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. 2019 Dec:98:104185.
doi: 10.1016/j.chiabu.2019.104185. Epub 2019 Sep 23.

Co-occurrence of adverse childhood experiences and its association with family characteristics. A latent class analysis with Dutch population data

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Free article

Co-occurrence of adverse childhood experiences and its association with family characteristics. A latent class analysis with Dutch population data

Carlijn Bussemakers et al. Child Abuse Negl. 2019 Dec.
Free article

Abstract

Background: Although adverse childhood experiences (ACEs) are relatively common among children, there is limited knowledge on the co-occurrence of such experiences.

Objective: The current study therefore investigates co-occurrence of childhood adversity in the Netherlands and whether specific clusters are more common among certain types of families.

Participants and setting: Representative data from the Family Survey Dutch population 2018 (N = 3,128) are employed.

Method: We estimate Latent Class Analysis (LCA) models to investigate co-occurrence of ACEs. As ACEs we examine maltreatment, household dysfunction, demographic family events, as well as financial and chronic health problems. Gradual measures for maltreatment and financial problems are studied to make it possible to differentiate with regard to the severity of experiences.

Results: Our results show that four ACE clusters may be identified: 'Low ACE', 'Moderate ACE: Household dysfunction', 'Moderate ACE: Maltreatment' and 'High ACE'. Regression analyses indicated that mother's age at first childbirth and the number of siblings were related to experiencing childhood adversity. We found limited evidence for ACEs to be related to a family's socioeconomic position.

Conclusion: The found clusters of ACEs reflect severity of childhood adversity, but also the types of adversity a child experienced. For screening and prevention of childhood adversity as well as research on its consequences, it is relevant to acknowledge this co-occurrence of types and severity of adversity.

Keywords: Adverse childhood experiences; Clustering; Latent class analysis; Social selection.

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