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JAMA Netw Open
JAMA Netw Open
JAMA Network Open
2574-3805
American Medical Association

39302675
10.1001/jamanetworkopen.2024.34923
zoi241035
Research
Original Investigation
Online Only
Pediatrics
Community Asset Density and Past-Year Mental Health Symptoms Among Youths
Community Asset Density and Mental Health Symptoms Among Youths
Community Asset Density and Mental Health Symptoms Among Youths
Szoko Nicholas MD 1
Ajith Aniruddh BS 2
Kurland Kristen BA 3 4
Culyba Alison J. MD PhD MPH 1 2
1 Division of Adolescent and Young Adult Medicine, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania
2 Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
3 School of Architecture, Carnegie Mellon University, Pittsburgh, Pennsylvania
4 H. John Heinz III College, Carnegie Mellon University, Pittsburgh, Pennsylvania
Article Information

Accepted for Publication: July 27, 2024.

Published: September 20, 2024. doi:10.1001/jamanetworkopen.2024.34923

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2024 Szoko N et al. JAMA Network Open.

Corresponding Author: Nicholas Szoko, MD, UPMC Children’s Hospital of Pittsburgh, 120 Lytton Ave, Pittsburgh, PA 15213 (nis165@pitt.edu).
Author Contributions: Dr Szoko had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Szoko, Kurland, Culyba.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Szoko, Ajith, Kurland.

Critical review of the manuscript for important intellectual content: Ajith, Kurland, Culyba.

Statistical analysis: Szoko, Kurland, Culyba.

Administrative, technical, or material support: Szoko, Culyba.

Supervision: Kurland, Culyba.

Conflict of Interest Disclosures: Dr Culyba reported receiving grants from the National Institutes of Health (NIH) and the Centers for Disease Control and Prevention during the conduct of the study. No other disclosures were reported.

Funding/Support: This study was supported in part by grant TL1TR001858 from the National Center for Advancing Translational Sciences, NIH (Dr Szoko and Mr Ajith).

Role of the Funder/Sponsor: The NIH had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2.

20 9 2024
9 2024
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Copyright 2024 Szoko N et al. JAMA Network Open.
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the CC-BY License.
jamanetwopen-e2434923.pdf

This cross-sectional study assessed which community asset categories may be associated with youth mental health symptoms.

Key Points

Question

What types of community assets (eg, schools, parks, libraries, and barbershops) may be associated with youth mental health?

Findings

In this cross-sectional study of 2162 adolescent youths surveyed across Allegheny County, Pennsylvania, living in zip codes with a high density of transportation assets, educational resources, and health services was associated with lower odds of past-year feelings of hopelessness.

Meaning

These findings suggest that community assets offered important touchpoints for adolescents that may be leveraged to support youths’ mental health.

Importance

Many youths experience mental health challenges. Identifying which neighborhood and community factors may influence mental health may guide health policy and practice.

Objective

To explore associations between community assets (eg, schools, parks, libraries, and barbershops) and past-year mental health symptoms among youths.

Design, Setting, and Participants

This cross-sectional study leveraged 3 datasets, which were linked by 26 zip codes: the Western Pennsylvania Regional Data Center, the Child Opportunity Index 2.0 database, and the Allegheny County Youth Risk Behavior Survey (YRBS). The YRBS was administered during the study period in 2018 to youths across 13 high schools in Allegheny County, Pennsylvania; the study dates were from October 15 to October 19, 2018. Dates of analysis were from August 1, 2023, to July 15, 2024.

Exposures

Asset density in each zip code across 8 asset categories (transportation, education, parks and recreation, faith-based entities, health services, food resources, personal care services, and social infrastructure) was calculated.

Main Outcomes and Measures

The main outcomes were mental health measures included in the past 12 months, which comprised feelings of hopelessness (feeing so sad or hopeless that you stopped doing activities), nonsuicidal self-injury (hurt yourself on purpose without wanting to die), and suicidal ideation (seriously considered attempting suicide). All were operationalized to any or none. Data were analyzed using multivariable generalized linear mixed models and were adjusted for age, sex assigned at birth, race and ethnicity, and identification as sexually or gender diverse.

