Abstract Background Early negative life events (NLE) have long‐lasting influences on neurodevelopment and psychopathology. Reduced orbitofrontal cortex (OFC) thickness was frequently associated with NLE and depressive symptoms. OFC thinning might mediate the effect of NLE on depressive symptoms, although few longitudinal studies exist. Using a complete longitudinal design with four time points, we examined whether NLE during childhood and early adolescence predict depressive symptoms in young adulthood through accelerated OFC thinning across adolescence. Methods We acquired structural MRI from 321 participants at two sites across four time points from ages 14 to 22. We measured NLE with the Life Events Questionnaire at the first time point and depressive symptoms with the Center for Epidemiologic Studies Depression Scale at the fourth time point. Modeling latent growth curves, we tested whether OFC thinning mediates the effect of NLE on depressive symptoms. Results A higher burden of NLE, a thicker OFC at the age of 14, and an accelerated OFC thinning across adolescence predicted young adults' depressive symptoms. We did not identify an effect of NLE on OFC thickness nor OFC thickness mediating effects of NLE on depressive symptoms. Conclusions Using a complete longitudinal design with four waves, we show that NLE in childhood and early adolescence predict depressive symptoms in the long term. Results indicate that an accelerated OFC thinning may precede depressive symptoms. Assessment of early additionally to acute NLEs and neurodevelopment may be warranted in clinical settings to identify risk factors for depression.
Preventable patient harm due to adverse events (AEs) is a significant health problem today facing contemporary health care. Knowledge of patients' experiences of AEs is critical to improving health care safety and quality. A systematic review of studies of patients' experiences of AEs was conducted to report their experiences, knowledge gaps and any challenges encountered when capturing patient experience data. Key words, synonyms and subject headings were used to search eight electronic databases from January 2000 to February 2015, in addition to hand-searching of reference lists and relevant journals. Titles and abstracts of publications were screened by two reviewers and checked by a third. Full-text articles were screened against the eligibility criteria. Data on design, methods and key findings were extracted and collated. Thirty-three publications demonstrated patients identifying a range of problems in their care; most commonly identified were medication errors, communication and coordination of care problems. Patients' income, education, health burden and marital status influence likelihood of reporting. Patients report distress after an AE, often exacerbated by receiving inadequate information about the cause. Investigating patients' experiences is hampered by the lack of large representative patient samples, data over sufficient time periods and varying definitions of an AE. Despite the emergence of policy initiatives to enhance patient engagement, few studies report patients' experiences of AEs. This information must be routinely captured and utilized to develop effective, patient-centred and system-wide policies to minimize and manage AEs.
ABSTRACT Alcohol misuse during adolescence (AAM) has been linked with disruptive structural development of the brain and alcohol use disorder. Using machine learning (ML), we analyze the link between AAM phenotypes and adolescent brain structure (T1-weighted imaging and DTI) at ages 14, 19, and 22 in the IMAGEN dataset ( n ∼ 1182). ML predicted AAM at age 22 from brain structure with a balanced accuracy of 78% on independent test data. Therefore, structural differences in adolescent brains could significantly predict AAM. Using brain structure at age 14 and 19, ML predicted AAM at age 22 with a balanced accuracy of 73% and 75%, respectively. These results showed that structural differences preceded alcohol misuse behavior in the dataset. The most informative features were located in the white matter tracts of the corpus callosum and internal capsule, brain stem, and ventricular CSF. In the cortex, they were spread across the occipital, frontal, and temporal lobes and in the cingulate cortex. Our study also demonstrates how the choice of the phenotype for AAM, the ML method, and the confound correction technique are all crucial decisions in an exploratory ML study analyzing psychiatric disorders with weak effect sizes such as AAM.
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