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Air pollutants and health outcomes: Assessment of confounding by influenza
Atmospheric Environment 44 (2010) 1437e1442
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Air pollutants and health outcomes: Assessment of confounding by influenza Thuan-Quoc Thach a, Chit-Ming Wong a, *, King-Pan Chan a, Yuen-Kwan Chau a, G. Neil Thomas b, Chun-Quan Ou c, Lin Yang a, Joseph S.M. Peiris d, Tai-Hing Lam a, Anthony J. Hedley a a
School of Public Health, The University of Hong Kong, Hong Kong, China Department of Public Health and Epidemiology, University of Birmingham, UK c Department of Biostatistics, Southern Medical University, Guangzhou, China d Department of Microbiology, The University of Hong Kong, Hong Kong, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 October 2009 Received in revised form 21 January 2010 Accepted 25 January 2010
We assessed confounding of associations between short-term effects of air pollution and health outcomes by influenza using Hong Kong mortality and hospitalization data for 1996e2002. Three measures of influenza were defined: (i) intensity: weekly proportion of positive influenza viruses, (ii) epidemic: weekly number of positive influenza viruses 4% of the annual number for 2 consecutive weeks, and (iii) predominance: an epidemic period with co-circulation of respiratory syncytial virus <2% of the annual positive isolates for 2 consecutive weeks. We examined effects of influenza on associations between nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter with aerodynamic diameter 10 mm (PM10) and ozone (O3) and health outcomes including all natural causes mortality, cardiorespiratory mortality and hospitalization. Generalized additive Poisson regression model with natural cubic splines was fitted to control for time-varying covariates to estimate air pollution health effects. Confounding with influenza was assessed using an absolute difference of >0.1% between unadjusted and adjusted excess risks (ER%). Without adjustment, pollutants were associated with positive ER% for all health outcomes except asthma and stroke hospitalization with SO2 and stroke hospitalization with O3. Following adjustment, changes in ER% for all pollutants were <0.1% for all natural causes mortality, but >0.1% for mortality from stroke with NO2 and SO2, cardiac or heart disease with NO2, PM10 and O3, lower respiratory infections with NO2 and O3 and mortality from chronic obstructive pulmonary disease with all pollutants. Changes >0.1% were seen for acute respiratory disease hospitalization with NO2, SO2 and O3 and acute lower respiratory infections hospitalization with PM10. Generally, influenza does not confound the observed associations of air pollutants with all natural causes mortality and cardiovascular hospitalization, but for some pollutants and subgroups of cardiorespiratory mortality and respiratory hospitalization there was evidence to suggest confounding by influenza. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Confounding Influenza activity Mortality Hospitalization Hong Kong
1. Introduction
Abbreviations: ALRI, acute lower respiratory infections; ARD, acute respiratory; CI, confidence interval; COPD, chronic obstructive pulmonary disease; ER%, excess risk in percent; HD, heart disease; IHD, ischemic heart disease; ICD-9, International Classification of Diseases, 9th Revision; ICD-10, International Classification of Diseases, 10th Revision; LRI, lower respiratory diseases; mg m3, microgram per cubic meter; mm, micron; NO2, nitrogen dioxide; O3, ozone; PM10, particulate matter with an aerodynamic diameter 10 mm; RSV, respiratory syncytial viruses; SO2, sulfur dioxide. * Corresponding author at: School of Public Health, The University of Hong Kong, 5th Floor, Faculty of Medicine Building, 21 Sassoon Road, Hong Kong, China. Tel.: þ852 2819 9109; fax: þ852 2855 9528. E-mail address: [email protected] (C.-M. Wong). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.01.036
There is strong evidence from Hong Kong and the rest of east Asia, as well as Western populations, that ambient air pollution is associated with adverse health effects in children and adults, often at levels well below air quality objectives currently adopted by many countries (Ong et al., 1991; Tam et al., 1994; Katsouyanni et al., 1997; Wong et al., 1998, 1999, 2001, 2002, 2008; Samet et al., 2000; Vichit-Vadakan et al., 2008). Time-series studies are commonly used to examine the association between short-term effects of air pollution and health. In such time-series, a core model is first constructed and the effects of an air pollutant are then assessed by adding a linear term for the pollutant to the core model. However, the effect estimate for the air pollutant could be biased if
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all potential confounders have not been adequately controlled for, such as the effect of influenza epidemics which may be seasonally correlated with both air pollution and health outcomes. Studies for the association between short-term effects of exposure to particulate matter and mortality due to all natural and cardiovascular causes showed that the effects were not confounded by influenza epidemics (Braga et al., 2000; Touloumi et al., 2005). In limited number of time-series studies describing the effect of air pollution, influenza epidemics were controlled for by adding one dummy variable to the core model usually based on non-virological data of respiratory mortality counts (Katsouyanni et al., 1996, 2001; Touloumi et al., 2004; Wong et al., 2008). However, this approach cannot account for possible heterogeneity over time in the effects of influenza. In other studies the assessment of confounding effects of influenza in relation to gaseous pollutants was not considered. We present a formal assessment of confounding effects by influenza activity of health effects related to the four criteria pollutants in Hong Kong from 1996 to 2002 using laboratory confirmed influenza isolates to define influenza activity. 2. Materials and methods 2.1. Mortality Daily mortality data for all deaths of Hong Kong residents for the seven-year period from January 1996 to December 2002 were obtained from the Census and Statistics Department. All natural causes of deaths coded with International Classification of Diseases version 9 (ICD-9)1e799; or version 10 (ICD-10) A00-R99 were studied. We also specifically analyzed deaths due to cardiovascular disease (ICD-9:390e459; ICD-10:I00eI99) with subgroups of stroke (ICD-9:430e438; ICD-10: I60eI69) and cardiac or heart disease (cardiac or HD; ICD-9:390e398, 410e429; ICD-10:I00eI09, I20eI52); respiratory disease (ICD-9:460e519; ICD-10:J00eJ98) with subgroups of lower respiratory infections (LRI; ICD-9:466, 480e487; ICD-10:J10eJ22), and chronic obstructive pulmonary diseases (COPD; ICD-9:490e496; ICD-10:J40eJ47). We chose all ages for the analyses. An assessment of the possible discrepancy between the classification of causes of deaths using ICD-9 and ICD10 showed that agreement between the two coding systems was over 90% (Hong Kong Department of Health, 2005). 2.2. Hospitalization The computerized hospital data used in this study were based on discharge diagnoses from January 1996 to December 2002, of the 19 hospitals within the Hospital Authority which managed over 95% of hospital bed days in Hong Kong (Leung et al., 2005). The disease rubrics retrieved from these records were only based on the ICD-9. The discharge diagnoses included were cardiovascular (ICD9:390e459), including the subgroups of stroke (ICD-9:430e438) and ischemic heart disease (IHD; ICD-9:410e414); and respiratory (ICD-9:460e519) with subgroups of acute respiratory disease (ARD; ICD-9: 460e466, 480e487), acute lower respiratory infections (ALRI; ICD-9: 480e487), COPD (ICD-9:490e496), asthma (ICD-9:493). We chose all ages for the analyses. 2.3. Air pollutant and meteorological conditions Data provided by the Environment Protection Department included daily 24-h mean concentrations of particulate matter of aerodynamic diameter 10 mm (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), and 8-h mean concentrations of ozone (O3) measured at eight monitoring sites across Hong Kong. Daily data with less than 18 hourly measurements for PM10, NO2 and SO2 and
less than six hourly measurements during the 10:00e18:00 period for O3 were regarded as missing. The daily means among the monitors for each pollutant were calculated using an algorithm that accounted for differences between each monitor (Wong et al., 2002). There were no missing daily estimates for the four pollutants during the period of study. The daily mean temperature (Celsius) and daily mean relative humidity (percent) were obtained from the Hong Kong Observatory. 2.4. Virology Hong Kong is geographically situated in the tropics, but has a subtropical climate. It lies in a region considered to be the hypothetical epicenter of a number of influenza pandemics (Shortridge and Stuart-Harris, 1982). Virological data on influenza and respiratory syncytial viruses (RSV) were routinely available on a weekly basis as part of the Government infectious disease surveillance. Weekly numbers of positive isolates of influenza A and B viruses (influenza AþB) and respiratory syncytial virus (RSV) as well as the total specimens tested were obtained from the Department of Microbiology, Queen Mary Hospital, Hong Kong. The laboratory received an annual mean number of 6249 (range 3098e8333) specimens for diagnosis of respiratory infections during the study period. We defined three measures of influenza activity using the virology data. 2.4.1. Influenza intensity Intensity was defined by the weekly proportion of positive isolates of influenza AþB of the total number of specimens submitted for laboratory diagnosis. Weekly proportions of specimens tested positive for influenza viruses have been widely adopted to assess influenza-associated mortality and morbidity (Thompson et al., 2003, 2004; Wong et al., 2004, 2006). Because the influenza intensity was available on a weekly basis, we assigned the same values of intensity for the entire week to be daily measure of intensity. 2.4.2. Influenza epidemics Periods in each year were categorized according to the weekly frequency of positive influenza AþB isolates. We defined (i) Epidemic: Weekly number of positive influenza isolates 4% of the annual total number of positive isolates for at least 2 consecutive weeks (Chiu et al., 2002). (ii) Epidemic baseline: Weekly number of positive influenza isolates <2% of the annual total number of positive isolates for at least 2 consecutive weeks. (iii) Intermediate: Period not belonging to (i) or (ii) above.
