Two Decades of Child Welfare System Contact in the Global North: A Research Note on Trends in 44 Countries

Abstract

Child maltreatment and child welfare system contact are both associated with an elevated risk of adverse outcomes in childhood, adolescence, and adulthood. Yet, data on variation in system contact are available for only a handful of countries, limiting knowledge about the societal correlates of system contact. As reported in this research note, we identified, collected, and harmonized administrative data on child welfare agency investigations, confirmed maltreatment, and placements into out-of-home care for 44 countries in the Global North. We analyzed 15 sociodemographic factors commonly associated with child maltreatment and child welfare system contact. Results support three core conclusions. First, data are much more available on late-stage system contact (e.g., foster care caseloads) than for early-stage system contact (e.g., investigations). Second, whereas early-stage contact tended to be on the rise in most countries, late-stage contact was stable or declining. Cross-national variation in these trends was generally less substantial than cross-national variation in levels of child welfare system contact, indicating relatively stable cross-national differences. Third, cross-national variation in out-of-home care largely reflected, but was not reducible to, regional and sociocultural variation: we find little evidence for universal drivers of foster care caseloads across the Global North.

Keywords: Administrative data; Child maltreatment; Child welfare; Cross-national research; Foster care.

Fig. 1

Range of data on child protective services investigations (panel a), cases of confirmed maltreatment (panel b), and out-of-home care (panel c) for 44 countries in the Global North, 2000–2020. Source: Roehrkasse et al. (2022).

Fig. 2

Annual incidence of child protective services investigations (panel a), annual incidence of maltreatment confirmed by child protective services (panel b), annual prevalence of children entering out-of-home care (panel c), and point prevalence of children’s residence in out-of-home care (panel d). Note the differing y-axis scales, as well as the axis break in panel d due to Greenland being an outlier. Source: Roehrkasse et al. (2022).

Fig. 3

Point prevalence of children’s residence in out-of-home care, by region/language. Source: Roehrkasse et al. (2022).

Fig. 4

Dendrogram tree showing the hierarchical clustering of countries based on the similarity of the pattern of the out-of-home care residence rate over time (using a dynamic time warping distance measure and the Ward D linkage method). Three major clusters were distinguished; they are indicated by color. The height of each branch is a relative measure that indicates the similarity of countries or groups thereof, with a smaller height between two vertical branches indicating more similarity. The order in which we present countries along the x-axis reflects groupings and emerging clusters but otherwise has no significant meaning (e.g., listing Armenia first and then Ireland is the same as if Ireland were listed first and then Armenia; it would make no difference if the whole dendrogram were mirrored down the middle such that the green group instead appeared on the right side). Source: Roehrkasse et al. (2022).

Fig. 5

Pairwise Pearson correlations between children’s out-of-home care residence rates with socioeconomic indicators within each country (panel a) and a dendrogram tree showing the hierarchical clustering of countries based on the similarity of the correlations calculated using a Euclidean distance measure and Ward’s D linkage method (panel b). The overall measures are not included in clustering because they are the total average correlation across rows (i.e., average within-country correlation) and the total correlation between countries (e.g., global; we averaged observations within each country over time and then calculated the correlation of measures between countries). Red cells indicate highly positive correlations, whereas blue cells indicate highly negative correlations. Variables were grouped by similar context, and countries were ordered by their appearance on the dendrogram. Source: Roehrkasse et al. (2022).