Latent Variables Quantifying Neighborhood Characteristics and Their Associations with Poor Mental Health

Local gun violence, mental health, and sleep: A neighborhood analysis in one hundred US Cities

RATIONALE

Community gun violence significantly shapes public health and collective well-being. Understanding how gun violence is associated with community health outcomes like mental health and sleep is crucial for developing interventions to mitigate disparities exacerbated by violence exposure.

OBJECTIVE

This study examines the associations between community gun violence , insufficient sleep, and poor mental health across neighborhoods in the United States.

METHODS

We utilized a novel database covering nearly 16,000 neighborhoods in 100 US cities from 2014 through 2019. Correlated trait fixed-effects models were employed to conduct all analyses while considering various neighborhood covariates such as concentrated disadvantage, demographic composition, population density, and proximity to trauma centers.

RESULTS

Our analysis revealed that greater gun violence is associated with both insufficient sleep and poor mental health in subsequent years. There is a reciprocal relationship between poor mental health and insufficient sleep, with each partially mediating the other's association with community gun violence. Notably, gun violence exhibits the strongest direct association with poor sleep rather than with poor mental health. We found a consistent reciprocal relationship between sleep and mental health at the community level.

CONCLUSIONS

The findings highlight a complex interplay between community violence, sleep, and mental health, underlining the importance of reducing community violence through numerous long-term interventions to address health disparities across the US.

A Multi-Level Analysis of Individual and Neighborhood Factors Associated with Patient Portal Use among Adult Emergency Department Patients with Multimorbidity

Background: Patient portals tethered to electronic health records (EHR) have become vital to patient engagement and better disease management, specifically among adults with multimorbidity. We determined individual and neighborhood factors associated with patient portal use (MyChart) among adult patients with multimorbidity seen in an Emergency Department (ED). Methods: This study adopted a cross-sectional study design and used a linked database of EHR from a single ED site to patients’ neighborhood characteristics (i.e., zip code level) from the American Community Survey. The study population included all adults (age > 18 years), with at least one visit to an ED and multimorbidity between 1 January 2019 to 31 December 2020 (N = 40,544). Patient and neighborhood characteristics were compared among patients with and without MyChart use. Random-intercept multi-level logistic regressions were used to analyze the associations of patient and neighborhood factors with MyChart use. Results: Only 19% (N = 7757) of adults with multimorbidity used the patient portal. In the fully adjusted multi-level model, at the patient level, having a primary care physician (AOR = 5.55, 95% CI 5.07−6.07, p < 0.001) and health insurance coverage (AOR = 2.41, 95% CI 2.23−2.61, p < 0.001) were associated with MyChart use. At the neighborhood level, 4.73% of the variation in MyChart use was due to differences in neighborhood factors. However, significant heterogeneity existed in patient portal use when neighborhood characteristics were included in the model. Conclusions: Among ED patients with multimorbidity, one in five adults used patient portals. Patient-level factors, such as having primary care physicians and insurance, may promote patient portal use.

Associations between neighborhood socioeconomic deprivation and severity of depression: Data from the National Health and Nutrition Examination Survey, 2011-2014

Neighborhood socioeconomic disadvantage may contribute to depression. This study examined associations between neighborhood socioeconomic disadvantage, measured as deprivation, and depression severity within a broadly representative sample of the U.S. adult population. The sample (n = 6308 U.S. adults) was from the 2011-2014 National Health and Nutrition Examination Survey. Neighborhood deprivation was calculated using the 2010 U.S. Census and shown in tertile form. Depression severity was calculated from responses to the Patient Health Questionnaire-9 (PHQ-9) as a continuous depression severity score and binary Clinically Relevant Depression (CRD). Multilevel modeling estimated the relationship between deprivation and depression (reference = low deprivation). Models were additionally stratified by gender and race/ethnicity. U.S. adults living in high deprivation neighborhoods were more likely to have a higher PHQ-9 score (p < 0.0001). In unadjusted models, living in high deprivation neighborhoods associated with higher PHQ-9 (β = 0.89, SE = 0.15, p < 0.0001) and higher odds of CRD (OR = 1.35, 95% CI = 1.20-1.51). Living in medium deprivation neighborhoods associated with higher PHQ-9 (β = 0.49, SE = 0.16, p = 0.0019). Associations between deprivation and depression severity lost significance after adjusting for individual-level SES. The results suggest that, for U.S. adults, the relationship between neighborhood-level disadvantage and depression may be attenuated by individual-level SES.

Landscape Efficiency Assessment of Urban Subway Station Entrance Based on Structural Equation Model: Case Study of Main Urban Area of Nanjing

Public landscape efficiency is one of the research hotspots in contemporary landscape performance. The renewal of micro landscape space has positive effects on community vitality and the sustainable development of landscape resources. The subway station entrance is a typical representative of the miniature landscape environment. To improve the construction of subway station entrances, clear cognition on the landscape efficiency of subway station entrances and their impact indicators is necessary. For this purpose, a structural equation model with a parameter system was established to measure landscape efficiency. There are four latent variables (e.g., traffic capacity) and 10 observed variables (e.g., visual level) composed of an estimation model. Researchers selected 131 subway station entrances in the main urban area of Nanjing as survey samples. Various methods, including investigation, image recognition, and modelling analysis, were comprehensively used to analyse the landscape characteristics of the subway station entrances quantitatively. A calculation was conducted to obtain the correlation coefficient of latent variables and the explanatory degree of observed variables. The result shows that green space landscapes and traffic capacity impresses the landscape efficiency of subway station entrances. Furthermore, all these variables have complex correlations. The fluctuation of any latent variable may cause the decay or enhancement of related variables. Therefore, designers should have a comprehensive cognition of the landscape environment of subway station entrances to enable them to propose balanced design strategies under traffic, visibility, plants and facilities. This paper aims to help designers gain an in-depth understanding of the ideal landscape construction forms at subway station entrances and facilitate the high-quality development of the urban landscape environment.

Editors' Comments on the Special Issue "Social Determinants of Mental Health"

Mental disorders are one of the greatest public health concerns of our time, and they are affected by social factors. To reduce the considerable burden of mental disorders, more global and systematic knowledge of the social determinants of mental health is necessary. This paper presents the results of the 27 studies included in the International Journal of Environmental Research and Public Health Special Issue, “Social Determinants of Mental Health”. The studies are grouped into four broad categories: social inclusion and mental health, young people’s mental health, mental health at work, and mental health service users. The results cover different countries, age populations, settings, and methodologies. Finally, the main findings on the relationship between social determinants and mental health are presented and summarized.