EUROURHIS

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(No Transcript)
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Workpackage 9 Workshop17th April 2007

• Dick Heller
• Islay Gemmell
• Arpana Verma
• Perihan Torun

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Workshop Groups

• Group 1
• Mr Constantinos Astreos, Manchester, UK
• Tina Blain, Brussells, Belgium
• Dr Neville Calleja, Malta
• Dr Guilia Cesaroni, Italy
• Norbert Kiss, Budapest, Hungary
• Dr Ifeoma Onyia, Liverpool, UK
• Ms Lesley Patterson, Manchester, UK
• Dr Jude Robinson, Liverpool, UK
• Dr Islay Gemmell, Manchester, UK
• Group 2
• Professor Michal Bedlicki, Krakow, Poland
• Professor Ioan Bocsan, Cluj-Napoca, Romania
• Dr Pais, Clemente, Lisboa, Portugal
• Professor Jurate Klumbiene, Kaunas, Lithuania
• Dr Andre Ochoa, Paris, France
• Ms Angela Pilkington Merseyside, UK
• Jolanta Skrule Riga, Latvia

• Group 3
• Mr Kaspar Bams, Utrecht, Netherlands
• Ms Jana Brozova, Prague, Czech Republic
• Dr Nesrin Cilingiroglu, Ankara, Turkey
• Dr Wolfgang Hellmeier, Bielefeld, Germany
• Dr Igor Krampac, Maribor, Slovenia
• Dr Alexis Macherianakis, Manchester, UK
• Dr Arpana Verma, Manchester, UK
• Dr Yannis Skalkidis, Athens, Greece
• Chris White, Brussells, Belgium
• Group 4
• Dr Christopher Birt, Liverpool, UK
• Dr Maria Irina Brumboiu, Cluj-Napoca, Romania
• Professor Alain Deccache, Brussels, Belgium
• Dominique Doumont, Brussels, Belgium
• Professor Dick Heller, Manchester, UK
• Ms Heidi Lyshol, Oslo, Norway
• Dr Iveta Rajnicova-Nagyova, Kosice, Slovakia

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Overview

• Introduction to workshop
• Allocation of groups
• Nominate spokesperson for feedback
• Breakout groups (40 mins discussion)
• Feedback
• Discussion and conclusions

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Workshop Objectives

• Demonstration of PIMs using
• URHIS 50
• Substance misuse
• Asthma
• Mortality
• Local data
• Manchester
• Greater London
• England

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Workshop Objectives

• Encourage discussion
• Urban areas in relation to PIMs
• Influencing policy with
• Evidence based risk estimation
• Accurate local data
• Application of PIMs
• Any modifications for improvement
• Local prioritisation

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Feedback Group 1

• General
• PIMs useful for certain aspects e.g. diabetes not
  others e.g. green space
• Literature difficult to obtain
• Look at URHIS-50 indicators to see if PIMS
  possible
• Look at regions in league tables
• Influence change
• PIMs help make case for areas that are
  underfunded
• Interventions have big effect in a population and
  not another
• Start at the intervention and look at multiple
  outcomes (targets)
• Decisions based on costs need to add in
• Cost/DALY/QALY

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Feedback Group 2

• Examples
• Mental health checklist
• Costs
• Vaccination Hep B
• HPV
• HbA1c
• Problems
• Evidence not always strong
• Data may not be available
• Do not understand background
• Not applicable to all URHIS-50
• Influence policy makers by demonstrating effect
  of e.g. noise where no proof of efficacy
• Cost Benefit Analysis on prevention

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Feedback Group 3

• General
• Policy makers may see PIMs as real life
  predictions
• Examples
• Diabetes control
• Methadone crime, blood borne viruses

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Feedback Group 4

• Examples
• Yes but dependant on the availability of the
  information
• How to get policy makers get info
• Modifications
• Need a Users manual
• How to use/misuse/misinterpretation
• Local Prioritisation
• Offer tools piloting plans
• Time evolution models re data
• PIMs may be used not to inform policy but to
  justify policy
• Use as a teaching tool for policy makers

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General Feedback

• Identification of relevant health indicators as
  an adjunct to monitoring, policy making PIMs
  important to show this
• Translation of evidence based medicine into lay
  terms to influence policy makers including costs
• Use to justify URHIS
• Knowledge of experts to useable data
• PIMs are one method
• Mass vaccination programmes
• Influence of vaccine manufacturers on prescribing
• Look at morbidity profile between cities
• May be appropriate for the Newsletters
• Policy makers may or may not use PIMs
• Media (cautious)
• Presenting the methods
• For use in local settings
• Can use examples for publication
• URHIS 2
• More tools with PIMs would be included in
  proposal