Moving to opportunity and mental health: Exploring the spatial context of neighborhood effects

Unravelling the dynamics of mental health inequalities in England: A 12-year nationwide longitudinal spatial analysis of recorded depression prevalence

Background

Depression is one of the most significant public health issues, but evidence of geographic patterns and trends of depression is limited. We aimed to examine the spatio-temporal patterns and trends of depression prevalence among adults in a nationwide longitudinal spatial study in England and evaluate the influence of neighbourhood socioeconomic deprivation in explaining patterns.

Methods

Information on recorded depression prevalence was obtained from the indicator Quality and Outcomes Framework: Depression prevalence that measured the annual percentage of adults diagnosed with depression for Lower Super Output Areas (LSOA) from 2011 to 2022. We applied Cluster and Outlier Analysis using the Local Moran's I algorithm. Local effects of deprivation on depression in 2020 examined with Geographically Weighted Regression (GWR). Inequalities in recorded prevalence were presented using Prevalence Rate Ratios (PRR).

Results

The North West Region of England had the highest concentration of High-High clusters of depression, with 17.4% of the area having high values surrounded by high values in both space and time and the greatest percentage of areas with a high rate of increase (43.1%). Inequalities widened among areas with a high rate of increase in prevalence compared to those with a lower rate of increase, with the PRR increasing from 1.66 (99% CI 1.61-1.70) in 2011 to 1.81 (99% CI 1.76-1.85) by 2022. Deprivation explained 3%-39% of the variance in depression in 2020 across the country.

Conclusions

It is crucial to monitor depression's spatial patterns and trends and investigate mechanisms of mental health inequalities. Our findings can help identify priority areas and target prevention and intervention strategies in England. Evaluating mental health interventions in different geographic contexts can provide valuable insights to policymakers on the most effective and context-sensitive strategies, enabling them to allocate resources towards preventing the worsening of mental health inequalities.

Investigating the spatiotemporal patterns and clustering of attendances for mental health services to inform policy and resource allocation in Thailand

BACKGROUND

Mental illness poses a substantial global public health challenge, including in Thailand, where exploration of access to mental health services is limited. The spatial and temporal dimensions of mental illness in the country are not extensively studied, despite the recognized association between poor mental health and socioeconomic inequalities. Gaining insights into these dimensions is crucial for effective public health interventions and resource allocation.

METHODS

This retrospective study analyzed mental health service utilization data in Thailand from 2015 to 2023. Temporal trends in annual numbers of individuals visiting mental health services by diagnosis were examined, while spatial pattern analysis employed Moran's I statistics to assess autocorrelation, identify small-area clustering, and hotspots. The implications of our findings for mental health resource allocation and policy were discussed.

RESULTS

Between 2015 and 2023, mental health facilities documented a total of 13,793,884 visits. The study found anxiety, schizophrenia, and depression emerged as the top three illnesses for mental health visits, with an increase in patient attendance following the onset of the COVID-19 outbreak. Spatial analysis identified areas of significance for various disorders across different regions of Thailand. Positive correlations between certain disorder pairs were found in specific regions, suggesting shared risk factors or comorbidities.

CONCLUSIONS

This study highlights spatial and temporal variations in individuals visiting services for different mental disorders in Thailand, shedding light on service gaps and socioeconomic issues. Addressing these disparities requires increased attention to mental health, the development of appropriate interventions, and overcoming barriers to accessibility. The findings provide a baseline for policymakers and stakeholders to allocate resources and implement culturally responsive interventions to improve mental health outcomes.

Transforming mental health systems globally: principles and policy recommendations

