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. 2026 May 19;66:103504. doi: 10.1016/j.pmedr.2026.103504

Associations of racialized economic segregation with the prevalence of unhealthy behaviors and poor mental health in the United States

Xiaofang Lai a, Qingzhao Yu b, Nova Yang c, K Robin Yabroff c, Ruofei Du d, Brian Witrick a, Xuesong Han c, Lu Zhang a,⁎
PMCID: PMC13199751  PMID: 42199668

Abstract

Objective

To examine the associations of racialized economic segregation with unhealthy behaviors and poor mental health.

Methods

We conducted an ecological study with census tract-level measures from the 2018–2022 American Community Survey and 2024 CDC PLACES. Exposure was racialized economic segregation, measured by the Index of Concentration at the Extremes (ICE). Outcomes were the prevalence of unhealthy behaviors (smoking, binge drinking, physical inactivity, obesity, and inadequate sleep) and poor mental health among adults aged ≥18 years. Covariates were census tract's socioeconomic status and metropolitan status. Associations were examined with multivariable linear regression models.

Results

Among 83,359 census tracts, the average prevalence was 15.1% for smoking, 17.2% for binge drinking, 25.4% for physical inactivity, 34.4% for obesity, 36.6% for inadequate sleep, and 17.1% for poor mental health. After adjustment, all outcomes except binge drinking showed negative dose-response associations with the ICE, i.e., higher privilege level, lower prevalence of unhealthy behaviors. The prevalence of poor mental health increased by 0.61 (0.55–0.66), 0.98 (0.92–1.04),1.48 (1.41–1.55), and 2.38 (2.30–2.45) percentage points in the four less privileged groups, compared to the most privileged group.

Conclusion

Racialized economic segregation is significantly associated with the prevalence of smoking, physical inactivity, obesity, inadequate sleep, and poor mental health.

Keywords: Census tract, Racialized economic residential segregation, The index of concentration at the extremes, Mental health, Unhealthy behaviors

Highlights

  • •

    Substantial place-based disparities in mental health persist in the U.S.

  • •

    Less privileged neighborhoods have a higher prevalence of unhealthy behaviors.

  • •

    Less privileged neighborhoods have a higher prevalence of poor mental health.

  • •

    Community-level interventions are needed to enhance healthy lifestyles.

1. Introduction

Residential segregation, geographic separation of population groups by age, race/ethnicity, or socioeconomic status, is a fundamental cause of health disparities in the United States (Williams and Collins, 2001). Communities in higher segregated areas often face disproportionate exposure to environmental pollutants, substandard housing, and systematic inequities in education, employment, and health care access (Williams and Collins, 2001). While research firmly links segregation to adverse outcomes in chronic disease (Zhang et al., 2023), maternal and childhood health (Mari et al., 2023), and overall mortality (Zhang et al., 2025), its associations with unhealthy behaviors and poor mental health is less clear defined. Given the significant consequences of these outcomes, addressing this gap is essential to inform targeted public health interventions.

Residential segregation could be associated with unhealthy behaviors through multiple pathways. First, limited resources, deteriorating housing conditions, and greater exposure to environmental stressors, such as ambient noise, artificial light, and neighborhood violence, may increase chronic stress among residents of segregated areas (Williams and Collins, 2001). Individuals living in chronically stressful environments often cope with stressors by engaging unhealthy behaviors (Jackson et al., 2010). Second, segregated neighborhoods often have a higher density of tobacco and alcohol outlets and advertising, as well as fast-food restaurants, which may result in smoking and drinking initiation at younger ages and contribute to unhealthy dietary patterns (Goodman et al., 2018; Kong et al., 2023). Third, these areas frequently lack resources that support unhealthy behaviors, such as safe public spaces, recreational facilities, and access to affordable, nutritious foods (Gelormino et al., 2015; Goodman et al., 2018). Together these structural conditions contribute to a higher prevalence of unhealthy behaviors in more segregated areas (Corral et al., 2012; Schwartz et al., 2022). However, the existing literature on this association is limited and has typically examined only a narrow set of behaviors, mostly obesity and smoking (Corral et al., 2012; Ghosh et al., 2025; Goodman et al., 2018), or have been restricted to small geographic settings (Goodman et al., 2018), leaving important gaps in understanding how multiple unhealthy behaviors occur across segregated areas at the national level.

