### 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 Lam 1

**1 Department of Community Medicine, The University of Hong Kong, Hong Kong, China;**  **2 Department of Public Health Sciences,**  **St. George’s Hospital Medical School, London, United Kingdom**



### There is now considerable evidence thatdaily 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 isconsistent 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 concerningresidual confounding, the effects of individual pollutants or mixtures, and other factorsthat 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 andpatterns of the respiratory and cardiac diseases. On the other hand, there are differences between the two cities in terms of anumber 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 ofthe 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-termhealth 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 theaccident and emergency departments, general outpatient departments, or directly to the inpatient wards on the grounds ofurgency. 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-9460–519) 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 PM 10 

### (particles with median aerodynamic diameter < 10 μm), nitrogen dioxide, andsulfur 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 O 3 

### ) hourly measurements were made. When data were available from more than one monitoring station, we used a simplefilling-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 (PM 10 

### ), 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 PM 10 

### , NO 2 

### , and SO 2 

### with similar lag patterns. Associations tended to be stronger in the cool season. The associations with NO 2 

### and SO 2 

### 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 O 3 

### 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 minimumspan 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 wereinvestigated 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 influenzaepidemics 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 assessedusing 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 (km 2 ) 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 m 2 a 85 m 2 f GDP per capita (with adjustment for purchasing power parity) U.S. $20,458 g U.S. $20,890 g 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.

a Data from the Hong Kong Annual Digest of Statistics ( 17 ). b Data 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 ). e Data 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

). g Data from Asia Week ( 23 ). h Department of Health. Department of Health Annual Report ( 24). iData 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 mortality c (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 rate c (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 PM 10 ) ( 1997) e

PM 10 (%) Traffic (vehicle, marine vessel, aircraft) 61 83 Industry 6 11 Power generation (and heating for London) 33 6 NO x (%) Traffic (vehicle, marine vessel, aircraft) 41 83 Industry 8 5 Power generation (and heating for London) 45 13 SO 2 (%) Traffic (vehicle, marine vessel, aircraft) 14 28 Industry 21 34 Power generation (and heating for London) 65 38
TSP, total suspended particulate.

a Data from the Hong Kong Department of Health ( 24). b Data from the Office of Population and Censuses Surveys ( 18 ). c

The standard population was adopted from Segi ( 26 ). dData from the Planning, Environment and Lands Bureau ( 27 ). e Data 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, whichindicated 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. 

## ResultsBackground 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 km 2 

### , 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 km 2 

### , 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 averageannual 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 aresimilar, 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 per1,000). 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 relativedistributions of subcategories of cardiac diseases were similar except that arrhythmias were more common in Hong Kong. PM 10 

### and NO 2 

### 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). SO 2 

### 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 causeand 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-thirdsthe 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 NO 2 

### , SO 2 

### , and O 3 

### were higher in London, whereas PM 10 

### levels in Hong Kong were almost double those in London (46.8 vs. 24.8 μg/m 3 

### ) (Table 3). In Hong Kong, there was amarked seasonal variation in ambient concentrations of NO 2 

### , O 3 

### , and PM 10 

### , all of which were lowest in the warm season and highest in the cool season. In contrast, SO 2 

### tended to show less seasonal variation and was highest in the warm season. In London, there was little seasonal variation in NO 2 

### , 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/m 3 )

NO 2 (24 hr) Hong Kong a 55.9 (48.1/63.8) 19.4 15.3 31.8 53.5 81.8 151.5 London b 64.3 (62.6/66.1) 20.4 23.7 42.3 61.2 88.8 255.8 O 3 (8 hr)

Hong Kong c 33.5 (32.0/35.1) 23.0 0 7.9 28.3 64.0 168.9 London d 34.9 (45.3/24.0) 23.1 2.4 8.6 32.0 60.1 159.8 PM 10 (24 hr)

