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

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 follo...

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Bibliographic Details
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
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Summary: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.
ISSN:1077-5595
1552-6119
1552-6119
DOI:10.1177/1077559515580392