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Article

Mental Health and Immigrant Perceptions of Inclusion

by
Samantha Chapa
and
Jeronimo Cortina
*
Department of Political Science, College of Liberal Arts and Social Science, University of Houston, Houston, TX 77204, USA
*
Author to whom correspondence should be addressed.
Psychol. Int. 2025, 7(2), 35; https://doi.org/10.3390/psycholint7020035
Submission received: 28 February 2025 / Revised: 18 April 2025 / Accepted: 24 April 2025 / Published: 29 April 2025
(This article belongs to the Section Neuropsychology, Clinical Psychology, and Mental Health)
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Versions Notes

Abstract

:
Previous research on immigrant integration indicates that contextual factors, such as access to health services, often lead to varying levels of immigrant incorporation and feelings of inclusion. Research has demonstrated the link between inclusion and physical health but less is known about the connection between mental health and immigrant inclusion. In this study, we examine the impact of the prevalence of mental health issues at the county level on immigrants’ perceptions of inclusion. We propose that a lower prevalence of mental health issues at the county level is associated with improved perceptions of inclusion among immigrants. To explore this relationship, we analyze data from the Kaiser Family Foundation, which covers feelings of immigrant inclusion from a racially and geographically diverse sample of immigrants across the United States. We pair these data with CDC PLACES data on the crude prevalence of mental health issues at the county level. Our findings reveal an association between the crude prevalence of mental health issues at the county level and immigrants’ feelings of inclusion. Through this analysis, we underscore the significance of understanding the contextual determinants that shape migrants’ perceptions of inclusion.