Results

Among 6306 students who were eligible for the YRBS based on their enrollment in participating high schools, 4487 students completed surveys, and 2162 were included in the analytic sample (mean [SD] age, 15.8 [1.2] years; 1245 [57.6%] were assigned female sex at birth). Over one-third of the participants (811 [37.5%]) reported past-year feelings of hopelessness; 587 (27.2%), past-year nonsuicidal self-injury; and 450 (20.8%), past-year suicidal ideation. High total asset population density (adjusted odds ratio [AOR], 0.85 [95% CI, 0.75-0.97]; P = .01), as well as population density of transportation assets (AOR, 0.77 [95% CI, 0.66-0.90]; P < .001), educational resources (AOR, 0.78 [95% CI, 0.67-0.92]; P = .002), and health services (AOR, 0.74 [95% CI, 0.60-0.91]; P = .006), were associated with lower odds of past-year hopelessness after adjusting for covariates. There were no correlations between asset density, Child Opportunity Index, and other mental health measures.

Conclusions and Relevance

The findings of this cross-sectional study suggest that access to certain community assets was associated with lower odds of feelings of hopelessness among youths. Ongoing work is needed to characterize other forms of social and cultural capital, which may mitigate negative mental health outcomes among adolescent youths.
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pmcIntroduction

Many youths in the US experience mental health challenges. In 2021, 42% of US high school students felt persistently sad or hopeless, and 22% had seriously considered suicide.1 Poor mental health in adolescence exerts wide-reaching impacts on overall health and well-being. Feelings of hopelessness may predict future thoughts of suicide and increase risk for violence perpetration and delinquency.2 Youths who participate in nonsuicidal self-injury (NSSI) are more likely to develop substance use disorders and report risky sexual behaviors.3 Risk and protective factors related to youth mental health have been described at individual, interpersonal, and community levels.4,5,6 There have thus been growing calls for cross-sector research, which considers adolescent well-being within a broader ecologic framework.7,8

Community assets, including parks, libraries, community centers, and salons or barbershops, represent important touchpoints for youths that may bolster mental health.9,10,11 Community gardens and green spaces have been associated with greater psychologic empowerment and reduced psychologic distress.12,13,14,15,16 Public libraries provide a safe and inclusive space for socialization and access to mental health resources, which may foster an increased sense of belonging.17 Barbershops and hair salons are cultural assets among Black communities and may act as nexuses for community-based mental health initiatives.18,19 Resources related to basic needs (eg, food security, stable housing) may also play a key role in supporting mental health service utilization.20,21,22

Geographic information systems involve mapping, visualizing, and analyzing spatial data patterns and have emerged as a powerful strategy in public health and medicine.23,24 Geographic information systems encompass numerous methods and leverage data from various sources. Geographic information system studies of green space may use satellite-derived variables such as a normalized difference vegetation index, green cover, and park density.14 Density-based measures have also been used to understand relationships between environmental features and health. For example, alcohol outlet density has shown associations with community violence,25 and concentration of fast food establishments has been associated with elevated body mass index.26 Given their potential for understanding the dynamic environments in which youths live and play, geospatial methods are particularly salient for understanding mental health access and outcomes.27

Composite indices, such as the Child Opportunity Index (COI)28 and the Social Deprivation Index,29 represent additional geospatial measures that have been used to explore ecologic influences on adolescent health. Derived primarily from census-level metrics (eg, percentage living in poverty, educational attainment), such indices allow for grading of zip codes or census tracts across specified parameters, generating a summary score for socioeconomic context.30 These measures have been associated with a variety of adolescent psychologic outcomes, including depressive symptoms, subjective well-being, and externalizing behaviors.31,32 Although these indices provide some insight into place-based determinants of mental health, they may inadvertently reinforce assumptions related to historic forms of oppression. Limitations in resolution (ie, the level of measurement) as well as the summarization strategy used (ie, ranking, normalization) may obscure variability within specific indicators or across smaller geographic areas.30 Many indices assess areas through a lens of deprivation, which fails to acknowledge the strength and vitality of communities.

Considering these challenges, we sought to leverage novel geographic information system methods to better understand associations between multiple types of community assets (eg, schools, parks, libraries, and barbershops) and youth mental health. Specifically, we examined associations between the density of community assets and youth hopelessness, NSSI, and suicidal ideation. Our primary goal was to better understand which community assets may serve as protective factors for youths’ mental health to inform future prevention and intervention programs.

