|
Executive
Summary
Background
and objectives: Locally derived data are needed for the SAR
Government and regulatory agencies to support the drafting
and implementation of effective legislation for the management
and control of the environment. In 1996, the Environmental
Protection Department commissioned the Chinese University
of Hong Kong to carry out a study on health effects of air
pollution and in 1997 the University of Hong Kong to carry
out a follow-up validation study. The study performed by the
University of Hong Kong identified some irregularities in
the way the data were selected, managed and analysed in the
previous study. It also obtained effect estimates which were
more comparable to the results obtained by the collaborating
centres of Air Pollution on Health: a European Approach (APHEA).
Finally it pointed to new directions in methodology and in
modelling methods adopted by the APHEA Phase Two study which
could be used in Hong Kong.
In this
study, reported here, the problems of the earlier Hong Kong
studies were resolved in that (i) three years of data were
used to obtain better models; (ii) non-linearity effects in
the pollutant and covariates were modelled using a generalized
additive modelling method; and (iii) patterns in the daily
variations were explained as far as possible by using more
appropriate wave lengths, introducing influenza epidemics
in the models and using more recently developed statistical
methods to control for autocorrelation in the data.
Health
outcomes: Daily counts of hospital admissions with a discharge
diagnosis of respiratory disease (ICD9 460-519), chronic obstructive
pulmonary disease (COPD) (490-496 excluding 493), asthma (493),
cardiac disease (390-429) and ischaemic heart disease (IHD)
(410-414), for all ages, as well as hospital deaths due to
respiratory (460-519) and cardiovascular (390-459) diseases,
for all ages, were used as the 7 major health outcomes. Among
them, admissions were sub-categorised as follows: for respiratory
disease, into 0-14, 15-64, 65-74 and 75+ age groups; for asthma,
into 0-14 and 15-64 age groups; for cardiac disease, into
15-64, 65-74 and 75 age groups and for IHD, into 15-64, 65-74
and 75+ age groups. These categories for all ages and specific
age groups formed 19 categories of health outcomes for analysis.
Summary
of findings: The effects for each pollutant without adjustment
for co-pollutant, on the above-mentioned 19 health outcomes
and with adjustment for the most confounding co-pollutant,
on the above 7 major health outcomes are summarized in this
section.
NO2:
NO2 was shown to have strong effects on all the
19 health outcomes under study without adjustment for co-pollutants,
except on the IHD 15-64 and all ages groups. The effects on
the 7 major health outcomes, with adjustment for co-pollutant
and autocorrelation, were significant at the 5% level except
IHD admissions and cardiovascular hospital deaths for all
ages. The effects on asthma admissions and respiratory deaths
for all ages were likely to be non-linear. The effects on
respiratory, COPD, cardiac and IHD admissions for all ages
were higher in the cool than in the warm season.
SO2:
SO2 did not have an effect on most of the 19 health
outcomes under study without adjustment for co-pollutant.
With adjustment for co-pollutant and autocorrelation, it had
an effect on COPD admissions for all ages (p=0.005). The effect
on cardiac admissions for all ages was higher in the cool
than in the warm season.
RSP: RSP
was associated with most of the 19 health outcomes under study
without adjustment for co-pollutant. With adjustment for co-pollutants
and autocorrelation, it had an effect on respiratory (p=0.023)
and asthma (p=0.045) admissions for all ages and the effects
were likely to be non-linear. The effect on cardiac admissions
for all ages was higher in the cool than in the warm season.
O3:
O3 had effects on most of the 19 health outcomes
under study without adjustment for co-pollutant. With adjustment
for co-pollutant and autocorrelation, it had strong effects
on respiratory and asthma admissions for all ages (p=0.000).
The effects on COPD, cardiac and IHD admissions for all ages
were stronger in the cool than in warm season.
FSP: FSP
(PM2.5) had effects on most of the 19 health outcomes
under study without adjustment for co-pollutant. With adjustment
for co-pollutant, the effects were not significant for all
of the 7 major health outcomes for all ages (p>0.192). But
as the pollutant was measured in only one monitoring station
and there were missing data problems, the effects need to
be further investigated.
Public
health implications: The results from this study showed strong
short-term effects of air pollutants on hospital admissions
and hospital deaths.
In the
daily number of hospital admissions for all ages, there was
a 4-8% increase due to respiratory disease; 5-15% increase
due to chronic obstructive pulmonary disease and 11-22% increase
due to asthma. There was also a 3-7% increase in admissions
due to a cardiac disease. There was a 3-5% increase due to
a cardiovascular disease (ICD9 390-459), which includes cardiac
disease as well as cerebrovascular disease and diseases of
arteries, arterioles, capillaries, veins and lymphatics. These
percentage increases are all expressed as rates per 50 ug/m3
increase in the concentration of air pollutants (NO2,
SO2, O3 and RSP).
In the
daily number of hospital deaths for all ages, there was a
10% increase due to respiratory disease and 8% increase due
to cardiovascular disease for every 50 ug/m3 increase
in NO2. For other pollutants the increases were
not significant and they need to be further investigated.
The results
suggest that air pollution may show stronger health effects
in Hong Kong than those obtained in European cities.
Conclusions:
The results from this study show that short-term effects of
air pollutants on health are strong. It is both feasible and
worthwhile to develop further studies on the health effects
of air pollution with a longer series of data, using the methodology
adopted by the APHEA protocol. Territory-wide SAR mortality,
based on daily mortality counts from the Census and Statistics
Department, should be included as one of the main health outcomes
to be studied.
1.0
Background and introduction
In 1997,
the Department was offered a contract by the Environmental
Protection Department (EPD) to study the short-term effects
of ambient air pollution on public health using hospital admission
data in Hong Kong for the period January 1995 to June 19971
and to validate the findings of another similar study on data
for the period January 1994 to June 1996.2 The
results revealed that, similar to other studies overseas,
there were significant effects on morbidity and mortality
of individual pollutants i.e. nitrogen dioxide (NO2),
sulphur dioxide (SO2), respirable suspended particulates
(RSP) and ozone (O3). The study performed by the
Department modified the way the data were selected, managed
and analysed in the previous study and obtained effect estimates
which were more comparable to the results obtained by collaborating
centres of Air Pollution on Health: a European approach (APHEA).
In line with new directions in methodology and in modelling
methods adopted by the APHEA-2 protocol,3 our report
also pointed out some limitations of both of the Hong Kong
studies and indicated what should be done in any future studies.
The limitations
in the Hong Kong studies will be further resolved and discussed
in this new phase of study. First, with three years of data,
a better prediction model will be developed. Second, non-linearity
effects in the pollutant(s) or covariates will be modelled
using a generalized additive model (in statistical package
S-Plus). Third, unexplained patterns in the daily variations
will be explained as far as possible by (i) using more appropriate
wave lengths for seasonality and terms for time trends, to
eliminate as far as possible all unobserved confounding effects;
(ii) introducing influenza epidemics in the model to control
as much of the observed confounding effects as possible in
addition to those due to air temperature and humidity etc;
and (iii) using more recently developed statistical methods
to control for any auto-correlation and/or extra-Poisson variations
in the data.
2.0
Scope and objectives
The following
tasks will be completed by the Department of Community Medicine.
-
Single
pollutant: A model for each health outcome is fitted with
terms to account for all long-term and seasonal patterns
and other possible confounding effects. Short-term daily
variations are then studied and accounted for by daily
variations in SO2, NO2, RSP and
O3 individually, based on data from 1 January
1995 to 31 December 1997.
-
Multiple
pollutants: From each single pollutant model, the joint
effects of each pollutant with other pollutants are studied
by putting them in the model. Interactions and multi-collinearity
among pollutants are also studied and taken account of,
based on the same data as in a) above.
-
Single
pollutant PM2.5: The effect due to PM2.5
is studied in a similar way as in a) above, but using
data from 1 August 1995 to 31 December 1997 when the PM2.5
concentrations are available from the EPD.
-
Multiple
pollutants with PM2.5: Study b) above but with
PM2.5 instead of RSP. Compare the models with
either PM2.5 or RSP in terms of stability (reliability)
in effect estimates.
-
Verification
of model: The best model developed from a) to d) above
is then used to obtain the predicted daily numbers of
admissions for each health outcome. The observed number
of admissions in the data 1 January 1998 to 30 June 1998
are compared with the predicted for verification (i.e.
predictive validity) of model.
3.0
Materials and methods
| 3.1 |
Study
design
The
same design as in the previous report was used. It utilized
routinely collected hospital admission data, air pollutant
concentration data and meteorological data by the Hospital
Authority, Environmental Protection Department and Observatory.
Variations in the daily number of hospital admissions
were studied, and their relationships with each of the
pollutants were modelled to assess the effects of air
pollution on health after adjustment for confounding
factors.
|
| |
|
| 3.2 |
Databases
In
the APHEA-2 protocol there were changes in age group
definitions and missing data handling methods from those
in APHEA. All the changes were incorporated in this
report as far as possible. In order to be more comparable
with results from APHEA studies, there were also some
alterations in the choice of health outcomes, in that
the category of chronic obstructive pulmonary disease
(ICD9 490-496) excluded asthma (ICD9 493), and cardiac
diseases (ICD9 390-429) was used instead of circulatory
disease (ICD9 390-459) in the admission categories.
Hospital
admission data: The hospitals included for compilation
of hospital admissions were the publicly funded hospitals
(accounting for 90% of hospital beds in Hong Kong) with
an accident & emergency department, or those which
were the referral base from the accident & emergency
department of another hospital or had a 24-hour outpatient
department. One other hospital which was the only hospital
in the most polluted district in Hong Kong was also
included. All hospitals should have a computerized system
for inputting and retrieval of patient data (Basic Table
A1).
