Estimating the Health Effects of Ambient Air Pollution and ... · Estimating the Health Effects of...
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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
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
PM2.5 is a heterogeneous mixture of
solids and liquids
Particles
Particles
Gas to particle conversion Directly Emitted
Particles
HOW SMALL IS PM2.5?
Human Hair
(60 μm diameter)
PM10
(10 μm)
PM2.5
(2.5 μm)
Hair cross section (60 μm)
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
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
Concerns?
• Confounding by smoking, occupational
exposure, BMI, indoor pollution?
• Confounding by weather, seasonality,
time?
TS Methodology I
• Daily counts of mortality (hospital admits)
modeled as Poisson regression
conditional on time-varying covariates
(time, weather, day of week)
• Log(Mt) = o+ *PM2.5t+ 1*day of week+
+ time + tempt-1 + humidityt-1
• = % change in mortality per microgram
per cubic meter of pollution
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)
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
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
Time-Stratified
Case-crossover Method
CONTROL
PERIOD(S)
CASE
PERIOD
CONTROL
PERIOD(S)
-t Between 0-4 before
case period
t0
Death day
+t Between 0-4 after
case period
T-14 T-7 T0
CASE
T+7 T-21
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
Location Lead Author % change per 10
μg/m3 (95% CI)
90 U.S. Domenici (2003) 0.21 (0.04 – 0.33)
20 largest U.S. Daniels (2003) 0.28 (0.16 – 0.48)
10 U.S. Schwartz (2003) 0.55 (0.39 – 0.70)
29 European WHO (2004) 0.60 (0.40 – 0.80)
8 Canadian Burnett (2003) 0.70 (0.26 – 1.11)
4 Asian HEI (2004) 0.49 (0.23 – 0.76)
Multi-city study results for PM10
Note: Small RR!
Summary of TS Findings I
• Studies on PM now completed in 5 continents
• Most indicate 0.2 -1.6% increase in daily deaths per 10 μg/m3 PM10 or about 1 - 2% for 10 μg/m3 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
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, co-
pollutants
• Effects also observed for daily exposure to
ozone, which doesn’t appear to confound
PM effects
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
The Direct Health Effects of
Temperature Increases in California
1. Do we observe direct health effects in California from higher average (non-heat wave) temperatures?
2. Are these effects independent of those from air pollution?
3. Can we identify subgroups that are particularly susceptible?
4. What were the full effects of the 2006 heat wave? How high are the effects/degree?
Data Collected for 9 California Counties:
May-September 1999-2003
• 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)
– PM2.5, O3, CO, NO2
Mean Daily Apparent Temperature (°F) for
Nine California Counties, May-September 1999-2003
Kern 78
Fresno 75
Riverside 75
San Diego 71
Los Angeles 69
Santa Clara 65
Sacramento 71
15600 Contra Costa
67
Orange 72
28400
Map by Rachel Broadwin Aug 2006
County Mean Apparent Temp
Mean Apparent Temp deg F
65 - 68 69 - 70 71 - 73 74 - 75 76 - 78
Color symbols: ColorBrewer.org
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
Apparent Temperature and All-cause Mortality for
Alternative Lags and Methods (% change and 95% CI per 10oF from meta analysis)
2.3
Source: Basu et al. (2008) Epidemiology
All-cause mortality and Apparent
temperature race/ethnic group
Perc
ent
Change in M
ort
alit
y (
95%
CI)
0
2
4
6
8
WHITE BLACK HISPANIC
2.5
4.9
1.8
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
What is the relationship of temp and
pollution: confounder, effect modifier or
independent?
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. PM2.5 2.9 (1.7, 4.0)
Parallel Harvard Study
• 9 non-Cal cities
• 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
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. PM2.5 2.76 (1.75, 3.78) 1.69 (0.88, 2.51)
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
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
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)