Health impacts of air pollution in London: from understanding to forecasting

Transcription

1 Health impacts of air pollution in London: from understanding to forecasting John Gulliver University of the West of Scotland Marta Blangiardo, David Briggs, Anna Hansell Imperial College London

2 Study aim: demonstrate the use of GEMS data for retrospective health assessment via epidemiological analysis of air pollution episodes Objectives: 1. Develop an air pollution exposure model to consider both spatial and temporal variations in air pollution 2. Undertake a health impact assessment for particulate air pollution in London to investigate how rates and geographic patterns of risks vary between air pollution episodes and non-episodes 3. Undertake an epidemiological study to derive exposure response functions for long-range (and local) air pollution 4. Assess the extent to which these risks can be predicted through the use of GEMS data

3 Relative risk for all-cause mortality and a 10 µg/m 3 increase in PM 10 Source: WHO Adopted estimates (increase in mortality per 10 µg/m 3 increase in PM 10 ) : All-cause 0.8% Cardio / respiratory 0.11%

4 Spatial pattern in not constant over time! Monitored daily meanpm 10 concentrations, London (2003)

5 Locally derived PM modelling Integration of local and long-range modelling Trans-boundary PM modelling Traffic counts / simulation Traffic Models UK / EU emissions models Road geography EMIT modelling Point / area sources Long-range modelling Meteorology ADMS Modelling Residential locations Locally derived exposure estimates Trans-boundary PM and temperature exposure estimates receptors Bayesian modelling Site type; Season; Day of the week calibration Air pollution monitoring data validation Combined exposure estimates Health data Exposure-Response Functions Health Impact Assessment Health geography (i.e. Output areas)

6 PM 10 model components for 2002 and 2003 Explanatory variables Long-range component Local traffic component Local non-traffic component Temporal structure Spatial structure Statistical regression model: Uncertainty on the parameters is included in the model Bayesian approach simultaneously deals with space and time components Framework for analysis: Training set (to estimate the parameters of the model) Validation set (to evaluate prediction capability of the model)

7 Data: Training set 25 sites with coverage >90% for each year

8 Data: Validation set 20 sites with coverage >50% for each year

9 PM10 monitoring sites used in model development and validation

10 Data: temporal effects A day is considered only if the information about PM10 is present for all the 25 sites. The total number of days analysed is 297. To include a temporal effect: 4 seasons are obtained using the quartiles of the temperature for 2002 and 2003 The days of the week have been grouped in Monday-Friday, Saturday and Sunday. Using this aggregation the number of days for each group is as follows:

11 Bayesian hierarchical exposure model PM 10ts 1 LTts 2 LNTts LRt ts LT - local Traffic sources LNT - local non-traffic sources LR - the long-range component s is the index for site (s=1,,45), with the first 25 sites used as training set and the remaining 20 sites used for validation t is the index for time (t=1,, 297), with 148 days for 2002 and 149 days for Additional structure: Temporal PM 10ts 1LTts 2 LNTts seasont dowt LRt ts Temporal and site type PM 10ts 1, site types LTts 2 LNTts seasont dowt LRt ts

12 Indexes for model comparison For comparing the goodness of fit: Deviance Information Criterion (DIC) D I C = E µ [D (µ)] + pd Deviance of the model Number of parameters in the model For comparing the prediction capability: Predictive Mean Square Error (PMSE) Nominal Coverage (NC) R2

13 Nominal coverage The Nominal coverage calculates the proportion of observed data included in the 95% credibility interval of the predicted data P P t pr ed < P M 10 < 97:5%P M 10pr ed ) I (2:5%P M 10 ts ts ts s SxT The larger the proportion, the better the ability of prediction of the model (a small fraction of the predicted values is far away from the corresponding observed value) Comparing the Nominal coverage shows that the model that includes the information on site type has a marginally better prediction capability.

14 Model performance Model DIC PMSE NC R2 Non hierarchical Hierarchical (season and day of the week) Hierarchical (site type, season and day of the week)

15 Comparison with NAME Changing the long range component has an impact on the prediction capability of the model: RURAL (R2 = 0.54): rural monitoring site (Harwell) c.60km west of London NAME (R2 = 0.39): modelling by the UK Met Office RURAL NAME RURAL NAME From left to right the (1) benchmark, (2) hierarchical model on season and day of the week and (3) hierarchical model on season, day of the week and site type

16 Model performance by site type Urban Background Suburban R2 = 0.64 Kerbside R2 = 0.64 Roadside R2 = 0.44 R2 = 0.56

17 Modelled TOTAL exposures by postcode in London: 8th August 2003

18 Predicted change in mortality due to 2003 air pollution episodes Episode Actual change in deaths between 2002 and 2003 Estimated deaths February March April August Episodes: 1. 13th to 26th February (14 days) 2. 16th to 29th March (14 days) 3. 14th to 27th April (14 days) 4. 1st to 15th August (15 days)

19 Observed versus estimated death rates (per 100,000 of the population) by district (n=33) during two 2003 episodes April August

20 Summary of findings from health impact assessment Estimated health impacts are largely due to long-range air pollution Exposure model reasonably explains spatial patterns of mortality Spatial patterns of mortality changes between episode and non-episode periods Results indicate need for addition of other pollutants, especially ozone, in epidemiological analysis

21 Epidemiological study Hypothesis: long range particulate air pollution has a different composition from local traffic and local other sources and this leads to different biological properties and therefore different health impacts Aim: Estimating the health impact of long-range and local air pollution IN AIR POLLUTION EPISODES Overview Analyses use a Poisson log-linear regression in Bayesian framework Comparison of all-cause mortality (excl. accidents), and mortality and emergency hospital admissions for cardiorespiratory disease in air pollution episodes in 2003 vs. control periods without episodes in 2002 Analyses compare rates by statistical ward on episode days with (matched) non-episode days in preceding years same-day and lagged short and long-term modelled PM10 exposure and relevant confounders of temperature, day of week

22 Key areas of uncertainty: Transferability of exposure-response function Long-range modelling (credibility of models) Local modelling (emissions inventory) Characterisation of site types Exposure misclassification (residential address) Combination of pollutants (PM + O3) Spatial variations in temperature