EUROHEIS 2 Dr Linda Beale October 2007 – September 2010

EUROHEIS2 objectives General objective Develop further methods for integrating and analysing information on environmental exposure and human health Strategic objectives Linking data on environmental pollutants to (routinely collected) health data Collaboration with other EU funded projects (INTARESE, HEIMTSA et al.)

Small Area Health Statistics Unit (SAHSU) Developed methods and eventually a tool for SAHSU staff to analyse UK’s routinely collected health/population data Used to investigate environmental and other factors in explaining local geographic variations in disease with respect to other factors such as demographic, environmental, socio-economic risk factors.

The RIF The RIF is a tool that allows users to assess relationships between the environment and health Links spatial and non-spatial data Embedded in ESRI ® ArcGIS Risk analysis around putative hazardous sources Disease mapping

Data requirements Accurate health event data, located geographically to a place of residence or small geographical area Population data (e.g. from a national census) by small geographical area, and by age and gender Spatial data of area boundaries/point locations that link to the health and population data Optional: Covariate data e.g. socio-economic status, income or ethnicity Exposure data

Linking spatial and non-spatial data in the RIF ACCESS/ORACLE database Geographical areas (administrative/ hierarchical geography) Numerator data (cancer registrations, mortality data, hospital admissions, congenital malformation registrations Denominator data (population census output) Covariate data (SES, ethnicity, income…) Define study area ArcGIS ODBC Define comparison area spatial select db select Spatial Data Geographical boundaries Exposure data (land use, TRI sites,…) Contextual Information Define investigation Do study View data Study report WinBUGS SaTScan Maps Output & Export Run the RIF Run external models

Types of analysis 1. Risk analysis Allows assessment as to whether a risk factor has a statistical association with a health outcome in a local population selected by: distance bands around one or more user defined point or area sources modelled exposure 2. Disease mapping Allows a user to visualise mortality or morbidity rates and spatial patterns of health outcomes, selecting by: Variables stored in the database Spatially selected areas

Output: Rates and risks Directly standardised rates Apply the study area stratum-specific rate to the comparison area population + Can be directly compared between exposure groups - Can be unstable if small populations/rare diseases Indirectly standardised risks Apply the comparison area stratum-specific rate to the study area population + More stable as based on larger comparison population rates - Not directly comparable between different exposure groups (esp where population structure significantly different).

Directly Standardised Rates (DSR) DSR i is a weighted average of the specific rates, using as weights the population of the comparison region Calculation of DSR i can be seen as a projection of the area specific rates of the study region onto the population of the comparison region. Confidence intervals

Standardised Mortality/Morbidity Ratio (SMR) SMR i provides a measure of the relative risk of area i compared to that of the comparison region. Confidence intervals:

Further analysis Empirical Bayes smoothing »Low counts of observed cases/ small populations »Both rates and SMRs become numerically unstable (rates even more than SMRs) Chi square tests for homogeneity and linear trend (with accompanying p values) »test global association between a distance/exposure and relative risks Graphs of the risks as a function of exposure per band (risks plotted on a log-scale) Full Bayes smoothing (WinBUGs) Spatial scan - Statistically significant clusters (SatScan)

EUROHEIS2 specific objectives To enhance and test the RIF user interface further to make it more user friendly and readily transferable to other EU countries To arrange workshops in partner countries to discuss methodology and suggest enhancements to the RIF within the EUROHEIS framework comprehensive workshop reports will be produced

Technical and statistical qualities of the RIF Enhance the import and export functions within the RIF »These should include additional ability to export selected data from the RIF »Import and export of a range of commonly used EU data types and sources will be ensured, including country specific denominator data and a range of local geographies »This work will extend compatibility with other approaches and methods Include spatio-temporal methods for disease mapping in RIF To add measures of uncertainty to disease mapping, and visualise this uncertainty in the maps

User interface and test cases in new countries To incorporate the capability to include EU country specific indices of socio-economic status (SES) »to enable the user to choose from a selection of indices to standardise for in analyses of environmental health risks To use data on SES and environmental pollution to allow users to assess inequalities in health as well as environmental equity Test the user interface and the expanded RIF software To set up a web-based support tool (web-forum) assisting member countries in implementing and operating the system

Dissemination Disseminate the RIF software as freeware via the internet Supply training courses and material to interested EU countries Organise an end of project conference showing the advances made during the project and summarise the overall project strategic developments Identify dissemination mechanisms for reaching target audiences

Involvement of policy makers To interact with stakeholders at relevant workshops, ensuring the policy relevance of project work To raise awareness of the policy implications of the issues and trade-offs surrounding data governance, data protection, privacy and data quality issues Raise awareness of accurate (health) data collection, across the EU as an input to spatial epidemiological analyses

Good practice recommendations and future work Recommend data quality indicators to aid interpretation of the results Identify issues in integrating the RIF into existing spatial data infrastructures, such as SMASH and the Health Atlas

EUROHEIS2 Work packages WP 1. Coordination WP 2. Dissemination WP 3. Evaluation WP 4. Adaptation and enhancements of the current RIF to EU conditions WP 5. Evaluation of RIF for integrated assessment of environment and health risks WP 6. Spatio-temporal methods for disease mapping WP 7. Exposure databases and GIS methods WP 8. Health and Environment Information System in Poland WP 9. Health and Environment Information System in Hungary WP 10. Integration of RIF into existing spatial data Infrastructures

Partners OrganisationTown / CityCountry University of ValenciaValenciaSpain National Public Health Institute KuopioFinland National Institute of Environmental Health BudapestHungary Dublin City UniversityDublinIreland National Institute for Public Health and the Environment BilthovenThe Netherlands Nofer Institute of Occupational Medicine LodzPoland Lund UniversityLundSweden Imperial College LondonLondonUK