Maternal mental health (MMH), particularly depression and anxiety are crucial to the well-being of pregnant women and affecting 1 in 5 pregnant individuals every year in the U.S. Socioeconomic status (SES) is increasingly recognized as a key factor influencing MMH outcomes. Understanding the complex interplay between socioeconomic status and MMH is crucial for developing effective preventive measures and treatment strategies aimed at reducing maternal and fetal mortality. Our study utilized data from the All of Us research program to examine the relationship between SES factors and MMH, especially depression and anxiety. We further developed predictive models for early prediction of MMH based on socioeconomic factors. Our findings demonstrate the potential of statistical and machine learning approaches to uncover the risk factors and enhance early detection strategies, which could contribute to improve the maternal and fetal health outcomes.

Background: Thyroid dysfunction affects cardiovascular health through metabolic alterations. However, the effects of psychological stress and anxiety remain understudied. This study examines how chronic stress and anxiety influence cardiovascular disease (CVD) in patients with thyroid disorders.

Methods: We analyzed electronic health records (EHRs) from patients aged 18-80 years diagnosed with thyroid dysfunction between January 2018 and July 2022 in the All of Us research program. Predictors included stress and anxiety (ICD-10 codes), while outcomes were CVD diagnoses (CHF, COPD, arrhythmias). Using logistic regression adjusted for age and sex, we assessed associations between mental health conditions and CVD, ensuring thyroid diagnosis preceded cardiovascular events. Models were stratified by sex to examine effect modification.

Results: The cohort of 27,441 people was 78.9% female and 19.1% male, with a mean age of 58.6 years (SD 12.4). Racial/ethnic composition included: 65.5% White, 15.5% Hispanic/Latino, 11.3% Black, 2.5% Asian, 1.2% Other, 1.2% multiracial, and 2.9% unknown. CVD prevalence was 24.5% overall, with arrhythmias (20.3%, p<0.001), CHF (5.3%, p<0.001), and COPD (4.8%, p<0.001) being most common. Patients with mental health conditions had 2.06 times higher odds of CVD (95% CI: 1.93-2.18) than those without. The association was stronger in women (OR=2.14, CI:2.00-2.29) than men (OR=1.80, CI:1.62-2.00), with a significant interaction (p=0.01). Each additional year of age was associated with a 2.1% increase in CVD odds (OR=1.021, CI:1.018-1.022). Conclusion: Thyroid patients with stress or anxiety had higher CVD risks, especially women (2.1 times vs 1.8 times higher odds). Since 1 in 4 thyroid patients developed CVD, mental health screening may help with reducing risks.

The United States has the highest infant & maternal Mortality rates of any comparable developed nation. Amongst black women and infants these rates are even higher. States with restrictive abortion laws have higher mortality rates. There are many types of abortion laws. However, studies on the association of abortion laws with infant and maternal mortality have typically been done based on an index of state restrictiveness, rather than based on specific laws.

In this study we examine the association of specific types of abortion laws with maternal, infant and combined (maternal + infant) mortality rates, as measured per 1,000 births.

The data for the study were gathered and joined from 18 independent sources, including various datasets within the CDC WONDER Database, U.S. Census and LawAtlas.com. Mann Whitney U tests were conducted for each law and outcome to assess univariate association. For multivariate studies, several regression models including Linear Regression, Lasso, Ridge, Random Forest Regression and Linear Mixed Effects models were conducted using an 80/20 train-test split with 5-fold cross validation and feature selection methods integrated into their pipeline. Model fit was assessed based on r-squared values and root mean squared error. Finally, the Mixed Effects Linear Model was used to determine the significance and effect size of predictors and confounders based on p-values and coefficients.

In the univariate analysis, all types of laws significantly increased maternal, infant and combined mortality except for bans6 weeks after a woman’s last menstrual period (LMP), which was only associated with a significant increase in maternal mortality and bans 7-14 weeks LMP which had no association with any of the outcomes.

The multivariate analyses all fit well (r-squared >0.7) to the combined and infant mortality rates, but poorly to the maternal mortality rates. This indicated that the combined mortality fit was likely driven by infant mortality.

The mixed effects linear model proved that the only law that was significantly increased infant and combined mortality rates after accounting for covariates was banning abortions between 15-20 weeks LMP (infant: B = 0.435, p=0.026, combined: B=0.494, p= 0.012). Significant covariates included natural log of the percent of births paid for by private insurance (infant: B=2.157, p=0.015, combined: B=2.061, p=0.019), natural log of the percent of births to mothers less than 19 years old (infant: B=1.499, p=0.044, combined: B=1.466, p=0.047), natural log of the percent of birth paid by self-pay (infant: B=0.380, p=0.007, combined: B=0.380, p=0.007), percent of births to minority mothers(infant: B=0.041, P<0.001, combined: B= 0.040, p<0.001), average interval since last other pregnancy outcome (infant: B= -0.066, p=0.019,combined: B=-0.065, p=0.02). It is possible that bans in the 15-20thweek LMP are associated with higher infant mortality because this is approximately the timeframe when a mother can determine if her growing baby has a fetal abnormality. The inability to terminate pregnancies that are ultimately not viable may be resulting in higher infant mortality rates. Further study should be conducted to examine if this is, in fact, occurring.