The Environmental Health Atlas for England and Wales...2014/06/05 · environmental health atlas...

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MRC-HPA Centre for Environment and HealthImperial CollegeLondon

The Environmental Health Atlas for England and Wales

MRC-HPA Centre for Environment and Health

Imperial CollegeLondon

Small Area Health Statistics Unit (SAHSU)Linda Beale, Lars Jarup, Carlos Abellan, Daniela Fecht, Paul Elliott

Outline• The main aim of the project is to compile and produce an

environmental health atlas for England and Wales, as a basis for informing policy-makers and the public on geographic patterns of disease and potential environmental exposure to pollutants.

• The two main sections of the atlas: – describe patterns of exposure to environmental hazards across

England and Wales– describe selected health outcomes on a geographic scale

• It is important to note that we will not attempt to make or suggest any causal links between the mapped environmental exposures and health outcomes.

Proposed indicatorsEnvironmental Maps:• Air pollution (NO2, PM10)• Landfill sites• Light pollution• Metals (Cadmium, Arsenic)• Pesticides• Radon• Trihalomethanes (THMs)• UV-light

Contextual Maps:• Administrative boundaries• Population density• Urban/rural distribution• SES (Carstairs)

Diseases:• Lung cancer• Breast cancer (female)• Prostate cancer (male)• Colorectal cancer• Leukaemia• Malignant melanoma• Bladder cancer• Mesothelioma• Stomach cancer• Laryngeal cancer• Oesophagus cancer• Liver cancer• Pancreatic cancer• Brain cancer• Ischaemic heart disease• COPD• Kidney disease mortality

Approaches

• Combines a number of different disciplines and skills:– Epidemiology– Environmental science– Statistics– Geographical information science– Cartography

• GIS has been used for all spatial analysis and mapping• The Rapid Inquiry Facility (RIF) has been used for the health

analysis: designed for spatial epidemiology studies (such as disease mapping)

Environmental data

• Environmental datasets are increasingly being made available but:– often interpolation is required to translate a discrete set of known

data points to a surface– Different approaches depending on data sources

• For inclusion in the atlas some of the datasets were further modelled using GIS to show population weighted annual exposure

Air pollution (NO2 and PM10)

Land use regression models were employed to derive 100m x 100m NO2 and PM10 maps. The models use 2001 annual mean concentrations from the national air quality network and predictor variables related to traffic, population, land use and topography

Data from other sources

• Difficulties in gaining permission to use data from other sources– Need to use values and map the data

• The variation in radon potential map is based on the ‘Indicative map’ of radon published by the British Geological Survey and the Health Protection Agency. The map has a resolution of 1km x 1km

• Night-time artificial light emissions represent an increasing source of environmental pollution throughout much of the developing world

Disease Mapping• The study population is England and Wales covering 25

years (1980/1981-2005/6)• All selected diseases will be mapped for the whole of

England and Wales– Inset maps of major conurbations (e.g. Greater London,

Manchester-Liverpool and Tyneside)

• We will produce maps of standardised incidence/mortality ratios as risk indicators, adjusted by age and sex– Include maps adjusted for socio-economic status– Include smoothed maps to account for over dispersion caused by

sparseness• together with a summary of the posterior distribution of

disease risk in each area

The Rapid Inquiry Facility (RIF)• Combines methods from GIS, statistics and epidemiology to

assess relationships between the environment and health– Originally developed as a tool for SAHSU staff– Transformed for European countries (EUROHEIS & EUROHEIS2

projects)– Re-developed and significantly enhanced with funding from the US

Centers for Disease Control (Environmental Public Health Tracking Network)

• Bayesian modelling was run using 3 models and 4 priors• Convergence of the chains of simulations were assessed (e.g.

Brooks-Gelman-Rubin diagnostic).

Temporal analysis

• Temporal trends are shown with graphs, using both incidence and mortality data for over 30 years across England and Wales.

All calculated using the RIF software

Malignant Melanoma

Issues with boundariesThe 25 year period of the atlas covers three different census periods (1981, 1991 and 2001),each used slightly different census boundaries. Boundary changes are accounted for using data held at postcode level which is then aggregated in ArcGIS using yearly postcode look up tables.e.g. Population data for 1981 and 1991 collected at ED level was disaggregated to postcodes from the relevant year and then re-aggregated to 2001 ward boundaries.

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Census boundaries

Scale of analysis

Map Design• Using distinct hues facilitates pattern and rate recognition and increasing

darkness of a hue represents increasing rate

• A diverging or bi-polar scheme allows extremes around a risk of 1.0 to be easily recognised

• The different map types reflect different analyses and, therefore, different colour schemes are used to readily distinguish between them

• All disease maps include a graph showing the distribution of incidence combined with the colour ramp to show the data spread. In areas where the numbers are low (for example in the cases of rare diseases) faint cross hatching in used, to demonstrate that these are areas of unstable risks

Relative Risks Adjusted for Age, Sex Adjusted for Age, Sex and SES

Smoothed Relative Risks and PP valuesAdjusted for Age, Sex and SES

Data Interpretation

Interpretation of geographical variations in relative risk is complicated by a number of factors including: • variations in data quality between and within regions, which may lead to

significant, but artefactual differences in risk estimate between smallareas

• long latency times between any causal agent (e.g. an environmentalexposure) and the onset of disease– People move homes over time, and such migration effects are likely to

reduce the ability to detect any true variation in risk

• Data quality is fundamental to interpretation– observed patterns may be reflecting data quality

Conclusion

• The atlas aims to provide readers with a picture of many diseases, primarily cancers, over the last 25 years across England and Wales that have been carefully analysed and mapped

The Environmental Health Atlas for England and Wales...2014/06/05 · environmental health atlas...

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