Estimating the Health Effects of Ambient Air Pollution and Temperature

Transcription

1 Estimating the Health Effects of Ambient Air Pollution and Temperature Bart Ostro, Ph.D., Chief Air Pollution Epidemiology Section Office of Environmental Health Hazard Assessment California EPA

2 Hundreds of Epidemiological Studies of Air Pollution and Dozens on Temperature Many different study designs Pros/cons for each Fostered by availability of health data and monitoring of temperature and ubiquitous pollutants Control for confounders Supported by human clinical and toxicological studies

3 PM2.5 is a heterogeneous mixture of solids and liquids Particles Particles Particles Gas to particle conversion Directly Emitted

4 HOW SMALL IS PM2.5? Hair cross section (60 μm) Human Hair (60 μm diameter) PM10 (10 μm) PM2.5 (2.5 μm)

5 Types of Air Pollution Epidemiological Study Designs 1. Cross-sectional 2. Time-series and Case-crossover 3. Comparison with reference period (observed versus expected) 4. Prospective cohort 5. Panel studies

6 Time Series Methods Examines association over time in one area between daily changes in pollution (or temperature) and daily counts of mortality or morbidity (all-cause, disease-specific, age>65, other subgroups) Only a few confounders that vary with both pollution and health and can control for effects of weather, DOW, season Exposure measurement error can cause bias O Enhanced by multi-city studies, new methodologies and statistical software O Can be replicated in many cities at low cost

7 Concerns? Confounding by smoking, occupational exposure, BMI, indoor pollution? Confounding by weather, seasonality, time?

8 TS Methodology I Daily counts of mortality (hospital admits) modeled as Poisson regression conditional on time-varying covariates (time, weather, day of week) Log(M t ) = o+ *PM2.5 t + 1 *day of week+ + time + temp t-1 + humidity t-1 = % change in mortality per microgram per cubic meter of pollution

9 TS Methodology II: Controlling for Time and Season Pollution, mortality poll mort time

10 Previous methods used for controlling for weather and season 1. Comparison with other cites 2. Dummy variables for quarter and year 3. Stratification by a given season 4. Successive deletion of high temp 5. Linear temperature term 6. Non-linear terms or dichot extreme values) 7. Smoothing techniques (time/season)

11 TS Methodology III Use smoothing splines (natural or penalized) to control for time, temperature and humidity (splines = non-linear data-driven functions that smooth the relation of mortality and time) Typically involves weighted average of observations or piecewise cubic function (with penalty) to estimate new shape of function Useful to determine shape of function or to control for potential confounders Common feature now in software packages

12 Mortality and Time

13 Mortality and Time with Smooth

14

15 Curriero et al., AJE 2002

16 Case-Crossover Method Compare temperature on day of hospitalization (case) to temperature on different days for same person when hosp did not occur (control) To eliminate bias, choose control periods within the same year/month as the cases Addresses most concerns about effects of seasonality and other time-varying factors Uses logistic function

17 Time-Stratified Case-crossover Method CONTROL PERIOD(S) -t Between 0-4 before case period CASE PERIOD t 0 Death day CONTROL PERIOD(S) +t Between 0-4 after case period T-21 T-14 T-7 T0 CASE T+7

18 PM Time-Series Study Characteristics Studies conducted and associations reported with PM10 over a wide range of: Climates and seasonal patterns PM concentrations and mixtures Co-pollutants and weather covariations Population characteristics Housing stock, etc

19 Location Multi-city study results for PM10 Lead Author % change per 10 μg/m 3 (95% CI) 90 U.S. Domenici (2003) 0.21 ( ) 20 largest U.S. Daniels (2003) 0.28 ( ) 10 U.S. Schwartz (2003) 0.55 ( ) 29 European WHO (2004) 0.60 ( ) 8 Canadian Burnett (2003) 0.70 ( ) 4 Asian HEI (2004) 0.49 ( ) Note: Small RR!

