Child Abuse Pediatrics Research Network: The CAPNET Core Data Project

Yield of skeletal surveys in national network of child abuse pediatricians: Age is key

BACKGROUND

Skeletal surveys (SS) are recommended for the evaluation of suspected physical abuse in children <2 years old. No guidelines exist for SS completion in children between 2 and 5 years old.

OBJECTIVE

To determine rates of SS completion by age and examine variables associated with occult fracture identification in older children.

PARTICIPANTS AND SETTING

Observational cross-sectional multi-center study of 10 US pediatric centers 2/2021-9/2022 including children <6 years old evaluated for physical child abuse.

METHODS

The principal outcome is occult fracture identified on SS. Non-parametric tests were conducted from comparison between age groups and those with and without occult fractures.

RESULTS

The rate of SS completion declined with increasing age from a high of 91 % in infants <6 months old to 7 % in children 5-5.9 years old. The proportion of SS with occult fractures also decreased with age. Of 450 children 2-5 years old with a SS, 20 [4 % (95 % CI: 3-8 %)] had an occult fracture. The rate of occult fractures among children 2-5 years old who were diagnosed with abuse and not admitted to the hospital was 0.3 % (95 % CI 0-0.6 %)]. Over 30 % of children 2-5 years old were diagnosed with child abuse by a child abuse pediatrician without completion of a SS.

CONCLUSION

In children 2-5 years of age being evaluated for physical abuse, use of SS and the rate of occult fractures is low. The number of SS performed in children in this age group could potentially be decreased by up to 60 % by limiting SS to children admitted to the hospital.

Pilot study using machine learning to improve estimation of physical abuse prevalence

BACKGROUND

International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes have been shown to underestimate physical abuse prevalence. Machine learning models are capable of efficiently processing a wide variety of data and may provide better estimates of abuse.

OBJECTIVE

To achieve proof of concept applying machine learning to identify codes associated with abuse.

PARTICIPANTS AND SETTING

Children <5 years, presenting to the emergency department with an injury or abuse-specific ICD-10-CM code and evaluated by the child protection team (CPT) from 2016 to 2020 at a large Midwestern children's hospital.

METHODS

The Pediatric Health Information System (PHIS) and the CPT administrative databases were used to identify the study sample and injury and abuse-specific ICD-10-CM codes. Subjects were divided into abused and non-abused groups based on the CPT's evaluation. A LASSO logistic regression model was constructed using ICD-10-CM codes and patient age to identify children likely to be diagnosed by the CPT as abused. Performance was evaluated using repeated cross-validation (CV) and Reciever Operator Characteristic curve.

RESULTS

We identified 2028 patients evaluated by the CPT with 512 diagnosed as abused. Using diagnosis codes and patient age, our model was able to accurately identify patients with confirmed PA (mean CV AUC = 0.87). Performance was still weaker for patients without existing ICD codes for abuse (mean CV AUC = 0.81).

CONCLUSIONS

We built a model that employs injury ICD-10-CM codes and age to improve accuracy of distinguishing abusive from non-abusive injuries. This pilot modelling endeavor is a steppingstone towards improving population-level estimates of abuse.

Child Health Needs and the Child Abuse Pediatrics Workforce: 2020-2040

Child abuse pediatrics (CAP) subspecialists evaluate, diagnose, and treat children when abuse or neglect is suspected. Despite the high rates of child maltreatment across the United States, CAP remains the smallest pediatric subspecialty. The CAP workforce faces numerous challenges, including few fellows entering the field, decreased financial compensation compared with other fields of medicine, and threats to workforce retention, including secondary trauma and harmful exposure in the media. A microsimulation model that estimates the future of the US CAP workforce over the next 20 years shows that, although the number of child abuse pediatricians in the field is expected to increase, the growth is smaller than that of every other pediatric subspecialty. In addition to the low overall CAP workforce in the United States, other workforce issues include the need to increase CAP subspecialists who are underrepresented in medicine and unequal geographic distribution across the country. To meet the medical needs of suspected victims of maltreatment, especially in CAP-underserved areas, many children are evaluated by providers who are not board-certified in CAP, such as general pediatricians, family medicine physicians, emergency medicine physicians, and advanced practice providers, whose CAP experience and training may vary. Current child abuse pediatricians should continue introducing the field to medical students and residents, especially those who identify as underrepresented in medicine or are from CAP-underserved areas, and offer mentorship, continuing education, and oversight to non-CAP physicians meeting this population's medical needs.

Using Child Abuse Specialists to Reduce Unnecessary Child Protective Services Reports and Investigations

This Viewpoint informs clinicians of the potential harms of unnecessary reporting of child abuse and highlights the possibility of consulting a child abuse pediatrician prior to reporting in a subset of cases in which the concern for child abuse is low.