Area-based disparities in poor mental health are documented, especially by metropolitan status or neighborhood socioeconomic status. Individuals living in urban areas or socioeconomically disadvantaged neighborhoods have higher incidence and prevalence of poor mental health, such as anxiety, depression, and stress (Bonnell et al., 2022; Kirkbride et al., 2024). However, evidence on the association between residential segregation and poor mental health is less definitive. Studies using traditional segregation measures based on racial/ethnic density or distribution, such as the isolation index and the dissimilarity index, often emphasize potential protective features like social cohesion and support, yielding mixed findings (Bécares et al., 2018). In contrast, the Index of Concentration at the Extremes (ICE), which captures spatial social polarization involving both income and race and ethnicity, has shown more consistent association with poor mental health (i.e., lower ICE values representing lower privilege are associated with higher likelihood of poor mental health) (Acker et al., 2023; Sugg et al., 2023). To date, only two state-level studies have examined this association, and both focused exclusively on adolescents (Acker et al., 2023; Sugg et al., 2023), highlighting the need for confirmation using nationwide data that captures broader geographic variation and a wider range of age groups.

In this study, we examined the associations of racialized economic segregation, measured by the ICE, with the prevalence of unhealthy behaviors and poor mental health at the census tract level across the U.S.

2. Methods

2.1. Study design and data sources

This was an ecological study, and all measures were at the census tract level. We used 2018–2022 American Community Survey (ACS) (U.S. Census Bureau, 2022) five-year estimates to calculate the ICE, 2024 CDC PLACES (Centers for Disease Control and Prevention, 2023) to identify prevalence of unhealthy behaviors and poor mental health, Social Vulnerability Index 2022 from CDC Place and Health (Centers for Disease Control and Prevention et al., 2022) to measure socioeconomic status, and 2020 Rural-Urban Commuting Area (RUCC) Codes (U.S. Department of Agriculture, E. R. S., 2025) to measure metropolitan status (Appendix Table 1). Because all data used in this study were publicly available and aggregated, the study was exempted from Institutional Review Board review.

2.2. Measures

Exposure variable. Racialized economic segregation was measured by the ICE, which quantifies spatial inequality capturing the extent to which privilege (wealthy White individuals) and deprivation (poor Black individuals) are concentrated within a geographic area (Krieger et al., 2016; Massey, 1996). We focused on Black-White polarization because anti-Black structural racism has played a central role in shaping health inequalities in the U.S. and Black-White disparities are the most persistent and historically rooted health inequalities in the U.S. The ICE ranges from −1 to +1 and was categorized into quintiles, in which the first quintile represents the least privileged group, and the fifth quintile represents the most privileged group. The formula and calculation of the ICE is provided in Appendix Textbox 1.

Outcome variables. Unhealthy behaviors included census tract-level prevalence of smoking, binge drinking, physical inactivity, obesity, and inadequate sleep among people aged ≥18 years. Smoking was defined as having smoked ≥100 cigarettes in their lifetime and currently smoking every day or some days. Binge drinking was defined as having ≥5 drinks (men) or ≥4 drinks (women) on ≥1 occasion in the past 30 days. Physical inactivity was defined as having no leisure time physical activity in the past month. Obesity was defined as having a body mass index (BMI) ≥30.0 kg/m2. Inadequate sleep was defined as usually getting <7 h sleep on average during a 24-h period. The prevalence of poor mental health was measured as the proportion of people aged ≥18 years who reported that during the past 30 days, there were 14 or more days when their mental health was not good (Centers for Disease Control and Prevention, 2023).

