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The Coronavirus Disease-2019 (COVID-19) pandemic has had a profound impact on suboptimal health status, depression, and anxiety, necessitating a comprehensive understanding of their inter-relationships at the national level. This study aims to investigate the inter-relationships among suboptimal health status, depression, and anxiety using a network analysis approach. We conducted a national survey between June 20 and August 31, 2022. Three network models were constructed and analyzed to independently examine the inter-relationships among suboptimal health status, depression, and anxiety. A total of 26,152 participants were included in this study. The study network analysis indicated that item 9 (i.e., Slow response) exhibited the highest node strength within the suboptimal health status questionnaire-short form (SHSQ-SF) network, followed by item 5 (i.e., Breathlessness at rest). Additionally, positive correlations were observed between depression and anxiety severity and most of the SHSO-SF items. This study provided valuable insights into inter-relationships between suboptimal health status, depression, and anxiety, informing the development of comprehensive intervention strategies for the general population. These findings have important implications for promoting the well-being and mental health of individuals during and beyond the COVID-19 pandemic. • The study found that item "Slow response" had the highest node strength. • Positive correlations between depression and anxiety severity and most SHSQ-SF items were observed. • This study provided valuable insights that informed the development of comprehensive intervention strategies. • These findings carry vital implications for bolstering individuals' well-being and mental health amidst a major crisis. [ABSTRACT FROM AUTHOR]

Limited understanding exists regarding the associations of traffic-related pollution with depression and anxiety symptoms in individuals residing within low- and middle-income countries. Data for this study were extracted from the Psychology and Behavior Investigation of Chinese Residents (PBICR) survey, implemented between June 20 and August 31, 2023. We determined residential proximity to major roadways through self-reports and evaluated depression symptoms using the Patient Health Questionnaire-9 (PHQ-9), along with anxiety symptoms assessed through the Generalized Anxiety Disorder-7 (GAD-7). We examined the associations between residential proximity to major roadways and depression and anxiety symptoms using logistic regressions and generalized linear models, while controlling for potential confounding variables. This study comprised a total of 22,723 participants. The adjusted odds ratios (OR) for depression symptoms were 1.34 (95 % confidence interval (CI) 1.20, 1.51), 1.29 (95 % CI 1.17, 1.43), 1.34 (95 % CI 1.20, 1.49), and 1.32 (95 % CI 1.17, 1.49) among individuals residing within <50 m, 50–100 m, 101–200 m, and 201–300 m, respectively, in comparison to those residing >300 m from a major roadway. Individuals residing <50 m, 50–100 m, 101–200 m, and 201–300 m from a major roadway exhibited adjusted OR for anxiety symptoms of 1.49 (95 % CI 1.30, 1.69), 1.21 (95 % CI 1.07, 1.37), 1.38 (95 % CI 1.21, 1.56), and 1.38 (95 % CI 1.20, 1.59), respectively, in contrast to those residing >300 m. This study provides valuable insights into the associations between environmental factors and mental health. The findings underscore the importance of integrating environmental considerations into comprehensive mental health frameworks, especially for individuals residing near high-traffic areas. • Traffic-related pollution is considered a component of door air pollution. • Living near a major road linked to higher depression and anxiety symptom rates. • Research stresses need to integrate environment in mental health frameworks. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has significantly impacted individuals' mental health, resulting in a higher incidence of depression, anxiety, and changes in eating behaviors. The objective of this study is to examine the inter-relationships among obesity-related eating behavior, depression, and anxiety in adults during the COVID-19 pandemic, utilizing a network analysis method. We conducted a cross-sectional survey among a representative sample of 9091 adults between July 10 and September 15, 2021. Participants completed self-report measures to assess obesity-related eating behavior and symptoms of depression and anxiety. Network analysis was employed to investigate the inter-relationships among these variables. The network analysis revealed that item 2 (i.e., Do not feel satisfied unless I eat until full) exhibited the highest node strength within the network, followed by item 5 (i.e., Like oily foods). In addition, positive correlations were found between the severity of depression and anxiety and most of the obesity-related eating behavior items. These findings offer valuable insights into the interplay between obesity-related eating behavior, depression, and anxiety during the COVID-19 pandemic, underscoring the significance of considering these factors in comprehending and addressing mental health and well-being in adults. Further research is warranted to explore potential interventions and treatment approaches that specifically target the identified relationships. [ABSTRACT FROM AUTHOR]

Background: Depression is a common mental disorder with a much higher prevalence in women than in men. Although there has been a gradual increase in research on the association between reproductive health and depression, there is still some inconsistency in the evidence of the relationship between pregnancy loss and depression. This study aimed to investigate the relationship between pregnancy loss and depressive symptoms. Methods: We analyzed data from the 2007–2020 National Health and Nutrition Examination Survey (NHANES), which included female participants aged 20 to 80 years. Pregnancy loss was determined based on participants' self-reported number of pregnancies and pregnancy outcomes. Depressive symptoms were measured using the Patient Health Questionnaire (PHQ-9, score ≥ 10). Multivariate logistic regression, smoothed curve fitting, and generalized additive modeling were used to examine the association between pregnancy loss and depression. We also performed sensitivity analyses and subgroup analyses to verify the robustness and specificity of the findings. Results: A total of 12,873 female participants were included in our study, of which 1,595 (12.39%) were categorized as depressed. Multivariate logistic regression results indicated that experiencing a pregnancy loss increased the risk of prevalence of depression in women (for 1 loss: OR = 1.31, 95% CI 1.15,1.50; for 2 or more losses: OR = 1.58, 95% CI 1.38, 1.81). When sensitivity analyses were performed, an association between pregnancy loss and depression was found in both multivariate linear regressions with PHQ-9 scores as a continuous variable and multivariate logistic regressions with a threshold of 5 PHQ-9 scores. The association between pregnancy loss and depression remained stable across subgroups. Conclusion: Pregnancy loss correlated with elevated PHQ-9 scores and a heightened risk of depression in adult women across the United States. Focusing on the incidence of adverse pregnancy events in the female population may help prevent or early recognize the onset of depression. Highlights: We analyzed the relationship between pregnancy loss and depression in American adult women. The sample size was large and nationally representative. Pregnancy loss was associated with higher PHQ-9 scores and an increased likelihood of depression among adult women. Our study is expected to provide more effective strategies to mitigate the impact of adverse pregnancy events on women's mental health. [ABSTRACT FROM AUTHOR]