Multidimensional Approach to Exploring Neighborhood Determinants and Symptom Severity Among Individuals With Psychosis

Importance The impact of cumulative exposure to neighborhood factors on psychosis, depression, and anxiety symptom severity prior to specialized services for psychosis is unknown. Objective To identify latent neighborhood profiles based on unique combinations of social, economic, and environmental factors, and validate profiles by examining differences in symptom severity among individuals with first episode psychosis (FEP). Design, Setting, and Participants This cohort study used neighborhood demographic data and health outcome data for US individuals with FEP receiving services between January 2017 and August 2022. Eligible participants were between ages 14 and 40 years and enrolled in a state-level coordinated specialty care network. A 2-step approach was used to characterize neighborhood profiles using census-tract data and link profiles to mental health outcomes. Data were analyzed March 2023 through October 2023. Exposures Economic and social determinants of health; housing conditions; land use; urbanization; walkability; access to transportation, outdoor space, groceries, and health care; health outcomes; and environmental exposure. Main Outcomes and Measures Outcomes were Community Assessment of Psychic Experiences 15-item, Patient Health Questionnaire 9-item, and Generalized Anxiety Disorder 7-item scale. Results The total sample included 225 individuals aged 14 to 36 years (mean [SD] age, 20.7 [4.0] years; 152 men [69.1%]; 9 American Indian or Alaska Native [4.2%], 13 Asian or Pacific Islander [6.0%], 19 Black [8.9%], 118 White [55.1%]; 55 Hispanic ethnicity [26.2%]). Of the 3 distinct profiles identified, nearly half of participants (112 residents [49.8%]) lived in urban high-risk neighborhoods, 56 (24.9%) in urban low-risk neighborhoods, and 57 (25.3%) in rural neighborhoods. After controlling for individual characteristics, compared with individuals residing in rural neighborhoods, individuals residing in urban high-risk (mean estimate [SE], 0.17 [0.07]; P = .01) and urban low-risk neighborhoods (mean estimate [SE], 0.25 [0.12]; P = .04) presented with more severe psychotic symptoms. Individuals in urban high-risk neighborhoods reported more severe depression (mean estimate [SE], 1.97 [0.79]; P = .01) and anxiety (mean estimate [SE], 1.12 [0.53]; P = .04) than those in rural neighborhoods. Conclusions and Relevance This study found that in a cohort of individuals with FEP, baseline psychosis, depression, and anxiety symptom severity differed by distinct multidimensional neighborhood profiles that were associated with where individuals reside. Exploring the cumulative effect of neighborhood factors improves our understanding of social, economic, and environmental impacts on symptoms and psychosis risk which could potentially impact treatment outcomes.

as a component of a walkability score: The measure displays a score from zero to 100 with zero being only one land use type and 100 being an equal distribution of six land use types (education, entertainment, single-family residence, multi-family residence, retail, office).
Within the WTN, decile rankings reflect "Poor Land-Use Mix," such that higher rankings correspond to lower-diversity land use.

Healthcare Professional Shortage Areas
Health Provider Shortage were determined based on data on Primary Care, Mental Health, and Dental Healthcare Shortage Area (HPSA) datasets maintained by the Health Resources and Services Administration's (HRSA) Division of Policy and Shortage Designation.
Scores from primary health care, mental health, and dental health HPSAs were summed across census tracts.Scores for each shortage type range from 0 to 26.The scores are based on the number of providers per capita and information on the intensity of the need for providers in that area.

LPA Indicators from the Washington Tracking Network's Information by Location tool
In addition to raw data and percentile scores on individual data indicators, the WTN has developed composite metrics for the Map (University of Washington Department of Environmental & Occupational Health Sciences, 2019), which are available via the WTN's Information by Location (IBL) mapping tool.To create composites, the WTN percentile scores for all indicators are averaged within a given tract and averages are ranked using deciles (1 decile = 10 percent).Each decile represents about 10 percent of the values in the dataset.In the current study, we used the IBL to collect information on economic characteristics, social determinants of health, nearby housing conditions and land use, access to transportation, area environmental exposure, and population health outcomes.Additional details about indicators for each are available within the WTN data portal.

Economic Determinants Composite
Data informing the Economic Determinants composite variable were drawn from the U.S. Census Bureau's American Community Survey 5-year estimates for 2015-19 (U.S. Census Bureau, 2019).Indicators included in the composite metric are shown in eTable 1.

