Evidence and theory for lower rates of depression in larger US urban areas

Savior or saboteur? A nationwide study on digital economy and depression in China

The regional boom in digital economy has provided people with remote conversations and socialization while reducing the risk of depression. This study aims to elucidate whether regional digital economy can be a savior for individual depression. The multi-source dataset collects 11,845 individuals from the China Health and Retirement Longitudinal Study 2018 (CHARLS), with the combination of corresponding regional data from China City Statistical Yearbooks. A series of regressions with integrated mediation and moderation analyses are employed to bridge the link between the digital economy and depression. The results suggest that people living in areas with a higher level of digital economy are less likely to suffer from depression. The development of the digital economy helps people find solace or air grievances more easily, thereby reducing the risk of depression. Individual information and communications technology (ICT) engagement is found to mediate the relationship between the regional digital economy and individual depression. Residence type moderates the association between the three casual pairs of digital economy, ICT engagement, and depression. Improved digitization stimulates personal engagement with ICTs, which in turn expands social connections and support. Strengthened social interactions naturally keep depression away. Moreover, the urban-rural differences further confirm the underlying mechanism. Properly embracing the new digital world can therefore benefit from the transformative potential and mitigate depressive outcomes.

Development and validation of a nomogram to predict the depressive symptoms among older adults: A national survey in China

BACKGROUND

Depressive symptoms (DS) have become a global public health problem. However, a risk prediction model for DS in the elderly population has not been established. The purpose of this study was to develop and validate a predictive nomogram to screen for DS in the elderly population.

METHODS

A cross-sectional data of 3396 participants aged 60 and over were obtained from the China Health and Retirement Longitudinal Study 2018 (CHARLS). Participants were divided into the development and validation set. Predictive factors were selected through a single-factor analysis, and then a predictive model nomogram was established. The discrimination, calibration, and clinical validity were evaluated using the receiver operating characteristic (ROC) curves, Hosmer-Lemeshow tests, and decision curve analyses (DCA).

RESULTS

A total of 2379 and 1017 participants were included in the development and validation set, respectively. The analysis found that gender, residence, dyslipidemia, self-rated health, and ADL disability were risk factors for DS in older adults, and were included in the final model. This nomogram showed an acceptable predictive performance as evaluated by the area under the ROC curve with values of 0.684 (95 % confidence interval (CI): 0.663-0.706) and 0.687 (95 % CI: 0.655-0.719) in the development and validation set, respectively. The calibration curve indicated that the model was accurate, and DCA demonstrated a good clinical application value.

CONCLUSION

Five factors were selected to establish a nomogram for predicting DS in older adults. The nomogram has a good evaluation performance and can be used as a reliable tool to predict DS among older adults.

Changing Neighborhood Income Deprivation Over Time, Moving in Childhood, and Adult Risk of Depression

Importance

Complex biological, socioeconomic, and psychological variables combine to cause mental illnesses, with mounting evidence that early-life experiences are associated with adulthood mental health.

Objective

To evaluate whether changing neighborhood income deprivation and residential moves during childhood are associated with the risk of receiving a diagnosis of depression in adulthood.

Design, Setting, and Participants

This cohort study included the whole population of 1 096 916 people born in Denmark from January 1, 1982, to December 31, 2003, who resided in the country during their first 15 years of life. Individuals were followed up from 15 years of age until either death, emigration, depression diagnosis, or December 31, 2018. Longitudinal data on residential location was obtained by linking all individuals to the Danish longitudinal population register. Statistical analysis was performed from June 2022 to January 2024.

Exposures

Exposures included a neighborhood income deprivation index at place of residence for each year from birth to 15 years of age and a mean income deprivation index for the entire childhood (aged ≤15 years). Residential moves were considered by defining "stayers" as individuals who lived in the same data zone during their entire childhood and "movers" as those who did not.

Main Outcomes and Measures

Multilevel survival analysis determined associations between neighborhood-level income deprivation and depression incidence rates after adjustment for individual factors. Results were reported as incidence rate ratios (IRRs) with 95% credible intervals (95% CrIs). The hypotheses were formulated before data collection.