2.4.3. Influenza predominance RSV has been regarded as a major cause of hospitalization in both children and the elderly and exhibits clinical syndromes very similar to influenza (Nicholson, 1996; Han et al., 1999; Zambon et al., 2001). We defined (i) Predominance: Weekly number of positive influenza isolates 4% of the annual total number, and weekly number of positive RSV isolates <2% of the annual total number of positive isolates of the respective viruses, both for at least 2 consecutive weeks. (ii) Predominance baseline: Weekly number of positive influenza isolates <2% of the annual total number, and RSV isolates also <2% of the annual total number of positive isolates of the respective viruses, both for at least 2 consecutive weeks. (iii) Intermediate: Period not belonging to (i) or (ii) above.
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2.5. Statistical analysis
3. Results
We first developed core models with a generalized additive Poisson regression method allowing for overdispersion in the model. For each health outcome, variations in seasonality, trends, temperature and relative humidity for that day were fitted with natural smoothing splines, and dummy variables were used to control variations for days of the week and holiday. We chose 4e7 degrees of freedom (df) per year for the smoothing splines of the trends and 3e6 df for temperature and humidity. The choice of the df for each smoothing function in the core models was made on the basis of observed residual autocorrelations using Partial AutoCorrelation Function (PACF). To reduce the autocorrelations that could not be minimized through smoothing, autoregressive terms were also added into the core model. For the core models fitted to the mortality data, the time-varying confounding factors were considered as adequately controlled if the absolute values of PACF coefficients were <0.1 for the first two lag days and there were no systematic patterns in the PACF plots (Wong et al., 2008). Due to the high autocorrelations within the hospitalization data, we included autoregressive terms up to the fourth order in the core models. Following the construction of an adequate core model for each health outcome we entered the pollutant variable linearly into the regression and examined the effects for the average of the current and previous days (lag 0e1). We expressed the effect of air pollution as the percentage change in the mean number of the daily health outcomes associated with an increase of 10 mg m3 of pollutant concentrations, calculated as 100 [exp(b 10) 1], where b is the estimated Poisson regression coefficient, and referred to this as unadjusted excess risk (ER%). We assessed the confounding of air pollutant effects by adding each of the three measures of influenza activity in turn into the model, treating intensity as continuous with both epidemics and predominance as categorical variables. The ER% was then reestimated to obtain adjusted ER%, assuming linear associations between intensity and health outcomes. Based on the criterion that if the absolute difference between the unadjusted and adjusted ER % estimates was >0.1%, then influenza activity was regarded as a confounder of the association between an air pollutant and a health outcome. The analyses were performed using R statistical software (R Development Core Team, 2009).
We report the results only for four causes of mortality: stroke, cardiac or HD, LRI and COPD and four causes of hospitalization: stroke, IHD, ARD and COPD. The results for other health outcomes are available in the Supplementary material. From 1996 to 2002 the mean concentrations of NO2, SO2, PM10 and O3 were 58.7, 17.8, 51.6, and 36.9 mg m3 respectively. There were more deaths and hospitalizations for all health outcomes under study with colder conditions in influenza epidemics and predominance periods than in their corresponding baseline periods. Relative humidity was lowest in the baseline periods. The duration of influenza epidemics and predominance (336 and 238 days) was longer than that of their respective baseline periods (1602 and 888 days). There was no difference between the influenza epidemics and predominance, and their baseline periods in the mean concentrations for NO2, SO2 and PM10. O3 showed higher levels during epidemics and predominance baseline periods (Table 1 and Table 4). The Spearman correlation between influenza intensity and pollutant concentrations was negative for NO2 (r ¼ 0.22), for PM10 (r ¼ 0.07), and for SO2 (r ¼ 0.31) whereas it was positive for O3 (r ¼ 0.20) [data not shown]. The intensities were 10.1% for influenza and 8.8% for RSV. The pattern of influenza intensity in 1996e2002 showed that except for the years 1997 and 2001, the seasonal pattern of influenza intensity comprised two peaks each year in January to March and July to August [data not shown]. 3.1. Mortality 3.1.1. ER estimates without adjustment for influenza activity The ER% estimates associated with the four pollutants were positive for all mortality outcomes under study (Table 2 and Table 5). A 10 mg m3 increase in NO2 concentrations at lag 0e1 day was associated with an ER% estimate ranging from 1.03 for all natural causes to 2.08 for cardiac or HD. The corresponding ER% estimates for SO2, PM10 and O3 ranged from 0.54 for COPD to 2.72 for cardiac or HD, from 0.40 for COPD to 1.11 for LRI, and from 0.34 for all natural causes to 0.94 for COPD, respectively. 3.1.2. Change in ER estimates with adjustment for influenza activity The direction of associations remained positive for all pollutants and mortality outcomes with adjustment for influenza (Table 2 and
Table 1 Mean and (standard deviation) of meteorological, air pollution, mortality and hospitalization data. 1996e2002
Influenza epidemics (N ¼ 336 days)
Epidemic baseline (N ¼ 1602 days)
Influenza predominance (N ¼ 238 days)
Predominance baseline (N ¼ 888 days)
Temperature ( C)
23.7 (4.9)
19.5 (5.0)
24.1 (4.6)
17.4 (3.3)
22.4 (4.4)
Humidity (%)
