Neighborhood Racial Composition,Socioeconomic Status,and Obesity Among Older Black Adults

Abstract

Objectives

Obesity is common among older Black adults, but its underlying drivers, such as neighborhood racial composition (NRC), remain understudied. This study examined the association between NRC and obesity among older Black adults, evaluating the role of individual-level socioeconomic status (SES).

Methods

Data from the Nashville Stress and Health Study was linked to five-year estimates from the American Community Survey. Obesity was defined as body mass index ≥30 kg/m². NRC was measured with the Index of Concentration at the Extremes.

Results

After adjusting for demographic characteristics and health behaviors, older Black adults living in more Black-concentrated neighborhoods had lower obesity prevalence (PR = 0.76, 95% CI: 0.63–0.93) compared to those in more White-concentrated neighborhoods. However, after controlling for individual-SES this association was not significant (PR = 0.82, 95% CI: 0.65–1.04).

Conclusion

Individual-SES may account for the relationship between NRC and obesity among older Black adults.

Keywords

obesity segregation aging and Blacks

Introduction

Obesity affects approximately 41% of older adults in the United States, and nearly 47% of non-Hispanic Black adults meet criteria for obesity (Ward et al., 2019). These figures are expected to rise in the coming years (Ward et al., 2019). Severe obesity is more common among older adults, Black Americans, and individuals with lower incomes (Lincoln, 2020). Obesity in this group is a significant public health concern, contributing to higher rates of chronic conditions such as hypertension, type 2 diabetes, and cardiovascular disease (Bae et al., 2025; Emmerich et al., 2024; Ezendu et al., 2025). These health consequences are further compounded by reduced mobility, poorer quality of life, and increased healthcare costs in later life (Fjeldstad et al., 2008; Rejeski et al., 2010). Structural racism and lifetime exposure to chronic stress, often described as “weathering,” have also been linked to elevated obesity risk and metabolic dysregulation among older Black adults (Geronimus et al., 2006).

Research indicates that social determinants of health, including neighborhood food environments, income inequality, and access to safe spaces for physical activity, play a central role in shaping obesity risk (Rundle et al., 2009; Suglia et al., 2016; Wang et al., 2023; Yang et al., 2023; Zare et al., 2021). One factor that has gained increasing attention is neighborhood racial composition (NRC), commonly used as a proxy for racial segregation and defined as the proportion of individuals from different racial groups within a geographic area (Ruel et al., 2010). Several studies have linked NRC to obesity prevalence among adults. For example, a 2015 study of New York City residents found that the percentage of Black residents in a neighborhood accounted for much of the disparity in obesity between Black and White adults (Lim & Harris, 2015). In general, obesity risk has been observed to be higher in neighborhoods with larger Black populations. However, much less is known about how this relationship operates among older adults. For older Black adults in particular, the cumulative effects of structural racism and discrimination over the life course may shape how neighborhood context influences health (Boardman et al., 2005). Place has long been recognized as a critical determinant of health, and the added influence of long-term residential exposure underscores the potential importance of NRC in later life (Gaskin et al., 2014).

At the same time, socioeconomic status (SES) is a well-documented determinant of obesity at the individual level. Our study builds on this literature by seeking to understand the impact of individual SES on the observed association between NRC and obesity (Anekwe et al., 2020). Existing research shows that older Black adults and those with lower SES are both more likely to experience obesity compared to older White adults and individuals with higher SES (Conklin et al., 2013; Lincoln, 2020; Lincoln et al., 2014). In one study of late-middle-aged Black adults, those with higher SES were more likely to report health behaviors protective against obesity (Cockerham et al., 2022). Additionally, cumulative stress exposure related to discrimination, a process known as weathering, has been shown to increase biological vulnerability to obesity through mechanisms such as chronic inflammation and hormonal dysregulation (Scommegna, 2021). These intersecting risks raise important questions about whether neighborhood-level segregation and individual-level SES jointly contribute to obesity in later life. This study examines whether the relationship between NRC and obesity persists after adjusting for individual SES.

Although prior studies have separately examined NRC and individual SES in relation to obesity, few have tested both in tandem among older Black adults (Anekwe et al., 2020). In this study, we assess whether individual SES impacts the association between NRC and obesity. Specifically, we evaluate whether living in a more Black-concentrated versus White-concentrated neighborhood is associated with obesity risk after adjusting for individual SES. To operationalize NRC, we use the Index of Concentration at the Extremes (ICE), a validated measure of racial segregation that captures the spatial concentration of privilege and disadvantage by comparing the distribution of Black and White residents in a given census tract (Krieger et al., 2016). This approach allows us to examine how structural features of residential segregation shape obesity risk among older Black adults. We hypothesize that the association between NRC and obesity will be at least partially accounted for by individual SES.

