Presentation on theme: "Environment and Health Atlas for England and Wales Frontiers in Spatial Epidemiology Symposium Anna L Hansell Assistant director of Small Area Health Statistics."— Presentation transcript:
Environment and Health Atlas for England and Wales Frontiers in Spatial Epidemiology Symposium Anna L Hansell Assistant director of Small Area Health Statistics Unit MRC-HPA Centre for Environment and Health School of Public Health, Imperial College London
Overview of the Environment and Health Atlas for England and Wales What is the aim of the EHA? To provide information about geographical variation of disease that may be related to environmental pollutants To provide information about geographical variation of selected pollutants To form a basis for development of hypotheses, further research and public health action. How can we access it? Print version On-line interactive version The print version will be available both as hard copy to purchase and as downloadable pdf chapters.
The Environment and Health Atlas 17 authors 22 reviewers 24 Sense About Science attendees 12 data providers 7 SAHSU team members 4 audiences Researchers Public health Policy makers Public
Environmental Exposures and Health Outcomes Environmental Exposures Health Outcomes Birth Outcomes -Air Pollution NO 2, PM 10 -Radon -Metals cadmium, lead -Agricultural Pesticides -Sunshine Duration -Chlorination disinfection by-products ⁻Lung cancer ⁻Breast cancer ⁻Prostate cancer ⁻Malignant melanoma ⁻Bladder cancer ⁻Leukaemia ⁻Brain cancer ⁻Liver cancer ⁻Mesothelioma ⁻Coronary heart disease mortality ⁻COPD mortality ⁻Kidney disease mortality -Still Births -Low birth Weight
Exposures Air pollution (NO 2, PM 10 ): Land Use Regression for 2001 annual mean concentrations from the national air quality network and predictor variables related to traffic, population, land use and topography Radon Sunshine duration Metals in soil (Cd, Pb) Agricultural pesticides Chlorination disinfection byproducts
Statistical methods Poisson framework with BYM model used for all analyses Allows to overcome the excess variability due to small numbers (rare disease/small areas) Takes into account the spatial dependence in risks using –Global smoothing (unstructured heterogeneity) –Local smoothing (spatial heterogeneity) a parameter in one area is influenced by the average value of its neighbours variability quantified by a conditional variance depending on the number of neighbours y i ~ Poisson(λ i ) Log λ i = α + U i + V i U ~ CAR( W, σ 2 u ) V i ~ Normal( 0, σ 2 v )
Smoothed Relative Risk of male lung cancer incidence adjusted for age and deprivation Ward-level maps 1985-2009 Male/female separately Chloropleth maps A diverging or bi-polar scheme is used as risks are below or above average. Health outcomes use same nine point scale. Each increment represents 12% increase in the log RR Colour scheme can be read by colour blind individuals
Smoothed and posterior probability maps Posterior probabilities of male lung cancer incidence adjusted for age and deprivation Smoothed Relative Risk of male lung cancer incidence adjusted for age and deprivation Posterior probabilities may be interpreted as the strength of (statistical) evidence of an excess/reduced risk in each area
Contextual maps –Topography –Administrative boundaries –Population density (1981, 1991 & 2001) –Urban/rural distribution –SES (Carstairs)
Health outcome chapters – context Each chapter contains a brief overview of the disease including background, risk factors and time trends to provide context for the maps. Age-standardised lung cancer incidence and mortality in England and Wales 1985-2009 Lung cancer incidence rates in males and females in 2002
Health outcome chapters – text Text presents information about the outcome and important risk factors based on authoritative reviews and meta-analyses Key points are displayed in text boxes. Summary text box Lung cancer is one of the commonest cancers and is strongly associated with cigarette smoking. In England and Wales there were approximately 35,000 (19,800, male and 15,600 female) new cases of lung cancer in 2009, the last year of data for the maps. The maps show highest risks for lung cancer in conurbations and industrial areas of England and Wales. This is likely to reflect past smoking patterns and occupational exposures (e.g. to asbestos) with a smaller potential contribution from air pollution exposures.
Health outcome chapters – statistics Male lung cancer age- standardised incidence rates per 100,000 people by region of England and Wales, 1985-2009 Statistical summary: Male lung cancer incidence. Observed and expected numbers, Standardised Incidence Ratios (SIRs) and smoothed Relative Risk (RRs) by census ward 1985-2009
Public Engagement Working with Sense about Science, a charitable organisation that aim to improve understanding of science and evidence - workshops and meetings with representative target audience 1.Workshop 1: Interpretation of the maps 2.Workshop 2: Chapter content 3.Workshop 3: Online interactive atlas Presentations and discussions with the MRC-HPA Centre Community Advisory Board
Consultation Why haven’t you done x? You will alarm people who live in an area at higher risk You should have done (the whole atlas like) this Giving people information on exposure implies it is a health risk and is irresponsible I’ve no idea what this means People will overlay the health and exposure maps
Feedback from consultation – 1 -One colour ramp instead of a divergent colour ramp -The same scale to be used across all health outcome maps to allow them to be comparable -Defined categories instead of a continuous scale -Provide the highest and lowest number of cases -Revise text so that the chapter acts as an easy reference guide for interpreting the maps -Provide a complete worked example of how to interpret the mpas -Remove statistical methods and move to an appendix Feedback on interpretation of the maps: Feedback on disease maps:
Feedback from consultation – 2 Traffic light colours: Red=danger Displaying uncertainty on maps Interpretation Interpretation when not shown on other environmental exposure maps
Simplification of a usually complicated subject Balancing scientific rigour against getting a message across Public Health Message Will the maps tell me if my area is bad? Will I be able to tell if the exposure in my area is giving me cancer? Everyone becomes an expert if they think they understand it! Scientific discussion Why haven’t you presented unadjusted maps? Is drinking tap water a risk factor for bladder cancer in men? You should use different statistical methods It’s of statistical interest only It’s probably right when nobody is happy!
Conclusions Using sophisticated (statistical, cartography, literature review) techniques doesn’t mean the target audience will understand! Output may need to be simplified to be accessible and meaningful Risks oversimplification Presenting information to a range of different audiences is difficult Consultation results in a more useful output but Takes a lot of time Throws up the unexpected Needs a thick skin!
SAHSU Atlas Team Paul ElliottAnna Hansell Peter Hambly Kees de Hoogh Lea FortunatoDaniela FechtRebecca Ghosh Linda Beale Federico Fabbri Kevin GarwoodEllen McRobie Oliver RobinsonClare Pearson Helga Laszlo Lars Jarup
Exposures Air Pollution (NO 2, PM 10 ) – Land Use Regression model (100m x100m grid) using data from National Air Quality Archive’s Radon Potential- Modelled by the HPA and the British Geological Survey (BGS) Sunshine Duration - Meteorological Office, through MIDAS Land Surface Station Data Metals (Cadmium, lead) - Data collected and analysed by the Centre for Ecology and Hydrology as part of the 2000 Countryside Survey Agricultural Pesticides - Data from The Pesticides Usage Survey, conducted by The Food and Environment Research Agency Chlorination Disinfection By-products – Data from 10 Water companies