78.0 (10.0)
79.2 (10.2)
76.4 (10.5)
78.2 (10.9)
73.6 (11.2)
Pollutant (mg m3) NO2 SO2 PM10 O3
58.7 17.8 51.6 36.9
(20.0) (12.1) (25.3) (23.0)
62.5 16.7 55.8 31.4
(20.2) (10.9) (28.8) (21.2)
60.0 17.6 53.7 40.1
(20.3) (11.8) (25.0) (23.9)
66.4 15.8 59.3 33.7
(16.9) (9.8) (25.5) (20.6)
64.0 16.1 60.3 43.9
(20.0) (11.5) (24.7) (22.2)
Mortality (no.) Stroke Cardiac or HD LRI COPD
8.9 12.0 9.3 5.9
(3.3) (4.1) (3.7) (2.9)
10.6 15.5 11.9 8.2
(3.6) (4.6) (3.8) (3.3)
8.6 11.4 8.8 5.3
(3.2) (3.8) (3.5) (2.6)
11.2 16.6 11.7 8.6
(3.5) (4.4) (3.8) (3.3)
8.7 11.6 8.9 5.3
(3.1) (3.9) (3.7) (2.6)
47.1 46.1 104.9 91.5
(10.0) (13.1) (29.8) (20.0)
47.8 46.4 140.9 107.9
(10.0) (12.4) (35.4) (22.0)
46.3 45.9 94.3 87.8
(9.6) (12.9) (22.9) (18.6)
49.9 48.2 140.9 113.8
(9.9) (12.5) (39.0) (20.9)
46.7 46.0 85.4 90.0
(10.1) (13.3) (18.1) (18.7)
Hospitalization (no.) Stroke IHD ARD COPD
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Table 2 Confounding by influenza of effect of air pollution on cardiorespiratory mortality: excess risk (ER%) and 95% confidence interval (95% CI) of mortality per 10 mg m3 increase in average concentration of air pollutants, for lag 0e1 day with and without adjustment of three measures of influenza. Without adjustment
With adjustment for influenza Intensity
Epidemics
Predominance
ER%
95% CI
ER%
95% CI
ER%
95% CI
ER%
95% CI
Stroke
NO2 SO2 PM10 O3
1.13 1.08 0.81 0.54
0.19, 0.36, 0.03, 0.35,
2.08 2.53 1.60 1.43
1.09 0.97 0.85 0.52
0.14, 2.04 L0.47, 2.44 0.07, 1.64 0.37, 1.41
1.02 1.00 0.77 0.50
0.08, 1.98 0.44, 2.46 0.01, 1.56 0.39, 1.40
1.05 1.02 0.80 0.46
0.10, 0.42, 0.02, 0.43,
Cardiac or HD
NO2 SO2 PM10 O3
2.08 2.72 0.96 0.61
1.10, 1.22, 0.15, 0.32,
3.07 4.23 1.78 1.54
2.09 2.65 1.10 0.59
1.12, 3.07 1.16, 4.17 0.29, 1.91 0.33, 1.51
1.98 2.70 0.94 0.56
1.00, 1.21, 0.13, 0.37,
2.97 4.22 1.75 1.49
1.97 2.74 0.99 0.43
0.99, 2.95 1.24, 4.25 0.18, 1.81 L0.49, 1.35
LRI
NO2 SO2 PM10 O3
1.75 2.21 1.11 0.41
0.74, 0.71, 0.27, 0.52,
2.77 3.73 1.95 1.35
1.73 2.13 1.16 0.41
0.72, 0.62, 0.33, 0.52,
2.74 3.66 2.00 1.35
1.64 2.11 1.07 0.41
0.63, 2.65 0.60, 3.63 0.24, 1.90 0.52, 1.34
1.62 2.11 1.09 0.30
0.61, 2.63 0.60, 3.64 0.26, 1.93 L0.62, 1.24
COPD
NO2 SO2 PM10 O3
1.39 0.54 0.40 0.94
0.18, 1.29, 0.59, 0.20,
2.61 2.41 1.41 2.09
1.30 0.30 0.52 0.90
0.10, 2.51 L1.53, 2.17 L0.46, 1.52 0.23, 2.05
1.17 0.37 0.34 0.88
L0.04, 2.39 L1.46, 2.24 0.65, 1.34 0.26, 2.03
1.22 0.47 0.44 0.72
0.01, 2.44 1.37, 2.35 0.55, 1.45 L0.42, 1.87
2.00 2.49 1.59 1.36
Note: ER% and 95% CI in bold if the absolute change in ER% following adjustment for influenza activity >0.1%.
Table 5). No discernable pattern in the changes of effect estimate for any particular air pollutant was found during epidemic and predominance periods. 3.1.2.1. Influenza intensity. The changes in the effect estimate for all associations were <0.1% except for associations between SO2 and stroke and COPD and between PM10 and cardiac or HD and COPD for which changes were >0.1%. 3.1.2.2. Influenza epidemic. The changes in the effect estimate for all associations were <0.1% except for associations between NO2 and stroke, LRI and COPD; as well as between SO2 and COPD for which changes were >0.1%. The initial association between NO2 and COPD (p < 0.05) became non-significant. 3.1.2.3. Influenza predominance. The changes in the effect estimate for all associations were <0.1% except for associations between NO2
and cardiac or HD, LRI and COPD; O3 and cardiovascular, cardiac or HD, LRI and COPD for which changes were >0.1%. The initial associations between O3 and all natural causes (p < 0.05) and cardiovascular disease (p < 0.05) became both non-significant. 3.2. Hospitalization 3.2.1. ER estimates without adjustment for influenza activity The effects of the four pollutants on hospitalization were positive for all hospitalization outcomes under study (Table 3 and Table 6), except for SO2 and stroke and asthma, and O3 and stroke hospitalizations which were all negatively associated and non-significant (p > 0.05). The ER% estimates per 10 mg m3 increase in concentration of NO2 in lag 0e1 day ranged from 0.33 for stroke to 1.94 for COPD. The corresponding estimates for SO2, PM10 and O3 ranged from 0.17 for stroke to 0.98 for cardiovascular, 0.12 for stroke to 1.32 for COPD, and 0.05 for stroke to 1.55 for ARD, respectively.
Table 3 Confounding by influenza of effect of air pollution on cardiorespiratory hospitalization: excess risk (ER%) and 95% confidence interval (95% CI) of hospitalization per 10 mg m3 increase in average concentration, for air pollutants in lag 0e1 day with and without adjustment of three measures of influenza. Without adjustment
With adjustment for influenza Intensity
ER%
95% CI
Epidemics
ER%
95% CI
Predominance ER%
95% CI
Stroke
NO2 SO2 PM10 O3
0.33 0.17 0.12 0.05
0.09, 0.80, 0.23, 0.43,
0.76 0.47 0.48 0.33
0.32 0.22 0.12 0.04
0.10, 0.86, 0.24, 0.42,
0.75 0.42 0.48 0.34
0.29 0.19 0.09 0.10
0.14, 0.82, 0.27, 0.48,
0.72 0.46 0.45 0.28
0.33 0.15 0.13 0.11
0.10, 0.79, 0.23, 0.49,
0.76 0.50 0.49 0.27
IHD
NO2 SO2 PM10 O3
0.94 0.93 0.72 0.26
0.46, 0.21, 0.32, 0.17,
1.42 1.66 1.13 0.69
0.96 0.93 0.75 0.27
0.48, 0.21, 0.35, 0.16,
1.44 1.66 1.16 0.70
0.94 1.01 0.73 0.22
0.46, 0.28, 0.33, 0.22,
1.43 1.74 1.14 0.65
0.96 1.02 0.76 0.20
0.47, 0.29, 0.35, 0.24,
1.45 1.75 1.16 0.63
ARD
NO2 SO2 PM10 O3
1.22 0.55 0.88 1.55
0.74, 0.18, 0.49, 1.11,
1.71 1.29 1.28 1.99
0.90 0.11 0.85 1.25
0.47, 1.32 L0.53, 0.75 0.50, 1.20 0.86, 1.63
0.82 0.25 0.65 1.14
0.40, L0.38, 0.30, 0.76,
1.25 0.89 1.00 1.53
0.71 0.28 0.68 0.94
0.26, L0.39, 0.31, 0.54,
1.15 0.95 1.04 1.35
COPD
NO2 SO2 PM10 O3
1.94 0.70 1.32 1.54
1.55, 0.10, 0.99, 1.17,
2.33 1.31 1.65 1.92
1.92 0.62 1.33 1.54
1.86 0.62 1.28 1.50
1.47, 0.02, 0.96, 1.14,
2.25 1.22 1.61 1.88
1.92 0.70 1.35 1.47
1.53, 0.10, 1.03, 1.09,
2.31 1.31 1.68 1.84
1.53, 0.01, 1.01, 1.16,
2.32 1.22 1.66 1.91
ER%
95% CI
Note: ER% and 95% CI in bold if the absolute change in ER% following adjustment for influenza activity >0.1%.