Confounders. Metropolitan status and socioeconomic status were selected as confounders based on prior literature (Acker et al., 2023; Corral et al., 2012; Sugg et al., 2023) identifying them as associated with the exposure, a risk factor for the outcome, but not on the causal pathway between exposure and outcome. Metropolitan status meets these criteria. Socioeconomic status was included as a conservative confounder. While the ICE and socioeconomic status are related to each other, they capture distinct constructs. ICE reflects social polarization within a census tract, contrasting concentrations of privilege and deprivation, whereas socioeconomic status represents average area-level socioeconomic conditions. Therefore, it was included in the model to provide more conservative estimates of the association of ICE with the prevalence of unhealthy behaviors and poor mental health.

Census tracts were categorized into metropolitan (RUCA: 1–3) and nonmetropolitan areas (RUCA: 4–9) (U.S. Department of Agriculture, E. R. S., 2025). Census tract's socioeconomic status was measured by the socioeconomic status theme of Social Vulnerability Index (SVI) 2022 (Centers for Disease Control and Prevention et al., 2022), which summarizes the percentile ranking for the prevalence of poverty, unemployment, housing cost burden, no high school diploma, and no health insurance at the census tract level, ranging from 0 to 1. Higer values indicated worse neighborhood socioeconomic status. For example, a census tract with a value of 0.80 indicates that it has higher socioeconomic vulnerability (lower socioeconomic status) than about 80% of census tracts in the U.S.

2.3. Statistical analysis

Census tracts' characteristics across ICE quintiles were compared with descriptive statistics, including Chi-square test and ANOVA test. Univariate and multivariable linear regressions were applied to evaluate the crude and adjusted associations of the categorical ICE quintiles and the prevalence of unhealthy behaviors and poor mental health. Metropolitan status and socioeconomic status were added sequentially to the multivariable models. P-values for trend (P-trend) were obtained from linear regressions with ICE quintiles treated as a continuous variable. All analysis was conducted in SAS 9.4. All statistical tests were 2-sided and P-value <0.05 was considered statistically significant.

3. Results

The study included 83,359 census tracts in U.S. (Appendix Fig. 1). Compared to the most privileged census tracts (i.e., the fifth ICE quintile), other areas had higher proportions of non-Hispanic Black and Hispanic residents, but lower proportions of non-Hispanic White and Asian residents (Table 1). Less privileged areas also had higher proportions of population in poverty. The average prevalence of unhealthy behaviors was 15.1% for smoking, 17.2% for binge drinking, 25.4% for physical inactivity, 34.4% for obesity and 36.6% for inadequate sleep across all U.S. census tracts (Appendix Table 2). The prevalence of smoking, physical inactivity, obesity, and inadequate sleep decreased from the least to the most privileged areas, while binge drinking increased (Fig. 1). For example, the prevalence of inadequate sleep in the five quintiles from the least to the most privileged groups was 43.1%, 38.3%, 36.0%, 34.2%, and 31.8%. The average prevalence of poor mental health was 17.1% across all U.S. census tracts, decreasing from 20.4% in the least privileged group to 13.8% in the most privileged group.

Table 1.

Characteristics of the U.S. census tracts by quintiles of the Index of Concentration at the Extremes, United States, 2018–2022.*.
The Index of Concentration at the Extremes
All 1st Quintile
(Least privileged)		 2nd Quintile 3rd Quintile 4th Quintile 5th Quintile
(Most privileged)
Census tract, N 83,359 16,151 16,642 16,854 16,856 16,856
Female, % 49.6 48.3 49.9 49.9 49.9 49.8
Age 65+, % 17.3 14.2 15.9 18.7 19.0 18.8



Race and Ethnicity, %
NH White 59.7 28.3 49.1 69.7 73.0 76.8
NH Black 12.9 42.6 8.9 5.9 4.9 3.5
Asian 5.2 3.1 5.7 4.8 5.6 6.8
Hispanic 17.5 21.6 30.8 14.7 12.0 8.7
Other race/ethnicity 4.7 4.3 5.6 4.8 4.5 4.3



