Neighborhood Context and Substance Use Disorder Risk: Comparing Immigrants and Nonimmigrants in a Community Health Setting

Neighborhood Context and Substance Use Disorder Risk: Comparing Immigrants and Nonimmigrants in a Community Health Setting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Neighborhood Context and Substance Use Disorder Risk: Comparing Immigrants and Nonimmigrants in a Community Health Setting Kenan Sualp This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8379948/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Substance use disorders remain a major public health concern, yet less is known about how neighborhood context shapes clinically documented substance use disorder diagnoses among immigrants and other underserved patients. We linked de identified electronic medical record data from a large integrated care facility serving uninsured and underinsured adults in Central Florida from 2010 to 2019 with American Community Survey 5-year census tract measures from 2013 to 2017 using ArcGIS. The analytic sample included 2,725 adults at or below 200 percent of the federal poverty line, including 403 immigrants. The outcome was a binary substance use disorder diagnosis based on ICD 9 CM codes. We examined neighborhood concentrated disadvantage and neighborhood immigrant density, with immigrant status as a potential moderator, using multilevel Bernoulli models estimated in HLM. Overall, 10.4 percent of patients had a documented substance use disorder diagnosis, with lower prevalence among immigrants (5.2 percent) than nonimmigrants (11.3 percent). In adjusted models, immigrant status was strongly protective (OR 0.27, p < .01). Higher neighborhood immigrant density was also protective, with each 1 percentage point increase associated with lower odds of diagnosis (OR = 0.96, p < .01). Neighborhood concentrated disadvantage was not significant, and moderation by immigrant status was not supported. These findings extend the immigrant paradox to clinically documented substance use disorder diagnoses in a low-income safety net population and suggest that immigrant dense neighborhoods may confer community level protection. Future research should test mechanisms such as social cohesion, collective efficacy, cultural norms, stigma, and service access using longitudinal and mixed methods approaches, and should examine heterogeneity across immigrant subgroups, generations, and local contexts. Immigrants Neighborhood characteristics Immigrant Health Paradox Substance Use Disorder Figures Figure 1 Figure 2 Introduction Substance use disorders (SUDs) remain a persistent public health challenge in the United States. According to national estimates, more than 20 million adults meet diagnostic criteria for SUDs annually, yet fewer than 11% receive specialized treatment, and over 70,000 lives were lost to drug overdoses in 2019 (National Institute on Drug Abuse, 2022). These numbers underscore the urgent need to understand not only individual risk factors but also the social and structural contexts that shape SUD vulnerability, diagnosis, and treatment. For social work practice, where the emphasis is on person-in-environment, examining how neighborhood structures and immigrant status interact to influence SUD outcomes is essential to designing equitable, context-sensitive interventions. A substantial body of research documents the “immigrant paradox,” a counterintuitive pattern whereby first-generation immigrants exhibit better health outcomes, including lower prevalence of SUDs, than U.S.-born peers, despite socioeconomic disadvantage and barriers to care (Salas-Wright et al., 2014; Salas-Wright & Vaughn, 2014). Nationally representative studies consistently show that immigrants are significantly less likely to meet diagnostic criteria for substance use disorders compared to U.S.-born adults, with differences most pronounced among first-generation immigrants and attenuating across subsequent generations (Mancini et al., 2015; Vaeth et al., 2017). Recent evidence also highlights a “refugee paradox,” whereby refugees are three to six times less likely than both U.S.-born and non-refugee immigrant adults to be diagnosed with SUDs (Salas-Wright & Vaughn, 2014). At the same time, studies of acculturation, enculturation, and acculturative stress suggest a more nuanced picture: assimilation into U.S. cultural norms is often associated with increased risk for alcohol and substance misuse, while strong ethnic identity and social support networks serve as protective factors (Alamilla et al., 2020; Lopez-Tamayo et al., 2016). Parallel to research on the immigrant paradox, scholars have long examined how neighborhood structures shape behavioral health. Classic work using the National Institute of Mental Health’s Epidemiologic Catchment Area data demonstrated that neighborhood concentrated disadvantage, characterized by high poverty, unemployment, single-parent households, and racial segregation, was positively associated with psychiatric disorders, including substance use (Silver et al., 2002). Subsequent studies corroborated that neighborhood disadvantage contributes to depressive symptoms (Kim, 2010; Haines et al., 2011), adolescent behavioral health issues (Roosa et al., 2010), and treatment attrition for substance abuse (Jacobson, 2004). In contrast, immigrant concentration has been conceptualized as a protective factor. Jackson and colleagues (2016) found that higher immigrant concentration in adolescents’ residential and non-residential activity spaces was associated with reduced alcohol use. Similarly, Author et al., (2024) and Cho et al. (2013) reported that outpatient treatment completion was more likely in facilities located in neighborhoods with higher immigrant density, suggesting that cultural and community contexts exert an influence on service engagement. These findings reflect broader theories of collective efficacy and the “cultural armamentarium,” in which immigrant-dense neighborhoods foster social cohesion, protective norms, and informal controls that mitigate risk behaviors. Despite these insights, the social work and behavioral health fields continue to grapple with significant disparities in service access, treatment engagement, and outcomes. Latino and immigrant populations, for example, are less likely to access specialty SUD treatment, more likely to rely on family or social services, and report higher dropout and lower satisfaction compared to White counterparts (Mancini et al., 2015). Scholars have argued that culturally responsive interventions must move beyond individual-level adaptation to also consider structural barriers and contextual determinants, including neighborhood composition and acculturation-related stressors (Alamilla et al., 2020; Lopez-Tamayo et al., 2016). Programmatic responses increasingly emphasize addressing discrimination, acculturative stress, and transportation burdens in treatment design, while leveraging cultural strengths such as familism and ethnic identity as resilience resources. Although prior research has identified neighborhood disadvantage as a risk factor and immigrant concentration as a potential protective factor, few studies have tested whether immigrant status moderates the relationship between neighborhood structural characteristics and SUD diagnoses. Much of the existing literature relies on self-reported substance use or treatment completion, rather than clinically documented diagnoses. Moreover, studies often focus on national samples, leaving safety-net and low-income populations underexamined. Another gap concerns the spatial unit of analysis: while some studies assess residential neighborhoods, others focus on treatment facility neighborhoods or non-residential activity spaces, raising questions about which contexts matter most for substance use outcomes. The current study addresses these gaps by merging electronic medical record (EMR) data from a safety-net clinic serving uninsured and underinsured adults in Central Florida with census tract-level data from the American Community Survey. Using hierarchical linear modeling and Geographic Information Systems (GIS), this study investigates (1) whether neighborhood concentrated disadvantage and immigrant density are associated with the likelihood of an SUD diagnosis, (2) whether being an immigrant is associated with lower