Hong Kong e 51.8 (42.2/61.6) 25.0 14.1 24.7 46.8 87.2 163.8 London f 28.5 (28.2/28.8) 13.7 6.8 15.8 24.8 46.4 99.8 SO 2 (24 hr)

Hong Kong g 17.7 (18.3/17.2) 12.3 1.1 6.2 14.5 32.8 90.0 London h 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.

a r = 0.65–0.90 between seven stations. b r = around 0.8 between three stations. c r = 0.79 between two stations. dr = 0.95 between two stations. e r = 0.92–0.97 between five stations. f Only one station involved. g r = 0.44–0.81 between five stations. h r = –0.1 to 0.8 (median 0.5) in six stations.

### SO 2 

### , or PM 10 

### , but there was marked seasonal variation in O 3 

### , which was highest in the warm season. Thus, the only pollutant with a similar seasonal pattern in both cities was SO 2 

### . 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 highestin 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 ineither of the cities. For respiratory admissions, we found small, positive, and statistically significant associations with all four pollutants in Hong Kong. By contrast, only O 3 

### was significantly associated with respiratory admissions in London. For cardiac diseases, both cities showed significant positive associations of comparable size with NO 2 

### , PM 10 

### , and SO 2 

### . There were no significant positive associations with O 3 

### 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 O 3 

### ) and none were significant. In London, the relative risks for IHD were similar to those for all cardiacdiseases and all were significant; the association with O 3 

### 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 resultsare 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 NO 2 

### , PM 10 

### , and SO 2 

### 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/m 3 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

NO 2 – 0.6 (

– 2.1–1.0) 1.0 (0.0–2.1) O 3 0.0 (

– 1.3–1.4)

– 0.7 (

– 1.8–0.4) PM 10 – 1.1 (

– 2.4–0.1) 1.4 (

– 0.1–3.0) SO 2 – 0.1 (

– 2.4–2.2) 0.7 (

– 1.0–2.5) Respiratory, ≥ 65 years

NO 2 1.8 (1.2– 2.4)

– 0.1 (

– 0.6–0.5) O 3 0.8 (0.3–1.3) 0.8 (0.2–1.4) PM 10 1.0 (0.5–1.5) 0.4 (

– 0.3–1.2) SO 2 1.8 (0.9–2.6) 0.2 (

– 0.6–1.1) Cardiac, all ages

NO 2 1.4 (0.9–2.0) 0.7 (0.3–1.0) O 3 0.3 (

– 0.2–0.7)

– 0.6 (

– 1.0–

– 0.1) PM 10 0.7 (0.3–1.1) 0.8 (0.3–1.4) SO 2 2.1 (1.3–2.8) 1.6 (1.0–2.2) IHD, all ages NO 2 0.6 (

– 0.2–1.4) 0.7 (0.2–1.2) O 3 0.4 (

– 0.3–1.1)

– 0.8 (

– 1.4–

– 0.2) PM 10 0.5 (

– 0.1–1.1) 0.9 (0.1–1.6) SO 2 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/m 3 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

NO 2 1

– 1.3 (

– 2.6–0.1) 2 1.1 (0.2–2.0) O 3 2 1.2 (0.0–2.4) 0

– 0.7 (

– 1.7–0.3) PM 10 0

– 1.1 (

– 2.1–0.0) 2 2.2 (0.8–3.6) SO 2 2

– 1.5 (

– 3.4–0.5) 3 2.1 (0.7–3.6) Respiratory, ≥ 65 years

NO 2 0 1.3 (0.8–1.8) 3 0.9 (0.5–1.3) O 3 1 0.6 (0.2–1.0) 0 0.6 (0.1–1.2) PM 10 0 0.7 (0.3–1.0) 3 1.5 (0.8–2.2) SO 2 0 1.7 (1.0–2.4) 3 1.2 (0.5–2.0) Cardiac, all ages