1. Introduction

Although immigrant integration is a polarizing issue in American society, it remains an important topic to understand. Immigrants currently account for around 14% of the U.S. population, and immigration to the U.S. is currently the highest it has been in decades, leaving local, state, and national experts grappling with the challenges that come with immigrant integration (Moslimani & Passel, 2024). As research demonstrates, many factors—such as social, economic, and political factors—contribute to immigrant integration (Joppke, 2013; Kivisto, 2015; van Tubergen et al., 2004). While many studies have focused on the policy dimensions of immigrant inclusion, fewer studies have focused on the impact that community health has on feelings of immigrant inclusion (Aboii, 2016; Ortiz et al., 2021). In this paper, we examine how community mental health (including stress, depression, and problems with emotions) impacts feelings of immigrant inclusion.
We demonstrate the relationship between the crude prevalence of mental health at the county level (i.e., the crude prevalence among adults aged ≥ 18 years who report that their mental health, including stress, depression, and problems with emotions was not good for 14 or more days during the past 30 days) and feelings of immigrant inclusion through the combination of two datasets. Our independent variable comes from the CDC’s Population-Level Analysis and Community Estimates dataset, which provides estimates of various health outcomes at the county level. For our dependent variable, we use data from the Kaiser Family Foundation and the Los Angeles Times on self-reported feelings of immigrant inclusion. The data include a nationally representative survey on migrant perceptions of health and feelings of inclusion at the neighborhood, state, and national levels. We create an additive index from feelings of inclusion at these three levels. We regress this index onto the prevalence of mental health issues through multilevel models that vary both slopes and intercepts to account for the nested nature of the data. We find that as the crude prevalence of mental health at the county level increases, self-reported feelings of inclusion decrease.
Several mechanisms may explain why we find that feelings of immigrant inclusion decrease along with higher rates of mental health issues at the county level. High levels of mental health challenges at the county level may be indicative of social withdrawal among community members, lower civic engagement, and reduced resources devoted to immigrant inclusion. First, a higher crude prevalence of mental health issues tends to correlate with social isolation and reduced interpersonal trust (Elsina & Martinsone, 2020; Hall et al., 2020; Segrin, 1996). In communities where mental health issues are a challenge, residents may be less likely to interact with each other or engage in community activities that foster immigrant inclusion, such as attending events at local community centers (Mccann et al., 2012). In this way, social isolation and withdrawal may reduce opportunities for immigrants to build local networks, participate in civic life, and feel a sense of belonging.
Second, given the current levels of polarization surrounding immigration (Ollerenshaw & Jardina, 2023), mental health issues can affect the establishment of social networks necessary for inclusion. Prior work demonstrates that individuals who experience mental health issues, like depression, for instance, are more likely to suffer from low feelings of efficacy and are less likely to engage in community civic activities such as advocacy efforts (Baum et al., 2024; Bernardi et al., 2023; Landwehr & Ojeda, 2021; Ojeda, 2015). Civic engagement, such as volunteering, can be beneficial in establishing a sense of community, as interacting with others generally improves feelings of inclusion (Fenn et al., 2024; Hrivnák et al., 2023; Nelson et al., 2019).
Third, counties with a high prevalence of mental health issues often grapple with finding the resources necessary to address mental health concerns, which are often indicative of poor economic conditions. Poor economic conditions may generate feelings of depression, which in turn foster resentment toward newcomers, particularly if economic hardships create competition for jobs and services (Bhavnani & Lacina, 2018; Goldstein & Peters, 2014). Poor economic conditions also limit the amount of resources that can go toward mental health resources and immigrant integration, generating a negative feedback cycle of poor mental health outcomes and exclusion. Recent data suggest that the mental healthcare professionals need met is only 28% across the U.S. (https://www.kff.org/other/state-indicator/mental-health-care-health-professional-shortage-areas-hpsas, accessed on 25 February 2025). Mental health challenges often strain local healthcare and social service systems and draw attention and funding away from immigrant integration programs such as language training, job placement services, and legal aid (Graaf et al., 2016; Pilkauskas et al., 2012; Starr, 2011). The strain can also make it more difficult for local governments and non-profits to implement policies that facilitate immigrant inclusion.
The contributions of this paper are twofold. First, this is one of the first studies that focuses on the impact of contextual variables on a immigrants’ perceptions of inclusiveness. Second while other works that focus on self-reported mental and physical health among immigrant or other vulnerable populations tend to use data from particular subsections of the population, our data are unique in that they include the first nationally representative survey of immigrants in the United States (Crawford et al., 2024; Hong & Yao, 2024; Mboweni et al., 2023; Salami et al., 2023; Sui & Yeung, 2024; Wieland et al., 2012). While other accounts on immigrant health are limited in their generalizability, the representative nature of our data allows us to paint a broad picture of immigrant health across the nation, providing a distinct contribution to the literature on both immigrant inclusion and health. To our knowledge, be it self-reported or observational data, no other representative sample on immigrant health in the United States exists besides the data we use in our analysis.

2. Materials and Methods

2.1. Data

To explore the relationship between inclusivity and immigrant health, we use a survey conducted by the Kaiser Family Foundation (KFF) in partnership with the Los Angeles Times. The nationally representative survey collected data on migrants’ perceptions of how welcoming their neighborhoods, states of residence, and other parts of the U.S. are toward immigrants (i.e., those born outside the U.S. and U.S. territories, including naturalized U.S. citizens, legal permanent residents, those with visas, and those who entered the country without inspection). The sample, comprising 3358 observations, matches the characteristics of the adult immigrant population in the U.S. based on the 2021 American Community Survey. The survey used a mix-mode data collection where responses were collected online, by telephone, and by mail through address-based sampling and random digit dialing from April to June 2023. All respondents were paid for their time. In addition, the surveys were offered in ten languages: English, Spanish, Chinese, Korean, Vietnamese, Portuguese, Haitian-Creole, Arabic, French, and Tagalog.
The dependent variable consists of an additive index measuring the cumulative effect of three inclusiveness measures at the neighborhood and state levels and in other parts of the U.S., ensuring consistency in units, magnitude, and conceptual focus. Perceived inclusivity captures the belief that a neighborhood, state, or any other place in the U.S. is inclusive or welcoming, irrespective of actual policies and outcomes. We assessed the validity of our index using principal components factor analysis to determine if the three variables—measuring welcoming feelings at the neighborhood, state, and national levels—converge into a single dimension of inclusiveness. The analysis retained one factor with an eigenvalue of 1.64, which exceeds the Kaiser criterion (1958) of 1 and accounts for over half of the total variance. This indicates that our composite index effectively captures a single dimension of overall inclusion (see Appendix A). We call this independent variable our Welcome Index, which captures how included immigrants feel in their communities.
The primary independent variable is the crude prevalence of mental health issues at the ZIP code tabulation area (ZCTA) level (i.e., respondents aged ≥ 18 years who reported that their mental health including stress, depression, and problems with emotions was not good for 14 or more days during the past 30). The data come from the latest release of the Population Level Analysis and Community Estimates (PLACES) (www.cdc.gov/places/, accessed on 18 February 2025), a collaboration between the Centers for Disease Control and Prevention (CDC), the Robert Wood Johnson Foundation, and the CDC Foundation, which provides model-based estimates of health measures. The model-based estimates were produced by the CDC using data sources such as the Behavioral Risk Factor Surveillance System (BRFSS) 2020, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates.
We include control variables in line with previous research on immigrant health (Goel et al., 2004; Lara et al., 2005; Riosmena et al., 2015; Saasa et al., 2022), including whether survey respondents have perceived being treated worse in a restaurant, with their interactions with the police, and in the housing market. In addition, we control for respondents’ English ability, their level of education, sex, total household income, the number of years living in the U.S., if they are employed, and their race and ethnicity. We do not have access to respondents’ immigration status to protect respondents’ anonymity. Table 1 summarizes the data.