odds of an SUD diagnosis after controlling for neighborhood and individual factors, and (3) whether immigrant status moderates the association between neighborhood structural characteristics and SUD diagnoses. By explicitly testing moderation effects in a vulnerable population, this study advances understanding of the immigrant paradox in the context of substance use and highlights the role of neighborhood structure in shaping risk and protection. These findings can inform social work research, practice, and policy by identifying multilevel intervention points and underscoring the importance of structural context in addressing SUD disparities. Conceptual Framework The conceptual framework guiding this study is presented in Figure 1. It illustrates how neighborhood concentrated disadvantage and immigrant density are hypothesized to influence substance use disorder (SUD) diagnosis, with immigrant status moderating these relationships. This model integrates social disorganization and collective efficacy perspectives with the immigrant paradox framework, providing a multilevel view of structural and cultural determinants of SUDs. Figure 1 Conceptual Framework Goes here è Figure 1. Conceptual framework illustrating hypothesized relationships between neighborhood concentrated disadvantage, immigrant density, immigrant status, and substance use disorder diagnosis. Methods This study employed a cross-sectional, multilevel quantitative design to examine the impact of neighborhood structural characteristics on the likelihood of receiving a substance use disorder (SUD) diagnosis, with immigrant status considered as a potential moderator. Guided by the immigrant health paradox and social disorganization frameworks, the study integrated individual-level clinical data with neighborhood-level structural indicators using Geographic Information Systems (GIS) and hierarchical linear modeling (HLM). Two data sources were combined for this analysis: 1. Electronic Medical Records (EMRs): Individual-level data were obtained from a large integrated care facility in Central Florida that provides physical and mental health services to uninsured and underinsured populations. Records spanning 2010–2019 were extracted in de-identified form at the researcher’s request. The EMRs included demographic information, immigrant status, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnostic codes for substance use disorders. 2. American Community Survey (ACS): Neighborhood-level variables were generated using the ACS 5-year estimates (2013–2017). Census tract identifiers were derived from block-level residential addresses provided in the EMRs. ArcGIS software was used to merge census tract structural characteristics with the individual-level EMR data. Sample The analytic sample included 2,725 adults whose household income was at or below 200% of the federal poverty line. Of these, 403 were immigrants (defined as foreign-born, first-generation individuals) and 2,322 were non-immigrants. Within the sample, 284 individuals (10.4%) received a clinically documented SUD diagnosis, while 2,441 (89.6%) did not. Participants ranged in age from 18 to 81 years, with both genders and multiple racial/ethnic groups represented.. Dependent Variable Substance Use Disorder Diagnosis: The outcome variable was a binary indicator (0 = no diagnosis, 1 = diagnosis) derived from ICD-9-CM codes documented in the EMRs. Diagnoses included tobacco-related disorders and psychoactive substance use disorders, following standard coding protocols (see Table 1). Independent Variables (Neighborhood Structural Characteristics) - Neighborhood Concentrated Disadvantage (CDI): CDI scores were computed at the census tract level using the PhenX Toolkit protocol (Hamilton et al., 2011). Six measures were included: percentage of households below poverty level, percentage unemployed, percentage of female-headed households, percentage of households receiving public assistance, percentage of Black residents, and percentage of residents under age 18. Scores were standardized and summed to form a composite CDI index. - Neighborhood Immigrant Density: Immigrant density was operationalized as the percentage of foreign-born residents in each census tract, derived from ACS estimates. Moderator - Immigrant Status: Immigrant status was coded dichotomously from EMRs (1 = foreign-born, first-generation immigrant; 0 = U.S.-born). Control Variables Individual-level covariates included: - Age (continuous, 18–81 years) - Gender (male/female) - Race/Ethnicity (White, Black, Other; with dummy variables created for White (White vs Non-White) and Black (Black vs Non Black)) - Federal Poverty Level (FPL): Categorical variable indicating household income relative to federal poverty thresholds (below 100%, 100–125%, 125–150%, 150–200%). - Preferred Language: Dichotomized as English (1) versus Spanish/Other (0). Procedures This study was reviewed and approved by the University of Central Florida Institutional Review Board (IRB #XXXX). The analysis is based on de-identified data previously collected for the author’s dissertation study. All procedures complied with ethical standards for human subjects research, and because the data were de-identified, informed consent was waived. All study procedures were approved by the university’s Institutional Review Board (IRB). De-identified EMR data were stored on an encrypted flash drive accessible only to the primary investigator. Individual-level residential addresses were geocoded to census tracts using ArcGIS, after which tract-level data from ACS were merged with the EMR dataset. Prior to analysis, data were examined for missingness, outliers, and violations of statistical assumptions. Data Analysis Strategy Analyses were conducted in multiple stages. First, descriptive statistics were generated to characterize the sample and distribution of key study variables. Second, bivariate analyses were performed to examine associations between individual and neighborhood variables and SUD diagnosis. Third, given the nested nature of individuals within neighborhoods, hierarchical linear modeling (HLM) for Bernoulli outcomes was employed to estimate the effects of individual- and neighborhood-level predictors on the likelihood of SUD diagnosis. The multilevel models included: 1. Level 1 (individual-level): Age, gender, race, FPL, language, and immigrant status. 2. Level 2 (neighborhood-level): Concentrated disadvantage index and immigrant density. 3. Cross-level interactions: Immigrant status × concentrated disadvantage, and immigrant status × immigrant density. All continuous predictors were grand-mean centered to aid interpretation of coefficients. Odds ratios and 95% confidence intervals were reported for all fixed effects. Model fit was evaluated using deviance statistics. Analyses were conducted in HLM software and validated in Stata. Findings Descriptive analyses indicated that out of the total sample of 2,725 adults, 10.4% (n = 284) had a documented substance use disorder (SUD) diagnosis. The prevalence of SUD was markedly lower among immigrants (5.2%) compared to non-immigrants (11.3%), providing initial evidence consistent with the immigrant health paradox. Individuals with SUD diagnoses were more likely to be younger, male, White, English-speaking, and living at the lowest levels of the federal poverty line compared to those without diagnoses. At the neighborhood level, participants resided in census tracts characterized by varying levels of concentrated disadvantage and immigrant density; however, only immigrant density appeared to differ meaningfully between those with and without SUD diagnoses. [insert Table 1 about here] Table 1. Sample Characteristics and SUD Prevalence: This table presents descriptive statistics. Results from the multilevel hierarchical linear models are presented in Table 2. At the individual level, several predictors were significantly associated with the likelihood of an SUD diagnosis. Age was positively associated with SUD, such that each additional year slightly increased the odds of diagnosis. Gender was a significant factor, with males more likely than females to be diagnosed. Racial differences emerged, with White participants having higher odds of SUD diagnosis compared to others, while Black participants did not significantly differ. Higher household income relative to the federal poverty line was protective, reducing the likelihood of diagnosis. Participants who reported English as their preferred language were also significantly more likely to have a SUD diagnosis. Most notably, immigrant status itself was strongly protective: foreign-born individuals were about 73% less likely to receive a diagnosis compared to U.S.