Download Imperial College London

EUROHEIS 2
October 2007 – September 2010
Dr Linda Beale
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
ArcGIS
ACCESS/ORACLE
database
Geographical areas
(administrative/
hierarchical geography)
Covariate data
(SES, ethnicity, income…)
Numerator data
(cancer registrations,
mortality data, hospital
admissions, congenital
malformation registrations
Denominator data
(population census output)
ODBC
Run the RIF
db select
spatial select
Define study area
Define comparison area
Define investigation
Do study
View data
Spatial Data
Geographical
boundaries
Exposure data
(land use, TRI sites,…)
Contextual Information
Output & Export
Study report
Run external models
WinBUGS
SaTScan
Maps
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)
N *j
DSRi   rij *  105
N
j
•DSRi is a weighted average of the specific rates, using as weights
the population of the comparison region
•Calculation of DSRi can be seen as a projection of the area
specific rates of the study region onto the population of the
comparison region.
•Confidence intervals
If Oi  100, calculatio n is based on the assumption that log (DSRi ) are
approximat ely normally distribute d
If Oi  100, the CI95% are obtained from the statistical tables of the Poisson
Standardised Mortality/Morbidity Ratio (SMR)
Oi
SMRi 
Ei
•SMRi provides a measure of the relative risk of area i compared to
that of the comparison region.
Ei   rj* N ij : total number of adjusted expected cases in area i if that
j
area had experience d the same mortality as in the comparison region
•Confidence intervals:
If Oi  100 is based on the assumption that log (SMRi ) are approximat ely
normaly distribute d
If Oi  100, the CI95% are obtained from the statistical tables of the Poisson
(via its mathematic al relationsh ip with the  2 distributi on)
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
Organisation
Town / City
Country
University of Valencia
Valencia
Spain
National Public Health
Institute
Kuopio
Finland
National Institute of
Environmental Health
Budapest
Hungary
Dublin City University
Dublin
Ireland
National Institute for
Public Health and the
Environment
Bilthoven
The Netherlands
Nofer Institute of
Occupational Medicine
Lodz
Poland
Lund University
Lund
Sweden
Imperial College London
London
UK