1. Time-series studies for the relationship between air pollution and the population health in Beijing Xiao-chuan Pan Dept. of Occupational and Environmental Health Peking University School of Public Health

2. Part one Time-series study for the correlation of daily hospital outpatient visits and ambient air pollution in Beijing

3. Materials and Method Study setting. The study was conducted in 3 community-based hospitals in Beijing: ShiJingShan hospital, which located in the industrial area, western Beijing ; LongFu hospital, in the old downtown area and ChangPing hospital, in north suburb of Beijing. They represented the general situation for the effects of air pollution on outpatient visits of the hospitals in Beijing.

4. Materials and Method(2) Air pollution data collection The air pollution data by 1998-2000 was obtained from the air pollution monitoring station nearby the hospitals. Pollutant indicators in the study were (1) daily mean levels of Total Suspended Particulates(TSP); (2) daily mean levels of Particulate Matter(PM 10 ) (3) 24-hr average levels of nitrogen dioxide NO 2, (4) sulfur dioxide SO 2 levels, and (5) carbon monoxide (CO) levels.

5. The Map of Monitoring Substations in whole Beijing

6. Materials and Method(3) Collection for data of the hospitals. The latest 3 years data of daily hospital outpatient/emergency room visits from 3 community-based hospitals were collected for respiratory diseases, cardiovascular diseases and others. The classification of the disease diagnosis from that the outpatients suffer is based on ICD-9.

7. Materials and Method(4) Data analysis: 1. Interpolation for the missing values of the time- series data. 2. The wave smooth for control of Meteorological conditions, influenza epidemics and public holidays. 3. Spectrum analysis for Control of work days, seasons and years. 4. The data of outpatient visits lag 3 days behind the air… 5. Principal Component Analysis. 6. Poisson regression was used for exposure-response functions between air pollutants and related diseases.

8. Fig.2 Analysis procedure Computer Treatment Poisson analysis Interpolate Missing value Confounding Factors control Collection of Data Main component analysis

9. Results

10. Fig1. The interpolation for the missing values of daily outpatient visits in internal medicine of the hospitals in 1998 － 2000(about 1100 days)

11. Fig2. The interpolation for the missing values of TSP concentration (1998 － 2000)

12. Fig.3 The spectrum analysis for daily outpatient visits of AURI in the hospitals AURI: acute upper respiratory infection

13. Fig.4 The spectrum analysis for daily outpatient visits of CHD in the hospitals CHD:coronary heart disease

14. Fig.5 The spectrum analysis for daily outpatient visits of in the hospitals Fig.5 The spectrum analysis for daily outpatient visits of pediatrics in the hospitals

15. Fig.6 Wave smooth for Flu in the daily outpatient visits in internal medicine of the hospitals

16. SO 2 Levels of air in 3 monitoring substations of Beijing monthly

17. TSP Levels of air in 3 monitoring substations of Beijing monthly

18. NO 2 Levels of air in 3 monitoring substations of Beijing monthly

19. Fig. 11 The Outpatient visits of AURI, CHD and HD in internal medicine of the hospitals CHD:coronary heart disease AURI: acute upper respiratory infection HD: hypertension diseases

20. Fig. 12 The Outpatient visits of CRI, CEVD and TOV in internal medicine of the hospitals CRI: chronic respiratory infection CEVD: cerebrovascular disease TOV: total outpatient visits

21. Fig.13 The Outpatient visits of AUTI, HD and TOV in pediatrics of the hospitals

22. Regression Equation for exposure- response functions : Ln(AURI) ＝ 3.3467 ＋ 0.0269SO 2 r ＋ 0.0379NO X r ＋ 0.0356COr ＋ 0.0164TSPr ＋ 0.0219PM10r -0.0260Tr ＋ 0.0084Hr Ln(RD) ＝ 3.6317 ＋ 0.0144SO 2 r ＋ 0.0148NO X r ＋ 0.0143COr ＋ 0.0293TSPr ＋ 0.0034PM10r-0.0116Tr-0.0096Hr Ln(CHD) ＝ 2.7005 ＋ 0.0052SO 2 r ＋ 0.0477NO X r ＋ 0.0479COr -0.0346TSPr -0.0117PM10r -0.0202Tr ＋ 0.1128Hr Ln(HD) ＝ 3.1228-0.0307SO 2 r ＋ 0.0089NO X r ＋ 0.0198COr ＋ 0.0121TSPr ＋ 0.0586PM10r ＋ 0.0286Tr ＋ 0.0388Hr Ln(CVD) ＝ 3.7562-0.0087SO 2 r ＋ 0.0214NO X r ＋ 0.0260COr ＋ 0.0050TSPr ＋ 0.0076PM10r ＋ 0.0026Tr ＋ 0.0567Hr

23. Fig14. The percentages of increase in hospital outpatient visits with per unit increase(100mcg/m 3 ) of the air pollutants