-born individuals, even after accounting for individual sociodemographics and neighborhood context. At the neighborhood level, concentrated disadvantage was not significantly associated with SUD diagnoses, suggesting that economic and structural deprivation in residential tracts did not independently increase risk in this sample. In contrast, immigrant density was a significant protective factor: for each percentage point increase in the proportion of immigrants in a neighborhood, the odds of an SUD diagnosis decreased by approximately 4%. These findings align with theories of cultural armamentarium and collective efficacy, which posit that immigrant-dense neighborhoods may foster norms and social controls that discourage substance use. Finally, interaction terms tested whether immigrant status moderated the effects of neighborhood concentrated disadvantage or immigrant density on SUD diagnosis. Neither interaction reached statistical significance. This indicates that the protective effect of immigrant density was present for both immigrants and non-immigrants, and the lack of association between concentrated disadvantage and SUD was consistent across groups. [insert Table 2 about here] Table 2. Hierarchical Bernoulli model predicting Substance Use Disorder Diagnosis. This table will report Hierarchical Model for Individual-level predictors, Neighborhood-level predictors and Cross-level interactions. [Insert Figure 2 (GIS MAP) about here] Figure 2: Neighborhood Immigrant Density and Substance Use Map Taken together, these findings provide strong support for the immigrant paradox in relation to SUD, while also highlighting the independent protective role of neighborhood immigrant density. In contrast, concentrated disadvantage - long considered a risk factor in national studies - was not associated with SUD diagnoses in this sample of underserved patients. These results suggest that immigrant concentration may serve as a protective neighborhood-level factor for all residents, whereas the influence of structural disadvantage on SUD may operate differently in populations already experiencing economic marginalization. Discussion This study sought to examine whether neighborhood structural characteristics are associated with substance use disorder diagnoses, whether immigrant status confers protective effects, and whether immigrant status moderate’s neighborhood influences. The findings provide three key insights. First, consistent with the immigrant health paradox literature, immigrants in this sample were significantly less likely to have a clinically documented SUD diagnosis compared to non-immigrants, even after accounting for sociodemographic and neighborhood characteristics. This protective effect highlights the resilience of immigrant populations despite their concentration in economically disadvantaged and underserved clinical settings. While previous studies have relied heavily on self-reported data, the use of EMR-based diagnoses in this study strengthens the validity of the immigrant paradox as applied to SUD outcomes. Second, neighborhood immigrant density emerged as a significant protective factor. Higher concentrations of immigrants in residential tracts were associated with reduced odds of SUD diagnosis for both immigrants and non-immigrants. This aligns with prior research demonstrating that immigrant enclaves may foster cultural norms, collective efficacy, and social controls that discourage substance use. Importantly, the finding that immigrant density protects non-immigrants as well underscores the broader community-level benefits of immigrant concentration. These results provide empirical support for theories of cultural armamentarium and collective efficacy, suggesting that immigrant-dense neighborhoods may serve as a buffer against substance misuse across populations. Third, contrary to much of the extant literature, concentrated disadvantage was not associated with SUD in this sample. One explanation may be that all participants were drawn from a low-income safety-net clinic population already living below 200% of the federal poverty line. In such a uniformly disadvantaged context, variation in tract-level disadvantage may exert less influence on SUD risk. This finding underscores the need to consider sample composition when interpreting neighborhood effects, as mechanisms linking concentrated disadvantage to SUD may operate differently in highly marginalized populations. The moderation analyses revealed no evidence that immigrant status altered the effects of neighborhood disadvantage or immigrant density on SUD diagnosis. This suggests that while immigrants benefit from protective individual-level and contextual factors, these benefits do not differentially amplify or diminish neighborhood influences compared to non-immigrants. Instead, immigrant density appears to exert a broadly protective neighborhood effect. These findings have several implications for social work. At the research level, they highlight the value of integrating EMR data with census-based neighborhood measures to produce more rigorous, clinically anchored evidence on SUD disparities. At the practice level, the results suggest that interventions to reduce SUD may be strengthened by leveraging protective aspects of immigrant communities, such as cultural norms and social cohesion. At the policy level, the findings argue against deficit-based stereotypes of immigrant communities and emphasize the potential role of immigrant density as a community asset in promoting health equity. Several limitations warrant consideration. The data were limited to one safety-net clinic in Central Florida, which may restrict generalizability. Because only first-generation versus U.S.-born status was measured, the study could not capture generational differences in SUD risk, which prior research has shown to be significant. Additionally, while EMR-based diagnoses strengthen validity compared to self-report, they may undercount SUDs due to underdiagnosis or barriers to care. Finally, this cross-sectional design precludes causal inference. Future research should explore longitudinal designs, differentiate across immigrant subgroups and generations, and examine how neighborhood immigrant density interacts with other structural factors such as crime, policing, and access to treatment facilities. Mixed-methods studies may further illuminate the mechanisms such as cultural norms, stigma, and collective efficacy through which immigrant density reduces SUD risk. This study contributes to the literature by extending the immigrant paradox framework to clinically diagnosed SUDs in an underserved population and by identifying neighborhood immigrant density as a robust protective factor across groups. By highlighting both individual- and neighborhood-level protective influences, the findings underscore the importance of multi-level, contextually informed approaches in social work research and practice addressing substance use disparities. Declarations Funding Statement: No funding was received for conducting this study Author Contribution The author conceived and designed the study, conducted the analyses, interpreted the findings, and drafted and revised the manuscript. References Alamilla, S. G., Barney, B. J., Small, R., Wang, S. C., Schwartz, S. J., Donovan, R. A., & Lewis, C. (2020). Explaining the immigrant paradox: The influence of acculturation, enculturation, and acculturative stress on problematic alcohol consumption. Behavioral Medicine, 46 (1), 21–33. Cho, Y. I., Johnson, T. P., Fendrich, M., & Pickup, L. (2013). Treatment facility neighborhood environment and outpatient treatment completion. Journal of Drug Issues, 43( 3), 374–385. Hamilton, C. M., Strader, L. C., Pratt, J. G., Maiese, D., Hendershot, T., Kwok, R. K., ... & Haines, J. (2011). The PhenX Toolkit: get the most from your measures. American journal of epidemiology , 174 (3), 253-260. https://doi.org/10.1093/aje/kwr193 Haines, V. A., Beggs, J. J., & Hurlbert, J. S. (2011). Neighborhood