T.-Q. Thach et al. / Atmospheric Environment 44 (2010) 1437e1442
3.2.2. Change in ER estimate with adjustment for influenza activity The direction of associations remained unchanged for all pollutants and hospitalization outcomes under study following adjustment for three measures of influenza. There were no consistent trends in the changes of effect estimate for any pollutant (Table 3 and Table 6). 3.2.2.1. Influenza intensity. The changes in the effect estimate for all associations were <0.1% except for associations between the gaseous pollutants and ARD and for PM10 and ALRI for which changes were >0.1%. 3.2.2.2. Influenza epidemic and predominance. The changes in the effect estimate for all associations were <0.1% except for gaseous pollutants and ARD for which changes were >0.1%. We examined both natural and penalized spline models, but do not report results for penalized spline. We found both splines generate relatively similar results in the effect estimates, consistent with a previous study (Dominici et al., 2003). 4. Discussion We found positive associations between the four criteria air pollutants and all health outcomes under study except for SO2 effects on hospitalization for asthma and stroke; and O3 effects on hospitalization for stroke which were negative but non-significant without adjustment for influenza and these associations persisted after adjustment for three measures of influenza. There were some changes in the estimates, but they were generally small suggesting little or no confounding effects on the associations between the air pollutants and health outcomes except for some cardiorespiratory mortality outcomes: stroke with NO2 and SO2, cardiac or HD with NO2, PM10 and O3, LRI with NO2 and O3 and COPD with all pollutants; and respiratory hospitalization outcomes: ARD with NO2, SO2 and O3 and ALRI with PM10. A change in statistical significance occurred in only two out of fifteen modified effect estimates for mortality and in none out of twelve for hospitalization. Although there was no widely accepted criterion for defining an influenza epidemic, in our study we defined the epidemic period as the point when the frequency of positive isolates exceeds the baseline average frequency of positive isolates, known as the epidemic threshold. The beginning of an epidemic period can be identified only after two consecutive weeks with positive isolates above the threshold. These two measures of influenza activity based on epidemic and predominance periods and intensity have been used to assess influenza-associated mortality and morbidity in Hong Kong and found to be valid (Wong et al., 2004, 2006). There is evidence for modification by influenza of the health effects of air pollution (Wong et al., 2009). So inadequate adjustment for influenza epidemics may confound interpretation of the impact of air pollutants but mainly for ARD hospitalizations which include pneumonia and influenza. Previously, only a limited number of studies have examined potential confounding by influenza epidemics for PM10 and mortality outcomes. A study of seven European cities which assessed 10 methods to control for influenza, including excess event counts and virology monitoring found that PM10 and all natural causes and cardiovascular mortality were robust to adjustment by influenza epidemics (Touloumi et al., 2005). Braga et al. (2000) found that effects for PM10 did not markedly alter the PM10 effect estimate following adjustment for respiratory epidemics which was based on pneumonia-related hospitalization for five US cities. Tobias and Campbell (1999) reported that, although controlling for daily influenza cases resulted >10% change in black smoke effect on total mortality the association remained
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significant. Our findings cannot be directly compared with previous studies because the type of influenza data being used and the definition of epidemic and predominance periods are different. There is no consensus on the cutoff value for determining confounding especially for air pollution and health studies and it is expected that changing the cutoff value will ultimately affect the results. Our study used a criterion based on the absolute changes of >0.1% between unadjusted and adjusted ER% to define confounding rather than changes in relative risk (RR) ratio estimate (RRadjusted/ RRunadjusted) as suggested by Maldonado and Greenland (1993). Sensitivity analyses showed that the two results are in agreement in determining confounding. This is because the relative risk associated with air pollution is generally small and close to unity and the magnitude of absolute and relative change is comparable. We assessed the confounding of influenza intensity by assuming a linear association with the health outcomes and did not investigate any other functional forms. If the shape of association between influenza intensity and health outcomes is nonlinear, and the model is fitted assuming a linear association with influenza intensity, then confounding may not have been adequately adjusted for. Surveillance data from the Hong Kong Department of Health was only available from 1998. We used virology data from Queen Mary Hospital for 1996e2002 which accounted for about 40% of the surveillance data for the whole territory. We found the correlation between the two virology datasets was high for period 1998e2002 (r ¼ 0.8). Owing to the compact geographical area of Hong Kong these data should be representative of influenza virus activity. The validity of our findings must include the recognition that we examined a large number of models to assess the confounding effects of influenza activity on fifteen health outcomes, for four air pollutants and three measures of influenza. Because of a large number of comparisons, the chances of detecting a spurious finding are increased. We did not adjust the p-values to take into account the multiple comparisons performed in our study because the adjustment has been criticized on the grounds that it induces more problems than it intends to solve (Rothman, 1990). The findings are particularly relevant to environments where influenza circulation and epidemics and air pollution, are currently both high. Funding source This study was supported by grant 4713-RFIQ03-3/04-9 from the Health Effects Institute. Acknowledgment We thank the Health Effects Institute for funding this study. Appendix. Supplementary material Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.atmosenv.2010.01.036. References Braga, A.L., Zanobetti, A., Schwartz, J., 2000. Do respiratory epidemics confound the association between air pollution and daily deaths? The European Respiratory Journal 16, 723e728. Chiu, S.S., Lau, Y.L., Chan, K.H., Wong, W.H., Peiris, J.S., 2002. Influenza-related hospitalizations among children in Hong Kong. The New England Journal of Medicine 347, 2097e2103. Dominici, F., McDermott, A., Daniels, M., Zeger, S.L., Samet, J.M., 2003. A Special Report to the Health Effects Institute on the Revised Analyses of the NMMAPS II Data. The Health Effects Institute, Cambridge, MA. Han, L.L., Alexander, J.P., Anderson, L.J., 1999. Respiratory syncytial virus pneumonia among the elderly: an assessment of disease burden. The Journal of Infectious Diseases 179, 25e30.
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Hong Kong Department of Health, 2005. Comparability of Cause-of-Death Coding between ICD-9 and ICD-10. (accessed January 2010). Katsouyanni, K., Schwartz, J., Spix, C., Touloumi, G., Zmirou, D., Zanobett, i A., Wojtyniak, B., Vonk, J.M., Tobias, A., Pönkä, A., Medina, S., Bachárová, L., Anderson, H.R., 1996. Short term effects of air pollution on health: a European approach using epidemiologic time series data: the APHEA protocol. Journal of Epidemiology and Community Health 50 (Suppl. 1), 12e18. Katsouyanni, K., Touloumi, G., Spix, C., Schwartz, J., Balducci, F., Medina, S., Rossi, G., Wojtyniak, B., Sunyer, J., Bacharova, L., Schouten, J.P., Ponka, A., Anderson, H.R., 1997. Short-term effects of ambient sulphur dioxide and particulate matter on mortality in 12 European cities: results from time series data from the APHEA project. Air Pollution and Health: a European Approach. British Medical Journal 314, 1658e1663. Katsouyanni, K., Touloumi, G., Samoli, E., Gryparis, A., Le Tertre, A., Monopolis, Y., Rossi, G., Zmirou, D., Ballester, F., Boumghar, A., Anderson, H.R., Wojtyniak, B., Paldy, A., Braunstein, R., Pekkanen, J., Schindler, C., Schwartz, J., 2001. Confounding and effect modification in the short-term effects of ambient particles on total mortality: results from 29 European cities within the APHEA2 project. Epidemiology 12, 521e531. Leung, G.M., Wong, I.O., Chan, W.S., Choi, S., Lo, S.V., Health Care Financing Study Group, 2005. The ecology of health care in Hong Kong. Social Science and Medicine 61, 577e590. Maldonado, G., Greenland, S., 1993. Simulation study of confounder-selection strategies. American Journal of Epidemiology 138, 923e936. Nicholson, K.G., 1996. Impact of influenza and respiratory syncytial virus on mortality in England and Wales from January 1975 to December 1990. Epidemiology and Infection 116, 51e63. Ong, S.G., Liu, J., Wong, C.M., Lam, T.H., Tam, A.Y., Daniel, L., Hedley, A.J., 1991. Studies on the respiratory health of primary school children in urban communities of Hong Kong. The Science of the Total Environment 106, 121e135. R Development Core Team, 2009. R: A Language and Environment for Statistical Computing. Available from:. R Foundation for Statistical Computing, Vienna, Austria http://www.R-project.org (accessed March 2009). Rothman, K.J., 1990. No adjustments are needed for multiple comparisons. Epidemiology 1, 43e46. Samet, J.M., Zeger, S.L., Dominici, F., Curriero, F., Coursac, I., Dockery, D.W., Schwartz, J., Zanobetti, A., 2000. The National Morbidity, Mortality, and Air Pollution Study. Part II: Morbidity and Mortality from Air Pollution in the United States. Health Effects Institute, Cambridge, MA. Shortridge, K.F., Stuart-Harris, C.H., 1982. An influenza epicentre? Lancet 2, 812e813. Tam, A.Y., Wong, C.M., Lam, T.H., Ong, S.G., Peters, J., Hedley, A.J., 1994. Bronchial responsiveness in children exposed to atmospheric pollution in Hong Kong. Chest 106, 1056e1060. Thompson, W.W., Shay, D.K., Weintraub, E., Brammer, L., Cox, N., Anderson, L.J., Fukuda, K., 2003. Mortality associated with influenza and respiratory syncytial
