Epidemiologic studies have reported associations of outdoor air pollution with daily mortality and morbidity from cardiorespiratory

diseases (Goldberg et al. 2003). Multicity

analyses conducted in the United States,

Canada, and Europe provide further evidence

supporting coherence and plausibility of the

associations (Burnett et al. 2000; Dominici

et al. 2006; Katsouyanni et al. 1997, 2001;

Samet et al. 2000a). Recently, interest has

been focused on the possible modifying effect

of season (Peng et al. 2005; Touloumi et al.

2006; Zeka et al. 2006), preexisting health

status (Bateson and Schwartz 2004; Goldberg

et al. 2001; Katsouyanni et al. 2001), and

population demographic characteristics such

as sex and age (Atkinson et al. 2001; Bateson

and Schwartz 2004; Cakmak et al. 2006;

Katsouyanni et al. 2001) on the relation

between air pollution and daily mortality. It

is also hypothesized that the effects of air

pollution exposure on health are greater in

people with lower socioeconomic status (SES)

(O’Neill et al. 2003). However, prior findings

about the modifying effect of SES remain

inconsistent: some studies found evidence of

modification (Finkelstein et al. 2003; Jerrett

et al. 2004; Krewski et al. 2005; Zeka et al.

2006), but others did not (Bateson and

Schwartz 2004; Cakmak et al. 2006; Samet

et al. 2000b; Zanobetti and Schwartz 2000).

Moreover, most of these studies were conducted in developed countries, and only a

small number of studies have been conducted

in Asia (Health Effects Institute 2004). The

need remains for studies of cities in developing

countries, where characteristics of outdoor air

pollution (e.g., air pollution level and mixture,

transport of pollutants), meteorological conditions, and sociodemographic patterns may differ from those in North America and Europe. 

Better knowledge of these modifying factors will help in public policy making, risk

assessment, and standard setting, especially in

cities of developing countries with fewer

existing studies. In the present study, we conducted a time-series analysis to examine the

modifying effect of season, sex, age, and education on the association between outdoor air

pollutants [particulate matter < 10 μm in

diameter (PM10), sulfur dioxide, nitrogen

dioxide, and ozone] and daily mortality in

Shanghai, China. This study is a part of the

joint Public Health and Air Pollution in Asia

(PAPA) program supported by the Health

Effects Institute (HEI).

Materials and Methods

Data. Shanghai, the most populous city in

China, comprises urban/suburban districts and

counties, with a total area of 6,341 km2 and

had a population of 13.1 million by the end of

2004. Our study area was limited to the traditional nine urban districts of Shanghai

(289 km2). The target population includes all

permanent residents living in the area—

around 6.3 million in 2004. In the target

population, the male/female ratio was 100.9%,

and the elderly (> 65 years of age) accounted

for 11.9% of the total population.

Daily nonaccidental mortality data from

1 January 2001 to 31 December 2004 were

collected from the database of the Shanghai

Municipal Center of Disease Control and

Prevention (SMCDCP). Death certificates are

completed either by community doctors for

deaths at home or by hospital doctors for

deaths in hospitals. The information on the

certificates is then sent to the SMCDCP

through their internal computer network. In

Shanghai, all deaths must be reported to

appropriate authorities before cremation. The

database for 2001 and 2002–2004 was coded

according to the International Classification of

Diseases, Revision 9 [ICD-9; World Health

Organization (WHO) 1978] and Revision 10

Environmental Health Perspectives • VOLUME 116 | NUMBER 9 | September 2008 1183

Research

Address correspondence to H. Kan, Department of

Environmental Health, School of Public Health,

Fudan University, Shanghai 200032, China.

Telephone: 86 21 64046351. Fax: 86 21 64046351.

E-mail: haidongkan@gmail.com

This study was funded by the Health Effects

Institute through grant 4717-RFIQ03-3/04-13. The

research was also supported by the Division of

Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health,

U.S. Department of Health and Human Services.

The views expressed in this article are those of the

authors and do not necessarily reflect the views of

the Health Effects Institute or its sponsors.

The authors declare they have no competing

financial interests.

Received 5 September 2007; accepted 26 June 2008.

