Environmental Health Perspectives • VOLUME 110 | NUMBER 1 | January 2002 67

A Tale of Two Cities: Effects of Air Pollution on Hospital Admissions in Hong

Kong and London Compared

Chit-Ming Wong,1 Richard W Atkinson,2 H. Ross Anderson,2 Anthony Johnson Hedley,1 Stefan Ma,1

Patsy Yuen-Kwan Chau,1 and Tai-Hing Lam1

1Department of Community Medicine, The University of Hong Kong, Hong Kong, China; 2Department of Public Health Sciences, 

St. George’s Hospital Medical School, London, United Kingdom

There is now considerable evidence that

daily hospital admissions for cardiorespiratory diseases are linked to levels of particulate and gaseous ambient air pollution on

the same or previous days (1–3). This is

consistent with even more substantial evidence concerning daily mortality. In the

formulation of public health policy it has

been assumed that these associations have a

causal basis, but at the scientific level there

remain important questions concerning

residual confounding, the effects of individual pollutants or mixtures, and other factors

that may modify health effects. Further evidence on these issues will have an important bearing on conclusions about the cause

and mechanisms of the health effects of air

pollution.

Because populations are exposed to mixtures rather than to individual pollutants,

multicity studies have the potential to create

added insights into some of these issues.

Those that have been established so far, using

the approaches of APHEA (Air Pollution and

Health: a European Approach) Phase I (4)

and Phase II (5) and NMMAPS (National

Mortality and Morbidity Air Pollution

Study) (6,7), are confined to the temperate

climatic zones. Hong Kong is a large city in

a subtropical region where there is evidence

of adverse effects of air pollution (8,9).

London, United Kingdom, is a city of similar size for which adverse health effects of

air pollution have also been reported

(10,11). On one hand, there are similarities

between the two cities in terms of their

main sources and levels of pollutants and

patterns of the respiratory and cardiac diseases. On the other hand, there are differences between the two cities in terms of a

number of factors that might influence confounding or effect modification; these

include demography, climate, housing,

lifestyle, patterns of disease, the health care

system, and seasonal cycles of both weather

and pollution variables. 

We have conducted parallel analyses of

the short-term associations between air pollution and daily hospital admissions in

Hong Kong and London to compare and

contrast the health effects of air pollution in

the two cities. This comparison has relevance

to the understanding of the short-term

health effects of air pollution, their consistency, and the factors that may modify their

effects. 

Methods

Daily emergency hospital admissions for

respiratory and cardiac diseases were

obtained from routine hospital information

systems for Hong Kong (1995–1997) and

London (1992–1994). The data included in

this study are from patients admitted to

hospitals immediately either through the

accident and emergency departments, general outpatient departments, or directly to

the inpatient wards on the grounds of

urgency. The series that we chose for comparison were those selected by the APHEA2 collaboration; these included asthma

[International Classification of Diseases,

Revision 9 (ICD-9) code 493] (12) for ages

15–64 years, respiratory disease (ICD-9

460–519) for ages 65 and over, cardiac diseases (ICD-9 396–429) for all ages, and

ischemic heart disease (IHD; ICD-9

410–414) for all ages. 

Daily average 24-hr concentrations of

PM10 (particles with median aerodynamic

diameter < 10 μm), nitrogen dioxide, and

sulfur dioxide and average 8-hr concentrations of ozone were collected from background monitoring stations in each city.

Only stations able to provide data for 75%

or more days during the study periods were

used. A daily concentration was accepted as

valid if more than 17/24 or 5/8 (in the case

of O3) hourly measurements were made.

When data were available from more than

one monitoring station, we used a simple

filling-in procedure to improve data completeness. Missing values were replaced with

the mean of values from those stations with

available data. The pollutant measures from

Address correspondence to A.J. Hedley, Department

of Community Medicine, The University of Hong

Kong, Patrick Manson Building South Wing, 7

Sassoon Road, Pokfulam, Hong Kong, China.

Telephone: (852) 2819-9282. Fax: (852) 2855-9528.

E-mail: commed@hkucc.hku.hk

We thank the Hong Kong Environmental

Protection Department for data on air pollutant

concentrations and the Hong Kong Hospital

Authority for information on hospital admissions;

we also thank T.Q. Thach for statistical analysis

and M. Chi for clerical support.

This project was sponsored by the British Council,

the Research Grants Council of Hong Kong, and L.

Shiu. The Royal Society Kan Tong Po Visiting

Professorship provided support to H.R. Anderson. 

Received 15 December 2000; accepted 15 June

2001.

Articles

The causal interpretation of reported associations between daily air pollution and daily admissions requires consideration of residual confounding, correlation between pollutants, and effect

modification. If results obtained in Hong Kong and London—which differ in climate, lifestyle,

and many other respects—were similar, a causal association would be supported. We used identical statistical methods for the analysis in each city. Associations between daily admissions and

pollutant levels were estimated using Poisson regression. Nonparametric smoothing methods

were used to model seasonality and the nonlinear dependence of admissions on temperature,

humidity, and influenza admissions. For respiratory admissions (≥ 65 years of age), significant

positive associations were observed with particulate matter < 10 μm in aerodynamic diameter

(PM10), nitrogen dioxide, sulfur dioxide, and ozone in both cities. These associations tended to

be stronger at shorter lags in Hong Kong and at longer lags in London. Associations were

stronger in the cool season in Hong Kong and in the warm season in London, periods during

which levels of humidity are at their lowest in each city. For cardiac admissions (all ages) in both

cities, significant positive associations were observed for PM10, NO2, and SO2 with similar lag

patterns. Associations tended to be stronger in the cool season. The associations with NO2 and

SO2 were the most robust in two-pollutant models. Patterns of association for pollutants with

ischemic heart disease were similar in the two cities. The associations between O3 and cardiac

admissions were negative in London but positive in Hong Kong. We conclude that air pollution

has remarkably similar associations with daily cardiorespiratory admissions in both cities, in spite

of considerable differences between cities in social, lifestyle, and environmental factors. The

results strengthen the argument that air pollution causes detrimental short-term health effects.

Key words: air pollution, cardiac and respiratory hospital admissions, daily time-series, Hong

Kong, London. Environ Health Perspect 110:67–77 (2002). [Online 18 December 2001]

http://ehpnet1.niehs.nih.gov/docs/2002/110p67-77wong/abstract.html
all stations providing data were then averaged to provide city-wide daily estimates. 

