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Growing service demand is a perennial challenge for child protection systems, with the number of children in out-of-home care (OOHC) increasing in the USA and Australia since earlier in the decade. Placement in OOHC is costly and its effects on outcomes for children and youth are ambiguous. While factors influencing placement in OOHC have been studied extensively, few have applied mathematical dynamical models of entire child protection systems, from maltreatment investigations to re-entry following children’s exits. This study uses population dynamics modelling to analyze and understand broad mechanisms driving child welfare system behavior. It generates a theoretical model that identifies and unravels key underlying dynamical structures of child protection subpopulations, simulates their interactions, and elucidates effects such as “churn” – repeated entry into and exit from care.
Methods
We constructed a population dynamics model of a generic child protection system that can be applied to jurisdictions that are similar in structure and/or processes. The model consists of four subpopulations: 1. Children who have not yet been involved with child protection services (CPS); 2. Children who were the subject of at least one investigation but never placed in OOHC; 3. Children currently in OOHC; and 4. Children who exited OOHC. Analytical solutions of the systems equations were derived to examine interactions among the subpopulations and various flow rate regimes. Numerical simulations were carried out using annual aggregate child protection population data for New South Wales (NSW), Australia and California, USA to understand the morphology of the subpopulations, including the effect of “churn.”
Results
Simulations of the four subpopulations for NSW and California were able to replicate the observed figures of child protection data in 2016, with repeat clients in NSW accounting for 71%, 97% and 97% of children who were subjects of investigations, children entering OOHC, and children in OOHC, respectively. The corresponding figures for California were 70%, 21% and 27%. We also established the lower bounds of investigation rates for new clients and repeat clients, showing that repeat clients were much more likely to receive an investigation upon being referred to CPS. We showed that for model simulations to replicate the highly lopsided structure in NSW where 97% of the children in OOHC were repeat clients, the notification rate for repeat clients could be up to 7 times that for new clients.
Conclusions
Simulations showed that repeat clients are much more likely to be the subjects of child protection notifications, and to be investigated upon notifications to CPS, as compared to new clients. The results call for serious reflection on the use of past maltreatment history as a predictor and the extent to which the use of such tools can fuel repeated involvement with CPS. The population dynamics approach in this study can be applied to jurisdictions with similar practices. Further, the model may help decision makers and researchers identify specific decision points in their attempts to ameliorate rising demands for CPS, and to evaluate the impact of specific policies on child protection populations.