Methods

Study Design and Data Sources

This cross-sectional study used data from the Western Pennsylvania Regional Data Center (WPRDC), the COI 2.0 database, and the Allegheny County Youth Risk Behavior Survey (YRBS) (eTable 1 in Supplement 1). Data from the WPRDC were accessed online as a comma-separated values text file with latitude and longitude data for over 30 000 assets in Allegheny County, Pennsylvania, compiled from January 1, 2019, to March 15, 2020, and active as of March 1, 2020, prior to the onset of the COVID-19 pandemic.33 The COI 2.0 database was downloaded directly from the host web page and included the population younger than 18 years in each zip code, derived from 2015 American Community Survey estimates and zip code COI scores across 3 domains: education, health and environment, and social and economic.34 The COI scores were aggregated by zip code boundaries from January 15, 2020, to align with asset data from the WPRDC. The Allegheny County YRBS was administered during the study period in 2018 using paper-and-pencil surveys to 9th through 12th grade students across 13 high schools in Pittsburgh, Pennsylvania, in partnership with the Allegheny County Health Department and Pittsburgh Public Schools; the study dates were from October 15 to October 19, 2018. The current study used data from respondents living in zip codes with at least 5 youths sampled. Comparisons between youths with and without missing zip code data are presented separately (eTable 2 in Supplement 1). Parents or guardians received informational letters prior to the YRBS survey with the option to opt out of their child’s participation by signing and returning the letter, and students could also decline to participate. All students who were present in school on the day of survey administration and whose parents or guardians had not opted out were eligible to participate. This study was deemed exempt from review by the University of Pittsburgh Institutional Review Board because it used deidentified data. This study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Measures

Demographics

Youths reported their age in years and the sex (female or male) that was listed on their birth certificate. Race was measured by asking youths to select all categories that applied, including American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, White, and other (for which no racial categories were specified). Ethnicity was assessed with a single item regarding Hispanic or Latino heritage (self-reported yes or no). A collapsed race and ethnicity variable was used in analyses, which included Hispanic, multiracial, or other; non-Hispanic Black; or non-Hispanic White. Youths who were sexually and gender diverse included those reporting any sexual identity other than heterosexual or any gender identity other than cisgender, using a 2-step measure of gender identity.35,36 Race and ethnicity and sexual and gender diversity were included in the study due to known inequities related to mental health and well-being.

Asset Density

Assets were classified into 8 categories that were hypothesized to be associated with mental health based on a review of existing literature9,10,12,13,14,15,16,17,18,19,37,38: transportation, education, parks and recreation, faith-based entities, health services, food resources, personal care services, and social infrastructure (eTable 3 in Supplement 1). There were 9338 (28.4%) of 32 938 assets that did not fall into 1 of these categories and were only included when calculating total asset density. Assets were visualized as point overlays on choropleth maps. In addition, assets were summarized by calculating the number of assets per zip code and dividing by the estimated population under 18 (density per population). We obtained asset density z scores by normalizing across the 26 zip codes included in the analysis.

COI

The COI included 3 domains, each made up of multiple indicators obtained from census-level data: education (eg, high school graduation rate), health and environment (eg, walkability), and social and economic (eg, employment rate). The COI scores were obtained by ordering more than 72 000 census tracts across indicators and assigning a score from 1 to 100 based on percentile rank, with higher scores indicating more favorable opportunity. The COI scores were normalized to generate z scores across zip codes included in the study sample.

Mental Health

Mental health measures included feelings of hopelessness, NSSI, and suicidal ideation in the past 12 months. Hopelessness was assessed with a single item that asked youths to indicate whether they experienced sadness or hopelessness that limited usual activities for 2 weeks or more (yes or no). Youths indicated NSSI by reporting the frequency with which they hurt themselves on purpose without wanting to die; responses were operationalized to a binary indicator variable (any or none). For suicidal ideation, youths were asked whether they had seriously considered attempting suicide (yes or no).