We used a statistical approach that

closely followed the one adopted by the

APHEA-2 study. Poisson regression was

used to model the associations between the

dependent variable, daily admission counts,

and independent variables including nonparametric smooth functions of time, temperature, humidity, and influenza. We used

loess functions (13) of time with a minimum

span of 60 days to model seasonal fluctuations in admission counts. Temperature and

humidity recorded on the day of admission

and up to 3 days before admission were

investigated and modeled using nonparametric smooth functions, with the degree of

smoothing determined by the exposure–

response curves and the Akaike’s Information

Criteria (14). In addition, dummy variables

for days of the week, holidays, and unusual

events such as thunderstorms and influenza

epidemics were included as other independent variables. Daily admission counts for

influenza at the 4th quartile (for each week)

were used as indicators of influenza epidemics. Models were fitted using a quasilikelihood method assuming constant

over-dispersion over time. This modeling

procedure was carried out for each series

studied, and the core models were assessed

using plots of model residuals and fitted values and plots of the estimated partial autocorrelation functions. Pollution measures

were then added in turn, and if necessary,

both overdispersion and autocorrelation

were further adjusted for using statistical

procedures implemented in S-PLUS

(Insightful Corporation, Seattle, WA, USA)

(15). We examined concentrations on the

day of admission and on the previous 3

Articles • Wong et al.

68 VOLUME 110 | NUMBER 1 | January 2002 • Environmental Health Perspectives

Table 1. Comparison of environmental factors of Hong Kong and London.

Environmental factor Hong Kong London

Population (millions) 6.2 (1995)a 6.9 (1992)b

Area (km2) 1,092 1,580

Climate Subtropical, with rain and tropical cyclones in the Maritime, with mild winters and temperate

summer months summers

Mean January/July temperatures (°C) 16/29 3/23 

Rainfall 224 cm, most falling in the summer months 58 cm, evenly distributed through the year

Topography Peninsula with offshore islands Estuarine river basin

Lifestyle

Smoking rates (≥ 15 years of age) Male 26.7%; female 3.1%c Male 28%; female 27%d

Regular alcohol consumers Male 20.0%; female 2.0%e Male 27%; female 11%d

Health care system Primary care services provided mainly by private sector (85%) National Health Service

Hospital services provided mainly by public sector (86%)

Median size of private dwellings 40.0–69.9 m2a 85 m2f

GDP per capita (with adjustment for purchasing power parity) U.S. $20,458g U.S. $20,890g

Leading causes of death (1996 data)h (1996 data)i

1. Malignant neoplasms, 31.3% 1. Circulatory diseases, 42.6%

2. Heart diseases, 15.8% 2. Malignant neoplasms, 25.0%

3. Cerebrovascular disease, 10.7% 3. Respiratory diseases, 15.9%

4. Pneumonia (all forms), 10.6% 4. Digestive diseases, 3.6%

5. Injury and poisoning, 5.1% 5. Injury and poisoning, 2.9%

GDP, gross domestic product.

aData from the Hong Kong Annual Digest of Statistics (17). bData from the Office of Population and Censuses Surveys (18). c

Data from the Census and Statistics Department (19). dData from

Statistics on Smoking: England, 1976 to 1996 (20). eData from Janus et al. (21); alcohol consumption at least once per week (25–74 years of age). f

Data from the Office of National Statistics

(22). gData from Asia Week (23). hDepartment of Health. Department of Health Annual Report (24). i

Data from the Office of National Statistics (25). 

Table 2. Comparison of selected health and air pollution statistics between Hong Kong and London.

Health variable Hong Kong London

Population < 15/> 65 years of age (%) 18.9/10.0 (1996)a 18.8/13.9 (1992)b

Infant mortality rate (per 1,000 live births) 4.0 7.2

Age-standardized mortalityc (per 1,000 population)

From all causes 3.7 4.5

From respiratory diseases 0.7 0.5

From cardiovascular diseases 0.9 1.9

Emergency admissions for respiratory disease

Respiratory (% of all causes) 10.0 (1996) 5.1 (1992/1993)

Age standardized ratec (per 1,000 population) 12.9 8.0 (1992–1994)

Age distribution (%)

0–14 years 33 35

15–64 years 22 26

≥ 65 years 45 39

Subcategories (%)

Lower respiratory infections (ICD-9 466, 480–487) 23 22

Asthma (ICD-9 493) 13 25

COPD (ICD-9 490–496, excluding 493) 24 15

Emergency admissions for cardiovascular disease

Cardiovascular (% of all causes) 7.6 (1996) 5.9 (1992/1993)

Age standardized rate (per 1,000 population) 5.8 5.5 (1992–1994)

Age distribution (%)

0–14 years 2 0

15–64 years 37 32

≥ 65 years 61 68

Subcategories (%)

Stroke (ICD-9 430–438) 22 19

Cardiac (ICD-9 390–429) 63 70

[Ischemic heart disease (ICD-9 410–414)] 37 30

[Arrhythmias (ICD-9 427)] 20 9

[Cardiac failure (ICD-9 428)] 22 18

Sources of pollutant emissions (1997)d (TSP including PM10) (1997)e

PM10 (%)

Traffic (vehicle, marine vessel, aircraft) 61 83

Industry 6 11

Power generation (and heating for London) 33 6

NOx (%)

Traffic (vehicle, marine vessel, aircraft) 41 83

Industry 8 5

Power generation (and heating for London) 45 13

SO2 (%)

Traffic (vehicle, marine vessel, aircraft) 14 28

Industry 21 34

Power generation (and heating for London) 65 38

TSP, total suspended particulate.

aData from the Hong Kong Department of Health (24).bData from the Office of Population and Censuses Surveys (18). cThe

standard population was adopted from Segi (26). dData from the Planning, Environment and Lands Bureau (27). eData

from the London Research Centre (28).
days, and the means of the current day and

the previous day (lag 0–1).

Any linear effect of the pollutant could

be assessed by adding a pollutant measure

into the model described above. A possible

nonlinear effect of the pollutant was further

assessed by an exposure–response relationship

generated by generalized additive modeling

(16). The procedure involved symmetrical

application of a loess smoothing function on

a number of pollutant measures around a

specific point and estimation of the risk at

that specific point. A plot of risk against all

the specific points along the x-axis produced

an exposure–response curve.

To investigate seasonal differences in the

pollution effects, dummy variables were

added to the models to indicate season and

pollutant-season interaction terms. The

warm season was defined as April–September

and the cool season was October–March. We

derived estimates of the pollutant effects in

each season from the models together with

p-values for the interaction terms, which

indicated whether or not the observed seasonal differences were statistically significant. 

We used two-pollutant models to estimate the effects of one pollutant at mean

cumulative lag 0–1 days after controlling for

another pollutant also at mean cumulative

lag 0–1 days. 

Results

Background demographic, health, and environmental data. The background characteristics of the two cities have been summarized

(Table 1). Hong Kong has a population of

over 6 million and occupies an area of

approximately 1,000 km2, comprising two

major islands, some smaller outer islands, a

peninsula, the lands adjacent to the mainland,

and some reclaimed areas. It is situated at

22.5°N latitude at the mouth of the Pearl

River, which opens into the South China sea.

Hong Kong has a subtropical climate that

tends toward the temperate for nearly half the

year. The average annual rainfall is 224 cm,

most of which falls in the summer months.

