Preventing Adolescent Maltreatment: A Focus on Child Welfare, Juvenile Justice, and Sexual Exploitation

Dierkhising, Carly B.; Geiger, Jennifer M.; Hurst, T.; Panlilio, Carlomagno C.; Schelbe, Lisa

doi:10.1007/978-3-319-16327-7_3

Cited by 2 publications

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“…Results of the ROC analyses indicated that the screener was good at discriminating high versus low risk for CSE (area under the curve = 0.85 [95% CI, 0.77-0.92]; P < .001; Figure ). (Additional tables with different values for Se, Sp, and J across different test totals [ie, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] are available from the authors upon request. )…”

Section: Resultsmentioning

confidence: 99%

“…It is noteworthy, however, that child welfare involvement also increases the risk for CSE. 11,12 This risk often relates to the traumatic experiences that precipitated entry into the child welfare system, along with related factors such as housing instability and continued experiences of maltreatment. 11,13 Therefore, the child welfare system likely serves many children and adolescents who are at high risk for CSE.…”

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confidence: 99%

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Evaluating and Validating the Classification Accuracy of a Screening Instrument to Assess Risk for Commercial Sexual Exploitation of Child Welfare–Involved Children and Adolescents

Panlilio

Dierkhising

Richardson³

et al. 2022

Public Health Rep

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Objectives: Identifying children and adolescents within child welfare at risk for commercial sexual exploitation (CSE) can ensure referrals to appropriate services. However, screening measures to understand the prevalence of CSE are missing in child welfare. We evaluated the classification accuracy of a screener developed for the purpose of this study, guided by the Sexual Exploitation among Youth (SEY) risk assessment framework used in practice with child welfare–involved young people, (1) to identify young people at high versus low risk for experiencing CSE and (2) to estimate the prevalence of CSE risk for child welfare–involved children and adolescents. Methods: We used extant data from the National Survey of Child and Adolescent Well-being study with a nationally representative sample of children and adolescents aged 11-17 years (n = 1054) investigated by child welfare from February 2008 to April 2009. The 26-item screener showed acceptable reliability (α = .73) and test-criterion validity evidence using a CSE proxy outcome (ie, narrowly defined as being paid for sexual relations). We used the receiver-operating curve to classify risk and calculate the optimal cutoff score. Results: Higher scores on the SEY screener (range, 0-20 points) increased the odds of experiencing CSE by 34%. The screener was good at discriminating CSE risk at the 6-point cutoff, with 26.7% of child welfare–involved young people identified as being at high risk for CSE. Conclusions: Given the absence of accurate prevalence rates of CSE risk in the population, a theoretical cutoff index using an established method can provide an objective decision on how to distinguish risk levels. Prevalence estimates for CSE risk highlight the need for systematic screening in child welfare to identify and provide services for young people at risk.

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