NO 2 0 1.2 (0.7–1.7) 0 0.7 (0.4–1.0) O 3 2 0.5 (0.1–0.8) 0

– 0.8 (

– 1.2–

– 0.4) PM 10 0 0.5 (0.2–0.9) 0 1.1 (0.5–1.5) SO 2 0 1.6 (1.0–2.2) 0 1.4 (0.9–1.9) IHD, all ages NO 2 3 0.7 (0.1–1.4) 0 0.7 (0.2–1.1) O 3 3 0.5 (0.0–1.0) 0

– 0.9 (

– 1.4–

– 0.3) PM 10 2 0.5 (

– 0.1–1.0) 3 0.3 (

– 0.5–1.0) SO 2 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).
SO 2 PM 10 O 3 Temperature Humidity Hong Kong Whole year

NO 2 0.37 0.82 0.43 –0.45 –0.35 SO 2 0.30 –0.18 0.17 –0.16 PM 10 0.54 –0.42 –0.53 O 3 –0.14 –0.59 Temperature 0.19 Warm season

NO 2 0.28 0.80 0.54 –0.43 –0.18 SO 2 0.22 –0.14 0.37 –0.16 PM 10 0.65 –0.25 –0.40 O 3 –0.17 –0.57 Temperature –0.26 Cool season

NO 2 0.61 0.72 0.23 0.10 –0.36 SO 2 0.53 –0.21 0.13 –0.20 PM 10 0.36 0.01 0.55 O 3 0.05 –0.60 Temperature 0.21 SO 2 PM 10 O 3 Temperature Humidity
London Whole year

NO 2 0.71 0.68 –0.29 –0.16 0.01 SO 2 0.64 –0.25 –0.13 –0.15 PM 10 0.17 0.02 –0.05 O 3 0.47 –0.52 Temperature –0.27 Warm season

NO 2 0.66 0.68 0.05 0.08 –0.09 SO 2 0.56 0.14 0.26 –0.33 PM 10 0.27 0.32 –0.14 O 3 0.26 –0.53 Temperature 0.26 Cool season

NO 2 0.76 0.68 –0.61 –0.36 0.01 SO 2 0.70 –0.58 –0.46 –0.05 PM 10 –0.56 0.23 0.01 O 3 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 NO 2 

### , O 3 

### , and SO 2 

### . PM 10 

### 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 NO 2 

### and SO 2 

### (Table 7). In London, the pattern was reversed with greater effects in the warm season, significantly so for NO 2 

### and PM 10

### (Table 7). The two cities were similar in having larger estimates of cardiac admissions in the cool season (with the exception of O 3 

### for London); all of these seasonal interactions were significant for Hong Kong, but only one (PM 10 

### ) 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 PM 10 

### and SO 2 

### associations were substantially reduced after NO 2 

### was added to the models. In London, associations between respiratory admissions and NO 2 

### , PM 10 

### , and SO 2 

### were nonsignificant and remained unchanged after the addition of a second pollutant. The significant O 3 

### associations found in London were robust to the inclusion of an additional pollutant. For cardiac admissions in Hong Kong, the addition of NO 2 

### or SO 2 

### reduced the magnitude and statistical significance of NO 2 

### , SO 2 

### , and PM 10 

### associations (O 3 

### was not found to be significant in single-pollutant models). These results were largely replicated in the London analyses, although in a model containing NO 2 

### and SO 2 

### , SO 2 

### was clearly the “most robust” pollutant, retaining both the magnitude and statistical significance of its association after the inclusion of NO 2 

### . 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 3RR(95% CI) NO 2(24-hr) O 3 (8-hr) PM 10(24-hr) SO 2(24-hr) NO 2(24-hr) O 3(8-hr) PM 10(24-hr) SO 2(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 3RR(95% CI) NO 2(24-hr) O 3 (8-hr) PM 10(24-hr) SO 2(24-hr) NO 2(24-hr) O 3(8-hr) PM 10(24-hr) SO 2(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/m 3 increase in concentration.

