274379 Utility of a Child Abuse Screening Algorithm in an Urban Pediatric Emergency Department

Sunday, October 28, 2012

Karla Lawson, PhD , Trauma Services, Dell Children's Hospital, Austin, TX
Nicole Higginbotham, RN, NP , Neurosurgery, Dell Children's Medical Center, Austin, TX
Kelly Gttig, RN, NP , Trauma Services, Dell Children's Medical Center of Central Texas, Austin, TX
Sarah Duzinski, MPH , Trauma Services, Dell Children's Medical Center of Central Texas, Austin, TX
Eric Higginbotham, MD , Emergency Medicine, Dell Children's Medical Center of Central Texas, Austin, TX
George Edwards, MD , Pediatrics, Dell Children's Medical Center of Central Texas, Austin, TX
Tim George, MD , Neurosurgery, Dell Children's Medical Center of Central Texas, Austin, TX
Nilda Garcia, MD , Trauma Services, Dell Children's Medical Center of Central Texas, Austin, TX
Background: Previous studies have found racial and socio-economic status bias in the way clinicians screen for and detect child abuse in patients presenting to the emergency room. Methods: An algorithm for child abuse screening in patients less than one year of age presenting with fractures was developed for a pediatric trauma center emergency room. Data was collected 1.5 years before and after implementation of the algorithm to investigate implementation success. Data was compared pre- and post- algorithm using Chi2 and univariate logistic regression analysis. Results: The characteristics of patients with fractures were similar before and after algorithm implementation. Implementation of the algorithm was related to a significant increase in algorithm required screenings: skeletal survey (p < 0.001), urinalysis (p <0.001), and transaminase levels (p < 0.001). The racial composition of those screened did not change after implementation of the protocol. Children with government subsidized or no insurance were more likely to be screened for child abuse via skeletal survey pre-algorithm compared to those with private insurance (OR=2.7, 95% CI = 1.2-6.0, p=0.017). This relationship did not exist post-algorithm (OR=1.2, 95% CI = 0.56-2.46, p = 0.66). Final determination of child abuse was related to insurance status both pre- and post- algorithm. Conclusion: A child abuse screening algorithm was successfully implemented in an urban trauma center. After implementation, screening was no longer associated with socio-economic status of the patient's family, though final determination of child abuse still was. Additional research is needed to determine utility of un-biased screening on patient outcomes.

Learning Areas:
Clinical medicine applied in public health
Conduct evaluation related to programs, research, and other areas of practice

Learning Objectives:
Recount the literature regarding child abuse detection bias List the success metrics of child abuse detection algorithm implementation List the outcome of algorithm implementation on child abuse screening bias.

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a PhD and post doctoral trained scientist. I am principle investigator on a number of studies. I am an assistant professor of public health, surgery, and pediatrics at UTSPH, UT Southwestern Medical School, and UTMB.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

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