Statistical Analysis

Dates of analysis were from August 1, 2023, to July 15, 2024. Geospatial analyses were performed in ArcGIS Pro, version 3.1 (Esri), and statistical analyses were completed in R, version 3.16.0 (R Project for Statistical Computing). Choropleth maps were created to display the percentage of youths reporting each mental health measure by zip code. To identify potential hotspots, we also conducted multivariate clustering analyses (eFigure 2 in Supplement 1). Quantitative geospatial data, including asset density and COI, were appended to the YRBS dataset according to respondent zip code. We used multivariable generalized linear mixed models to explore associations among asset density, COI, and each mental health measure, adjusting for age, sex assigned at birth, race and ethnicity, and identification as sexually or gender diverse. Models used a logit-link function and accounted for school-level clustering with a random intercept. Covariates were selected based on prior literature1 and hypothesized association with mental health measures. In asset density models, we included COI as a covariate to control for neighborhood contextual factors. We also examined correlations between total asset density and COI using Pearson product moment correlation. As an additional sensitivity analysis, we assessed associations between spatial asset density (calculated by dividing the number of assets by zip code area) and mental health measures given that certain asset categories (eg, parks and recreation) may depend on geographic area. We also conducted regression analyses using nationally normed COI z scores. Model fit was assessed by reviewing bayesian information criteria. Residual plots approximated a normal distribution without evidence of underdispersion or overdispersion. Variance inflation factors (all <3) did not suggest multicollinearity. Results of regression are presented as adjusted odds ratios (AORs) with 95% CIs. Unadjusted analyses and alternative multilevel models are available in eTables 4 and 5 in Supplement 1.

Results

Out of 6306 students enrolled, 4487 completed surveys (71.2% response rate), with 280 surveys excluded due to being unreadable or too incomplete for analysis (eFigure 1 in Supplement 1). A total of 2162 youths across 26 zip codes were included in the analysis (Table 1). The mean (SD) age was 15.8 (1.2) years; 1245 (57.6%) were assigned female sex and 899 (41.6%) were assigned male sex at birth. Among the participants, 25 (1.2%) reported being American Indian or Alaska Native; 88 (4.1%), Asian; 610 (28.2%), Black or African American; 181 (8.4%), Hispanic or Latino; 9 (0.4%), Native Hawaiian or Other Pacific Islander; 1035 (47.9%), White; and 375 (17.3%), other racial identity. One-quarter of the participants (552 [25.5%]) identified as being sexually or gender diverse. Over one-third (811 [37.5%]) reported feelings of hopelessness in the past 12 months; NSSI and suicidal ideation were also common (NSSI: 587 participants [27.2%]; suicidal ideation: 450 participants [20.8%]).

Table 1. Demographic Characteristics of Patients

Characteristic	Values (N = 2162)a	
Age, mean (SD), y	15.8 (1.2)	
Sex assigned at birth		
Female	1245 (57.6)	
Male	899 (41.6)	
Raceb		
American Indian or Alaska Native	25 (1.2)	
Asian	88 (4.1)	
Black or African American	610 (28.2)	
Native Hawaiian or Other Pacific Islander	9 (0.4)	
White	1035 (47.9)	
Otherc	375 (17.3)	
Hispanic or Latino ethnicityd	181 (8.4)	
Sexual or gender diversitye	552 (25.5)	
Mental health measure		
Hopelessness	811 (37.5)	
NSSI	587 (27.2)	
Suicidal ideation	450 (20.8)	
Abbreviation: NSSI, nonsuicidal self-injury.

a Data are presented as No. (%) of patients unless indicated otherwise. Due to missing data, numbers or percentages may not match the total number of patients or sum to 100%.

b Respondents were instructed to select all identities that applied.

c Other did not include specific racial categories.

d Assessed as a single item with a yes or no response.

e Includes those reporting any sexual identity other than heterosexual or any gender identity other than cisgender.

Choropleth maps for mental health measures are shown in the Figure. Adjusting for covariates, high total asset density was associated with lower odds of hopelessness using both population and spatial density measures (population: AOR, 0.85 [95% CI, 0.75-0.97]; P = .01; spatial: AOR, 0.87 [95% CI, 0.78-0.98]; P = .02). Similar findings were observed for 3 asset density subcategories, including transportation (population: AOR, 0.77 [95% CI, 0.66-0.90]; P < .001; spatial: AOR, 0.83 [95% CI, 0.73-0.95]; P = .006), education (population: AOR, 0.78 [95% CI, 0.67-0.92]; P = .002; spatial: AOR, 0.87 [95% CI, 0.77-0.98]; P = .02), and health services (population: AOR, 0.74 [95% CI, 0.60-0.91]; P = .006; spatial: AOR, 0.80 [95% CI, 0.67-0.95]; P = .01) (Table 2). Faith-based asset population density (AOR, 0.85 [95% CI, 0.73-0.99]; P = .04) and food resource spatial density (AOR, 0.89 [95% CI, 0.81-0.98]; P = .02) were also associated with lower odds of hopelessness.

