Articles



# Air 

# Pollution and 

# Daily Hospital 

# Admissions in 

# Metropolitan Los Angeles

William S. 

### Linn,12 Yaga 

### Szlachcic,1'3 HenryGong, Jr.,123 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, NewYork, NewYork, USA

We used

### daily time-series analysis to evaluate associations 

### between ambient cabon monoxide, nitrogen 

### dioxide, particulate matter



### . 10 

### pm in

### aerodynamicdiameter 

### (PM0), or ozone concentrations, andhospitaladmissions for

### cardiopulmonaryilinesses inmetropolitanLos

### Anglesduring 1992-1995. We

### performedPoisson

### regsions fortheentirepatientpopulationandforsubgroups defined

### byseason, region, or

### perso'nal characteristics, 

### allowing for 

### effects of

### temporal variation, 

### weather, andautocorrelation. CO showedthe most 

### significant 

### (p 0.05) reationships
to 

### cardiovascular admissions. Awintertime 

### 25th-75thpercentile increase in CO (1.1-2.2 ppm) predicted an 

### increase of 4% in 

### cardiovascular admissions. 

### NO2, and, to a lesser extent, 

## PMIO tracked.CO andshowedsimilarassociationswith

### cardiovacular die, but 03wasnegativelyor nonsignificantly associated. No significant demographic 

### differences were found, 

### although increased 

### cardiovascular effects were suggested in 

### diabetics, in

### whites and 

### blacks (relative to Hispanics andAsians), and in

### persons older 

### than 65 

### years of

### age. Pulmonary

### disease admissions 

### associated more 

### with NO2 

### and 

### PM10 tan with CO. 

### Pulmonary efiects were

### generallysmaler than cardiovascular

### effet and were more sensitive to the choice of

### modd. We conclude that in LosAngeles, 

### atmospheric stagnationwith

### highprimary 

## (COINO2jPM1Q) pollution, most common in

### antumn/winter, increase the

### riskof

### hospitalizationfir

### cardiopulmonaryillness. Summer 

### photochemicalpollution 

### (highO3) 

### apparentlypresents 

### less risk 

### KIywords air

### pollutants, carbon monoxide, epidemiology, Los Angeles, morbidity, nitrogen dioxide, ozone, 

### particulate matter. EnvironHealtb

### Perpect108:427434 (2000). 

### [Online 27

### March

### 2000] 

### hip.x//ehpnetl.niehs.

### nih.ov/docs/2000/1

### 08p427431innlabstrt btmI



### Time-series 

### analyses of daily 

### mortality or 

### morbidityhave 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 

### ofscientificand

### regulatory attention 

### (1,2,4-6). However, recent 

### studies in a 

### number 

### ofNorth American 

### cities 

### also associate 

### cardiovascular and/or 

### pulmonary disease 

### incidencewith

### pollutant gases such as 

### carbon 

### monoxide, nitrogen 

### dioxide, and/or

ozone 

### (7-14. Where 

### theyare not

### highlycor

### related, gas and 

### particulate pollutants appear to 

### have separate 

### statistically and 

### medically significant influences on 

### cardiopulmonary morbidity 

### (9,11,14M. 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 

### (a 14 

### million); detailed monitoring 

### of air 

### quality and

### hospital 

### admissions; mild

### climate, which should limit 

### confoundingof

### pollutioneffects 

### 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 met

### ropolitan 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 

### (03), or

### particulate mattermore 

### 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 ana

### lyzed 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, 03, and 

### particulate matter < 10 

### pm in

### aerodynamic diameter 

## (PM0O). Methods

Data 

### acquisition andmanagement. After its 

### institutional review 

### board verified con

### fidentiality 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 ofDiseases 

### (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); 

### andabdominal-anegative control category

### thought to 

### beunrelatedto

### pollution 

### (APR-DRG 

### 146-207). More-specific princi

### pal 

### diagnoses, thought likelyto associatewith air 

### pollution on 

### the 

### basis of previous epi

### demiologic or 

### toxicologic evidence, were 

### also 

### analyzed: congestive heart 

### failure (CHF) (APR-DRG 127); 

### myocardial infarction (APR-DRG 1 1

### 1, 11

### 5, 

### 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 

### ofage 

### (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 thankJ. Solomon forprogramming and dataquality
assurance; C. Linn for consultation on hospital admissiondata interpretation; S. Terrell forcomputer artwork; and KI Clark, V. Valdez, and M. Avila for administrative assistance. We thank M.P. Jones, M. Lipsett, andJ. Schwartz for advice on datahandling andstatisticalanalysis. Major support for this project wasprovidedby the American HeartAssociation, Western StatesAffiliate (grant 1134-GIl). 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.

Environmental Health Perspectives * VOLUME 108 NUMBER 5I May2000 427Articles

. Linn et al.



