Comparison of Poisson and Bernoulli spatial cluster analyses of pediatric injuries in a fire district

Abstract

Background: With limited resources available, injury prevention efforts need to be targeted both geographically and to specific populations. As part of a pediatric injury prevention project, data was obtained on all pediatric medical and injury incidents in a fire district to evaluate geographical clustering of pediatric injuries. This will be the first step in attempting to prevent these injuries with specific interventions depending on locations and mechanisms.

Results: There were a total of 4803 incidents involving patients less than 15 years of age that the fire district responded to during 2001-2005 of which 1997 were categorized as injuries and 2806 as medical calls. The two cohorts (injured versus medical) differed in age distribution (7.7 +/- 4.4 years versus 5.4 +/- 4.8 years, p < 0.001) and location type of incident (school or church 12% versus 15%, multifamily residence 22% versus 13%, single family residence 51% versus 28%, sport, park or recreational facility 3% versus 8%, public building 8% versus 7%, and street or road 3% versus 30%, respectively, p < 0.001). Using the medical incident locations as controls, there was no significant clustering for environmental or assault injuries using the Bernoulli method while there were four significant clusters for all injury mechanisms combined, 13 clusters for motor vehicle collisions, one for falls, and two for pedestrian or bicycle injuries. Using the Poisson cluster method on incidence rates by census tract identified four clusters for all injuries, three for motor vehicle collisions, four for fall injuries, and one each for environmental and assault injuries. The two detection methods shared a minority of overlapping geographical clusters.

Conclusion: Significant clustering occurs overall for all injury mechanisms combined and for each mechanism depending on the cluster detection method used. There was some overlap in geographic clusters identified by both methods. The Bernoulli method allows more focused cluster mapping and evaluation since it directly uses location data. Once clusters are found, interventions can be targeted to specific geographic locations, location types, ages of victims, and mechanisms of injury.

Figure 1

Study area: Tualatin Valley Fire & Rescue District (TVF&R). This map shows the boundary of the fire district, the major cities, county boundaries, fire station locations and their respective first-due response areas, the major highway and street system, and the location of community hospitals and the two pediatric trauma centers.

Figure 2

Census tract population less than 15 years old. This map demonstrates the population of each census tract or partial census tract of children less than 15 years old for the year 2000. The tracts are shaded according to the quantile method.

Figure 3

Distribution of all injuries and medical calls locations. This map reveals a subtle difference in the distribution of these two cohorts but both are primarily located in population centers and along the road and street network when superimposed on these.

Figure 4

Total injuries cluster analysis. The significant clusters by the SaTScan Poisson (green-shaded census tracts) and Bernoulli (green circles and green incident locations) methods are superimposed on this map. Each cluster is identified by its rank followed by its relative risk (RR), associated p-value and the number of cases in the cluster.

Figure 5

Motor vehicle collision injuries cluster analysis. The significant clusters by the SaTScan Poisson (blue-shaded census tracts) and Bernoulli (blue circles and blue incident locations) methods are superimposed on this map. Each cluster is identified by its rank followed by its relative risk (RR), associated p-value and the number of cases in the cluster.

Figure 6

Pedestrian and bicycle injuries cluster analysis. The significant clusters by the SaTScan Poisson (yellow-shaded census tracts) and Bernoulli (orange circles and orange incident locations) methods are superimposed on this map. Each cluster is identified by its rank followed by its relative risk (RR), associated p-value and the number of cases in the cluster.

Figure 7

Fall injuries cluster analysis. The significant clusters by the SaTScan Poisson (pink-shaded census tracts) and Bernoulli (red circles and red incident locations) methods are superimposed on this map. Each cluster is identified by its rank followed by its relative risk (RR), associated p-value and the number of cases in the cluster.

Figure 8

Environmental injuries cluster analysis. The significant cluster by the SaTScan Poisson (purple-shaded census tract) on this map is presented on this map. The cluster is identified by its rank followed by its relative risk (RR), associated p-value and the number of cases in the cluster.

Figure 9

Assault injuries cluster analysis. The significant cluster by the SaTScan Poisson (brown-shaded census tract) is presented on this map. The cluster is identified by its rank followed by its relative risk (RR), associated p-value and the number of cases in the cluster.