The Geography of Child Maltreatment: A Spatiotemporal Analysis Using Bayesian Hierarchical Analysis With Integrated Nested Laplace Approximation

Abstract

This study quantifies the spatiotemporal risk of child abuse and neglect in Los Angeles at the census tract level over a recent 4-year period, identifies areas of increased risk, and evaluates the role of structural disadvantage in substantiated child maltreatment referrals. Child maltreatment data on 83,379 child maltreatment cases in 1,678 census tracts spanning 2006-2009 were obtained from the Los Angeles County Department of Children and Family Services. Substantiated referral counts were analyzed across census tracts with Bayesian hierarchical spatial models using integrated nested Laplace approximations. Results showed that the unadjusted yearly rate of child abuse and neglect held fairly steady over the study period decreasing by only 2.57%. However, the temporal term in the spatiotemporal model reflected a downward trend beginning in 2007. High rates of abuse and neglect were predicted by several neighborhood-level measures of structural burden. Every 1-unit decrease in the social vulnerability index reduced the risk of child abuse and neglect by 98.3% (95% CrI = 1.869-2.1042) while every 1-unit increase in the Black-White dissimilarity index decreased child abuse and neglect risk by 70.6%. The interaction of these variables demonstrated the protective effect of racial heterogeneity in socially vulnerable neighborhoods. No such effect was found in neighborhoods characterized by low levels of vulnerability. Population-based child abuse and neglect prevention and intervention efforts should be aided by the characteristics of neighborhoods that demonstrate strong spatial patterns even after accounting for the role of race and place.

A spatiotemporal analysis of the impact of COVID-19 on child abuse and neglect in the city of Los Angeles, California

“…The use of social disorganization theory emphasizing the role of social conditions and neighborhood economic status to explain why areas with these structural risk factors have higher rates of child abuse ( Coulton, Korbin, Su, & Chow, 1995 ; Coulton, Korbin, & Su, 1999 ; Freisthler, Midanik, & Gruenewald, 2004 ; Klein & Merritt, 2014 ; Schuck & Widom, 2005 ) can also explain why some communities are disproportionately impacted by COVID-19. As well, research has uncovered that these structural inequalities result from spatial heterogeneity in the risk factors that underliechild maltreatment in different geographical contexts ( Barboza, 2019 ). One study examining spatial ‘regimes’ of child maltreatment allegations in San Diego, for example, found different clusters of neighborhoods with different risk factors meaning that the same risk factors were differentially associated with child welfare in different areas.…”

Social determinants of health and maxillofacial injuries in children and adolescents victims of violence: A novel GIS‐based modelling application

“…To date, no specific research has been identified for this specific topic, making further comparisons impossible. Some studies restricted to the evaluation of the spatial distribution of cases of child maltreatment have been recently conducted in the USA, but without considering maxillofacial traumas as exposure markers . It is important to carry out studies considering maxillofacial traumas as a result of exposition to physical violence, since this pattern of injury may characterize a mode of insidious violence, permeated by humiliation and disrespect, which usually occurs silently, and may represent the start point for a more severe or fatal outcome …”

“…In a study carried out in Los Angeles, California, the spatiotemporal risk of child abuse and neglect was assessed over a 4‐year period. The authors reported that the unadjusted annual rate of child abuse and neglect remained fairly stable during the study period, decreasing only by 2.57%.…”

“…Some studies restricted to the evaluation of the spatial distribution of cases of child maltreatment have been recently conducted in the USA, but without considering maxillofacial traumas as exposure markers. 23,24 It is important to carry out studies considering maxillofacial traumas as F I G U R E 3 Spatial autocorrelation analysis for cases of violence against children and adolescents that resulted in oral and maxillofacial trauma using the Getis-Ord (Gi *) statistics. A, Getis-Ord cluster map showing hot and coldspots.…”

A Secondary Spatial Analysis of Gun Violence near Boston Schools: a Public Health Approach

“…A large body of work has shown that violence is spatially clustered across space and remains stable over time depending on local context [10][11][12][13][14]. In Boston, for instance, most fatal and non-fatal shootings incidents derive from only a fraction of the city's census block groups and local trends in gun violence have been attributed to a few Bhot spot^locations in disadvantaged urban neighborhoods [11][12][13].…”