disadvantage, network social capital, and depressive symptoms . Journal of Health and Social Behavior, 52(1) , 58–73. Jackson, A. L., Browning, C. R., Krivo, L. J., Kwan, M. P., & Washington, H. M. (2016). The role of immigrant concentration within and beyond residential neighborhoods in adolescent alcohol use. Journal of Youth and Adolescence, 45(1) , 17–34. Jacobson, J. O. (2004). Place and attrition from substance abuse treatment. Journal of Drug Issues, 34 (1), 23–49. Kim, J. (2010). Neighborhood disadvantage and mental health: The role of neighborhood disorder and social relationships. Social Science Research, 39 (2), 260–271. Lopez-Tamayo, R., DiGangi, J., Segovia, G., Leon, G., Alvarez, J., & Jason, L. A. (2016). Psychosocial factors associated with substance abuse and anxiety on immigrant and U.S. born Latinos. Journal of Addiction & Prevention, 4( 1), 10. Mancini, A. D., Salas-Wright, C. P., Vaughn, M. G., & Maynard, B. R. (2015). Drug use and service utilization among American immigrants: A review. Social Psychiatry and Psychiatric Epidemiology, 50 (10), 1679–1689. National Institute on Drug Abuse. (2022). Fiscal year 2022 budget information: Congressional justification. National Institutes of Health. https://nida.nih.gov/about-nida/legislative-activities/budget-information/fiscal-year-2022-budget-information-congressional-justification-national-institute-drug-abuse/ic-fact-sheet-2022 Roosa, M. W., Burrell, G. L., Nair, R. L., Coxe, S., Tein, J. Y., & Knight, G. P. (2010). Neighborhood disadvantage, stressful life events, and adjustment among Mexican American early adolescents. The Journal of early adolescence , 30 (4), 567-592. Salas-Wright, C. P., & Vaughn, M. G. (2014). A “refugee paradox” for substance use disorders? Drug and Alcohol Dependence , 142, 345–349. Salas-Wright, C. P., Vaughn, M. G., Clark, T. T., Terzis, L. D., & Córdova, D. (2014). Substance use disorders among first- and second-generation immigrant adults in the United States: Evidence of an immigrant paradox? Journal of Studies on Alcohol and Drugs, 75 (6), 958–967. Author. (2021). Title omitted for blinded review [Doctoral dissertation]. Institution name and link omitted for review. Author. (2024). Title and journal details omitted for blinded review. Silver, E., Mulvey, E. P., & Swanson, J. W. (2002). Neighborhood structural characteristics and mental disorder: Faris and Dunham revisited. Social Science & Medicine, 55 (8), 1457–1470. Vaeth, P. A. C., Wang-Schweig, M., & Caetano, R. (2017). Drinking, alcohol use disorder, and treatment access and utilization among U.S. racial/ethnic groups. Alcoholism: Clinical and Experimental Research, 41 (1), 6–19. Tables Table 1 Demographic characteristics (N=2725) Frequency Percentage Gender Male (1) 946 34.7 Female (2) 1779 65.3 Immigrant Status Immigrant (1) 403 14.8 Non-Immigrant (0) 2322 85.2 Federal Poverty Line Below 100 FPL (1) 919 34.2 100-125 FPL (2) 472 17.6 125-150 FPL (3) 808 30.0 150-200 FPL (4) 489 18.2 Preferred Language English (1) 2131 78.2 Spanish (2) 495 18.2 Others (3) 99 3.6 Race White (1) 1512 55.5 Black (2) 640 23.5 Others (3) 573 21 Ethnic Hispanic (1) 1082 39.7 Non-Hispanic (2) 1623 59.6 Unknown (3) 20 0.7 Substance Use No (0) 2441 89.6 Yes (1) 284 10.4 White or Not Not White (0) 1213 44.5 White (1) 1512 55.5 Black or Not Not Black (0) 2085 76.5 Black (1) 640 23.5 Age 18-24 332 12.2 25-34 365 13.4 35-44 572 21 45-54 607 22.3 55-64 645 23.6 Over 65 204 7.5 Table 2 Substance Use Disorders. Hierarchical Bernoulli models predicting Mental Health b SE Model (Substance use Disorder Y/N) Intercept -2.37 ** 0.11 Level 1 (Individual) Age 0.02 ** 0.01 Gender -0.33 (p =0.017) * 0.14 FPL -0.18 (p =0.008) ** 0.06 White 0.55 (p =0.003) ** 0.18 Black -0.21 (p =0.41) 0.26 English 0.76 (p =0.003) ** 0.25 Immigrant -1.29 (p =0.005) ** 0.46 CDI Interaction -0.09 (p =0.175) 0.07 Immigrant Density Interaction -0.01 (p =0.886) 0.04 Level 2 (Neighborhood) Concentrated Disadvantage Index 0.01 (p =0.914) 0.01 Immigrant Density Percentage -0.03 (p =0.003) ** 0.01 *p < .05; **p < .01. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8379948","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":565500152,"identity":"2a59027b-42db-4652-afd0-1dc950579ebd","order_by":0,"name":"Kenan Sualp","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACCTiLnYHxAQODHJCVQKwWZgZmAwYGY9K0sEkQpUWyvffg5wKGusT+Zt5j1Tw1Bgz87DkGeLVI85xLlp7BcDhxxmG+tNs8xwwYJHve4NciJ5FjIM3DcCCx4TCP2W3ehj8MBjcI2CIn/8b4Nw/QYfOBWop5GwwY7AlpkZbgMQPawpy4AaiFGaTFQIKAFsmeHDNrHoPDxhsP8yVLzjlmwCNx5lkBXi0Sx88Y3+apqJOdd7z34Ic3NQZy/O3JG/BqgQCwS3gYECSRgCTFo2AUjIJRMJIAABfNO0P/ZGmMAAAAAElFTkSuQmCC","orcid":"","institution":"University of Central Florida","correspondingAuthor":true,"prefix":"","firstName":"Kenan","middleName":"","lastName":"Sualp","suffix":""}],"badges":[],"createdAt":"2025-12-16 22:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8379948/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8379948/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102593229,"identity":"7e5f302d-b2e6-42d4-990a-713ef0555a6c","added_by":"auto","created_at":"2026-02-13 11:47:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40964,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework illustrating hypothesized relationships between neighborhood concentrated disadvantage, immigrant density, immigrant status, and substance use disorder diagnosis.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8379948/v1/f85f4b3fd1d2e6c4b5ce7968.jpg"},{"id":102747847,"identity":"2a6e4ae5-2bd9-43f1-98dc-a1099feac8be","added_by":"auto","created_at":"2026-02-16 09:05:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":182911,"visible":true,"origin":"","legend":"\u003cp\u003eNeighborhood Immigrant Density and Substance Use Map\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8379948/v1/fede9973d679be4ee1e83ef6.png"},{"id":109405598,"identity":"ba072cd8-8acd-4a17-967b-8e836588a0ff","added_by":"auto","created_at":"2026-05-17 13:19:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":389021,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8379948/v1/e047345f-5160-45f8-80d6-adac74138f76.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neighborhood Context and Substance Use Disorder Risk: Comparing Immigrants and Nonimmigrants in a Community Health Setting","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSubstance use disorders (SUDs) remain a persistent public health challenge in the United States. According to national estimates, more than 20 million adults meet diagnostic criteria for SUDs annually, yet fewer than 11% receive specialized treatment, and over 70,000 lives were lost to drug overdoses in 2019 (National Institute on Drug Abuse, 2022). These numbers underscore the urgent need to understand not only individual risk factors but also the social and structural contexts that shape SUD vulnerability, diagnosis, and treatment. For social work practice, where the emphasis is on person-in-environment, examining how neighborhood structures and immigrant status interact to influence SUD outcomes is essential to designing equitable, context-sensitive interventions.\u003c/p\u003e\n\u003cp\u003eA substantial body of research documents the \u0026ldquo;immigrant paradox,\u0026rdquo; a counterintuitive pattern whereby first-generation immigrants exhibit better health outcomes, including lower prevalence of SUDs, than U.S.-born peers, despite socioeconomic disadvantage and barriers to care (Salas-Wright et al., 2014; Salas-Wright \u0026amp; Vaughn, 2014). Nationally representative studies consistently show that immigrants are significantly less likely to meet diagnostic criteria for substance use disorders compared to U.S.-born adults, with differences most pronounced among first-generation immigrants and attenuating across subsequent generations (Mancini et al., 2015; Vaeth et al., 2017). Recent evidence also highlights a \u0026ldquo;refugee paradox,\u0026rdquo; whereby refugees are three to six times less likely than both U.S.-born and non-refugee immigrant adults to be diagnosed with SUDs (Salas-Wright \u0026amp; Vaughn, 2014). At the same time, studies of acculturation, enculturation, and acculturative stress suggest a more nuanced picture: assimilation into U.S. cultural norms is often associated with increased risk for alcohol and substance misuse, while strong ethnic identity and social support networks serve as protective factors (Alamilla et al., 2020; Lopez-Tamayo et al., 2016).