Posted to allstat by Anthony Ledford on behalf of Trevor Sweeting.

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          Royal Statistical Society Ordinary Meeting             

                       12 January 2000

  Combining evidence on air pollution and daily mortality from
  the 20 largest US cities: a hierarchical modelling strategy

  by FRANCESCA DOMINICI, JONATHAN M. SAMET and SCOTT L. ZEGER
       (all at John Hopkins University, Baltimore, USA)

Synopsis: Daily time series studies throughout the world have linked
higher levels of airborne particulate matter (PM) to greater 
mortality. A hierarchical regression model was developed and applied
to data from the 20 largest US cities to estimate the effect of PM at
the national level while taking account of the temporal and spatial 
associations among daily mortality counts. The methods provide 
policy-relevant summaries of risk and have potential application to 
other environmental agents.

Time and place:  The meeting starts at 5pm on Wednesday 12 
January 2000, with tea at 4.30pm.  The meeting takes place at
the Royal Statistical Society, 12 Errol Street, London EC1 
(nearest underground stations are Old Street, Moorgate and
Barbican).

The paper: Copies of the paper are available in advance from 
Valerie Evans, PA to the Executive Secretary, at the RSS.
(e-mail: [log in to unmask], or write to Valerie at the above 
address) 

Discussion contributions:  Contributions to the discussion
following presentation of the paper are very welcome.  Anybody,
whether or not a Fellow of the Society, is welcome to contribute.
Please contact Valerie Evans if you would like to make a
contribution to the discussion immediately following the paper.
If you are unable to attend the meeting, you may send a written
contribution.  The written version should not exceed 400 words in
length.  Written contributions can be sent for reading at the
meeting, or after the meeting for inclusion in the written
discussion in Series A of the Society's journal.


Trevor Sweeting
Honorary Meetings Secretary
Royal Statistical Society





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