A burgeoning mental health crisis is emerging globally, regardless of each country's human resources or spending. We argue that effectively responding to this crisis is impeded by the dominant framing of mental ill health through the prism of diagnostic categories, leading to an excessive reliance on interventions that are delivered by specialists; a scarcity of widespread promotive, preventive, and recovery-oriented strategies; and failure to leverage diverse resources within communities. Drawing upon a series of syntheses, we identify five principles to transform current practices; namely, address harmful social environments across the life course, particularly in the early years; ensure that care is not contingent on a categorical diagnosis but aligned with the staging model of mental illness; empower diverse front-line providers to deliver psychosocial interventions; embrace a rights-based approach that seeks to provide alternatives to violence and coercion in care; and centre people with lived experience in all aspects of care. We recommend four policy actions which can transform these principles into reality: a whole of society approach to prevention and care; a redesign of the architecture of care delivery to provide a seamless continuum of care, tailored to the severity of the mental health condition; investing more in what works to enhance the impact and value of the investments; and ensuring accountability through monitoring and acting upon a set of mental health indicators. All these actions are achievable, relying-for the most part-on resources already available to every community and country. What they do require is the acceptance that business as usual will fail and the solutions to transforming mental health-care systems are already present within existing resources.

Examining neighborhood effects on mental health utilizing a novel two-stage modeling approach

PURPOSE

Neighborhood conditions may affect health, but health may also determine a preference for where to live. This study estimates the effect of neighborhood characteristics on mental health while aiming to adjust for this residential self-selection.

METHODS

A two-step method was implemented using register data from Statistics Netherlands from all residents of the city of Rotterdam relocating within the city in 2013 (N = 12,456). First, using a conditional logit model, we estimated for each individual the probability of relocating to a neighborhood over all other neighborhoods in Rotterdam, based on personal and neighborhood characteristics in 2013. Second, we corrected this selection process in a model investigating the effects of neighborhood characteristics in 2014 on reimbursed anti-depressant or anti-psychotic medication in 2016.

RESULTS

Personal and neighborhood characteristics predicted neighborhood choice, indicating strong patterns of selection into neighborhoods. Unadjusted for selection log neighborhood income was associated with reimbursed medication (β = -0.040, 95% CI = -0.060, -0.020), but the association strongly attenuated after controlling for self-selection into neighborhoods (β = -0.010, 95% CI = -0.030, 0.011). The opposite was observed for contact with neighbors; unadjusted for self-selection there was no association (β = -0.020, 95% CI = -0.073, 0.033), but after adjustment increased neighborhood contact was associated with an 8.5% relative reduction in reimbursed medication (β = -0.075, 95% CI = -0.126, -0.025).

CONCLUSIONS

The method illustrated in this study offers new opportunities to disentangle selection from causation in neighborhood health research.

Urban Stress and its Association with Symptoms of Depression, Fatigue, and Sleep Disruption in Women in Mexico City

Women in urban neighborhoods often face disproportionately higher levels of environmental and social stressors; however, the health effects from urban stressors remains poorly understood. We aimed to evaluate the association between urban stress and symptoms of depression, fatigue, and sleep disruption in a cohort of 460 women in Mexico City. To assess urban stress, women were administered the Urban Annoyances (Nuisances Environnementales) scale. Six constructs were summarized to create an overall index. Depressive symptoms were assessed using the Edinburgh Depression Scale; the Patient-Reported Outcomes Information System scales were used to assess sleep disruption and fatigue. Linear regression models were used to estimate the association with continuous symptoms comparing women with high urban stress to those with lower levels. Models were adjusted for socioeconomic status, education, age, social support, and previous depressive symptoms. High urban stress was associated with greater depressive symptoms (β: 1.77; 95%CI: 0.83, 2.71), fatigue (β: 2.47; 95%CI: 0.87, 4.07), and sleep disruption (β: 2.14; 95%CI: 0.54, 3.73). Urban stress plays an important role in women's psychological and physical health, highlighting the importance of including these measures in environmental health studies. Urban interventions, such as promoting alternative transport options, should additionally be addressed to improve health of urban populations.

Pessimistic cognitive biases mediate socioeconomic status and children's mental health problems

Low socioeconomic status (SES) is associated with higher rates of emotional disorders in childhood and beyond. Here we assessed one possible contributor to this disparity, a cognitive bias in the interpretation of negative events, in a group of 341 9-year-olds (49% female, 94% White) ranging widely in SES. This cognitive bias, known as pessimism in the attributional style literature, is the tendency to interpret negative events as persistent (Stable) and pervasive (Global). It was found to be more common among lower SES children (effect sizes = 0.18-0.24 depending on SES measures: income to needs ratio, proportion of poverty from birth to age 9, and parental educational attainment). Moreover, persistent, pervasive adversity in children's lives predicted this bias and mediated the SES-pessimism link. Pessimistic attributional style, in turn, was related to childhood emotional problems and mediated the relation between SES and these problems. Finally, evidence for serial mediation of the SES-mental health problems relationship was found via persistent, pervasive adversity and pessimism, respectively.

