Applying Neural Network Models to Predict Recurrent Maltreatment in Child Welfare Cases with Static and Dynamic Risk Factors

Risk assessment in child welfare has a long tradition of being based on models that assume the likelihood of recurrent maltreatment is a linear function of its various predictors: Gambrill & Shlonsky, 2000). Despite repeated testing of many child, parent, family, maltreatment incident, and service delivery variables, no consistent set of findings have emerged to describe the set of risk and protective factors that best account for increases and decreases in the likelihood of recurrent maltreatment. Shifts in predictors\u27 statistical significance, strength, and direction of effects coupled with evidence of risk assessment models\u27 poor predictive accuracy have led to questions regarding the fit between assumptions of linearity and the true relationship between the likelihood of recurrent maltreatment and its predictors: Gambrill & Shlonsky, 2000, 2001; Knoke & Trocmé, 2005). Hence, this dissertation study uses a distinctly nonlinear approach to modeling the likelihood of recurrent maltreatment by employing a combination of random forest and neural network models to identify the predictors that best explain the risk of recurrent maltreatment. The risk of recurrent maltreatment was assessed for a cohort of children living in a large Midwestern metropolitan area who were first reported for maltreatment between January 1, 1993 and January 1, 2002. Administrative child welfare records for 6,747 children were merged with administrative records from income maintenance, mental health, special education, juvenile justice, and criminal justice systems in order to identify the effects that various public sector service system contacts have on the risk of recurrent maltreatment. Each child was followed for a period of at least seven years to identify the risk of recurrent maltreatment in relationship to a second report for maltreatment. Post-hoc analyses comparing the predictive validity of the neural network model and a binary logistic regression model with random intercepts shows that the neural network model was superior in its predictive validity with an area under the ROC curve of 0.7825 in comparison with an area under the ROC curve of 0.7552 for the logistic regression model. Additional post-hoc analyses provided empirical insight into the four prominent risk factors and four risk moderating service variables that best explain variation in the risk of recurrent maltreatment. Specifically, the number of income maintenance spells received, community-level poverty, the child\u27s age at the first maltreatment report, and the parent\u27s status as the perpetrator of the first maltreatment incident defined 21 risk-based groups where the average probability of recurrent maltreatment was dependent upon values for the four primary risk factors, and the risk of maltreatment was moderated by juvenile court involvement, special education eligibility, receipt of CPS family centered services, and the child\u27s receipt of a mental health/substance abuse service in the community. Findings are discussed within a Risk-Need-Responsivity theory of service delivery: Andrews & Bonta, 2006), which links the empiricism of risk assessment with the clinical implementation of a preventive service delivery plan through the identified modifiable risk factors that drive the likelihood of recurrent maltreatment

Risk factors of subsequent allegations of child maltreatment

Providing effective risk assessments of the likelihood of recurrent maltreatment is one of the key responsibilities of child welfare services in preventing subsequent maltreatment of a child who is a victim of abuse or neglect. This dissertation aimed to address foundational research questions about risk factors of subsequent allegations of child maltreatment that have been highlighted in theory but understudied in prior research. Study 1 aimed to synthesize results from prior research to understand the effect sizes of risk factors of subsequent allegations of child maltreatment reports. Based on the decision-making ecology framework (Fluke et al., 2020), risk factors from 69 studies were analyzed using meta-analysis. The study found several risk factors at child (e.g. disabilities, younger age), caregiver (childhood abuse history, parenting, mental health and substance abuse problems), family (welfare assistance receipt, domestic violence), and case-level (prior reports, neglect) that were associated with increased risk of re-rereports. Study 2 aimed to understand how accurately can maltreatment re-reports be predicted using machine learning approaches and to explore complex interactions and non-linear relationships among risk factors. Using longitudinal data from NSCAW, regularized logistic regression and random forests were used to analyze data of 59 risk factors collected from 2,162 children to predict subsequent maltreatment re-report. The best predictive performance was 67% (classification accuracy) which improved over the benchmark rate. Key interactions and non-linear relationships were found for risk factors such as harsh parenting, caregiver age, and prior maltreatment reports. Study 3 aimed to understand how community factors such as economic disadvantage, crime, and supportive services influence trends in county-level maltreatment re-reports. Using 2015-2017 data from administrative sources, Bayesian spatiotemporal models found that counties with higher percentages of unemployment population and single-parent households had higher re-reports. Supportive services in the community such as substance abuse treatment facilities had beneficial effects on reducing re-reports. Overall, this dissertation provided evidence for key risk factors of maltreatment re-reports, identified potential complex interactions among risk factors, introduced the use of innovative methods for predictive modeling, and made significant contributions to child welfare risk research that can inform practice, policy, and interventions to reduce child maltreatment re-reports.Doctor of Philosoph

Metoder for innhenting, systematisering og vurdering av informasjon i barnevernets undersøkelsessaker