Information
for patients admitted to the above-mentioned hospitals
between 1.1.1995 and 30.6.1998, on date of admission,
discharge diagnosis (in ICD9 codes) and age (and other
information not required in this study) were retrieved
from the databases. The number of cases by discharge
diagnosis and age, grouped according to APHEA-2 protocol
(hospital deaths not in APHEA-2 categories, were also
included) were summarized in Table 1 below.
| Table
1: |
Number
of patients admitted between 1.1.1995 and 30.6.1998
by discharge diagnoses and age groups |
| Number
of cases |
| Discharge
diagnosis (ICD9 codes) |
Age
groups |
1995-1997 |
1998
(Jan-June) |
| Respiratory
disease (460-519) |
All
ages* |
223,225 |
46,877 |
| 0-14
yrs |
74,164 |
16,450 |
| 15-64
yrs |
48,909 |
9,451 |
| 65-74
yrs |
38,783 |
7,640 |
| 75+ |
61,318 |
13,329 |
Chronic
obstructive pulmonary disease
(490-496 excluding 493) |
All
ages* |
53,374 |
9,795 |
| Asthma
(493) |
All
ages* |
27,080 |
4,286 |
| 0-14
yrs |
14,005 |
2,133 |
| 15-64
yrs |
8,520 |
1,332 |
| Cardiac
disease (390-429) |
All
ages* |
108,150 |
18,835 |
| 15-64
yrs |
37,195 |
5,961 |
| 65-74
yrs |
32,205 |
5,715 |
| 75+ |
37,356 |
6,976 |
| Ischaemic
heart disease (410-414) |
All
ages* |
39,483 |
6,940 |
| 15-64
yrs |
14,259 |
2,402 |
| 65-74
yrs |
13,969 |
2,507 |
| 75+ |
11,201 |
2,029 |
| Deaths:
Respiratory (460-519) |
All
ages* |
11,798 |
2,095 |
| Cardiovascular
(390-459) |
All
ages* |
11,202 |
1,779 |
*
Including patients without information on age.
Pollution
data: Hourly concentrations of air pollutants (Table
2) by monitoring stations (Table 3) were provided by
the Air Services Group of the Environmental Protection
Department.
| Table
2: |
List
of pollutants used in the study |
| Pollutant |
Unit |
| 1.
Nitrogen Dioxide {24-hr} (NO2) |
micrograms/cubic
metre |
| 2.
Sulphur Dioxide {24-hr} (SO2) |
micrograms/cubic
metre |
| 3.
Respirable Suspended Particulates {24-hr} (RSP)* |
micrograms/cubic
metre |
| 4.
Ozone {8-hr} (O3) |
micrograms/cubic
metre |
| 5.
Fine Suspended Particulates {24-hr} (FSP) |
micrograms/cubic
metre |
*
Measured by Tapered Element Oscillating Microbalance
(TEOM), an instrument for the continuous measurement
of particulate matter in air.
| Table
3: |
List of air pollution monitoring stations |
| Station |
Sampling
height |
Above
ground |
Date
start operation |
| 1.
Central/West (C/W) |
78m |
18m
(4th floor) |
11/83 |
| 2.
Kwai Chung (KC) |
82m |
25m
(6th floor) |
7/88 |
| 3.
Kwun Tong (KT) |
34m |
25m
(6th floor) |
7/83 |
| 4.
Sham Shui Po (SSP) |
21m |
17m
(4th floor) |
7/84 |
| 5.
Shatin (ST) |
27m |
21m
(5th floor) |
7/91 |
| 6.
Tai Po (TP) |
31m |
25m
(6th floor) |
2/90 |
| 7.
Tsuen Wan (TW) |
21m |
17m
(4th floor) |
8/88 |
Two
stations were excluded, one in Yuen Long (in suburban
area) due to the extent of missing data and one in Mong
Kok (in urban area) which provided concentrations measured
on ground level.
In
each station, for O3 the 8-hourly (9 am -
5 pm when O3 concentrations were highest)
means were taken as daily data if there were at least
6 valid hourly data each day; and for the other pollutants
the 24-hourly means were used as daily data if there
were at least 18 valid hourly data. Other data were
missing data. The percentages of non-missing daily data
were shown in Table 4 below.
| Table
4: |
Percentage
of valid measures of air pollutants in each monitoring
station |
|
Station# |
| Pollutant |
C/W |
KC |
KT |
SSP |
ST |
TP |
TW |
| NO2
(24-hr average) |
| 1995 |
93 |
92 |
96 |
89 |
97 |
97 |
95 |
| 1996 |
93 |
96 |
95 |
90 |
96 |
95 |
93 |
| 1997 |
91 |
90 |
87 |
98 |
93 |
89 |
85 |
| 1995-97 |
92 |
93 |
93 |
92 |
95 |
93 |
91 |
| 1998* |
97 |
96 |
94 |
85 |
97 |
98 |
96 |
| SO2
(24-hr average) |
| 1995 |
98 |
97 |
97 |
93 |
99 |
- |
95 |
| 1996 |
98 |
95 |
98 |
89 |
99 |
- |
98 |
| 1997 |
92 |
93 |
93 |
96 |
94 |
- |
82 |
| 1995-97 |
96 |
95 |
97 |
93 |
97 |
- |
91 |
| 1998* |
97 |
98 |
94 |
86 |
97 |
- |
94 |
| RSP
(24-h average) |
| 1995 |
85 |
95 |
79 |
- |
87 |
- |
94 |
| 1996 |
99 |
98 |
92 |
- |
87 |
- |
86 |
| 1997 |
90 |
92 |
85 |
- |
91 |
- |
90 |
| 1995-97 |
91 |
95 |
85 |
- |
89 |
- |
90 |
| 1998* |
99 |
95 |
95 |
- |
99 |
- |
92 |
| O3
(8-hr average) |
| 1995 |
94 |
93 |
- |
- |
- |
- |
- |
| 1996 |
92 |
95 |
- |
- |
- |
- |
- |
| 1997 |
87 |
90 |
- |
- |
- |
- |
- |
| 1995-97 |
91 |
93 |
- |
- |
- |
- |
- |
| 1998* |
92 |
96 |
- |
- |
- |
- |
- |
| FSP
(24-hr average) |
| 1995
(Aug-Dec) |
- |
- |
- |
- |
- |
- |
94 |
| 1996 |
- |
- |
- |
- |
- |
- |
82 |
| 1997 |
- |
- |
- |
- |
- |
- |
92 |
| 1995
(Aug)-97 |
- |
- |
- |
- |
- |
- |
88 |
| 1998* |
- |
- |
- |
- |
- |
- |
97 |
*
the data are available only for first half year of 1998
- not available
# Abbreviations referred to Table 3.
Daily
mean concentrations representing the pollutant levels
in all Hong Kong areas were obtained from non-missing
data for each of the pollutants under study by means
of the following steps:
-
Estimate
the annual mean concentration of each pollutant
in each monitoring station and subtract this annual
mean from the daily concentrations of the corresponding
year. The resulting series is regard as "centered".
-
Take the arithmetic mean over all monitoring stations
of these centered series day by day.
-
Finally add the annual mean of all stations to the
series obtained in step 2. The series for each health
outcome is used for the analysis.
Overall,
daily means obtained using APHEA-2 method were highly
correlated (Spearman's correlation coefficient above
0.99) with those obtained using the previous APHEA method
(Basic Table D3).
Meteorological
data: Daily means of temperature and relative humidity
were obtained from the Environmental Protection Department
which derived the data from the Observatory.
|
| |
|
| 3.3 |
Statistical modelling
The
statistical modelling methods followed the guideline
recommended by the APHEA-2 protocol and are outlined
as follows:
| a) |
Poisson
regression with adjustment for over-dispersion using
quasi-likelihood4 approach was used to
model the hospital admission counts of the health
outcomes under study. |
| |
|
| b) |
Using
Poisson regression mentioned in a) above, a core
model was established to remove all the long term
trends and seasonal variations by means of the
following covariates:
| Covariate |
Explanation |
| t
(day) |
Daily
trend |
| t2 |
Daily
curvature |
| cos
(2?kt/365.25) |
Seasonality |
| sin
(2?kt/365.25) |
for
k cycles per year to be determined by spectral
analysis |
| Monday |
Monday
vs Sunday |
| Tuesday |
Tuesday
vs Sunday |
| Wednesday |
Wednesday
vs Sunday |
| Thursday |
Thursday
vs Sunday |
| Friday |
Friday
vs Sunday |
| Saturday |
Saturday
vs Sunday |
| Holiday1 |
Holidays |
| Holiday2 |
Day
after holidays |
| Temperature |
Linear
effect of temperature |
| Humidity |
Linear
effect of relative humidity |
| Influenza |
-
Dummy variable for weeks with number of
influenza admissions (ICD9 487) above
75 percentile in a year
|
The
numbers of seasonal cycles per year were obtained
from spectral analysis5 instead of
1-4 cycles used in the previous study.
|
| |
|
| c)
|
The
residuals from fitting the core model for each health
outcome, were examined for any discernible patterns,
lack of normality and autocorrelation in them. |
| |
|
| d) |
If
there are no discernible patterns and lack of normality
in the residuals, then potential confounding is
not evident in the daily variations. Air pollutant
concentrations were then fitted to obtain the air
pollution effects for pollutants. Autocorrelations
were taken account of for identified models. |
| |
|
| e) |
In
order to obtain an indicator of the amount (proportion)
of variations in the health outcome explained, the
number of admissions was tranformed by taking the
logarithm and multiple regression was then applied.