Greater London has a population of

about 7 million people and occupies a

roughly circular basin of 1,600 km2, which

is bisected east to west by the River Thames

and bounded to the north and south by low

hills. It lies at a latitude of 45°N and has a

temperate maritime climate. The average

annual rainfall is 58 cm, which falls throughout the year.

Table 2 shows a comparison of relevant

demographic, health, and environmental

characteristics for Hong Kong and London.

The age distributions of the two cities are

similar, but age-standardized annual mortality rates are lower in Hong Kong than in

London for deaths from all causes and from

cardiovascular diseases. Standardized annual

rates for admission to public hospitals are

higher in Hong Kong than in London for

respiratory disease (12.9 vs. 8.0 per 1,000)

and for cardiovascular disease (5.8 vs. 5.5 per

1,000). Among respiratory hospital admissions, the proportions due to respiratory

infections are similar, but admissions due to

chronic obstructive pulmonary disease are

greater in Hong Kong and those due to

asthma are higher in London. The relative

distributions of subcategories of cardiac diseases were similar except that arrhythmias

were more common in Hong Kong. 

PM10 and NO2 emissions in London

were both predominantly from traffic (83%

and 83%, respectively); in Hong Kong they

were from both traffic (61% and 41%,

respectively) and power generation (33%

and 45%, respectively). SO2 in London was

almost equally derived from traffic, industry,

and power generation (28%, 34%, and

38%, respectively), but in Hong Kong they

were mainly from power generation (65%)

and industry (21%). 

Daily time-series data. Summary statistics for daily counts of admissions, by cause

and age, pollutant concentrations, and meteorologic variables are shown in Table 3.

Correlations between these variables are

shown in Table 4. London had almost twice

the median daily count of admissions for

asthma as Hong Kong but only two-thirds

the median number of admissions for respiratory disease. The numbers of cardiac

admissions were more comparable, but

London had almost 50% more emergency

admissions for IHD than Hong Kong.

The concentrations of NO2, SO2, and

O3 were higher in London, whereas PM10

levels in Hong Kong were almost double

those in London (46.8 vs. 24.8 μg/m3)

(Table 3). In Hong Kong, there was a

marked seasonal variation in ambient concentrations of NO2, O3, and PM10, all of

which were lowest in the warm season and

highest in the cool season. In contrast, SO2

tended to show less seasonal variation and

was highest in the warm season. In London,

there was little seasonal variation in NO2,

Articles • Health effects of air pollution in Hong Kong and London

Environmental Health Perspectives • VOLUME 110 | NUMBER 1 | January 2002 69

Table 3. Summary statistics for daily hospital admissions, pollutant concentrations, and meteorologic

measurements in Hong Kong (1995–1997) and London (1992–1994); n = 1,096 days.

Percentile

Variable, city Mean (Warm/cool) SD Min 10th 50th 90th Max

Emergency hospital admission (no./day) 

Asthma (ICD-9 493), 15–64 years

Hong Kong 7.8 (7.0/8.6) 3.4 0.0 4.0 7.0 12.0 24.0

London 14.1 (13.0/15.1) 5.8 2.0 8.0 13.0 21.0 85.0

Respiratory (ICD-9 460–519), ≥ 65 years

Hong Kong 91.3 (86.7/96.1) 22.5 45.0 64.0 88.0 122.0 174.0

London 58.3 (49.5/67.4) 19.4 13.0 37.0 55.0 82.0 150.0

Cardiac (ICD-9 390–429), all ages

Hong Kong 98.7 (94.0/103.4) 23.3 40.0 67.0 101.0 127.5 176.0

London 121.1 (118.3/124.0) 23.4 50.0 89.0 121.0 152.0 196.0

IHD (ICD-9 410–414), all ages

Hong Kong 36.0 (35.3/36.7) 10.3 8.0 23.0 35.0 49.0 76.0

London 51.3 (50.5/52.0) 10.0 22.0 39.0 51.0 64.0 86.0

Pollutant concentration (daily μg/m3)

NO2 (24 hr)

Hong Konga 55.9 (48.1/63.8) 19.4 15.3 31.8 53.5 81.8 151.5

Londonb 64.3 (62.6/66.1) 20.4 23.7 42.3 61.2 88.8 255.8

O3 (8 hr)

Hong Kongc 33.5 (32.0/35.1) 23.0 0 7.9 28.3 64.0 168.9

Londond 34.9 (45.3/24.0) 23.1 2.4 8.6 32.0 60.1 159.8

PM10 (24 hr)

Hong Konge 51.8 (42.2/61.6) 25.0 14.1 24.7 46.8 87.2 163.8

Londonf 28.5 (28.2/28.8) 13.7 6.8 15.8 24.8 46.4 99.8

SO2 (24 hr)

Hong Kongg 17.7 (18.3/17.2) 12.3 1.1 6.2 14.5 32.8 90.0

Londonh 23.7 (22.2/25.3) 12.3 6.2 13.2 20.6 38.1 113.6

Meteorologic measurements (daily)

Temperature (°C)

Hong Kong 23.2 (27.2/19.0) 5.0 6.9 16.0 24.3 29.1 30.9

London 11.9 (15.5/8.3) 5.0 –0.8 5.6 11.8 18.6 25.5

Humidity (%)

Hong Kong 77.7 (80.7/74.7) 10.6 31.0 64.0 79.0 90.0 97.0

London 70.6 (67.5/73.7) 10.9 41.0 56.0 70.0 85.0 97.0

Abbreviations: Max, maximum; Min, minimum.

ar = 0.65–0.90 between seven stations. br = around 0.8 between three stations. cr = 0.79 between two stations. dr = 0.95

between two stations. er = 0.92–0.97 between five stations. f

Only one station involved. gr = 0.44–0.81 between five stations. hr = –0.1 to 0.8 (median 0.5) in six stations.
SO2, or PM10, but there was marked seasonal

variation in O3, which was highest in the

warm season. Thus, the only pollutant with a

similar seasonal pattern in both cities was

SO2. Mean daily temperature was twice as

high in Hong Kong as in London (23°C vs.

12°C), and the mean relative humidity was

also higher in Hong Kong. The two cities

have similar cycles of temperature, but their

seasonal patterns for humidity differ

markedly; humidity in Hong Kong is highest

in the warm season, but in London it is highest in the cool season (Table 3).

Single-pollutant models. The associations

between pollutants (a priori mean lag 0–1

days) and the four admission outcomes are

shown in Table 5. We found no statistically

significant associations between asthma

admissions and any of the four pollutants in

either of the cities. For respiratory admissions, we found small, positive, and statistically significant associations with all four

pollutants in Hong Kong. By contrast, only

O3 was significantly associated with respiratory admissions in London. For cardiac diseases, both cities showed significant positive

associations of comparable size with NO2,

PM10, and SO2. There were no significant

positive associations with O3 in Hong Kong,

whereas in London it was significantly negative. The direction of effects for IHD was

the same as for all cardiac diseases in both

cities, but the estimates were lower in Hong

Kong than in London (except O3) and none

were significant. In London, the relative risks

for IHD were similar to those for all cardiac

diseases and all were significant; the association with O3 was negative. 

