Temporal trends of co-diagnosis of depression and/or anxiety among female maternal and non-maternal hospitalizations: Results from Nationwide Inpatient Sample 2004-2013

Decision tree-Markov model of perinatal depression screening: a cost-utility analysis

Background

Perinatal depression affects the physical and mental health of pregnant women. It also has a negative effect on children, families, and society, and the incidence is high. We constructed a cost-utility analysis model for perinatal depression screening in China and evaluated the model from the perspective of health economics.

Methods

We constructed a Markov model that was consistent with the screening strategy for perinatal depression in China, and two screening strategies (screening and non-screening) were constructed. Each strategy was set as a cycle of 3 months, corresponding to the first trimester, second trimester, third trimester, and postpartum. The state outcome parameters required for the model were obtained based on data from the National Prospective Cohort Study on the Mental Health of Chinese Pregnant Women from August 2015 to October 2016. The cost parameters were obtained from a field investigation on costs and screening effects conducted in maternal and child health care institutions in 2020. The cost-utility ratio and incremental cost-utility ratio of different screening strategies were obtained by multiplicative analysis to evaluate the health economic value of the two screening strategies. Finally, deterministic and probabilistic sensitivity analyses were conducted on the uncertain parameters in the model to explore the sensitivity factors that affected the selection of screening strategies.

Results

The cost-utility analysis showed that the per capita cost of the screening strategy was 129.54 yuan, 0.85 quality-adjusted life years (QALYs) could be obtained, and the average cost per QALY gained was 152.17 yuan. In the non-screening (routine health care) group, the average cost was 171.80 CNY per person, 0.84 QALYs could be obtained, and the average cost per QALY gained was 205.05 CNY. Using one gross domestic product per capita in 2021 as the willingness to pay threshold, the incremental cost-utility ratio of screening versus no screening (routine health care) was about -3,126.77 yuan, which was lower than one gross domestic product per capita. Therefore, the screening strategy was more cost-effective than no screening (routine health care). Sensitivity analysis was performed by adjusting the parameters in the model, and the results were stable and consistent, which did not affect the choice of the optimal strategy.

Conclusion

Compared with no screening (routine health care), the recommended perinatal depression screening strategy in China is cost-effective. In the future, it is necessary to continue to standardize screening and explore different screening modalities and tools suitable for specific regions.

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.

Trends in and complications associated with mental health condition diagnoses during delivery hospitalizations

BACKGROUND

Mental health conditions during delivery hospitalizations are not well characterized.

OBJECTIVE

This study aimed to characterize the prevalence of maternal mental health condition diagnoses and associated risk during delivery hospitalizations in the United States.

STUDY DESIGN

The 2000 to 2018 National Inpatient Sample was used for this repeated cross-sectional analysis. Delivery hospitalizations of women aged 15 to 54 years with and without mental health condition diagnoses, including depressive disorder, anxiety disorder, bipolar spectrum disorder, and schizophrenia spectrum disorder, were identified. Temporal trends in mental health condition diagnoses during delivery hospitalizations were determined using the National Cancer Institute's Joinpoint Regression Program to estimate the average annual percent change with 95% confidence intervals. The trends in chronic conditions associated with mental health condition diagnoses, including asthma, pregestational diabetes mellitus, chronic hypertension, obesity, and substance use, were analyzed. The association between mental health conditions and the following adverse outcomes was determined: (1) severe maternal morbidity, (2) preeclampsia or gestational hypertension, (3) preterm delivery, (4) postpartum hemorrhage, (5) cesarean delivery, and (6) maternal mortality. Regression models for each outcome were performed with unadjusted and adjusted risk ratios as measures of effects.

