1370Introducing network analysis to measure early life adversity. (2nd September 2021)

• Record Type: Journal Article
• Authors: De Vries, Tjeerd Rudmer; Arends, Iris; Rod, Naja Hulvej; Oldehinkel, Albertine J.; Bültmann, Ute
• Is Part Of: International journal of epidemiology. Volume 50(2021)Supplement 1
• Publication Date: 2021-09-02
• Subjects: Epidemiology -- Periodicals; 614.4
• DOI: 10.1093/ije/dyab168.153
• Languages: English
• ISSNs: 0300-5771

Abstract

Abstract: Focus of Presentation: Many studies have investigated associations between early life adversity (ELA) and outcomes across the life course. A defining characteristic of ELA is its complex nature, as many individual adverse experiences (e.g., parental mental health problems, financial difficulties) co-occur and interact over time. Commonly used methods for measuring ELA have not been able to elucidate pathways through which individual AEs are associated with each other during early life. We propose using network analysis to overcome this research gap. Findings: Figure 1 shows the conditional associations between AEs in childhood and adolescence in an undirected network model, based on empirical data from the longitudinal TRAILS cohort. First, we found that the network model allows us to explain co-occurrences between AEs. For example: the co-occurrence of parental illness and financial difficulties in childhood is likely due to parental unemployment. Second, we identified which AEs are associated over time, e.g., familial conflicts in childhood and adolescence are strongly associated, the latter being associated with parental divorce in adolescence. Conclusions/Implications: These findings add to the literature by providing insight into how individual AEs are conditionally associated, in distinct developmental periods and over time. The findings can be used in future research on pathways between AEs and guide the development of interventions. Key messages: Undirected network models are a promising alternative approach to measuring ELA that can provide insight into pathways through which AEs co-occur and interact over time.