Season, Sex, Age, and Education as Modifiers of the Effects 

of Outdoor Air Pollution on Daily Mortality in Shanghai, China: 

The Public Health and Air Pollution in Asia (PAPA) Study

Haidong Kan,1,2 Stephanie J. London,2 Guohai Chen,3 Yunhui Zhang,1 Guixiang Song,4 Naiqing Zhao,5 Lili Jiang,4

and Bingheng Chen1

1Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China; 2Epidemiology Branch, National

Institute of Environmental Health Sciences, National Institutes of Health, U.S. Department of Health and Human Services, Research

Triangle Park, North Carolina, USA; 3Shanghai Environmental Monitoring Center, Shanghai, China; 4Shanghai Municipal Center of Disease

Control and Prevention, Shanghai, China; 5Department of Health Statistics, School of Public Health, Fudan University, Shanghai, China

BACKGROUND: Various factors can modify the health effects of outdoor air pollution. Prior findings

about modifiers are inconsistent, and most of these studies were conducted in developed countries. 

OBJECTIVES: We conducted a time-series analysis to examine the modifying effect of season, sex,

age, and education on the association between outdoor air pollutants [particulate matter < 10 μm

in aerodynamic diameter (PM10), sulfur dioxide, nitrogen dioxide, and ozone] and daily mortality

in Shanghai, China, using 4 years of daily data (2001–2004). 

METHODS: Using a natural spline model to analyze the data, we examined effects of air pollution

for the warm season (April–September) and cool season (October–March) separately. For total

mortality, we examined the association stratified by sex and age. Stratified analysis by educational

attainment was conducted for total, cardiovascular, and respiratory mortality. 

RESULTS: Outdoor air pollution was associated with mortality from all causes and from cardiorespiratory diseases in Shanghai. An increase of 10 μg/m3 in a 2-day average concentration of

PM10, SO2, NO2, and O3 corresponds to increases in all-cause mortality of 0.25% [95% confidence interval (CI), 0.14–0.37), 0.95% (95% CI, 0.62–1.28), 0.97% (95% CI, 0.66–1.27), and

0.31% (95% CI, 0.04–0.58), respectively. The effects of air pollutants were more evident in the

cool season than in the warm season, and females and the elderly were more vulnerable to outdoor

air pollution. Effects of air pollution were generally greater in residents with low educational attainment (illiterate or primary school) compared with those with high educational attainment (middle

school or above). 

CONCLUSIONS: Season, sex, age, and education may modify the health effects of outdoor air pollution in Shanghai. These findings provide new information about the effects of modifiers on the

relationship between daily mortality and air pollution in developing countries and may have implications for local environmental and social policies.

KEY WORDS: air pollution, modifiers, mortality, time-series studies. Environ Health Perspect

116:1183–1188 (2008). doi:10.1289/ehp.10851 available via http://dx.doi.org/ [Online 9 July 2008]
(ICD-10; WHO 1993), respectively. The

mortality data were classified into deaths due

to all nonaccidental causes (ICD-9 codes

< 800; ICD-10 codes A00–R99), cardiovascular diseases (ICD-9 codes 390–459;

ICD-10 codes I00–I99), and respiratory diseases (ICD-9 codes 460–519; ICD-10 codes

J00–J98). The data were also classified by sex

and age (0–4, 5–44, 45–64, and ≥ 65 years)

for all-cause deaths. Educational attainment

has often been used as a surrogate indicator of

SES in time-series studies (Cakmak et al.

2006; Jerrett et al. 2004; Zanobetti and

Schwartz 2000; Zeka et al. 2006). We therefore classified all-cause, cardiovascular, and

respiratory deaths by educational attainment

(low, illiterate or primary school; high, middle

school or above). 

Daily air pollution data, including PM10,

SO2, NO2, and O3, were retrieved from the

database of the Shanghai Environmental

Monitoring Center, the government agency in

charge of collection of air pollution data in

Shanghai. The daily concentrations for each

pollutant were averaged from the available

monitoring results of six fixed-site stations in

the nine urban districts and covered by China

National Quality Control. These stations are

mandated to be located away from major

roads, industrial sources, buildings, or residential sources of emissions from the burning of

coal, waste, or oil; thus, our monitoring results

reflect the background urban air pollution

level in Shanghai rather than local sources

such as traffic or industrial combustion. 