Gender Disparities Between Neighborhood Social Vulnerability and Psychological Distress Among Patients with Heart Disease

Background: Psychological stress disorders are twice as prevalent in women with ischemic heart disease compared to men. The disproportionate psychological health experience of these women is not well understood. The objective of this study was to examine whether neighborhood social factors are associated with disparities in psychological health by gender. Materials and Methods: We studied 286 patients with heart disease recruited from Emory-based hospitals in the Myocardial Infarction and Mental Stress 2 Study (n = 286). A global measure of psychological distress was calculated by taking an average of ranks across symptom scales for depression, post-traumatic stress disorder, anxiety, anger, and perceived stress. The social vulnerability index (SVI) was developed by the Centers for Disease Control and Prevention and was used to rank patients' census tracks on 14 social factors. Beta coefficients for mean ranks in psychological distress scores were estimated per 10-unit increase in SVI percentile ranking using multilevel regression models. Results: The mean age of the sample was 51 years, 49% were women, and 66% African American. After adjusting for demographics, cardiovascular risk factors, and antidepressant use, each 10-unit increase in SVI percentile ranking was associated with 4.65 (95% CI: 0.61-8.69; p = 0.02) unit increase in mean scores for psychological distress among women only (SVI-by-gender-interaction = 0.01). These associations were driven by the SVI themes of lower socioeconomic status and poorer access to housing and transportation. Conclusion: Neighborhood social vulnerability may be a psychosocial stressor that potentiates women's susceptibility to adverse psychological and cardiovascular health.

Connected in health: Place-to-place commuting networks and COVID-19 spillovers

Biweekly county COVID-19 data were linked with Longitudinal Employer-Household Dynamics data to analyze population risk exposures enabled by pre-pandemic, country-wide commuter networks. Results from fixed-effects, spatial, and computational statistical approaches showed that commuting network exposure to COVID-19 predicted an area's COVID-19 cases and deaths, indicating spillovers. Commuting spillovers between counties were independent from geographic contiguity, pandemic-time mobility, or social media ties. Results suggest that commuting connections form enduring social linkages with effects on health that can withstand mobility disruptions. Findings contribute to a growing relational view of health and place, with implications for neighborhood effects research and place-based policies.

The Intersection of Neighborhood Environment and Adverse Childhood Experiences: Methods for Creation of a Neighborhood ACEs Index

This study evaluated methods for creating a neighborhood adverse childhood experiences (ACEs) index, a composite measure that captures the association between neighborhood environment characteristics (e.g., crime, healthcare access) and individual-level ACEs exposure, for a particular population. A neighborhood ACEs index can help understand and address neighborhood-level influences on health among individuals affected by ACEs. Methods entailed cross-sectional secondary analysis connecting individual-level ACEs data from the Philadelphia ACE Survey (n = 1677) with 25 spatial datasets capturing neighborhood characteristics. Four methods were tested for index creation (three methods of principal components analysis, Bayesian index regression). Resulting indexes were compared using Akaike Information Criteria for accuracy in explaining ACEs exposure. Exploratory linear regression analyses were conducted to examine associations between ACEs, the neighborhood ACEs index, and a health outcome—in this case body mass index (BMI). Results demonstrated that Bayesian index regression was the best method for index creation. The neighborhood ACEs index was associated with higher BMI, both independently and after controlling for ACEs exposure. The neighborhood ACEs index attenuated the association between BMI and ACEs. Future research can employ a neighborhood ACEs index to inform upstream, place-based interventions and policies to promote health among individuals affected by ACEs.

Dietary patterns in middle childhood and behavior problems in adolescence

BACKGROUND/OBJECTIVES

Adherence to a "Western" style dietary pattern has been related to behavior problems in children in high-income countries; however, dietary patterns may differ in countries undergoing the nutrition transition. Associations of dietary patterns with behavior problems in a Latin American context have not been evaluated.