Metropolitan status, %
Metropolitan 80.4 85.8 75.1 66.7 79.9 94.7
Non-metropolitan 19.6 14.2 24.9 33.3 20.1 5.3



Proportion of population in poverty
<10% 47.5 8.9 24.2 43.3 69.0 90.2
10% - <20% 31.0 30.2 43.7 45.0 27.2 9.0
≥20% 21.5 60.9 32.1 11.7 3.8 0.8
Socioeconomic Status, %** 0.5 0.8 0.7 0.5 0.4 0.2
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Notes

*Values represent mean percentage of each characteristic.

**Socioeconomic status represents the percentile ranking of each census tract's socioeconomic vulnerability, ranging from 0 to 1, with higher values indicating worse neighborhood-level socioeconomic status.

Fig. 1.

Fig. 1

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The proportion of unhealthy behaviors and poor mental health by the Index of Concentration at the Extremes, United States Census Tracts, 2018–2020, N = 83,359.

The associations of the ICE with smoking, physical inactivity, obesity, and inadequate sleep were consistent, with lower levels of privilege corresponding to a higher prevalence of these unhealthy behaviors (Table 2). Compared to the most privileged group (i.e., the fifth ICE quintile), the prevalence of smoking increased by 3.22 (95% confidence interval [CI] 3.13, 3.30), 5.57 (5.48, 5.66), 6.93 (6.84, 7.02) and 9.90 (9.81, 9.99) percentage points (ppt) in the four less privileged groups, respectively (P-trend <0.01). Adjustment for metropolitan status (i.e. Adjusted model 1) slightly attenuated the estimates but did not materially change the associations. Further adjustment for socioeconomic status (i.e. Adjusted model 2) attenuated the magnitude of the association; however, a clear dose-response pattern remained (1.28 [1.19, 1.36], 2.08 [1.98, 2.17], 2.33 [2.22, 2.43], and 4.65 [4.53, 4.77] ppt in the four groups, respectively, P-trend <0.01). Similar patterns were observed for physical inactivity, obesity, and inadequate sleep.

Table 2.

Associations between racialized economic segregation measured by the Index of Concentration at the Extremes and unhealthy behaviors and poor mental health from linear regressions, United States Census Tracts, N = 83,359, 2018–2022.

 Index of Concentration at the Extremes
1st Quintile
(Least privileged)		 2nd Quintile 3rd Quintile 4th Quintile 5th Quintile
(Most privileged)		 P-trend
Coefficient (95% CI) Coefficient (95% CI) Coefficient (95% CI) Coefficient (95% CI) Coefficient (95% CI)



Unhealthy behaviors
Smoking
Crude model 9.90 (9.81, 9.99) 6.93 (6.84, 7.02) 5.57 (5.48, 5.66) 3.22 (3.13, 3.30) Ref <0.01
Adjusted model 1 9.63 (9.54, 9.71) 6.32 (6.23, 6.41) 4.71 (4.62, 4.80) 2.76 (2.67, 2.85) Ref <0.01
Adjusted model 2 4.65 (4.53, 4.77) 2.33 (2.22, 2.43) 2.08 (1.98, 2.17) 1.28 (1.19, 1.36) Ref <0.01



Binge drinking
Crude model −3.61 (−3.67, −3.54) −1.87 (−1.94, −1.81) −1.28 (−1.34, −1.22) −0.58 (−0.65, −0.52) Ref <0.01
Adjusted model 1 −3.54 (−3.61, −3.48) −1.69 (−1.75, −1.62) −1.00 (−1.07, −0.94) −0.44 (−0.51, −0.38) Ref 0.01
Adjusted model 2 −2.19 (−2.29, −2.10) −0.61 (−0.69, −0.52) −0.29 (−0.36, −0.21) −0.04 (−0.11, −0.02) Ref 0.06



