air pollution and children’s - bristol
TRANSCRIPT
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Air pollution and children’s
respiratory health:
Do English parents respond to
air quality information?
Katharina Janke
20th June 2012
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Introduction
• Epidemiological research of short-term
effects of air pollution: time-series studies
Single city
Populations serve as their own controls
Weather-driven variations in pollutant levels
• Limitations
Central monitoring site
Single-pollutant models
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Fixed effects approach
• Panel data (longitudinal data)
• 89 English local authorities in 2003 to 2007
Reduces city selection bias
Areas smaller => reduces measurement error
• Control for unobserved factors by adding
local authority dummies
outcomea,t = 1exposurea,t + 2controlsa,t
+ a + ea,t
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Specification
admissionsa,t,w,q,y =
4i=0 [i
NONOa,t-i + iOZOZa,t-i]
+ 4i=0 M’a,t-ii
+ Xt’
+ a,q,y + w,y + ea,t,w,q,y
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Air Pollution Forecast
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Control for avoidance
admissionsa,t,w,q,y =
4i=0 [i
NONOa,t-i + iOZOZa,t-i]
+ 4i=0 iforecasta,t-i
+ 4i=0 M’a,t-ii
+ Xt’
+ a,q,y + w,y + ea,t,w,q,y
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Air Pollution Forecast
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Data • Hospital Episode Statistics (HES)
Hospital emergency admissions for respiratory
diseases and symptoms, age 5 to 19
(ICD-10 codes J00-J99, R05, R06)
Patient’s local authority: daily admission counts
Rates (per 100,000)
• UK Air Quality Archive
Distance-weighted mean of pollutant
concentrations at monitors in 5/10/15/20 km
radius around local authority’s population-
weighted centroid
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Results
Without forecast
NO2 / 10 0.033*** (0.007)
O3 / 10 0.024*** (0.006)
Forecast
Robust standard errors in (round brackets), clustered at county
level. Coefficients are sum of coefficients on contemporaneous
value and four lags of pollutants. 148,210 observations in 89
local authorities with 23 county clusters.
* significant at 5%, ** significant at 1%, *** significant at 0.1%.
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• Elasticity at mean:
• Example for NO2:
0.033 x (3.46/1.36) = 0.08
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admissions
NOi
NOi
24
0
2
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Results
Without forecast
NO2 / 10 0.033*** (0.007)
[0.083]
O3 / 10 0.024*** (0.006)
[0.099]
Forecast
Robust standard errors in (round brackets), clustered at county
level. Numbers in [square brackets] are elasticities at the mean.
Coefficients are sum of coefficients on contemporaneous value
and four lags of pollutants. 148,210 observations in 89 local
authorities with 23 county clusters.
* significant at 5%, ** significant at 1%, *** significant at 0.1%.
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Results
Without forecast With forecast
NO2 / 10 0.033*** (0.007)
[0.083]
0.035*** (0.007)
[0.088]
O3 / 10 0.024*** (0.006)
[0.099]
0.025*** (0.006)
[0.103]
Forecast -0.033 (0.041)
[-2.398] Robust standard errors in (round brackets), clustered at county level.
Numbers in [square brackets] are elasticities at the mean for NO2 and O3
and percent change in admission rate (evaluated at the mean) for discrete
change of Forecast from 0 to 1. Coefficients are sum of coefficients on
contemporaneous value and four lags of pollutants. 148,210 observations in
89 local authorities with 23 county clusters.
* significant at 5%, ** significant at 1%, *** significant at 0.1%.
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Results
• Subset of respiratory diseases:
Asthma
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Asthma
Without forecast With forecast
NO2 / 10 0.011* (0.006)
[0.086]
0.013** (0.006)
[0.103]
O3 / 10 0.005 (0.003)
[0.067]
0.006* (0.003)
[0.080]
Forecast -0.035*** (0.010)
[-8.243] Robust standard errors in (round brackets), clustered at county level.
Numbers in [square brackets] are elasticities at the mean for NO2 and O3
and percent change in admission rate (evaluated at the mean) for discrete
change of Forecast from 0 to 1. Coefficients are sum of coefficients on
contemporaneous value and four lags of pollutants. 148,210 observations in
89 local authorities with 23 county clusters.
* significant at 5%, ** significant at 1%, *** significant at 0.1%.
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Conclusions
• 10% increase in NO2 or O3 increases
children’s hospital emergency admissions
for respiratory diseases by 1%
• For asthma admissions:
Moderate or high air pollution forecast reduces
admissions by 8%
15% bias in pollutant coefficient estimates
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Visitor data from Bristol Zoo
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Daily visitor counts (Bristol Zoo)
Day visitors Members
Forecast 0.023 (0.030) -0.070** (0.028)
Rain -0.015*** (0.002) -0.013*** (0.002)
Max. temperature 0.027*** (0.005) 0.022*** (0.005)
Min. temperature -0.016*** (0.005) -0.017*** (0.005)
Wind speed -0.018*** (0.003) -0.021*** (0.003
Newey-West standard errors allowing for autocorrelation up to lag 10 in
(round brackets). Regressions include year-month dummies, dummies for
day of week, public holidays in winter, public holidays in summer, bank
holiday weekends, school holidays and school holiday weekends. 2,382
observations. * significant at 5%, ** significant at 1%, *** significant at 0.1%.