Our institution is a large pediatric tertiary care center with Level I trauma status with a large referral base. Institutional Review Board approval was obtained before data collection. Since 1998, we have maintained an extensive database (Suspected Child Abuse and Neglect [SCAN]) that examines all potential cases of suspected child abuse and/or neglect. This database contains information identifying child abuse victims, demographic factors, and ICD-9 and CPT codes relating to their concomitant diagnoses and treatment. From that database, we identified all children from birth to 48 months of age who had the diagnosis of child abuse (ICD-9 code 995.5x) and the diagnosis of femur fracture (ICD-9 code 820.x and 821.x). In addition, we reviewed the general trauma database from our institution for all children presenting to the emergency room and/or hospitalized with traumatic injuries for children of the same age from 2000 to 2003 with the diagnosis of femur fracture (ICD-9 code 820.x and 821.x). The general trauma database contains data identifying trauma patients that fit these characteristics, demographic information, information regarding injury diagnoses, and subsequent treatment. Information other than demographic information, injury code data and identifying information were abstracted from the electronic medical record.
We calculated a power analysis for multiple logistic regression as described by Hsieh et al. [10
]. The power analysis assumes a background abuse rate of 30% [9
] in patients with femur fractures in this age group with a difference in the rate in any given independent variable of 20%. The power analysis was conducted with a desired two-sided alpha of 0.05 and a desired power of 0.80. The analysis assumes a variance inflation rate (adjustment for increased variability caused by multiple regressors) of 25% for multiple regression. With these characteristics, our total sample size would need to be 196 patients. Because our analysis assumes a 30% event (abuse) [9
] rate in femur fractures, we would need 30% of our total cases to be from abuse and 70% from accidental trauma (59 abuse cases and 137 accidental trauma cases).
Patients were identified in the study as a potential control (accidental) trauma case if they were found in the general trauma database and did not have a diagnosis of child abuse in their record and were not included in the SCAN database. We excluded patients from the study if they were younger than 48 months of age (except for one patient, the SCAN database contained only patients 48 months of age and younger), if we could not adequately confirm whether the case represented child abuse or accidental injury as a result of incomplete medical records (three children), and if cases were erroneously placed in either of the two databases (ie, did not represent either child abuse or accidental trauma). This was the case in one child in whom a diagnosis of femur fracture by ICD-9 code was found but no record of this injury could be identified. In addition, we excluded patients with known metabolic bone disease or osteogenesis imperfecta. Seventy femur fractures were from the child abuse cohort and 139 femur fractures were from the accidental trauma cohort. These 209 total femur fractures represented the cases and controls that would be analyzed in our study. The child abuse cohort had a median age of 4.0 months. Of these 70 patients, 63 were younger than 18 months of age and seven were older than 18 months of age. The accidental trauma cohort had a mean age of 26.2 months. Of these 139 patients, 44 were younger than 18 months of age and 95 were older than 18 months of age.
Patients were included in the SCAN database if hospital personnel determined the patient was a victim of abuse or neglect. The SCAN team consisted of an independent group of nonorthopaedic physicians, nurses, and social workers with advanced training in child abuse who examined these children independently in the emergency room and/or as inpatients and made the determination whether the patients should be diagnosed with child abuse and when child protective services should be activated. Patients were included in the general trauma database if they presented to the emergency room and/or were hospitalized with organ system trauma that necessitated an evaluation from the hospital’s trauma service, died in the emergency department as a result of their traumatic injuries, or were initially seen in the emergency department and transferred to another trauma center for management of traumatic injuries. Both databases were collected and maintained outside of the orthopaedic department at our institution and were initiated many years before the retrospective review of the data that was performed by the authors.
Data parameters collected from the patients’ medical records included age at the time of injury, gender, insurance status, presence of current polytrauma (another concurrent long bone, clavicle or axial skeletal fracture, or other body system injury that would require hospitalization), and physical and/or radiographic evidence of prior trauma. Furthermore, one of the authors (HW) who was blinded to the SCAN status of patients examined the paper and electronic medical records of the children and briefly described the history of each patient. Subsequently, this author categorized in a separate list the plausibility of each patient’s history as either suspicious for abuse (unwitnessed accident, witnessed abuse, delayed presentation greater than 48 hours, a mechanism that would not normally cause a fracture, or different stories provided by different witnesses), or consistent with accidental trauma in cases in which these criteria were not fulfilled, and then transferred the list to the database. Two other authors (NP and KB) reviewed the patient histories to determine if they were consistent with accidental trauma or suspicious for abuse. An intraclass correlation coefficient was generated based on average measures (ICC 0.902, p value < 0.001) indicating agreement. Since many of the original radiographs for patients were no longer available, we examined radiology reports to determine the location of the femur fracture in all patients. The fractures were classified as “proximal,” “diaphyseal,” or “distal” (each representing roughly one-third of the length of the femur: subtrochanteric region, shaft region, and distal metaphyseal region).