2.2. Methods

The PLACES and the KFF data were merged into a tabular cross-sectional database. The final data include individual and ZCTA data, producing a multi-level data structure with individuals nested within ZCTAs. Given the hierarchical nature of the data, we fit a varying-intercept, varying-slope multilevel model in which both intercepts and coefficients vary across groups simultaneously. Given that the number of observations is lower than the number of ZCTAs, we instead choose to model the hierarchical nature of the data using counties as the second level to avoid making the random effects parameters and the residual variance unidentifiable (Gelman & Hill, 2007). We collapsed the ZCTA crude prevalence of mental health to the county level to reflect, more accurately, survey respondents’ place of residence rather than using the overall crude prevalence at the county level from PLACES since not all ZCTAs in a county were included. All models are run using R version 4.4.2.
First, we run a linear model, ignoring the hierarchical nature of the data. Equation (1) summarizes the model:
Welcome Index = β0 + β1 Mental Health + β2 D.Restaurant + β3 D.Police + β4 D.Housing + β5 English + β6 Education + β7 Sex + β8 Income + β9 Years US + β10 Employment + β11 Race/Ethnicity + ε.
Our next step is to run a varying-intercepts/varying-slopes multilevel model of the following form (see Equation (2)). This multilevel model predicts an immigrant’s feelings of inclusion, or welcome index, based on a set of individual and county level predictors—including the crude prevalence of mental health, categorical indicators for restaurant, police, and housing discrimination perceptions, as well as English proficiency, education, sex, income, years in the U.S., employment status, and race/ethnicity—while also incorporating group-level variability. Specifically, the model allows both the intercept (baseline level of the welcome index) and the slope for the crude prevalence of mental health, our independent variable, to vary across counties through the random effects terms b 0 j and b 1 j . This structure captures the idea that not only does the overall level of inclusion perceptions (i.e., welcome index) differ by county, but the impact of the crude prevalence of mental health on the welcome index can also change from one county to another, thus accounting for unobserved heterogeneity, such as differences between local institutions and policies, at the county level.
Welcome Indexij = β0 + β1 Mental Health ij + β2 D.Restaurantij + β3 D.Policeij + β4 D.Housingij + β5 English ij + β6Educationij + β7 Sexij + β8Iincomeij + β9 Years USij + β10Employmentij + β11 Race/Ethnicityij + b0j + b1j Mental Healthij + εij.
The random effects are assumed to follow a joint multivariate normal distribution:
b 0 j b 1 j ~ N 0 0 , σ b 0 2 σ b 0 b 1 σ b 0 b 1 σ b 1 2 ,
and the individual-level errors are assumed to be
ε i j ~ N 0 , σ 2