### exceptions 

### noted in 

### "Results") andprescheduled

### 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

## PMIO from six 

### SCAQMD stations 

### with continuous monitors; 

### hourlyCO, 

### 03, NO2, temperature, 

### and relative

### humidity from those stations 

### plus 

### others; 

### 24-hr-average 

## PMIO measured every 

### sixth day

### by

### high-volume samplers at or near 

### eachcontinuous

## PMIO station; 

### andbarometric pressure 

### and rainfall at 

### the Los 

### Angeles InternationalAirport. Figure 1 

### shows monitor 

### locations. Analyses 

### related 

### daily 

### admission counts 

### with 24-hr averages 

### of environmental variables. For 

### 03, maximum 

### hourly concentrations were 

### also 

### analyzed; 

### they correlated 

### highly with 24-hr averages in 

### all seasons 

### (r 2 0.79) 

### and showed 

### similar 

### relationships to 

### daily

### morbidity ("Results"). We 

### did not ana

### lyze relative

### humiditybecausemany

### datawere 

### missing or out 

### of range. 

### Stations 

### differed in 

### their continuous 

## PM1O monitoring techniques 

### and their 

### relationships of continuous to

### high-volume 

### samplerdata. 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 

### PM0o 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) bymountains, 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 

### byaveraging across all monitoring stations 

### within it. 

### Missing air 

### monitoring data 

### (4.4% for

### PM1O, smaller

### percentages forother 

### variables) were 

### replaced using

### analysis ofvariance 

### with maximum 

### likelihood estimation of missing 

### values. For 

### PM10, data from all 

### days in

### the same season

### andall stations in

### thebasin

were 

### used in 

### estimation; for other 

### variables, 

### onlystations in

### the same regionwere

### used. Statistical 

### analyses. We 

### used BMDP software (SPSS Inc., 

### Chicago) and SAS software (SAS Institute, Inc., Cary, 

### NC) for 

### statistical 

### analyses. Descriptive statistics 

### and 

### correlationpatternswere 

### 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 andsecular trends. Predicted 

### values were 

### from regressions 

### with 

### indicator variables for the 

### day of the week

PM1, + gas +tsmperaturemonitor M Temperature + gasmonitor

* Gasmonitor

A WeatherIrain, barometricpressure) monitor 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 airquality regions analyzed (seetext). and for

### weekdayholidays, with 

### longer term variation 

### modeled 

### by fitting cubic 

### splines to successive 

### 28-day intervals ofdata. We 

### then 

### compared weather and 

### pollution statistics 

### between 

### days with

### high admissions 

### (residual

> 

### 85th

### percentile) and low

### admissions 

### (residual < 

### 15th

### percentile), aswell as the immedi

### atelypreceding

### days. Time-series 

### analytical approaches induded 

### a) ordinary least 

### squares 

### (OLS) 

### regression with 

### admission count and 

### atmospheric data 

### filtered 

### by the 

### Shumway 19-day 

### weighted movingaverage

### procedure 

### (13), withorwithout 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 auto

### correlation, adapted from the 

### analytical strat

### egy of the 

### Partide 

### Epidemiology Evaluation 

### Project 

### (16) with

### modifications. In

### principle, 

### daily counts are Poisson 

### distributed and 

### require 

### approach 

### c; however, given the 

### gen

### erally large counts with 

### filtering or smooth

### ing, 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 theprimary

### 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 

### studyperiod), cold

### days (minimumtemperature < 

### 15thpercentile), and rain

### days 

### (> 0.01 inches at the Los 

### Angeles International 

### Airport); continuous 

### atmospheric 

### variables 

### (one or more 

### pollutant concentrations, 

### barometricpressure, andmean 

### 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 ofwinter 

### infectious disease 

### outbreaks varied 

### fromyear to year. 

### Thus, cubic 

### splines were 

### determined at 

### 28-day intervals when 

### smoothing 

### pulmonary disease counts, andat 

### 4-month

### intervals otherwise. 

### Results Seasonalair

### qualityand

### hospitaladmissions. Table 1 presents seasonal 

### pollution, weather, and

### hospital admissionstatistics 

### for the entire 

### basin for 1992-1995. Overall means ± SDs

VOLUME 1081 NUMBER 5 May2000 * Environmental Health Perspectives 428

### Articles

. 

### Daily 

### pollution and morbidity in Los 

### Angeles

were 1.5 ± 0.8 ppm 

### for CO, 3.4 ± 1.3 parts per 

### hundred million 

### (pphm) for NO2, 

### 45 ± 18 

## pg/m3 for

## PM1O, and 2.4 + 1.2 

### pphm for 03. We 

### determined basinwide means 

### from the six 

### regional values 

### by 

### weighting each region 

### according to its proportion 

### of car

### diovascular 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 

### ofdailyadmissions were 

### 428 ± 

### 76 for

### cardiovascular, 207 ± 

### 54 for 

### pulmonary, 

### 74 ± 

### 14 

### for

### cerebrovascular, and 244 ± 39 

### forabdominal 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 cerebrovascu

### lar, and 67% 

### forabdominal 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 PM

### O.0 3 was

### positivelycorrelatedwith all three other 

### pollutants only in 

### the spring, 

### and most 

### strongly with PM

## O0 03 showed a 

### weaker positive 

### relationship to 

## PM1O in 

### the

summer 

### and a negative 

### relationship to CO 

### and NO2 in 

### the winter. 

### Higher mean tem

### peratures were 

### associated with 

### higher 

### pollutant 

### levels in 

### all seasons, 

### with 

### the exception 

### of CO in 

### the autumn. Barometric pressure 

### showedvarying 

### relationships with

### pollutants. Expressing the data as 

### residuals from cubic

### spline smoothing 

### brought about no 

### marked 

### changes in 

### these 

### correlations, except 

### that in autumn 

### the positive 

### relationship between 

### 03 and temperature 

### became 

### nonsignificant. Regional measurements 

### and basinwide averages 

### correlated 

### stronglyfor every

### pollutant in every season 

### (r > 

### 0.7), except 

### for 

## PM1O 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 ofthis 

### generallysimilarbehavior 

### ofairquality 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, 

## PM1O, and 

### 03. The mean

### andvariance 

### ofCO 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 

### inlandregion 6, 

### but seasonal variation was 

### less 

### for

## PMIO than 

### for CO. 

### 03 was 

### highest in 

### the spring 

### and summer, particularlyin inlandregions 3 and 6. 

### Contrast ofatmospheric 

### 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 unusu

### ally

### high

### andunusuallylow

### 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 

### significantlyelevated 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

## PMIO tended to accompany

### elevated

Table 1. Aira and hospital admission statistics for the entire South CoastAirBasin, byseason. Variable (units) Season
CO Winter (ppm) Spring