\u003c/p\u003e\n\u003cp\u003eParallel to research on the immigrant paradox, scholars have long examined how neighborhood structures shape behavioral health. Classic work using the National Institute of Mental Health\u0026rsquo;s Epidemiologic Catchment Area data demonstrated that neighborhood concentrated disadvantage, characterized by high poverty, unemployment, single-parent households, and racial segregation, was positively associated with psychiatric disorders, including substance use (Silver et al., 2002). Subsequent studies corroborated that neighborhood disadvantage contributes to depressive symptoms (Kim, 2010; Haines et al., 2011), adolescent behavioral health issues (Roosa et al., 2010), and treatment attrition for substance abuse (Jacobson, 2004).\u003c/p\u003e\n\u003cp\u003eIn contrast, immigrant concentration has been conceptualized as a protective factor. Jackson and colleagues (2016) found that higher immigrant concentration in adolescents\u0026rsquo; residential and non-residential activity spaces was associated with reduced alcohol use. Similarly, Author et al., (2024) and Cho et al. (2013) reported that outpatient treatment completion was more likely in facilities located in neighborhoods with higher immigrant density, suggesting that cultural and community contexts exert an influence on service engagement. These findings reflect broader theories of collective efficacy and the \u0026ldquo;cultural armamentarium,\u0026rdquo; in which immigrant-dense neighborhoods foster social cohesion, protective norms, and informal controls that mitigate risk behaviors.\u003c/p\u003e\n\u003cp\u003eDespite these insights, the social work and behavioral health fields continue to grapple with significant disparities in service access, treatment engagement, and outcomes. Latino and immigrant populations, for example, are less likely to access specialty SUD treatment, more likely to rely on family or social services, and report higher dropout and lower satisfaction compared to White counterparts (Mancini et al., 2015). Scholars have argued that culturally responsive interventions must move beyond individual-level adaptation to also consider structural barriers and contextual determinants, including neighborhood composition and acculturation-related stressors (Alamilla et al., 2020; Lopez-Tamayo et al., 2016). Programmatic responses increasingly emphasize addressing discrimination, acculturative stress, and transportation burdens in treatment design, while leveraging cultural strengths such as familism and ethnic identity as resilience resources.\u003c/p\u003e\n\u003cp\u003eAlthough prior research has identified neighborhood disadvantage as a risk factor and immigrant concentration as a potential protective factor, few studies have tested whether immigrant status moderates the relationship between neighborhood structural characteristics and SUD diagnoses. Much of the existing literature relies on self-reported substance use or treatment completion, rather than clinically documented diagnoses. Moreover, studies often focus on national samples, leaving safety-net and low-income populations underexamined. Another gap concerns the spatial unit of analysis: while some studies assess residential neighborhoods, others focus on treatment facility neighborhoods or non-residential activity spaces, raising questions about which contexts matter most for substance use outcomes.\u003c/p\u003e\n\u003cp\u003eThe current study addresses these gaps by merging electronic medical record (EMR) data from a safety-net clinic serving uninsured and underinsured adults in Central Florida with census tract-level data from the American Community Survey. Using hierarchical linear modeling and Geographic Information Systems (GIS), this study investigates (1) whether neighborhood concentrated disadvantage and immigrant density are associated with the likelihood of an SUD diagnosis, (2) whether being an immigrant is associated with lower odds of an SUD diagnosis after controlling for neighborhood and individual factors, and (3) whether immigrant status moderates the association between neighborhood structural characteristics and SUD diagnoses.\u003c/p\u003e\n\u003cp\u003eBy explicitly testing moderation effects in a vulnerable population, this study advances understanding of the immigrant paradox in the context of substance use and highlights the role of neighborhood structure in shaping risk and protection. These findings can inform social work research, practice, and policy by identifying multilevel intervention points and underscoring the importance of structural context in addressing SUD disparities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptual Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe conceptual framework guiding this study is presented in Figure 1. It illustrates how neighborhood concentrated disadvantage and immigrant density are hypothesized to influence substance use disorder (SUD) diagnosis, with immigrant status moderating these relationships. This model integrates social disorganization and collective efficacy perspectives with the immigrant paradox framework, providing a multilevel view of structural and cultural determinants of SUDs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1 Conceptual Framework Goes here\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026egrave;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1. Conceptual framework illustrating hypothesized relationships between neighborhood concentrated disadvantage, immigrant density, immigrant status, and substance use disorder diagnosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study employed a cross-sectional, multilevel quantitative design to examine the impact of neighborhood structural characteristics on the likelihood of receiving a substance use disorder (SUD) diagnosis, with immigrant status considered as a potential moderator. Guided by the immigrant health paradox and social disorganization frameworks, the study integrated individual-level clinical data with neighborhood-level structural indicators using Geographic Information Systems (GIS) and hierarchical linear modeling (HLM).\u003c/p\u003e\n\u003cp\u003eTwo data sources were combined for this analysis:\u003cbr\u003e1. Electronic Medical Records (EMRs): Individual-level data were obtained from a large integrated care facility in Central Florida that provides physical and mental health services to uninsured and underinsured populations. Records spanning 2010\u0026ndash;2019 were extracted in de-identified form at the researcher\u0026rsquo;s request. The EMRs included demographic information, immigrant status, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnostic codes for substance use disorders.\u003cbr\u003e2. American Community Survey (ACS): Neighborhood-level variables were generated using the ACS 5-year estimates (2013\u0026ndash;2017). Census tract identifiers were derived from block-level residential addresses provided in the EMRs. ArcGIS software was used to merge census tract structural characteristics with the individual-level EMR data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e The analytic sample included 2,725 adults whose household income was at or below 200% of the federal poverty line. Of these, 403 were immigrants (defined as foreign-born, first-generation individuals) and 2,322 were non-immigrants. Within the sample, 284 individuals (10.4%) received a clinically documented SUD diagnosis, while 2,441 (89.6%) did not. Participants ranged in age from 18 to 81 years, with both genders and multiple racial/ethnic groups represented..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDependent Variable\u003c/strong\u003e\u003cbr\u003eSubstance Use Disorder Diagnosis: The outcome variable was a binary indicator (0 = no diagnosis, 1 = diagnosis) derived from ICD-9-CM codes documented in the EMRs. Diagnoses included tobacco-related disorders and psychoactive substance use disorders, following standard coding protocols (see Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent Variables (Neighborhood Structural Characteristics)\u003c/strong\u003e\u003cbr\u003e\u003cem\u003e- Neighborhood Concentrated Disadvantage (CDI):\u003c/em\u003e CDI scores were computed at the census tract level using the PhenX Toolkit protocol (Hamilton et al., 2011). Six measures were included: percentage of households below poverty level, percentage unemployed, percentage of female-headed households, percentage of households receiving public assistance, percentage of Black residents, and percentage of residents under age 18. Scores were standardized and summed to form a composite CDI index.