3. Results

The results are summarized in Table 2. Model 1 presents the results after running a linear regression predicting the levels of migrant inclusiveness. Overall, as the crude prevalence of mental health increases, so do migrants’ feelings of perceived lack of inclusion (β = 0.0197, p < 0.05). In other words, poor mental health results in feelings of exclusion, whereas good mental health results in feelings of inclusion. Feelings of discrimination or mistreatment in restaurants, by the police, or housing, are also associated with feelings of exclusion. As migrants’ English proficiency increases, they also tend to have more positive feelings of inclusion. As respondents live in the U.S. for longer, feelings of inclusion seem to decrease, plausibly because immigrants realize the dichotomy between immigrants and non-immigrants that exists within American society (Cortina, 2024).
Turning to Model 2, the multilevel model, the fixed effects results indicate that county-level crude prevalence of mental health is significantly associated with feelings of exclusion (β = 0.0309, p < 0.05), suggesting that migrants living in counties with a higher prevalence of poor mental health tend to experience slightly higher levels of exclusion, or lack of inclusion. An increase of 1 percentage point in the overall rate of mental health issues is associated with a 0.03 unit rise in the extent to which migrants feel excluded. Among the strongest predictors, discrimination in restaurants (β = 0.3211, p < 0.001) and discrimination during interactions with the police (β = 0.1919, p < 0.01) significantly influence perceived immigrant inclusion, indicating that experiences of discrimination play a major role in shaping perceptions of feeling unwelcome. Additionally, discrimination in housing (β = 0.1242, p < 0.05) also has a significant effect. English proficiency has a negative association (β = −0.2217, p < 0.001), implying that individuals with higher English proficiency may feel more included, possibly due to stronger community networks. Years in the U.S. also has a small but significant positive effect (β = 0.0041, p < 0.01), suggesting that the longer immigrants reside in the U.S., the less they feel included.
Several demographic variables, such as education, income, employment status, and sex, do not appear to have a statistically significant effect on immigrant inclusion. However, race and ethnicity (β = 0.1667, p < 0.05) show a positive correlation, indicating that racial and ethnic minorities tend to feel less welcome than non-Hispanic white immigrants. Figure 1 summarizes the results.
In terms of the random effects, the variance estimates show substantial variation in inclusion perceptions across counties (σ2 = 0.855 for intercepts). The slope for county-level mental health prevalence has a small variance (σ2 = 0.0038), meaning that while its effect varies slightly across counties, it may not be a dominant force. The strong negative correlation (−1.00) between county-level mental health prevalence and the intercept suggests that for migrants living in counties with lower baseline inclusion scores, the crude prevalence of mental health tends to have a more substantial negative effect on inclusion.
One of the most important limitations is that the model did not fully converge, suggesting potential instability in the estimates, possibly due to collinearity or insufficient data points per county, a common challenge when working with observational data. Additionally, while the variance components suggest meaningful between-county differences, the small effect sizes of some predictors warrant further investigation.
Despite these limitations, this analysis highlights the importance of mental health prevalence and discrimination experiences in shaping immigrant inclusion. Both discrimination and the prevalence of mental health issues negatively affect inclusion.