Table 7. Summary of single-pollutant results in excess risk (ER) and 95% confidence interval (CI) for a 10 μg/m 3 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

NO 2 – 0.5 (

– 2.7–1.6)

– 0.6 (

– 2.8–1.6) O 3 – 0.3 (

– 2.0–1.3) 0.6 (

– 1.4–2.6) PM 10 – 1.0 (

– 2.8–0.8)

– 1.2 (

– 2.8–0.4) SO 2 1.5 (

– 1.5–4.6)

– 2.0 (

– 5.4–1.4) Respiratory, ≥ 65 years

NO 2 0.8 (0.1–1.6) 3.0 (2.1–3.9) p ≤ 0.001 O 3 0.8 (0.2–1.4) 1.0 (0.2–1.7) PM 10 0.8 (0.1–1.4) 1.2 (0.6–1.9) SO 2 1.1 (0.0–2.2) 2.7 (1.4–4.0) p ≤ 0.05 Cardiac, all ages

NO 2 0.3 (

– 0.4–1.0) 2.6 (1.9–3.3) p ≤ 0.001 O 3 0.0 (

– 0.5–0.6) 0.9 (0.2–1.6) p ≤ 0.05 PM 10 0.0 (

– 0.6–0.6) 1.3 (0.8–1.9) p ≤ 0.001 SO 2 1.0 (0.0–2.0) 3.3 (2.1–4.4) p ≤ 0.01 IHD, all ages NO 2 0.1 (

– 0.9–1.2) 1.2 (0.0–2.3) O 3 0.4 (

– 0.4–1.2) 0.6 (

– 0.5–1.6) PM 10 0.2 (

– 0.7–1.0) 0.8 (

– 0.1–1.6) SO 2 – 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

NO 2 0.6 (

– 0.8–2.0) 1.3 (

– 0.1–2.8) O 3 – 0.1 (

– 1.4–1.2)

– 2.6 (

– 4.6–

– 0.5) p ≤ 0.05 PM 10 0.6 (

– 1.9–3.1) 1.6 (

– 0.3–3.6) SO 2 – 1.4 (

– 4.7–1.9) 1.6 (

– 0.5–3.8) Respiratory, ≥ 65 years

NO 2 0.6 (

– 0.2–1.4)

– 0.7 (

– 1.4–0.0) p ≤ 0.01 O 3 1.0 (0.3–1.7) 0.2 (

– 0.7–1.2) PM 10 1.8 (0.5–3.1)

– 0.5 (

– 1.5–0.5) p ≤ 0.01 SO 2 1.3 (

– 0.5–3.1)

– 0.3 (

– 1.3–0.8) Cardiac, all ages

NO 2 0.4 (

– 0.1–0.9) 0.8 (0.3–1.4) O 3 – 0.2 (

– 0.7–0.3)

– 1.1 (

– 1.8–

– 0.4) PM 10 0.1 (

– 0.7–1.0) 1.2 (0.5–2.0) p ≤ 0.01 SO 2 0.6 (

– 0.6–1.7) 1.9 (1.2–2.7) IHD, all ages NO 2 0.4 (

– 0.3–1.1) 1.0 (0.2–1.7) O 3 – 0.5 (

– 1.2–0.2)

– 1.3 (

– 2.3–

– 0.3) PM 10 0.1 (

– 1.1–1.4) 1.3 (0.3–2.3) SO 2 1.0 (

– 0.6–2.6) 2.0 (0.9–3.1)

### exposure–response relationship was observed for concentrations of O 3 

### < 20 μg/m 3 

### (mainly in the warm season); in London (mainly in the cool season), a neutral relationship was found. For levels of O 3 

### > 20 μg/m 3 

### , there were similar positive linear relationships in both cities (Figure 3). For cardiac admissions and PM 10 

### between the 10th and 90th percentiles (i.e., 25–87 μg/m 3 

### in Hong Kong and 16–46 μg/m 3 

### inLondon), both cities showed positive exposure–response relationships (Figure 3). We observed a negative linear association for O 3 

### 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 O 3 

### (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 theapproach 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 beenfound 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 defi ned in an identical manner. We didnot 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. Inany 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/m 3 increase in concentration in mean lag 0–1 days.