24. Tab1. The percentages of increase in hospital outpatient visits with per unit increase(100mcg/m3) of the air pollutants pollu tants internal medicine internal medicine emergency pediatrics AUR I CHDHDCVDAUR I CVD AUR I Pn Tr SO 2 2.7270.521 －－ 0.3712.5931.1671.4088.513 NO X 3.8244.8860.894 2.163 1.4005.3221.85713.3411.082 CO3.6244.907 2.0002.634 1.4914.5171.50110.219.516 PM102.214 － 6.035 0.763 0.894 － 1.0357.6677.037 AURI: acute upper respiratory infection CHD:coronary heart disease HD: hypertension diseases Pn: pneumonia Tr: tracheitis CVD: cardiovascular disease

25. Fig.5 The Means of Daily Air Temperature in Beijing in 1998 － 2000

26. Fig.6 The Means of Daily Air Humidity in Beijing in 1998 － 2000

27. Part two Time-Series Analysis on the Relationship between Air Pollution and Daily Mortality in Beijing

28. Methods The data of daily cause-specific mortality of respiratory diseases, cardiovascular diseases and cancer in Beijing urban residents have been collected. The other ways were similar as part two.

29. selection of study parameters selection of study parameters pollutants: NO X 、 SO2 、 CO 、 TSP 、 PM10 health outcomes ： respiratory deaths 、 cardiovascular and cerebrovascular deaths 、 chronic obstructive pulmonary deaths 、 coronary heart disease deaths.

30. Fig.1 Analysis procedure Compute treatment Poisson analysis Interpolate Missing value Confounding Factors control Collection of Data

31. Results

32. Fig.2 Death counts of Respiratory Disease after control flu factor with spectrum analysis

33. Regression equations for related diseases: Ln(RD) ＝ 1.9780+0.4222SO 2 +0.3139TSP+0.0044H. Ln(CVD)=3.5731+0.3896SO 2 +0.0614TSP+0. 003T Ln(CHD)=2.6636+1.0151SO2+0.0044T Ln(COPD) ＝ 1.7365+1.7576 SO 2 -0.0097T +0.0021H RD: respiratory diseases, CVD: Cardiovascular diseases; CHD: coronary heart disease, COPD: chronic obstructive pulmonary diseases

34. Fig.7 The percentages of over the air quality standard in average levels of SO 2 monthly in Beijing

35. Fig.8 The percentages of over the air quality standard in monthly average levels of CO

36. Fig.9 The percentages of over the air quality standard in monthly average levels of NO 2

37. Fig.10 The percentages of over the air quality standard in monthly average levels of TSP

38. Chart. cause-specific mortality during 1994-2000 in Beijing

39. Fig.15 The percentages of increase in The cause-specific mortality with per unit increase(100mcg/m3) of the air pollutants pollutantrespiratory Cardiovascular and cerebrovascular coronary heart disease COPD Neoplasm of digestive system NOx4.272.568.9545.25 － SO 2 3.561.746.7730.98 － CO0.200.110.431.84 － TSP2.740.73 － 11.391.55 PM104.084.983.77*4.95 －

40. Conclusion We have got the newer exposure-response functions between ambient air pollution and hospital outpatient visits in Beijing by time-series analysis. It provides an important fundament for estimates of population morbidity and prevalence in relations with ambient air pollution in Beijing. We have also updated the results of epidemiological studies conducted previously in Beijing for correlations of population mortality with ambient air pollution in Beijing. We have evaluated primarily the roles of indoor air pollution on the health of exposure population.

41. The Health Data we have got as follows: The cause-specific mortality in 1994-2000 The age-specific mortality in 1994-2000 The location-specific mortality in 8 urban districts of Beijing in 1994-2000 Prevalence of the respiratory diseases in a case study(3000 subjects) of Beijing conducted in 2000 Outpatient/emergency visits of the hospital

42. Further study for the project in future 1. Integrated evaluation for links between energy type/use, air pollution and related health effects in Beijing by combining study results from Beijing, other cities of China and other countries. 2. Calculating exposure-response functions between ambient air pollution and health effects based on health risk assessment approach recommended by USEPA, as the fundament of quantitative assessment for health effects of air pollution. 3. Economic valuation of health impacts of air pollution, using a “benefits transfer” approach. Dr. Hong Wang in Yale University will join in this work.

43. In final report we could provide: 1. Integrated evaluation for the changes in health effects of air pollution previously in Beijing. 2. Based on exposure-response functions we setup from this project, estimate the anticipated health effects scenario with the related environmental data in different pollution controls and energy use, in 2005, 2008, even in 2020 of Beijing. 3. Economic benefits evaluation related for the anticipated health effects scenario of air pollution. 4. Further study ideas for national level’s in China.

44. Thank you!