Summer Autumn NO2 Winter (pphm) Spring

Summer Autumn 

### PM10 Winter 

### (pg/mi3) Spring

Summer Autumn 03 Winter (pphm) Spring

Summer Autumn Temperature Winter (mean, 'C) Spring

Summer Autumn Rain Winter 

### (% of

### days) Spring

Summer Autumn Cardiovascular Winter adm/day Spring

Summer Autumn Pulmonary Winter adm/day Spring

Summer Autumn Cerebrovascular Winter adm/day Spring

Summer Autumn Mean ± SD
1.7± 0.8 1.0 ± 0.3 1.2 ± 0.4 2.1 ± 0.8 3.4 ± 1.3 2.8 ± 0.9 3.4 ± 1.0 4.1 ±1.4 37 ± 19 42 ± 12 49 ± 10 54 ± 22
1.4 ± 0.7 3.2 ± 1.0 3.3 ± 0.8 1.5 ± 0.9 14.8 ± 2.7 19.2 ± 3.0 23.9 ± 2.3 16.4 ± 3.7 28 4 1 10 450 ± 77 428 ± 76 406 ± 70 428 ± 76 241 ± 54 196 ± 34 172 ± 27 220 ± 63
77 14 74 14 72 14 75 14 Min
0.5 0.4 0.3 0.6 1.1 1.1 0.7 1.6
5 14 14 15 0.2 0.9 0.4 0.1 8.4 9.5 19.0 8.7

300 277 239 273
117 118 107 115
44 43 41 45 Max
5.3 2.2 2.7 4.3 9.1 6.1 6.7 8.4
115 83 78 132 4.4 7.0 6.3 4.7 23.2 29.2 31.2 26.7

607 586 559 610
574 329 256 595 126 116 114 117

Abbreviations: adm, admissions; max, maximum; min, minimum; pphm, parts perhundred million. "Pollutant concentrations are averaged across six regions, each weighted according to its proportion of cardiovascular plus pulmonary admissions. primarypollutants on 

### days withhigh cardio

### vascular orpulmonaryadmissions; 

## PMIO also

was 

### associated with high abdominal admissions in the spring. 03 was increased (along 

### with the other 

### pollutants) on days 

### with high 

### pulmonary admissions in spring 

### and summer, 

### the seasons 

### ofthe

### highestmean 03 concentrations 

### (Table 1). By contrast, 03 was 

### decreased on 

### days with 

### high cardiovascular admissions in 

### the winter, 

### when 03 was gen

### erally low and 

### negatively correlated 

### with the otherpollutants. Table 4 

### shows mean 

### weather 

### and 

### pollution 

### conditions on 

### days 

### ofhigh and low car

### diovascular 

### 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 

## PM1O, and 

### significantly lower 

### probabiliy ofrain, 

### than low-admission days. In

### region 6, 

### these 

### tendencieswere 

### less 

### obvious, but CO 

### and NO2 were 

### significantly elevated

on 

### the 

### days 

### precedinghigh-admission days. In 

### similar 

### analyses of 

### pulmonary diseases (not 

### tabulated), we 

### found only a 

### few 

### significant associations 

### with 

### high admissions: 

### high 

### same-day

## PMIO in region 4 in 

### the winter,

Table 2. Pairwise correlation coefficients (r) for atmospheric factors expressed as basinwide 24- hraverages,a byseason.*

Factor Season
CO Winter Spring Summer Autumn NO2 Winter Spring Summer Autumn PM10 Winter Spring Summer Autumn 03 Winter Spring Summer Autumn 

### Tmean Winter Spring Summer Autumn 

## NO2 0.89 0.92 

### 0.94 0.84 

## PM10 0.78 0.54 0.72 0.58 0.88 0.67 

### 0.80 0.80 

### 03 -0.43 0.29 0.03 -0.36 

### -0.23 0.35 0.11 -0.00 -0.01 

### 0.63 0.40 0.28 

### Tmean0.22 0.38 

### 0.51 -0.08 0.38 0.53 

### 0.51 0.28 0.37 0.64 

### 0.44 0.40 0.33 

### 0.57 0.18 0.62 Bpa 

### 0.43 0.15 

### -0.17 0.38 

### 0.38 0.03 -0.21 0.12 0.39 

### -0.18 -0.30 -0.03
-0.11 -0.23 

### -0.05 -0.42 0.13 

### -0.34 -0.20 -0.39

Abbreviations: BP, barometric pressure; T, temperature. "BP 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.

Environmental Health Perspectives * VOLUME 108 NUMBER 5 May 2000 429Articles * Linn et al.



### high previous-day CO 

### and 

### low

### previous-day 

### 03 in 

### region 6 in 

### thewinter, 

### and

### high previ

### ous-day NO2 and 

### 03 in 

### region 6 in 

### the

summer. 

