California's Most Vulnerable Parents: When Maltreated Children Have Children

This report takes an in-depth look at the intersection between teen births, child maltreatment, and involvement with the child protection system. Putnam-Hornstein, along with other researchers at USC and the University of California, Berkeley, linked and then analyzed roughly 1.5 million California birth records and 1 million CPS records, with a second phase of research focusing on the maltreatment risk of children born to adolescent mothers.In 2012, California became one of the first states in the nation to extend foster youth status until age 21. Different programs and services will likely be required to adequately respond to the needs and circumstances of non-minor youth who remain in the foster care system, particularly in the area of parenting supports. This report finds that as many as one in three female youth in California may be parenting by the time they exit the foster care system on their 21st birthday

Policy Recommendations for Meeting the Grand Challenge to Harness Technology for Social Good

This brief was created forSocial Innovation for America’s Renewal, a policy conference organized by the Center for Social Development in collaboration with the American Academy of Social Work & Social Welfare, which is leading theGrand Challenges for Social Work initiative to champion social progress. The conference site includes links to speeches, presentations, and a full list of the policy briefs

What Does Child Protective Services Investigate as Neglect? A Population-Based Study

Most child protective services (CPS) investigations involve allegations of neglect. Broad and vague definitions have led to concerns that CPS-investigated neglect is driven by poverty-based material hardship. In a representative sample of 295 neglect investigations in California in 2017, structured data and narrative text fields were used to characterize the types of neglect and concurrent parental risk factors investigated by CPS and to assess the rate and nature of investigated physical neglect, defined as inadequate food, housing, or hygiene. The most common types of neglect were inadequate supervision (44%) and failure to protect (29%), followed by physical neglect (14%). Common risk factors identified in neglect investigations were parental substance use (41%), domestic violence (21%), mental illness (18%), and co-reported physical or sexual abuse (29%). Nearly all investigations of physical neglect (99%) included concerns related to substance use, domestic violence, mental illness, co-reported abuse or an additional neglect allegation (i.e., abandonment). Given concerns identified in neglect investigations, economic supports are likely insufficient without an array of behavioral-health supports

The Children’s Data Network

The Children's Data Network (CDN) is a data and research collaborative focused on the linkage and analysis of administrative records. In partnership with public agencies, philanthropic funders, affiliated researchers, and community stakeholders, we seek to generate knowledge and advance evidence-rich policies that improve the health, safety, and well-being of the children of California. Given our experience negotiating access to and working with existing administrative data (and importantly, data stewards), the CDN has demonstrated its ability to perform cost-effective and rigorous record linkage, answer time-sensitive policy- and program-related questions, and build the public sector's capacity to do the same. Owing to steadfast and generous infrastructure and project support, close collaboration with public partners, and strategic analyses and engagements, the CDN has promoted a person-level and longitudinal understanding of children and families in California and in so doing, informed policy and program development nationwide. We sincerely hope that our experience—and lessons learned—can advance and inform work in other fields and jurisdictions

Do "Accidents" Happen? An Examination of Injury Mortality Among Maltreated Children

This dissertation is based on a unique dataset constructed by probabilistically linking records across three independent sources of data from California: 1) vital birth records, 2) administrative child protective service records, and 3) vital death records. The final dataset captures 4.3 million children born in California between 1999 - 2006 and includes maltreatment allegation information for over 500,000 children who were reported to child protective services (CPS), as well as death reports on 2,000 children who were fatally injured before age five. Three research questions were examined in the context of a prospective birth cohort analysis: 1) Is a referral to child protective services an independent risk factor for injury mortality? 2) Is allegation disposition associated with injury fatality risk? 3) Does injury fatality risk vary across maltreatment allegation types? To answer these three questions, a series of multivariate survival models were specified. Separate models were estimated for overall risk of injury death, risk of unintentional injury death, and risk of intentional injury death. Findings indicate that an earlier report to CPS is the strongest predictor of a child's injury death during the first five years of life. Children previously reported for maltreatment died from accidental injuries at twice the rate of their unreported, demographically similar peers, and from intentional injuries at five times the rate. After adjusting for other characteristics, children whose report of maltreatment was evaluated out without an in-person investigation by CPS died of injuries at significantly higher rates than children who had never been reported. Children with a substantiated allegation of maltreatment and no foster care placement died of intentional injuries at over 10 times the rate of children who had not been reported. Placement in foster care for even a single day was protective. Children with a prior allegation of physical abuse died from injuries at rates that were notably higher than not only unreported children, but also children reported for reasons of sexual abuse, neglect, or other forms of maltreatment. When only intentional injury fatalities were modeled, a prior allegation of physical abuse was associated with a rate of death that was 38 times that of children who had not been reported.This study represents the most rigorous longitudinal analysis of mortality outcomes following a report to CPS to date, with several key implications for practice and policy emerging. First, these data underscore that a child's report to CPS is not random, nor is it simply a function of poverty. Rather, a report to CPS signals a level of risk, including a risk of death, that is greater than sociodemographic factors would alone predict. A second and related point is that children evaluated out after a CPS hotline call reflect a group whose risk of injury death is far greater than their unreported sociodemographic peers. The decision to screen these children out without an investigation, under the logic that these children were assessed to be at no greater risk of harm than other demographically similar children, is not supported by the empirical evidence generated from this study. Third, these data highlight that although there has been a recent emphasis on the unmet service needs of children reported for neglect, it is young children reported for physical abuse who face the greatest risk of death. Given that physical abuse allegations represent a minority of reports received by CPS, these data suggest that a different protocol for investigating and intervening in cases in which physical abuse is alleged may be justified. Finally, the finding that a prior allegation of maltreatment is the single greatest predictor of not just intentional injury death, but also unintentional injury death, lends support to calls that have been made for child welfare services to be pursued under a broader, public health-oriented agenda, focused on the prevention of all manners of injury death

