Can coders abstract child maltreatment variables from child welfare administrative data and case narratives for public health surveillance in Canada?

Background: Public health surveillance is essential to inform programs that aim to eradicate child maltreatment (CM) and to provide services to children and families. However, collection of CM data imposes a burden on child welfare workers (CWWs). This study assesses the feasibility of hiring coders to abstract the required information from administrative records and case narratives.
Methods: Based on a convenience sample of child welfare data from Manitoba, Canada, two coders abstracted information on 181 alleged CM cases. The coders completed a short web-based questionnaire for each case to identify which of five types of CM had been investigated, level of substantiation for each type, and risk of future CM. The CWWs responsible for each case completed the same questionnaire. Percentages of the occurrence of CM by the three sources were compared. The validity of the coders' classifications was assessed by calculating sensitivity, specificity, and positive and negative predictive values, against the CWWs' classifications as the "gold standard." Cohen's kappa was also calculated.
Results: The coders' classifications of physical abuse, sexual abuse and neglect generally matched those of CWWs; for exposure to intimate partner violence, agreement was weak for one coder. Coding of emotional maltreatment and risk investigations could not be evaluated.
Conclusion: Results were promising. Abstraction was not time-consuming. Differences between coders and CWWs can be largely explained by the administrative data system, child welfare practice, and legislation. Further investigation is required to determine if additional training could improve coders' classifications of CM.
(Copyright © 2019. Published by Elsevier Ltd.)