The value R2 was used to quantify the
proportion of variations explained by the core model. |
| |
|
| f) |
Effects
were obtained per unit change in pollutant levels
measured by current day and average of current day
up to a previous day, called the best lag day, to
be determined by AkaikeÕs Information Criterion
with the minimum value. The best lag day was determined
up to previous 5 days for ozone and up to previous
3 days for the other pollutants. |
| |
|
| g)
|
Interaction
effect between each pollutant (in the original scale)
and each of the other pollutants was checked by
first dichotomizing the other pollutants using the
median and multiplying each of them to obtain the
interaction term, and then introducing the interaction
term into the model. When there was an interaction
effect, the air pollutant effect was estimated for
high and low levels of the other pollutant. |
| |
|
| h) |
Interactions
between pollutant concentrations and season were
checked by defining a season dummy variable with
1 for cool (January-April; November-December) and
0 for warm (May-October) season, multiplying the
season dummy variable with concentrations to obtain
the interaction term and fit each interaction term
to the model. When there was an interaction effect,
relative risks in cool and warm seasons were obtained
respectively. |
| |
|
| i)
|
Effects
due to copollutants were estimated by putting the
other pollutants one by one in the model. Effect
with adjustment for a copollutant which was associated
with the maximum adjusting effect was used. |
| |
|
| j)
|
Autocorrelations
were adjusted for using Newy-West method.6
Non-linear copollutant effects were studied by
means of generalised additive model.7
Non-linear
dose-response relationships of pollutants were
also studied using generalised additive model.
|
| |
|
| k)
|
Validation
of the models for 1995-97 was achieved by comparing
the observed numbers of health outcomes in the first
half year of 1998 with the predicted from the model
developed using 1995-97 data. |
|
4.0
Findings
| 4.1 |
Descriptive
statistics
Summary
statistics of all the health outcomes used in this study
are shown in Tables 5-6 below.
| Table
5: |
Summary
statistics of daily hospital admissions (including
deaths) by disease groups (1995-97) |
| Admission |
No.
(days) |
Mean |
SD |
Min |
P25 |
Median |
P75 |
Max |
| Respiratory |
1096 |
203.7 |
38.49 |
116 |
175 |
197 |
227 |
335 |
| Asthma |
1096 |
24.7 |
8.59 |
7 |
19 |
23 |
29 |
63 |
| COPD |
1096 |
48.7 |
12.27 |
22 |
40 |
48 |
56 |
94 |
| Cardiac |
1096 |
98.7 |
23.28 |
40 |
80 |
101 |
114 |
179 |
| IHD |
1096 |
35.0 |
10.26 |
8 |
28 |
35 |
44 |
76 |
| Death: |
|
Cardiovascular
|
1096 |
10.2 |
3.77 |
1 |
8 |
10 |
13 |
24 |
|
Respiratory
|
1096 |
10.8 |
4.06 |
1 |
8 |
10 |
13 |
27 |
Min:
Minimum; P25: 25% centile; P75: 75% centile; Max: Maximum
Note
: COPD - Chronic obstructive pulmonary disease excluding
asthma
IHD
- Ischaemic heart disease
| Table
6: |
Summary
statistics of daily hospital admissions (including
deaths) by disease groups (1998 Jan-June) |
| Admission |
No.
(days) |
Mean |
SD |
Min |
P25 |
Median |
P75 |
Max |
| Respiratory |
181 |
259.0 |
43.39 |
174 |
227 |
259 |
285 |
389 |
| Asthma |
181 |
23.7 |
7.2 |
10 |
19 |
22 |
28 |
48 |
| COPD |
181 |
54.1 |
11.86 |
32 |
45 |
53 |
62 |
90 |
| Cardiac |
181 |
104.1 |
22.27 |
48 |
88 |
106 |
118 |
166 |
| IHD |
181 |
38.3 |
10.52 |
15 |
31 |
39 |
45 |
65 |
| Death: |
|
Cardiovascular
|
181 |
9.8 |
3.81 |
3 |
7 |
9 |
12 |
23 |
|
Respiratory
|
181 |
11.6 |
4.15 |
3 |
8 |
11 |
14 |
23 |
Min:
Minimum; P25: 25% centile; P75: 75% centile; Max: Maximum
Note
: COPD - Chronic obstructive pulmonary disease excluding
asthma
IHD
- Ischaemic heart disease
Summary
statistics of daily mean temperature, relative humidity,
and mean concentration of each of the pollutants are
shown in the Basic Tables B1-2.
The
correlations among the five pollutants, and between
pollutants and health outcomes are shown in Table 7
below.
| Table
7: |
SpearmanÕs
rank correlation coefficient (r) between daily concentrations
of pollutants, meteorological measures and hospital
admissions (1995-97) |
|
Respiratory |
COPD* |
Asthma |
Cardiac |
IHD |
|
r
(p-value) |
r
(p-value) |
r
(p-value) |
r
(p-value) |
r
(p-value) |
| SO2
(24-hr) |
0.10
(0.00) |
0.14
(0.00) |
-0.04
(0.15) |
0.15
(0.00) |
0.07
(0.02) |
| NO2
(24-hr) |
0.11
(0.00) |
0.24
(0.00) |
0.27
(0.00) |
0.23
(0.00) |
0.15
(0.00) |
| RSP
(24-hr) |
-0.01
(0.73) |
0.13
(0.00) |
0.27
(0.00) |
0.16
(0.00) |
0.11
(0.00) |
| O3
(8-hr) |
-0.07
(0.03) |
-0.08
(0.01) |
0.26
(0.00) |
-0.02
(0.68) |
0.01
(0.85) |
| FSP
(24-hr) |
-0.06
(0.12) |
0.10
(0.01) |
0.17
(0.00) |
0.08
(0.08) |
0.02
(0.49) |
| Temperature |
-0.01
(0.76) |
-0.35
(0.00) |
-0.21
(0.00) |
-0.14
(0.00) |
-0.04
(0.16) |
| Humidity |
0.10
(0.00) |
0.04
(0.20) |
-0.30
(0.00) |
-0.07
(0.02) |
-0.06
(0.03) |
*
excluding asthma
| Table
8: |
Matrix
of SpearmanÕs rank corelation coefficient (r) between
daily concentrations of pollutants and meteorological
measures (1995-97) |
|
SO2 |
RSP |
O3
(8-hr) |
FSP |
Temperature |
Humidity |
| NO2 |
0.37 |
0.82 |
0.43 |
0.79 |
-0.45 |
-0.35 |
| SO2 |
|
0.30 |
-0.18 |
0.33 |
0.17 |
-0.16 |
| RSP |
|
|
0.54 |
0.91 |
-0.42 |
-0.53 |
| O3
(8-hr) |
|
|
|
0.47 |
-0.14 |
-0.59 |
| FSP |
|
|
|
|
-0.39 |
-0.39 |
| Temperature |
|
|
|
|
|
0.19 |
Note:
all p values are <0.0001
|
| |
|
| 4.2 |
Statistical
modelling
| 4.2.1 |
Core
models for hospital admissions
The
core models, i.e. models with terms which explain
long term trends, seasonal patterns and other
variations due to potential confounding factors
for daily counts of admission, are shown in Basic
Tables E2-20. The numbers of cycles for all the
19 health outcomes are shown in Basic Table E1.
Overall,
8-9 numbers of cycles, each ranging from 0.33
to 5.98 cycles per year, were required to explain
the seasonal patterns in each health outcome.
For
all ages, the core models explained 41-72% of
the total variations, in respiratory (R2
68%), COPD (53%), asthma (41%), cardiac (72%)
and IHD (56%) admissons. In specific age groups,
the core model explained less variations in the
older than the younger age groups. In hospital
deaths, the core model explained the least variations
(26% for respiratory deaths; 21% for cardiovascular
deaths).
Trends
and seasonal patterns varied extensively among
individual health outcomes.
Monday
to Friday, except for asthma in all age groups,
were associated with increased admissions relative
to Sunday (0.000<p<0.799), and Saturday
had different effects in different health outcomes.
For asthma, Monday was associated with increased
admissions (0.000<p<0.100), and Wednesday
to Saturday were associated with decreased admissions
relative to Sunday (0.000<p<0.939), while
Tuesday did not show a clear pattern of association.
Holidays,
except for asthma for all age groups and respiratory
0-14 age group, were associated with decreased
admissions (0.000<p<0.045). Asthma all age
groups and respiratory 0-14 age group were associated
with increased admissions (0.002<p<0.288).
Days
after holidays, except for IHD 65-74 age group
(p=0.633), were associated with increased admissions
(0.000<p<0.404).
Influenza,
except for asthma 0-14 age group (p=0.098), was
associated with increased admissions due to respiratory
diseases (0.000<p<0.864).
Temperature,
except for asthma all age groups, was associated
with increased admissions (0.000<p<0.864).
For asthma, it was associated with decreased admissions
(0.000<p<0.134).