Table 8. Excess risk (ER) and 95% confidence interval (CI) for a 10 μg/m 3 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 NO 2 O 3 PM 10 SO 2 admission ER (95% CI) ER (95% CI) ER (95% CI) ER (95% CI) Respiratory

NO 2 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) O 3

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) PM 10

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) SO 2

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

NO 2 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) O 3

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) PM 10

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) SO 2

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

a Estimates from the single-pollutant model. Respiratory admissions ( ≥ 65)

Cardiac admissions (all ages) warm Season 1.06

1.04

1.02

1.00

0.98

0.96RR(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 NO 2(24-hr) Hong Kong London Hong Kong London O 3 (8-hr) Hong Kong London PM 10(24-hr) Hong Kong London SO 2(24-hr)

NO 2(24-hr) Hong Kong London Hong Kong London O 3 (8-hr) Hong Kong London PM 10(24-hr) Hong Kong London SO 2(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 thebest 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 O 3 

### , 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 PM 10 

### , NO 2 

### , and SO 2 

### 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 homeand for controlling access to hospital facilities. We observed that the lag patterns for respiratory mortality associated with NO 2 

### , PM 10 

### , and SO 2 

### (Figure 8) ( 34,35) also followed the respective patterns for respiratory admissions (Figure 1). The other difference in the effects of NO 2 

### , PM 10 

### , and SO 2 

### 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 largesteffects. It may also be relevant that the average levels of all pollutants apart from SO 2 

### are highest during the cool season in Hong Kong.The strong associations between respiratory admissions and NO 2 

### and PM 10 

### suggest that traffic may be an important source oftoxic pollution. In both cities, it has been estimated that only a minority (about 20–40%) of PM 10 

### 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 NO 2 

### retains its strong association in two pollutant models while PM 10 

### does not. O 3 

### showed consistent significant effects on respiratory admissions, irrespective ofwhether the mean lag 0–1 days or best single day lag was chosen. The exposure– response relationships with O 3 

### were linear in both cities when concentrations were > 20 μg/m 3 

### , but in Hong Kong, a negative relationship was observed below this level. Itcould be postulated that the Hong Kong population would be more resistant to O 3 

### because the diet is higher in antioxidants and because air conditioning is used in most closed spaces, but our data suggest that bothpopulations are equally susceptible. It is relevant to note that O 3 

### 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 O 3 

### 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 NO 2 

### , PM 10 

### , and SO 2 

### . This was irrespective of whether the mean lag 0–1 or the best lag was chosen, because in contrastto respiratory admissions, both cities displayed the same lag patterns, with lower risksat 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 O 3 

### in the cool season on admissions for these cardiac events in the elderly in Hong Kong ( 8). The exposure–response relationships with NO 2 

### , PM 10 

### , and SO 2 

### 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 NO 2 

### being robust to the inclusion of PM 10 

### in the models but affected to some extent by SO 2 

### . In both cities, the effect of SO 2 

### 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/m 3 ) and response relationships for O 3 and respiratory admissions and for PM 10 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 8 0 100 120 120 100 80 60 40 20 140
Concentration O 3 in lag 0–1 day Concentration PM 10 in lag 0–1 day

Concentration PM 10 in lag 0–1 day Concentration O 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 6 0 80 100 120 140 20 40 60 80Log-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 NO 2 in lag 0–1 day Concentration SO 2 in lag 0–1 dayLog-risk(deviated from overall mean)Log-risk(deviated from overall mean) –0.05 0.15

0.10

0.05

0.0

–0.05

20 0 40 60Log-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 0.10Log-risk(deviated from overall mean)

Concentration NO 2 in lag 0–1 day Concentration SO 2 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 PM 10 in lag 0–1 day Concentration O 3 in lag 0–1 dayLog-risk(deviated from overall mean)Log-risk(deviated from overall mean) –0.05 0.15