RESULTS

Of 73,109,791 delivery hospitalizations, 2,316,963 (3.2%) had ≥1 associated mental health condition diagnosis. The proportion of delivery hospitalizations with a mental health condition increased from 0.6% in 2000 to 7.3% in 2018 (average annual percent change, 11.4%; 95% confidence interval, 10.3%-12.6%). Among deliveries in women with a mental health condition diagnosis, chronic health conditions, including asthma, pregestational diabetes mellitus, chronic hypertension, obesity, and substance use, increased from 14.9% in 2000 to 38.5% in 2018. Deliveries to women with a mental health condition diagnosis were associated with severe maternal morbidity (risk ratio, 1.88; 95% confidence interval, 1.86-1.90), preeclampsia and gestational hypertension (risk ratio, 1.59; 95% confidence interval, 1.58-1.60), preterm delivery (risk ratio, 1.35; 95% confidence interval, 1.35-1.36), postpartum hemorrhage (risk ratio, 1.37; 95% confidence interval, 1.36-1.38), cesarean delivery (risk ratio, 1.20; 95% confidence interval, 1.20-1.20), and maternal death (risk ratio, 1.31; 95% confidence interval, 1.12-1.56). The increased risk was retained in adjusted models.

CONCLUSION

The proportion of delivery hospitalizations with mental health condition diagnoses increased significantly throughout the study period. Mental health condition diagnoses were associated with other underlying chronic health conditions and a modestly increased risk of a range of adverse outcomes. The findings suggested that mental health conditions are an important risk factor in adverse maternal outcomes.

Symptom profiles of women at risk of mood disorders: A latent class analysis

BACKGROUND

Depression is the leading cause of disease burden among women worldwide. However, an understanding of symptom profiles among women at risk of mood disorders is limited. We determined distinct profiles of affective symptoms among high risk women, along with their distinguishing characteristics.

METHODS

Women were recruited from 17 clinical sites affiliated with the National Network of Depression Centers. They completed measures of depression (Patient Health Questionnaire - 9) and anxiety (Generalized Anxiety Disorder - 7) as well as questions regarding demographics, reproductive status, behavioral/mental health history, and life stress/adversity. Latent class analysis and multinomial logistic regression were used to identify and characterize symptom profiles.

RESULTS

5792 women participated, ages 18 to 90 (M = 38). Three latent classes were identified: generally asymptomatic (48%), elevated symptoms of comorbid anxiety and depression (16%), and somatic symptoms (36%). Financial security and greater social support were protective factors that distinguished asymptomatic women. The profile of the class with elevated anxiety/depressive symptoms constituted a complex mix of adverse social determinants and potentially heritable clinical features, including a diagnosis of Bipolar Disorder. Women in the 3rd latent class were characterized by menstrual irregularity and a stronger expression of neurovegetative symptoms, especially sleep disturbance and fatigue.

LIMITATIONS

Limitations included less than optimal racial diversity of our sample and reliance on self-report.

CONCLUSIONS

Different symptom profiles may reflect distinct subtypes of women at risk of mood disorders. Understanding the etiology and mechanisms underlying clinical and psychosocial features of these profiles can inform more precisely targeted interventions to address women's diverse needs.

Pregnancy hypertension and its association with maternal anxiety and mood disorders: A population-based study of 9 million pregnancies

BACKGROUND

Evidence on whether anxiety or mood disorders increases the risk of hypertensive disorders of pregnancy (HDP) has been conflicting. We aimed to evaluate the prevalence of maternal mental disorders over time and their associations with HDP.

METHODS

This was a population-based retrospective study involving 9,097,355 pregnant women using Healthcare Cost and Utilization Project Nationwide Inpatient Sample (HCUP-NIS) data from 2004 through 2014. We calculated the prevalence of maternal anxiety, depression, bipolar disorder and mood disorder and trends of gestational hypertension, preeclampsia and eclampsia during the study period. Multivariate logistic regression was used to examine the association between each mental disorder and HDP.

RESULTS

Mental disorders showed increasing trends among pregnant women, with anxiety showing the greatest increase in rates. Unadjusted associations suggest all mental disorders increase the likelihood of HDP. When adjusted for sociodemographic characteristics and comorbidities, only anxiety showed consistently increased risk of gestational hypertension (adjusted odds ratio (aOR) 1.324, 95% CI 1.255-1.397), preeclampsia (aOR 1.522, 95% CI 1.444-1.604), with the strongest association with eclampsia (aOR 1.813, 95% CI 1.260-2.610).

LIMITATIONS

Information on medication use is not available in the HCUP-NIS database and might have been contributory to our findings.

CONCLUSIONS

Rates of maternal psychopathology are rising in the United States. Our study suggests that pregnant women with anxiety are at increased risk of HDP. Targeted screening for mental disorders as possible clinical risk markers may allow for timely prophylaxis and surveillance for the development of HDP.