Physical inactivity
Crude model 16.13 (16.02, 16.25) 11.83 (11.71, 11.94) 7.82 (7.71, 7.94) 4.34 (4.22, 4.45) Ref <0.01
Adjusted model 1 15.90 (15.78, 16.01) 11.32 (11.20, 11.43) 7.08 (6.97, 7.20) 3.95 (3.84, 4.07) Ref <0.01
Adjusted model 2 6.80 (6.65, 6.95) 4.02 (3.88, 4.15) 2.27 (2.15, 2.39) 1.24 (1.13, 1.34) Ref <0.01



Obesity
Crude model 12.81 (12.69, 12.94) 7.89 (7.77, 8.01) 6.37 (6.25, 6.49) 3.87 (3.74, 3.99) Ref <0.01
Adjusted model 1 12.46 (12.34, 12.58) 7.12 (6.99, 7.24) 5.29 (5.17, 5.41) 3.29 (3.17, 3.41) Ref <0.01
Adjusted model 2 8.81 (8.64, 9.99) 4.19 (4.03, 4.35) 3.36 (3.22, 3.50) 2.20 (2.08, 2.33) Ref 0.01



< 7-h sleep
Crude model 11.28 (11.20, 11.37) 6.45 (6.37, 6.54) 4.12 (4.04, 4.20) 2.41 (2.32, 2.49) Ref 0.01
Adjusted model 1 11.34 (11.26, 11.42) 6.59 (6.50, 6.67) 4.31 (4.23, 4.40) 2.50 (2.42, 2.59) Ref 0.01
Adjusted model 2 7.46 (7.34, 7.58) 3.47 (3.37, 3.58) 2.26 (2.27, 2.35) 1.35 (1.26, 1.43) Ref 0.01



Poor mental health
Crude model 6.56 (6.50, 6.61) 4.88 (4.83, 4.94) 3.28 (3.22, 3.33) 1.90 (1.85, 1.96) Ref <0.01
Adjusted model 1 6.51 (6.45, 6.57) 4.80 (4.74, 4.86) 3.17 (3.11, 3.23) 1.84 (1.78, 1.90) Ref <0.01
Adjusted model 2 2.38 (2.30, 2.45) 1.48 (1.41, 1.55) 0.98 (0.92, 1.04) 0.61 (0.55, 0.66) Ref <0.01
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Abbreviations: CI: confidence interval.

Adjusted model 1 adjusted for metropolitan status.

Adjusted model 2 adjusted for metropolitan status and socioeconomic status.

In contrast to these four unhealthy behaviors, binge drinking showed an inverse association. Compared to the most privileged group, less privileged tracts had lower prevalences of binge drinking with coefficients of −0.58 (−0.65, −0.52), −1.28 (−1.34, −1.22), −1.87 (−1.94, −1.81) and − 3.61 (−3.67, −3.54) ppt in the four other groups, respectively, from the crude model. The adjustment for metropolitan status and socioeconomic status reduced the magnitude of the association, but coefficients remained statistically significant with dose-response pattern: −0.04 (−0.11, −0.02), −0.29 (−0.36, −0.21), −0.61 (−0.69, −0.52), and − 2.19 (−2.29, −2.10) ppt in the four groups, respectively (P-trend = 0.06).

Compared to the most privileged (i.e., the fifth ICE quintile) census tracts, the prevalence of poor mental health increased by 1.90 (1.85, 1.96), 3.28 (3.22, 3.33), 4.88 (4.83, 4.94), and 6.56 (6.50, 6.61) ppt in the four other groups, respectively (Table 2). The association remained significant with a clear dose-response pattern after adjustment for metropolitan status and socioeconomic status, with coefficients of 0.61 (0.55, 0.66), 0.98 (0.92, 1.04), 1.48 (1.41, 1.55), and 2.38 (2.30, 2.45) ppt in the four groups, respectively (P-trend <0.01).