SUBJECTS/METHODS

Mothers of 385 children 5 to 12 y old completed a food frequency questionnaire (FFQ) on the children's usual intake at enrollment into a cohort study. Four dietary patterns were identified through principal component analysis of the FFQ: animal protein, snacking, cheaper protein, and traditional/starch. After a median 6 y follow-up, adolescents reported behavior problems via the Youth Self Report, a standardized questionnaire. We compared the continuous distributions of externalizing and internalizing behavior problems and their subscales across quartiles of adherence to the four dietary patterns using multivariable linear regression.

RESULTS

Boys in the highest quartile of adherence to the animal protein pattern in middle childhood had an adjusted 5.5 units lower (95% CI -9.5, -1.5) mean total externalizing problems score compared with boys in the lowest quartile (P trend = 0.008). Adherence to the animal protein pattern was also inversely related to the aggressive behavior externalizing subscale in a dose-response manner among boys (P trend = 0.009). There were no associations between adherence to other dietary patterns and externalizing problems in boys or girls. There were no associations with internalizing problems.

CONCLUSION

Adherence to an animal protein dietary pattern in middle childhood was associated with less externalizing behavior problems in adolescent boys.

Neighborhood socioeconomic deprivation and psychiatric medication purchases. Different neighborhood delineations, different results? A nationwide register-based multilevel study

Previous studies of neighborhood socioeconomic deprivation and mental health treatment have shown mixed results. Multiple reviews have highlighted that the definition and measurement of neighborhoods can lead to different results, providing one explanation for these mixed findings. This study compares the use of micro-areas created using an automated redistricting algorithm and divided by physical barriers with the use of two administrative units, Danish parishes and postal codes. The geographical data are linked to Danish register data of the Danish population from age 16 in 2017, N = 4,347,001, to measure the association between the purchase of psychiatric medication and neighborhood socioeconomic deprivation using logistic multilevel models. Neighborhood socioeconomic deprivation is associated with a slightly increased probability of redeeming prescriptions for psychiatric medication after controlling for individual sociodemographic composition. However, this association was present only for micro-areas and not for parishes or postal codes. The results indicate that neighborhood effects on psychiatric medication purchases are affected by the neighborhood delineations used and that future studies should carefully consider how neighborhoods are defined and measured.

An Ecological Model to Frame the Delivery of Pediatric Preventive Care

Screening and surveillance are integral aspects of child health promotion and disease prevention. The American Academy of Pediatrics recommends that primary care clinicians screen children and adolescents for a broad array of conditions, conduct surveillance of growth and development, identify social determinants of health, and identify protective and risk factors that might impact health over time. However, access to and outcomes of preventive services vary based on features of children’s social ecology, including family and community contexts. The proposed five-stage socio-ecological model considers multiple contextual dimensions of pediatric screening: (1) individual, (2) interpersonal, (3) organizational, (4) community/population, and (5) public policy. Incorporating this model into routine care might improve outcomes at the individual and population level. Future endeavors should focus on integration of this model with validated risk screening tools as part of a supportive electronic health record, culture, and incentive structure. Further research assessing the contributors and outcomes of differences in beliefs, resources, practices, and opportunities among individuals, families, providers, primary care organizations, communities, health systems, and policy partners will be essential in advancing knowledge and policies to improve preventive services delivery.

Savings, home ownership, and depression in low-income US adults

PURPOSE

While the association between income and depression is well established, less explored is the relation between wealth and depression, particularly among low-income adults. We studied the relation between two types of assets-savings and home ownership-and probable depression to understand how access to different assets may shape depression among low-income US adults.

METHODS

Study sample We conducted a serial cross-sectional, observational study with 12,019 adults with low-income in the United States using National Health and Nutrition Examination Survey (NHANES) data from 2007 to 2016. Measures We measured probable major depressive disorder (MDD) with impairment using the Patient Health Questionnaire-9. Low savings was defined as having $5000 or less in family savings. Statistical analysis We estimated adjusted and unadjusted prevalence, odds ratios, and predicted probability of probable MDD across asset groups.