Trends in Selected Chronic Conditions and Related Risk Factors Among Women of Reproductive Age: Behavioral Risk Factor Surveillance System, 2011-2017

Introduction: Chronic diseases in the United States are the leading drivers of disability, death, and health care costs. In women of reproductive age (WRA), chronic disease and related risk factors can also affect fertility and reproductive health outcomes. This analysis of trends from 2011 to 2017 adds additional indicators and updates an analysis covering 2001-2009. Methods: Data from the 2011-2017 Behavioral Risk Factor Surveillance System were analyzed for 265,544 WRA (18-44 years). To assess trends in 12 chronic conditions and related risk factors, we calculated annual prevalence estimates and adjusted prevalence ratios (APRs) with predicted marginals accounting for age, race, Hispanic ethnicity, education, and health care coverage. Results: From 2011 to 2017, prevalence decreased for current smoking (20.7%-15.9%; p < 0.001), gestational diabetes (3.1%-2.7%; p = 0.003), and high cholesterol (19.0%-16.7%; p < 0.001); prevalence increased for depression (20.4%-24.9%; p < 0.001) and obesity (24.6%-27.6%; p < 0.001). After adjustment, in 2017 WRA were more likely to report asthma (APR = 1.06; 95% confidence interval [CI] = 1.01-1.11), physical inactivity (APR = 1.08; 95% CI = 1.04-1.12), obesity (APR = 1.15; 95% CI = 1.11-1.19), and depression (APR = 1.29; 95% CI = 1.25-1.34) compared with 2011. They were less likely to report high cholesterol (APR = 0.89; 95% CI = 0.85-0.94) in 2015 compared with 2011, and current smoking (APR = 0.86; 95% CI = 0.82-0.89) and gestational diabetes (APR = 0.84; 95% CI = 0.75-0.94) in 2017 compared with 2011. Conclusions: Some chronic conditions and related risk factors improved, whereas others worsened over time. Research clarifying reasons for these trends may support the development of targeted interventions to promote improvements, potentially preventing adverse reproductive outcomes and promoting long-term health.

Trends in prevalence and treatment of depressive symptoms in adult patients with epilepsy in the United States

OBJECTIVE

Depressive symptoms are a common comorbidity among adults with epilepsy (AWE). Prior estimates regarding prevalence and treatment of depressive symptoms in AWE have been largely based on samples of tertiary care cohorts that may not be generalizable. We aimed to provide a representative population estimate of the prevalence and treatment of depressive symptoms over time in AWE in the United States as measured by a validated depression screen.

METHOD

Data from the Medical Expenditure Panel Survey (MEPS) were analyzed from 2004 to 2015 to determine the prevalence of "screen positive" depressive symptoms (SPDS) among AWE as evaluated by the Patient Health Questionnaire-2 (PHQ-2). We defined pharmacotherapy for depressive symptoms as the prescription of any antidepressant, antipsychotic, anxiolytic, or central nervous system stimulant for the "Clinical Classification Code" of mood disorders within the year sampled, and psychotherapy as any outpatient or office-based visit for "mood disorders" for that year sampled. We analyzed temporal trends and explanatory variables for treatment using the Cochran-Armitage test and logistic regression, respectively.

RESULTS

Our sample included 2024 AWE, representing 1,736,023 patients nationwide. This included 517 AWE with SPDS (AWE-SPDS), representing 401,452 AWE, and 1507 AWE who screened negative for depressive symptoms (AWE-SNDS), representing 1,334,571 AWE. The prevalence of SPDS was 23.1% (95% confidence interval [CI]: 20.6%-25.8%). Women (odds ratio [OR]: 1.40, 95% CI: 1.05-1.87), patients ages 35-49 (OR: 1.83, 95% CI: 1.23-2.72; compared with patients ages 18-34), and patients with Charlson Comorbidity Index ≥1 (OR: 1.92, 95% CI: 1.41-2.61) had higher odds of SPDS. There was no significant change in depressive symptoms' prevalence or treatment in AWE between the epochs of 2004-2006 and 2013-2015.

CONCLUSIONS

Despite a quarter of AWE in the United States with SPDS, fewer than half received treatment. This indicates a need for improved efforts to screen AWE for depression and treat appropriately.