Results

A total of 1 096 916 individuals (563 864 male participants [51.4%]) were followed up from 15 years of age. During follow-up, 35 098 individuals (23 728 female participants [67.6%]) received a diagnosis of depression. People living in deprived areas during childhood had an increased risk of depression (IRR, 1.10 [95% CrI, 1.08-1.12]). After full individual-level adjustment, the risk was attenuated (IRR, 1.02 [95% CrI, 1.01-1.04]), indicating an increase of 2% in depression incidence for each 1-SD increase in income deprivation. Moving during childhood, independent of neighborhood deprivation status, was associated with significantly higher rates of depression in adulthood compared with not moving (IRR, 1.61 [95% CrI, 1.52-1.70] for 2 or more moves after full adjustment).

Conclusions and Relevance

This study suggests that, rather than just high or changing neighborhood income deprivation trajectories in childhood being associated with adulthood depression, a settled home environment in childhood may have a protective association against depression. Policies that enable and support settled childhoods should be promoted.

The U-shape Association between Population Agglomeration and Individual Depression: the Role of Dialect Diversity

Depression is a relevant mental illness affecting hundreds of millions of people worldwide. As urbanization accelerates, agglomeration of populations has altered individual social network distances and life crowding, which in turn affects depressive prevalence. However, the association between depression and population agglomeration (PA) remains controversial. This study aims to explore whether and how PA could influence individual depression. Based on the China Health and Retirement Longitudinal Study (CHARLS) 2018, the empirical results showed that there was a U-shaped association between PA and individual CES-D scores. As PA increases, the risk of depression first decreases and then increases. CES-D was lowest at moderate aggregation. Dialect diversity (DD) was positively related to the incidence of individual depression. The higher the DD, the higher the risk of depression. Meanwhile, DD also played a moderating role in the association between PA and individual depression. Our observations suggest that the optimistic level of agglomeration for individual mental health is within 1500 to 2000 persons per square kilometer.

The association between urbanization and depression in the elderly: A network analysis from the complexity science perspective

IMPORTANCE

The global increase in urbanization has coincided with a rise in depression prevalence. However, the effect of urbanization on depression remains controversial, especially for the elderly.

OBJECTIVE

To clarify how urbanization impacts depression in the elderly from a network perspective.

DESIGN, SETTING, AND PARTICIPANTS

This sectional cohort study used data from China Health and Retirement Longitudinal Study (CHARLS).

MAIN OUTCOMES AND MEASURES

The occurrence of depressive symptoms in urban and rural elderly residents. Network metrics of depressive symptoms.

RESULTS

Of the 13,993 participants, lower incidence of depressive symptoms was observed in urban (26.3 %, 95 % CI, 24.7 %-27.8 %) than in rural (40.4 %, 95 % CI, 39.5 %-41.3 %, P < 0.0001) residents. However, higher incidence of depressive symptoms was observed in urban (26.3 %, 95 % CI, 25.2 %-28.4 %) than in rural (17.5 %, 95 % CI, 16.1 %-18.9 %, P < 0.0001) residents in a subset of 2898 pairs of participants after PSM. No difference in the network structure and metrics between urban and rural residents before (M = 0.071, p = 0.054, S = 0.037, p = 0.80) and after (M = 0.085, p = 0.133, S = 0.086, p = 0.47) PSM was detected. The networks structure revealed that negative affect was strongly connected to somatic symptoms and that the two anhedonic symptoms were also strongly connected.

CONCLUSIONS

The current study indicated the homogeneity of the ultimate nature of depression between rural and urban residents from the network perspective, supporting the viewpoint that urbanization might not impose influence on depression. Further researches delving deeper into the complexity of the issue may provide new insights into our understanding of depression in an urban environment among the elderly.

