Understanding Overrepresentation of Indigenous Children in Child Welfare Data: An Application of the Drake Risk and Bias Models

• Published in: Child maltreatment 2015-08, Vol.20 (3), p.170-182
• Main Authors: Cram, Fiona, Gulliver, Pauline, Ota, Rissa, Wilson, Moira
• Format: Article
• Language: English
• Subjects: Child; Child Abuse; Child abuse & neglect; Child Abuse - prevention & control; Child Abuse - statistics & numerical data; Child Health Services - organization & administration; Child Neglect; Child welfare; Child Welfare - statistics & numerical data; Estimation bias; Female; Health Services, Indigenous - statistics & numerical data; Humans; Infant mortality; Male; Models, Statistical; Native peoples; New Zealand; Performance evaluation; Risk Factors; Statistics
• ISSN: 1077-5595; 1552-6119; 1552-6119
• DOI: 10.1177/1077559515580392

Child welfare data collected for administrative purposes are often used as a source of information for understanding the population impact of child abuse and neglect (CA/N). This study used administrative data linked at the individual level for a cohort of Aotearoa New Zealand (NZ) children to follow and extend a model developed by Drake et al. In this investigation, we aimed to build an understanding of the high representation of indigenous NZ children in administratively sourced measures of CA/N. Variation in rate ratios (RRs) within infant mortality and birth outcomes considered as possible proxies for actual CA/N RRs leaves open a range of interpretations. Our findings indicate that a more nuanced interpretation of the overrepresentation of indigenous children in administratively recorded maltreatment statistics is required. Rather than considering risk and bias as competing explanations, we suggest an acknowledgment of the impact of colonization and the existence of systemic bias generating increased risk as key drivers. As linked administrative data are increasingly used for research and evaluation, and considered for use in supporting decision making, there is a need for a deeper understanding of the drivers of administratively recorded CA/N in order to effectively address the needs of indigenous populations.