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. 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 (<1, 1-4, 5-9, 10-14, 15-19 and 20-24). Eighteen local health area characteristics are studied. They include a broad spectrum of socio-economic indicators, residential environment indicators (roads and parks), medical services availability and utilisation, population health, proportion of recent immigrants, crime rates, rates of speeding charge and rates of seatbelt violation. Our study indicates a large regional variation in MVAI in males aged 0-24 in British Columbia, Canada, in 1990-1999, and that adjusting for appropriate risk factors eliminates nearly all the variation observed. Socio-economic influence on MVAI was profoundly apparent in young males of all ages with the injury being more common in communities of lower socio-economic status. High adult male crime rates were significantly associated with high injury rates of boys aged 1-14. Seatbelt violations and excess speeding charges were found to be positively associated with the injury rates of young men aged 20-24. This and similar ecological studies shed light on reasons for regional variations in accident occurrence as well as in the resulting injuries and hospital utilisation. Thereby they are potentially useful in identifying priority areas for injury/accident prevention and in informing regional health planning and policy development.
MacNab
Ying C
YC
Department of Health Care and Epidemiology, Division of Epidemiology and Biostatistics, University of British Columbia, Vancouver, BC V6H 3V4, Canada. ymacnab@cw.bc.ca
D000059:Accidents / Q000706:statistics & numerical data*; D000063:Accidents, Traffic / Q000706:statistics & numerical data; D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D001499:Bayes Theorem; D001955:British Columbia / Q000453:epidemiology; D002648:Child; D002675:Child, Preschool; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016014:Linear Models; D008297:Male; D008962:Models, Theoretical; D011159:Population Surveillance / Q000379:methods*; D011204:Poverty Areas; D012111:Residence Characteristics; D012307:Risk Factors; D012737:Sex Factors; D017062:Small-Area Analysis; D014947:Wounds and Injuries / Q000453:epidemiology*