4. Discussion

High rates of mental health issues, including stress, depression, and problems with emotions at the county level, may be indicative of several factors that contribute to low feelings of inclusion. They may be reflective of greater social withdrawal among members of the community, whether materialized via social isolation, low levels of civic engagement, or fewer resources to promote immigrant inclusion. First, in communities where mental health issues are prevalent, individuals may be less likely to interact with one another, which limits opportunities for immigrants to build networks and communities necessary for integration. Second, at the individual level, those who experience discrimination may also be less likely to engage in community life and thus withdraw from positive social interactions. Third, communities with high levels of mental health challenges tend to strain social service systems in addition to local healthcare, which limits resources that can potentially be devoted to integration.
Our analysis is not to say that other factors do not influence feelings of inclusion and exclusion among immigrants. Rather, an extensive body of work grapples with these various factors. Work concerning the social determinants of health argues that economic, societal, and political conditions also contribute to health outcomes, which in turn affect immigrant health (Marmot & Wilkinson, 2006; Short & Mollborn, 2015). Robust work in both the American and European contexts find that policy adoption, such as exclusionary policies, negatively affect immigrant mental health (Dourgnon et al., 2023; Malmusi, 2015; Vargas et al., 2017). Anti-immigrant policies not only reduce access to health services but also provoke stress, anxiety, and depression among immigrants particularly (Bratter & Eschbach, 2005; Eschbach et al., 2004; Kaestner et al., 2009; Martinez et al., 2015). In addition, restrictive federal policies often limit immigrants’ access to public assistance, contributing to declining mental and physical health among foreign-born populations (Gelatt, 2016; Pitkin Derose et al., 2007; Vargas & Pirog, 2016). Moreover, institutional, county-level factors, such as available social support services at the county level, employment, and labor conditions also contribute to inclusion and exclusion (Van Kempen, 2019). Other structural barriers, including geographic distance from health facilities, and intersecting socioeconomic variables such as income, racial and ethnic background, and legal status are also strong determinants of healthcare quality and overall wellbeing (Williams et al., 2010).
Collectively, this body of research highlights how social, structural, contextual, and institutional dynamics play a critical role in shaping immigrant health outcomes, be they physical or mental, which ultimately influence social inclusion (Anderson & Olson, 2019; Lamont, 2009; Wright & Stickley, 2013). Our work contributes to this existing body of literature by suggesting that community-level health mental health may point to several mechanisms at play, such as high levels of social isolation, more instances of discrimination, or strained social services, that may also affect individual feelings of inclusion. We make the case that, to create thriving communities, community mental health must also be assessed.
Future work should focus on identifying which of these mechanisms contribute to the link between increased county-level depression rates and lower feelings of inclusion. Longitudinal data on feelings of immigrant inclusion and county-level mental health may provide more insight into the specific mechanisms that connect these variables. Lastly, future work may explore the geographic variation inherent in this relationship. For example, rural versus urban areas may have different patterns of mental health challenges and patterns of inclusion (Cortina & Hardin, 2023). Future work may also focus on how local level factors, such as county and city level policies contribute to immigrant health and social inclusion.
Immigrant integration greatly benefits American society—immigrants contribute significantly to the economy as taxpayers, consumers, and business owners (Abramitzky & Boustan, 2017; Borjas, 1995). Not only do immigrants contribute to our community fabric economically, but they also contribute socially, enriching our society through the introduction of new cultural traditions, food, music, and art as previous immigrant waves have done. Moreover, immigrants who feel included in our society also actively contribute through volunteer work, civic participation, and advocacy (Bloemraad, 2006; De Graauw & Bloemraad, 2017). In many ways, immigrants revitalize our society by choosing to take part in the American Dream (Brubaker, 1990; Smith, 2008). The more included immigrants feel in society, the more they can contribute in these varying ways, creating thriving communities across the U.S. For this reason, it is important to understand how community-level factors influence feelings of immigrant inclusion.

Author Contributions

Conceptualization, S.C. and J.C.; methodology, S.C. and J.C.; software, S.C. and J.C.; validation, S.C. and J.C.; formal analysis, S.C. and J.C.; investigation, S.C. and J.C.; resources, S.C. and J.C.; data curation, S.C. and J.C.; writing—original draft preparation, S.C. and J.C.; writing—review and editing, S.C. and J.C.; visualization, S.C. and J.C.; supervision, S.C. and J.C.; project administration, S.C. and J.C.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study as we use secondary data previously collected by the Kaiser Family Foundation and the Los Angeles Times.

Informed Consent Statement

Not applicable.

Data Availability Statement

Restrictions apply to the availability of these data. The data were obtained from the Kaiser Family Foundation and are available at https://www.kff.org/, with the permission of the Kaiser Family Foundation following the completion of a data use agreement.