We abstracted the daily 24-hr mean concentrations for PM10, SO2, and NO2, and

maximal 8-hr mean concentrations for O3.

The maximal 8-hr mean was used because the

WHO (2000) recommended that the 8-hr

mean reflects the most health-relevant exposure

to O3. For the calculation of both 24-hr mean

concentrations of PM10, SO2, and NO2, as

well as maximal 8-hr mean O3 concentrations,

at least 75% of the 1-hr values must have been

available on that particular day. 

To allow adjustment for the effect of

weather conditions on mortality, we obtained

daily mean temperature and humidity data

from the Shanghai Meteorological Bureau

database. The weather data were measured at

a single fixed-site station in the Xuhui District

of Shanghai.

All of the mortality, weather, and air pollution data were validated by an independent

auditing team assigned by the HEI. The team

checked a sample of the original death certificates and monitoring records and validated the

generation process of mortality, weather, and

air pollution data used for the time-series

analysis.

Statistical methods. Our statistical analysis

followed the Common Protocol of the PAPA

program. We used a generalized linear model

(GLM) with natural splines (ns) to analyze the

data. First, we built the basic models for various mortality outcomes excluding the air pollution variables. We incorporated the ns

functions of time and weather conditions,

which can accommodate nonlinear and nonmonotonic relationships of mortality with

time and weather variables, offering a flexible

modeling tool (Hastie and Tibshirani 1990).

We used the partial autocorrelation function

(PACF) to guide the selection of degrees of

freedom (df) for time trend (Katsouyanni

et al. 2001; Touloumi et al. 2004, 2006).

Specifically, we used 4–6 df per year for time

trend. When the absolute magnitude of the

PACF plot was < 0.1 for the first two lag days,

the basic model was regarded as adequate; if

this criterion was not met, autoregression terms

for lag up to 7 days were introduced to

improve the model. In this way, 4, 4, and 5 df

per year for time trend, as well as 3, 2, and 4

lag-day autoregression terms, were used in our

basic models for total, cardiovascular, and respiratory mortality, respectively. In addition, we

used 3 df (whole period of study) for temperature and humidity because this has been shown

to control well for their effects on mortality

(Dominici et al. 2006; Samet et al. 2000a).

Day of the week was included as a dummy

variable in the basic models. We examined

residuals of the basic models to determine

whether there were discernable patterns and

autocorrelation by means of residual plots

and PACF plots. After we established the

basic models, we introduced the pollutant

variables and analyzed their effects on mortality outcomes. 

Briefly, we fit the following log-linear

GLM to obtain the estimated pollution logrelative rate β in Shanghai:

logE(Yt) = βZt + DOW + ns(time, df) 

+ ns(temperature/humidity, 3) 

+ intercept, [1]

where E(Yt) represents the expected number

of deaths at day t; β represents the log-relative

rate of mortality associated with a unit

increase of air pollutants; Zt indicates the

pollutant concentrations at day t; DOW is

dummy variable for day of the week;

ns(time,df) is the ns function of calendar time;

and ns(temperature/humidity, 3) is the ns function for temperature and humidity with 3 df.

Current-day temperature and humidity (lag 0)

and 2-day moving average of air pollutant concentrations (lag 01) were used in our analyses. 

We assessed both total nonaccidental and

cause-specific mortality. We were able to stratify by sex and age only for total mortality. We

analyzed effects of air pollution separately for

the warm season (April–September) and the

cool season (October–March) as well as for

the entire year (Peng et al. 2005; Touloumi

et al. 2006). The basic models of seasonal

analyses were different from those of wholeperiod analyses, using various dfs for time

trend. Analyses by educational attainment

were conducted for total, cardiovascular, and

respiratory mortality. We tested the statistical

significance of differences between effect estimates of the strata of a potential effect modifier (e.g., the difference between females and

males) by calculating the 95% confidence

interval (CI) as

, [2]

where Q

^

1and Q

^

2 are the estimates for the two

categories, and SE^

1 and SE^2 are their respective SEs (Zeka et al. 2006). Regardless of significance, we considered modification of

effect by a factor of ≥ 2 to be important and

worthy of attention (Zeka et al. 2006). 

