Environment and Health Atlas for England and Wales

Frontiers in Spatial Epidemiology Symposium

Anna L Hansell

Assistant director of Small Area Health Statistics UnitMRC-HPA Centre for Environment and HealthSchool 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.

Example: Exposure and Health Outcome Maps

The Environment and Health Atlas

17 authors

22 reviewers

24 Sense About Science

attendees

12 data providers

7 SAHSU team members

4 audiencesResearchersPublic healthPolicy makers

Public

Environmental Exposures and Health Outcomes

Environmental Exposures

Health Outcomes

Birth Outcomes

- Air Pollution • NO2, PM10

- 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 (NO2, PM10): 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

yi ~ Poisson(λi)

Log λi = α + Ui + Vi

U ~ CAR( W, σ2u )

Vi ~ Normal( 0, σ2v )

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

RegionRate per

100,000 (age adjusted)

95% Confidence Intervals

Rate per 100,000 (age and

deprivation adjusted)

95% Confidence Intervals

East Midlands 85.08 (84.29 to 85.88) 86.02 (85.21 to 86.83)East of England 76.04 (75.38 to 76.70) 82.17 (81.37 to 82.98)London 94.10 (93.39 to 94.82) 84.63 (83.86 to 85.41)North East 120.20 (118.99 to 121.42) 100.94 (99.66 to 102.24)North West 103.94 (103.24 to 104.64) 98.41 (97.74 to 99.09)South East 75.79 (75.25 to 76.34) 86.35 (85.56 to 87.15)South West 71.08 (70.45 to 71.72) 79.85 (78.89 to 80.82)Wales 92.89 (91.93 to 93.87) 89.47 (88.47 to 90.48)West Midlands 89.57 (88.84 to 90.30) 85.73 (85.02 to 86.46)Yorkshire & the Humber 98.85 (98.06 to 99.65) 92.13 (91.36 to 92.91)

Development of interactive Atlas

Interactive Atlas

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

Difficult decisions!

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 Fortunato Daniela Fecht Rebecca Ghosh

Linda Beale

Federico Fabbri

Kevin GarwoodEllen McRobie

Oliver RobinsonClare Pearson

Helga Laszlo

Lars Jarup

Exposures

• Air Pollution (NO2, PM10) – 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