Abstract
Integrative data analysis (IDA) was used to derive developmental models of depression, externalizing problems, and self-regulatory processes in three prevention trials of the Family Check-Up and one longitudinal, community-based study of girls over a 10-year span covering early to late adolescence (N = 4,773; 74.9% female, 41.7% white). We used moderated nonlinear factor analysis to create harmonized scores based on all available items for a given participant in the pooled dataset while accounting for potential differences in both the latent factor and the individual items as a function of observed covariates. We also conducted latent growth model analyses to examine developmental trajectories of risk. Results indicated a bidirectional relationship between depression and externalizing problems, with greater baseline externalizing problems and depression predicting growth in inhibitory control difficulties. Furthermore, initial level of inhibitory control difficulties was associated with growth in depression. We did not, however, find a relationship between early inhibitory control difficulties and growth in externalizing problems. This work illustrates the utility of IDA techniques to harmonize data across multiple studies to identify risk factors for the development of depression and externalizing problems that can be targeted by prevention efforts.
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The research reported in this paper was supported by grants from the National Institute on Mental Health (MH122213 and MH056630), National Institute on Drug Abuse (DA25630, DA26222, and DA07031), and the National Institute on Alcoholism and Alcohol Abuse to (AA022071). The content is solely the responsibility of the authors and does not necessarily reflect the official views of the National Institute of Mental Health, National Institute on Drug Abuse, or the National Institute on Alcoholism and Alcohol Abuse.
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Magee, K.E., Connell, A., Hipwell, A.E. et al. Developmental Models of Depression, Externalizing Problems, and Self-regulatory Processes: Integrated Data Analysis Across Four Longitudinal Studies of Youth. Prev Sci 24, 1499–1509 (2023). https://doi.org/10.1007/s11121-022-01441-w
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DOI: https://doi.org/10.1007/s11121-022-01441-w