### Analyses ofadmission counts in 

### broad disease 

### categories. 

### Table 5 

### presents the 

### results from 

### single-pollutant autoregressive Poisson 

### models, indudingall ofthe time 

### andweather 

### predictors mentioned in 

### "Methods," relating 

### daily average concentrations 

### with 

### same-day 

### hospital admission counts over 

### the entire 

### basin for each broad disease 

### category. 

### Admissions ofpatients under 30 years 

### ofage
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. 

## PMIOshowed a

### similarpattern 

### of 

### relationships, but was 

### nonsignificant in 

### the summer as 

### well as 

### the 

### spring. The 

### cardiovascular 

### disease/primarypollution rela

### tionship was not

### very

### sensitive to 

### incusion or 

### exclusion ofweather or 

### other 

### pollutant vari

### ables in 

### the 

### models. In 

### the winter, 

### the 

### interquartile range 

### of CO concentrations

was 1.1 to 2.2 

### ppm, and the 

### corresponding



### 51:1
, ...

E-

.a

' C.)

120_

.: :: CL 100

eLs0 [

E

C2. CL

m 0

winter Spring Summer Autumn Figure 2. Air quality in six regions (see Figure 1), 1992-1995. Bar = seasonal mean; flag = seasonal SD. 

### predicted increase in 

### cardiovascular 

### admissions was 

### 4.2%, which 

### represented approxi

### mately 20 

### extra 

### admissions/day. Pulmonary-disease admissions were 

### significantly related to 

### NO2 or 

## PMIO 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 signifi

### cantly related 

### only to 

### NO2, and only in 

### the 

### year-roundanalysis. 03 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-03 conditions, 

### by expressing exposure in terms 

### of

### dailymaxi

### ma or in exceedances 

### ofanassumed 

### threshold,

or

### byrestricting 

### the analysis to the 

### three

### high03 

### inland regions, 

### still 

### showed no 

### significant positive associations. In 

### Poisson 

### models excluding mean 

### temperature and 

### barometric pressure, 

### daily mean 0 showed 

### significant positive associations wit

### 1 pulmonarydiseases in 

### the spring

### andyear-round. 

### Table 6 

### illustrates the sensitivity 

### of results to 

### the choice 

### ofregression procedure 

### andmodel 

### for the cardiovascular

### disease/CO relationship in 

### thewinter 

### and the

### pulmonary 

### disease/03 relationship in 

### the spring. Across
a 

### broad range of models with and without 

### weather 

### and other pollutants as 

### predictors,

Table 3 

### Atmospheric variables showing significant (p <0.05)

### differences between days with high and low hospital admissions inthe entire basin, byseason." Disease category Season CO NO2 PM10 03 Thigh T,w BP Rain
Cardiovascular Winter + + +

- + +

Spring + + + b Summer + Autumn + + +

,,.!,8,:. -,; a

. . -. . ' t.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~b.... Cerebrovascular Winter b

Spring + + b + Autumn b + +

.p.r.

............ ............ ;,.

............. Abbroviatons: BP, baromeric pressure; T, temperature. 'High- and low-admission days (> 85th and < 15th percentiles, respectively) are determined by residuals from regressions accounting fortemporal effects (seetext). Asignificant increase inatmospheric variables onhigh-admissiondays relative to low-admission days land/or the immediately preceding days) is indicated by+; a significant decrease by-. the change in atmospheric measurement from the preceding daywas significantly more positive onhigh- than lowadmission days, alfthough values measured on high and lowdays were not significantlydifferent 'Forpulmonarydiseases inwinter, high-admission days' increases inNO2,

### PM,,, and hightemperature approached significance (p <0.10). %orabdominaldiseases inwinter, high-admission days' increases inCO approached significance (p<0.10).

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 (pg/m3) Low 27.6 38.0 39.1 84.6

High 42.8* 36.9 48.5 83.5 03 (pphm) Low 1.4 2.8 1.9 4.4

High 1.4 2.9 1.8 4.5 Thigh (0C) Low 17.9 25.6 19.2 33.7

High 20.1* 25.6 19.6 33.9 

### T1ow(OC) 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(% ofdays) Low 38 0 26 0



### High 12* 0 22 2

'SeeTable 3 fordefinition ofhigh- and low-admission days. bMeasured atthe LosAngeles International Airport, closerto 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 dayswas significant (p < 0.05).