As a sensitivity analysis, we also examined

the impact of model specifications such as lag

structure and df selection on the effects of air

pollutants (Welty and Zeger 2005). We did

not find substantial differences using alternative specifications.

All analyses were conducted in R, version

2.5.1, using the mgcv package (R Development Core Team 2007). The results are presented as the percent change in daily mortality

per 10-μg/m3 increase of air pollutants. 

Results

Data description. From 2001 to 2004 (1,461

days), a total of 173,911 deaths (82,597

females and 91,314 males) were registered in

the study population. The percentages of total

deaths by age group were 0.3% for 0–4 years,

3.2% for 5–44 years, 13.0% for 45–64 years,

and 83.5% for ≥ 65 years. On average, there

were approximately 119 nonaccidental deaths

per day, including 44 from cardiovascular 

diseases and 14 from respiratory diseases

(Table 1). Cardiorespiratory disease accounted

for 49.1% of total nonaccidental deaths. 

During our study period, the mean daily

average concentrations of PM10, SO2,

NO2, and O3 were 102.0, 44.7, 66.6, and

63.4 μg/m3, respectively. There were two

missing value days for O3 and none for the

other three pollutants. The mean daily average

temperature and humidity were 17.7°C and

72.9%, respectively, reflecting the subtropical

climate in Shanghai. 

Generally, PM10, SO2, and NO2 were relatively highly correlated with each other

(Pearson correlation coefficients ranged from

0.64 to 0.73). PM10/SO2/NO2 concentrations were negatively correlated with temperature and humidity. Maximal 8-hr mean O3

was weakly correlated with PM10, SO2, and

NO2 (Pearson correlation coefficients ranged

from 0.01 to 0.19) and moderately correlated

with temperature level (Pearson correlation

coefficient, 0.48). 

ˆ ˆ . Q Q SE SE ˆ ˆ 12 1 2 ( − ) ± + 1 96

Kan et al.

1184 VOLUME 116 | NUMBER 9 | September 2008 • Environmental Health Perspectives
Effects by season. In the whole-period

analyses, outdoor air pollution was associated

with mortality from all causes and from cardiopulmonary diseases in Shanghai (Table 2). An

increase of 10 μg/m3 of 2-day average concentrations of PM10, SO2, NO2, and O3 corresponds to 0.25% (95% CI, 0.14–0.37), 0.95%

(95% CI, 0.62–1.28), 0.97% (95% CI,

0.66–1.27), and 0.31% (95% CI, 0.04–0.58)

increase of all-cause mortality, respectively. 

There were more deaths, higher concentrations of pollutants (except for O3, which

had higher concentrations in the warm season), and drier weather conditions in the cool

season than in the warm season (Table 1). 

The effect estimates of PM10 on total mortality were similar in both seasons. Effect estimates were approximately 2–3 times higher

for SO2 and NO2 in the cool season compared

with the warm season. The effect estimate of

O3 was significant in both cool and warm seasons, and the magnitude of the O3-associated

increase in total mortality was approximately

5-fold higher in the cool season than in the

warm season. Between-season differences in

total mortality were significant for NO2 and

O3 but not for PM10 or SO2 (Table 2). 

For cardiovascular mortality, the effect

estimate of PM10 was similar in both seasons.

For SO2, NO2, and O3, the effect estimate in

the cool season were approximately 3–4 times

higher than in the warm season. Betweenseason differences in cardiovascular mortality

were insignificant for all four pollutants. 

For the smaller category of respiratory

mortality, the effect estimates of PM10, SO2,

and NO2 were significant only in the cool

season, and their between-season differences

were significant. The effect effect estimate of

O3 on respiratory mortality was insignificant

in either season. 

Effects by sex and age. The percent increase

associated with higher concentration levels of

air pollutants varied by sex or age group

(Table 3). The effect estimates of PM10 and

O3 among females were approximately twice

those among males, although their between-sex

differences were insignificant. The effect estimates of SO2 and NO2 on total mortality in

females were slightly higher than in males. 

