Environmental Health Sciences grant no. ES-11615.

Competing interests: None.

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Policy implications

c In addition to advisories to avoid outdoor activities that

increase exposure during wildfires, preventive measures need

to be taken where possible to reduce exacerbations of asthma

c Preventive measures may include advisories for the early use

of anti-inflammatory medications at the first sign of increasing

asthma symptoms.

c The health impacts of wildfires reported here are anticipated to

increase worldwide due to global warming, which has broad

policy implications.

Main messages

c Wildfire-related PM2.5 led to significantly increased asthma,

bronchitis and COPD hospital admissions.

c Sensitive subgroups included young children and the elderly.

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Original article

Occup Environ Med 2009;66:189–197. doi:10.1136/oem.2008.041376 197