4. Discussion

Using the contemporary data from all U.S. census tracts, we evaluated the associations between racialized economic segregation and the prevalence of certain unhealthy behaviors and poor mental health. We found that less privileged census tracts with extreme concentration of racial and economic disadvantages had significantly higher prevalence of poor mental health and higher prevalence of smoking, physical inactivity, obesity and inadequate sleep, but lower prevalence of binge drinking.

Residential segregation shapes neighborhood environments in ways that constrains health-promoting choices. Segregated areas tend to have weaker substance use policies, greater exposure to tobacco stores and related advertising, unhealthy foods, and stressful environmental conditions, along with limited resources for health promotion, which could result in higher prevalence of smoking, obesity, and physical inactivity in these areas (Williams and Collins, 2001). In addition, higher exposure to ambient noise, artificial light, air pollution, and neighborhood violence in segregated areas are established risk factors for poor sleep (Kim et al., 2022). Most importantly, individuals living in these highly segregated areas experience greater chronic stress and are more likely to adopt unhealthy behaviors as coping strategies (Jackson et al., 2010). Consistent with these mechanisms, prior studies reported positive association of segregation with obesity (Kershaw et al., 2013) and smoking (Bell et al., 2007; Ghosh et al., 2025). However, evidence regarding physical inactivity remains limited and inconsistent, which could be due to the use of different measures of residential segregation (Deng et al., 2025; Lopez, 2006). For example, a study using the AmeriSpeaker panel found no association between county-level segregation measured by the Isolation Index and physical activity (Deng et al., 2025), whereas another study reported that higher Dissimilarity Index scores were associated with increased physical inactivity (Lopez, 2006). In addition, although previous studies have examined neighborhood social environments as potential determinants of poor sleep health (Kim et al., 2022), few studies have investigated the association between residential segregation and sleep outcomes (Gaston et al., 2026). Our study contributes to the literature by examining multiple unhealthy behaviors simultaneously and suggest that elevated racialized economic segregation (less privileged areas) is associated with increased prevalence of smoking, physical inactivity, obesity, and inadequate sleep.

In contrast, binge drinking showed an inverse association with racialized economic segregation in our study. Areas with higher ICE values (more privileged areas) had higher prevalence of binge drinking. Prior evidence on the relationship between segregation and alcohol use is mixed. Some studies reported positive associations between neighborhood segregation and alcohol drinking (Woodard et al., 2025), while other studies found negative or null association between neighborhood segregation and alcohol drinking (Schwartz et al., 2022). The ICE captures the spatial concentration of racialized economic privilege; higher ICE values reflect areas with a greater proportion of affluent White residents and a lower proportion of low-income Black residents. The positive association between the ICE value and binge drinking prevalence observed in our study may align with prior evidence indicating that White populations have a higher prevalence of binge and heavy drinking than Black populations (Bohm et al., 2021).

The effect of residential segregation on mental health has been studied over decades with mixed findings (Acker et al., 2023; Bécares et al., 2018; Sugg et al., 2023). An important reason is that residential segregation is a multidimensional concept and its measurement has varied. Traditional measures of racial residential segregation, which focus on the density, distribution, or interaction of racial/ethnic groups, have shown inconsistent associations with mental health (Bécares et al., 2018; Bécares et al., 2014). For example, a meta-analysis found that for each 10 ppt increase in ethnic density, there was an 18 ppt reduction in relative odds of psychotic experiences and suicidal ideation among racial/ethnic minority group (Bécares et al., 2018), while another study found that very high racial segregation was associated with poorer mental health outcomes (Bécares et al., 2014). The ICE, a novel measurement of racialized economic segregation which quantifies geographic variations in social polarization, i.e., spatial concentration of extremes of socioeconomic privilege and deprivation, has shown more consistent associations with mental health and other health outcomes (Acker et al., 2023; Larrabee Sonderlund et al., 2022; Sugg et al., 2023). Segregation measured by the ICE has been linked with worse mental health in two previous state-level studies (Acker et al., 2023; Sugg et al., 2023). One study found that lower census tract-level ICE value (less privileged) was significantly associated with higher rates of depressive symptoms and suicidality among adolescent in Northern California (Acker et al., 2023). Another study reported higher likelihood of the spatial clusters of suicide and self-injury among adolescent and young adults in areas with lower ICE values (less privileged) at the zip code level in North Carolina (Sugg et al., 2023). Our study used national data and found a significant and dose-dependent association between census tract-level ICE and prevalence of poor mental health in adults in the U.S., confirming the associations of racialized economic segregation measured by the ICE with mental health outcomes identified in previous state-level studies in adolescents (Acker et al., 2023; Sugg et al., 2023).