Results for the most significant single

day lag from lags 0 to 3 are shown in Table

6 and illustrated along with the other single

day lags in Figure 1. Generally, these results

are similar in terms of direction and magnitude to the a priori choice of mean lags 0

and 1. One difference was that in London,

the associations between admissions for asthmatic attacks in the 15–64 age group, as well

as respiratory disease in the ≥ 65 age group

and NO2, PM10, and SO2 in the best single

lag days, were larger than the a priori (lag

0–1) choice and are statistically significant.

Articles • Wong et al.

70 VOLUME 110 | NUMBER 1 | January 2002 • Environmental Health Perspectives

Table 5. Summary of single-pollutant excess risk

(ER) and 95% confidence interval (CI) for a 10 μg/m3

change in pollutant concentration for mean lag 0–1

day: comparison between Hong Kong and London.

Emergency 

admission Hong Kong London

complaints, age ER (95% CI) ER (95% CI)

Asthma, 15–64 years

NO2 –0.6 (–2.1–1.0) 1.0 (0.0–2.1)

O3 0.0 (–1.3–1.4) –0.7 (–1.8–0.4)

PM10 –1.1 (–2.4–0.1) 1.4 (–0.1–3.0)

SO2 –0.1 (–2.4–2.2) 0.7 (–1.0–2.5)

Respiratory, ≥ 65 years

NO2 1.8 (1.2– 2.4) –0.1 (–0.6–0.5)

O3 0.8 (0.3–1.3) 0.8 (0.2–1.4)

PM10 1.0 (0.5–1.5) 0.4 (–0.3–1.2)

SO2 1.8 (0.9–2.6) 0.2 (–0.6–1.1)

Cardiac, all ages

NO2 1.4 (0.9–2.0) 0.7 (0.3–1.0)

O3 0.3 (–0.2–0.7) –0.6 (–1.0––0.1)

PM10 0.7 (0.3–1.1) 0.8 (0.3–1.4)

SO2 2.1 (1.3–2.8) 1.6 (1.0–2.2)

IHD, all ages

NO2 0.6 (–0.2–1.4) 0.7 (0.2–1.2)

O3 0.4 (–0.3–1.1) –0.8 (–1.4––0.2)

PM10 0.5 (–0.1–1.1) 0.9 (0.1–1.6)

SO2 0.1 (–1.1–1.2) 1.7 (0.8–2.6)

Table 6. Summary of single-pollutant excess risk (ER) and 95% confidence interval (CI) for a 10 μg/m3

change in pollutant concentration for the best single lag day: comparison between Hong Kong and

London.

Emergency admission Hong Kong London

complaints, age Lag ER (95% CI) Lag ER (95% CI)

Asthma, 15–64 years

NO2 1 –1.3 (–2.6–0.1) 2 1.1 (0.2–2.0)

O3 2 1.2 (0.0–2.4) 0 –0.7 (–1.7–0.3)

PM10 0 –1.1 (–2.1–0.0) 2 2.2 (0.8–3.6)

SO2 2 –1.5 (–3.4–0.5) 3 2.1 (0.7–3.6)

Respiratory, ≥ 65 years

NO2 0 1.3 (0.8–1.8) 3 0.9 (0.5–1.3)

O3 1 0.6 (0.2–1.0) 0 0.6 (0.1–1.2)

PM10 0 0.7 (0.3–1.0) 3 1.5 (0.8–2.2)

SO2 0 1.7 (1.0–2.4) 3 1.2 (0.5–2.0)

Cardiac, all ages

NO2 0 1.2 (0.7–1.7) 0 0.7 (0.4–1.0)

O3 2 0.5 (0.1–0.8) 0 –0.8 (–1.2––0.4)

PM10 0 0.5 (0.2–0.9) 0 1.1 (0.5–1.5)

SO2 0 1.6 (1.0–2.2) 0 1.4 (0.9–1.9)

IHD, all ages

NO2 3 0.7 (0.1–1.4) 0 0.7 (0.2–1.1)

O3 3 0.5 (0.0–1.0) 0 –0.9 (–1.4––0.3)

PM10 2 0.5 (–0.1–1.0) 3 0.3 (–0.5–1.0)

SO2 2 0.4 (–0.5–1.4) 0 1.4 (0.7–2.2)

Table 4. Matrix of Spearman’s rank correlation coefficient (r) between mean daily concentration of pollutants and meteorologic data (1995–1997).

SO2 PM10 O3 Temperature Humidity

Hong Kong

Whole year

NO2 0.37 0.82 0.43 –0.45 –0.35

SO2 0.30 –0.18 0.17 –0.16

PM10 0.54 –0.42 –0.53

O3 –0.14 –0.59

Temperature 0.19

Warm season

NO2 0.28 0.80 0.54 –0.43 –0.18

SO2 0.22 –0.14 0.37 –0.16

PM10 0.65 –0.25 –0.40

O3 –0.17 –0.57

Temperature –0.26

Cool season

NO2 0.61 0.72 0.23 0.10 –0.36

SO2 0.53 –0.21 0.13 –0.20

PM10 0.36 0.01 0.55

O3 0.05 –0.60

Temperature 0.21

SO2 PM10 O3 Temperature Humidity

London

Whole year

NO2 0.71 0.68 –0.29 –0.16 0.01

SO2 0.64 –0.25 –0.13 –0.15

PM10 0.17 0.02 –0.05

O3 0.47 –0.52

Temperature –0.27

Warm season

NO2 0.66 0.68 0.05 0.08 –0.09

SO2 0.56 0.14 0.26 –0.33

PM10 0.27 0.32 –0.14

O3 0.26 –0.53

Temperature 0.26

Cool season

NO2 0.76 0.68 –0.61 –0.36 0.01

SO2 0.70 –0.58 –0.46 –0.05

PM10 –0.56 0.23 0.01

O3 0.29 –0.37

Temperature 0.05
These stronger associations all occur with

longer lag 3 except once with lag 2. Another

clear difference between the two cities was

for admissions for IHD. In Hong Kong the

most significant associations occurred at lag

2 or 3 days for the four pollutants, whereas

in London they were at lag 0 days for NO2,

O3, and SO2. PM10 was the exception in

London, with the most significant lag

occurring at lag 3 days. In both cities however the magnitude of the effects were similar whether at mean lag 0–1 days or the

most significant day. 

