We used National Child Abuse and Neglect Data System and Census data to examine Black–White and Hispanic–White disparities in reporting, substantiation, and out-of-home placement both descriptively from 2005–2019 and in multivariate models from 2007–2017. We also tracked contemporaneous social risk (e.g., child poverty) and child harm (e.g., infant mortality) disparities using non-child protective services (CPS) sources and compared them to CPS reporting rate disparities. Black–White CPS reporting disparities were lower than found in non-CPS risk and harm benchmarks. Consistent with the Hispanic paradox, Hispanic–White CPS reporting disparities were lower than risk disparities but similar to harm disparities. Descriptive and multivariate analyses of data from the past several years indicated that Black children were less likely to be substantiated or placed into out-of-home care following a report than White children. Hispanic children were slightly more likely to be substantiated or placed in out-of-home care than White children overall, but this difference disappeared in multivariate models. Available data provide no evidence that Black children were overreported relative to observed risks and harms reflected in non-CPS data. Reducing reporting rates among Black children will require addressing broader conditions associated with maltreatment.

The past several years have seen calls from QuantCrit scholars to “disaggregate” samples into same-race groups. To date, however, there has been no attempt to empirically evaluate the benefits of disaggregation within a child welfare sample. Using a child maltreatment dataset derived from the National Child Abuse and Neglect Data System and Census data, we empirically evaluate the utility of employing sample disaggregation (in which separate records are created for White, Black and Latino populations in each county) as well as variable creation disaggregation (in which we avoid using “full county” economic measures, but instead employ “same race/ethnicity” measures). Using model fit and convergence with findings from individual-level studies as evaluation metrics, we find that both kinds of disaggregation are demonstrably beneficial. We recommend that sample and variable disaggregation be considered by any future researchers using national geographically structured child maltreatment data.

Purpose: This paper presents a re-analysis of the National Child Abuse and Neglect Data System (NCANDS) data presented by Briggs et al. (2022). Methods: We review five components of that article: The aims, variables, analytic strategy, analysis, and conclusions. Results: We conclude that several of the NCANDS variables used are invalid at the national level, and that this is sufficient to call the research into question. We find concerning issues in analytic strategy and analysis as well, many stemming from a failure to account for the serious underreporting of services in NCANDS, and the wide variability in data quality and consistency across states. We also found what we consider to be issues with their statistical analysis. Discussion: The reanalysis presented in this article shows no pattern of disparate within Child Protective Services (CPS) outcomes by race and, therefore, no support for the Briggs et al. claim of pervasive anti-Black racism within the CPS system.

Purpose: The National Child Abuse and Neglect Data System (NCANDS) Child File, the only national dataset cataloging child maltreatment reports. It includes variables representing economic distress frequently used in published research. At the national level, these variables are demonstrably implausible, substantially underestimating economic distress. Method: This paper reviews recent work using these variables, analyzes the NCANDS data directly, demonstrates why the economic variables in NCANDS are unusable at a national level, and provides recommendations for incorporating economic measures using NCANDS. Results: We find 19 articles that have used these variables within the past 10 years. Most states provide implausible estimates. Economic measures can be incorporated into NCANDS data by either subsetting to s states with plausible estimates of these variables in given years, or appending county-level economic Census data. Discussion: Without addressing these variables’ issues in plausibility, use of them will yield biased estimates.