Association between maternal employment status during pregnancy and risk of depressive symptomatology 1 month after childbirth: the Japan Environment and Children's Study

Exploring the association between non-regular employment and adverse birth outcomes: an analysis of national data in Japan

Background

As few studies have explored the association between non-regular or precarious employment in parents and adverse birth outcomes, this study aimed to investigate this association using national data in Japan.

Methods

This study utilized the census data from 2020 and birth data from the vital statistics in 2021 and 2022 in the analysis. Adverse birth outcomes, including preterm birth, term low birth weight (TLBW), and small-for-gestational-age, were examined. Data linkage was conducted between birth data and census data to link parental employment statuses and educational attainments with birth data. Rates of adverse birth outcomes were calculated for each parental employment status. Additionally, regression analysis was used to determine adjusted risk ratios (RRs) of parental employment statuses for each birth outcome.

Results

After data linkage, 334,110 birth records were included in the statistical analysis. Rates for non-regular workers were consistently higher than those for regular workers across all adverse birth outcomes for maternal employment status. Results of regression analyses indicated that the risks of preterm birth for non-regular workers were statistically significantly higher than those for regular workers, both in mothers and fathers with a RR (95% confidence intervals [CIs]) of 1.053 (1.004-1.104) and 1.142 (1.032-1.264), respectively. Furthermore, the risk of TLBW birth for non-regular workers was statistically significantly higher than that for regular workers in fathers (RR [95% CI]: 1.092 [1.043-1.143]).

Conclusions

Our findings demonstrate that non-regular workers have a higher risk of some adverse birth outcomes compared to regular workers.

Predicting prenatal depression and assessing model bias using machine learning models

Perinatal depression (PND) is one of the most common medical complications during pregnancy and postpartum period, affecting 10-20% of pregnant individuals. Black and Latina women have higher rates of PND, yet they are less likely to be diagnosed and receive treatment. Machine learning (ML) models based on Electronic Medical Records (EMRs) have been effective in predicting postpartum depression in middle-class White women but have rarely included sufficient proportions of racial and ethnic minorities, which contributed to biases in ML models for minority women. Our goal is to determine whether ML models could serve to predict depression in early pregnancy in racial/ethnic minority women by leveraging EMR data. We extracted EMRs from a hospital in a large urban city that mostly served low-income Black and Hispanic women (N=5,875) in the U.S. Depressive symptom severity was assessed from a self-reported questionnaire, PHQ-9. We investigated multiple ML classifiers, used Shapley Additive Explanations (SHAP) for model interpretation, and determined model prediction bias with two metrics, Disparate Impact, and Equal Opportunity Difference. While ML model (Elastic Net) performance was low (ROCAUC=0.67), we identified well-known factors associated with PND, such as unplanned pregnancy and being single, as well as underexplored factors, such as self-report pain levels, lower levels of prenatal vitamin supplement intake, asthma, carrying a male fetus, and lower platelet levels blood. Our findings showed that despite being based on a sample mostly composed of 75% low-income minority women (54% Black and 27% Latina), the model performance was lower for these communities. In conclusion, ML models based on EMRs could moderately predict depression in early pregnancy, but their performance is biased against low-income minority women.

Influence of chronotype on the incidence and severity of perinatal depression in the "Life-ON" study

BACKGROUND

Perinatal depression (PND) is a severe complication of pregnancy, but there are no established risk factors predicting the disease. Evening chronotype has been associated with unhealthy lifestyle habits and adverse outcomes during pregnancy. In this study, we aimed to clarify whether chronotype can predict symptoms and/or occurrence of PND.

METHODS

Two hundred ninety-nine women were followed-up from the first trimester of pregnancy until 6 months postpartum. Chronotype was assessed at baseline using the MEQ, while mood was repeatedly assessed by depression rating scales (EPDS, HDRS, MADRS). The influence of time and chronotype on EPDS, HDRS and MADRS, was estimated by constructing multilevel linear mixed regression models. A Cox proportional-hazard regression model was built to evaluate the association between chronotype and incidence of depression.

RESULTS

Chronotype modulated PND symptom severity depending on time of assessment, with evening chronotypes having a higher risk for developing PND symptoms, as assessed by EPDS, at postpartum visits V4 (5-12 days) and V5 (19-26 days). These also had less healthy lifestyle habits and were more likely to suffer from gestational diabetes mellitus and undergo cesarean delivery as compared to other chronotypes.

LIMITATIONS

Only a minority of women were classified as evening chronotypes. The long follow-up phase of the study led to missing data.

CONCLUSIONS

Pregnant evening chronotypes show unhealthy lifestyle habits and sociodemographic characteristics commonly associated with a higher risk for PND. They also have a higher risk of developing PND symptoms in the first month after delivery. Chronotype should therefore be routinely assessed during pregnancy to identify women potentially at risk for developing PND.