Estimates of pollution effects by season

(Table 7) showed contrasting patterns

between the two cities for respiratory disease

and similar patterns for cardiac disease

(Figure 2). In Hong Kong, pollution effects

on respiratory disease tended to be greater in

the cool season and significantly so for NO2

and SO2 (Table 7). In London, the pattern

was reversed with greater effects in the warm

season, significantly so for NO2 and PM10

(Table 7). The two cities were similar in having larger estimates of cardiac admissions in

the cool season (with the exception of O3 for

London); all of these seasonal interactions

were significant for Hong Kong, but only one

(PM10) was significant for London (Table 7).

Two-pollutant models. In Hong Kong,

associations between respiratory admissions

and each of the four pollutants studied

tended to be robust to inclusion of a second

pollutant into the models (Table 8). There

were two exceptions: the PM10 and SO2

associations were substantially reduced after

NO2 was added to the models. In London,

associations between respiratory admissions

and NO2, PM10, and SO2 were nonsignificant and remained unchanged after the addition of a second pollutant. The significant

O3 associations found in London were robust

to the inclusion of an additional pollutant.

For cardiac admissions in Hong Kong,

the addition of NO2 or SO2 reduced the

magnitude and statistical significance of

NO2, SO2, and PM10 associations (O3 was

not found to be significant in single-pollutant

models). These results were largely replicated

in the London analyses, although in a model

containing NO2 and SO2, SO2 was clearly

the “most robust” pollutant, retaining both

the magnitude and statistical significance of

its association after the inclusion of NO2. 

Exposure–response relationships. For respiratory admissions in Hong Kong, a negative

Articles • Health effects of air pollution in Hong Kong and London

Environmental Health Perspectives • VOLUME 110 | NUMBER 1 | January 2002 71

Respiratory admissions (≥ 65) Cardiac admissions (all ages)

Respiratory admissions (≥ 65) Cardiac admissions (all ages)

0

Single lag day

1.03

1.02

1.01

1.00

0.99

0.98

0.97 1 2 3 01 2 3 01 2 3 01 2 3 01 2 3 01 2 3 01 2 3 01 2 3

RR (95% CI)

NO2 (24-hr) O3 (8-hr) PM10 (24-hr) SO2 (24-hr) NO2 (24-hr) O3 (8-hr) PM10 (24-hr) SO2 (24-hr)

1.03

1.02

1.01

1.00

0.99

0.98

0.97

Single lag day

01 2 3 01 2 3 01 2 3 01 2 3 01 2 3 01 2 3 01 2 3 01 2 3

RR (95% CI)

NO2 (24-hr) O3 (8-hr) PM10 (24-hr) SO2 (24-hr) NO2 (24-hr) O3 (8-hr) PM10 (24-hr) SO2 (24-hr)

A

B

Figure 1. Effect of pollutants at a single lag day on hospital admissions due to cardiac and respiratory disease in (A) Hong Kong and (B) London. Values shown are relative risk (RR) and 95% confidence interval

(CI) for a 10 μg/m3 increase in concentration. 

Table 7. Summary of single-pollutant results in excess risk (ER) and 95% confidence interval (CI) for a 10 μg/m3 change in concentration at mean lag 0–1 day in

warm and cool seasons.

Emergency Significance for

admission Warm Cool pollutant by season

complaints, age ER (95% CI) ER (95% CI) interaction

Hong Kong

Asthma, 15–64 years

NO2 –0.5 (–2.7–1.6) –0.6 (–2.8–1.6)

O3 –0.3 (–2.0–1.3) 0.6 (–1.4–2.6)

PM10 –1.0 (–2.8–0.8) –1.2 (–2.8–0.4)

SO2 1.5 (–1.5–4.6) –2.0 (–5.4–1.4)

Respiratory, ≥ 65 years

NO2 0.8 (0.1–1.6) 3.0 (2.1–3.9) p ≤ 0.001

O3 0.8 (0.2–1.4) 1.0 (0.2–1.7)

PM10 0.8 (0.1–1.4) 1.2 (0.6–1.9)

SO2 1.1 (0.0–2.2) 2.7 (1.4–4.0) p ≤ 0.05

Cardiac, all ages

NO2 0.3 (–0.4–1.0) 2.6 (1.9–3.3) p ≤ 0.001

O3 0.0 (–0.5–0.6) 0.9 (0.2–1.6) p ≤ 0.05

PM10 0.0 (–0.6–0.6) 1.3 (0.8–1.9) p ≤ 0.001

SO2 1.0 (0.0–2.0) 3.3 (2.1–4.4) p ≤ 0.01

IHD, all ages

NO2 0.1 (–0.9–1.2) 1.2 (0.0–2.3)

O3 0.4 (–0.4–1.2) 0.6 (–0.5–1.6)

PM10 0.2 (–0.7–1.0) 0.8 (–0.1–1.6)

SO2 –0.6 (–2.0–0.8) 1.0 (–0.8–2.8)

Emergency Significance for

admission Warm Cool pollutant by season

complaints, age ER (95% CI) ER (95% CI) interaction

London

Asthma, 15–64 years

NO2 0.6 (–0.8–2.0) 1.3 (–0.1–2.8)

O3 –0.1 (–1.4–1.2) –2.6 (–4.6––0.5) p ≤ 0.05

PM10 0.6 (–1.9–3.1) 1.6 (–0.3–3.6)

SO2 –1.4 (–4.7–1.9) 1.6 (–0.5–3.8)

Respiratory, ≥ 65 years

NO2 0.6 (–0.2–1.4) –0.7 (–1.4–0.0) p ≤ 0.01

O3 1.0 (0.3–1.7) 0.2 (–0.7–1.2)

PM10 1.8 (0.5–3.1) –0.5 (–1.5–0.5) p ≤ 0.01

SO2 1.3 (–0.5–3.1) –0.3 (–1.3–0.8)

Cardiac, all ages

NO2 0.4 (–0.1–0.9) 0.8 (0.3–1.4)

O3 –0.2 (–0.7–0.3) –1.1 (–1.8––0.4)

PM10 0.1 (–0.7–1.0) 1.2 (0.5–2.0) p ≤ 0.01

SO2 0.6 (–0.6–1.7) 1.9 (1.2–2.7)

IHD, all ages

NO2 0.4 (–0.3–1.1) 1.0 (0.2–1.7)

O3 –0.5 (–1.2–0.2) –1.3 (–2.3––0.3)

PM10 0.1 (–1.1–1.4) 1.3 (0.3–2.3)

SO2 1.0 (–0.6–2.6) 2.0 (0.9–3.1)
exposure–response relationship was observed

for concentrations of O3 < 20 μg/m3 (mainly

in the warm season); in London (mainly in

the cool season), a neutral relationship was

found. For levels of O3 > 20 μg/m3, there

were similar positive linear relationships in

both cities (Figure 3). 