VOLUME 108 NUMBER 5 May2000 * 

### Environmental Health 

### Perspectives 

### B[,

430

### Articles * 

### Daily pollution and morbidity in Los 

### Angeles

the estimated winter CO 

### effect was 

### always 

### significant and was 

### reasonably consistent in 

### size. None 

### ofthe otherpollutants' 

### effectswas 

### significant when 

### included in 

### a 

### model with CO. The spring 0 

### effect on pulmonary 

### admissions was significant when 03 was the only atmospheric 

### factor in 

### the model, 

### predictinga 1.5% increase in

### admissions fora 1-pphmincrease in

### dailymean 03 concentration. 

### However, the 

### 03 

### 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 

### fordifferent pollutants 

### (19). Nevertheless, it seems 

### dear that in 

### the winter, CO was the analyzed atmos

### pheric 

### factor that was most closely linked with excess cardiovascular morbidity. In the 

### spring, 

### 03 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

### factorforcardiovasculardisease, we 

### rean

### alyzed cardiovascular admissions 

### separately fordiabetics 

### (all 

### ofthosewith 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 ± 

### SEwas 

### 0.039 ± 

### 0.006 

### fordiabetics as 

### compared to 

### 0.031 ± 

### 0.004 

### for others. Year-round 

### analyses of NO2 

### and 

## PMIO effects showed similar modest 

### slope increases 

### for 

### diabetics, as 

### did 

### single-season analyses. None 

### of the 

### slope 

### differences between diabetics andotherswas 

### statistically

### significant. Analyses ofcardiovasculardisease

### admission counts 

### byage, 

### sex, and

### ethniity. Table 7 presents results 

### from 

### single-pollutant autoregressive Poisson

### models 

### applied to car

### diovascular admission counts in 

### three 

### age strata 

### (30-64, 65-74, and 

### 2 75 years 

### of

### age) separately for men 

### and women. 

### Results 

### for 

### 03 (not

### tabulated) were never

### statisticallysignificant. CO 

### effects were 

### near-significant for

women 

### 30-64years 

### ofageand

### sig-nificant in 

### all other age-sexgroups in

### year-roundanalyses 

### 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. 

## PMIO effects were 

### also 

### significant year-round and/or in one season 

### for all 

### groups except men > 

### 75 

### years ofage. Table 8

### presents theresults 

### from

### single-pollutant 

### autoregressive Poisson

### models 

### applied to 

### cardiovascularadmission counts 

### for

### adults > 30 

### years of

### age in 

### four ethnic 

### categories-white (non-Hispanic), 

### black, Hispanic, and other. 

### The 

### other category 

### indudes 

### people ofAsian

### Pacific ancestry 

### (the large majority), Native 

### Americans, 

### and others not 

### dassifiable in the first three groups. 03 effects (not tabulated)

wereneversignificant. 

### Regressioncoefficients, 

### though not 

### significantly different, suggested 

### meaningful ethnic 

### differences in expo

### sure-response 

### relationships. CO, NO2, 

### and 

## PM0Oeffectswere

### significant inwhites inyear

### round, 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

### significantyear

### round 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 

### nonsignificantregression

### slopes. Analyses ofadumission countsfor more 

### specifi diagnoses. 

### Table 9 presents 

### results in 

### adults > 

### 30 years of 

### age 

### from single

### pollutant 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 03 in 

### the summer 

### only; and with CO, NO2, and

# PMIO year-round and in 

### at 

### least two 

### single-season analyses. Asthma, COPD, 

### and CHF were

Table 5. Poisson regression coefficients (SEs): hospital admissions in broad disease categories in the entire metropolitan area, versus same-daypollution levels.@ 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) 

### (pg/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) 0 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) 'Regression analyses used 24-hr average measurements of pollutants and same-dayadmission counts forpatients > 30 years of age throughout the South CoastAir Basin. Example interpretation: the coefficient 0.038 relating winter cardiovascularadmissions to CO indicates thatadmissions increase bya factorof

### aem., i.e., by3.9%, with a 1-ppm increase in CO concentration, afterallowing forthe effects oftime andweather on admission rates. *Significant in expected direction, p< 0.05. **Significant inthe 'wrong' direction, p< 0.05.

Table 6. Sensitivityofkeyresultsto choice ofregressionprocedure andmodel.
Procedure (additional predictors) Slope (SE) Relative risk8 CardiovascularadmissionsversusCO, winter Poissonautoregressive (time) 0.044 (0.005)* 1.050 Poissonautoregressive (time, weather) 0.038 (0.006)* 1.043 Poissonautoregressive (time, weather, 03) 0.036 (0.007)* 1.040 Poissonautoregressive (all above +PM10) 0.033(0.011)* 1.037 Poissonautoregressive (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 Pulmonaryadmissionsversus 03, spring Poissonautoregressive (time) 0.012 

### (0.004)* 1.015 Poissonautoregressive (time, weather) 0.003 (0.005) 1.003 Poissonautoregressive (time, weather, CO) 0.002 (0.005) 1.002 Poissonautoregressive (all above +PM10) 0.008 (0.006) 1.009 Poissonautoregressive (all above +NO2) 0.008 (0.006) 1.009 PDLb(time, weather) 0.003 

### (0.011) 1.004 OLSautoregressive (time, weather) 1.58 (1.79) 1.010 

### 'Predicted relative risk at the 75th percentile concentration of pollutant, versus the 25th percentile. hCubic polynomial distributed lagmodel, lag 0 slopegiven, lags 1-3slopesnonsignificant