0.10

0.05

0.0

20 0 40 60Log-risk(deviated from overall mean)

0.0

–0.05Log-risk(deviated from overall mean)

50 100 150 20 40 60 80 0.20

Concentration O 3 in lag 0–1 day Concentration PM 10 in lag 0–1 day 80 100 120 140

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 NO 2 in lag 0–1 day Concentration SO 2 in lag 0–1 dayLog-risk(deviated from overall mean)Log-risk(deviated from overall mean) –0.05 0.15

0.10

0.05

0.0

20 0 40 60Log-risk(deviated from overall mean)

0.0

–0.1Log-risk(deviated from overall mean)

50 100 150 20 40 60 80 0.0

Concentration NO 2 in lag 0–1 day Concentration SO 2 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 PM 10 in lag 0–1 day Concentration O 3 in lag 0–1 dayLog-risk(deviated from overall mean)Log-risk(deviated from overall mean) –0.05 0.0

20 0 40 60Log-risk(deviated from overall mean)

0.0

–0.05Log-risk(deviated from overall mean)

Concentration O 3 in lag 0–1 day Concentration PM 10 in lag 0–1 day 80 100 120 140

0.05 0.10 0.0 0.05

–0.10

–0.15

20 40 60 80 100 120 140 0 20 40 60 80 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 NO 2 in lag 0–1 day Concentration SO 2 in lag 0–1 dayLog-risk(deviated from overall mean)Log-risk(deviated from overall mean) –0.05 0.10

0.05

0.0

–0.05

20 0 40 60Log-risk(deviated from overall mean)

0.10

0.05

0.0

–0.05 0.20

0.15

–0.05

0.0 0.05 0.10Log-risk(deviated from overall mean)

Concentration NO 2 in lag 0–1 day Concentration SO 2 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 PM 10 in lag 0–1 day Concentration O 3 in lag 0–1 dayLog-risk(deviated from overall mean)Log-risk(deviated from overall mean) –0.05 0.0

20 0 40 60Log-risk(deviated from overall mean)

0.0Log-risk(deviated from overall mean)

Concentration O 3 in lag 0–1 day

Concentration PM 10 in lag 0–1 day 80 100 120 140

0.05 0.10

0.0 0.05

–0.10

20 40 60 80 100 120 140 0 20 40 60 80 –0.05 –0.10 –0.05 0.05 0.10

–0.10

A

B

0.0

20 40 60 80 100 120

Concentration NO 2 in lag 0–1 day Concentration SO 2 in lag 0–1 dayLog-risk(deviated from overall mean)Log-risk(deviated from overall mean) 0.0

–0.5

20 0 40 60Log-risk(deviated from overall mean)

0.0

–0.1

–0.2

–0.3 –0.3 0.0Log-risk(deviated from overall mean)

Concentration NO 2 in lag 0–1 day Concentration SO 2 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 PM 10 in lag 0–1 day Concentration O 3 in lag 0–1 dayLog-risk(deviated from overall mean)Log-risk(deviated from overall mean) 0.0

20 0 40 60Log-risk(deviated from overall mean)

0.0Log-risk(deviated from overall mean)

Concentration O 3 in lag 0–1 day

Concentration PM 10 in lag 0–1 day 80 100 120 140

0.05 0.10

0.0 0.2

20 40 60 80 100 120 140

0 20 40 60 80 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 inwhich concerns about unknown or inadequately controlled confounding can beaddressed. 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 affectconfounding 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 theeffects 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 O 3 

### 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 O 3 

### associations with respiratory disease are at least independent of other pollutants and in line with studies elsewhere. There is also evidence that O 3 

### is potentially toxic at near ambient levels ( 2 ). For cardiac admissions, the PM 10 

### associations were less independent of NO 2 

### (and in some cases SO 2 

### ) than the reverse. Toxicologic evidence suggests that NO 2 

### and SO 2 

### are unlikely to have effects at this level, which points toward them being surrogates for some other toxic component. PM 10 

