Longitudinal Understanding of Child Maltreatment Report Risks

Hyunil Kim, Brett Drake, Melissa Jonson-Reid

Research output: Contribution to journalArticlepeer-review


Background: Child maltreatment reports (CMR) are both common and strongly associated with various negative outcomes. Objective: To examine CMR risks by child age, early childhood context, current/cumulative economic status (welfare receipt), race, and other risk factors with a longitudinal dataset. Participants and Setting: The CAN sample included 2,111 children having a CMR ≤ age 3, suggestive of a harmful early childhood context. The AFDC sample included 1,923 children having AFDC but no CMR ≤ age 3, suggestive of early childhood protective factors despite poverty. Methods: We estimated the CMR likelihood at each age from 1–17 years based on various risk factors while following up children from 1995–2009. Results: During follow-up, CMR likelihoods were substantially higher for the CAN sample than for the AFDC sample. The age-CMR relationship was strongly negative for the CAN sample (OR = 0.87, 95% CI = 0.86–0.88). This relationship was weaker for the AFDC sample (OR = 0.92, 0.89–0.95) and became non-significant for children who exited welfare. Current welfare receipt remained a strong predictor of CMR likelihoods for both CAN (OR = 2.32, 1.98–2.71) and AFDC (OR = 2.08, 1.61–2.68) samples. Prior welfare receipt moderately increased CMR likelihoods among those not currently on welfare. Controlling for other risk factors, White children had the highest likelihood of CMR. Other child and parent level vulnerabilities also increased CMR risk over time. Conclusions: This study highlights the importance of longitudinal analytic approaches and the utility of cross-sector administrative data in improving our ability to understand and predict CMRs over time.

Original languageEnglish (US)
Article number104467
JournalChild Abuse and Neglect
StatePublished - Jun 2020


  • Administrative data analysis
  • Child abuse
  • Child maltreatment
  • Child protective services
  • Multilevel growth curve model

ASJC Scopus subject areas

  • Pediatrics, Perinatology, and Child Health
  • Developmental and Educational Psychology
  • Psychiatry and Mental health


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