Humidity
was associated with decreased admissions for all
categories of respiratory diseases (0.000<p<0.689);
and it did not show a consistent pattern of association
with categories of cardiovascular diseases (p>0.198).
|
| |
|
| 4.2.2 |
Core
models for hospital deaths
Hospital
deaths due to respiratory disease were associated
with Monday, higher temperature and lower humidity
(p<0.040); and deaths due to cardiovascular
disease were associated with lower temperature
and lower humidity (p<0.009).
|
| |
|
| 4.2.3 |
Models
for pollutants on hospital admissions
Effects
of single pollutants at current day (lag 0) and
average of best current day up to 3 or 5 previous
days (best lag 0-3 or lag 0-5 days) are shown
in Table 9.
| Table
9: |
Relative
risks (RR) and 95% confidence interval (95%
CI) for 50 ug/m3 increase in a
pollutant for each category of hospital admission
?1995-97 (Basic Table F1) |
| Respiratory
disease (ICD9: 460-519) |
|
|
Lag
0 |
|
|
Lag
(0-n)* |
|
| Pollutant |
Age |
RR
(95% CI) |
p-value |
Lag
: |
RR
(95% CI) |
p-value |
| NO2
(24-hr) |
All |
1.08
(1.06,1.10) |
0.0000 |
0-0
: |
1.08
(1.06,1.10) |
0.0000 |
|
<15 |
1.04
(1.01,1.07) |
0.0055 |
0-3
: |
1.08
(1.04,1.12) |
0.0001 |
|
15-64 |
1.06
(1.03,1.10) |
0.0002 |
0-0
: |
1.06
(1.03,1.10) |
0.0002 |
|
65-74 |
1.07
(1.03,1.10) |
0.0002 |
0-1
: |
1.09
(1.06,1.15) |
0.0000 |
|
75+ |
1.10
(1.07,1.14) |
0.0000 |
0-0
: |
1.10
(1.07,1.14) |
0.0000 |
| SO2
(24-hr) |
All |
1.04
(1.02,1.07) |
0.0024 |
0-0
: |
1.04
(1.02,1.07) |
0.0024 |
|
<15 |
1.01
(0.96,1.05) |
0.7884 |
0-1
: |
0.99
(0.94,1.04) |
0.6575 |
|
15-64 |
1.04
(0.99,1.09) |
0.0832 |
0-0
: |
1.04
(0.99,1.09) |
0.0832 |
|
65-74 |
1.06
(1.01,1.11) |
0.0239 |
0-0
: |
1.06
(1.01,1.11) |
0.0239 |
|
75+ |
1.14
(1.09,1.19) |
0.0000 |
0-0
: |
1.14
(1.09,1.19) |
0.0000 |
| RSP
(24-hr) |
All |
1.04
(1.02,1.06) |
0.0000 |
0-3
: |
1.06
(1.04,1.09) |
0.0000 |
|
<15 |
1.04
(1.01,1.07) |
0.0019 |
0-3
: |
1.09
(1.05,1.12) |
0.0000 |
|
15-64 |
1.03
(1.00,1.05) |
0.0596 |
0-0
: |
1.03
(1.00,1.05) |
0.0596 |
|
65-74 |
1.02
(0.99,1.05) |
0.1248 |
0-1
: |
1.05
(1.01,1.08) |
0.0075 |
|
75+ |
1.05
(1.03,1.08) |
0.0001 |
0-0
: |
1.05
(1.03,1.08) |
0.0001 |
| O3
(8-hr) |
All |
1.03
(1.01,1.05) |
0.0005 |
0-2
: |
1.08
(1.06,1.11) |
0.0000 |
|
<15 |
1.03
(1.00,1.06) |
0.0326 |
0-2
: |
1.07
(1.03,1.10) |
0.0001 |
|
15-64 |
1.00
(0.97,1.03) |
0.9632 |
0-2
: |
1.04
(1.00,1.07) |
0.0413 |
|
65-74 |
1.04
(1.01,1.07) |
0.0182 |
0-2
: |
1.09
(1.05,1.14) |
0.0000 |
|
75+ |
1.00
(0.98,1.13) |
0.7280 |
0-1
: |
1.06
(1.06,1.10) |
0.0010 |
| FSP
(24-hr) |
All |
1.05
(1.02,1.07) |
0.0002 |
0-3
: |
1.06
(1.03,1.10) |
0.0001 |
|
<15 |
1.02
(0.98,1.06) |
0.3335 |
0-3
: |
1.06
(1.01,1.11) |
0.0215 |
|
15-64 |
1.02
(0.98,1.06) |
0.2963 |
0-0
: |
1.02
(0.98,1.06) |
0.2963 |
|
65-74 |
1.04
(0.99,1.09) |
0.1026 |
0-1
: |
1.07
(1.01,1.12) |
0.0125 |
|
75+ |
1.08
(1.04,1.12) |
0.0001 |
0-0
: |
1.08
(1.04,1.12) |
0.0001 |
| COPD
(ICD9: 490-496 excluding 493) |
| NO2
(24-hr) |
All |
1.13
(1.09,1.16) |
0.0000 |
0-1
: |
1.14
(1.10,1.19) |
0.0000 |
| SO2
(24-hr) |
All |
1.15
(1.10,1.20) |
0.0000 |
0-0
: |
1.15
(1.10,1.20) |
0.0000 |
| RSP
(24-hr) |
All |
1.05
(1.02,1.08) |
0.0002 |
0-0
: |
1.05
(1.02,1.08) |
0.0002 |
| O3
(8-hr) |
All |
1.01
(0.98,1.03) |
0.6988 |
0-3
: |
1.08
(1.04,1.12) |
0.0001 |
| FSP
(24-hr) |
All |
1.06
(1.02,1.10) |
0.0049 |
0-0
: |
1.06
(1.02,1.10) |
0.0049 |
| Asthma
(ICD9 493) |
| NO2
(24-hr) |
All |
1.12
(1.07,1.17) |
0.0000 |
0-3
: |
1.22
(1.14,1.30) |
0.0000 |
|
<15 |
1.11
(1.04,1.19) |
0.0014 |
0-3
: |
1.33
(1.22,1.46) |
0.0000 |
|
15-64 |
1.12
(1.04,1.20) |
0.0031 |
0-0
: |
1.12
(1.04,1.20) |
0.0031 |
| SO2
(24-hr) |
All |
1.04
(0.97,1.11) |
0.2856 |
0-0
: |
1.04
(0.97,1.11) |
0.2856 |
|
<15 |
0.96
(0.87,1.06) |
0.3990 |
0-1
: |
0.95
(0.84,1.06) |
0.3547 |
|
15-64 |
1.14
(1.03,1.27) |
0.0116 |
0-0
: |
1.14
(1.03,1.27) |
0.0116 |
| RSP(24-hr) |
All |
1.05
(1.01,1.09) |
0.0108 |
0-3
: |
1.17
(1.11,1.23) |
0.0000 |
|
<15 |
1.09
(1.04,1.15) |
0.0011 |
0-3
: |
1.29
(1.21,1.38) |
0.0000 |
|
15-64 |
0.98
(0.92,1.04) |
0.5091 |
0-0
: |
0.98
(0.92,1.04) |
0.5091 |
| O3
(8-hr) |
All |
1.04
(1.00,1.09) |
0.0584 |
0-2
: |
1.17
(1.11,1.23) |
0.0000 |
|
<15 |
1.10
(1.04,1.17) |
0.0009 |
0-2
: |
1.24
(1.16,1.34) |
0.0000 |
|
15-64 |
0.98
(0.92,1.05) |
0.5653 |
0-2
: |
1.09
(1.01,1.18) |
0.0356 |
| FSP
(24-hr) |
All |
1.00
(0.94,1.06) |
0.9626 |
0-3
: |
1.11
(1.03,1.20) |
0.0066 |
|
<15 |
1.03
(0.95,1.12) |
0.4783 |
0-3
: |
1.25
(1.13,1.38) |
0.0000 |
|
15-64 |
0.92
(0.84,1.01) |
0.0804 |
0-0
: |
0.92
(0.84,1.01) |
0.0804 |
| Cardiac
disease (ICD9: 390-429) |
| NO2
(24-hr) |
All |
1.06
(1.04,1.09) |
0.0000 |
0-1
: |
1.07
(1.04,1.10) |
0.0000 |
|
15-64 |
1.06
(1.02,1.09) |
0.0029 |
0-0
: |
1.06
(1.02,1.09) |
0.0029 |
|
65-74 |
1.05
(1.01,1.09) |
0.0138 |
0-3
: |
1.09
(1.03,1.15) |
0.0018 |
|
75+ |
1.08
(1.05,1.12) |
0.0000 |
0-0
: |
1.08
(1.05,1.12) |
0.0000 |
| SO2
(24-hr) |
All |
1.07
(1.03,1.10) |
0.0001 |
0-0
: |
1.07
(1.03,1.10) |
0.0001 |
|
15-64 |
1.03
(0.98,1.09) |
0.1775 |
0-2
: |
1.06
(0.99,1.14) |
0.0712 |
|
65-74 |
1.04
(0.98,1.09) |
0.1714 |
0-1
: |
1.06
(1.00,1.13) |
0.0685 |
|
75+ |
1.09
(1.04,1.15) |
0.0004 |
0-0
: |
1.09
(1.04,1.15) |
0.0004 |
| RSP
(24-hr) |
All |
1.02
(1.01,1.05) |
0.0137 |
0-1
: |
1.03
(1.01,1.05) |
0.0071 |
|
15-64 |
1.02
(0.99,1.05) |
0.1590 |
0-3
: |
1.03
(0.99,1.07) |
0.1481 |
|
65-74 |
1.02
(0.99,1.05) |
0.2054 |
0-1
: |
1.03
(1.00,1.07) |
0.0767 |
|
75+ |
1.04
(1.01,1.07) |
0.0132 |
0-1
: |
1.05
(1.01,1.08) |
0.0000 |
| O3
(8-hr) |
All |
1.00
(0.98,1.02) |
0.8749 |
0-5
: |
1.04
(1.01,1.08) |
0.0058 |
|
15-64 |
1.01