Built Environment, Natural Environment, and Mental Health

Mental health disorders have become a global problem, garnering considerable attention. However, the root causes of deteriorating mental health remain poorly understood, with existing literature predominantly concentrating on socioeconomic conditions and psychological factors. This study uses multi-linear and geographically weighted regressions (GWR) to examine the associations between built and natural environmental attributes and the prevalence of depression in US counties. The findings reveal that job sprawl and land mixed use are highly correlated with a lower risk of depression. Additionally, the presence of green spaces, especially in urban area, is associated with improved mental health. Conversely, higher concentrations of air pollutants, such as PM2.5 and CO, along with increased precipitation, are linked to elevated depression rates. When considering spatial correlation through GWR, the impact of population density and social capital on mental health displays substantial spatial heterogeneity. Further analysis, focused on two high depression risk clustering regions (northwestern and southeastern counties), reveals nuanced determinants. In northwestern counties, depression rates are more influenced by factors like precipitation and socioeconomic conditions, including unemployment and income segregation. In southeastern counties, population demographic characteristics, particularly racial composition, are associated with high depression prevalence, followed by built environment factors. Interestingly, job growth and crime rates only emerge as significant factors in the context of high depression risks in southeastern counties. This study underscores the robust linkages and spatial variations between built and natural environments and mental health, emphasizing the need for effective depression treatment to incorporate these multifaceted factors.

Depressive disorder benefits of cities: Evidence from the China

BACKGROUND

Rapid urbanization is a major trend in global population migration. There is growing debate about whether this urban-rural disparity exacerbate depression at the individual level. This study aims to investigate how urban living has a beneficial impact on individual mental health.

METHODS

Based on the data of 15,764 participants in the 2018 China Health and Retirement Longitudinal Study (CHARLS), we perform analysis of variance to identify the gap in depression levels between urban and rural areas. Extensive comparisons and detailed statistical analyses are carried out to demonstrate the differences in social participation between urban and rural residents. Finally, we conduct a series of mediation and moderation analyses to reveal the underlying mechanisms of depressive disorder benefits of cities.

RESULTS

The results indicate that those who lived in urban areas were less likely to suffer from depression (β = -1.461, 95 % CI = [-1.691, -1.235], p < 0.001). Social engagement is found to mediate the relationship between residence type (β = 0.164, 95 % CI = [0.136, 0.193], p < 0.001) and individual depression (β = -0.462, 95 % CI = [-0.587, -0.337], p < 0.001). City size plays a moderating role in the association between urban living and social engagement.

LIMITATIONS

The mechanism is conducted through cross-sectional data. Self-reported depression status is accessed in this study, which could lead to measurement error.

CONCLUSION

This study demonstrates the beneficial effects of urban living on individual depression, and reveals the mechanism by which urbanization at different scales affects the prevalence of depression.

What words can tell us about social determinants of mental health: A multi-method analysis of sentiment towards migration experiences and community life in Lima, Perú

To support resilience in contexts of migration, a deeper understanding of the experiences of both receiving communities and migrants is required. Research on the impacts of migration on community life is limited in contexts with high internal migration (i.e., migrating within one's country of origin). Evidence suggests that cultural similarity, community relationships, and access to resources may be protective factors that could be leveraged to support the mental health of internal migrants. The current study uses data drawn from a sample of pregnant Peruvian women (N = 251), 87 of whom reported being internal migrants and 164 of whom reported being from the locale of the study (Lima, Perú). The aim was to better understand the social experience of internal migration for both local and migrant women. Inductive thematic analysis was used to examine migration experience and perceived impact of migration on community life. Internal migrants discussed three themes relative to their experiences: motivations, adjustment, and challenges. Experiences of women in receiving communities consisted of four themes related to migration: positive, negative, neutral, and mixed perceptions. Linguistic Inquiry and Word Count (LIWC-22) software was also used to assess sentiment towards migration. Across both analytic methods, migration motivations and perceptions were multifaceted and migrants reported a wide range of challenges before, during, and after migration. Findings indicated that attitudes toward migration are broadly positive, and that there is a more positive appraisal of migration's impact on the community life for internal as opposed to international migration.

Implicit racial biases are lower in more populous more diverse and less segregated US cities

Implicit biases - differential attitudes towards members of distinct groups - are pervasive in human societies and create inequities across many aspects of life. Recent research has revealed that implicit biases are generally driven by social contexts, but not whether they are systematically influenced by the ways that humans self-organize in cities. We leverage complex system modeling in the framework of urban scaling theory to predict differences in these biases between cities. Our model links spatial scales from city-wide infrastructure to individual psychology to predict that cities that are more populous, more diverse, and less segregated are less biased. We find empirical support for these predictions in U.S. cities with Implicit Association Test data spanning a decade from 2.7 million individuals and U.S. Census demographic data. Additionally, we find that changes in cities' social environments precede changes in implicit biases at short time-scales, but this relationship is bi-directional at longer time-scales. We conclude that the social organization of cities may influence the strength of these biases.

