Does child abuse predict a population segment with large economic burden?

Jana Runze; Geertjan Overbeek; Annemarie I Luik; Margreet Ten Have

doi:10.1016/j.puhe.2024.09.013

Does child abuse predict a population segment with large economic burden?

Public Health. 2024 Sep 18:236:347-353. doi: 10.1016/j.puhe.2024.09.013. Online ahead of print.

Authors

Jana Runze¹, Geertjan Overbeek², Annemarie I Luik³, Margreet Ten Have⁴

Affiliations

¹ Research Institute of Child Development and Education, University of Amsterdam, the Netherlands. Electronic address: j.runze@uva.nl.
² Research Institute of Child Development and Education, University of Amsterdam, the Netherlands.
³ Epidemiology, Trimbos Institute - The Netherlands Institute of Mental Health and Addiction, Utrecht, the Netherlands; Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
⁴ Epidemiology, Trimbos Institute - The Netherlands Institute of Mental Health and Addiction, Utrecht, the Netherlands.

PMID: 39299089
DOI: 10.1016/j.puhe.2024.09.013

Abstract

Objectives: The enormous societal and individual consequences of mental health disorders and detrimental health behaviours in the general population are of paramount concern. Many argue that 'prevention is the best cure', pushing for the implementation of early (preventive) interventions. Key questions regarding early interventions include which population segment to target for screenings and what information these screenings should focus on. In line with previous efforts, this study aimed to identify which population segment holds the majority (≥ 80 %) of different economically costly outcomes in society, and whether child abuse before the age of 16 years predicts being part of that population segment.

Study design: Epidemiological cohort study.

Methods: This study used the Netherlands Mental Health Survey and Incidence Study-2, a Dutch epidemiological cohort study including 6646 adults aged 18-64 years at baseline, spanning four timepoints from 2007 to 2018. Cumulative distributions were computed to identify high-cost population segments of economically costly outcomes in adulthood (i.e., mental and physical health [behaviours], unemployment and work absenteeism). Child abuse was examined as a potential predictor of these segments and the risk of multiple high-cost population segment membership was investigated by conducting Poisson regressions.

Results: A 20 % population segment carried between 42 % and 100 % of economically costly outcomes. Being exposed to more child abuse predicted being in a high-cost population segment, albeit with small effect sizes. Being exposed to more child abuse also predicted belonging to multiple high-cost population segments across different economically costly outcomes.

Conclusions: The study findings have implications for policy makers. Emphasis should be placed on prevention aimed at identifying potential members of multiple high-cost population segments.

Keywords: Child maltreatment; Childhood trauma; Epidemiology; Health; Mental disorders; Population-based study.