Purpose The National Centre for Health Statistics and the National Centre for Injury Prevention and Control have proposed surveillance case definitions and reporting frameworks for analysis of injury data coded using the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM). This presentation describes recent collaborative efforts to test the proposed definitions and the framework for reporting external cause (injury mechanism by intent).

Methods 1) Analysis of administrative data coded in ICD-10-CM, with comparison to historic results from data coded in ICD-9-CM, and 2) analysis of dual coded data (i.e., same medical record coded in both ICD-9-CM and ICD-10-CM).

Results Using dual-coded data, the proposed ICD-10-CM definition identified more injury hospitalizations than the ICD-9-CM definition. 95% of the hospitalizations identified using the ICD-10-CM definition but not the ICD-9-CM definition were ‘subsequent’ encounters. Comparison of results from the proposed ICD-10-CM external cause matrix to historic results using the ICD-9-CM matrix showed a decrease in the number of cases assigned to Unintentional Fall, Motor Vehicle Traffic (MVT)- Pedestrian, Other Pedalcyclist, Other Transportation, Overexertion and Undetermined Intent, while increases were seen in the number of cases assigned to Unintentional MVT-Pedalcyclist, Other Pedestrian and Unspecified Mechanism.

Conclusions Analysis results will be used to finalise the ICD-10-CM surveillance case definitions and the external cause matrix by deciding whether to: 1) include subsequent encounters, 2) limit case inclusion to a principal diagnosis of injury only or to include records with mentions of injury in other diagnosis fields, 3) include records based on presence of an external cause code even if the principal diagnosis is not an injury, and 4) whether re-assignment of codes to specific cells of the external cause matrix is needed.

Significance Use of finalised ICD-10-CM injury surveillance case definitions and reporting frameworks will help standardise the comparison of results across jurisdictions and time.