Human mobility networks reveal increased segregation in large cities

A long-standing expectation is that large, dense and cosmopolitan areas support socioeconomic mixing and exposure among diverse individuals1-6. Assessing this hypothesis has been difficult because previous measures of socioeconomic mixing have relied on static residential housing data rather than real-life exposures among people at work, in places of leisure and in home neighbourhoods7,8. Here we develop a measure of exposure segregation that captures the socioeconomic diversity of these everyday encounters. Using mobile phone mobility data to represent 1.6 billion real-world exposures among 9.6 million people in the United States, we measure exposure segregation across 382 metropolitan statistical areas (MSAs) and 2,829 counties. We find that exposure segregation is 67% higher in the ten largest MSAs than in small MSAs with fewer than 100,000 residents. This means that, contrary to expectations, residents of large cosmopolitan areas have less exposure to a socioeconomically diverse range of individuals. Second, we find that the increased socioeconomic segregation in large cities arises because they offer a greater choice of differentiated spaces targeted to specific socioeconomic groups. Third, we find that this segregation-increasing effect is countered when a city's hubs (such as shopping centres) are positioned to bridge diverse neighbourhoods and therefore attract people of all socioeconomic statuses. Our findings challenge a long-standing conjecture in human geography and highlight how urban design can both prevent and facilitate encounters among diverse individuals.

Urbanicity and depression: A global meta-analysis

BACKGROUND

Previous meta-analyses have revealed that in adult and older adult populations of developed countries, depression is more prevalent in urban than rural areas. No meta-analyses have identified the effects of urbanicity on the general age demographic for developing countries. We conducted a meta-analysis of urban-rural differences in depression across all age demographics for developed and developing countries.

METHODS

PubMed and PsycINFO databases were searched for studies published between 1980 and 2020. Studies were included if they reported prevalences of urban and rural depression, or odds ratios comparing urban-rural depression prevalence. Studies were excluded for: nonrepresentative samples, non-standard measures of depression, and reporting continuous outcomes only. Meta-analytic models of urban-rural differences in the odds of depression were conducted across country development levels and age demographics.

RESULTS

From 1597 records screened and 302 full texts assessed for eligibility, 80 studies (N = 539,557) were included for meta-analysis. Urban residence was significantly associated with a higher prevalence of depression in developed countries (OR = 1.30, 95 % CI [1.17, 1.46], z = 4.75, p < .001), which was primarily driven by urban-rural differences in the general population age demographic (OR = 1.37, 95 % CI [1.22, 1.54], z = 5.38, p < .001).

LIMITATIONS

Studies reporting urban-rural differences in depression in terms of continuous symptom severity scores were not included.

CONCLUSIONS

Urbanicity appears to uniquely be associated with a higher prevalence of depression in developed countries, but not in developing countries.

Spatial Health Predictors for Depressive Disorder in Manhattan: A 2020 Analysis

Background Urban cores often present extreme disparities in the distribution of wealth and income. They also vary in health outcomes, especially regarding mental welfare. Dense urban blocks agglomerate many residents of various backgrounds, and extreme differences in income, commerce, and health may lead to variations in depressive disorder outcomes. More research is needed on public health characteristics that may affect depression in dense urban centers. Methods Data on 2020 public health characteristics for Manhattan Island was collected using the Centers for Disease Control's (CDC's) PLACES project. All Manhattan census tracts were used as the spatial observations, resulting in [Formula: see text] observations. A cross-sectional generalized linear regression (GLR) was used to fit a geographically weighted spatial regression (GWR), with tract depression rates as the endogenous variable. Data on the following eight exogenous parameters were incorporated: the percentage without health insurance, the percentage of those who binge drink, the percentage who receive an annual doctor's checkup, the percentage of those who are physically inactive, the percentage of those who experience frequent mental distress, the percentage of those who receive less than 7 hours of sleep each night, the percentage of those who report regular smoking, and the percentage of those who are obese. A Getis-Ord Gi* model was built to locate hot and cold spot clusters for depression incidence and an Anselin Local Moran's I spatial autocorrelation analysis was undertaken to determine neighborhood relationships between tracts.  Results Depression hot spot clusters at the 90%-99% confidence interval (CI) were identified in Upper Manhattan and Lower Manhattan using the Getis-Ord Gi* statistic and spatial autocorrelation. Cold spot clusters at the 90%-99% CI were in central Manhattan and the southern edge of Manhattan Island. For the GLR-GWR model, only the lack of health insurance and mental distress variables were significant at the 95% CI, with an adjusted R-² of 0.56. Noticeable inversions were observed in the spatial distribution of the exogenous coefficients across Manhattan, with a higher lack of insurance coefficients observed in Upper Manhattan and higher frequent mental distress coefficients in Lower Manhattan. Conclusion The level of depression incidence does spatially track with predictive health and economic parameters across Manhattan Island. Additional research is encouraged on urban policies that may reduce the mental distress burden on Manhattan residents, as well as investigations of the spatial inversion observed in this study between the exogenous parameters.

