TY - JOUR
T1 - Geographic cluster analysis of injury severity and hospital resource use in a regional trauma system
AU - Warden, Craig
AU - Sahni, Ritu
AU - Newgard, Craig
PY - 2010
Y1 - 2010
N2 - Objectives. To determine clusters of trauma incidents with high injury severity and resource utilization and to test their association with census demographic information. Methods. Using "trauma band" unique identifiers and probabilistic linkage for unmatched cases, we matched injury location information collected from a centralized regional trauma communications center to the state trauma system registry for patients directly transported to two level I trauma centers for the years 20012003 in a three-county area. The injury locations were aggregated at the census tract level using a geographic information system (GIS). Moran's I analysis was used to determine clusters of census tracts that had a high incidence of either total trauma injuries, Injury Severity Scores (ISSs) >15, or high resource use (in-hospital mortality, admission to the intensive care unit, or major nonorthopedic surgery). These clusters were then tested for association with census tract demographics using logistic regression. Results. Eight thousand seven hundred fifty-one injured persons were directly transported from the tricounty area to a trauma center during the study period. The mean (± standard deviation) age was 37 ± 21 years, 67.4 were male, 18.9 had ISSs >15, and 29.8 had a high-resource-use indicator. Moran's I analysis demonstrated a single large cluster of incidents for total injuries, ISS >15, and occurrence of a high-resource-use indictor that overlapped except for one small census tract. Logistic regression revealed that the high-risk cluster was associated with a higher prevalence of nonwhite population and vacant housing and a lower prevalence of foreign-born residents and family housing. Conclusions. GIS cluster analysis demonstrated high-risk census tracts for trauma incidents and associated population demographics. Geospatial analyses may assist injury prevention interventions and emergency medical services deployment strategies for trauma.
AB - Objectives. To determine clusters of trauma incidents with high injury severity and resource utilization and to test their association with census demographic information. Methods. Using "trauma band" unique identifiers and probabilistic linkage for unmatched cases, we matched injury location information collected from a centralized regional trauma communications center to the state trauma system registry for patients directly transported to two level I trauma centers for the years 20012003 in a three-county area. The injury locations were aggregated at the census tract level using a geographic information system (GIS). Moran's I analysis was used to determine clusters of census tracts that had a high incidence of either total trauma injuries, Injury Severity Scores (ISSs) >15, or high resource use (in-hospital mortality, admission to the intensive care unit, or major nonorthopedic surgery). These clusters were then tested for association with census tract demographics using logistic regression. Results. Eight thousand seven hundred fifty-one injured persons were directly transported from the tricounty area to a trauma center during the study period. The mean (± standard deviation) age was 37 ± 21 years, 67.4 were male, 18.9 had ISSs >15, and 29.8 had a high-resource-use indicator. Moran's I analysis demonstrated a single large cluster of incidents for total injuries, ISS >15, and occurrence of a high-resource-use indictor that overlapped except for one small census tract. Logistic regression revealed that the high-risk cluster was associated with a higher prevalence of nonwhite population and vacant housing and a lower prevalence of foreign-born residents and family housing. Conclusions. GIS cluster analysis demonstrated high-risk census tracts for trauma incidents and associated population demographics. Geospatial analyses may assist injury prevention interventions and emergency medical services deployment strategies for trauma.
KW - Cluster analysis
KW - GIS
KW - Geographic information systems
KW - Morans I analysis
KW - Severity
KW - Trauma
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U2 - 10.3109/10903120903538682
DO - 10.3109/10903120903538682
M3 - Article
C2 - 20095830
AN - SCOPUS:77749292164
SN - 1090-3127
VL - 14
SP - 137
EP - 144
JO - Prehospital Emergency Care
JF - Prehospital Emergency Care
IS - 2
ER -