Prevalence, increase and predictors of family violence during the COVID-19 pandemic, using modern machine learning approaches.

Background: We are facing an ongoing pandemic of coronavirus disease 2019 (COVID-19), which is causing detrimental effects on mental health, including disturbing consequences on child maltreatment and intimate partner violence.
Methods: We sought to identify predictors of child maltreatment and intimate partner violence from 380 participants (mean age 36.67 ± 10.61, 63.2% male; Time 3: June 2020) using modern machine learning analysis (random forest and SHAP values). We predicted that COVID-related factors (such as days in lockdown), parents' psychological distress during the pandemic (anxiety, depression), their personality traits, and their intimate partner relationship will be key contributors to child maltreatment. We also examined if there is an increase in family violence during the pandemic by using an additional cohort at two time points (Time 1: March 2020, N = 434; mean age 35.67 ± 9.85, 41.69% male; and Time 2: April 2020, N = 515; mean age 35.3 ± 9.5, 34.33%).
Results: Feature importance analysis revealed that parents' affective empathy, psychological well-being, outdoor activities with children as well as a reduction in physical fights between partners are strong predictors of a reduced risk of child maltreatment. We also found a significant increase in physical punishment (Time 3: 66.26%) toward children, as well as in physical (Time 3: 36.24%) and verbal fights (Time 3: 41.08%) among partners between different times.
Conclusion: Using modernized predictive algorithms, we present a spectrum of features that can have influential weight on prediction of child maltreatment. Increasing awareness about family violence consequences and promoting parenting programs centered around mental health are imperative.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2022 Todorovic, O’Leary, Ward, Devarasetty, Lee, Knox and Andari.)