General and Specific Factors of Environmental Stress and Their Associations With Brain Structure and Dimensions of Psychopathology

Background

Early-life stressors can adversely affect the developing brain. While hierarchical modeling has established the existence of a general factor of psychopathology, no studies have modeled a general factor of environmental stress and related this factor to brain development. Using a large sample of children from the ABCD (Adolescent Brain Cognitive Development) Study, the current study aimed to identify general and specific factors of environmental stress and test their associations with brain structure and psychopathology.

Methods

In a sample of 11,878 children, bifactor modeling and higher-order (second-order) modeling identified general and specific factors of environmental stress: family dynamics, interpersonal support, neighborhood socioeconomic status deprivation, and urbanicity. Structural equation modeling was performed to examine associations between these factors and regional gray matter volume (GMV) and cortical thickness as well as general and specific factors of psychopathology.

Results

The general environmental stress factor was associated with globally smaller cortical and subcortical GMV as well as thinner cortices across widespread regions. Family dynamics and neighborhood socioeconomic status deprivation were associated with smaller GMV in focal regions. Urbanicity was associated with larger cortical and subcortical GMV and thicker cortices in frontotemporal regions. The environmental factors were associated with psychopathology in the expected directions. The general factors of environmental stress and psychopathology were both predictors of smaller GMV in children, while remaining distinct from each other.

Conclusions

This study reveals a unifying model of environmental influences that illustrates the inherent organization of environmental stressors and their relationship to brain structure and psychopathology.

Mental Health of the Russian Federation Population versus Regional Living Conditions and Individual Income

The objective of our study was to assess the impact of regional living conditions on the Russian population's mental health. For the analysis, we used data from the cross-sectional stage of a 2013-2014 study, "Epidemiology of Cardiovascular Diseases in the Regions of the Russian Federation (ESSE-RF)". The final sample included 18,021 men and women 25-64 years of age from 11 regions of Russia. Using principal component analysis, we performed an integral simultaneous assessment of stress, anxiety, and depression. To describe the regional living conditions, we utilized five regional indices, which were computed from publicly available data of the Federal State Statistics Service of Russia. Overall, mental health indicators were improved, on the one hand, with the deterioration of social conditions and an aggravation of the demographic depression in the region, but on the other hand, they were improved with an increase in economic and industrial development, along with economic inequality among the population. In addition, the impact of regional living conditions on mental health increased with a higher individual wealth. The obtained results provided new fundamental knowledge on the impact of the living environment on health, using the case study of the Russian population, which has been little studied in this regard.

Higher depression risks in medium- than in high-density urban form across Denmark

Urban areas are associated with higher depression risks than rural areas. However, less is known about how different types of urban environments relate to depression risk. Here, we use satellite imagery and machine learning to quantify three-dimensional (3D) urban form (i.e., building density and height) over time. Combining satellite-derived urban form data and individual-level residential addresses, health, and socioeconomic registers, we conduct a case-control study (n = 75,650 cases and 756,500 controls) to examine the association between 3D urban form and depression in the Danish population. We find that living in dense inner-city areas did not carry the highest depression risks. Rather, after adjusting for socioeconomic factors, the highest risk was among sprawling suburbs, and the lowest was among multistory buildings with open space in the vicinity. The finding suggests that spatial land-use planning should prioritize securing access to open space in densely built areas to mitigate depression risks.