The number of deaths for residents under

5 years of age was very low and therefore was

excluded from our analysis. We did not

observe significant effects of air pollution in

residents 5–44 years of age or 45–64 years of

age. Among those ≥ 65 years of age, the effect

estimates of all four pollutants were significant, and approximately 2–5 times higher

than among people 5–44 years of age or

45–64 years of age, although the between-age

differences among all three groups were

insignificant. 

Effects by education. Generally, residents

with low educational attainment (illiterate or

primary school) had a higher number of deaths

from air pollution–related effects than those

with high educational attainment (middle

school or above) (Table 4).

For total mortality, the effect estimates of

PM10, SO2, and NO2 were significant in both

education groups. The effect estimates of

these three pollutants were 1–2 times larger

among the low-education group compared

with the high-education group, although the

educational differences were significant only

for NO2 for total mortality. The effect estimate of O3 of total mortality were similar and

insignificant in both groups. 

For cardiovascular mortality, the effect

estimates of PM10 and NO2 were significant

or marginally significant in both education

groups; the effect estimate of SO2 was significant only in the low-education group; no significant effect of O3 was seen in either group.

The effect estimates of all four pollutants were

1–2 times larger among the low-education

group compared with the high-education

group. The educational differences in cardiovascular mortality were not significant for any

pollutants. 

For respiratory mortality, the effect estimates of PM10, SO2, and NO2 were significant only among those with low education,

whereas the effect estimate of O3 on respiratory mortality was not significant in either

group. The effect estimates of PM10, SO2,

and NO2 were several times larger among the

low-education group compared with the

Modifiers of health effects of air pollution

Environmental Health Perspectives • VOLUME 116 | NUMBER 9 | September 2008 1185

Table 1. Daily deaths, air pollutant concentrations, and weather conditions (mean ± SE) in Shanghai,

China, 2001–2004.

Warm season Cool season Entire period

(n = 729) (n = 732) (n = 1,461)

No. of daily deaths

Total (nonaccident) 106.1 ± 0.5 132.0 ± 0.8 119.0 ± 0.6

Cardiovascular 37.9 ± 0.3 50.5 ± 0.4 44.2 ± 0.3

Respiratory 11.4 ± 0.1 17.2 ± 0.3 14.3 ± 0.2

Air pollutant concentration (μg/m3)

a

PM10 87.4 ± 1.8 116.7 ± 2.8 102.0 ± 1.7

SO2 39.4 ± 0.7 50.1 ± 1.0 44.7 ± 0.6

NO2 57.3 ± 0.7 76.0 ± 1.0 66.6 ± 0.7

O3 78.4 ± 1.5 48.3 ± 0.9 63.3 ± 1.0

Meteorological measures

Temperature (°C) 24.3 ± 0.2 11.2 ± 0.3 17.7 ± 0.2

Humidity (%) 75.1 ± 0.4 70.6 ± 0.5 72.9 ± 0.3

aTwenty-four-hour average for PM10, SO2, and NO2; 8-hr (1000–1800 hours) average for O3.

Table 2. Percent increase [mean (95% CI)] of mortality outcomes of Shanghai residents associated with

10-μg/m3 increase in air pollutant concentrations by season, 2001–2004.a

Mortality Pollutant Warm season Cool season Entire period

Total PM10 0.21 (0.09 to 0.33) 0.26 (0.22 to 0.30) 0.25 (0.14 to 0.37)

SO2 0.57 (–0.03 to 1.18) 1.10 (0.66 to 1.53) 0.95 (0.62 to 1.28)

NO2 0.46 (–0.07 to 0.98) 1.24 (0.84 to 1.64)* 0.97 (0.66 to 1.27)

O3 0.22 (0.03 to 0.41) 1.19 (0.56 to 1.83)* 0.31 (0.04 to 0.58)

Cardiovascular PM10 0.22 (–0.14 to 0.58) 0.25 (0.05 to 0.45) 0.27 (0.10 to 0.44)

SO2 0.31 (–0.65 to 1.29) 1.02 (0.40 to 1.65) 0.91 (0.42 to 1.41)

NO2 0.30 (–0.54 to 1.14) 1.26 (0.68 to 1.84) 1.01 (0.55 to 1.47)

O3 0.32 (–0.05 to 0.69) 1.42 (0.51 to 2.33) 0.38 (–0.03 to 0.80)