For cardiac admissions and PM10 between

the 10th and 90th percentiles (i.e., 25–87

μg/m3 in Hong Kong and 16–46 μg/m3 in

London), both cities showed positive exposure–response relationships (Figure 3). We

observed a negative linear association for O3

across the range of the pollutant in London,

whereas in Hong Kong we observed a “J”-

shaped exposure–response relationship, indicating a positive association between cardiac

admissions and the higher levels of O3 (data

not shown). 

For the other exposure–response relationship, there were similarities as well as

dissimilarities between the two cities (Figures

4–7), which was quite in agreement with

those results presented in Table 5 for the

same lag 0–1 day effects. 

Discussion

Validity of results. The analytic method was

the same in each city and followed the

approach adopted by the APHEA collaboration. One of the present authors (R.W.A.)

was responsible for analyzing the APHEA 2

respiratory admissions data and worked

closely with researchers in Hong Kong to

ensure that the application of methods was

the same in each city. One feature of this

method of Poisson regression is that seasonal,

long-term trends and weather factors were

modeled using nonparametric methods. This

method is widely accepted and has been

found to yield comparable results to the earlier method, which uses sinusoidal models for

seasonal control (29). It also gives similar

results to methods that use a synoptic

approach to control for weather factors (30).

In a sensitivity analysis, the method of parametric seasonal control was applied to selected

series in both London (11) and Hong Kong;

results were similar to those observed using

the current method, which used generalized

additive models (data not shown). The data

on air pollution, weather, and outcomes

were defined in an identical manner. We did

not validate the consistency of hospital diagnosis, but since medical practice and the

death certification procedure in Hong Kong

has been strongly influenced by British and

Commonwealth medical education, it is

unlikely that there were major differences. In

any case, the adoption of some broad categories (lower respiratory disease and cardiac

disease) should have absorbed diagnostic

transfer within those groups. The lower level

of asthma admissions corresponds to the

Articles • Wong et al.

72 VOLUME 110 | NUMBER 1 | January 2002 • Environmental Health Perspectives

Figure 2. Comparison of pollutant effects in cool and warm seasons on hospital admissions due to respiratory and cardiac diseases. Values shown are relative risk (RR) and 95% confidence interval (CI) for a 10

μg/m3 increase in concentration in mean lag 0–1 days.

Table 8. Excess risk (ER) and 95% confidence interval (CI) for a 10 μg/m3 change in mean concentration of

lag 0–1 day in each air pollutant from a single- and co-pollutant model.

After adjusting for co-pollutant

Emergency NO2 O3 PM10 SO2

admission ER (95% CI) ER (95% CI) ER (95% CI) ER (95% CI)

Respiratory

NO2

Hong Kong 1.8 (1.2–2.4)a 1.6 (1.0–2.3) 1.7 (0.8–2.7) 1.6 (0.8–2.4)

London –0.1 (–0.6–0.5)a 0.1 (–0.5–0.6) –0.4 (–1.2–0.4) –0.2 (–0.9–0.5)

O3

Hong Kong 0.5 (0.0–1.0) 0.8 (0.3–1.3)a 0.5 (0.0–1.1) 1.0 (0.5–1.5)

London 0.8 (0.2–1.4) 0.8 (0.2–1.4)a 1.1 (0.5–1.7) 0.9 (0.3–1.5)

PM10

Hong Kong 0.0 (–0.7–0.7) 0.8 (0.3–1.3) 1.0 (0.5–1.5)a 0.6 (0.1–1.1)

London 0.9 (–0.3–2.0) 0.4 (–0.3–1.2) 0.4 (–0.3–1.2)a 0.7 (–0.5–1.8)

SO2

Hong Kong 0.3 (–0.7–1.4) 1.9 (1.1–2.8) 1.2 (0.3–2.2) 1.8 (0.9–2.6)a

London 0.5 (–0.7–1.7) 0.5 (–0.4–1.5) –0.4 (–1.8–1.0) 0.2 (–0.6–1.1)a

Cardiac

NO2

Hong Kong 1.4 (0.9–2.0)a 1.5 (0.9–2.0) 1.7 (0.9–2.5) 0.7 (0.1–1.4)

London 0.7 (0.3–1.0)a 0.7 (0.3–1.1) 0.6 (0.0–1.2) 0.1 (–0.3–0.6)

O3

Hong Kong –0.1 (–0.6–0.4) 0.3 (–0.2–0.7)a 0.0 (–0.5–0.5) 0.4 (–0.1–0.9)

London –0.5 (–0.9–0.0) –0.6 (–1.0––0.1)a –0.6 (–1.0––0.1) –0.3 (–0.8–0.1)

PM10

Hong Kong –0.3 (–0.9–0.4) 0.7 (0.3–1.2) 0.7 (0.3–1.1)a 0.1 (–0.4–0.6)

London 0.2 (–0.6–1.0) 0.8 (0.3–1.3) 0.8 (0.3–1.4)a –0.3 (–1.1–0.4)

SO2

Hong Kong 1.4 (0.4–2.3) 2.1 (1.4–2.9) 2.0 (1.1–2.8) 2.1 (1.3–2.8)a

London 1.4 (0.6–2.3) 1.6 (0.9–2.2) 2.2 (1.2–3.2) 1.6 (1.0–2.2)a

aEstimates from the single-pollutant model.

Respiratory admissions (≥ 65)

Cardiac admissions (all ages)

warm

Season

1.06

1.04

1.02

1.00

0.98

0.96

RR (95% CI) RR (95% CI)

Season

cool warm cool warm cool warm cool warm cool warm cool warm cool warm cool

warm cool warm cool warm cool warm cool warm cool warm cool warm cool warm cool

1.06

1.04

1.02

1.00

0.98

0.96

NO2

 (24-hr)

Hong Kong London Hong Kong London

O3 (8-hr)

Hong Kong London

PM10 (24-hr)

Hong Kong London

SO2 (24-hr)

NO2

 (24-hr)

Hong Kong London Hong Kong London

O3 (8-hr)

Hong Kong London

PM10 (24-hr)

Hong Kong London

SO2 (24-hr)
known lower prevalence of asthma in Hong

Kong (31,32). The lack of statistically significant association in asthmatic admissions may

be due to the small numbers, relative to the

other categories, and low statistical power to

detect a significant association.

Respiratory admissions. The results for

respiratory admissions were similar when the

best single day lag was chosen, with all pollutants showing significant effects in both cities.