### is widely regarded asimportant in spite of meagre human toxicologic corroboration. The cities were similar in the proportion of fine particles comprising PM 10 

### 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 ).

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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/m 3 increase in concentration. 0 1.04

1.02

1.00

0.98

0.96

1 2 3 0 1 2 3 0 1 2 3 0 1 2 3 01 2 3 0 1 2 3 0 1 2 3 01 2 3RR(95% CI)RR(95% CI) 1.04

1.02

1.00

0.98

0.96
Single lag day Respiratory mortality (all ages) Cardiovascular mortality (all ages) A

B 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3 01 2 3 0 1 2 3 0 1 2 3 01 2 3 NO 2(24-hr) O 3(8-hr) PM 10(24-hr) SO 2(24-hr) NO 2(24-hr) O 3 (8-hr) PM 10(24-hr) SO 2(24-hr)

NO 2(24-hr) O 3(8-hr) PM 10(24-hr) SO 2(24-hr) NO 2(24-hr) O 3 (8-hr) PM 10(24-hr) SO 2(24-hr)28. Buckingham C, Clewley L, Sadler L, Shah S. London Atmospheric Emissions Inventory. London:London Research Centre, 1997. 29. Kelsall JE, Samet JM, Zeger SL, Xu J. Air pollution and mortality in Philadelphia, 1974–1988. Am J Epidemiol 146:750–762 (1997). 30. Samet J, Zeger S, Kelsall J, Xu J, Kalkstein L. Doesweather confound or modify the association of particulate air pollution with mortality. Environ Res 77:9–19 (1998).31. Leung R, Ho P. Asthma, allergy, and atopy in three southeast Asian populations. Thorax 49:1205–1210 (1994).32. Worldwide variations in the prevalence of asthma symptoms: the International Study of Asthma and Allergies in Childhood (ISAAC). Eur Respir J 12:315–335 (1998). 33. Spix C, Anderson HR, Schwartz J, Vigotti MA, LeTertre A, Vonk JM, Touloumi G, Balducci F, Piekarski T, Bacharova L, et al. Short-term effects of air pollution on hospitaladmissions of respiratory diseases in Europe: a quantitative summary of APHEA study results. Air Pollution and Health: a European Approach. Arch Environ Health 53:54–64 (1998). 34. Bremner SA, Anderson HR, Atkinson RW, McMichaelAJ, Strachan DP, Bland JM, Bower JS. Short-term associations between outdoor air pollution and mortality in London 1992–4. Occup Environ Med 56:237–244 (1999).35. Wong CM, Ma S, Hedley AJ, Lam TH. Effect of air pollution on daily mortality in Hong Kong. Environ Health Perspect 109:335–340 (2001). 36. Stedman JR, Linehan E, Conlan B. Receptor modelling of PM 10 concentrations at a United Kingdom National Network Monitoring site in Central London. Atmos Environ 35:297–304 (2000). 37. Wu C. Personal communication. 38. Anderson HR, Ponce de Leon A, Bland JM, Bower JS, Emberlin J, Strachan DP. Air pollution, pollens, and daily admissions for asthma in London 1987–92. Thorax 53:842–848 (1998). 39. Environmental Protection Department. Air Quality in Hong Kong 1995. Hong Kong:Environmental Protection Department, 1996. 40. Environmental Protection Department. Air Quality in Hong Kong 1996. Hong Kong:Environmental Protection Department, 1997 41. Environmental Protection Department. Air Quality in Hong Kong 1997. Hong Kong:Environmental Protection Department, 1998.42. Levy JI, Hammitt JK, Spengler JD. Estimating the mortality impacts of particulate matter: what can be learned from between-study variability? Environ Health Perspect 108:109–117 (2000). 

### Articles • 

### Health effects of air pollution in Hong Kong and London

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

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