BACKGROUND: Previous studies have found racial and socioeconomic status bias in the way clinicians screen for and detect child abuse in patients presenting to the emergency department. We hypothesized that implementing a guideline for screening would attenuate this bias. METHODS: An algorithm for child abuse screening in patients younger than 1 year presenting with fractures was developed for a pediatric trauma center emergency department. Data were collected 1.5 years before and after implementation of the algorithm to investigate implementation success. Data were compared before and after the implementation of the algorithm using χ and univariate logistic regression analysis. RESULTS: The characteristics of patients with fractures were similar before and after the 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 the implementation of the protocol. Children with government-subsidized or no insurance were more likely to be screened for child abuse via skeletal survey before the algorithm implementation compared with those with private insurance (odds ratio, 2.7; 95% confidence interval, 1.2-6.0; p = 0.017). This relationship did not exist after the algorithm implementation (odds ratio, 1.2; 95% confidence interval, 0.56-2.46; p = 0.66). Final determination of child abuse was related to insurance status both before and after the algorithm implementation. CONCLUSION: A child abuse screening algorithm was successfully implemented in an urban trauma center. After implementation, screening was no longer associated with socioeconomic status of the patient's family, although final determination of child abuse still was. Additional research is needed to determine utility of unbiased screening on patient outcomes. LEVEL OF EVIDENCE: Therapeutic study, level IV.
BACKGROUND: Previous studies have found racial and socioeconomic status bias in the way clinicians screen for and detect child abuse in patients presenting to the emergency department. We hypothesized that implementing a guideline for screening would attenuate this bias. METHODS: An algorithm for child abuse screening in patients younger than 1 year presenting with fractures was developed for a pediatric trauma center emergency department. Data were collected 1.5 years before and after implementation of the algorithm to investigate implementation success. Data were compared before and after the implementation of the algorithm using χ and univariate logistic regression analysis. RESULTS: The characteristics of patients with fractures were similar before and after the 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 the implementation of the protocol. Children with government-subsidized or no insurance were more likely to be screened for child abuse via skeletal survey before the algorithm implementation compared with those with private insurance (odds ratio, 2.7; 95% confidence interval, 1.2-6.0; p = 0.017). This relationship did not exist after the algorithm implementation (odds ratio, 1.2; 95% confidence interval, 0.56-2.46; p = 0.66). Final determination of child abuse was related to insurance status both before and after the algorithm implementation. CONCLUSION: A child abuse screening algorithm was successfully implemented in an urban trauma center. After implementation, screening was no longer associated with socioeconomic status of the patient's family, although final determination of child abuse still was. Additional research is needed to determine utility of unbiased screening on patient outcomes. LEVEL OF EVIDENCE: Therapeutic study, level IV.
Authors: Joanne N Wood; M Katherine Henry; Rachel P Berger; Daniel M Lindberg; James D Anderst; Lihai Song; Russell Localio; Chris Feudtner Journal: Acad Pediatr Date: 2018-08-16 Impact factor: 3.107
Authors: Alex Aregbesola; Ahmed M Abou-Setta; George N Okoli; Maya M Jeyaraman; Otto Lam; Viraj Kasireddy; Leslie Copstein; Nicole Askin; Kathryn M Sibley; Terry P Klassen Journal: PLoS One Date: 2021-03-24 Impact factor: 3.240
Authors: Ruhee Shah; Alessandra Della Porta; Sherman Leung; Margaret Samuels-Kalow; Elizabeth M Schoenfeld; Lynne D Richardson; Michelle P Lin Journal: West J Emerg Med Date: 2021-10-27