These results are in line with many other

studies (33). However, when the 0–1 day lag

was compared, the cities were similar only for

O3, with only Hong Kong showing significant effects of the other pollutants. This may

be explained by the fact that in Hong Kong

the effects of PM10, NO2, and SO2 were

greatest at early lags, whereas in London the

effects were greater with later lags. We have

considered whether this difference in lags

could be explained by the different primary

health care systems. In Hong Kong this is a

combination of private practitioners (the great

majority) and public out-patient clinics, and

heavy use of hospital accident and emergency

departments. Perhaps this results in more

rapid referral to hospital of persons with

severe lower respiratory disease than in

London, where the state-provided primary

care system takes more responsibility for

treating moderately severe disease at home

and for controlling access to hospital facilities. We observed that the lag patterns for

respiratory mortality associated with NO2,

PM10, and SO2 (Figure 8) (34,35) also followed the respective patterns for respiratory

admissions (Figure 1). The other difference

in the effects of NO2, PM10, and SO2 was

that in Hong Kong, the effects were stronger

in the cool season, whereas they were

stronger in the warm season in London. One

common factor here is that the humidity is

lower in the season showing the largest

effects. It may also be relevant that the average levels of all pollutants apart from SO2

are highest during the cool season in Hong

Kong.

The strong associations between respiratory admissions and NO2 and PM10 suggest

that traffic may be an important source of

toxic pollution. In both cities, it has been estimated that only a minority (about 20–40%)

of PM10 particles in the ambient air (not just

for emissions from various sources) is derived

from local traffic (36,37). This is consistent

with the finding that NO2 retains its strong

association in two pollutant models while

PM10 does not.

O3 showed consistent significant effects

on respiratory admissions, irrespective of

whether the mean lag 0–1 days or best single day lag was chosen. The exposure–

response relationships with O3 were linear

in both cities when concentrations were

> 20 μg/m3, but in Hong Kong, a negative

relationship was observed below this level. It

could be postulated that the Hong Kong population would be more resistant to O3

because the diet is higher in antioxidants and

because air conditioning is used in most

closed spaces, but our data suggest that both

populations are equally susceptible. It is relevant to note that O3 also shows associations

with respiratory admissions in a range of

European cities, with little heterogeneity

(33,38). In both London and Hong Kong,

the association with O3 was very robust to the

inclusion of other pollutants in the model.

Cardiac admissions. The results for cardiac admissions were similar for both cities

in respect to NO2, PM10, and SO2. This was

irrespective of whether the mean lag 0–1 or

the best lag was chosen, because in contrast

to respiratory admissions, both cities displayed the same lag patterns, with lower risks

at longer lags. These results add to the accumulating evidence worldwide that air pollution has short-term effects on cardiac admissions. Our evidence indicates that within the

cardiac group of diagnoses, there are also

effects on ischemic heart disease, but we do

not know from this study if the same applies

to other diagnoses such as cardiac failure or

cardiac arrhythmias. However, we previously

demonstrated an effect of O3 in the cool season on admissions for these cardiac events in

the elderly in Hong Kong (8). The exposure–response relationships with NO2,

PM10, and SO2 were linear in both cities,

and there were similar seasonal associations,

with both cities having larger effects in the

cool season. The two-pollutant models also

showed considerable similarities, with NO2

being robust to the inclusion of PM10 in the

models but affected to some extent by SO2.

In both cities, the effect of SO2 retained its

statistical significance in the presence of all

the other pollutants.

Articles • Health effects of air pollution in Hong Kong and London

Environmental Health Perspectives • VOLUME 110 | NUMBER 1 | January 2002 73

Figure 3. Exposure (μg/m3) and response relationships for O3 and respiratory admissions and for PM10 and

cardiac admissions in (A) Hong Kong and (B) London. The density of the vertical bars on the x-axis shows

the distribution of the pollutant concentration data.

0.20

0.15

0.10

0.05

0.0

0.10

0.05

0.0

–0.05

0 20 40 60 80 100 120 120 20 140 40 60 80 100

Concentration O3 in lag 0–1 day Concentration PM10 in lag 0–1 day

Concentration PM10 Concentration O in lag 0–1 day 3 in lag 0–1 day

0.20

0.15

0.10

0.05

0.0

–0.05

0.10

0.05

0.0

0 20 40 60 80 100 120 140 20 40 60 80

Log-risk (deviated from overall mean) Log-risk (deviated from overall mean)

Log-risk (deviated from overall mean) Log-risk (deviated from overall mean)

Respiratory admissions (≥ 65) Cardiac admissions (all ages)

Respiratory admissions (≥ 65) Cardiac admissions (all ages)

A

B
Articles • Wong et al.

74 VOLUME 110 | NUMBER 1 | January 2002 • Environmental Health Perspectives

Figure 5. Exposure response curves for IHD (all ages) in (A) Hong Kong and (B) London for all pollutants under study.

Figure 4. Exposure response curves for respiratory admissions (≥ 65 years of age) in (A) Hong Kong and (B) London for all pollutants under study. 

0.20

0.15

0.10

0.05

0.0

20 40 60 80 100 120

Concentration NO2 in lag 0–1 day Concentration SO2 in lag 0–1 day

Log-risk (deviated from overall mean) Log-risk (deviated from overall mean) –0.05

0.15

0.10

0.05

0.0

–0.05

0 40 60 20

Log-risk (deviated from overall mean)

0.0

–0.1

–0.2

–0.3 –0.20

–0.15

–0.10

–0.05

0.0

0.05

0.10

Log-risk (deviated from overall mean)

Concentration NO2 in lag 0–1 day Concentration SO2 in lag 0–1 day

50 100 150 20 40 60 80

0.15

0.10

0.05

0.0

20 40 60 80 100 120

Concentration PM10 in lag 0–1 day Concentration O3 in lag 0–1 day

Log-risk (deviated from overall mean) Log-risk (deviated from overall mean) –0.05

0.15

0.10

0.05

0.0

0 40 60 20

Log-risk (deviated from overall mean)

0.0

–0.05

Log-risk (deviated from overall mean)

50 100 150 20 40 60 80

0.20

Concentration O3 Concentration PM in lag 0–1 day 10 in lag 0–1 day

140 80 100 120

0.05

0.10

0.0

0.05

0.10

0.15

0.20

0.25

A

B

0.10

0.05

0.0

20 40 60 80 100 120

Concentration NO2 in lag 0–1 day Concentration SO2 in lag 0–1 day

Log-risk (deviated from overall mean) Log-risk (deviated from overall mean) –0.05

0.15

0.10

0.05

0.0

0 40 60 20

Log-risk (deviated from overall mean)

0.0

–0.1

Log-risk (deviated from overall mean)

50 100 150 20 40 60 80

0.0

Concentration NO2 in lag 0–1 day Concentration SO2 in lag 0–1 day

–0.05

–0.10

0.1

0.2

0.3

0.1

0.2

–0.10

–0.15

0.15

0.10

0.05

0.0

20 40 60 80 100 120

Concentration PM10 in lag 0–1 day Concentration O3 in lag 0–1 day

Log-risk (deviated from overall mean) Log-risk (deviated from overall mean) –0.05 0.0

0 40 60 20

Log-risk (deviated from overall mean)

0.0

–0.05

Log-risk (deviated from overall mean)

Concentration O3 Concentration PM in lag 0–1 day 10 in lag 0–1 day

140 80 100 120

0.05

0.10 0.0

0.05

–0.10

–0.15

20 40 60 80 0 20 40 60 80 100 120 140

0.15

–0.05

–0.10

–0.15

0.1

0.2

–0.1

A

B
Articles • Health effects of air pollution in Hong Kong and London

Environmental Health Perspectives • VOLUME 110 | NUMBER 1 | January 2002 75

Figure 6. Exposure response curves for cardiac admissions (all ages) in (A) Hong Kong and (B) London for all pollutants under study.