Hovedbudskap Kunnskapssenteret for helsetjenesten ved Seksjon for velferdstjenester fikk i oppdrag av Barne‐, ung‐ doms‐ og familiedirektoratet å utføre et systematisk litteratursøk med påfølgende sortering av mulig re‐ levante publikasjoner. Oppdraget var å identifisere ulike metoder for innhenting, systematisering og vurdering av informasjon i barnevernets undersø‐ kelsessaker. Metode Vi utarbeidet søkestrategi for et systematisk litteratursøk. Det ble søkt i samfunnsvitenskaplige og medisinske databaser og i Google og Google Scholar. Søket ble utført i november og desember 2015. To forskere gikk uavhengig av hverandre gjennom identifiserte referanser og vurderte relevans i forhold til inklusjonskriteriene. Resultater • Litteratursøket gav 12 833 referanser etter dublettsjekk samt 428 leste referanser fra BASE, Google og Google Scholar • Vi identifiserte totalt 132 relevante referanser - 1 referanse var en mulig systematisk oversikt - 41 referanser omhandlet enkeltinstrumenter - 15 referanser omhandlet flere instrumenter - 39 referanser omhandlet enkeltmodeller - 18 referanser omhandlet flere modeller - 18 referanser omhandlet andre typer studier I dette systematiske litteratursøket med sortering har vi ikke lest publikasjonene i fulltekst og dermed ikke vurdert studienes kvalitet. Vi har kun sortert referansene etter type, basert på sammendragene.Methods We developed a search strategy for a systematic literature search. In November and December 2015, the search was carried out in social and medical scientific databases and in Google and Google Scholar. Two researchers independently screened all identified references to assess inclusion according to predefined criteria. Results • The literature search resulted in 12 833 references after duplicates were removed and 428 read references from BASE, Google and Google Scholar • In total, we identified 132 relevant references - 1 reference was a possible systematic review - 41 references dealt with single instruments - 15 referanser dealt with several instruments - 39 referanser dealt with single models - 18 referanser dealt with several models - 18 referanser dealt with other kinds of studies In this systematic literature search we have not read the publications in full and hence not critically evaluated the studies. We have only sorted the references by type, based on the abstracts.publishedVersio

Ethics review of machine learning in children’s social care

This report: – Reviews the ethical criteria that would make the use of machine learning (ML) in children’s social care (CSC) justifiable and examines the problematic contexts in which such criteria may not be met; – Identifies requirements and best practice for the responsible use of ML in CSC; – Presents recommendations for a way forward

Assessing parental capacity to change when children are on the edge of care: an overview of current research evidence

Assessing Parental Capacity to Change when Children are on the Edge of Care is an overview of current research evidence, bringing together some of the key research messages concerning factors which promote or inhibit parental capacity to change in families where there are significant child protection concerns. It is intended to serve as a reference resource for social workers in their work to support families where children’s safety and developmental functioning are at risk. Its purpose is also to assist social workers and children’s guardians in delivering more focused and robust assessments of parenting capability and parental capacity to change, and assist judges and other legal professionals in evaluating the quality of assessment work in court proceedings. The report brings together research findings from a wide range of disciplines, which are not otherwise readily available in one location for social workers, family justice professionals and other practitioners with safeguarding responsibilities. [Continues

Activation of the pro-resolving receptor Fpr2 attenuates inflammatory microglial activation

Poster number: P-T099 Theme: Neurodegenerative disorders & ageing Activation of the pro-resolving receptor Fpr2 reverses inflammatory microglial activation Authors: Edward S Wickstead - Life Science & Technology University of Westminster/Queen Mary University of London Inflammation is a major contributor to many neurodegenerative disease (Heneka et al. 2015). Microglia, as the resident immune cells of the brain and spinal cord, provide the first line of immunological defence, but can become deleterious when chronically activated, triggering extensive neuronal damage (Cunningham, 2013). Dampening or even reversing this activation may provide neuronal protection against chronic inflammatory damage. The aim of this study was to determine whether lipopolysaccharide (LPS)-induced inflammation could be abrogated through activation of the receptor Fpr2, known to play an important role in peripheral inflammatory resolution. Immortalised murine microglia (BV2 cell line) were stimulated with LPS (50ng/ml) for 1 hour prior to the treatment with one of two Fpr2 ligands, either Cpd43 or Quin-C1 (both 100nM), and production of nitric oxide (NO), tumour necrosis factor alpha (TNFα) and interleukin-10 (IL-10) were monitored after 24h and 48h. Treatment with either Fpr2 ligand significantly suppressed LPS-induced production of NO or TNFα after both 24h and 48h exposure, moreover Fpr2 ligand treatment significantly enhanced production of IL-10 48h post-LPS treatment. As we have previously shown Fpr2 to be coupled to a number of intracellular signaling pathways (Cooray et al. 2013), we investigated potential signaling responses. Western blot analysis revealed no activation of ERK1/2, but identified a rapid and potent activation of p38 MAP kinase in BV2 microglia following stimulation with Fpr2 ligands. Together, these data indicate the possibility of exploiting immunomodulatory strategies for the treatment of neurological diseases, and highlight in particular the important potential of resolution mechanisms as novel therapeutic targets in neuroinflammation. References Cooray SN et al. (2013). Proc Natl Acad Sci U S A 110: 18232-7. Cunningham C (2013). Glia 61: 71-90. Heneka MT et al. (2015). Lancet Neurol 14: 388-40

Pathways to Offending: Domestic Sex Trafficking

Multidisciplinary professionals across criminal justice, public policy, education, and health and human services have all attempted to understand the complex phenomenon of sex trafficking to assist victims, correct offenders, and prevent future abuse. However, current research has struggled to agree on terms, definitions of terms, best measures of prevalence, and recommendations to address sex trafficking in the United States. This review of current literature aims to offer a synthesized framework to conceptualize domestic sex trafficking perpetrator behaviors (what they do), their uses of force, fraud, and coercion (how they do it), and their motivations and justifications/rationalizations for those behaviors (why they do it). The resulting conceptual framework can serve as a roadmap to guide the development of tailored assessment instruments and evidence-based treatments as well as improve community prevention and education efforts