Predictors of child protective service contact between birth and age five: An examination of California's 2002 birth cohort

This study utilizes population-level birth data to describe those children who may be at greatest risk of maltreatment during the first five years of life. Based on a unique dataset constructed by linking California's administrative child welfare data to statewide vital birth records, a cohort study design was employed to track reports of maltreatment involving children born in 2002. Twelve variables captured in the birth record were selected for analysis. Generalized Linear Models were used to estimate adjusted risk ratios (RR) for each independent variable. Predicted probabilities of CPS contact were computed based on the count of risk factors present at birth. Results suggest that many of the associations previously observed between birth variables and subsequent maltreatment have sustained value in foretelling which children will be reported to CPS beyond infancy. Of the 531,035 children born in California in 2002, 14% (74,182) were reported for possible maltreatment before the age of five. Eleven of the twelve birth variables examined presented as significant predictors of contact with child protective services.Child Welfare Child Maltreatment Birth Cohort Risk Assessment Racial Disparities

Foster care reunification: An exploration of non-linear hierarchical modeling

Hierarchical or multilevel models are based on the same fundamental concepts that apply to simple linear models. The linear forms of these models can be interpreted with relative ease as parameter estimates do not differ in magnitude or interpretation from standard non-hierarchical models. Non-linear hierarchical models, however, are more complex as the introduction of a random intercept means that parameter estimates must be interpreted as "subject-specific" rather than "population-averaged". Depending on the specifics of the data being modeled, these parameters may be very different in magnitude. In this article we provide two examples of non-linear hierarchical modeling using administrative child welfare data. For each example, we estimate the odds of reunification for a cohort of children in California using both standard logistic regression models and random intercept models.Hierarchical models Reunification Clustered data Foster care

Injury and mortality among children identified as at high risk of maltreatment

OBJECTIVES: To determine if children identified by a predictive risk model as at "high risk" of maltreatment are also at elevated risk of injury and mortality in early childhood.METHODS: We built a model that predicted a child's risk of a substantiated finding of maltreatment by child protective services for children born in New Zealand in 2010. We assigned risk scores to the 2011 birth cohort, and flagged children as "very high risk" if they were in the top 10% of the score distribution for maltreatment. We also set a less conservative threshold for defining "high risk" and examined children in the top 20%. We then compared the incidence of injury and mortality rates between very high-risk and high-risk children and the remainder of the birth cohort.RESULTS: Children flagged at both 10% and 20% risk thresholds had much higher postneonatal mortality rates than other children (4.8 times and 4.2 times greater, respectively), as well as a greater relative risk of hospitalization (2 times higher and 1.8 times higher, respectively).CONCLUSIONS: Models that predict risk of maltreatment as defined by child protective services substantiation also identify children who are at heightened risk of injury and mortality outcomes. If deployed at birth, these models could help medical providers identify children in families who would benefit from more intensive supports

An open-source probabilistic record linkage process for records with family-level information: Simulation study and applied analysis.

Research with administrative records involves the challenge of limited information in any single data source to answer policy-related questions. Record linkage provides researchers with a tool to supplement administrative datasets with other information about the same people when identified in separate sources as matched pairs. Several solutions are available for undertaking record linkage, producing linkage keys for merging data sources for positively matched pairs of records. In the current manuscript, we demonstrate a new application of the Python RecordLinkage package to family-based record linkages with machine learning algorithms for probability scoring, which we call probabilistic record linkage for families (PRLF). First, a simulation of administrative records identifies PRLF accuracy with variations in match and data degradation percentages. Accuracy is largely influenced by degradation (e.g., missing data fields, mismatched values) compared to the percentage of simulated matches. Second, an application of data linkage is presented to compare regression model estimate performance across three record linkage solutions (PRLF, ChoiceMaker, and Link Plus). Our findings indicate that all three solutions, when optimized, provide similar results for researchers. Strengths of our process, such as the use of ensemble methods, to improve match accuracy are discussed. We then identify caveats of record linkage in the context of administrative data