Back

Childhood Adversity and Risk-taking Behaviors in Youth using National Representative Emergency Room Admission and Survey Data

Lichtenberg, B. N.; De Vries, T. R.; Ekstroem, C. T.; Rod, N. H.; Nielsen, J.

2026-06-26 public and global health
10.64898/2026.06.24.26356317 medRxiv
Show abstract

Background Childhood adversity can affect propensity to risk-taking behaviors. We aim to investigate the relation between childhood adversity and risk-taking behaviors in youth using emergency room (ER) admissions and survey data. Method Using the DANLIFE study, we included 1.2 million Danes. Individuals were assigned into five groups based on childhood adversity exposure from ages 0 to 15 years. We applied survival analyses on repeated outcomes to model ER-admissions due to substances, violence and unintentional injury in the full cohort between ages 16 and 24. We applied logistic regression models to weighted survey data on frequent binge drinking, cannabis use, drug use, and unsafe sex in a nested subsample of 34,064 18 year olds from the Danish National Birth Cohort. Results The high adversity group was at highest risk of ER-admissions due to substances (HR=3.27, 95% CI [3.10, 3.46]), violence (HR=2.67, 95% CI [2.58, 2.76]) and unintentional injuries (HR=1.30, 95% CI [1.28, 1.33]). In the nested subsample, the high adversity was at highest risk of cannabis use (OR=1.59, 95% CI [1.21, 2.09]), drug use (OR=2.44, 95% CI [1.71, 3.49]) and unsafe sex (OR=1.72, 95% CI [1.34, 2.22]), but at lower risk of frequent binge drinking (OR=0.57, 95% CI [0.37, 0.87]). Conclusion These findings highlight how childhood adversity is associated with increased engagement in and harm from risk-taking behaviors. To prevent inequalities in health in youth, there is a need for interventions and policies that promote child welfare, as well as targeted support for youth with harmful behavioral patterns.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.