\u003cbr\u003e\u003cem\u003e- Neighborhood Immigrant Density:\u003c/em\u003e Immigrant density was operationalized as the percentage of foreign-born residents in each census tract, derived from ACS estimates.\u003cbr\u003e\u003cstrong\u003e Moderator\u003c/strong\u003e\u003cbr\u003e\u003cem\u003e- Immigrant Status:\u003c/em\u003e Immigrant status was coded dichotomously from EMRs (1 = foreign-born, first-generation immigrant; 0 = U.S.-born).\u003cbr\u003e\u003cstrong\u003eControl Variables\u003c/strong\u003e\u003cbr\u003eIndividual-level covariates included:\u003cbr\u003e\u003cem\u003e- Age\u003c/em\u003e (continuous, 18\u0026ndash;81 years)\u003cbr\u003e\u003cem\u003e- Gender\u003c/em\u003e (male/female)\u003cbr\u003e\u003cem\u003e- Race/Ethnicity\u003c/em\u003e (White, Black, Other; with dummy variables created for White (White vs Non-White) and Black (Black vs Non Black))\u003cbr\u003e\u003cem\u003e- Federal Poverty Level (FPL):\u003c/em\u003e Categorical variable indicating household income relative to federal poverty thresholds (below 100%, 100\u0026ndash;125%, 125\u0026ndash;150%, 150\u0026ndash;200%).\u003cbr\u003e\u003cem\u003e- Preferred Language:\u003c/em\u003e Dichotomized as English (1) versus Spanish/Other (0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the University of Central Florida Institutional Review Board (IRB #XXXX). The analysis is based on de-identified data previously collected for the author\u0026rsquo;s dissertation study. All procedures complied with ethical standards for human subjects research, and because the data were de-identified, informed consent was waived.\u003c/p\u003e\n\u003cp\u003eAll study procedures were approved by the university\u0026rsquo;s Institutional Review Board (IRB). De-identified EMR data were stored on an encrypted flash drive accessible only to the primary investigator. Individual-level residential addresses were geocoded to census tracts using ArcGIS, after which tract-level data from ACS were merged with the EMR dataset. Prior to analysis, data were examined for missingness, outliers, and violations of statistical assumptions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses were conducted in multiple stages. First, descriptive statistics were generated to characterize the sample and distribution of key study variables. Second, bivariate analyses were performed to examine associations between individual and neighborhood variables and SUD diagnosis. Third, given the nested nature of individuals within neighborhoods, hierarchical linear modeling (HLM) for Bernoulli outcomes was employed to estimate the effects of individual- and neighborhood-level predictors on the likelihood of SUD diagnosis.\u003cbr\u003e\u003cbr\u003eThe multilevel models included:\u003cbr\u003e1. Level 1 (individual-level): Age, gender, race, FPL, language, and immigrant status.\u003cbr\u003e2. Level 2 (neighborhood-level): Concentrated disadvantage index and immigrant density.\u003cbr\u003e3. Cross-level interactions: Immigrant status \u0026times; concentrated disadvantage, and immigrant status \u0026times; immigrant density.\u003cbr\u003e All continuous predictors were grand-mean centered to aid interpretation of coefficients. Odds ratios and 95% confidence intervals were reported for all fixed effects. Model fit was evaluated using deviance statistics. Analyses were conducted in HLM software and validated in Stata.\u003c/p\u003e"},{"header":"Findings","content":"\u003cp\u003eDescriptive analyses indicated that out of the total sample of 2,725 adults, 10.4% (n = 284) had a documented substance use disorder (SUD) diagnosis. The prevalence of SUD was markedly lower among immigrants (5.2%) compared to non-immigrants (11.3%), providing initial evidence consistent with the immigrant health paradox. Individuals with SUD diagnoses were more likely to be younger, male, White, English-speaking, and living at the lowest levels of the federal poverty line compared to those without diagnoses. At the neighborhood level, participants resided in census tracts characterized by varying levels of concentrated disadvantage and immigrant density; however, only immigrant density appeared to differ meaningfully between those with and without SUD diagnoses.\u003c/p\u003e\n\u003cp\u003e[insert Table 1 about here]\u003c/p\u003e\n\u003cp\u003eTable 1. Sample Characteristics and SUD Prevalence: This table presents descriptive statistics.\u003c/p\u003e\n\u003cp\u003eResults from the multilevel hierarchical linear models are presented in Table 2. At the individual level, several predictors were significantly associated with the likelihood of an SUD diagnosis. Age was positively associated with SUD, such that each additional year slightly increased the odds of diagnosis. Gender was a significant factor, with males more likely than females to be diagnosed. Racial differences emerged, with White participants having higher odds of SUD diagnosis compared to others, while Black participants did not significantly differ. Higher household income relative to the federal poverty line was protective, reducing the likelihood of diagnosis. Participants who reported English as their preferred language were also significantly more likely to have a SUD diagnosis. Most notably, immigrant status itself was strongly protective: foreign-born individuals were about 73% less likely to receive a diagnosis compared to U.S.-born individuals, even after accounting for individual sociodemographics and neighborhood context.\u003c/p\u003e\n\u003cp\u003eAt the neighborhood level, concentrated disadvantage was not significantly associated with SUD diagnoses, suggesting that economic and structural deprivation in residential tracts did not independently increase risk in this sample. In contrast, immigrant density was a significant protective factor: for each percentage point increase in the proportion of immigrants in a neighborhood, the odds of an SUD diagnosis decreased by approximately 4%. These findings align with theories of cultural armamentarium and collective efficacy, which posit that immigrant-dense neighborhoods may foster norms and social controls that discourage substance use.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, interaction terms tested whether immigrant status moderated the effects of neighborhood concentrated disadvantage or immigrant density on SUD diagnosis. Neither interaction reached statistical significance. This indicates that the protective effect of immigrant density was present for both immigrants and non-immigrants, and the lack of association between concentrated disadvantage and SUD was consistent across groups.\u003c/p\u003e\n\u003cp\u003e[insert Table 2 about here]\u003c/p\u003e\n\u003cp\u003eTable 2. Hierarchical Bernoulli model predicting Substance Use Disorder Diagnosis. This table will report Hierarchical Model for Individual-level predictors, Neighborhood-level predictors and Cross-level interactions.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[Insert Figure 2 (GIS MAP) about here]\u003c/p\u003e\n\u003cp\u003eFigure 2: Neighborhood Immigrant Density and Substance Use Map\u003c/p\u003e\n\u003cp\u003eTaken together, these findings provide strong support for the immigrant paradox in relation to SUD, while also highlighting the independent protective role of neighborhood immigrant density. In contrast, concentrated disadvantage - long considered a risk factor in national studies - was not associated with SUD diagnoses in this sample of underserved patients. These results suggest that immigrant concentration may serve as a protective neighborhood-level factor for all residents, whereas the influence of structural disadvantage on SUD may operate differently in populations already experiencing economic marginalization.