### *Signficant inexpecteddirection, p< 0.05.

Environmental Health

### Perspectives * VOLUME 1081 NUMBER 5 May2000 

### 431

### Articles * Linn et al.



### significantly associated with 

### CO and 

### NO2 admissions, CO, and 

## PMIO in 

### southern 

### year-round and in 

### one or more single-season coastal region 4 

### and eastern 

### inland region 6. 

### analyses. Myocardial infarction was 

### associat- The two regions showed 

### reasonably 

### similar edwith 

### CO andNO2, 

### and arrhythmias with 

### daily admission counts 

### and similar positive 

### CO, in

### year-round analyses 

### only. 

### correlations of 

### daily CO 

### and 

## PMIO levels, We 

### also 

### analyzed asthma 

### admissions for but markedly different concentration ranges 

### patients 0-29 

### years 

### of age. In 

### year-round (Figure 2). By OLS regression 

### allowing 

### for analyses, slopes ± 

### SEs 

### were 0.036 ± 

### autocorrelation, a wintertime 

### 1-ppm rise in 

### 0.016/ppm CO, 0.024 ± 

### 0.008/pphm NO2, CO 

### predicted a 

### 9-pg/m3 rise in PM10 in 

### and 0.0011 ± 

### 0.0006/pg/i3 PM 

### -all sig- region 

### 4, but a 

### 25 

### pg/m3 rise in 

### region 

### 6. In 

### nificant (p < 

### 0.05) and 

### appreciably larger 

### single-pollutant autoregressive Poisson mod

### than the 

### slopes in 

### adults > 

### 30 years of age. els, region 4 

### showed 

### highly 

### significant rela0 

### effects were 

### nonsignificant. Most 

### of the 

### tionships between 

## PMIO and 

### admissions, 

# almitted patients in 

### this youngest group year-round and in 

### thewinter, 

### despite its 

### low

were 

### children: themean agewas 7. 

### PM10. In 

### region 6, despite its 

### high 

### PM1O, Relationships of

### cardiovascular disease regression slopes were 

### significantly lower 

### admissions to CO or

### PM10 in the two 

### most than in 

### region 

### 4, and were not 

### significantly contrasting 

### regions. Table 10 

### presents different from zero. 

### Admissions showed a 

### comparative statistics 

### for cardiovascular more 

### plausible relationship with CO across

Table 7. Poisson regression coefficients (SEs): cardiovascular disease admissions inthe entire metropolitan area versus same-daypollution levels, byage and sex."
Sex, age inyears Year-round Significant (p < 0.05) positive [countib 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)* Winter0.021 (0.008)



### PM10 0.0005 (0.0003) Winter 0.0012 (0.0005)
"rhese results are for 1992-1994 only. See footnote to Table 5 for explanation of regression procedure and coefficients. bAnnual mean dailyadmission count ± SD, forpatients
. 30years of age. 

### C03 results nottabulated; 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-daypollution levels, byethnic category.8 Category Year-round Significant (p< 0.05) positive [countib Pollutantc coefficient coefficients for separate seasons White CO 0.034 (0.005)* Winter 0.038 (0.008), autumn 0 .036 (0.008) [290 ± 531 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 (0.0091* None [63 ± 131 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 

### 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 

### indude exposure 

### misdassification, 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 ofparticulate pollutants and covarying gases (e.g., CO 

### and NO2). Small panel studies have suggested that personal particulate exposures 

### track background concentrations 

### dosely in 

### healthy olderadults andchildren inThe

### Netherlands (20,21), but not in older adults with COPD in Los Angeles (22). To our knowledge, no longitudinal studies ofpersonal CO exposure have beenreported. Response misclassification can result 

### from errors indiagnosis or inmedical 

### 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 

### ofdisease/pollution relationships, but should not introduce bias unless coding inaccuracies covarywithpollution. 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 havehad onlylimited 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.

VOLUME 108 NUMBER 5 May 2000 * Environmental Health Perspectives ,These results are for 1992-1994 only. See footnote to Table 5 for explanation ofregression procedure and coefficients. bAnnual meandaily admission count ± SD, for

### patients

. 30years of age. 

### CO3 results nottabulated; nonewas significantly positive. *Year-roundrelationshipsignificant, p < 0.05.



### 432

### Articles * 

### Daily pollution 

### and morbidity in 

### Los 

### Angeles

Limitations 

### of our 

### primary analytical to 

### indusionor

### exclusion of

### weatherandother 

### model 

### indude the use 

### ofonly one 

### pollutant pollutant variables. 

### Ifpollutants not in 

### the at a time 

### and possibly incomplete account- 

### model affected admissions, the likely result ing 

### for weather and temporal influences. would be to overestimate effects ofthe 

### modBecause 

### estimated CO effectswere 

### similar in 

### eled

### pollutant and 

### underestimate total effects various 

### single- and 

### multipollutant models of

### pollution (12). Thus, effects we associated 

### that accounted for seasonal 

### and weekly with CO 

### might be at least 

### partly due to 

### cycles, more 

### complete modeling ofweather covarying

### gases (e.g., oxides ofnitrogen) or to
or 

### temporal effects should not 

### change the particulate substances. 

### Similarly, 

### incomplete 

### conclusions concerning CO. 

### By contrast, accounting 

### for lagged effects would 

### likely estimates 

### of 03 

### effects were 

### highly sensitive 

### result in 

### overestimated effects ofvery recent

Table 9. Poisson regression coefficients (SEs): hospital admissions inmore specific disease categories in the entire metropolitan area, versus same-daypollution levels.a
Disease [countib Myocardial infarction [47

± 11]

Congestive heartfailure [49

± 11]

Cardiacarrhythmia [50 ± 10]

Occlusive stroke [45

± 101

Asthmac [38
± 91

COPD [89 + 19] Pollutant

CO NO2 PM10

03 CO NO2 PM10

03 CO NO2 PM10

03 CO

NO2 PM10

03 CO NO2 PM10

03 CO NO2 PM10 

### 03 Year-round

coefficient
0.040 (0.009)* 0.011 (0.005)* 0.0006 (0.0003) -0.007 (0.007) 0.025 (0.009)* 0.010 (0.005)* 0.0004 (0.0003) -0.001 (0.007) 0.023 (0.009)* 0.006(0.005) 0.0002 (0.0003) -0.001 (0.007)
0.044 (0.009)*

0.020 (0.005)* 0.0013 (0.0003)* 0.007 (0.007) 0.028 (0.010)* 0.014 (0.005)* 0.0003 (0.0004) -0.001 (0.008) 0.019 (0.007)* 0.008 (0.004)* 0.0003 (0.0002) -0.007 (0.005) Significant (p <0.05) positive

coefficients forseparate seasons
None None None None Summer 0.074 (0.038) Winter 0.019 (0.009) None None
None None None None Winter0.036 (0.017), summer 0.091 (0.039), autumn 0.032 (0.015) Winter0.027 (0.010), autumn 0.021 (.008) Winter 0.0024 (0.0006), autumn 0.0012 (0.0005) Summer 0.025 (0.012) Winter 0.045 (0.017), autumn 0.039 (0.016) Winter0.028 (0.010), autumn 0.019 (0.008) None None Winter0.035 (0.012), autumn 0.029 (0.011) Autumn 0.016 (0.006) None None j 'See footnote toTable 5forexplanation ofregression procedure and coefficients. bAnnual meandailyadmission count ± SD for patients 2 30 years of age. 0See 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.

