During the first year of the project, we updated all registers included in the present study and built various datasets that can be used more efficiently for data analyses than raw register data. Moreover, we built reproducible code on processes used across studies, which enables more efficient workflows. We have also developed methods for dealing with overlapping register entries in the health-care register data (Suokas et al., 2024) and build interactive websites for easier display of the project results. We are also in process of building social network data over the life course and have started developing methods that work efficiently with large scale social network data.
So far we have published two manuscript within the scope of the project. In our study including over 1.6 million parents from Finland and Denmark (Hakulinen et al., 2024), we showed that the risk of a parent receiving a mental disorder diagnosis was higher among those who had a child with a mental disorder compared to those who did not. Overall, the excess risk was at its highest in temporal proximity to the child’s mental disorder diagnosis among both women and men, and then declined over time. When examining specific disorders of a child, a similar time-dependent trend was generally observed across most diagnostic categories in women, while the temporal patterns in men were less consistent.
In our study that was published in JAMA Psychiatry (Alho et al., 2024), we found an association between having peers diagnosed with a mental disorder during adolescence and an increased risk of receiving a mental disorder diagnosis later in life. The risk we discovered was most pronounced in the first year of follow-up. Notably, the association showed a dose-response relationship, with higher risk when multiple diagnosed individuals were in the peer network. Of the mental disorders examined, the association was strongest for mood, anxiety, and eating disorders. These associations were not explained by differences in area-level general morbidity or socioeconomic characteristics, parental mental disorders or socioeconomic position during childhood, or random differences in predisposition to mental health problems occurring among schools’ student populations.