title: An overview of geospatial methods used in unintentional injury epidemiology.
abstract: BACKGROUND
  Injuries are a leading cause of death and disability around the world. Injury incidence is often associated with socio-economic and physical environmental factors. The application of geospatial methods has been recognised as important to gain greater understanding of the complex nature of injury and the associated diverse range of geographically-diverse risk factors. Therefore, the aim of this paper is to provide an overview of geospatial methods applied in unintentional injury epidemiological studies.
  
  
  METHODS
  Nine electronic databases were searched for papers published in 2000-2015, inclusive. Included were papers reporting unintentional injuries using geospatial methods for one or more categories of spatial epidemiological methods (mapping; clustering/cluster detection; and ecological analysis). Results describe the included injury cause categories, types of data and details relating to the applied geospatial methods.
  
  
  RESULTS
  From over 6,000 articles, 67 studies met all inclusion criteria. The major categories of injury data reported with geospatial methods were road traffic (n = 36), falls (n = 11), burns (n = 9), drowning (n = 4), and others (n = 7). Grouped by categories, mapping was the most frequently used method, with 62 (93%) studies applying this approach independently or in conjunction with other geospatial methods. Clustering/cluster detection methods were less common, applied in 27 (40%) studies. Three studies (4%) applied spatial regression methods (one study using a conditional autoregressive model and two studies using geographically weighted regression) to examine the relationship between injury incidence (drowning, road deaths) with aggregated data in relation to explanatory factors (socio-economic and environmental).
  
  
  CONCLUSIONS
  The number of studies using geospatial methods to investigate unintentional injuries has increased over recent years. While the majority of studies have focused on road traffic injuries, other injury cause categories, particularly falls and burns, have also demonstrated the application of these methods. Geospatial investigations of injury have largely been limited to mapping of data to visualise spatial structures. Use of more sophisticated approaches will help to understand a broader range of spatial risk factors, which remain under-explored when using traditional epidemiological approaches.
authors.affiliation: Australian Collaboration for Research into Injury in Sport and its Prevention (ACRISP), Federation University Australia, SMB Campus, PO Box 663, Ballarat, 3353, Australia. himalaya.singh@federation.edu.au.|School of Health Sciences and Psychology, Faculty of Health, Federation University Australia, Ballarat, Australia. himalaya.singh@federation.edu.au.
authors.forename: Himalaya
authors.initials: H
authors.lastname: Singh
authors.affiliation: Australian Collaboration for Research into Injury in Sport and its Prevention (ACRISP), Federation University Australia, SMB Campus, PO Box 663, Ballarat, 3353, Australia.
authors.forename: Lauren V
authors.initials: LV
authors.lastname: Fortington
authors.affiliation: Centre for eResearch and Digital Innovation (CeRDI), Federation University Australia, Ballarat, Australia.
authors.forename: Helen
authors.initials: H
authors.lastname: Thompson
authors.affiliation: Australian Collaboration for Research into Injury in Sport and its Prevention (ACRISP), Federation University Australia, SMB Campus, PO Box 663, Ballarat, 3353, Australia.
authors.forename: Caroline F
authors.initials: CF
authors.lastname: Finch
keywords: Cluster detection; Clustering; Ecological analysis; Geographical correlation; Geographical epidemiology; Mapping; Smoothing; Spatial analysis; Spatial epidemiology