Statistic
CVadmission count interquartile range

CO interquartile range (ppm)



### PM10 interquartile range 

### (pg/m3)

Correlation, COvs. 

### PM10

Correlation, COvs. PM10 residualsa

Regressionslope, CVvs. Cob

Regressionslope, CVvs. pM

## 1b Season
All Winter All Winter
All Winter All Winter All Winter All Winter All Winter Region 4

(Long Beach)
48-67 52-71 0.93-2.40 1.33-3.17 28-45 20-43 0.56* 0.70* 0.63* 0.72* 0.022 (0.005)* 0.027 (0.008)* 0.0012 (0.0003)* 0.0018 (0.0006)* Region 6

(Riverside)
43-57 44-59 0.81-1.58 0.74-1.70 44-86 24-61 0.55* 0.75* 0.69* 0.76* 0.012 (0.008) 0.017 (0.015) 0.0001 (0.0002) 0.0003 (0.0004) exposure, 

### but underestimated 

### cumulative



### effectsofrecent

### andearlier 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

## PMIO and/or NO2 are 

### associatedwith

### meaningful increases in 

### cardiovascular illnesses. We 

### found only

a 

### few 

### equivocally positive 

### relationships between 

### cardiopulmonary morbidity and 03, in 

### situations when other 

### pollutants and heat stress 

### increased along 

### with 0 

### This is surprising, in 

### light of severe 

### 03 potlution in Los

### Angeles, obvious acute respiratory

### effects of

## 03 in

### animal andhuman exposure 

### studies (2), 

### and recent 

### observations of 03-related hospital admissions in Toronto, Canada, 

### where 03 

### levels are 

### lower than in Los 

### Angeles 

### (12). 03 

### has been linked to 

### mortality in 

### Los 

### Angeles (13), although 

## PMIO 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 

## PMIO but not with 03. 

### The tendency 

### of 03 concentrations to 

### decrease indoors, 

### where most 

### people spend most 

### oftheir time 

### (27), 

### might

attenuate 

### morbidity/03 relationships, but would not 

### likely do so in Los 

### Angeles more than in 

### Toronto. In any event, our 

### results
suggest

### thatthe excess

### riskof

### hospitalization in Los 

### Angeles is greater on 

### high-primary-pollution 

### days than on 

### high-0 days. The greatest 

### risk of

### pollution-related hospital admissions
apparently occurs on

### autumn/winter

### dayswith weak SantaAna

### weather 

### conditions, when air incursion 

### fromthedesert approximately counterbalances that 

### from the ocean, 

### resulting in 

### maximal 

### atmosphericstagnation. We 

### could not 

### distinguish clearly among CO-, NO2- 

### and 

### particle-associated effects. CO showed 

### the strongest 

### statistical relation

### ships with most 

### indices of

### morbidity even in the 

### regions and seasons with the 

### highest and 

### widest ranging

### PM1O. NO2 

### trackedCO 

### dose

### ly 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

## PMIO were 

### inherently more toxic 

### (19). Too lirtle is known about the rela

### tionships between the ambient 

### background and

### personal exposures to 

### judgewhich

### pollutants are most 

### subject to exposure 

### misdassification. 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

Environmental Health Perspectives * VOLUME 1081 NUMBER 5 May 2000 'Observed values minus values predicted byday of the week and cubic-spline smooth of longer termtrends. 1From single-pollutantautoregressive Poissonmodels including temporal andweather effects. *Significant, p < 0.05.



