title: Does transport time help explain the high trauma mortality rates in rural areas? New and traditional predictors assessed by new and traditional statistical methods.
abstract: BACKGROUND
  Trauma is a leading global cause of death. Trauma mortality rates are higher in rural areas, constituting a challenge for quality and equality in trauma care. The aim of the study was to explore population density and transport time to hospital care as possible predictors of geographical differences in mortality rates, and to what extent choice of statistical method might affect the analytical results and accompanying clinical conclusions.
  
  
  METHODS
  Using data from the Norwegian Cause of Death registry, deaths from external causes 1998-2007 were analysed. Norway consists of 434 municipalities, and municipality population density and travel time to hospital care were entered as predictors of municipality mortality rates in univariate and multiple regression models of increasing model complexity. We fitted linear regression models with continuous and categorised predictors, as well as piecewise linear and generalised additive models (GAMs). Models were compared using Akaike's information criterion (AIC).
  
  
  RESULTS
  Population density was an independent predictor of trauma mortality rates, while the contribution of transport time to hospital care was highly dependent on choice of statistical model. A multiple GAM or piecewise linear model was superior, and similar, in terms of AIC. However, while transport time was statistically significant in multiple models with piecewise linear or categorised predictors, it was not in GAM or standard linear regression.
  
  
  CONCLUSIONS
  Population density is an independent predictor of trauma mortality rates. The added explanatory value of transport time to hospital care is marginal and model-dependent, highlighting the importance of exploring several statistical models when studying complex associations in observational data.
authors.affiliation: Department of Health Sciences, University of Stavanger, Stavanger, Norway Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
authors.forename: Jo
authors.initials: J
authors.lastname: Røislien
authors.affiliation: Department of Health Sciences, University of Stavanger, Stavanger, Norway Department of Research, Norwegian Air Ambulance Foundation, Drøbak, Norway.
authors.forename: Hans Morten
authors.initials: HM
authors.lastname: Lossius
authors.affiliation: Department of Research, Norwegian Air Ambulance Foundation, Drøbak, Norway Department of Anaesthesiology, Vestre Viken Hospital Trust, Drammen, Norway.
authors.forename: Thomas
authors.initials: T
authors.lastname: Kristiansen
mesh_terms: D000367:Age Factors; D006801:Humans; D015233:Models, Statistical; D009104:Multiple Trauma / Q000401:mortality*; D015999:Multivariate Analysis; D009664:Norway / Q000453:epidemiology; D011156:Population Density; D012042:Registries; D012307:Risk Factors; D012424:Rural Population / Q000706:statistics & numerical data*; D013997:Time Factors; D014187:Transportation of Patients / Q000706:statistics & numerical data*