Economic development, weak ties, and depression: Evidence from China

OBJECTIVES

Weak ties are becoming mainstream in daily relationships and play an essential role in the improvement of individuals' mental health. Despite growing concerns on depression, inclusion of weak ties is limited. To address the gap, this study empirically shed light on the role of weak ties on individual depression in the context of economic development.

METHOD

A cross-sectional study was conducted based on 2018 China Health and Retirement Longitudinal Study (CHARLS) with a sample of 16,545 individuals. A moderated mediation model is constructed to evaluate the impact of economic development (GDP) on the degrees of depression, the mediating effect of weak ties, and the moderating effect of residents' residence types (living in urban or rural areas).

RESULTS

Economic development exerts a significant direct impact on depression (β=-1.027, p<0.001). Weak ties are significantly negatively correlated with depression (β=-0.574, p<0.001), and act as a mediator between economic development and local individual depression. In addition, the residence type plays a moderating role between economic development and weak ties (β=0.193, p<0.001). That is, living in urban areas would introduce the higher the level of weak ties.

CONCLUSIONS

Higher economic development is largely conducive to alleviating the degrees of depression, weak ties play a mediating role in economic development and depression, and residence types have a positive moderating effect on the economic development and weak ties.

Urbanization and depressive symptoms among middle-aged and older adults in China

Aims

Urbanization plays an important role in individuals' health. However, it is difficult to isolate healthy migrant effect between urbanization and health. This study examined the effects of urbanization on depressive symptoms and its possible pathways among Chinese middle-aged and older adults independent of the influence of health-selective migration.

Methods

Using the baseline survey of the China Health and Retirement Longitudinal Study, this study compared the depressive symptoms among three groups (urbanized rural residents, rural non-migrants and urban non-migrants). The 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) short form was used to measure depressive symptoms. Logistic regression models and Structural Equation Model (SEM) were applied to examine the association between urbanization and depressive symptoms and the corresponding potential mechanisms.

Results

Our final sample contained 11,156 respondents with an average age of 58.91 (SD = 9.48), with 5,142 males (46.09%) and 6,014 females (53.91%). Compared with urbanized rural residents, rural residents were more likely to have depressive symptoms (OR = 1.19, 95% CI = 1.07, 1.32), and urban residents were associated with a decreased risk of depressive symptoms (OR = 0.81, 95% CI: 0.70, 0.94). A large proportion of the association between urbanization and depressive symptoms were mainly mediated by social participation, income and living conditions.

Conclusions

Planned urbanization had an independent impact on decreased depressive symptoms. Improvements in social participation, income and living conditions are the main drivers behind this relationship. Additionally, urbanization compensates for the negative impact of depressive symptoms from disadvantaged early life conditions, but it cannot eliminate the gap between urbanized rural people and urban non-migrants.

Postpartum depression and health-related quality of life: a Saudi Arabian perspective

Background and Objectives

The mental and physical functioning of an individual is partly determined by their health-related quality of life (HRQOL), which is a multifaceted component. Women who have recently given birth must have a good quality of life to provide proper care and development for their infant. The purpose of this study was to assess the relationship between postpartum depression (PPD) and HRQOL in Saudi Arabian women and to identify potential risk factors that could influence them.

Methods

This study comprised 253 mothers aged 1-24 weeks postpartum from several health centers in Saudi Arabia, recruited by random purposive sampling. The study's questionnaire featured three sections: the first section had demographic information; the second and third sections contained the Edinburgh Postnatal Depression Scale (EPDS) scale and the HRQOL scale (SF-12), respectively. Data were analyzed using descriptive statistics, chi square analyses, independent samples t-tests and binary logistic regression analysis using IBM SPSS 25.

Results

Results of current study indicate that 59.68% of the patients exhibited probable post-partum depression symptoms. Participants who were depressed had significantly lower mental component (MCS) and physical component scores than participants who were in good health. When compared to non-smokers, smokers have a 21-fold higher risk of developing depression. Similar to this, mothers who worked had a 3.98 times higher risk of depression, and patients with a history of depression had a 3.6 times higher chance of getting PPD. The probability of developing PPD was also significantly higher in those who lived outside the Riyadh region, had given birth more than twice before this time, and had experienced undesired pregnancies.