Respiratory PM10 –0.28 (–0.93 to 0.38) 0.58 (0.25 to 0.92)* 0.27 (–0.01 to 0.56)

SO2 –1.13 (–2.86 to 0.62) 2.47 (1.41 to 3.54)* 1.37 (0.51 to 2.23)

NO2 –1.37 (–2.86 to 0.15) 2.66 (1.67 to 3.65)* 1.22 (0.42 to 2.01)

O3 0.12 (–0.72 to 0.98) 0.94 (–0.60 to 2.50) 0.29 (–0.44 to 1.03)

aWe used current day temperature and humidity (lag 0) and 2-day moving average of air pollutant concentrations (lag 01), and

applied 3 df to temperature and humidity. *Significantly different from the warm season (p < 0.05).

Table 3. Percent increase [mean (95% CI)] in total mortality of Shanghai residents associated with a

10-μg/m3 increase in air pollutant concentrations by sex and age.a

Mean daily Pollutant

deaths (n) PM10 SO2 NO2 O3

Sex 

Female 56.5 0.33 (0.18 to 0.48) 1.06 (0.62 to 1.51) 1.10 (0.69 to 1.51) 0.40 (0.03 to 0.76)

Male 62.5 0.17 (0.03 to 0.32) 0.85 (0.43 to 1.28) 0.88 (0.49 to 1.28) 0.19 (–0.16 to 0.55)

Age (years)

5–44 3.7 0.04 (–0.52 to 0.59) 1.21 (–0.47 to 2.91) 0.52 (–1.01 to 2.08) –0.08 (–1.38 to 1.25)

45–64 15.5 0.17 (–0.11 to 0.45) 0.22 (–0.60 to 1.04) 0.64 (–0.11 to 1.40) 0.47 (–0.19 to 1.12)

≥ 65 99.6 0.26 (0.15 to 0.38) 1.01 (0.65 to 1.36) 1.01 (0.69 to 1.34) 0.32 (0.03 to 0.61)

aWe used current day temperature and humidity (lag 0) and 2-day moving average of air pollutant concentrations (lag 01),

and applied 3 df to temperature and humidity.
high-education group. The educational

differences in respiratory mortality were not

significant for any pollutants.

Discussion

Although the associations between outdoor

air pollution and daily mortality have been

well established in developed countries, the

question of the potential modifiers remains

inconclusive. As the U.S. National Research

Council (1998) pointed out, it is important

to understand the characteristics of individuals who are at increased risk of adverse events

due to outdoor air pollution. Our results suggest that season and individual sociodemographic factors (e.g., sex, age, SES) may

modify the health effects of air pollution in

Shanghai. Specifically, the association

between air pollution and daily mortality was

generally more evident for the cool season

than the warm season; females and the elderly

(≥ 65 years of age) appeared to be more vulnerable to air pollution than males and

younger people; and disadvantaged SES may

intensify the adverse health effects of outdoor

air pollution. 

Our finding of a stronger association

between air pollution and daily mortality in

the cool season is consistent with several prior

studies in Hong Kong (Wong et al. 1999,

2001) and Athens, Greece (Touloumi et al.

1996), but in contrast with others reporting

greater effects in the warm season (Anderson

et al. 1996; Bell et al. 2005; Nawrot et al.

2007). In Shanghai, the concentrations of

PM10, SO2, and NO2 were higher and more

variable in the cool season than in the warm

season (Table 1). Because these three pollutants were highly correlated, greater effects

observed during the cool season may also be

due to other pollutants that were also at higher

levels during that season. In contrast, the O3

level was higher in the warm season than in

the cool season, and our exposure–response

relationship also revealed a flatter slope at

higher concentrations of O3 for both sexes

(data not shown). At higher concentrations,

the risks of death could be reduced because

vulnerable subjects may have died before the

concentration reached the maximum level

(Wong et al. 2001).

