Do data from child protective services and the police enhance modelling of perinatal risk for paediatric abusive head trauma? A retrospective case-control study

OBJECTIVES: First, to investigate whether there is a relationship between a family being known to child protective services or police at the time of birth and the risk of abusive head trauma (AHT, formerly known as shaken baby syndrome). Second, to investigate whether data from child protective services or police improve a predictive risk model derived from health records. DESIGN: Retrospective case control study of child protective service and police records. SETTING: Nine maternity hospitals. PARTICIPANTS: 142 consecutive cases of AHT admitted to a tertiary children’s hospital from 1991 to 2010 and born in one of the nine participating maternity hospitals. 550 controls matched by the date and hospital of birth. OUTCOME MEASURE: Abusive head trauma. RESULTS: There is a relationship between families known to child protective services or police and the risk of AHT. Notification to child protective services: univariable OR 7.24 (95% CI 4.70 to 11.14). Involvement with youth justice: univariable OR 8.94 (95% CI 4.71 to 16.95). Police call-out for partner violence: univariable OR 3.85 (95% CI 2.51 to 5.91). Other violence offence: univariable OR 2.73 (95% CI 1.69 to 4.40). Drug offence: univariable OR 2.82 (95% CI 1.63 to 4.89). However, in multi-variable analysis with data from perinatal health records, notification to child protective services was the only one of these variables to remain in the final model (OR 4.84; 95% CI 2.61 to 8.97) and had little effect on overall predictive power. The area under the receiver operating characteristic curve was 89.5% (95% CI 86.6 to 92.5) using variables from health data alone and 90.9% (95% CI 88.0 to 93.7) when notification was added. CONCLUSIONS: Family involvement with child protective services or police is associated with increased risk of AHT. However, accessing such data at the time of birth would add little predictive power to a risk model derived from routine health information.