### 433

### Articles * Linn et 

### al.

component 

### of

## PM1O, 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 

### ofmetabolically 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 

### lowrange 

### of 

### monitored 

### background CO 

### does not neces

### sarily

### ruleout an

### effect ofCO on

### cardiovascular

### morbidity. The 

### observed association 

### of all 

### pulmonary 

### diseases 

### with 

## PMIO or NO2 more 

### than CO, 

### and 

### of all 

### cardiovascular diseases with CO more 

### than

## PM1O (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. Aprevious 

### findingof

### increased 

### plasma viscosity 

### during a primary 

### pollution 

### episode in 

### Germany 

### (2,9 also supports 

### that 

### hypothesis. By our 

### data, we cannot test Seaton et al.'s 

### (28) 

### attribution ofthe 

### inflammatory

### effect to

### ultrafineparticles. We 

### found 

### possibly meaningful demo

### graphic differences in 

### morbidity/pollution relationships, although none 

### ofthem

### attained statistical 

### significance. Persons



### . 65 years 

### of age 

### and

### diabetics showed somewhat increased cardiovascular disease 

### effects as 

### compared to 

### otherswithout 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 ethnicminorities 

### (30), whites usually 

### showed the 

### largestpollution-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, 03 exposures in the 

### basin

### appear

### higher forwhites than forblacks 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

### highproportions 

### of minority 

### residents. Thus, smaller effects in

some minorities 

### (if real) 

### probably are not 

### explained

### byless 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 con

### trol 

### of 0, 

### which in any case 

### depends on 

### control 

### ofprimarypollutants.

REFERENCES AND 

### NOTES

1. Lipfert FW. Air Pollution and Community Health. New York:Van Nostrand Reinhold, 1994;347-413, 516-536. 2. Committee of the Environmental and Occupational Health Assembly of the American Thoracic Society. State of the art: health effects of outdoor air pollution, part 1. AmJ Respir Crit Care Med 153:3-50 (1996). 3. Committee of the Environmental and Occupational Health Assembly of the American Thoracic Society. State of the art: health effects of outdoor air pollution, part 11. AmJ RespirCrit Care Med 153:477-498 (1996). 4. Vedal S. Ambient particles and health: lines that divide. J AirWaste ManagAssoc 47:551-581 (1997). 5. DockeryDW, Pope CA. Acute respiratoryeffects ofparticulate airpollution. Annu RevPublic Health 15:107-132 (1994). 6. Pope CA Ill, Bates DV, Raizenne ME. Health effects of particulate air pollution: time for reassessment? Environ Health Perspect 103:472-480 (1995). 7. Morris RD, Naumova EN, Munasinghe RL. Ambient air pollution and hospitalization for congestive heart failure
among elderly people in seven large US cities. Am J Public Health 85:1361-1365 (1995). 8. Burnett RT, Dales RE, Brook JR, Raizenne ME, Krewski D. Association between ambient carbon monoxide levels and hospitalizations for congestive heartfailure inthe elderlyin 10 Canadian cities. Epidemiology 8:162-167 (1997). 9. Schwartz J. Air pollution and hospital admissions for cardiovascular disease in Tucson. Epidemiology 8:371-377 (1997). 10. Morris RD, Naumova EN. Carbon monoxide and hospital admissions for congestive heart failure: evidence of an increased effect at low temperatures. Environ Health Perspect 106:649-653 (1998). 11. Schwartz J. Airpollution and hospital admissions for heart disease ineightUS counties. Epidemiology 10:17-22(1999). 12. Burnett RT, Smith-Doiron M, Stieb D, Cakmak S, Brook JR. Effects of particulate and gaseous air pollution on cardiorespiratory hospitalizations. Arch Environ Health 54:130-139 (1999). 13. Kinney PL, Ozkaynak H. Associations of daily mortality and air pollution in Los Angeles County. Environ Res 54:99-120 (1991). 14. Pope CA, Schwartz J. Time series forthe analysis of pulmonary health data. Am J Respir Crit Care Med 154:S229-S233 (1996). 15. Gold DR, DamokoshAl, Pope CA, Dockery DW, McDonnell WF, Serrano P, Retama A Castillejos M. Particulate and

ozone effects on the respiratory function of children in southwest Mexico City. Epidemiology 10:6-16 11999). 16. SametJM, Zeger SL, Berhane K. The association ofmortality and particulate air pollution. In: Particulate Air Pollution and Daily Mortality: Replication and Validation of Selected Studies. Cambridge, MA:Health Effects Institute, 1995;1-104. 17. Thurston GD, KinneyPLAirpollution epidemiology: considerations intime-seriesmodeling. InhalToxicol 7:71-83 (1995). 18. California Air Resources Board. Ten-Year Summary of California Air Quality Data, 1963-1972. Sacramento, CA:State of California Resources Agency, 1974. 19. Lipfert FW, Wyzga RE. Air pollution and mortality: the implications of uncertainties in regression modeling and exposure measurement. J Air Waste Manag Assoc 47:517-523 (1997). 20. Janssen NAH, Hoek G, Brunekreef B, Harssema H, Mensink I, Zuidhof A. Personal sampling of PM10 in adults: relation between personal, indoor, and outdoor concentrations. AmJ Epidemiol 147:537-547 (1998). 21. Janssen NAH, Hoek G, Harssema H, Brunekreef B. Personal exposure to fine particles in children correlates closely with ambient fine particles. Arch Environ Health 54:95-101 (1999). 22. Linn WS, Gong H, Clark KW, Anderson KR. Day-to-day particulate exposures and health changes in Los Angeles area residents with severe lung disease. J Air Waste ManagAssoc 49:PM108-PM115 (1999). 23. Hsia DC, Ahern CA, Ritchie BP, Moscoe LM, Krushat WM. Medicare reimbursement accuracy under the prospective payment system, 1985 to 1988. J Am Med Assoc 268:896-899 (1992). 24. Fisher ES, WhaleyFS, KrushatWM, Malenka DJ, Fleming C, Baron JA, Hsia DC. The accuracy of Medicare's hospital claims data. AmJ Public Health 82:243-248 (1992). 25. Kinney PL, Ito K, Thurston GD. A sensitivity analysis of mortality/PM10 associations in LosAngeles. Inhal Toxicol 7:59-69 (1995). 26. Nauenberg E, Basu K. Effectof insurance coverage onthe relationship between asthma hospitalizations and exposure to airpollution. Public Health Rep 114:135-148 (1999). 27. Hayes SR. Use of an indoor air quality model (IAQM) to estimate indoor ozone levels. J AirWaste Manag Assoc 41:161-170 (1991). 28. Seaton A, MacNee W, Donaldson K, Godden D. Particulate air pollution and acute health effects. Lancet 345:176-178 (1995). 29. Peters A, Doring A, Wichmann HE, Koenig W. Increased plasma viscosityduring an air pollution episode: a linkto mortality? Lancet 349:1582-1587 (1997). 30. Sexton K, Gong H, Bailar JC, Ford JG, Gold DR, Lambert WE, Utell MJ. Air pollution health risks: do class and

race matter? Toxicol Ind Health 9:843-878 (1993). 31. Korc ME. A socioeconomic assessment of human exposure to ozone in the South Coast Air Basin of California. J AirWaste Manag Assoc 46:547-557 (1996).

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