Figure 7. Exposure response curves for asthma (15–64 years of age) in (A) Hong Kong and (B) London for all pollutants under study.

0.15

0.10

0.05

0.0

20 40 60 80 100 120

Concentration NO2 in lag 0–1 day Concentration SO2 in lag 0–1 day

Log-risk (deviated from overall mean) Log-risk (deviated from overall mean) –0.05

0.10

0.05

0.0

–0.05

0 40 60 20

Log-risk (deviated from overall mean)

0.10

0.05

0.0

–0.05

0.20

0.15

–0.05

0.0

0.05

0.10

Log-risk (deviated from overall mean)

Concentration NO2 in lag 0–1 day Concentration SO2 in lag 0–1 day

50 100 150 20 40 60 80

0.25

0.10

0.05

0.0

20 40 60 80 100 120

Concentration PM10 in lag 0–1 day Concentration O3 in lag 0–1 day

Log-risk (deviated from overall mean) Log-risk (deviated from overall mean) –0.05

0.0

0 40 60 20

Log-risk (deviated from overall mean)

0.0

Log-risk (deviated from overall mean)

Concentration O3 Concentration PM in lag 0–1 day 10 in lag 0–1 day

140 80 100 120

0.05

0.10

0.0

0.05

–0.10

20 40 60 80 0 20 40 60 80 100 120 140

–0.05

–0.10

–0.05

0.05

0.10

–0.10

A

B

0.0

20 40 60 80 100 120

Concentration NO2 in lag 0–1 day Concentration SO2 in lag 0–1 day

Log-risk (deviated from overall mean) Log-risk (deviated from overall mean)

0.0

–0.5

0 40 60 20

Log-risk (deviated from overall mean)

0.0

–0.1

–0.2

–0.3 –0.3

0.0

Log-risk (deviated from overall mean)

Concentration NO2 in lag 0–1 day Concentration SO2 in lag 0–1 day

50 100 150 20 40 60 80

–0.2

–0.1

0.1

0.2

0.3

0.1

0.2

0.3

–0.4

–0.3

–0.2

–0.1

0.1

–0.1

–0.2

0.1

0.2

0.0

20 40 60 80 100 120

Concentration PM10 in lag 0–1 day Concentration O3 in lag 0–1 day

Log-risk (deviated from overall mean) Log-risk (deviated from overall mean)

0.0

0 40 60 20

Log-risk (deviated from overall mean)

0.0

Log-risk (deviated from overall mean)

Concentration O3 Concentration PM in lag 0–1 day 10 in lag 0–1 day

140 80 100 120

0.05

0.10

0.0

0.2

20 40 60 80 0 20 40 60 80 100 120 140

0.1

–0.1

0.15

–0.05

–0.10

–0.15

0.2

–0.2

–0.2

–0.1

0.1

0.2

0.3

0.4

A

B
Relevance of results. The principal aim of

this study was to determine if the effects of

air pollution on daily hospital admissions

are consistent between Hong Kong and

London. An important component of causal

thinking in observational studies is whether

the associations are consistent in widely

varying environments. This is one way in

which concerns about unknown or inadequately controlled confounding can be

addressed. In air pollution time-series studies, this is especially important because

other components of causal reasoning such

as size of effect, biological plausibility, and

coherence are less convincing than many

would wish. Hong Kong and London differ

markedly in many respects that could affect

confounding or effect modification, especially in climate and lifestyle. We have based

our comparison on the size, significance,

and direction of estimates of effect, lag pattern, exposure–response relationship, and

seasonal effects. We conclude overall that

there are considerable similarities in the

effects of each pollutant, although the explanations for some differences, including a

lack of association with asthma admissions

in adults in Hong Kong, differences in the

lag pattern for all respiratory admissions,

and opposite directions of effect for O3 and

cardiac admissions, remain uncertain.

Overall, we consider the similarities to outweigh the differences; thus, we conclude

that our study strengthens the argument for

the causality of air pollution associations

with hospital admissions.

The comparison has done less to clarify

which component of the pollution mixture is

important. The O3 associations with respiratory disease are at least independent of other

pollutants and in line with studies elsewhere.

There is also evidence that O3 is potentially

toxic at near ambient levels (2). For cardiac

admissions, the PM10 associations were less

independent of NO2 (and in some cases

SO2) than the reverse. Toxicologic evidence

suggests that NO2 and SO2 are unlikely to

have effects at this level, which points toward

them being surrogates for some other toxic

component. PM10 is widely regarded as

important in spite of meagre human toxicologic corroboration. The cities were similar in

the proportion of fine particles comprising

PM10 and in other components such as sulfate (as an indicator of secondary particles)

and carbon (as an indicator of primary particles) (36,39–41). Our study, like many others, suggests that traffic sources are important

but cannot be more specific. Analysis using

many, rather than only two, cities may be

one way of learning more about the effects of

different pollution mixtures (42).

REFERENCES AND NOTES

1. Pope III CA, Dockery DW. Epidemiology of particle effects.

In: Air Pollution and Health (Holgate ST, Samet JM, Koren

HS, Maynard RL, eds). San Diego, CA:Academic Press,

1999;673–705.

2. Thurston GD, Ito K. Epidemiological studies of ozone

exposure effects. In: Air Pollution and Health (Holgate

ST, Samet JM, Koren HS, Maynard RL, eds). San Diego,

CA:Academic Press, 1999;485–510.

3. Committee of the Environmental and Occupational

Healthy Assembly of the American Thoracic Society.

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76 VOLUME 110 | NUMBER 1 | January 2002 • Environmental Health Perspectives

Figure 8. Effect of pollutants at a single day lag on mortality due to cardiovascular and respiratory diseases in (A) Hong Kong and (B) London. Values shown are relative risk (RR) and 95% confidence interval

(CI) for a 10 μg/m3 increase in concentration. 

0

1.04

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RR (95% CI) RR (95% CI)

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Single lag day

Respiratory mortality (all ages) Cardiovascular mortality (all ages) A

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NO2

 (24-hr) O3 (8-hr) PM10 (24-hr) SO2 (24-hr) NO2 (24-hr) O3 (8-hr) PM10 (24-hr) SO2 (24-hr)

NO2

 (24-hr) O3 (8-hr) PM10 (24-hr) SO2 (24-hr) NO2 (24-hr) O3 (8-hr) PM10 (24-hr) SO2 (24-hr)
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