Conclusion

Our study demonstrated an inverse correlation between postpartum depression and health-related quality of life scores. Treatment for depression, particularly among mothers, is crucial for improving their quality of life and, as a result, creating a favorable environment for the development of newborn babies.

The Prevalence of Depression Symptoms and Their Socioeconomic and Health Predictors in a Local Community with a High Deprivation: A Cross-Sectional Studies

Depression is a heterogeneous and etiologically complex psychiatric syndrome thatshows a strong sexual dimorphism and often impacts people with a low socioeconomic status (SES). The aim of the study was to estimate the occurrence of depression symptoms in a local community with a high deprivation rate, the example being the inhabitants of the JanówLubelski County in eastern Poland. A cross-sectional study was carried out on 3752 people aged between 35 and 64. The prevalence of depression symptoms was assessed using the Patient Health Questionnaire-9 (PHQ-9) scale. In the screening for depression symptoms in the entire population we studied, the risk of depression symptoms was 16.1% (n = 605), with women having a significantly higher mean score than men (p < 0.001). Significant predictors associated with the achievement of 10 points and more in the PHQ-9 assessment in the case of women and men were: living alone, education and having comorbidities. Moreover, female participants living in rural areas were significantly more likely to exhibit depression symptoms, whereas smoking was a significant predictor of depressive symptoms in men. It was observed that in the case of obese women, the chance of being in the higher category of the PHQ-9 assessment was 1.41 times higher than in women with normal body weight. However, in the case of men, an increase in age by one year increased the chance of being in a higher category by 1.02 times. Moreover, the odds of falling into a higher category, as assessed by the PHQ-9 questionnaire, among men who drink alcohol more than once a week was 1.7 times higher than in men who do not drink or consume alcohol occasionally. Summarising the results of studies conducted in a local community characterised by a high deprivation rate, socioeconomic and health variables related to SES significantly impacted the incidence of depression, but they differ in terms of gender.

Characteristics and reasons for use associated with solitary alcohol and marijuana use among U.S. 12th Grade Students, 2015-2021

BACKGROUND

Little is known regarding what sociodemographic characteristics and reasons for use are associated with adolescent solitary alcohol and marijuana use.

METHODS

Data from 7845 12th grade students participating in the nationally-representative Monitoring the Future study from 2015 to 2021 were used to examine cross-sectional associations between sociodemographics, heavy drinking/marijuana use, reasons for use, and past 12-month solitary alcohol or marijuana use among past 12-month users. Historical trends and possible differences related to the COVID-19 pandemic also were examined.

RESULTS

Solitary use prevalence increased from 2015 to 2021 with no evidence of significant COVID-19 deviations. In 2021, solitary alcohol use was reported by 32.1% (SE 3.01) and solitary marijuana use by 55.8% (4.72) of those reporting past 12-month use. Common and substance-specific sociodemographic risk factors were observed. Binge drinking was associated with solitary alcohol use; frequent marijuana use was associated with solitary marijuana use. Reasons for use related to coping with negative affect were associated with solitary use. Compulsive use reasons were more strongly associated with solitary alcohol than marijuana use. Drinking to have a good time with friends was negatively associated with solitary alcohol use but this association was not seen for solitary marijuana use.

CONCLUSIONS

The percentage of adolescents who use alcohol or marijuana when they were alone has increased among those who report using each substance. Associations between solitary use and (a) higher levels of consumption and (b) coping with negative affect highlight the importance of solitary use as a risk indicator.

The impact of gardening on mental resilience in times of stress: A case study during the COVID-19 pandemic in Singapore

To curb the spread of Covid-19, Singapore, like other cities, had to impose movement restriction and social distancing measures that may affect the well-being of its residents. In this paper, we assessed the potential benefits of gardening on the mental well-being of Singapore residents, based on the concept of mental resilience. We hypothesized that gardening activities promote mental resilience. A survey was administered on 8,786 participants of a "Gardening with Edibles" programme, measuring their mental resilience status, engagement in gardening activities and socio-demographic information. The mental resilience scores of participants who engaged in weekly gardening were compared with the scores derived from another survey conducted during the pandemic on an online community comprising demographically representative respondents with an interest on Covid-19 related community care. The results showed that the mental resilience of those who gardened was statistically significantly higher than the online community. Within the gardening group, those with less than one hour of weekly gardening time had significantly lower scores in their total mental resilience, and five out of seven resilience factors, "emotional regulation", "relationship", "confidence", "positive thinking" and "spirituality", compared to those with more weekly gardening time, showing that the efficacy of the mediating effects may peak at a weekly gardening time of between one to four hours. Home gardening may be an effective way for people living in densely populated cities to interact with nature and build mental resilience during the pandemic.