(0.98,1.04) |
0.4747 |
0-5
: |
1.03
(0.98,1.08) |
0.2297 |
|
65-74 |
1.00
(0.97,1.04) |
0.8842 |
0-5
: |
1.08
(1.03,1.14) |
0.0023 |
|
75+ |
0.99
(0.96,1.03) |
0.7021 |
0-3
: |
1.07
(1.03,1.11) |
0.0016 |
| FSP
(24-hr) |
All |
1.02
(0.99,1.05) |
0.1452 |
0-3
: |
1.05
(1.01,1.09) |
0.0023 |
|
15-64 |
1.01
(0.97,1.06) |
0.6339 |
0-3
: |
1.02
(0.96,1.08) |
0.4812 |
|
65-74 |
1.01
(0.96,1.06) |
0.6678 |
0-3
: |
1.03
(1.07,1.10) |
0.2732 |
|
75+ |
1.05
(1.00,1.10) |
0.0309 |
0-3
: |
1.08
(1.03,1.14) |
0.0044 |
| IHD
(ICD9: 410-414) |
| NO2
(24-hr) |
All |
1.04
(1.00,1.07) |
0.0552 |
0-0
: |
1.04
(1.00,1.07) |
0.0552 |
|
15-64 |
0.99
(0.93,1.04) |
0.6226 |
0-0
: |
0.99
(0.93,1.04) |
0.6226 |
|
65-74 |
1.07
(1.01,1.13) |
0.0273 |
0-3
: |
1.11
(1.06,1.11) |
0.0116 |
|
75+ |
1.07
(1.01,1.14) |
0.0222 |
0-0
: |
1.07
(1.01,1.14) |
0.0222 |
| SO2
(24-hr) |
All |
1.01
(0.96,1.06) |
0.6082 |
0-1
: |
1.02
(0.96,1.08) |
0.5635 |
|
15-64 |
0.96
(0.89,1.04) |
0.3536 |
0-0
: |
0.96
(0.89,1.04) |
0.3536 |
|
65-74 |
1.09
(1.07,1.12) |
0.0000 |
0-1
: |
1.04
(0.95,1.14) |
0.3964 |
|
75+ |
1.08
(1.05,1.10) |
0.0000 |
0-3
: |
0.93
(0.81,1.05) |
0.2366 |
| RSP
(24-hr) |
All |
1.03
(1.00,1.06) |
0.0982 |
0-0
: |
1.03
(1.00,1.06) |
0.0982 |
|
15-64 |
0.99
(0.95,1.04) |
0.7484 |
0-1
: |
0.98
(0.93,1.03) |
0.4713 |
|
65-74 |
1.05
(1.00,1.09) |
0.0593 |
0-1
: |
1.07
(1.03,1.12) |
0.0161 |
|
75+ |
1.05
(1.00,1.10) |
0.0478 |
0-0
: |
1.05
(1.00,1.10) |
0.0478 |
| O3
(8-hr) |
All |
1.00
(0.97,1.03) |
0.9800 |
0-3
: |
1.02
(0.98,1.07) |
0.2944 |
|
15-64 |
1.01
(0.96,1.06) |
0.7614 |
0-1
: |
1.01
(0.95,1.07) |
0.7592 |
|
65-74 |
1.01
(0.96,1.06) |
0.8053 |
0-2
: |
1.05
(0.99,1.12) |
0.1246 |
|
75+ |
0.99
(0.94,1.04) |
0.7276 |
0-3
: |
1.04
(0.97,1.11) |
0.3225 |
| FSP
(24-hr) |
All |
0.99
(0.95,1.04) |
0.6478 |
0-0
: |
0.99
(0.95,1.04) |
0.6478 |
|
15-64 |
0.95
(0.89,1.02) |
0.1785 |
0-0
: |
0.95
(0.89,1.02) |
0.1785 |
|
65-74 |
1.00
(0.93,1.07) |
0.9873 |
0-1
: |
1.03
(0.95,1.11) |
0.4381 |
|
75+ |
1.04
(0.96,1.12) |
0.3375 |
0-1
: |
1.05
(0.97,1.14) |
0.2437 |
| *
Note: |
-
Lag 0-n denotes the cumulative effects
of the mean pollutant concentration of
lag from day 0 (same day) to day 1 up
to day n
-
using up to Lag 0-5 for ozone and up to
Lag 0-3 for other pollutants
|
NO2
was associated with increased admissions for all
health outcomes, except IHD 15-64 age group, at
current day and best lag day (p<0.055).
SO2
was associated with increased admissions at the
best lag day for respiratory disease all ages
and the older than 15 age group (p<0.083),
COPD all ages (p=0.000), asthma 15-64 age group
(p=0.012) and cardiac disease all ages and all
older than 15 age groups (p<0.071).
RSP
was associated with increased admissions at the
best lag day for respiratory disease in all ages
and all the age groups (p<0.060), for COPD
all ages (p=0.000), for asthma all ages and 0-14
age group (p=0.000), for cardiac disease in all
ages and older than 65 age groups (p<0.077)
and IHD all ages and older than 65 age groups
(p<0.098).
O3
was not associated with admissions for most outcomes
at the current day; but it was associated with
most outcomes at the best lag days for respiratory
diseases all ages and all age groups (p<0.041),
COPD all ages (p=0.000), asthma all ages and all
younger than 65 age groups (p<0.036) and cardiac
disease all ages and all older than 65 age groups
(p<0.006).
FSP
was associated with admissions for most outcomes
at the best lag days for respiratory diseases
all ages, 0-14 and older than 65 age groups (p<0.022),
for COPD (p=0.005), asthma all ages and all age
groups (p<0.080) and for cardiac disease all
ages and older than 75 age group (p<0.004).
|
| |
|
| 4.2.4 |
Models
for pollutants on hospital deaths
Hospital
deaths due to respiratory disease all ages, were
associated with NO2, SO2
and RSP (p<0.084). Hospital deaths due to cardiovascular
disease all ages, were associated with NO2
and RSP (p<0.079). The relative risks with
95% confidence intervals are shown in Table 10
below
| Table
10: |
Relative
risk (RR) and 95% confidence interval (95%
CI) for 50 ug/m3 increase in a
pollutant for each category of hospital deaths
1995-97 (Basic Table F2) ?all ages |
| Respiratory
disease (ICD9: 460-519) |
|
(Lag
0) |
|
|
(Lag
0-n)* |
|
| Pollutant |
RR
(95% CI) |
p-value |
Lag
: |
RR
(95% CI) |
p-value |
| NO2
(24-hr) |
1.08
(1.02,1.15) |
0.0140 |
0-1
: |
1.10
(1.03,1.18) |
0.0068 |
| SO2
(24-hr) |
1.08
(1.00,1.18) |
0.0638 |
0-0
: |
1.08
(1.00,1.18) |
0.0638 |
| RSP
(24-hr) |
1.04
(0.99,1.09) |
0.1655 |
0-1
: |
1.05
(0.99,1.11) |
0.0840 |
| O3
(8-hr) |
1.01
(0.95,1.06) |
0.7951 |
0-3
: |
1.06
(0.99,1.14) |
0.1043 |
| FSP
(24-hr) |
1.02
(0.94,1.10) |
0.6398 |
0-1
: |
1.04
(0.95,1.13) |
0.3981 |
| Cardiovascular
disease (ICD9: 390-459) |
| NO2
(24-hr) |
1.08
(1.02,1.15) |
0.0130 |
0-0
: |
1.08
(1.02,1.15) |
0.0130 |
| SO2
(24-hr) |
1.01
(0.92,1.11) |
0.8009 |
0-3
: |
0.95
(0.83,1.09) |
0.4560 |
| RSP
(24-hr) |
1.02
(0.97,1.07) |
0.4196 |
0-3
: |
1.06
(0.99,1.14) |
0.0789 |
| O3
(8-hr) |
1.00
(0.95,1.06) |
0.9917 |
0-4
: |
1.05
(0.97,1.14) |
0.1914 |
| FSP
(24-hr) |
1.02
(0.94,1.11) |
0.6596 |
0-3
: |
1.05
(0.95,1.17) |
0.3150 |
| *
Note: |
- Lag
0-n denotes the cumulative effects of
the mean pollutant concentration of lag
from day 0 (same day) to day 1 up to day
n
- using
up to Lag 0-5 for ozone and up to Lag
0-3 for other pollutants
|
|
| |
|
| 4.2.5 |
Effects
of pollutants adjusted for copollutants
Effect
of pollutant with adjustment for each of the other
pollutants at their best lag days are shown in
Tables 11-12 below. Only hospital admissions and
deaths for all ages are presented.