virus in the United States. The Journal of the American Medical Association 289, 179e186. Thompson, W.W., Shay, D.K., Weintraub, E., Brammer, L., Bridges, C.B., Cox, N.J., Fukuda, K., 2004. Influenza-associated hospitalizations in the United States. The Journal of the American Medical Association 292, 1333e1340. Tobias, A., Campbell, M.J., 1999. Modelling influenza epidemics in the relation between black smoke and total mortality. A sensitivity analysis. Journal of Epidemiology and Community Health 53, 583e584. Touloumi, G., Atkinson, R., Le Tertre, A., Samoli, E., Schwartz, J., Schindler, C., Vonk, J. M., Rossi, G., Saez, M., Rabszenko, D., Katsouyanni, K., 2004. Analysis of health outcome time series data in epidemiological studies. Environmetrics 15, 101e117. Touloumi, G., Samoli, E., Quenel, P., Paldy, A., Anderson, H.R., Zmirou, D., Galan, I., Forsberg, B., Schindler, C., Schwartz, J., Katsouyanni, K., 2005. Short-term effects of air pollution on total and cardiovascular mortality: the confounding effect of influenza epidemics. Epidemiology 16, 49e57. Vichit-Vadakan, N., Vajanapoom, N., Ostro, B., 2008. The Public Health and Air Pollution in Asia (PAPA) Project: estimating the mortality effects of particulate matter in Bangkok, Thailand. Environmental Health Perspectives 116, 1179e1182. Wong, C.M., Lam, T.H., Peters, J., Hedley, A.J., Ong, S.G., Tam, A.Y., Liu, J., Spiegelhalter, D.J., 1998. Comparison between two districts of the effects of an air pollution intervention on bronchial responsiveness in primary school children in Hong Kong. Journal of Epidemiology and Community Health 52, 571e578. Wong, C.M., Ma, S., Hedley, A.J., Lam, T.H., 1999. Does ozone have any effect on daily hospital admissions for circulatory diseases? Journal of Epidemiology and Community Health 53, 580e581. Wong, C.M., Ma, S., Hedley, A.J., Lam, T.H., 2001. Effect of air pollution on daily mortality in Hong Kong. Environmental Health Perspectives 109, 335e340. Wong, C.M., Atkinson, R.W., Anderson, H.R., Hedley, A.J., Ma, S., Chau, Y.K., Lam, T.H., 2002. A tale of two cities: effects of air pollution on hospital admissions in Hong Kong and London compared. Environmental Health Perspectives 110, 67e77. Wong, C.M., Chan, K.P., Hedley, A.J., Peiris, J.S.M., 2004. Influenza-associated mortality in Hong Kong. Clinical Infectious Diseases 39, 1611e1617. Wong, C.M., Yang, L., Chan, K.P., Leung, G.M., Chan, K.H., Guan, Y., Lam, T.H., Hedley, A.J., Peiris, J.S.M., 2006. Influenza-associated hospitalization in a subtropical city. PLoS Medicine 3, e121. Wong, C.M., Vichit-Vadakan, N., Kan, H., Qian, Z., 2008. Public Health and Air Pollution in Asia (PAPA): a multicity study of short-term effects of air pollution on mortality. Environmental Health Perspectives 116, 1195e1202. Wong, C.M., Yang, L., Thach, T.Q., Chau, P.Y., Chan, K.P., Thomas, G.N., Lam, T.H., Wong, T.W., Hedley, A.J., Peiris, J.S., 2009. Modification by influenza on health effects of air pollution in Hong Kong. Environmental Health Perspectives 2, 248e253. Zambon, M.C., Stockton, J.D., Clewley, J.P., Fleming, D.M., 2001. Contribution of influenza and respiratory syncytial virus to community cases of influenza-like illness: an observational study. Lancet 358, 1410e1416.
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Air pollutants and health outcomes: Assessment of confounding by influenza Thuan-Quoc Thach a, Chit-Ming Wong a, *, King-Pan Chan a, Yuen-Kwan Chau a, G. Neil Thomas b, Chun-Quan Ou c, Lin Yang a, Joseph S.M. Peiris d, Tai-Hing Lam a, Anthony J. Hedley a a
School of Public Health, The University of Hong Kong, Hong Kong, China Department of Public Health and Epidemiology, University of Birmingham, UK c Department of Biostatistics, Southern Medical University, Guangzhou, China d Department of Microbiology, The University of Hong Kong, Hong Kong, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 October 2009 Received in revised form 21 January 2010 Accepted 25 January 2010
We assessed confounding of associations between short-term effects of air pollution and health outcomes by influenza using Hong Kong mortality and hospitalization data for 1996e2002. Three measures of influenza were defined: (i) intensity: weekly proportion of positive influenza viruses, (ii) epidemic: weekly number of positive influenza viruses 4% of the annual number for 2 consecutive weeks, and (iii) predominance: an epidemic period with co-circulation of respiratory syncytial virus <2% of the annual positive isolates for 2 consecutive weeks. We examined effects of influenza on associations between nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter with aerodynamic diameter 10 mm (PM10) and ozone (O3) and health outcomes including all natural causes mortality, cardiorespiratory mortality and hospitalization. Generalized additive Poisson regression model with natural cubic splines was fitted to control for time-varying covariates to estimate air pollution health effects. Confounding with influenza was assessed using an absolute difference of >0.1% between unadjusted and adjusted excess risks (ER%). Without adjustment, pollutants were associated with positive ER% for all health outcomes except asthma and stroke hospitalization with SO2 and stroke hospitalization with O3. Following adjustment, changes in ER% for all pollutants were <0.1% for all natural causes mortality, but >0.1% for mortality from stroke with NO2 and SO2, cardiac or heart disease with NO2, PM10 and O3, lower respiratory infections with NO2 and O3 and mortality from chronic obstructive pulmonary disease with all pollutants. Changes >0.1% were seen for acute respiratory disease hospitalization with NO2, SO2 and O3 and acute lower respiratory infections hospitalization with PM10. Generally, influenza does not confound the observed associations of air pollutants with all natural causes mortality and cardiovascular hospitalization, but for some pollutants and subgroups of cardiorespiratory mortality and respiratory hospitalization there was evidence to suggest confounding by influenza. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Confounding Influenza activity Mortality Hospitalization Hong Kong
1. Introduction
Abbreviations: ALRI, acute lower respiratory infections; ARD, acute respiratory; CI, confidence interval; COPD, chronic obstructive pulmonary disease; ER%, excess risk in percent; HD, heart disease; IHD, ischemic heart disease; ICD-9, International Classification of Diseases, 9th Revision; ICD-10, International Classification of Diseases, 10th Revision; LRI, lower respiratory diseases; mg m3, microgram per cubic meter; mm, micron; NO2, nitrogen dioxide; O3, ozone; PM10, particulate matter with an aerodynamic diameter 10 mm; RSV, respiratory syncytial viruses; SO2, sulfur dioxide. * Corresponding author at: School of Public Health, The University of Hong Kong, 5th Floor, Faculty of Medicine Building, 21 Sassoon Road, Hong Kong, China. Tel.: þ852 2819 9109; fax: þ852 2855 9528. E-mail address: [email protected] (C.-M. Wong). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.01.036
There is strong evidence from Hong Kong and the rest of east Asia, as well as Western populations, that ambient air pollution is associated with adverse health effects in children and adults, often at levels well below air quality objectives currently adopted by many countries (Ong et al., 1991; Tam et al., 1994; Katsouyanni et al., 1997; Wong et al., 1998, 1999, 2001, 2002, 2008; Samet et al., 2000; Vichit-Vadakan et al., 2008). Time-series studies are commonly used to examine the association between short-term effects of air pollution and health. In such time-series, a core model is first constructed and the effects of an air pollutant are then assessed by adding a linear term for the pollutant to the core model. However, the effect estimate for the air pollutant could be biased if
