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Validity of Diagnostic and Procedure Codes in Administrative Data to Identify Pediatric Physical Abuse
Erika Obikane1, Tomoko Nishino1, Harumi Sonehara2
1Department of Social Medicine, National Center for Child Health and Development, Setagaya, Tokyo, Japan.
Insights
International Classification of Diseases, 10th Revision (ICD-10) codes are unreliable for identifying child physical abuse. Combining diagnostic codes with specific medical procedures can improve detection for future surveillance.
Area of Science:
- Pediatric Health
- Public Health Surveillance
- Medical Informatics
Background:
- Many countries lack robust systems for tracking child abuse using diagnostic codes.
- This study addresses the inadequacy of current diagnostic coding for child abuse identification.
Purpose of the Study:
- To evaluate the validity of International Classification of Diseases, 10th Revision (ICD-10) codes for identifying child physical abuse.
- To develop algorithms for detecting physical abuse in administrative data.
Main Methods:
- Analysis of hospitalized injury cases in children under 10 years in Japan (April 2013-March 2023).
- Utilized child protection team records as the reference standard.
- Employed machine learning to assess diagnostic, procedure, and medication codes.
Main Results:
- Only one of 53 physical abuse cases used an abuse-specific ICD-10 code, showing low reliability.
- Combining X-ray and fundus examinations yielded high sensitivity and specificity for detecting abuse in children under 10 and under 6.
- Machine learning models (Lasso, random forest, B-CRT) provided consistent findings.
Conclusions:
- ICD-10 codes alone are insufficient for accurate identification of child physical abuse.
- Integrating specific medical procedures with diagnostic codes can enhance case identification for epidemiological surveillance.
Background:
Many countries lack comprehensive systems or standardized use of diagnostic codes to track child abuse. This study evaluated the validity of the International Classification of Diseases, 10th Revision (ICD-10) diagnostic codes and developed algorithms to identify physical abuse using administrative data.
Methods:
We analyzed children under 10 years hospitalized for injuries between April 2013 and March 2023 at a children's hospital in Japan. Child protection team records notifying local services were used as the reference standard. We assessed diagnostic, procedure, and medication codes using machine learning.
Results:
Among 1375 injury cases, 53 involved physical abuse, but only one used an abuse-specific ICD-10 code, indicating limited reliability. Combining X-ray and fundus examinations achieved 73.6% sensitivity (95% CI, 59.7-84.7) and 85.7% specificity (95% CI, 83.7-87.5) among children aged < 10 years and 80.9% sensitivity (95% CI, 66.7-90.9) and 79.1% specificity (95% CI, 76.2-81.7) among children aged < 6 years. Several machine learning approaches, including Lasso regression, random forest, and bootstrap classification-and-regression-tree models, yielded broadly consistent findings.
Conclusions:
ICD-10 codes alone are insufficient for identifying physical abuse combining specific procedures may improve case identification for future epidemiological surveillance.
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