Modification by influenza on air pollution effects Data analysis. We used generalized additive modeling in this study (Hastie and Tibshirani 1990). To control for potential con‑ founding factors due to time‑ varying covari‑ ates, we first built the core model on the daily counts of hospitalization or mortality for each disease category, with dummy variables for the day of the week and public holidays to control for the variation associated with these factors. We controlled the weather conditions by adding into the core model natural spline smoothing functions of daily mean tempera‑ ture and relative humidity, each with three degrees of freedom. Additional adjustments in the core model included natural spline smooth‑ ing for time trend and seasonality. We chose the degrees of freedom for each smoothing function according to the observed autocor‑ relations of the residuals using partial autocor‑ relation function (PACF) plots. To reduce the autocorrelations that could not be minimized through smoothing, we also added autoregres‑ sive terms into the core model. For the core models fitted to the mortality data, we consid‑ ered the time‑ varying confounding factors as adequately controlled if the absolute values of PACF coefficients were < 0.1 for the first 2 lag days and there were no systematic patterns in the PACF plots. Because of the large autocor‑ relations within the hospitalization data, we added autoregressive terms until all the PACF coefficients at the first 30 lag days were < 0.1. After the core model for each health out‑ come had been established, we entered single air pollutant concentrations, influenza inten‑ sity, and the interaction term defined by the cross‑ product between these two variables into the core model to obtain the interaction model. To adjust for lag effects, we used the concentrations of air pollutants averaged over the current day and the previous day (lag 0–1). We added RSV proportions in order to control for the potential confounding of RSV with influenza. By assuming that the influenza intensity of the same day was equal to zero, we calculated from the interaction model the excess risk (ER) of hospitalization or mortal‑ ity per 10 µg/m3 increase of each air pollut‑ ant as the baseline effect of air pollution. We also calculated the ER per 10µ‑g/m 3 increase of each air pollutant when we assumed the influenza intensity to be its mean level of 10%, and the change in ER for each air pol‑ lutant associated hospitalization or mortal‑ ity (a measure for the modifying effect of influenza) from the interaction model, under the assumption that the influenza intensity increased from 0% to 10%. We completed all the statistical analyses using the statistical software package R version 2.5.1 with mgcv package version 1.3–25 (R Foundation for Statistical Computing, Vienna, Austria). Results During the study period of 1996–2002, influ‑ enza intensity had an average of 10.1% and ranged from 0% to 54.6%. The average con‑ centrations of air pollutants were 58.7, 17.8, 51.6, and 36.9 µg/m3 for NO2, SO2, PM10, and O3, respectively (Table 1). Table 1 also shows the summary statistics for meteorologic data as well as daily mortality and hospitaliza‑ tion outcomes. On average, mortality counts of CVD were higher than those of RD, but for hospitalization it was the opposite (Table 1). The baseline effects of the air pollutants on mortality, which we calculated under the assumption of zero influenza intensity, were Table 1. Summary statistics of influenza, air pollution, and meteorologic data. Mean ± SD Minimum Maximum the daily numbers of deaths for the above dis‑ ease categories over the study period from the Census and Statistics Department of Hong Kong. Because of relatively small numbers of daily mortality, we did not perform the age‑ and sex‑ stratified analyses when assessing the effect modification of influenza on the effects of air pollution on mortality of RD, COPD, and CVD. Daily mean temperature and relative humidity were provided by the Hong Kong Observatory (Hong Kong Observatory 2005). We obtained air pollutant concentrations from eight monitoring stations from the Hong Kong Environmental Protection Department (HKEPD). We derived daily 24‑ hr mean con‑ centrations of NO2, SO2, and PM10 and 8‑ hr (1000–1800 hours) mean concentration of O3 from the HKEPD database. We defined daily concentrations as nonmissing if at least 18 of 24 hourly concentrations of NO2, SO2, and PM10, and six of eight hourly concentrations of O3 were available. We first centered non‑ missing daily means for each station i [i.e., we subtracted individual daily concentrations (Xij) by an annual station mean (Xi) for each day j]. We then combined the centered data from all stations and added them into the annual mean of all stations (X) to form X´ij = (Xij – Xi + X). We computed the daily (mean) concentrations of individual pollutants for analysis by tak‑ ing the mean of centered X´ij over all stations (Wong et al. 2001). We obtained weekly numbers of speci‑ mens positive for influenza viruses A and B (Flu A+B) or respiratory syncytial virus (RSV) and total numbers of specimens tested from the Microbiology Laboratory of Queen Mary Hospital (Hong Kong). This laboratory rou‑ tinely collects the respiratory specimens from the patients with influenza‑like symptoms (fever > 38°C, with cough and/or sore throat) over the Hong Kong Island and conducted the immunofluorescent antigen test for Flu A+B. We derived the proportions of positive isolates of Flu A+B and RSV from the total specimens tested, which we then used to assess the effects of influenza and to adjust for the potential confounding effect of RSV in the model. The proportion of positive isolates of Flu A+B, which is defined as influenza intensity in this study, has been used in assessment of influ‑ enza effects on hospitalization and mortality (Wong et al. 2004, 2006). We used the pro‑ portions of specimens positive for influenza as a continuous measure for influenza activity instead of defining influenza epidemics. This allowed us to avoid the bias potentially posed by unpredictable seasonality of influenza in Hong Kong (Yang et al. 2008). We assumed constant activity of influenza virus and RSV within a week and interpolated the weekly proportions to the daily data, which we set the same from Sunday to Saturday. aValue for O3 concentration is negative because air pollutant concentration data were standardized using the centering method. Environmental Health Perspectives • volume 117 | number 2 | February 2009 249 Variables Temperature (°C) Humidity (%) Influenza (%) Air pollutant (µg/m3) NO2 SO2 PM10 O3 Mortality, all ages (no.) RD COPD CVD Hospitalization, all ages (no.) RD ARD COPD CVD Hospitalization (no.) RD, ≥ 65 age group ARD, 0–14 age group COPD, ≥ 65 age group CVD, ≥ 65 age group 23.7 ± 4.7 78 ± 7.8 10.1 ± 10.1 58.7 ± 20 17.8 ± 12.1 51.6 ± 25.3 36.9 ± 23 16.2 ± 5.2 5.9 ± 2.9 23.8 ± 6.5 270.3 ± 56.3 104.9 ± 29.8 91.5 ± 20 203.5 ± 48.5 138.5 ± 36.7 60.1 ± 19.5 59.6 ± 16.7 130.8 ± 30.1 8.9 40.6 0 10.1 1.8 13.5 –8.2a 3 0 6 143 48 41 75 57 22 19 47 25th 19.6 74.7 2.2 45.1 9.6 31.8 19.2 12 4 19 230 84 77 166 112 46 48 108 Percentile 50th 24.7 79.8 7.2 56.3 14.7 45.5 31.7 16 6 23 266 99 89 202 135 56 58 128 75th 27.8 83.3 14.5 69.6 22.1 66.7 50.8 19 8 28 303 122 104 241 160 72 70 152 31.7 92 54.6 168 109.4 188.5 196.6 34 19 54 586 275 176 345 302 149 124 222