| Table
11: |
Relative
risk (RR) and 95% confidence interval (95%
CI) for 50 ug/m3 increase in a
pollutant adjusted for a copollutant which
was associated with the greatest adjustment
effect ?hospital admissions 1995-97 |
| • |
Pollutant |
RR
(95% CI) |
p-value |
[Copollutant] |
| Respiratory
disease all ages |
| • |
NO2 |
1.06
(1.04, 1.08) |
0.000 |
[O3] |
| • |
SO2 |
0.96
(0.92, 0.99) |
0.016 |
[NO2] |
| • |
RSP |
1.03
(1.00, 1.06) |
0.023 |
[O3] |
| • |
O3 |
1.07
(1.04, 1.09) |
0.000 |
[NO2] |
| • |
FSP |
1.04
(0.98, 1.10) |
0.192 |
[RSP] |
| COPD
all ages |
| • |
NO2 |
1.11
(1.06, 1.16) |
0.000 |
[SO2] |
| • |
SO2 |
1.08
(1.02, 1.13) |
0.005 |
[NO2] |
| • |
RSP |
0.98
(0.95, 1.01) |
0.253 |
[NO2] |
| • |
O3 |
1.03
(0.99, 1.07) |
0.123 |
[NO2] |
| • |
FSP |
1.02
(0.95, 1.09) |
0.655 |
[RSP] |
| Asthma
all ages |
| • |
NO2 |
1.13
(1.02, 1.25) |
0.018 |
[RSP] |
| • |
SO2 |
0.96
(0.90, 1.04) |
0.316 |
[NO2] |
| • |
RSP |
1.09
(1.00, 1.18) |
0.045 |
[NO2] |
| • |
O3 |
1.11
(1.05, 1.18) |
0.000 |
[RSP] |
| • |
FSP |
0.99
(0.90, 1.10) |
0.913 |
[NO2] |
| Cardiac
disease all ages |
| • |
NO2 |
1.06
(1.02, 1.09) |
0.001 |
[SO2] |
| • |
SO2 |
1.03
(0.99, 1.07) |
0.128 |
[NO2] |
| • |
RSP |
0.98
(0.95, 1.01) |
0.244 |
[NO2] |
| • |
O3 |
1.02
(0.99, 1.05) |
0.213 |
[NO2] |
| • |
FSP |
1.01
(0.96, 1.05) |
0.770 |
[NO2] |
| IHD
all ages |
| • |
NO2 |
1.03
(0.98, 1.08) |
0.289 |
[RSP] |
| • |
SO2 |
0.99
(0.93, 1.06) |
0.858 |
[RSP] |
| • |
RSP |
1.01
(0.97, 1.05) |
0.664 |
[NO2] |
| • |
O3 |
1.01
(0.97, 1.06) |
0.603 |
[NO2] |
| • |
FSP |
1.00
(0.95, 1.06) |
0.905 |
[SO2] |
NO2
was associated with all categories of respiratory
admissions including COPD and asthma (p<0.018)
and with cardiac admissions (p=0.001).
SO2
was associated with admissions due to respiratory
disease (p=0.016) and COPD (p=0.005).
RSP
was associated with admissions due to respiratory
disease (p=0.023) and asthma (p=0.045).
O3
was associated with admissions due to respiratory
disease (p=0.000) and asthma (p=0.000).
FSP
was not associated with any of the admission categories
under study (p>0.192).
No
pollutants, except NO2, were associated
with admissions due to cardiac disease (p>0.128);
and no pollutants were associated with admissions
due to IHD (p>0.289).
| Table
12: |
Relative
risk (RR) and 95% confidence interval (95%
CI) for 50 ug/m3 increase in a
pollutant adjusted for a copollutant which
was associated with the greatest adjustment
effect ?hospital deaths 1995-97 |
| • |
Pollutant |
RR
(95% CI) |
p-value |
[Copollutant] |
| Respiratory
deaths all ages |
| • |
NO2 |
1.09
(1.00, 1.19) |
0.044 |
[SO2] |
| • |
SO2 |
1.02
(0.92, 1.13) |
0.669 |
[NO2] |
| • |
RSP |
0.99
(0.91, 1.07) |
0.761 |
[NO2] |
| • |
O3 |
1.03
(0.95, 1.11) |
0.457 |
[NO2] |
| • |
FSP |
1.00
(0.90, 1.12) |
0.959 |
[O3] |
| Cardiovascular
deaths all ages |
| • |
NO2 |
1.07
(1.00, 1.15) |
0.057 |
[RSP] |
| • |
SO2 |
0.87
(0.74, 1.02) |
0.079 |
[RSP] |
| • |
RSP |
1.03
(0.95, 1.11) |
0.449 |
[NO2] |
| • |
O3 |
1.03
(0.94, 1.12) |
0.564 |
[RSP] |
| • |
FSP |
1.00
(0.89, 1.13) |
0.967 |
[RSP] |
NO2
was associated with respiratory deaths (p=0.044)
and with cardiovascular deaths (p=0.057) for all
ages. For the other pollutants they were not associated
with both respiratory and cardiovascular deaths
(p>0.079).
|
| |
|
| 4.2.6 |
Models
with adjustment for autocorrelation
Figures
5 (a) - (e) show the residual plots and partial
autocorrelation function plots for all the hospital
admission categories. There were some but non-regular
patterns in the residual plots and some autocorrelation
(r<0.4) in respiratory disease for all ages
and all age groups and in asthma 0-14 age group.
For the other admission categories, there were
no patterns in the residual plots with very little
autocorrelation (r<0.25).
Adjustment
for autocorrelation did not affect the RR estimates;
but it would affect the standard errors, if autocorrelation
exists, and therefore the 95% confidence intervals
will be wider and the p-values larger. The estimated
95% CIs and p-values, if they differred from the
original estimates after the adjustment, are shown
in Basic Tables G1.
Hospital
deaths did not show autocorrelations in the residuals.
|
| |
|
| 4.2.7 |
Non-linear
effects
Generalized
additive modelling (GAM) was used to assess the
sensitivity of using non-linear assumption for
meteorological covariates (temperature and humidity)
on effect estimates of pollutants. The RRs were
practically the same as those presented in Tables
11-12. Those which show some slight variations
were COPD due to O3 (1.04 in GAM estimate
vs 1.03 in original estimate); asthma due to NO2
(1.10 vs 1.13), SO2 (0.97 vs 0.96)
and O3 (1.10 vs 1.11); cardiac disease
due to O3 (1.03 vs 1.02) and IHD due
to SO2 (1.00 vs 0.99). (Data were not
presented in table.)
Non-linear
dose-response relationships for all ages and in
models with adjustment for copollutant were found
in NO2 on asthma and respiratory deaths
(p<0.008); RSP on respiratory and asthma admissions
(p<0.036); and FSP on respiratory admissions
(p=0.040). The others were non-significant (p>0.050).
(Data were not presented in table.)
|
| |
|
| 4.2.8 |
Models
for interactions between pollutants
Only
admissions for all ages were studied and those
significant interactions (p<0.05) between pollutants
were presented in Table 13 below.
| Table
13: |
Relative
risk (RR) and 95% confidence interval (95%
CI) for 50 ug/m3 increase of a
pollutant at low and high level of a copollutant
?hospital admission 1995-97 (Basic Table
H1) |
1.
Respiratory all ages
| Copollutant |
NO2
RR (95% CI) |
p-value |
| SO2 |
Low |
1.12
(1.08, 1.16) |
0.000 |
|
High |
1.07
(1.05, 1.10) |
0.000 |
2.
COPD all ages
| Copollutant |
NO2
RR (95% CI) |
p-value |
| O3 |
Low |
1.09
(1.05, 1.14) |
0.000 |
|
High |
1.17
(1.12, 1.23) |
0.000 |
|
|
SO2
RR (95% CI) |
|
| NO2 |
Low |
1.22
(1.12, 1.33) |
0.000 |
|
High |
1.08
(1.03, 1.14) |
0.002 |
|
|
O3
RR (95% CI) |
|
| NO2 |
Low |
0.94
(0.90, 0.99) |
0.011 |
|
High |
1.01
(0.97, 1.04) |
0.739 |
3.
Asthma all ages
| Copollutant |
NO2
RR (95% CI) |
p-value |
| SO2 |
Low |
1.27
(1.16, 1.39) |
0.000 |
|
High |
1.10
(1.04, 1.17) |
0.002 |
|
|
O3
RR (95% CI) |
|
| SO2 |
Low |
1.10
(1.04, 1.16) |
0.001 |
|
High |
1.00
(0.95, 1.06) |
0.893 |
Interactions
between pollutants were found in admissions due
to the respiratory disease (between SO2
?NO2); due to COPD (O3
?NO2; NO2 ?SO2;
NO2 ?O3) and due to asthma
(SO2 ?NO2; SO2
?O3), but they were not found in admissions
due to cardiovascular disease.
|
| |
|
| 4.2.9 |
Models
with interaction between pollutants and seasons
Only
admissions for all ages were studied and those
with a significant difference (p<0.05) between
cool and warm seasons are presented in Table 14.
| Table
14: |
Relative
risk (RR) and 95% confidence interval (95%
CI) for 50 ug/m3 increase of a
pollutant in cool and warm seasons ?hospital
admission 1995-97 (Basic Table I1) |
a.
Respiratory all ages
| Relative
Risk (95% CI) |
| Pollutant |
Cool |
Warm |
| NO2 |
1.10
(1.07, 1.13) |
1.05
(1.02, 1.08) |
| FSP |
1.09
(1.04, 1.53) |
0.99
(0.93, 1.05) |
a.
COPD all ages
| Relative
Risk (95% CI) |
| Pollutant |
Cool |
Warm |
| NO2 |
1.17
(1.12, 1.22) |
1.07
(1.02, 1.12) |
| O3 |
1.07
(1.04, 1.11) |
1.02
(0.97, 1.06) |
b.
Cardiac all ages
| Relative
Risk (95% CI) |
| Pollutant |
Cool |
Warm |
| NO2 |
1.11
(1.07, 1.14) |
1.01
(0.98, 1.04) |
| SO2 |
1.11
(1.06, 1.16) |
1.04
(0.99, 1.08) |
| RSP |
1.03
(1.00, 1.06) |
0.98
(0.95, 1.00) |
| O3 |
1.05
(1.02, 1.08) |
0.99
(0.96, 1.02) |
b.
IHD all ages
| Relative
Risk (95% CI) |
| Pollutant |
Cool |
Warm |
| NO2 |
1.08
(1.03, 1.14) |
0.99
(0.94, 1.04) |
| O3 |
1.05
(1.01, 1.09) |
0.99
(0.95, 1.04) |
When
there were interactions, the effects were stronger
and were greater than unity in the cool season
than in the warm season.
|
|
| |
|
| 4.3 |
Validity
and reliability of models
| 4.3.1 |
Predictive
validity of 1995-97 models
Figures
7 (a)-(e) depict the discrepancies between the
observed in the first half year of 1998 and the
predicted counts of hospital admissions due to
the 5 categories of diseases for all ages, according
to the model derived from 1995-97 data. Figures
8(a)-(b) depict the discrepancies for hospital
deaths due to respiratory and cardiovascular diseases.
