Literature DB >> 30776738

County-level socioeconomic and crime risk factors for substantiated child abuse and neglect.

Matthew C Morris1, Miriam Marco2, Kathryn Maguire-Jack3, Chrystyna D Kouros4, Wansoo Im5, Codi White6, Brooklynn Bailey5, Uma Rao7, Judy Garber8.   

Abstract

Rates of substantiated child abuse and neglect vary significantly across counties. Despite strong cross-sectional support for links between social-contextual characteristics and abuse and neglect, few longitudinal studies have tested relations between these risk factors and substantiated rates of abuse/neglect. The goal of this study was to identify county-level socioeconomic and crime factors associated with substantiated abuse/neglect rates over 13 years (2004-2016). Annual county-level data for Tennessee, obtained from the KIDS COUNT Data Center, included rates of substantiated child abuse and neglect, children's race and ethnicity, births to unmarried women, teen birth rate, children in families receiving Supplemental Nutrition Assistance Program (SNAP) benefits, and children in families receiving Temporary Assistance for Needy Families. Annual county-level crime report data, obtained from the Tennessee Incident Based Reporting System, included sexual offenses, non-sexual assaults, stalking incidents, thefts, property damage, and drug-related offenses. Bayesian spatio-temporal models indicated that substantiated child abuse and neglect rates were independently and positively associated with teen birth rates, percentages of births to unmarried mothers, drug-related offenses, and percentages of children receiving SNAP benefits. In contrast, substantiated child abuse and neglect rates were negatively associated with percentages of African-American youth. The findings highlighted distinct demographic, socioeconomic, and crime factors associated with substantiated child abuse and neglect rates and have the potential to enhance identification of high-risk counties that could benefit from targeted abuse and neglect prevention efforts.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Abuse; Bayesian spatio-temporal; Child; County; Crime; Neglect

Mesh:

Year:  2019        PMID: 30776738      PMCID: PMC6422336          DOI: 10.1016/j.chiabu.2019.02.004

Source DB:  PubMed          Journal:  Child Abuse Negl        ISSN: 0145-2134


  52 in total

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