Figure. Mental Health and Community Assets in Allegheny County, Pennsylvania

Choropleth maps show percentages of youths reporting past-year hopelessness (A), nonsuicidal self-injury (NSSI) (B), and suicidal ideation (C) across 26 zip codes in Allegheny County, Pennsylvania. Insets show community asset locations for transportation (D), educational resources (E), and health services (F). Data are derived from the results of the 2018 Allegheny County Youth Risk Behavior Survey.

Table 2. Associations Between Community-Level Asset Density and Mental Healtha

Asset category	Mental health measure	
Hopelessness	NSSI	Suicidal ideation	
AOR (95% CI)	P value	AOR (95% CI)	P value	AOR (95% CI)	P value	
Transportation	
Population density	0.77 (0.66-0.90)	<.001	1.02 (0.87-1.21)	.80	0.87 (0.72-1.04)	.13	
Spatial density	0.83 (0.73-0.95)	.006	0.97 (0.84-1.12)	.68	0.97 (0.83-1.14)	.71	
Education	
Population density	0.78 (0.67-0.92)	.002	1.00 (0.84-1.18)	.97	0.88 (0.74-1.06)	.18	
Spatial density	0.87 (0.77-0.98)	.02	0.97 (0.85-1.11)	.69	0.99 (0.86-1.14)	.91	
Parks and recreation	
Population density	0.86 (0.74-1.01)	.06	1.05 (0.89-1.24)	.57	0.93 (0.78-1.12)	.46	
Spatial density	0.90 (0.78-1.04)	.15	1.01 (0.87-1.18)	.89	1.02 (0.87-1.20)	.81	
Faith-based entities	
Population density	0.85 (0.73-0.99)	.04	1.10 (0.93-1.29)	.26	0.94 (0.78-1.12)	.47	
Spatial density	0.93 (0.84-1.04)	.22	1.02 (0.91-1.16)	.70	1.03 (0.90-1.17)	.65	
Health services	
Population density	0.74 (0.60-0.91)	.006	1.03 (0.85-1.25)	.77	0.85 (0.67-1.08)	.19	
Spatial density	0.80 (0.67-0.95)	.01	1.01 (0.84-1.20)	.95	0.94 (0.77-1.15)	.54	
Food resources	
Population density	0.90 (0.79-1.02)	.11	0.95 (0.82-1.10)	.48	0.98 (0.84-1.14)	.80	
Spatial density	0.89 (0.81-0.98)	.02	0.95 (0.85-1.06)	.39	1.02 (0.90-1.14)	.77	
Personal care services	
Population density	0.97 (0.85-1.11)	.65	0.96 (0.83-1.11)	.57	0.99 (0.85-1.16)	.92	
Spatial density	0.92 (0.78-1.09)	.35	1.00 (0.83-1.19)	.96	1.05 (0.87-1.27)	.60	
Social infrastructure	
Population density	0.93 (0.85-1.02)	.14	1.04 (0.94-1.15)	.44	0.97 (0.87-1.08)	.62	
Spatial density	0.94 (0.86-1.03)	.16	1.04 (0.95-1.13)	.44	0.99 (0.90-1.09)	.83	
Total assets	
Population density	0.85 (0.75-0.97)	.01	1.05 (0.92-1.21)	.45	0.93 (0.80-1.08)	.34	
Spatial density	0.87 (0.78-0.98)	.02	1.04 (0.91-1.18)	.56	0.98 (0.86-1.13)	.82	
Abbreviations: AOR, adjusted odds ratio; NSSI, nonsuicidal self-injury.

a Generalized linear mixed models were adjusted for age, sex assigned at birth, race and ethnicity, identification as sexually or gender diverse, Child Opportunity Index score (national norm; scores range from 1 to 100 based on percentile rank, with higher scores indicating more favorable opportunity), and school-level clustering.

In models examining COI scores, the overall COI score was significantly associated with lower odds of hopelessness (AOR, 0.89 [95% CI, 0.81-0.97]). A similar association was noted for health and environment (AOR, 0.90 [95% CI, 0.82-0.99]) and social and economic (AOR, 0.88 [95% CI, 0.81-0.96]) domains of the COI but not for education (AOR, 0.92 [95% CI, 0.83-1.01]) (Table 3). There were no correlations among asset density, COI, and other mental health measures. Total asset density did not show significant correlations with overall COI score (r = −0.26 [95% CI, −0.59 to 0.19]; P = .19).