Exposure patterns may contribute to our

season-specific observation. During the warm

season, Shanghai residents tend to use air conditioning more frequently because of the relatively higher temperature and humidity, thus

reducing their exposure. For example, in a survey of 1,106 families in Shanghai, 32.7% of

the families never turn on air conditioners in

the winter compared with 3.7% in the summer

(Long et al. 2007). Heavy rain in the warm

season may reduce time outdoors, thus reducing personal exposure. In contrast, the cool

season in Shanghai is drier and less variable, so

people are more likely to go outdoors and

open the windows. Nevertheless, the fact that

a consistently significant health effect of air

pollution was observed only in the cool season

in two subtropical Asian cities [Shanghai (present study) and Hong Kong (Wong et al.

1999, 2001)] suggests that the interaction of

air pollution exposure and season may vary by

location.

Unlike the gaseous pollutants, the constituents of the complex mix of PM10 may vary

by season. Therefore, another potential explanation for the seasonal difference in the effects

of PM10 is that the most toxic particles may

have a cool-season maximum in Shanghai. 

We found a greater effect of ambient air

pollution on total mortality in females than in

males. Results of prior studies on sex-specific

acute effects of outdoor air pollution were discordant. For example, Ito and Thurston

(1996) found the highest risk of mortality

related with air pollution exposure among

black women. Hong et al. (2002) found that

elderly women were most susceptible to the

adverse effects of PM10 on the risk of acute

mortality from stroke. However, Cakmak

et al. (2006) found that sex did not modify

the hospitalization risk of cardiac diseases due

to air pollution exposure. 

The reasons for our sex-specific observations are unclear and deserve further investigation. In Shanghai, females have a much lower

smoking rate than males (0.6% in females vs.

50.6% in males) (Xu 2005). One study suggested that effects of air pollution may be

stronger in nonsmokers than in smokers

(Künzli et al. 2005). Oxidative and inflammatory effects of smoking may dominate to such

an extent that the additional exposure to air

pollutants may not further enhance effects

along the same pathways in males. In addition, females have slightly greater airway reactivity than males, as well as smaller airways

(Yunginger et al. 1992); therefore, dose–

response relations might be detected more easily in females than in males. Deposition of

particles in the lung varies by sex, with greater

lung deposition fractions of 1-μM particles in

all regions for females (Kim and Hu 1998;

Kohlhaufl et al. 1999). Sunyer et al. (2000)

suggested that differing particulate deposition

patterns between females and males may

partly explain the difference between the sexes.

Moreover, compared with males, females in

Shanghai had a lower education level (73.9%

in females vs. 41.0% in males); thus, lower

SES might contribute to the observed larger

effects of air pollution in females. 

As in a few other studies (Gouveia and

Fletcher 2000; Katsouyanni et al. 2001), we

found the elderly were most vulnerable to the

effects of air pollution. Low numbers of deaths

in the 0- to 4-year age group limited our

power to detect the effects of air pollution on

mortality, even if they exist. Two groups, the

elderly and the very young, are presumed to be

at greater risk for air pollution–related effects

(Gouveia and Fletcher 2000; Schwartz 2004).

For the elderly, preexisting respiratory or

cardiovascular conditions are more prevalent

than in younger age groups; thus, there is

some overlap between potentially susceptible

groups of older adults and people with heart

or lung diseases. 

It has long been known that SES can

affect health indicators such as mortality

(Mackenbach et al. 1997). Recently, studies

have started to examine the role of SES in

the vulnerability of subpopulations to outdoor air pollution, especially for particles

and O3, although the results remain inconsistent (O’Neill et al. 2003). For example,

Zeka et al. (2006) found that individuallevel education was inversely related to the

risk of mortality associated with PM10.

Another cohort study with small-area measures of SES in Hamilton, Ontario, Canada,

found important modification of the particle

effects by social class (Finkelstein et al. 2003;

Kan et al.

