Air pollution and children s respiratory health: Do English parents respond to air quality information?

Transcription

1 Air pollution and children s respiratory health: Do English parents respond to air quality information? Katharina Janke 20 th June

2 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 2

3 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 outcome a,t = 1 exposure a,t + 2 controls a,t + a + e a,t 3

4 Specification admissions a,t,w,q,y = 4 i=0 [ i NO NO a,t-i + i OZ OZ a,t-i ] + 4 i=0 M a,t-i i + X t + a,q,y + w,y + e a,t,w,q,y 4

5 Air Pollution Forecast 5

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8 Control for avoidance admissions a,t,w,q,y = 4 i=0 [ i NO NO a,t-i + i OZ OZ a,t-i ] + 4 i=0 i forecast a,t-i + 4 i=0 M a,t-i i + X t + a,q,y + w,y + e a,t,w,q,y 8

9 Air Pollution Forecast 9

10 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 populationweighted centroid 10

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12 Results Without forecast NO 2 / *** (0.007) O 3 / *** (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%. 12

13 Elasticity at mean: 4 i 0 NO i NO2 admissions 2 Example for NO 2 : x (3.46/1.36) =

14 Without forecast Results NO 2 / *** (0.007) [0.083] O 3 / *** (0.006) Forecast [0.099] 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. 14 * significant at 5%, ** significant at 1%, *** significant at 0.1%.

15 Without forecast Results NO 2 / *** (0.007) [0.083] O 3 / *** (0.006) [0.099] With forecast 0.035*** (0.007) [0.088] 0.025*** (0.006) [0.103] Forecast (0.041) [-2.398] Robust standard errors in (round brackets), clustered at county level. Numbers in [square brackets] are elasticities at the mean for NO 2 and O 3 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%. 15

16 Results Subset of respiratory diseases: Asthma 16

17 Without forecast NO 2 / * (0.006) [0.086] O 3 / (0.003) [0.067] Asthma With forecast 0.013** (0.006) [0.103] 0.006* (0.003) [0.080] Forecast *** (0.010) [-8.243] Robust standard errors in (round brackets), clustered at county level. Numbers in [square brackets] are elasticities at the mean for NO 2 and O 3 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%. 17

18 Conclusions 10% increase in NO 2 or O 3 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 18

19 Visitor data from Bristol Zoo 19

20 Daily visitor counts (Bristol Zoo) Day visitors Members Forecast (0.030) ** (0.028) Rain *** (0.002) *** (0.002) Max. temperature 0.027*** (0.005) 0.022*** (0.005) Min. temperature *** (0.005) *** (0.005) Wind speed *** (0.003) *** (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, observations. * significant at 5%, ** significant at 1%, *** significant at 0.1%.