Multiple pathways have been proposed to explain the association between residential segregation and poor physical health, including socioeconomic factors (Poulson et al., 2023), food environment (Goodman et al., 2018), and healthcare access (Janevic et al., 2020). In contrast, fewer studies have examined pathways between residential segregation and mental health outcomes. Unhealthy behaviors could be a potential pathway. Limited resources for health promotion and increased exposure to factors that encourage unhealthy behaviors in highly segregated areas may contribute to a higher likelihood of engaging in unhealthy behaviors (Williams and Collins, 2001). In turn, smoking, physical inactivity, overeating, and inadequate sleep have been identified as risk factors for poor mental health (Vermeulen-Smit et al., 2015), with evidence suggesting that individuals engaging in these behaviors may have approximately twice the risk of depression, anxiety, and loneliness (Vermeulen-Smit et al., 2015). Our study identified associations between residential segregation and clustered unhealthy behaviors, as well as between residential segregation and poor mental health. However, given the ecological study design, we were unable to directly assess the pathway linking residential segregation to unhealthy behaviors and, subsequently, to mental health outcomes. Future longitudinal studies incorporating both area- and individual-level data are needed to evaluate the potential mediating role of unhealthy behaviors in this relationship.

Our study has limitations. First, associations at the census tract level do not necessarily reflect individual-level associations. Second, the temporal relationships cannot be established in this ecological study, as both exposure and outcome variables were measured during the same time period. In addition, the prevalence of poor mental health was for a 30-day, which may not fully capture the severity and specificity of mental health conditions.

5. Conclusion

Using multiple contemporary nationwide data sources, our study identified significant place-based disparities in mental health and unhealthy behaviors, i.e., less privileged neighborhoods had significantly higher prevalence of unhealthy behaviors and poor mental health. Future studies are needed to elucidate the causal pathways underlying these disparities and to identify actionable, targeted interventions to improve mental health and behaviors health equity.

CRediT authorship contribution statement

Xiaofang Lai: Writing – review & editing, Writing – original draft, Formal analysis, Data curation, Conceptualization. Qingzhao Yu: Writing – review & editing, Formal analysis, Conceptualization. Nova Yang: Writing – review & editing, Formal analysis, Conceptualization. K. Robin Yabroff: Writing – review & editing, Conceptualization. Ruofei Du: Writing – review & editing, Formal analysis, Conceptualization. Brian Witrick: Writing – review & editing, Formal analysis, Conceptualization. Xuesong Han: Writing – review & editing, Funding acquisition, Formal analysis, Conceptualization. Lu Zhang: Writing – review & editing, Writing – original draft, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Funding

This study was supported by a Discovery Boost Grant, DBG-23-1036396-01-HOPS, from the American Cancer Society (PI: Lu Zhang; co-I: Xuesong Han) and funding from Mary Lohr Endowment from Clemson University (PI: Lu Zhang). Sponsors did not play any role in study design; collection, analysis, and interpretation of data; writing the report; and the decision to submit the report for publication.

Declaration of competing interest

K. Robin Yabroff has received honoraria from the American Society of Clinical Oncology, funding from research grants from the National Cancer Institute and Blood Cancer United. All other authors declare no conflict of interest.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pmedr.2026.103504.

Appendix A. Supplementary data

Supplementary material

mmc1.docx (34.5KB, docx)

Data availability

All data are publicly available and websites are provided in the paper.

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