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all potential confounders have not been adequately controlled for, such as the effect of influenza epidemics which may be seasonally correlated with both air pollution and health outcomes. Studies for the association between short-term effects of exposure to particulate matter and mortality due to all natural and cardiovascular causes showed that the effects were not confounded by influenza epidemics (Braga et al., 2000; Touloumi et al., 2005). In limited number of time-series studies describing the effect of air pollution, influenza epidemics were controlled for by adding one dummy variable to the core model usually based on non-virological data of respiratory mortality counts (Katsouyanni et al., 1996, 2001; Touloumi et al., 2004; Wong et al., 2008). However, this approach cannot account for possible heterogeneity over time in the effects of influenza. In other studies the assessment of confounding effects of influenza in relation to gaseous pollutants was not considered. We present a formal assessment of confounding effects by influenza activity of health effects related to the four criteria pollutants in Hong Kong from 1996 to 2002 using laboratory confirmed influenza isolates to define influenza activity. 2. Materials and methods 2.1. Mortality Daily mortality data for all deaths of Hong Kong residents for the seven-year period from January 1996 to December 2002 were obtained from the Census and Statistics Department. All natural causes of deaths coded with International Classification of Diseases version 9 (ICD-9)1e799; or version 10 (ICD-10) A00-R99 were studied. We also specifically analyzed deaths due to cardiovascular disease (ICD-9:390e459; ICD-10:I00eI99) with subgroups of stroke (ICD-9:430e438; ICD-10: I60eI69) and cardiac or heart disease (cardiac or HD; ICD-9:390e398, 410e429; ICD-10:I00eI09, I20eI52); respiratory disease (ICD-9:460e519; ICD-10:J00eJ98) with subgroups of lower respiratory infections (LRI; ICD-9:466, 480e487; ICD-10:J10eJ22), and chronic obstructive pulmonary diseases (COPD; ICD-9:490e496; ICD-10:J40eJ47). We chose all ages for the analyses. An assessment of the possible discrepancy between the classification of causes of deaths using ICD-9 and ICD10 showed that agreement between the two coding systems was over 90% (Hong Kong Department of Health, 2005). 2.2. Hospitalization The computerized hospital data used in this study were based on discharge diagnoses from January 1996 to December 2002, of the 19 hospitals within the Hospital Authority which managed over 95% of hospital bed days in Hong Kong (Leung et al., 2005). The disease rubrics retrieved from these records were only based on the ICD-9. The discharge diagnoses included were cardiovascular (ICD9:390e459), including the subgroups of stroke (ICD-9:430e438) and ischemic heart disease (IHD; ICD-9:410e414); and respiratory (ICD-9:460e519) with subgroups of acute respiratory disease (ARD; ICD-9: 460e466, 480e487), acute lower respiratory infections (ALRI; ICD-9: 480e487), COPD (ICD-9:490e496), asthma (ICD-9:493). We chose all ages for the analyses. 2.3. Air pollutant and meteorological conditions Data provided by the Environment Protection Department included daily 24-h mean concentrations of particulate matter of aerodynamic diameter 10 mm (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), and 8-h mean concentrations of ozone (O3) measured at eight monitoring sites across Hong Kong. Daily data with less than 18 hourly measurements for PM10, NO2 and SO2 and
less than six hourly measurements during the 10:00e18:00 period for O3 were regarded as missing. The daily means among the monitors for each pollutant were calculated using an algorithm that accounted for differences between each monitor (Wong et al., 2002). There were no missing daily estimates for the four pollutants during the period of study. The daily mean temperature (Celsius) and daily mean relative humidity (percent) were obtained from the Hong Kong Observatory. 2.4. Virology Hong Kong is geographically situated in the tropics, but has a subtropical climate. It lies in a region considered to be the hypothetical epicenter of a number of influenza pandemics (Shortridge and Stuart-Harris, 1982). Virological data on influenza and respiratory syncytial viruses (RSV) were routinely available on a weekly basis as part of the Government infectious disease surveillance. Weekly numbers of positive isolates of influenza A and B viruses (influenza AþB) and respiratory syncytial virus (RSV) as well as the total specimens tested were obtained from the Department of Microbiology, Queen Mary Hospital, Hong Kong. The laboratory received an annual mean number of 6249 (range 3098e8333) specimens for diagnosis of respiratory infections during the study period. We defined three measures of influenza activity using the virology data. 2.4.1. Influenza intensity Intensity was defined by the weekly proportion of positive isolates of influenza AþB of the total number of specimens submitted for laboratory diagnosis. Weekly proportions of specimens tested positive for influenza viruses have been widely adopted to assess influenza-associated mortality and morbidity (Thompson et al., 2003, 2004; Wong et al., 2004, 2006). Because the influenza intensity was available on a weekly basis, we assigned the same values of intensity for the entire week to be daily measure of intensity. 2.4.2. Influenza epidemics Periods in each year were categorized according to the weekly frequency of positive influenza AþB isolates. We defined (i) Epidemic: Weekly number of positive influenza isolates 4% of the annual total number of positive isolates for at least 2 consecutive weeks (Chiu et al., 2002). (ii) Epidemic baseline: Weekly number of positive influenza isolates <2% of the annual total number of positive isolates for at least 2 consecutive weeks. (iii) Intermediate: Period not belonging to (i) or (ii) above.
2.4.3. Influenza predominance RSV has been regarded as a major cause of hospitalization in both children and the elderly and exhibits clinical syndromes very similar to influenza (Nicholson, 1996; Han et al., 1999; Zambon et al., 2001). We defined (i) Predominance: Weekly number of positive influenza isolates 4% of the annual total number, and weekly number of positive RSV isolates <2% of the annual total number of positive isolates of the respective viruses, both for at least 2 consecutive weeks. (ii) Predominance baseline: Weekly number of positive influenza isolates <2% of the annual total number, and RSV isolates also <2% of the annual total number of positive isolates of the respective viruses, both for at least 2 consecutive weeks. (iii) Intermediate: Period not belonging to (i) or (ii) above.
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2.5. Statistical analysis
3. Results
We first developed core models with a generalized additive Poisson regression method allowing for overdispersion in the model. For each health outcome, variations in seasonality, trends, temperature and relative humidity for that day were fitted with natural smoothing splines, and dummy variables were used to control variations for days of the week and holiday. We chose 4e7 degrees of freedom (df) per year for the smoothing splines of the trends and 3e6 df for temperature and humidity. The choice of the df for each smoothing function in the core models was made on the basis of observed residual autocorrelations using Partial AutoCorrelation Function (PACF). To reduce the autocorrelations that could not be minimized through smoothing, autoregressive terms were also added into the core model. For the core models fitted to the mortality data, the time-varying confounding factors were considered as adequately controlled if the absolute values of PACF coefficients were <0.1 for the first two lag days and there were no systematic patterns in the PACF plots (Wong et al., 2008). Due to the high autocorrelations within the hospitalization data, we included autoregressive terms up to the fourth order in the core models. Following the construction of an adequate core model for each health outcome we entered the pollutant variable linearly into the regression and examined the effects for the average of the current and previous days (lag 0e1). We expressed the effect of air pollution as the percentage change in the mean number of the daily health outcomes associated with an increase of 10 mg m3 of pollutant concentrations, calculated as 100 [exp(b 10) 1], where b is the estimated Poisson regression coefficient, and referred to this as unadjusted excess risk (ER%). We assessed the confounding of air pollutant effects by adding each of the three measures of influenza activity in turn into the model, treating intensity as continuous with both epidemics and predominance as categorical variables. The ER% was then reestimated to obtain adjusted ER%, assuming linear associations between intensity and health outcomes. Based on the criterion that if the absolute difference between the unadjusted and adjusted ER % estimates was >0.1%, then influenza activity was regarded as a confounder of the association between an air pollutant and a health outcome. The analyses were performed using R statistical software (R Development Core Team, 2009).
