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Citation |
Eastman AL, Mitchell MN, Putnam-Hornstein E. Child Abuse Negl. 2016; 55: 22-31. |
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Affiliation |
Children's Data Network, School of Social Work, University of Southern California, United States. |
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Copyright |
(Copyright © 2016, Elsevier Publishing) |
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DOI |
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PMID |
27082751 |
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Abstract |
A key challenge facing child protective services (CPS) is identifying children who are at greatest risk of future maltreatment. This analysis examined a cohort of children with a first report to CPS during infancy, a vulnerable population at high risk of future CPS reports. Birth records of all infants born in California in 2006 were linked to CPS records; 23,871 infants remaining in the home following an initial report were followed for 5 years to determine if another maltreatment report occurred. Latent class analysis (LCA) was used to identify subpopulations of infants based on varying risks of re-report. LCA model fit was examined using the Bayesian information criterion, a likelihood ratio test, and entropy. Statistical indicators and interpretability suggested the four-class model best fit the data. A second LCA included infant re-report as a distal outcome to examine the association between class membership and the likelihood of re-report. In Class 1 and Class 2 (lowest risk), the probability of a re-report was 44%; in contrast, the probability in Class 4 (highest risk) was 78%. Two birth characteristics clustered in the medium- and highest-risk classes: lack of established paternity and delayed or absent prenatal care. Two risk factors from the initial report of maltreatment emerged as predictors of re-report in the highest-risk class: an initial allegation of neglect and a family history of CPS involvement involving older siblings. Language: en |