Apologies for cross-postings -

Please find below the programme and abstracts for the meeting "Environment 
and Health", organised by the Environmental Statistics Section of the Royal 
Statistical Society,
Richard
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MEETING TITLE: Environment and Health
DATE:   6th December 2006
TIME:   14:00-17:00
VENUE: The Royal Statistical Society, 12 Errol Street, London EC1Y 8LX (see 
<http://www.rss.org.uk/main.asp?page=1759>http://www.rss.org.uk/main.asp?page=1759 
for location details)

SUMMARY: NERC's directed Environment and Human Health Programme, with 
funding starting this year, should boost the UK research effort in this 
area and indicates growing public and government interest. Statistics has a 
major part to play in uncovering the links between environmental factors 
and individual health outcomes, and this meeting will bring together 
leading epidemiologists and statisticians to discuss their current research.

ORGANISERS: Ron Smith ([log in to unmask]), Marian Scott and Nicky Best.
REGISTRATION: There is no charge for this event. Pre-registration is not 
required, but would be appreciated: please email [log in to unmask]

PROGRAMME (abstracts are below):

2.00-2.50 Paul Elliott (Imperial College, London): "Spatial epidemiology - 
challenges and opportunities"

2.50-3.40 Jon Wakefield (University of Washington, Seattle): "The efficient 
combination of ecological and individual-level data"

3.40-4.00 tea

4.00-4.50 Frank Dunstan (University of Cardiff): "What is the effect of the 
environment on the incidence of infectious diseases? - an investigation of 
the seasonality of campylobacter infections"

Talks are 45 minutes long, with 5 minutes for questions/discussion.
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ABSTRACTS

Spatial Epidemiology: Challenges and Opportunities

Paul Elliott

Department of Epidemiology and Public Health, Faculty of Medicine, Imperial 
College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK
[log in to unmask]

Spatial epidemiology is concerned with the analysis of geographic patterns 
in disease risk with respect to environmental, demographic, socioeconomic, 
and other factors.  Such studies provide an efficient approach to 
investigate disease risk in populations exposed to a putative environmental 
risk factor using routinely available data. While such analyses provide 
valuable public health risk assessments (eg, answers to the question as to 
whether there is excess risks of disease in a particular area or areas), 
interpretations as to etiology are more complex because of issues of 
confounding, migration and often the lack of individual data on exposure 
patterns and susceptibility. As with other approaches in environmental 
epidemiology, exposure assessment has often been the weak link in such 
studies due to uncertainty in whether living in a specific area serves as 
an accurate proxy for actual exposure. Modelling of exposure patterns 
coupled with collection of individual-level data on sub-samples of the 
population should lead to improved risk estimates (ie, less potential for 
bias) and strengthen etiological inference. Examples are given of this 
approach both for ecologic (small-area) and semi-individual (case-control) 
designs.


Controlling for Ecological Bias Using Individual Level Data

Jon Wakefield

Departments of Statistics and Biostatistics, University of Washington, 
Seattle, USA; International Agency for Research on Cancer, Lyon, France.

In an ecological study outcome and exposure/confounder data are
available on groups of individuals, rather than on the individuals
themselves. Such studies are often used to investigate associations
with environmental risk factors, but suffer from a number of problems
due to within-group variability in exposures and confounders, an
umbrella term for which is ecological bias. The only solution to
removing such bias is to supplement ecological data with individual
samples. In this talk, after detailing different sources of ecological
bias we describe aggregate data, embedded case-control, and two-phase
designs.


What is the effect of the environment on the incidence of infectious 
diseases? * an investigation of the seasonality of campylobacter infections.

Frank Dunstan, Sofia Pedro

Cardiff University

Campylobacter is the most common bacterial gastrointestinal infection in 
Europe and other temperate parts of the world.  Although there are some 
established risk factors, such as exposure to animals and eating 
undercooked food, none explains a large percentage of the cases.  The 
incidence of the disease displays a striking seasonality, with high peaks 
in late spring in the northern hemisphere, and there is published evidence 
that this seasonality varies between countries.  There is concern that 
global warming may increase the burden of the disease.

The aim of our project is to link variation in incidence with climate and 
environmental data to try to establish relationships which might give some 
clues about the aetiology of the disease.  This talk uses data on over 
500,000 cases over 13 years in England and Wales, and data for shorter 
periods from other European countries, to investigate the seasonality and, 
in particular, to examine spatial variation in this seasonality.  The data 
consist, for a given geographical area, of a time series of weekly counts 
of notifications of the disease.  The talk will discuss modelling these 
series and also the modelling of the spatial patterns using Bayesian 
methods of spatial smoothing.  Preliminary results show a surprising 
pattern and it is hoped that they may give a clue to the aetiology of the 
disease. Links to different environmental variables, ranging from climate 
to bird migration, will also be discussed.


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			Richard E. Chandler
			^^^^^^^^^^^^^^^^^^^
Room 135, Dept of Statistical Science, University College London,
1-19 Torrington Place, London WC1E 6BT, UK
Tel: +44 (0)20 7679 1880		Fax: +44 (0)20 7383 4703

Internet: http://www.ucl.ac.uk/Stats       		(department)
           http://www.homepages.ucl.ac.uk/~ucakarc	(personal)
email:    [log in to unmask]
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