title: Risk of re-report: A latent class analysis of infants reported for maltreatment.
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. Findings suggest that statistical techniques can be used to identify families with a heightened risk of experiencing later CPS contact.
authors.affiliation: Children's Data Network, School of Social Work, University of Southern California, United States. Electronic address: lanea@usc.edu.
authors.forename: Andrea Lane
authors.initials: AL
authors.lastname: Eastman
authors.affiliation: Children's Data Network, School of Social Work, University of Southern California, United States.
authors.forename: Michael N
authors.initials: MN
authors.lastname: Mitchell
authors.affiliation: Children's Data Network, School of Social Work, University of Southern California, United States.
authors.forename: Emily
authors.initials: E
authors.lastname: Putnam-Hornstein
mesh_terms: D000293:Adolescent; D000328:Adult; D002140:California / Q000453:epidemiology; D002648:Child; D002649:Child Abuse / Q000706:statistics & numerical data*; D002675:Child, Preschool; D015331:Cohort Studies; D004522:Educational Status; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D008423:Maternal Age; D008498:Medical Record Linkage; D010334:Paternity; D012008:Recurrence; D012107:Research Design / Q000706:statistics & numerical data; D035862:Vulnerable Populations; D055815:Young Adult
keywords: Child maltreatment; Childhood adversity; Infant vulnerability; Latent class analysis; Recurrence