Evaluation of Community- Based Injury Prevention Programs PhD course febr 2007 Leif Svanström.

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Evaluation of Community-Based Injury Prevention

Programs

PhD course febr 2007

Leif Svanström

Evaluation of Community-Based Injury Prevention

Programs

First exampleLong-term evaluation of Vaeroy

Vaeroy, Norway

A lot of photos A lot of photos excluded!excluded!

The Role of the The Role of the Health SectorHealth Sector

!!

VAEROY, NORWAY

1982-83 30% Reduction

1982-90 58% Reduction

PhD course jan- febr 2008Vaeroy

Multifactorial injury prevention programme started 1981 in a population of 1.000 population and lasted to 1987.

Method:Registration 1970-2001

Results: from 17.7% to 9.7% during 1981-7 and 96% by the year 2001

Conclusion: Community program with defined aims will lead to a considerable and long-lasting reduction of

injuries.

Evaluation of Community-Based Injury Prevention

Programs

Second exampleLidköping Accid Prev Programme- children

Lidköping, Sweden

PhD course jan- febr 2008Lidköping Children

Multifactorial injury prevention programme started 1984 in a population of about 30,000 population and evaluation

lasted to 1991.

Method:Surveillance hospital in-patient data 1983 to 1991

Results: Annual decrease of 2.4% for boysand 2.1% for girls. Smaller decrease in control areas.

Conclusion: Community program with defined aims will lead to a considerable reduction of injuries.

Evaluation of Community-Based Injury Prevention

Programs

Third exampleHelmet Wearing Programme- Sweden

Evaluation of Community-Based Injury Prevention

Programs

4th example

Intra-Country Differences in Child Bicycle-related Injuries in Sweden?

PhD course jan- febr 2008

Intra-Country DifferencesAnalyze differences in trend of childhood bicycle-related in

Sweden.

Method:Surveillance hospital in-patient data 1987 to 1996. Children 0-14 years.Statistical method with both linear

and quadratic modelling.Results: No differences South-North, except for children 7-

14 years. Substantial differences urban/rural areas.

Conclusion: This method gives a better understanding of time trends than the traditional linear regression model

only.