RESULTS

Of low-income US adults, 5.4% had probable MDD with impairment, 85.9% had low savings, and 54.9% rented their home. Persons with low savings had 2.34 (95% CI 1.44-3.79) times the odds of having probable MDD relative to those with high savings. Home owners had 2.14 (95% CI 1.20-3.86) and home renters had 3.65 (95% CI 1.45-9.20) times the odds of having probable MDD if they had low savings relative to high savings.

CONCLUSION

Family savings and home ownership are associated with lower burden of depression among low-income adults in the US.

Creative Approaches for Assessing Long-term Outcomes in Children

Advances in new technologies, when incorporated into routine health screening, have tremendous promise to benefit children. The number of health screening tests, many of which have been developed with machine learning or genomics, has exploded. To assess efficacy of health screening, ideally, randomized trials of screening in youth would be conducted; however, these can take years to conduct and may not be feasible. Thus, innovative methods to evaluate the long-term outcomes of screening are needed to help clinicians and policymakers make informed decisions. These methods include using longitudinal and linked-data systems to evaluate screening in clinical and community settings, school data, simulation modeling approaches, and methods that take advantage of data available in the digital and genomic age. Future research is needed to evaluate how longitudinal and linked-data systems drawing on community and clinical settings can enable robust evaluations of the effects of screening on changes in health status. Additionally, future studies are needed to benchmark participating individuals and communities against similar counterparts and to link big data with natural experiments related to variation in screening policies. These novel approaches have great potential for identifying and addressing differences in access to screening and effectiveness of screening across population groups and communities.

Area-Level Variation and Human Papillomavirus Vaccination among Adolescents and Young Adults in the United States: A Systematic Review

Understanding how human papillomavirus (HPV) vaccination coverage varies by geography can help to identify areas of need for prevention and control efforts. A systematic review of the literature was conducted using a combination of keywords (HPV vaccination, geography, neighborhoods, and sociodemographic factors) on Medline and Embase databases. Studies had to provide information on HPV vaccination by area-level variables, be conducted in the United States, and be published in English (analyzing data from January 2006 to February 2020). Conference abstracts and opinion pieces were excluded. Of 733 records identified, 25 were included for systematic review. Across studies, the average initiation rate was 40.5% (range, 6.3%-78.0%). The average rate of completion was 23.4% (range, 1.7%-55.2%). Geographic regions and area-level factors were associated with HPV vaccination, including zip code tabulation area-level poverty, urbanicity/rurality, racial/ethnic composition, and health service region characteristics. Only three studies utilized geospatial approaches. None accounted for geospatial-temporal associations. Individual-level and area-level factors and their interactions are important for characterizing HPV vaccination. Results demonstrate the need to move beyond existing multilevel methods and toward the adoption of geospatial approaches that allow for the mapping and detection of geographic areas with low HPV vaccination coverage.

Predicting psychosis-spectrum diagnoses in adulthood from social behaviors and neighborhood contexts in childhood

Research showing that risk for schizophrenia, bipolar disorder with psychosis, and other psychosis-spectrum diagnoses in adulthood is multidetermined has underscored the necessity of studying the additive and interactive factors in childhood that precede and predict future disorders. In this study, risk for the development of psychosis-spectrum disorders was examined in a 2-generation, 30-year prospective longitudinal study of 3,905 urban families against a sociocultural backdrop of changing economic and social conditions. Peer nominations of aggression, withdrawal, and likeability and national census information on neighborhood-level socioeconomic disadvantage in childhood, as well as changes in neighborhood socioeconomic conditions over the lifespan, were examined as predictors of diagnoses of schizophrenia, bipolar disorder, and other psychosis-spectrum disorders in adulthood relative to developing only nonpsychotic disorders or no psychiatric disorders. Individuals who were both highly aggressive and highly withdrawn were at greater risk for other psychosis-spectrum diagnoses when they experienced greater neighborhood disadvantage in childhood or worsening neighborhood conditions over maturation. Males who were highly aggressive but low on withdrawal were at greater risk for schizophrenia diagnoses. Childhood neighborhood disadvantage predicted both schizophrenia and bipolar diagnoses, regardless of childhood social behavior. Results provided strong support for multiple-domain models of psychopathology, and suggest that universal preventive interventions and social policies aimed at improving neighborhood conditions may be particularly important for decreasing the prevalence of psychosis-spectrum diagnoses in the future.