We report the results only for four causes of mortality: stroke, cardiac or HD, LRI and COPD and four causes of hospitalization: stroke, IHD, ARD and COPD. The results for other health outcomes are available in the Supplementary material. From 1996 to 2002 the mean concentrations of NO2, SO2, PM10 and O3 were 58.7, 17.8, 51.6, and 36.9 mg m3 respectively. There were more deaths and hospitalizations for all health outcomes under study with colder conditions in influenza epidemics and predominance periods than in their corresponding baseline periods. Relative humidity was lowest in the baseline periods. The duration of influenza epidemics and predominance (336 and 238 days) was longer than that of their respective baseline periods (1602 and 888 days). There was no difference between the influenza epidemics and predominance, and their baseline periods in the mean concentrations for NO2, SO2 and PM10. O3 showed higher levels during epidemics and predominance baseline periods (Table 1 and Table 4). The Spearman correlation between influenza intensity and pollutant concentrations was negative for NO2 (r ¼ 0.22), for PM10 (r ¼ 0.07), and for SO2 (r ¼ 0.31) whereas it was positive for O3 (r ¼ 0.20) [data not shown]. The intensities were 10.1% for influenza and 8.8% for RSV. The pattern of influenza intensity in 1996e2002 showed that except for the years 1997 and 2001, the seasonal pattern of influenza intensity comprised two peaks each year in January to March and July to August [data not shown]. 3.1. Mortality 3.1.1. ER estimates without adjustment for influenza activity The ER% estimates associated with the four pollutants were positive for all mortality outcomes under study (Table 2 and Table 5). A 10 mg m3 increase in NO2 concentrations at lag 0e1 day was associated with an ER% estimate ranging from 1.03 for all natural causes to 2.08 for cardiac or HD. The corresponding ER% estimates for SO2, PM10 and O3 ranged from 0.54 for COPD to 2.72 for cardiac or HD, from 0.40 for COPD to 1.11 for LRI, and from 0.34 for all natural causes to 0.94 for COPD, respectively. 3.1.2. Change in ER estimates with adjustment for influenza activity The direction of associations remained positive for all pollutants and mortality outcomes with adjustment for influenza (Table 2 and
Table 1 Mean and (standard deviation) of meteorological, air pollution, mortality and hospitalization data. 1996e2002
Influenza epidemics (N ¼ 336 days)
Epidemic baseline (N ¼ 1602 days)
Influenza predominance (N ¼ 238 days)
Predominance baseline (N ¼ 888 days)
Temperature ( C)
23.7 (4.9)
19.5 (5.0)
24.1 (4.6)
17.4 (3.3)
22.4 (4.4)
Humidity (%)
78.0 (10.0)
79.2 (10.2)
76.4 (10.5)
78.2 (10.9)
73.6 (11.2)
Pollutant (mg m3) NO2 SO2 PM10 O3
58.7 17.8 51.6 36.9
(20.0) (12.1) (25.3) (23.0)
62.5 16.7 55.8 31.4
(20.2) (10.9) (28.8) (21.2)
60.0 17.6 53.7 40.1
(20.3) (11.8) (25.0) (23.9)
66.4 15.8 59.3 33.7
(16.9) (9.8) (25.5) (20.6)
64.0 16.1 60.3 43.9
(20.0) (11.5) (24.7) (22.2)
Mortality (no.) Stroke Cardiac or HD LRI COPD
8.9 12.0 9.3 5.9
(3.3) (4.1) (3.7) (2.9)
10.6 15.5 11.9 8.2
(3.6) (4.6) (3.8) (3.3)
8.6 11.4 8.8 5.3
(3.2) (3.8) (3.5) (2.6)
11.2 16.6 11.7 8.6
(3.5) (4.4) (3.8) (3.3)
8.7 11.6 8.9 5.3
(3.1) (3.9) (3.7) (2.6)
47.1 46.1 104.9 91.5
(10.0) (13.1) (29.8) (20.0)
47.8 46.4 140.9 107.9
(10.0) (12.4) (35.4) (22.0)
46.3 45.9 94.3 87.8
(9.6) (12.9) (22.9) (18.6)
49.9 48.2 140.9 113.8
(9.9) (12.5) (39.0) (20.9)
46.7 46.0 85.4 90.0
(10.1) (13.3) (18.1) (18.7)
Hospitalization (no.) Stroke IHD ARD COPD
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Table 2 Confounding by influenza of effect of air pollution on cardiorespiratory mortality: excess risk (ER%) and 95% confidence interval (95% CI) of mortality per 10 mg m3 increase in average concentration of air pollutants, for lag 0e1 day with and without adjustment of three measures of influenza. Without adjustment
With adjustment for influenza Intensity
Epidemics
Predominance
ER%
95% CI
ER%
95% CI
ER%
95% CI
ER%
95% CI
Stroke
NO2 SO2 PM10 O3
1.13 1.08 0.81 0.54
0.19, 0.36, 0.03, 0.35,
2.08 2.53 1.60 1.43
1.09 0.97 0.85 0.52
0.14, 2.04 L0.47, 2.44 0.07, 1.64 0.37, 1.41
1.02 1.00 0.77 0.50
0.08, 1.98 0.44, 2.46 0.01, 1.56 0.39, 1.40
1.05 1.02 0.80 0.46
0.10, 0.42, 0.02, 0.43,
Cardiac or HD
NO2 SO2 PM10 O3
2.08 2.72 0.96 0.61
1.10, 1.22, 0.15, 0.32,
3.07 4.23 1.78 1.54
2.09 2.65 1.10 0.59
1.12, 3.07 1.16, 4.17 0.29, 1.91 0.33, 1.51
1.98 2.70 0.94 0.56
1.00, 1.21, 0.13, 0.37,
2.97 4.22 1.75 1.49
1.97 2.74 0.99 0.43
0.99, 2.95 1.24, 4.25 0.18, 1.81 L0.49, 1.35
LRI
NO2 SO2 PM10 O3
1.75 2.21 1.11 0.41
0.74, 0.71, 0.27, 0.52,
2.77 3.73 1.95 1.35
1.73 2.13 1.16 0.41
0.72, 0.62, 0.33, 0.52,
2.74 3.66 2.00 1.35
1.64 2.11 1.07 0.41
0.63, 2.65 0.60, 3.63 0.24, 1.90 0.52, 1.34
1.62 2.11 1.09 0.30
0.61, 2.63 0.60, 3.64 0.26, 1.93 L0.62, 1.24
COPD
NO2 SO2 PM10 O3
1.39 0.54 0.40 0.94
0.18, 1.29, 0.59, 0.20,
2.61 2.41 1.41 2.09
1.30 0.30 0.52 0.90
0.10, 2.51 L1.53, 2.17 L0.46, 1.52 0.23, 2.05
1.17 0.37 0.34 0.88
L0.04, 2.39 L1.46, 2.24 0.65, 1.34 0.26, 2.03
1.22 0.47 0.44 0.72
0.01, 2.44 1.37, 2.35 0.55, 1.45 L0.42, 1.87
2.00 2.49 1.59 1.36
Note: ER% and 95% CI in bold if the absolute change in ER% following adjustment for influenza activity >0.1%.
Table 5). No discernable pattern in the changes of effect estimate for any particular air pollutant was found during epidemic and predominance periods. 3.1.2.1. Influenza intensity. The changes in the effect estimate for all associations were <0.1% except for associations between SO2 and stroke and COPD and between PM10 and cardiac or HD and COPD for which changes were >0.1%. 3.1.2.2. Influenza epidemic. The changes in the effect estimate for all associations were <0.1% except for associations between NO2 and stroke, LRI and COPD; as well as between SO2 and COPD for which changes were >0.1%. The initial association between NO2 and COPD (p < 0.05) became non-significant. 3.1.2.3. Influenza predominance. The changes in the effect estimate for all associations were <0.1% except for associations between NO2
and cardiac or HD, LRI and COPD; O3 and cardiovascular, cardiac or HD, LRI and COPD for which changes were >0.1%. The initial associations between O3 and all natural causes (p < 0.05) and cardiovascular disease (p < 0.05) became both non-significant. 3.2. Hospitalization 3.2.1. ER estimates without adjustment for influenza activity The effects of the four pollutants on hospitalization were positive for all hospitalization outcomes under study (Table 3 and Table 6), except for SO2 and stroke and asthma, and O3 and stroke hospitalizations which were all negatively associated and non-significant (p > 0.05). The ER% estimates per 10 mg m3 increase in concentration of NO2 in lag 0e1 day ranged from 0.33 for stroke to 1.94 for COPD. The corresponding estimates for SO2, PM10 and O3 ranged from 0.17 for stroke to 0.98 for cardiovascular, 0.12 for stroke to 1.32 for COPD, and 0.05 for stroke to 1.55 for ARD, respectively.