Raw data from the databases were pooled, and means, SDs, and/or percentages were calculated for age at time of injury, gender, insurance status, presence of current polytrauma, physical and/or radiographic evidence of prior trauma, history plausibility (consistent with accidental trauma or suspicious for abuse), and radiographic femur fracture location (proximal, diaphyseal, distal).
Because our age data were skewed we used the Mann Whitney U test for independent samples to determine if there was a difference in age at the time of injury between the child abuse and accidental trauma patients. The chi square test with Yates’ correction for independence or the Fisher’s exact test in cases where the assumptions of a chi square test were violated were used to determine differences in binary variables. We used binary logistic regression to calculate adjusted odds ratios for a femur fracture representing abuse as opposed to accidental trauma using age at time of injury, gender, insurance status, presence of current polytrauma, physical and/or radiographic evidence of prior trauma, history plausibility, and femur fracture location as risk factors. Ninety-five percent confidence intervals were calculated for proportions and interquartile ranges for median values.
Child abuse victims were younger than accidental trauma victims (median age 4.0 months compared with 26.2 months) and were more often female (49% versus 32%) (Table ). There was no difference (p = 0.77) between the abuse and accidental trauma groups in terms of insurance status. Patients with femur fractures who were victims of abuse had a greater frequency of current polytrauma (53% versus 8%), physical and/or radiographic evidence of prior trauma (62% versus 4%), and histories that were deemed suspicious for abuse (33% versus 4%) (Table ). Patients with accidental trauma more often had diaphyseal femur fractures (46% versus 66%), abuse victims more often had distal femur fractures (37% versus 20%), and there was no difference in proximal femur fractures between groups (Table ). The odds of a femur fracture being the result of abuse rather than accidental trauma was greater for children younger than 18 months (19.4 times) and patients of female gender (2.0 times) (Table ). Furthermore, abuse was associated more frequently with current polytrauma (13.0 times), physical and/or radiographic evidence of prior trauma (37.5 times), and patients with a suspicious history (10.8 times) (Table ).
Demographic information for child abuse and accidental trauma patients for age, gender, and insurance status
Comparison of current polytrauma, prior trauma, and history plausibility in child abuse and accidental trauma patients with femur fractures
Comparison of femur fracture location in child abuse and accidental trauma patients
Finally, we used a multiple logistic regression model to calculate a prediction rule for the chance of abuse given the aforementioned risk factors. All of the variables that were significant in any portion of the study were used. In addition, variables previously judged important were included in the initial multiple logistic regression model. The variables were entered using the backward likelihood ratio method for selection of variables. All variables were entered and criteria of 0.10 by the −2 log likelihood method was used for removal of variables. In the backward regression analysis, only age younger than 18 months, physical and/or radiographic evidence of prior injury, and history of plausibility were predictive of an abusive etiology for child abuse. In this model, patients with a femur fracture whose age was younger than 18 months had a 10 times greater chance of being the victim of abuse than older children. Patients who had the presence of prior trauma on physical and/or radiographic examination had a 16.8 times greater chance of having been a victim of abuse than patients who had no such findings. Finally, patients with a suspicious history had a 7.4 times greater chance of having been abused than patients with a plausible history after considering the other factors in the model.
Given these findings, a new simpler logistic regression model was built based on the outcome of the first model (Table ). This model was built with the three significant variables in the first model. The variables entered were whether the patient had one, two, or three risk factors (risk factors from the first model: age younger than 18 months, physical and/or radiographic evidence of old trauma, and history suspicious for abuse). This final model did not differ (p = 0.69) from the old model using the −2 log likelihood method of comparison.
Multiple logistic regression model with number of risk factors (risk factors: age younger than 18 months, physical and/or radiologic evidence of prior trauma, and suspicious history)