When does geography matter most? Age-specific geographical effects in the patterning of, and relationship between, mental wellbeing and mental illness

Mental illness and mental wellbeing are related but distinct constructs. Despite this, geographical enquiry often references the two as interchangeable indicators of mental health and assumes the relationship between the two is consistent across different geographical scales. Furthermore, the importance of geography in such research is commonly assumed to be static for all age groups, despite the large body of evidence demonstrating contextual effects in age-specific populations. We leverage simultaneous measurement of a mental illness and mental wellbeing metric from Understanding Society, a UK population-based survey, and employ bivariate, cross-classified multilevel modelling to characterise the relationship between geographical context and mental health. Results provide strong evidence for contextual effects for both responses before and after covariate adjustment, with weaker evidence for area-classification and PSU-level contextual effects for the GHQ-12 after covariate adjustment. Results support a two-continua model of mental health at the individual level, but indicates that consensual benefit may be achieved across both dimensions by intervening at household and regional levels. There is also some evidence of a greater contextual effects for mental wellbeing than for mental illness. Results highlight the potential of the household as a target for intervention design for consensual benefit across both constructs. Results also suggest the increased importance of geographical context for older respondents across both responses. This research supports an area-based approach to improving both mental illness and mental wellbeing in older populations.

No man is an island: spatial clustering and access to primary care as possible targets for the development of new community mental health approaches

BACKGROUND

to understand if patients seen at Centro Hospitalar Psiquiátrico de Lisboa (CHPL) live in geographical clusters or randomly throughout the city, as well as determine their access to the psychiatric hospital and primary care facilities (PCF).

METHODS

spatial autocorrelation statistics were performed (queen criterion of contiguity), regarding all patients observed at CHPL in 2017 (at the census subsection level), and considering not only their overall number but also main diagnosis, and admission to the psychiatric ward - voluntary or compulsory. Distance to the hospital and to the closest PCF was measured (for each patient and the variables cited above), and the mean values were compared. Finally, the total number of patients around each PCF was counted, considering specified radius sizes of 656 and 1000 m.

RESULTS

All 5161 patients (509 psychiatric admissions) were geolocated, and statistical significance regarding patient clustering was found for the total number (p-0.0001) and specific group of disorders, namely Schizophrenia and related disorders (p-0.007) and depressive disorders (p-0.0002). Patients who were admitted in a psychiatric ward live farther away from the hospital (p-0.002), with the compulsory admissions (versus voluntary ones) living even farther (p-0.004). Furthermore, defining a radius of 1000 m for each PCF allowed the identification of two PCF with more than 1000 patients, and two others with more than 800.

CONCLUSIONS

as patients seem to live in geographical clusters (and considering PCFs with the highest number of them), possible locations for the development of programs regarding mental health treatment and prevention can now be identified.

Influence of Individual and Contextual Perceptions and of Multiple Neighborhoods on Depression

The risk of depression is related to multiple various determinants. The consideration of multiple neighborhoods daily frequented by individuals has led to increased interest in analyzing socio-territorial inequalities in health. In this context, the main objective of this study was (i) to describe and analyze the spatial distribution of depression and (ii) to investigate the role of the perception of the different frequented spaces in the risk of depression in the overall population and in the population stratified by gender. Data were extracted from the 2010 SIRS (a French acronym for "health, inequalities and social ruptures") cohort survey. In addition to the classic individual characteristics, the participants reported their residential neighborhoods, their workplace neighborhoods and a third one: a daily frequented neighborhood. A new approach was developed to simultaneously consider the three reported neighborhoods to better quantify the level of neighborhood socioeconomic deprivation. Multiple simple and cross-classified multilevel logistic regression models were used to analyze the data. Depression was reported more frequently in low-income (OR = 1.89; CI = [1.07-3.35]) or middle-income (OR = 1.91; CI = [1.09-3.36]) neighborhoods and those with cumulative poverty (OR = 1.64; CI = [1.10-2.45]). In conclusion, a cumulative exposure score, such as the one presented here, may be an appropriate innovative approach to analyzing their effects in the investigation of socio-territorial inequalities in health.