Table 3 Confounding by influenza of effect of air pollution on cardiorespiratory hospitalization: excess risk (ER%) and 95% confidence interval (95% CI) of hospitalization per 10 mg m3 increase in average concentration, for air pollutants in lag 0e1 day with and without adjustment of three measures of influenza. Without adjustment
With adjustment for influenza Intensity
ER%
95% CI
Epidemics
ER%
95% CI
Predominance ER%
95% CI
Stroke
NO2 SO2 PM10 O3
0.33 0.17 0.12 0.05
0.09, 0.80, 0.23, 0.43,
0.76 0.47 0.48 0.33
0.32 0.22 0.12 0.04
0.10, 0.86, 0.24, 0.42,
0.75 0.42 0.48 0.34
0.29 0.19 0.09 0.10
0.14, 0.82, 0.27, 0.48,
0.72 0.46 0.45 0.28
0.33 0.15 0.13 0.11
0.10, 0.79, 0.23, 0.49,
0.76 0.50 0.49 0.27
IHD
NO2 SO2 PM10 O3
0.94 0.93 0.72 0.26
0.46, 0.21, 0.32, 0.17,
1.42 1.66 1.13 0.69
0.96 0.93 0.75 0.27
0.48, 0.21, 0.35, 0.16,
1.44 1.66 1.16 0.70
0.94 1.01 0.73 0.22
0.46, 0.28, 0.33, 0.22,
1.43 1.74 1.14 0.65
0.96 1.02 0.76 0.20
0.47, 0.29, 0.35, 0.24,
1.45 1.75 1.16 0.63
ARD
NO2 SO2 PM10 O3
1.22 0.55 0.88 1.55
0.74, 0.18, 0.49, 1.11,
1.71 1.29 1.28 1.99
0.90 0.11 0.85 1.25
0.47, 1.32 L0.53, 0.75 0.50, 1.20 0.86, 1.63
0.82 0.25 0.65 1.14
0.40, L0.38, 0.30, 0.76,
1.25 0.89 1.00 1.53
0.71 0.28 0.68 0.94
0.26, L0.39, 0.31, 0.54,
1.15 0.95 1.04 1.35
COPD
NO2 SO2 PM10 O3
1.94 0.70 1.32 1.54
1.55, 0.10, 0.99, 1.17,
2.33 1.31 1.65 1.92
1.92 0.62 1.33 1.54
1.86 0.62 1.28 1.50
1.47, 0.02, 0.96, 1.14,
2.25 1.22 1.61 1.88
1.92 0.70 1.35 1.47
1.53, 0.10, 1.03, 1.09,
2.31 1.31 1.68 1.84
1.53, 0.01, 1.01, 1.16,
2.32 1.22 1.66 1.91
ER%
95% CI
Note: ER% and 95% CI in bold if the absolute change in ER% following adjustment for influenza activity >0.1%.
T.-Q. Thach et al. / Atmospheric Environment 44 (2010) 1437e1442
3.2.2. Change in ER estimate with adjustment for influenza activity The direction of associations remained unchanged for all pollutants and hospitalization outcomes under study following adjustment for three measures of influenza. There were no consistent trends in the changes of effect estimate for any pollutant (Table 3 and Table 6). 3.2.2.1. Influenza intensity. The changes in the effect estimate for all associations were <0.1% except for associations between the gaseous pollutants and ARD and for PM10 and ALRI for which changes were >0.1%. 3.2.2.2. Influenza epidemic and predominance. The changes in the effect estimate for all associations were <0.1% except for gaseous pollutants and ARD for which changes were >0.1%. We examined both natural and penalized spline models, but do not report results for penalized spline. We found both splines generate relatively similar results in the effect estimates, consistent with a previous study (Dominici et al., 2003). 4. Discussion We found positive associations between the four criteria air pollutants and all health outcomes under study except for SO2 effects on hospitalization for asthma and stroke; and O3 effects on hospitalization for stroke which were negative but non-significant without adjustment for influenza and these associations persisted after adjustment for three measures of influenza. There were some changes in the estimates, but they were generally small suggesting little or no confounding effects on the associations between the air pollutants and health outcomes except for some cardiorespiratory mortality outcomes: stroke with NO2 and SO2, cardiac or HD with NO2, PM10 and O3, LRI with NO2 and O3 and COPD with all pollutants; and respiratory hospitalization outcomes: ARD with NO2, SO2 and O3 and ALRI with PM10. A change in statistical significance occurred in only two out of fifteen modified effect estimates for mortality and in none out of twelve for hospitalization. Although there was no widely accepted criterion for defining an influenza epidemic, in our study we defined the epidemic period as the point when the frequency of positive isolates exceeds the baseline average frequency of positive isolates, known as the epidemic threshold. The beginning of an epidemic period can be identified only after two consecutive weeks with positive isolates above the threshold. These two measures of influenza activity based on epidemic and predominance periods and intensity have been used to assess influenza-associated mortality and morbidity in Hong Kong and found to be valid (Wong et al., 2004, 2006). There is evidence for modification by influenza of the health effects of air pollution (Wong et al., 2009). So inadequate adjustment for influenza epidemics may confound interpretation of the impact of air pollutants but mainly for ARD hospitalizations which include pneumonia and influenza. Previously, only a limited number of studies have examined potential confounding by influenza epidemics for PM10 and mortality outcomes. A study of seven European cities which assessed 10 methods to control for influenza, including excess event counts and virology monitoring found that PM10 and all natural causes and cardiovascular mortality were robust to adjustment by influenza epidemics (Touloumi et al., 2005). Braga et al. (2000) found that effects for PM10 did not markedly alter the PM10 effect estimate following adjustment for respiratory epidemics which was based on pneumonia-related hospitalization for five US cities. Tobias and Campbell (1999) reported that, although controlling for daily influenza cases resulted >10% change in black smoke effect on total mortality the association remained
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significant. Our findings cannot be directly compared with previous studies because the type of influenza data being used and the definition of epidemic and predominance periods are different. There is no consensus on the cutoff value for determining confounding especially for air pollution and health studies and it is expected that changing the cutoff value will ultimately affect the results. Our study used a criterion based on the absolute changes of >0.1% between unadjusted and adjusted ER% to define confounding rather than changes in relative risk (RR) ratio estimate (RRadjusted/ RRunadjusted) as suggested by Maldonado and Greenland (1993). Sensitivity analyses showed that the two results are in agreement in determining confounding. This is because the relative risk associated with air pollution is generally small and close to unity and the magnitude of absolute and relative change is comparable. We assessed the confounding of influenza intensity by assuming a linear association with the health outcomes and did not investigate any other functional forms. If the shape of association between influenza intensity and health outcomes is nonlinear, and the model is fitted assuming a linear association with influenza intensity, then confounding may not have been adequately adjusted for. Surveillance data from the Hong Kong Department of Health was only available from 1998. We used virology data from Queen Mary Hospital for 1996e2002 which accounted for about 40% of the surveillance data for the whole territory. We found the correlation between the two virology datasets was high for period 1998e2002 (r ¼ 0.8). Owing to the compact geographical area of Hong Kong these data should be representative of influenza virus activity. The validity of our findings must include the recognition that we examined a large number of models to assess the confounding effects of influenza activity on fifteen health outcomes, for four air pollutants and three measures of influenza. Because of a large number of comparisons, the chances of detecting a spurious finding are increased. We did not adjust the p-values to take into account the multiple comparisons performed in our study because the adjustment has been criticized on the grounds that it induces more problems than it intends to solve (Rothman, 1990). The findings are particularly relevant to environments where influenza circulation and epidemics and air pollution, are currently both high. Funding source This study was supported by grant 4713-RFIQ03-3/04-9 from the Health Effects Institute. Acknowledgment We thank the Health Effects Institute for funding this study. Appendix. Supplementary material Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.atmosenv.2010.01.036. References Braga, A.L., Zanobetti, A., Schwartz, J., 2000. Do respiratory epidemics confound the association between air pollution and daily deaths? The European Respiratory Journal 16, 723e728. Chiu, S.S., Lau, Y.L., Chan, K.H., Wong, W.H., Peiris, J.S., 2002. Influenza-related hospitalizations among children in Hong Kong. The New England Journal of Medicine 347, 2097e2103. Dominici, F., McDermott, A., Daniels, M., Zeger, S.L., Samet, J.M., 2003. A Special Report to the Health Effects Institute on the Revised Analyses of the NMMAPS II Data. The Health Effects Institute, Cambridge, MA. Han, L.L., Alexander, J.P., Anderson, L.J., 1999. Respiratory syncytial virus pneumonia among the elderly: an assessment of disease burden. The Journal of Infectious Diseases 179, 25e30.
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