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Rates of common mental health problems (depression/anxiety) rise sharply in adolescence and peak in young adulthood, often coinciding with the transition to parenthood. Little is known regarding the persistence of common mental health problems from adolescence to the perinatal period in both mothers and fathers.
A total of 393 mothers (686 pregnancies) and 257 fathers (357 pregnancies) from the intergenerational Australian Temperament Project Generation 3 Study completed self-report assessments of depression and anxiety in adolescence (ages 13–14, 15–16, 17–18 years) and young adulthood (ages 19–20, 23–24, 27–28 years). The Edinburgh Postnatal Depression Scale was used to assess depressive symptoms at 32 weeks pregnancy and 12 months postpartum in mothers, and at 12 months postpartum in fathers.
Most pregnancies (81%) in which mothers reported perinatal depression were preceded by a history of mental health problems in adolescence or young adulthood. Similarly, most pregnancies (83%) in which fathers reported postnatal depression were preceded by a preconception history of mental health problems. After adjustment for potential confounders, the odds of self-reporting perinatal depression in both women and men were consistently higher in those with a history of persistent mental health problems across adolescence and young adulthood than those without (ORwomen 5.7, 95% CI 2.9–10.9; ORmen 5.5, 95% CI 1.03–29.70).
Perinatal depression, for the majority of parents, is a continuation of mental health problems with onsets well before pregnancy. Strategies to promote good perinatal mental health should start before parenthood and include both men and women.
Maternal mental health during pregnancy and postpartum predicts later emotional and behavioural problems in children. Even though most perinatal mental health problems begin before pregnancy, the consequences of preconception maternal mental health for children's early emotional development have not been prospectively studied.
We used data from two prospective Australian intergenerational cohorts, with 756 women assessed repeatedly for mental health problems before pregnancy between age 13 and 29 years, and during pregnancy and at 1 year postpartum for 1231 subsequent pregnancies. Offspring infant emotional reactivity, an early indicator of differential sensitivity denoting increased risk of emotional problems under adversity, was assessed at 1 year postpartum.
Thirty-seven percent of infants born to mothers with persistent preconception mental health problems were categorised as high in emotional reactivity, compared to 23% born to mothers without preconception history (adjusted OR 2.1, 95% CI 1.4–3.1). Ante- and postnatal maternal depressive symptoms were similarly associated with infant emotional reactivity, but these perinatal associations reduced somewhat after adjustment for prior exposure. Causal mediation analysis further showed that 88% of the preconception risk was a direct effect, not mediated by perinatal exposure.
Maternal preconception mental health problems predict infant emotional reactivity, independently of maternal perinatal mental health; while associations between perinatal depressive symptoms and infant reactivity are partially explained by prior exposure. Findings suggest that processes shaping early vulnerability for later mental disorders arise well before conception. There is an emerging case for expanding developmental theories and trialling preventive interventions in the years before pregnancy.
This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.
We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.
Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.
Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.
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