Table 3. Associations Between Locally Normalized Child Opportunity Index and Mental Healtha

COI domainb	Mental health measure	
Hopelessness	NSSI	Suicidal ideation	
AOR (95% CI)	P value	AOR (95% CI)	P value	AOR (95% CI)	P value	
Education	0.92 (0.83-1.01)	.09	1.00 (0.89-1.11)	.95	0.97 (0.86-1.10)	.65	
Health and environment	0.90 (0.82-0.99)	.02	0.97 (0.88-1.08)	.60	0.92 (0.82-1.03)	.15	
Social and economic	0.88 (0.81-0.96)	.003	0.97 (0.89-1.07)	.57	0.93 (0.84-1.03)	.19	
Overall	0.89 (0.81-0.97)	.006	0.98 (0.89-1.07)	.65	0.94 (0.84-1.04)	.21	
Abbreviations: AOR, adjusted odds ratio; COI, Child Opportunity Index; NSSI, nonsuicidal self-injury.

a Generalized linear mixed models were adjusted for age, sex assigned at birth, race and ethnicity, identification as sexually or gender diverse, and school-level clustering.

b Scores range from 1 to 100 based on percentile rank, with higher scores indicating more favorable opportunity.

Discussion

In this cross-sectional study that leveraged a unique county-specific geospatial dataset, we demonstrated significant associations between community assets and youths’ mental health. We found that youths living in zip codes with a high density of transportation assets (eg, bus stops, light-rail stations), educational centers (eg, schools, colleges), faith-based entities (churches, synagogues, or temples), health services (eg, hospitals, doctors’ offices), and food resources (eg, grocery stores, food banks) reported lower odds of hopelessness. These findings were supported using a census-derived COI score. Our study builds on existing work9,10,12,13,14,15,16,17,18,19,37,38 by incorporating innovative quantitative methods and exploring a wide range of community assets. In addition, the specificity of data in this study allowed for a more nuanced understanding of the associations among spatial factors, resource access, and mental health.

The association between health service density and hopelessness among youths builds on existing geospatial research involving mental health care access. One prior study in an adult sample suggested that proximity to a primary care office was associated with depressive symptoms.39 That said, most other studies in this domain have focused on operationalizing measures of spatial access (eg, transit time) or describing specific access determinants (eg, proximity to transportation)40,41,42 rather than exploring associations with mental health symptoms. Although we did not analyze youths’ actual service utilization, the observed correlation may support theories of early service access associated with reduced mental health morbidity.43 This notion may be further supported by the inverse associations noted between transportation-related assets and hopelessness in our study. A high density of health clinics may also indicate greater clinician availability, a known facilitator of mental health care engagement among young people.44

In terms of other community assets, our findings regarding food resources support prior work, which has shown that food security may predict better mental health.45,46,47 Educational assets, such as high schools and colleges, showed similar negative associations with hopelessness, which may indicate the role of embedded mental health supports (eg, school counselors), classroom size, or other elements of school climate in promoting positive psychologic outcomes.48,49 In contrast, we noted no associations between the presence of parks and recreational spaces with the mental health measures examined. This may be due to the structure of our assets’ database. Each asset was coded as a point location (latitude and longitude) and thus did not capture areal factors, such as the spatial proportion of each zip code covered in green space.

Notably, significant associations were observed for hopelessness but not other mental health measures (ie, NSSI or suicidal ideation). A majority of geospatial studies involving youth mental health have focused on suicidal ideation, self-injury, or suicide attempts, with fewer considering other depressive symptoms.50,51,52 This is an important distinction, as increasing hope and goals for the future (ie, future orientation) has emerged as a cross-cutting protective factor for multiple adolescent health outcomes, especially among youths living in areas with a concentrated disadvantage.53,54,55 Among previous studies that have specifically considered hopelessness, the findings have generally supported the role of certain aspects of the built environment, such as housing occupancy and physical disorder (eg, graffiti, litter, or broken glass), as environmental determinants of these symptoms.56,57,58 That said, such studies have primarily assessed adult populations, and a majority have focused on census-based indicators included in composite deprivation indices such as the COI. There is a relative dearth in research examining community-protective factors and hopelessness, and our analyses suggest that certain assets may interact independently with youth mental health even after controlling for differences in neighborhood context.

