Predicting regional variations in mortality from motor vehicle crashes.

Abstract

To show that the previously-observed inverse relationship between population density and per-capita mortality from motor vehicle crashes can be derived from a simple mathematical model that can be used for prediction. The authors proposed models in which the number of fatal crashes in an area was directly proportional to the population and also to some power of the mean distance between hospitals. Alternatively, these can be parameterized as Weibull survival models. Using county and state data from the U.S. Census, the authors fitted linear regression equations on a logarithmic scale to test the validity of these models. The southern states conformed to a different model from the other states. If an indicator variable was used to distinguish these groups, the resulting model accounted for 74% of the variation from state to state (Alaska excepted). After controlling for mean inter-hospital distance, the southern states had a per-capita mortality 1.37 times that of the other states. Simply knowing the mean distance between hospitals in a region allows a fiarly accurate estimate of its per-capita mortality from vehicle crashes. After controlling for this factor, vehicle crash mortality per capita is higher in the southern states, for reasons yet to be explained.