City Environment and Occurrence of Neural Autoantibodies in Psychiatric Patients

BACKGROUND

City living might lead to a higher risk of psychiatric disease, but to date there is no evidence of any correlation between an urban environment and the occurrence of neural autoantibodies in psychiatric disease. Our aim is to identify whether the number of patients with and without neural autoantibodies living in diverse rural and urban environments differ.

METHODS

We enrolled retrospectively a cohort of 167 psychiatric patients via a cross-sectional design from the Department of Psychiatry and Psychotherapy University Medical Center Göttingen and determined serum and/or CSF neural autoantibodies in them. The patients live in the German states of Lower Saxony, Thuringia, and Hessen. Their data were investigated in conjunction with the location of their primary residence. We categorized them into five different categories depending upon their primary residence: one rural and four different urban environments depending on their population numbers.

RESULTS

We identified 36 psychiatric patients with neural autoantibodies, and 131 psychiatric patients with none. In total, 24 psychiatric patients with neural autoantibodies were classified as sharing a possible, probable, or definitive autoimmune origin according to our recently set criteria. We observed as a non-significant trend that more psychiatric patients with neural autoantibodies and a probable or definitive autoimmune origin (45.8%) live in a major city with over 100,000 inhabitants than do psychiatric patients presenting no evidence of autoantibodies (26.4%). However, we identified no relevant differences between (1) psychiatric patients with and without neural autoantibodies or between (2) psychiatric patients with a possible, probable, or definitive autoimmune origin and those without such autoantibodies in relation to the diverse rural and urban environmental settings.

CONCLUSION

The inherently different aspects of rural and urban environments do not appear to be relevant in determining the frequency of neural autoantibodies in psychiatric patients in Lower Saxony, Thuringia, and Hessen in Germany. Furthermore, large-scale studies involving other states across Germany should be conducted to exclude any regional differences and to examine the tendency of a higher frequency in large cities of autoimmune-mediated psychiatric syndromes.

Modeling Pedestrian Flows: Agent-Based Simulations of Pedestrian Activity for Land Use Distributions in Urban Developments

Pedestrian activity is a cornerstone for urban sustainability, with key implications for the environment, public health, social cohesion, and the local economy. Therefore, city planners, urban designers, and decision-makers require tools to predict pedestrian mobility and assess the walkability of existing or planned urban environments. For this purpose, diverse approaches have been used to analyze different inputs such as the street network configuration, density, land use mix, and the location of certain amenities. This paper focuses on the location of urban amenities as key elements for pedestrian flow prediction, and, therefore, for the success of public spaces in terms of the social life of city neighborhoods. Using agent-based modeling (ABM) and land use floor space data, this study builds a pedestrian flow model, which is applied to both existing and planned areas in the inner city of Hamburg, Germany. The pedestrian flows predicted in the planned area inform the ongoing design and planning process. The flows simulated in the existing area are compared against real-world pedestrian activity data for external validation to report the model accuracy. The results show that pedestrian flow intensity correlates to the density and diversity of amenities, among other KPIs. These correlations validate our approach and also quantify it with measurable indicators.

Evidence and theory for lower rates of depression in larger US urban areas

It is commonly assumed that cities are detrimental to mental health. However, the evidence remains inconsistent and at most, makes the case for differences between rural and urban environments as a whole. Here, we propose a model of depression driven by an individual's accumulated experience mediated by social networks. The connection between observed systematic variations in socioeconomic networks and built environments with city size provides a link between urbanization and mental health. Surprisingly, this model predicts lower depression rates in larger cities. We confirm this prediction for US cities using four independent datasets. These results are consistent with other behaviors associated with denser socioeconomic networks and suggest that larger cities provide a buffer against depression. This approach introduces a systematic framework for conceptualizing and modeling mental health in complex physical and social networks, producing testable predictions for environmental and social determinants of mental health also applicable to other psychopathologies.