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study sought to examine whether neighborhood structural characteristics are associated with substance use disorder diagnoses, whether immigrant status confers protective effects, and whether immigrant status moderate\u0026rsquo;s neighborhood influences. The findings provide three key insights.\u003c/p\u003e\n\u003cp\u003eFirst, consistent with the immigrant health paradox literature, immigrants in this sample were significantly less likely to have a clinically documented SUD diagnosis compared to non-immigrants, even after accounting for sociodemographic and neighborhood characteristics. This protective effect highlights the resilience of immigrant populations despite their concentration in economically disadvantaged and underserved clinical settings. While previous studies have relied heavily on self-reported data, the use of EMR-based diagnoses in this study strengthens the validity of the immigrant paradox as applied to SUD outcomes.\u003c/p\u003e\n\u003cp\u003eSecond, neighborhood immigrant density emerged as a significant protective factor. Higher concentrations of immigrants in residential tracts were associated with reduced odds of SUD diagnosis for both immigrants and non-immigrants. This aligns with prior research demonstrating that immigrant enclaves may foster cultural norms, collective efficacy, and social controls that discourage substance use. Importantly, the finding that immigrant density protects non-immigrants as well underscores the broader community-level benefits of immigrant concentration. These results provide empirical support for theories of cultural armamentarium and collective efficacy, suggesting that immigrant-dense neighborhoods may serve as a buffer against substance misuse across populations.\u003c/p\u003e\n\u003cp\u003eThird, contrary to much of the extant literature, concentrated disadvantage was not associated with SUD in this sample. One explanation may be that all participants were drawn from a low-income safety-net clinic population already living below 200% of the federal poverty line. In such a uniformly disadvantaged context, variation in tract-level disadvantage may exert less influence on SUD risk. This finding underscores the need to consider sample composition when interpreting neighborhood effects, as mechanisms linking concentrated disadvantage to SUD may operate differently in highly marginalized populations.\u003c/p\u003e\n\u003cp\u003eThe moderation analyses revealed no evidence that immigrant status altered the effects of neighborhood disadvantage or immigrant density on SUD diagnosis. This suggests that while immigrants benefit from protective individual-level and contextual factors, these benefits do not differentially amplify or diminish neighborhood influences compared to non-immigrants. Instead, immigrant density appears to exert a broadly protective neighborhood effect.\u003c/p\u003e\n\u003cp\u003eThese findings have several implications for social work. At the research level, they highlight the value of integrating EMR data with census-based neighborhood measures to produce more rigorous, clinically anchored evidence on SUD disparities. At the practice level, the results suggest that interventions to reduce SUD may be strengthened by leveraging protective aspects of immigrant communities, such as cultural norms and social cohesion. At the policy level, the findings argue against deficit-based stereotypes of immigrant communities and emphasize the potential role of immigrant density as a community asset in promoting health equity.\u003c/p\u003e\n\u003cp\u003eSeveral limitations warrant consideration. The data were limited to one safety-net clinic in Central Florida, which may restrict generalizability. Because only first-generation versus U.S.-born status was measured, the study could not capture generational differences in SUD risk, which prior research has shown to be significant. Additionally, while EMR-based diagnoses strengthen validity compared to self-report, they may undercount SUDs due to underdiagnosis or barriers to care. Finally, this cross-sectional design precludes causal inference.\u003c/p\u003e\n\u003cp\u003eFuture research should explore longitudinal designs, differentiate across immigrant subgroups and generations, and examine how neighborhood immigrant density interacts with other structural factors such as crime, policing, and access to treatment facilities. Mixed-methods studies may further illuminate the mechanisms such as cultural norms, stigma, and collective efficacy through which immigrant density reduces SUD risk.\u003c/p\u003e\n\u003cp\u003eThis study contributes to the literature by extending the immigrant paradox framework to clinically diagnosed SUDs in an underserved population and by identifying neighborhood immigrant density as a robust protective factor across groups. By highlighting both individual- and neighborhood-level protective influences, the findings underscore the importance of multi-level, contextually informed approaches in social work research and practice addressing substance use disparities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Statement:\u003c/h2\u003e \u003cp\u003eNo funding was received for conducting this study\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author conceived and designed the study, conducted the analyses, interpreted the findings, and drafted and revised the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlamilla, S. G., Barney, B. J., Small, R., Wang, S. C., Schwartz, S. J., Donovan, R. A., \u0026amp; Lewis, C. (2020). Explaining the immigrant paradox: The influence of acculturation, enculturation, and acculturative stress on problematic alcohol consumption. \u003cem\u003eBehavioral Medicine, 46\u003c/em\u003e(1), 21\u0026ndash;33.\u003c/li\u003e\n \u003cli\u003eCho, Y. I., Johnson, T. P., Fendrich, M., \u0026amp; Pickup, L. (2013). Treatment facility neighborhood environment and outpatient treatment completion. \u003cem\u003eJournal of Drug Issues, 43(\u003c/em\u003e3), 374\u0026ndash;385.\u003c/li\u003e\n \u003cli\u003eHamilton, C. M., Strader, L. C., Pratt, J. G., Maiese, D., Hendershot, T., Kwok, R. K., ... \u0026amp; Haines, J. (2011). The PhenX Toolkit: get the most from your measures. \u003cem\u003eAmerican journal of epidemiology\u003c/em\u003e, \u003cem\u003e174\u003c/em\u003e(3), 253-260. https://doi.org/10.1093/aje/kwr193\u003c/li\u003e\n \u003cli\u003eHaines, V. A., Beggs, J. J., \u0026amp; Hurlbert, J. S. (2011). Neighborhood disadvantage, network social capital, and depressive symptoms\u003cem\u003e. Journal of Health and Social Behavior, 52(1)\u003c/em\u003e, 58\u0026ndash;73.\u003c/li\u003e\n \u003cli\u003eJackson, A. L., Browning, C. R., Krivo, L. J., Kwan, M. P., \u0026amp; Washington, H. M. (2016). The role of immigrant concentration within and beyond residential neighborhoods in adolescent alcohol use. \u003cem\u003eJournal of Youth and Adolescence, 45(1)\u003c/em\u003e, 17\u0026ndash;34.\u003c/li\u003e\n \u003cli\u003eJacobson, J. O. (2004). Place and attrition from substance abuse treatment. \u003cem\u003eJournal of Drug Issues, 34\u003c/em\u003e(1), 23\u0026ndash;49.\u003c/li\u003e\n \u003cli\u003eKim, J. (2010). Neighborhood disadvantage and mental health: The role of neighborhood disorder and social relationships. \u003cem\u003eSocial Science Research, 39\u003c/em\u003e(2), 260\u0026ndash;271.\u003c/li\u003e\n \u003cli\u003eLopez-Tamayo, R., DiGangi, J., Segovia, G., Leon, G., Alvarez, J., \u0026amp; Jason, L. A. (2016). Psychosocial factors associated with substance abuse and anxiety on immigrant and U.S. born Latinos. \u003cem\u003eJournal of Addiction \u0026amp; Prevention, 4(\u003c/em\u003e1), 10.\u003c/li\u003e\n \u003cli\u003eMancini, A. D., Salas-Wright, C. P., Vaughn, M. G., \u0026amp; Maynard, B. R. (2015). Drug use and service utilization among American immigrants: A review. \u003cem\u003eSocial Psychiatry and Psychiatric Epidemiology, 50\u003c/em\u003e(10), 1679\u0026ndash;1689.\u003c/li\u003e\n \u003cli\u003eNational Institute on Drug Abuse. (2022). Fiscal year 2022 budget information: Congressional justification. National Institutes of Health. https://nida.nih.gov/about-nida/legislative-activities/budget-information/fiscal-year-2022-budget-information-congressional-justification-national-institute-drug-abuse/ic-fact-sheet-2022\u003c/li\u003e\n \u003cli\u003eRoosa, M. W., Burrell, G. L., Nair, R. L., Coxe, S., Tein, J. Y., \u0026amp; Knight, G. P. (2010). Neighborhood disadvantage, stressful life events, and adjustment among Mexican American early adolescents. \u003cem\u003eThe Journal of early adolescence\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(4), 567-592.