Rural Urban Commuting Area Codes
Rurality was determined using the Rural-Urban Commuting Area (RUCA) taxonomy, a classification system developed by the United States Department of Agriculture (USDA, 2013) that categorizes U.S. census tracts based on their degree of urbanization and commuting patterns (USDA, 2013).RUCA codes are based on measures of population density, urbanization, and commuter flows.These codes range from 1 (most urban) to 10 (most rural).Categorically, codes between 1 and 3 reflect metropolitan areas, between 4 and 6 micropolitan areas, between 7 and 9 small-town areas, and codes of 10 reflect rural areas.

Food Access Research Atlas
The Food Access Research Atlas was developed by the USDA to provide detailed information on food access across the U.S. and highlight areas where individuals may have difficulty accessing healthy and affordable food options (USDA, 2019).In the current study, tract access was measured as the percentage of the population living further than one mile away from a supermarket.

National Walkability Index
The National Walkability Index is a quantitative assessment tool used to evaluate and rank the pedestrian-friendliness of different neighborhoods (U.S. EPA, 2017).It considers sidewalk availability, connectivity, traffic safety, proximity to amenities, public transportation access, street design, safety from crime, and walkability infrastructure investments.

Department of Transportation, Transportation Barriers Score
The degree of transportation barriers for each census tract was characterized using scores developed by the Washington State Department of Transportation (DOT; U.S. DOT, 2022).The DOT transportation barriers scores quantify the difficulty of travel to essential services based on road metrics, transit availability, and travel time to key destinations like grocery stores, doctors, and schools.Census tracts receive a score from 0 to 100, with lower scores indicating more barriers to transportation such as limited access to vehicles, long travel times, and lack of public transit options.The scores were developed using travel demand modeling and accessibility analysis based on the street network, transit network, and locations of essential services.
Composite © 2024 Oluwoye O et al.JAMA Network Open.Data informing the Social Determinants variable were drawn from the U.S. Census Bureau's American Community Survey 5-year estimates for 2015-19 (U.S. Census Bureau, 2019).Indicators included in the composite metric are shown in eTable 2. Poor Health Outcomes Composite Data informing Poor Health Outcomes were drawn from Washington State Department of Health (WA DOH).Indicators included in the composite metric are shown in eTable 3. Housing Conditions Composite Data informing the Housing Conditions variable were drawn from the U.S. Census Bureau's American Community Survey 5-year estimates for 2015-19 (U.S. Census Bureau, 2019).Indicators included in the composite metric are shown in eTable 4. Environmental Exposures Composite Data informing the Environmental Exposures composite was drawn from the Washington State Department of Ecology's 2014 Comprehensive Emissions Inventory (Washington State Department of Ecology, 2022) and Washington State University's Air Indicator Report for Public Awareness and Community Tracking (AIRPACT) modeling domain (Vaughn et al, 2002), Washington State Department of Transportation's vehicular traffic data monitoring program (WA DOT, 22019), and the Environmental Protection Agency's Risk Screening tool (U.S. EPA, 2021).Indicators included in the composite metric are shown in eTable 5.

eTable 1 .
Indicators included in Washington Tracking Network's Information by Location Economic Determinants Composite Metricon housing costs.There are three categories under "Selected Monthly Costs as Percentage of Household Income'': households with mortgages, households without mortgages, and rentals."Unaffordable housing" is defined as households spending greater than 30 percent of their income on housing costs.The housing burden indicator displays the modeled percent of income spent on housing for a fourperson household making the median household income.
O et al.JAMA Network Open.
Class Solutions and Model Fit Indices for Latent Profile Analysis of Washington Neighborhoods Notes.AIC = Akaike Information Criterion; Approximate Weight of Evidence Criterion; BIC = Bayesian Information Criterion; BLRT = bootstrapped likelihood ratio test; CLC = Classification Likelihood Criterion; Kullback Information Criterion.eFigure.Class Solutions and Model Fit Indices for Latent Profile Analysis of Washington NeighborhoodsNotes.AIC = Akaike Information Criterion; Approximate Weight of Evidence Criterion; BIC = Bayesian Information Criterion; CLC = Classification Likelihood Criterion; Kullback Information Criterion.