Statistical methods for evaluating air pollution and temperature effects on human health

Transcription

1 Statistical methods for evaluating air pollution and temperature effects on human health Ho Kim School of Public Health, Seoul National University ISES-ISEE 2010 Workshop August 28, 2010, COEX, Seoul, Korea

2 # Hour Description File names 1 8:30-9:30 Introduction to Air pollution Epidemiology 1Intro.pdf 2 9:30-10:00 Introduction to R (1) 2introR.pdf,2introR.r prerequirement.r 10:00-10:30 Coffee break (R install and/or lab) Introduction to R (2) Exploring data, Introduction to R data, reading Korea data, Descriptive statistics 3expl.pdf 3 10:30-11:30 Box plot, Summary 3expl.r Time-series plot for air pollution, temperature, and mortality 4 11:30-12:30 Time-series analysis (1) 4times1.pdf 12:30-2:00 Lunch 5 2:00-3:00 Statistical analysis 5times1.pdf GAM/GLM 5times1.r 3:00-3:30 Coffee break 6 3:30-4:00 Time-series analysis (2) 6times2.pdf 7 4:00-5:00 Multisite studies, Threshold model 7times2.pdf,7times :00-6:00 Case-crossover analysis - Matching variables - Stratum 8cc1.pdf, 8cc2.pdf 8cc2.r

3 Introduction and Basic Theories Outline Introduction to Environmental Epidemiology (Air Pollution and Temperature) Time-series analysis Lag structure Multi-pollutant models Sensitivity check (df, lag, ) Meta analysis Case-crossover analysis Non-linear problems (threshold model)

4 Air pollution epidemiology (Environmental epidemiology) Assessing Air Pollution Effects on Human Health Air pollution Health Outcome Confounders (weather, time trend, Seasonality, etc)

5 Environmental epidemiology Assessing Air Pollution & Meteorological Effects on Human Health Air pollution, weather Health Outcome Confounders (time trend, Seasonality, etc)

6 Environmental epidemiology Assessing Air Pollution & Meteorological Effects on Human Health Weather Mean, Max, & Air Min pollution Temp Humidity, PM10, Ozone, SO2, etc. Confounders Daily Mortality Number in Korea (time trend, seasonality) Number # of daily events: Health Outcome Death, hospitalization Time

7 Environmental Data Time and Space Time series analysis Reduce spatial complexity: calculate daily mean PM10 value over 27 monitoring stations Date temp PM10 Humidity SO2 Ozone 2010/1/ /1/ Number Daily Mortality Number in Korea Time

8 Spatial analysis Modeling spatial dependencies between geographical sites Park et al. Locating major PM10 source areas in Seoul using multivariate receptor modeling, Environmental and Ecological Statistics 2004;11:9-19

9 Health Data Several problems Accuracy of cause of death (disease) Morbidity data is not easy to collect and analyze # daily Daily hospitalization Haspital Number in Korea in Seoul National Health Insurance Data are excellent sources of health information, BUT Number Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q Time

10 Health Data Several problems Accuracy of cause of death (disease) Morbidity data is not easy to collect and analyze # daily Daily hospitalization Haspital Number in Korea in Seoul Monday Sundays and holidays Number Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q Time Adjustment of weekdays is essential!

11 Association between air-pollution and health outcomes Time-series plots Daily Mortality Number in Korea Number Time Correlations: Pearson corr and Spearman s rank correlations 0: No association

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13 Q: Air Pollution,health outcome is it possible to have positive association? A H

14 Q: Air Pollution,health outcome is it possible to have positive association? YES A H

15 Q: Air Pollution,health outcome is it possible to have positive association? YES A We want to remove 1) long-term time trend 2) Seasonal trend H After controlling for those confounding factors, We want to compare dayto-day variations between air pollution and health outcomes

16 Q: Air Pollution,health outcome is it possible to have positive association? Long-term trend removed A We want to remove 1) long-term time trend 2) Seasonal trend H After controlling for those confounding factors, We want to compare dayto-day variations between air pollution and health outcomes

17 Nonparametric Smoothing: lowess Smoothing Consider X Y plot. Draw a regression line which requires no parametric assumptions The regression line is not linear The regression line is totally dependent on the data Two components of smoothing Kernal function : How to calculate weighted mean Bandwidth : width of the window (span), determines the smoothness of the regression line; wider > smoother

18 Nonparametric Smoothing: lowess Uniform Kernel

19 Nonparametric Smoothing: lowess Uniform Kernel

20 Nonparametric Smoothing: lowess Uniform Kernel

21 Nonparametric Smoothing: lowess Uniform Kernel

22 Nonparametric Smoothing: lowess Triangular Kernel

23 Nonparametric Smoothing: lowess Normal Kernel

24 Nonparametric Smoothing: lowess Linear assumptions at the both ends Default Lowess line : Span=0.5 Use 50% of data to calculate 1 point

25 Nonparametric Smoothing: lowess Lowess line : Span=0.2

26 Nonparametric Smoothing: lowess Lowess line : Span=0.1 Caution: Linear assumptions at the ends

27 Natural cubic spline df=4 Cubic polynomials with same values and derivatives at the bounds

28 ## Types of degree of freedom in ns (PM 10 in Seoul, )

29 Choosing df is not an easy task Typically 4(seasons) time # years We are interested in robustness of the estimator of air pollution -> sensitivity analysis

30 temperature-health study

31 IPCC 2007b

32 Global Average Surface Temperature IPCC 2007b

33 Surface Temperature Anomalies IPCC 2007b

34 Health Impacts of Climate Change McMichael et al. 2003a

35 Direction and Magnitude of Climate Change Health Impacts IPCC 2007a

36 UNEP 2009 Total CO 2 Emissions

37 Health Burden of Climate Change Impacts Deaths from malaria and dengue fever, diarrhoea, malnutriti on, flooding, and (in OECD countries) heatwaves

38 Vulnerable Groups Subgroup analysis Interactions in a model

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47 2003 European heat wave killed more than 35,000 people These conditions are forecasted to be common by Image courtesy of NASA Earth Observatory

48 Daily mortality in Paris during summer 2003 Source: Institut de Veille Sanitaire, France

49 Daily mortality in London, summer 2003 Daily mortality Mean temperature 01jun jul aug sep2003 Date Source: Hajat Observed deaths Mean temperature Baseline deaths

50 Why was Paris so badly affected? Temperature extremes - high minimum temperature Poor meteorological forecast Institutional failures - hospital and care home staff on holiday No health surveillance No previous experience/knowledge - no public health measures Source: Hajat

51 Time-series analysis Daily mortality in London: Daily mortality Mean temperature Source: Hajat

52 Daily mortality in London: Daily mortality Source: Hajat Mean temperature Heat threshold Heat slope

53 FC Curriero et al. AJE 2002

54 Ho Kim, Jong-Sik Ha, Jeongim Park, High Temperature, heat index, and mortality in six major cities in South Korea Archives of Environmental and Occupational Health, 2006;61(6):

55 Curriero et al., (2002) AJE 55

56 Vulnerable population groups The elderly Infants and Children People with Chronic Diseases People taking certain medications People whose socioeconomic status may make them more vulnerable People in certain occupation Source: Heat-related Action Plans, WHO EUROPE