For
admissions due to respiratory disease, the model
(core model plus a pollutant) predicted the admission
counts closely for the first quarter of the year;
but under-estimated the numbers markedly during
the fourth and fifth months of 1998 (Figure 7(a)).
For
COPD, the 1995-97 models predicted the admission
counts closely (Figure 7(b)).
For
asthma, the 1995-97 models predicted the admission
counts closely except in the fourth month of 1998
(Figure 7(c)).
For
cardiac, the 1995-97 models predicted the admission
counts well with a small amount of the observed
above the predicted. The discrepancies were consistent
throughout the first half year period of 1998
(Figure 7(d)).
For
IHD, the prediction was similar to that for cardiac
admissions (Figure 7(e)).
For
deaths due to respiratory disease, the 1995-97
models under estimated the observed especially
during the second month of 1998 (Figure 8(a)).
For
deaths due to cardiovascular disease, the 1995-97
models predicted the observed counts closely (Figure
8(b)).
Overall,
all the models appear to predict the observed
closely except those for admissions and deaths
due to respiratory disease.
|
| |
|
| 4.3.2 |
Comparison
of 1995-97 with 1995-96 models
Estimates
for effects of each pollutant for health outcomes
defined as in the previous study, at the best
lag day, are compared between those estimated
from the 1995-96 and 1995-97 models in Tables
15-16.
| Table
15: |
Comparison
between relative risks (RR) and 95% confidence
intervals (95% CI) for 50 ug/m3
increase in a pollutant estimated from the
1995-96 and 1995-97 models ?hospital admissions |
| (i)
Cardiovascular + Respiratory |
| |
995-96
(Lag 0-n)#1 |
1995-97
(Lag 0-n)# |
| Pollutant |
Lag |
:RR
(95% CI) |
Lag |
:RR
(95% CI) |
| NO2
(24-hr) |
0-0 |
:1.07
(1.04,1.09)*** |
0-0 |
:1.06
(1.05,1.08)*** |
| SO2
(24-hr) |
0-0 |
:1.04
(1.01,1.07)* |
0-0 |
:1.04
(1.02,1.07)*** |
| RSP
(24-hr) |
0-1 |
:1.04
(1.02,1.06)*** |
0-3 |
:1.05
(1.03,1.07)*** |
| O3
(8-hr) |
0-2 |
:1.07
(1.05,1.10)*** |
0-3 |
:1.05
(1.03,1.07)*** |
| (ii)
Cardiovascular |
| |
1995-96
(Lag 0-n)# |
1995-97
(Lag 0-n)# |
| Pollutant |
Lag |
:RR
(95% CI) |
Lag |
:RR
(95% CI) |
| NO2
(24-hr) |
0-2 |
:1.08
(1.04,1.11)*** |
0-0 |
:1.04
(1.02,1.06)*** |
| SO2
(24-hr) |
0-1 |
:1.05
(1.01,1.10)* |
0-0 |
:1.05
(1.02,1.08)*** |
| RSP
(24-hr) |
0-3 |
:1.03
(1.00,1.06)* |
0-3 |
:1.03
(1.00,1.05)* |
| O3
(8-hr) |
0-5 |
:1.07
(1.03,1.10)*** |
0-5 |
:1.03
(1.00,1.05)* |
| (iii)
Respiratory |
| |
1995-96
(Lag 0-n)# |
1995-97
(Lag 0-n)# |
| Pollutant |
Lag |
:RR
(95% CI) |
Lag |
:RR
(95% CI) |
| NO2
(24-hr) |
0-0 |
:1.08
(1.05,1.11)*** |
0-0 |
:1.08
(1.06,1.10)*** |
| SO2
(24-hr) |
0-0 |
:1.03
(1.00,1.08) |
0-0 |
:1.04
(1.02,1.07)** |
| RSP
(24-hr) |
0-1 |
:1.05
(1.02,1.07)*** |
0-3 |
:1.06
(1.04,1.09)*** |
| O3
(8-hr) |
0-2 |
:1.10
(1.07,1.13)*** |
0-2 |
:1.08
(1.06,1.11)*** |
| (iv)
Asthma |
| |
1995-96
(Lag 0-n)# |
1995-97
(Lag 0-n)# |
| Pollutant |
Lag |
:RR
(95% CI) |
Lag |
RR
(95% CI) |
| NO2
(24-hr) |
0-3 |
:1.12
(1.02,1.22)* |
0-3 |
:1.22
(1.14,1.30)*** |
| SO2
(24-hr) |
0-3 |
:0.91
(0.78,1.06) |
0-0 |
:1.04
(0.97,1.11) |
| RSP
(24-hr) |
0-3 |
:1.10
(1.03,1.18)** |
0-3 |
:1.17
(1.11,1.23)*** |
| O3
(8-hr) |
0-2 |
:1.16
(1.08,1.24)*** |
0-2 |
:1.11
(1.03,1.20)** |
| (v)
Chronic obstructive pulmonary disease |
| |
1995-96
(Lag 0-n)# |
1995-97
(Lag 0-n)# |
| Pollutant |
Lag |
:RR
(95% CI) |
Lag |
:RR
(95% CI) |
| NO2
(24-hr) |
0-0 |
:1.13
(1.09,1.17)*** |
0-0 |
:1.11
(1.08,1.14)*** |
| SO2
(24-hr) |
0-0 |
:1.12
(1.06,1.18)*** |
0-0 |
:1.10
(1.06,1.14)*** |
| RSP
(24-hr) |
0-3 |
:1.08
(1.04,1.13)*** |
0-3 |
:1.09
(1.06,1.13)*** |
| O3
(8-hr) |
0-2 |
:1.14
(1.09,1.18)*** |
0-3 |
:1.10
(1.06,1.13)*** |
| (vi)
Ischaemic heart disease |
| |
1995-96
(Lag 0-n)# |
1995-97
(Lag 0-n)# |
| Pollutant |
Lag |
:RR
(95% CI) |
Lag |
:RR
(95% CI) |
| NO2
(24-hr) |
0-2 |
:1.09
(1.03,1.16)** |
0-0 |
:1.04
(1.00,1.07) |
| SO2
(24-hr) |
0-1 |
:1.03
(0.95,1.12) |
0-1 |
:1.02
(0.96,1.08) |
| RSP
(24-hr) |
0-1 |
:1.04
(1.00,1.09) |
0-3 |
:1.02
(0.98,1.07) |
| O3
(8-hr) |
0-2 |
:1.04
(0.99,1.10) |
0-0 |
:0.99
(0.95,1.04) |
*
p < 0.05; ** p < 0.01; *** p < 0.001
| #
Note: |
-
Lag 0-n denotes the cumulative effect
of the mean pollutant concentration of
lag from day 0 (same day) up to previous
n days
-
the cumulative lag days were up to 5 days
for ozone and up to 3 days for other pollutants
|
Overall,
the effect estimates from the two sets of models
were comparable with their corresponding 95% confidence
intervals all overlapping the other.
| Table
16: |
Comparison
between relative risk (RR) and 95% confidence
interval (95% CI) for 50 ug/m3
increase in a pollutant estimated from 1995-96
and 1995-97 models ?hospital deaths |
| (i)
Cardiovascular + Respiratory |
| |
1995-96
(Lag 0-n)# |
1995-97
(Lag 0-n)# |
| Pollutant |
Lag |
:RR
(95% CI) |
Lag |
:RR
(95% CI) |
| NO2
(24-hr) |
0-1 |
:1.12
(1.05,1.20)*** |
0-1 |
:1.09
(1.04,1.15)*** |
| SO2
(24-hr) |
0-3 |
:0.93
(0.81,1.06) |
0-0 |
:1.05
(0.99,1.12) |
| RSP(24-hr) |
0-1 |
:1.04
(0.99,1.09) |
0-3 |
:1.06
(1.01,1.11)* |
| O3
(8-hr) |
0-2 |
:1.07
(1.05,1.10)*** |
0-4 |
:1.05
(1.00,1.11) |
| (ii)
Cardiovascular |
| |
1995-96
(Lag 0-n)# |
1995-97
(Lag 0-n)# |
| Pollutant |
Lag |
:RR
(95% CI) |
Lag |
:RR
(95% CI) |
| NO2
(24-hr) |
0-1 |
:1.10
(1.01,1.21)* |
0-0 |
:1.08
(1.02,1.15)* |
| SO2
(24-hr) |
0-3 |
:0.89
(0.74,1.07) |
0-3 |
:0.95
(0.83,1.09) |
| RSP
(24-hr) |
0-3 |
:1.05
(0.96,1.14) |
0-3 |
:1.06
(0.99,1.14) |
| O3
(8-hr) |
0-5 |
:1.15
(1.03,1.28)** |
0-4 |
:1.05
(0.97,1.14) |
| (iii)
Respiratory |
| |
1995-96
(Lag 0-n)# |
1995-97
(Lag 0-n)# |
| Pollutant |
Lag |
:RR
(95% CI) |
Lag |
:RR
(95% CI) |
| NO2
(24-hr) |
0-1 |
:1.14
(1.04,1.25)** |
0-1 |
:1.10
(1.03,1.18)** |
| SO2
(24-hr) |
0-0 |
:1.08
(0.96,1.22) |
0-0 |
:1.08
(1.00,1.18) |
| RSP
(24-hr) |
0-1 |
:1.05
(0.97,1.13) |
0-1 |
:1.05
(0.99,1.11) |
| O3
(8-hr) |
0-5 |
:1.22
(1.09,1.35)*** |
0-3 |
:1.06
(0.99,1.14) |
*
p < 0.05;
** p < 0.01;
*** p < 0.001
| #
Note: |
-
Lag 0-n denotes the cumulative effect
for the mean pollutant concentration of
lag from day 0 (same day) up to previous
n days
-
the cumulative lag days were up to 5 days
for ozone and up to 3 days for other pollutants
|
Overall,
for deaths the effect estimates derived from both
1995-96 and 1995-97 models were comparable with
the corresponding 95% confidence intervals overlapping
each other. However the RRs for ozone decreased
substantially in the 1995-97 estimates.
|
|
5.0
Discussion
| 5.1
|
Validity
and reliability of the models
Predictive
validity: The models derived from the 1995-97 data showed
a high level of predictive validity (much better that
those derived from 1995-96 data) although there are
some slight distinguishable discrepancies between the
observed and predicted for some categories of health
outcomes.