1186 VOLUME 116 | NUMBER 9 | September 2008 • Environmental Health Perspectives

Table 4. Percent increase in number of deaths due to total, cardiovascular, and respiratory causes associated with a 10-μg/m3 increase in air pollutants by educational attainment.a

Educational Mean daily Pollutant

Mortality attainment deaths (n) PM10 SO2 NO2 O3

Total Low 67.3 0.33 (0.19 to 0.47) 1.19 (0.77 to 1.61) 1.27* (0.89 to 1.66) 0.26 (–0.09 to 0.60)

High 42.1 0.18 (0.01 to 0.36) 0.66 (0.16 to 1.17) 0.62 (0.15 to 1.09) 0.30 (–0.11 to 0.71)

Cardiovascular Low 27.8 0.30 (0.10 to 0.51) 1.08 (0.47 to 1.69) 1.15 (0.58 to 1.72) 0.39 (–0.13 to 0.90)

High 16.4 0.23 (–0.03 to 0.50) 0.57 (–0.20 to 1.35) 0.73 (0.01 to 1.45) 0.26 (–0.38 to 0.91)

Respiratory Low 8.9 0.36 (0.00 to 0.72) 1.54 (0.43 to 2.66) 1.59 (0.57 to 2.62) 0.20 (–0.74 to 1.16)

High 5.4 0.02 (–0.43 to 0.47) 0.73 (–0.61 to 2.09) 0.34 (–0.89 to 1.60) 0.27 (–0.86 to 1.41)

aWe used current day temperature and humidity (lag 0) and 2-day moving average of air pollutants concentrations (lag 01) and we applied 3 df to temperature and humidity. *Significantly

different from high educational attainment (p < 0.05).
Jerrett et al. 2004). In contrast, Gouveia and

Fletcher (2000) observed a larger effect of air

pollution in areas of higher SES level; Bateson

and Schwartz (2004) found no indication

that susceptibility to air pollution varied by

group-level SES measures. In the present

study, using individual-level education as a

measure of SES, we found that residents with

low educational attainment were more sensitive to air pollution exposure than those with

high educational attainment. Our results provide the first evidence in Mainland China

that lower SES may compose a risk factor for

air pollution–related health effects. 

SES factors such as educational attainment

may modify the health effects of outdoor air

pollution in several pathways. People with

lower SES may be more sensitive to air pollution–related health hazards because they have a

higher prevalence of preexisting diseases that

confer a greater risk of dying associated with air

pollution exposure, and they may also receive

inferior medical treatment for preexisting diseases. Disadvantaged living conditions may

contribute to the modification effect; people

with lower SES may have more limited access

to fish, fresh fruits, and vegetables, resulting in

reduced intake of antioxidant polyunsaturated

fatty acids and vitamins that may protect

against adverse consequences of particle exposure (Romieu et al. 2005). Additionally, exposure patterns may contribute to effect

modification by SES. Persons with lower SES

are less likely to have air conditioning (Long

et al. 2007) and more likely to live near busy

roadways and have coexposures due to either

poor housing or occupation. For example, disadvantaged groups have been found to be

more highly exposed to some air pollutants

(Sexton et al. 1993). Scandinavian studies have

shown differential personal exposures to particles and other pollutants by education and

occupation (Rotko et al. 2000, 2001), and a

study in the U.S. Great Lakes region indicates

differences in exposure to gaseous pollutants by

occupation and education, minority status, and

income (Pellizzari et al. 1999). Finally, as

Jerrett et al. (2004) pointed out, persons with

lower education are less mobile and experience

less exposure measurement error, thereby

reducing bias toward the null.

The limitations of our analysis should be

noted. As in other studies in this field, we used

available outdoor monitoring data to represent

the population exposure to air pollutants. Our

assessment of weather conditions was derived

entirely from one monitoring station.

Measurement error may have substantial implications for interpreting epidemiologic studies

on air pollution, particularly for the time-series

design (Zeger et al. 2000). It is possible that

this type of error may introduce bias to the

results of our analysis; however, because of lack

of available information on personal exposure

to air pollutants, we could not quantify such a

bias. Compared with other studies in Europe

and North America, the data we collected were

limited in being only one city, in sample size,

and in duration. In addition, high correlation

between particulate matter and gaseous pollutants in Shanghai limited our ability to separate

the independent effect for each pollutant. 

In summary, in this time-series analysis,

we found that outdoor air pollution was associated with mortality from all causes and from

cardiopulmonary diseases in Shanghai during

2001–2004. Furthermore, our results suggest

that season and sociodemographic factors

(e.g., sex, age, SES) may modify the acute

health effects of air pollution. These findings

provide new information about the effects of

modifiers on the relationship between daily

mortality and air pollution in developing

countries and may have implications for local

environmental and social policies.

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