INTRODUCTION
A significant proportion of pregnant women and women in the early postpartum period suffer from mental health problems. The COVID-19 pandemic represents a unique stressor during this period and many studies across the world have shown elevated rates of postpartum depression (PPD).
METHODS
In this multicenter two-phase observational prospective cohort study, we aim to assess the prevalence of anxiety prior to labor (Generalized Anxiety Disorder-7), as well as PPD at 6–8 weeks postpartum using the Edinburgh Postnatal Depression Scale (EPDS).
RESULTS
Of the 330 women analyzed, 13.2% reported symptoms of depression using EPDS cut-off score ≥13. High antenatal levels of anxiety (24.8% scored ≥10 in GAD-7) were documented. A significant proportion of postpartum women reported a decrease in willingness to attend antenatal education courses (36%) and fewer antenatal visits to their obstetrician (34%) due to pandemic. Higher antenatal anxiety increased the odds of being depressed at 6–8 weeks postpartum (EPDS ≥13).
CONCLUSIONS
Compared to reported prevalence of PPD from previous studies before the COVID-19 era in Greece, we did not find elevated rates during the first wave of the pandemic. High anxiety levels were observed indicating that there is a need for close monitoring in pregnancy during the pandemic and anxiety screening to identify women who need support in the pandemic era. A well-planned maternity program should be employed by all the associated care providers to maintain the proper antenatal care adjusted to the pandemic strains as well as a follow-up after labor.
The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the identification of COVID-19’s impact on the mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the changes in the mood state of children and adolescents during the first lockdown. Therefore, in this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best-performing model; and (vi) a post hoc interpretation of the features’ impact on the best-performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of the COVID-19 pandemic on the mood state of children and adolescents.
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