It is important to note that both COI and asset density do not measure all forms of social, economic, and cultural capital in a neighborhood. For example, there were 2 zip codes with relatively low asset density that exhibited lower rates of hopelessness than what would be expected based on anticipated associations (eFigure 3 in Supplement 1). These neighborhoods have been disproportionately impacted by historical forms of oppression, which may be reflected as poorer scores on measures of deprivation (ie, low COI); however, findings of low hopelessness indicated that youths in such neighborhoods may have been buffered against negative mental health outcomes due to other unmeasured resources in the area. In addition, asset density did not show apparent correlations with COI, further distinguishing community assets as a measure that does not solely represent the absence of deprivation. Future work must involve partnerships with community members to comprehensively characterize neighborhood-level assets.59

Strengths and Limitations

Notable strengths of our study include the specificity offered by our measurement of community assets and the broad range of asset categories—features that complement existing measures of community opportunity. Our sample included youths with diverse racial, ethnic, sexual, and gender identities, which supports the generalizability of our findings. Because our study leveraged a unique locally derived dataset, we have been able to share findings with community collaborators to foster data transparency and promote translation of research findings. Obtaining additional qualitative data from youths through participatory mapping60 and walking interviews45,46 may further illuminate assets that support youths’ mental health. Focus groups or surveys with community members may allow identification of other assets not represented in existing repositories. Linking asset data to mental health measures collected at the census tract level may offer insight into neighborhood variability not apparent at the zip code level. Sharing data with policy makers and funders may inform structural interventions that fortify existing bright spots.

This study also has several key limitations. Our analysis was limited by geocoding at the zip code level, which may have obscured important heterogeneity within zip codes. In addition, zip codes are comprised of addresses defined yearly by the US Postal Service and do not represent distinct geographic boundaries, which creates challenges related to data aggregation.61,62 Although our study considered over 20 000 community assets, the specific services provided by each resource (eg, whether they offer youth-serving programs) were not included in the repository, making it difficult to quantify reach or associations with these locations. The YRBS was administered to students who were enrolled in urban high schools, which may cause sampling bias by excluding youths in rural areas or those with chronic absenteeism. Furthermore, our study only included youths from 26 of over 100 zip codes in Allegheny County, which may be due, in part, to a large percentage of missing zip code data in the overall YRBS dataset. Youths with missing zip code data were more likely to be younger, male, and Black, highlighting the need for more focused efforts to understand geospatial context in these subgroups.

In this study, we relied on cross-sectional data collected at multiple points in time. The WPRDC asset database was compiled between 2019 and 2020, the year after the YRBS survey was administered, and while most assets demonstrated relative stability prior to the COVID-19 pandemic, there were likely more prominent shifts after March 1, 2020. The COI data were derived from census-based indicators collected in 2015, which predates both the YRBS and WPRDC data. Because of these challenges, we could not examine longitudinal trends in asset density or mental health measures, and inferences related to causation were also limited. That said, many elements of neighborhood context, such as transportation, safety, and educational opportunity, do not change rapidly due to long-standing histories of structural oppression, reinforcing the critical importance of identifying and elevating community-level protective factors.

Conclusions

Methods to understand and analyze community assets through geospatial mapping continue to evolve. This cross-sectional study highlights opportunities to integrate strength-based frameworks in describing neighborhood context and suggests that certain asset types may be particularly relevant for addressing youths’ mental health. Future work must synergize existing data sources with expertise from community members to collect additional spatial metrics, implement novel analytic approaches, and develop interventions that fortify neighborhood resources to support youths in the areas in which they live and play.

Supplement 1. eTable 1. Data Sources and Measures

eFigure 1. Study Flow Diagram

eTable 2. Comparison of Youth With and Without Missing Zip Code Data

eTable 3. Asset Categories Derived From Allegheny County Assets Database

eFigure 2. Multivariate Cluster Analysis of Mental Health Measures

eTable 4. Associations Between Community-Level Asset Density and Mental Health: Unadjusted Odds Ratios (95% CIs)

eTable 5. Associations between Community-Level Asset Density and Mental Health: Adjusted Odds Ratios (95% CIs)

eFigure 3. “Bright Spot” Analysis: Asset Density Versus Proportion of Youth Reporting Hopelessness

Supplement 2. Data Sharing Statement
==== Refs
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