\u003c/li\u003e\n \u003cli\u003eSalas-Wright, C. P., \u0026amp; Vaughn, M. G. (2014). A \u0026ldquo;refugee paradox\u0026rdquo; for substance use disorders? \u003cem\u003eDrug and Alcohol Dependence\u003c/em\u003e, 142, 345\u0026ndash;349.\u003c/li\u003e\n \u003cli\u003eSalas-Wright, C. P., Vaughn, M. G., Clark, T. T., Terzis, L. D., \u0026amp; C\u0026oacute;rdova, D. (2014). Substance use disorders among first- and second-generation immigrant adults in the United States: Evidence of an immigrant paradox? \u003cem\u003eJournal of Studies on Alcohol and Drugs, 75\u003c/em\u003e(6), 958\u0026ndash;967.\u003c/li\u003e\n \u003cli\u003eAuthor. (2021). Title omitted for blinded review [Doctoral dissertation]. Institution name and link omitted for review.\u003c/li\u003e\n \u003cli\u003eAuthor. (2024). Title and journal details omitted for blinded review.\u003c/li\u003e\n \u003cli\u003eSilver, E., Mulvey, E. P., \u0026amp; Swanson, J. W. (2002). Neighborhood structural characteristics and mental disorder: Faris and Dunham revisited. \u003cem\u003eSocial Science \u0026amp; Medicine, 55\u003c/em\u003e(8), 1457\u0026ndash;1470.\u003c/li\u003e\n \u003cli\u003eVaeth, P. A. C., Wang-Schweig, M., \u0026amp; Caetano, R. (2017). Drinking, alcohol use disorder, and treatment access and utilization among U.S. racial/ethnic groups. \u003cem\u003eAlcoholism: Clinical and Experimental Research, 41\u003c/em\u003e(1), 6\u0026ndash;19.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Demographic characteristics\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(N=2725)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmigrant Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eImmigrant (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-Immigrant (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFederal Poverty Line\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBelow 100 FPL (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100-125 FPL (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e125-150 FPL (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e150-200 FPL (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreferred Language\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEnglish (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpanish (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOthers (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWhite (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBlack (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOthers (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHispanic (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnknown (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubstance Use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e89.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhite or Not\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot White (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWhite (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlack or Not\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot Black (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBlack (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOver 65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Substance Use Disorders.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eHierarchical Bernoulli models predicting Mental Health\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel \u0026nbsp; \u0026nbsp; (Substance use Disorder Y/N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.37 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel 1 (Individual)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.33 \u003cem\u003e(p\u003c/em\u003e=0.017) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; FPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.18 \u003cem\u003e(p\u003c/em\u003e=0.008) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55 \u003cem\u003e(p\u003c/em\u003e=0.003) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.21 \u003cem\u003e(p\u003c/em\u003e=0.41)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; English\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.76 \u003cem\u003e(p\u003c/em\u003e=0.003) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Immigrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.29 \u003cem\u003e(p\u003c/em\u003e=0.005) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CDI Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.09 \u003cem\u003e(p\u003c/em\u003e=0.175)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Immigrant Density Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.01 \u003cem\u003e(p\u003c/em\u003e=0.886)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel 2 (Neighborhood)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Concentrated Disadvantage Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01 \u003cem\u003e(p\u003c/em\u003e=0.914)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Immigrant Density Percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.03 \u003cem\u003e(p\u003c/em\u003e=0.003) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e*p \u0026lt; .05; \u0026nbsp;**p \u0026lt; .01.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Immigrants, Neighborhood characteristics, Immigrant Health Paradox, Substance Use Disorder","lastPublishedDoi":"10.21203/rs.3.rs-8379948/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8379948/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSubstance use disorders remain a major public health concern, yet less is known about how neighborhood context shapes clinically documented substance use disorder diagnoses among immigrants and other underserved patients. We linked de identified electronic medical record data from a large integrated care facility serving uninsured and underinsured adults in Central Florida from 2010 to 2019 with American Community Survey 5-year census tract measures from 2013 to 2017 using ArcGIS. The analytic sample included 2,725 adults at or below 200 percent of the federal poverty line, including 403 immigrants. The outcome was a binary substance use disorder diagnosis based on ICD 9 CM codes. We examined neighborhood concentrated disadvantage and neighborhood immigrant density, with immigrant status as a potential moderator, using multilevel Bernoulli models estimated in HLM. Overall, 10.4 percent of patients had a documented substance use disorder diagnosis, with lower prevalence among immigrants (5.2 percent) than nonimmigrants (11.3 percent). In adjusted models, immigrant status was strongly protective (OR 0.27, p\u0026thinsp;\u0026lt;\u0026thinsp;.01). Higher neighborhood immigrant density was also protective, with each 1 percentage point increase associated with lower odds of diagnosis (OR\u0026thinsp;=\u0026thinsp;0.96, p\u0026thinsp;\u0026lt;\u0026thinsp;.01). Neighborhood concentrated disadvantage was not significant, and moderation by immigrant status was not supported. These findings extend the immigrant paradox to clinically documented substance use disorder diagnoses in a low-income safety net population and suggest that immigrant dense neighborhoods may confer community level protection. Future research should test mechanisms such as social cohesion, collective efficacy, cultural norms, stigma, and service access using longitudinal and mixed methods approaches, and should examine heterogeneity across immigrant subgroups, generations, and local contexts.\u003c/p\u003e","manuscriptTitle":"Neighborhood Context and Substance Use Disorder Risk: Comparing Immigrants and Nonimmigrants in a Community Health Setting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 11:47:09","doi":"10.21203/rs.3.rs-8379948/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e4093ad-09f0-46c7-b2a3-a6f88eee0c5e","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T10:25:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 11:47:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8379948","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8379948","identity":"rs-8379948","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}