20 Summary of TS Findings I Studies on PM now completed in 5 continents Most indicate % increase in daily deaths per 10 μg/m 3 PM10 or about 1-2% for 10 μg/m 3 PM2.5 Results fairly robust but can be impacted by treatment of weather and time trend Fairly similar results using case-crossover Greater effects for cardiopulmonary disease and for those age > 65

21 Summary of TS Findings II Effects increase using cumulative average exposures Effects persist over a wide range of climates, demographics, co-morbidity, access to health care, housing characteristics, copollutants Effects also observed for daily exposure to ozone, which doesn t appear to confound PM effects

22 Morbidity Effects Are Also Assessed Using These Time-Series Analysis O Hospital admissions for cardiovascular or respiratory disease (or separately for outcomes such as asthma, COPD, pneumonia, MI, CHD) O Emergency department visits

23 The Direct Health Effects of Temperature Increases in California Do we observe direct health effects in California from higher average (non-heat wave) temperatures? Are these effects independent of those from air pollution? Can we identify subgroups that are particularly susceptible? What were the full effects of the 2006 heat wave? How high are the effects/degree?

24 Data Collected for 9 California Counties: May-September Mean (min and max) daily apparent temperature (EPA AIRS database, ARB, NCDC) Incorporates temperature and relative humidity Vital statistics of mortality and hospital admissions (CDPH) All-cause Disaggregated by disease, age and race Air pollutants (ARB) PM 2.5, O 3, CO, NO 2

25 Mean Daily Apparent Temperature ( F) for Nine California Counties, May-September Mean Apparent Temp deg F Contra Costa 67 Sacramento Santa Clara 65 Fresno 75 Kern 78 Riverside 75 County Mean Apparent Temp Los Angeles 69 Orange San Diego 71 Color symbols: ColorBrewer.org Map by Rachel Broadwin Aug 2006

26 Methodology Time-series and case-crossover methods Separate analyses by county County estimates combined through meta-analysis Parallel study by Harvard of 9 non-ca counties

27 Apparent Temperature and All-cause Mortality for Alternative Lags and Methods (% change and 95% CI per 10 o F from meta analysis) 2.3 Source: Basu et al. (2008) Epidemiology

28 All-cause Mortality and Daily Apparent Temperature by Age (% change per 10 o F)

29 All-cause mortality and Apparent temperature race/ethnic group Percent Change in Mortality (95% CI) WHITE BLACK HISPANIC

30 What is the relationship of temp and pollution: confounder, effect modifier or independent? To examine the impact of pollution, two different methods used, depending on the frequency of monitoring data 1. For ozone, measured daily, we matched on days with fairly similar concentrations and then examined the effects of temperature 2. For PM2.5, measured every sixth day, we added it as a covariate

31 All-cause Mortality and Apparent Temperature Adjusted by Pollutant (% change per 10 o F)

32 Pooled Results for Apparent Temp: CA study (% change per 10 o F) Model Time-Series Case-Crossover Basic 2.3 (1.0, 3.6) 2.3 (1.0, 3.6) w. ozone 2.8 (1.3, 4.2) Match ozone 2.9 (1.8, 4.0) w. PM (1.7, 4.0)

33 9 non-cal cities Parallel Harvard Study Used natural spline smooth of temp -mort to determine linear segment of relationship Estimated function with TS and CC analysis Added ozone and PM2.5 to the model and also matched by ozone

34 Pooled Results for Apparent Temp: Harvard study (% change per 10 o F) Model Time-Series Case-Crossover Basic 2.74 (2.01, 3.48) 1.78 (1.09, 2.48) w. ozone 2.07 (1.34, 2.81) 0.99 (0.31, 1.68) Match ozone 1.81 (-.34, 4.01) w. PM (1.75, 3.78) 1.69 (0.88, 2.51)

35 Other studies provide mixed evidence Medina-Ramon (2007): CC of 50 U.S. cities: zone reduced temp effect Staffogia (2006): CC of 4 Italian cities: ozone not confounder Basu (2005): CC of 20 U.S. cities: ozone not founder Renn (2008): TS of 95 U.S. cities: ozone effect modifier in some cities Filleul (2006): TS of 9 French cities in heat wave: ozone has different effect on temp in each city

36 Conclusions 1. Ozone and PM associated with mortality even after controlling for temp 2. Temp associated with mortality, after controlling for pollution 3. Matching in case-crossover most effective method 4. Evidence of effect modification mixed with some evidence of interaction 5. Results depending on cities, levels, methods

37 Future Issues Relative role of temperature and pollution during heat waves Additional analysis of interactions Other outcomes: birth outcomes (prematurity, birth weight, spontaneous abortions), emergency room visits Quantification of mitigation (air conditioning, reducing exercise, time outdoors, cooling shelters, warnings, thresholds)