A spatial analysis of functional outcomes and quality of life outcomes after pediatric injury

BACKGROUND: Changes in health-related quality of life (HRQoL) are more regularly being monitored during the first year after injury. Monitoring changes in HRQoL using spatial cluster analysis can potentially identify concentrations of geographic areas with injury survivors with similar outcomes, thereby improving how interventions are delivered or in how outcomes are evaluated. METHODS: We used a spatial scan statistic designed for oridinal data to test two different spatial cluster analysis of very low, low, high, and very high HRQoL scores. Our study was based on HRQoL scores returned by children treated for injury at British Columbia Children’s Hospital and discharged to the Vancouver Metropolitan Area. Spatial clusters were assessed at 4 time periods – baseline (based on pre-injury health as reported prior to discharge from hospital), and one, four, and twelve months after discharge. Outcome data were measured used the PedsQL™ outcome scale. Outcome values of very low, low, high, and very high HRQoL scores were defined by classifying PedsQL™ scores into quartiles. In the first test, all scores were assessed for clustering without specifying whether the response score was from a baseline or follow-up response. In the second analysis, we built a space-time model to identify whether HRQoL responses could be identified at specific time points. RESULTS: Among all participants, geographic clustering of response scores were observed globally and at specific time periods. In the purely spatial analysis, five significant clusters of ‘very low’ PedsQL physical and psychosocial health outcomes were identified within geographic zones ranging in size from 1 to 21 km. A space-time analysis of outcomes identified significant clusters of both ‘very low’ and ‘low’ outcomes between survey months within zones ranging in size from 3 to 5 km. CONCLUSION: Monitoring patient health outcomes following injury is important for planning and targeting interventions. A common theme in the literature is that future prevention efforts may benefit from identifying those most a risk of developing ongoing problems after injury in effort to target resources to those most in need. Spatial scan statistics are tools that could be applied for identifying concentrations of poor recovery outcomes. By classifying outcomes as a categorical variable, clusters of ‘potentially low’ outcomes can also be mapped, thereby identifying populations whose recovery status may decrease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40621-014-0016-1) contains supplementary material, which is available to authorized users.