Trajectories of Depressive Symptoms and Neighborhood Changes from Adolescence to Adulthood: Latent Class Growth Analysis and Multilevel Growth Curve Models

The role of neighborhood socioeconomic status (SES) and racial/ethnic composition on depression has received considerable attention in the United States. This study examines associations between trajectory patterns of neighborhood changes and depressive symptoms using data from Waves I-IV of the National Longitudinal Study of Adolescent to Adult Health. We used latent class growth analysis to determine the number and distribution of person-centered trajectories for neighborhood characteristics, and multilevel growth curve models to examine how belonging to each class impacted depression trajectories from ages 13 to 32 among non-Hispanic Whites (NHW), non-Hispanic Blacks (NHB), Hispanics, and non-Hispanic Others (NHO). The distribution of neighborhood SES classes across racial/ethnic groups suggests significant levels of economic inequality, but had no effect on depressive symptoms. A more complex picture emerged on the number and distribution of racial/ethnic composition latent class trajectories. Compared to NHB peers who lived in predominantly NHW neighborhoods from adolescence to adulthood, NHBs in more diverse neighborhoods had lower risk for depressive symptoms. Conversely, Hispanics living in neighborhoods with fewer NHWs had higher risk for depressive symptoms. Among NHOs, living in neighborhoods with a critical mass of other NHOs had a protective effect against depressive symptoms.

Neighborhoods, psychological distress, and the quest for causality

Neighborhood characteristics have been associated with psychological distress, but it is uncertain whether these associations are causal. The current article reviews data from interventions and quasi-experimental studies that have addressed the question of causality of neighborhood associations. Overall, data from neighborhood interventions, longitudinal studies, and twin studies have provided only limited and inconsistent evidence to support causal interpretation of neighborhood associations with psychological distress: very few findings have been replicated across different samples, and many associations have been observed only with some of the multiple measures included the studies. Studies that examine the effects of neighborhood change on people's wellbeing are needed to improve causal inference and policy relevance of neighborhood studies.

Spatial structure of depression in South Africa: A longitudinal panel survey of a nationally representative sample of households

Wider recognition of the mental health burden of disease has increased its importance as a global public health concern. However, the spatial heterogeneity of mental disorders at large geographical scales is still not well understood. Herein, we investigate the spatial distribution of incident depression in South Africa. We assess depressive symptomatology from a large longitudinal panel survey of a nationally representative sample of households, the South African National Income Dynamics Study. We identified spatial clusters of incident depression using spatial scan statistical analysis. Logistic regression was fitted to establish the relationship between clustering of depression and socio-economic, behavioral and disease risk factors, such as tuberculosis. There was substantial geographical clustering of depression in South Africa, with the excessive numbers of new cases concentrated in the eastern part of the country. These clusters overlapped with those of self-reported tuberculosis in the same region, as well as with poorer, less educated people living in traditional rural communities. Herein, we demonstrate, for the first time, spatial structuring of depression at a national scale, with clear geographical ‘hotspots’ of concentration of individuals reporting new depressive symptoms. Such geographical clustering could reflect differences in exposure to various risk factors, including socio-economic and epidemiological factors, driving or reinforcing the spatial structure of depression. Identification of the geographical location of clusters of depression should inform policy decisions.

Neighborhood conditions and trajectories of alcohol use and misuse across the early life course

While neighborhood conditions have been linked to alcohol misuse, less is known about the long-term consequences of exposure to adverse neighborhood conditions early in the life course. Using data from the National Longitudinal Survey of Adolescent to Adult Health, we examined how trajectories of alcohol behaviors from ages 12 to 32 varied according to neighborhood disorder, disadvantage, and advantage. Early exposure to adverse neighborhood conditions placed individuals at greater risk of being a current drinker and alcohol misuse, though these individuals never reached the same levels as those in more stable, advantaged neighborhoods. Early exposure appears to place individuals at risk for alcohol misuse across the early life course.