2018 Volume 83 Issue 747 Pages 843-850
1. Background and purpose
Since the 2011 Great East Japan Earthquake, the Japanese national government and municipalities have reviewed disaster prevention plans and have improved damage estimations. In this study, we aimed to use public data to construct a method for estimating the number of severely injured within a disaster medical sphere. Our estimation method targets districts with a high risk of building collapse.
2. Creation of severely-injured mesh considering disaster risk
In its Estimated Earthquake Damage report, the Tokyo Metropolitan Government (TMG) calculated the number of severely injured in each “ward, municipality, and village”. Also, the Seventh Community Earthquake Risk Assessment Study calculated building collapse risk at the town and neighborhood level.
For Tokyo's 23 wards, we created a 250-m mesh file that includes data on the estimated severely injured (hereafter referred to as the severely-injured (risk) mesh). We calculated the number of severely injured in each mesh by allocating the total number of severely injured in each ward with the distribution of building collapse risk (Fig. 1).
3. Determining the disaster medical sphere
The TMG has established a scheme to accept and triage injured persons at emergency medical relief stations. Since these stations are set up in the vicinity of disaster base hospitals and disaster base coordination hospitals, we determined the disaster medical sphere using Voronoi division for the two types of hospital.
Also, since severely injured persons are treated by disaster base hospitals, it was assumed that the severely injured of disaster base coordination hospitals included within the disaster medical sphere of a disaster base hospital would be transported to the base hospital (Fig. 4).
4. Estimation of the number of severely injured coming to a hospital
We extracted severely-injured (risk) meshes contained within the disaster medical sphere and summed the severely injured data. The number of severely injured treated at a disaster base hospital is the sum of the number of injured coming directly to this hospital and the number transported from coordination hospitals.
Next, we compared this estimation result with the estimation using the severely-injured (population) mesh (previous paper). For many hospitals there was no significant difference between the two estimates (Fig. 7). However, for 9% of hospitals, the estimation result using risk-mesh was greater than using population-mesh (Table 1).
5. Conclusion
The disaster risk level for each ward is reflected in the total number of severely injured for that ward. For many hospitals, the number of severely injured can be estimated using the severely-injured (population) mesh. However, for hospitals having high-risk districts within the disaster medical sphere, estimations with the severely-injured (risk) mesh method are useful in formulating disaster response plans.
In addition, wards' medical relief action plans for disasters do not currently describe the correlation between disaster base hospitals and base coordination hospitals. This study has shown that it is meaningful to delineate this correlation.