The
discrepancies may be due to incomplete return of the
discharge diagnoses for 1998 validation data at the
time of data collection. These may also be due to inadequacy
of the models for some health outcomes (3 years of data
are minimum for development of a predictive model).
The discrepancies may reflect some changes which might
have occurred in the environment. In any case, the presence
of these discrepancies would not invalidate the models
in estimating the health effects of air pollution.
Reliability
between years of the study: The relative risks estimated
from 1995-96 and from 1995-97 models are highly reproducible.
In most cases, th RRs were within 5% of each other in
the two sets of estimates; but for asthma the discrepancies
ranged from 5% to 13%. These discrepancies may just
be random.
|
| |
|
| 5.2 |
Comparison
with other APHEA studies
Respiratory
admissions: The RRs (95% CI) summarized from four to
five European cities8 as compared to those
from this Hong Kong study are shown in Table 17.
| Table
17: |
Relative
risk (RR) with 95% confidence interval (95% CI)
for 50 ug/m3 increase of a pollutant
on daily respiratory admissions summarized from
four to five European cities compared to those from
this Hong Kong study |
|
|
European |
|
Hong
Kong |
|
|
RR
(95% CI) |
|
RR
(95% CI) |
| NO2 |
15-64 |
1.00
(0.99,1.11) |
|
1.06
(1.03,1.10) |
|
65+ |
1.02
(0.98,1.06) |
|
1.11
(1.08,1.15) |
| SO2 |
15-64 |
1.01
(0.99,1.03) |
|
1.04
(0.99,1.09) |
|
65+ |
1.02
(1.01,1.05) |
|
1.11
(1.07,1.15) |
| TSP |
15-64 |
1.01
(0.99,1.03) |
RSP |
1.03
(1.00,1.05) |
|
65+ |
1.02
(0.99,1.04) |
|
1.06
(1.03,1.08) |
| O3 |
15-64 |
1.03
(1.01,1.05) |
|
1.04
(1.00,1.07) |
|
65+ |
1.04
(1.02,1.06) |
|
1.07
(1.04,1.11) |
Both
European and Hong Kong studies demonstrated that air
pollutants had a stronger effect in the 65+ than in
the 15-64 age group. The effects in Hong Kong were consistently
stronger than those in European cities.
Asthmatic
admissions: The RRs (95% CI) summarized from four European
cities,9 compared to those from this Hong
Kong study, are shown in Table 18.
| Table
18: |
Relative
risk (RR) with 95% confidence interval (95% CI)
for 50 ug/m3 increase of a pollutant
on daily asthmatic admissions summarized from four
European cities compared to those from this Hong
Kong study |
|
|
European |
|
Hong
Kong |
|
|
RR
(95% CI) |
|
RR
(95% CI) |
| NO2 |
<15 |
1.04
(1.00,1.07) |
|
1.33
(1.22,1.46) |
|
15-64 |
1.04
(1.01,1.07) |
|
1.12
(1.04,1.20) |
| SO2 |
<15 |
1.06
(1.00,1.13) |
|
0.95
(0.84,1.06) |
|
15-64 |
1.00
(0.96,1.05) |
|
1.14
(1.03,1.27) |
| BS |
<15 |
1.05
(0.98,1.12) |
RSP |
1.29
(1.21,1.38) |
|
15-64 |
1.03
(0.98,1.08) |
|
0.98
(0.92,1.04) |
| O3 |
<15 |
1.01
(0.98,1.04) |
|
1.24
(1.16,1.34) |
|
15-64 |
1.04
(0.94,1.14) |
|
1.09
(1.01,1.18) |
For
the 0-14 age group, the effect estimates due to NO2,
RSP (compared with black smoke) and O3 were
significantly greater in the Hong Kong than in the European
studies. For the 15-64 age group the effects due to
NO2, SO2 and O3 were
also greater in Hong Kong than in European cities but
thee differences were not statistically significant.
In the Hong Kong study the differences between the <15
and 15-64 age groups were great.
COPD
admissions: The RRs (95% CI) summarized from four to
six European cities,10 compared to those
from this Hong Kong study, are shown in Table 19.
| Table
19: |
Relative
risk (RR) with 95% confidence interval (95% CI)
for 50 ug/m3 increase of a pollutant
on daily COPD admissions summarized from four to
six European cities compared to those from this
Hong Kong study |
|
|
European |
|
Hong
Kong |
|
|
RR
(95% CI) |
|
RR
(95% CI) |
| NO2 |
All
ages |
1.03
(1.00,1.04) |
NO2 |
1.14
(1.10,1.19) |
| SO2 |
All
ages |
1.02
(1.00,1.05) |
SO2 |
1.15
(1.10,1.20) |
| TSP |
All
ages |
1.03
(0.99,1.07) |
RSP |
1.05
(1.02,1.08) |
| O3 |
All
ages |
1.05
(1.02,1.08) |
O3 |
1.08
(1.04,1.12) |
The
effects of pollutants estimated from the Hong Kong study
were all greater than those estimated from the European
studies. The differences were significant for NO2
and SO2.
Cardiovascular
admissions: There are no equivalent estimates from APHEA
studies published yet. From an American study11
the RR (95% CI) for cardiac admissions due to RSP was
1.06 (1.01,1.12) in 65+ age group as compared to that
estimated from this Hong Kong study of 1.03 (1.00,1.07)
in 65-74 and 1.05 (1.01,1.08) in 75+ age groups.
|
| |
|
| 5.3 |
Limitations
of the Hong Kong study
Non-linear
effects: In the main analysis of this study, a linear
association between air pollutants and health risks
was assumed. However attempts were made to assess whether
there were any non-linear dose-response relationships
and to assess the sensitivity of using a non-linear
assumption for the effect of meteorological variables,
using generalized additive modelling. In this study
the dose-response relationships between pollutants and
health outcomes were reasonably linear and estimates
were robust to non-linearity in the temperature and
humidity covariates used in the models.
Unexplained
patterns: Attempts were made in this study to estimate
the numbers of cycles present in the time-series data
in all core models systematically. However, there were
still some discernible patterns in the residuals of
the core models for respiratory admissions, indicating
that the amount of data were not sufficient to establish
the models in these outcomes. Time-series data longer
than three years are required. But it is not advisable
to eliminate all discernible patterns by using arbitrarily
defined dummy variables, for this would lead to problems
of over fitting and will disable the models to be used
for prediction purposes.
Effects
on mortality: Only deaths after admission to acute hospitals
(accounting for 50% of all deaths in Hong Kong) were
analysed in this study. As daily mortality data is now
available from the Census and Statistics Department,
they should be studied in order to be in line with the
APHEA protocol.
|
| |
|
| 5.4 |
Significance
of study results
The
effects of air pollutants estimated from this study
were to a large extent greater than those summarised
from APHEA studies in Europe. It is difficult to assess
the reasons which may contribute to the greater effects
in Hong Kong. The general explanations might include
over-crowding in the population, easy access to and
high utilization rates of hospital services and the
Government announcement of the air pollution index which
raised the awareness of the public to air pollution.
Further detailed analysis and interpretation of the
possible public health significance of this observation
were beyond the scope of this study.
There
are no studies from the Asia Pacific region, except
one similar study from Sydney Australia12,
for us to make comparison with. In the Sydney study,
NO2 has a primary effect on hospital admissions
for asthma, has a stronger effect than particles on
COPD admissions, and also has effects on heart disease
admissions (ICD9 410, 413, 427 and 428). The results
from the Sydney study were similar to those from this
Hong Kong study. In Hong Kong NO2 had an
effect on asthma, COPD and cardiac disease (ICD9 390-429)
(including the heart disease categories studied in Sydney),
but in addition it was also associated with admissions
for a broad respiratory disease category. Other pollutants
including SO2, RSP and O3 were
also associated with some disease categories. They could
be considered as proxy indices for the effects of the
complex mix of air pollution in large urban centre like
Sydney.12
|
6.0
Conclusions
The results
from this study show that short-term effects of air pollutants
on health are strong. Given the consistency and reproducibility
of the associations between air pollutants and cardio-respiratory
diseases and the biological plausibility, these short term
effects are likely to be causal. It is both feasible and necessary
to develop further studies on the health effects of air pollution
with a longer series of data, using the methodology adopted
by the APHEA protocol, to strengthen and clarify some of the
relationships observed in this study. Territory-wide SAR mortality
should also be included as one of the main health outcomes
to be studied. This would provide a sound basis on which to
assess the health gains from future air quality interventions
in Hong Kong.
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|
|