Risk of re-report: A latent class analysis of infants reported for maltreatment
Andrea Lane Eastman1, Michael N Mitchell1, Emily Putnam-Hornstein1
1Children's Data Network, School of Social Work, University of Southern California, United States.
Insights
Identifying children at high risk for child maltreatment is crucial. This study used latent class analysis to find that factors like lack of paternity and delayed prenatal care predict higher re-report risks for infants in child protective services.
Area of Science:
- Child welfare research
- Pediatric public health
- Social epidemiology
Background:
- Child protective services (CPS) face challenges in predicting future child maltreatment.
- Infants with an initial CPS report are a vulnerable group at high risk for subsequent reports.
Purpose of the Study:
- To identify distinct risk subpopulations of infants with initial CPS reports.
- To examine predictors of future maltreatment reports in this high-risk cohort.
Main Methods:
- Linked California birth records (2006) with CPS records for 23,871 infants.
- Utilized latent class analysis (LCA) to identify risk classes based on re-report probability.
- Followed infants for 5 years to determine subsequent maltreatment reports.
Main Results:
- A four-class LCA model best fit the data, with re-report probabilities ranging from 44% (lowest risk) to 78% (highest risk).
- Lack of established paternity and delayed/absent prenatal care were associated with medium- and highest-risk classes.
- Initial neglect allegations and prior family CPS involvement predicted higher re-report risk in the highest-risk class.
Conclusions:
- Statistical modeling, specifically LCA, can identify infants at heightened risk for future child protective services contact.
- Birth characteristics and initial maltreatment report details are key indicators for risk stratification.
- Findings can inform targeted interventions to prevent future child 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.
Related Concept Videos
01:20Attachment
01:21The Nativist Approach
01:30Socioemotional Development during Infancy
Primary Temperament Types