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.

Figure 1

Bivariate associations between mortality rates in 434 Norwegian municipalities and population density and transportation to hospital care. Scatterplot of raw data (A and B); log data with lowess line superimposed (C and D); categorized predictors according to quartiles and predefined categories (E and F). In figures C–F the outlier Træna has been removed. In the accompanying statistical analyses data were weighted with respect to municipality population.

Figure 2

Generalized Additive Models (GAM) with pre-hospital mortality rates in 434 Norwegian municipalities as outcome and population density and travel time to hospital care as predictors; univariate models (dotted line) and multiple model (solid line). See Table 3 for AIC, and p-values.