Bayesian spatial and ecological models for small-area accident and injury analysis

In this article, recently developed Bayesian spatial and ecological regression models are applied to analyse small-area variation in accident and injury. This study serves to demonstrate how Bayesian modelling techniques can be implemented to assess potential risk factors measured at group (e.g. are...

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Bibliographic Details
Published in:Accident analysis and prevention 2004-11, Vol.36 (6), p.1019-1028
Main Author: MacNab, Ying C.
Format: Article
Language:English
Subjects:
Accident
Accidents - statistics & numerical data
Accidents, Traffic - statistics & numerical data
Adolescent
Adult
Age Distribution
Bayes Theorem
Bayesian spatial and ecological models
British Columbia - epidemiology
Child
Child, Preschool
Ecological study
Group level risk factors
Hospitalisation due to motor vehicle accident injury
Humans
Infant
Infant, Newborn
Injury surveillance and prevention
Linear Models
Male
Models, Theoretical
Population Surveillance - methods
Poverty Areas
Residence Characteristics
Risk Factors
Sex Factors
Small-Area Analysis
Wounds and Injuries - epidemiology
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Summary:In this article, recently developed Bayesian spatial and ecological regression models are applied to analyse small-area variation in accident and injury. This study serves to demonstrate how Bayesian modelling techniques can be implemented to assess potential risk factors measured at group (e.g. area) level. Presented here is a unified modelling framework that enables thorough investigations into associations between injury rates and regional characteristics, residual variation and spatial autocorrelation. Using hospital separation data for 83 local health areas in British Columbia (BC), Canada, in 1990–1999, we explore and examine ecological/contextual determinants of motor vehicle accident injury (MVAI) among male children and youth aged 0–24 and for those of six age groups (
ISSN:0001-4575
1879-2057
DOI:10.1016/j.aap.2002.05.001