Acknowledgments

We thank the Kaiser Family Foundation and the Los Angeles Times for their generosity in providing us with the immigrant health data used in this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
KFFKaiser Family Foundation
PLACESPopulation Level Analysis and Community Estimates
CDCCenters for Disease Control and Prevention
ZCTAZIP code tabulation area

Appendix A

Table A1. Principal-Component Factor Analysis.
Table A1. Principal-Component Factor Analysis.
FactorEigenvalueDifferenceProportionCumulative
Factor 11.640.790.550.55
Factor 20.850.340.280.83
Factor 30.510.171.00
LR test: independent vs. saturated: chi2(3) = 1118.73 Prob>chi2 = 0.0000
Factor loadings (pattern matrix) and unique variances
VariableFactor 1Uniqueness
Neighborhood0.800.37
State0.830.31
Other parts of the U.S.0.570.68
Observations: 3244
Factors retained: 1

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Psycholint 07 00035 g001
Figure 1. Fixed effects’ estimates for Model 2. Note: p < 0.001 ***, p < 0.01 **, p < 0.05 *.
Figure 1. Fixed effects’ estimates for Model 2. Note: p < 0.001 ***, p < 0.01 **, p < 0.05 *.
Psycholint 07 00035 g001
Table 1. Summary Statistics.
Table 1. Summary Statistics.
VariableObs.MeanStd. Dev.MinMax
Inclusiveness/Welcoming Index32441.49691.018903
County Mental Health (Crude Prevalence)303815.43872.9155727.6
Discrimination in Restaurants (0 = No, 1 = Yes)32480.26880.443401
Discrimination by Police (0 = No, 1 = Yes)32110.20960.407101
Housing Discrimination (0 = No, 1 = Yes)32250.17180.377301
English Proficiency (Not at all … Very well)25142.99640.958514
Education (HS… College +)32151.15930.894002
Sex (0 = Man, 1 = Woman)32070.49080.500001
Income (<USD 40K… >USD 90K)31790.91540.815302
Years living in the U.S.320325.881117.0335086
Employment Status (0 = Unemployed, 1 = Employed)32340.67720.467601
Race/Ethnicity (0 = NH White, 1 = minority)32800.85150.355601
Table 2. Linear regression results (Model 1) and multilevel regression results (Model 2).
Table 2. Linear regression results (Model 1) and multilevel regression results (Model 2).
VariableModel 1Model 2
Mental Health (Crude Prevalence)0.0197 *
(0.0077)		0.0309 *
(0.0123)
Discrimination in Restaurants0.3198 ***
(0.0538)		0.3211 ***
(0.0536)
Discrimination by Police0.1867 **
(0.0584)		0.1920 ***
(0.0582)
Housing Discrimination0.1288 *
(0.0634)		0.1242 *
(0.0633)
English−0.2218 ***
(0.0275)		−0.2217 ***
(0.0276)
Education0.028
(0.0285)		0.0210
(0.0276)
Sex−0.0067
(0.0429)		−0.0017
(0.0428)
Income0.0049
(0.0276)		0.0020
(0.0275)
Years0.0043 **
(0.0014)		0.0041 **
(0.0014)
Employment Status−0.0447
(0.0483)		−0.0428
(0.0481)
Race/Ethnicity0.1727 *
(0.0706)		0.1667 *
(0.0705)
Intercept1.4441 ***
(0.1739)		1.2805 ***
(0.2258)
p < 0.001 ***, p < 0.01 **, p < 0.05 *.
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Chapa, S.; Cortina, J. Mental Health and Immigrant Perceptions of Inclusion. Psychol. Int. 2025, 7, 35. https://doi.org/10.3390/psycholint7020035

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Chapa S, Cortina J. Mental Health and Immigrant Perceptions of Inclusion. Psychology International. 2025; 7(2):35. https://doi.org/10.3390/psycholint7020035

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Chapa, Samantha, and Jeronimo Cortina. 2025. "Mental Health and Immigrant Perceptions of Inclusion" Psychology International 7, no. 2: 35. https://doi.org/10.3390/psycholint7020035

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Chapa, S., & Cortina, J. (2025). Mental Health and Immigrant Perceptions of Inclusion. Psychology International, 7(2), 35. https://doi.org/10.3390/psycholint7020035

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