Methods

Sample

Data was obtained from the Nashville Stress and Health Study (NSAHS), a population-based sample of Black and White adults aged 21 to 69 residing in the city of Nashville and surrounding areas within Davidson County, Tennessee. The NSAHS used a stratified, multi-stage probability sampling design, and employed post-stratification and design weights to ensure representativeness of the county’s adult population. Sampling weights allowed for generalizability of the county population despite oversampling of Black households. While the final response rate, calculated using the American Association for Public Opinion Research (AAPOR) Response Rate 1 (RR1), was 30.2%, the cooperation rate was high at 74.2%, and the majority of nonresponse was due to difficulty contacting selected individuals. The weighted data are considered representative of non-Hispanic Black and White adults aged 25 to 65 living in Davidson County at the time of the study (Brown et al., 2016). All analyses apply survey weights and incorporate complex survey design corrections to support generalizability to similar urban Black populations, particularly in Southern metropolitan areas like Nashville.

Information was collected between 2011 and 2014 through computer-assisted, race-matched interviews and in-home clinician visits. A total of 1,252 respondents provided information about their personal and family backgrounds, stress and coping experiences, and health histories during the interview portion. The following day prior to breakfast, clinicians obtained 12-hour urine samples and collected blood samples, measured blood pressure, took waist, hip, height, and weight measurements, and documented prescription medication usage. The Vanderbilt University Institutional Review Board approved the NSAHS and all study procedures. These details may be found elsewhere (Brown et al., 2016).

Demographic and health information from the NSAHS were linked to neighborhood-level data from the American Community Survey (ACS), a nationwide, continuous survey that provides reliable annual estimates of social, economic, housing, and demographic characteristics (United States Census Bureau, 2024a; 2024b). Approximately 3.5 million addresses are sampled each year, with responses collected online, by mail, or via computer-assisted interviews. The present study used five-year estimates from the ACS, given their reliability and larger sample size (United States Census Bureau, 2024c).

This study includes a subset of older Black adults (n = 273) within the NSAHS sample. Fewer than 1% of participants were missing sociodemographic or biological information due to issues such as insufficient blood draw, specimen contamination, or refusal of the clinician visit. This localized, tract-linked, and weighted design enables the investigation of within-county variation in NRC and its association with health among older Black residents. Analyses are weighted to account for the complex sampling design of the NSAHS and to ensure population-level representativeness.

Outcome Variable

The dependent variable in this analysis was obesity. Body mass index (BMI) was derived by calculating each respondent’s measured height and weight as obtained in the medical examination. Obesity was defined as BMI greater than or equal to 30 kg/m² (Centers for Disease Control and Prevention, 2023; National Institutes of Health, 2020).

Main Independent Variable

The independent variable of this study was neighborhood racial composition (NRC). NRC was operationalized using the Index of Concentration at the Extremes for race (ICE_race), a validated measure of racial concentration and segregation at the neighborhood level (Krieger et al., 2016). ICE_race quantifies the extent to which residents in each area are concentrated at one end of the racial distribution compared to the other. Specifically, it is calculated as

{I C E}_{r a c e} = \frac{(N u m b e r o f B l a c k r e s i d e n t s - N u m b e r o f W h i t e r e s i d e n t s)}{T o t a l p o p u l a t i o n i n t h e c e n s u s t r a c t}

ICE_race scores were calculated using census tract-level data from the ACS 5-Year Estimates (2010–2014) and linked to individual-level data from the NSAHS. The resulting variable captures the degree of racial concentration in respondents’ neighborhoods and serves as a proxy for structural racial segregation. These scores quantify the extent to which residents in each area are concentrated at one end of the racial distribution compared to the other. This variable was modeled continuously in all analyses, and values ranged from −1 to +1. Higher (positive) values reflect a greater concentration of Black residents relative to White residents in the census tract, while lower (more negative) values indicate a higher concentration of White residents. A value of zero indicates equal numbers of Black and White residents in the census tract. The resulting number is interpreted in alignment with the established use of ICE as a relative measure of racialized residential context (Westrick et al., 2020).

While Krieger originally introduced ICE as a measure of social stratification, defining values in terms of relative “privilege” versus “deprivation,” such framings can oversimplify the meaning of racial concentration—particularly in studies focused on racially minorityized populations. In this study, we use ICE_race to center Black neighborhood concentration as the primary dimension of interest, aligning with the broader goal of understanding how racialized residential contexts shape health among Black Americans. Rather than positioning Black racial density as a proxy for deprivation, this approach allows for a more grounded, asset-aware, and interpretable investigation of how racial context operates within a diverse metropolitan setting like Nashville.

Covariates

Demographics included age, gender, and marital status. Age was self-reported in years. Gender was reported as woman or man. Marital status was reported as married, never married, or other (i.e., widowed, divorced). Health behaviors included smoking status, alcohol consumption, and physical activity. Health behaviors included smoking, alcohol consumption, and physical activity. Smoking status was based on reports of being a current smoker, ex-smoker, or have never smoked. A binary variable was created to identify those who were current smokers compared to ex-smokers and never smokers. Based on federal guidelines (U.S. Department of Health and Human Services, 2018), physical activity was based on a binary variable that was created to compare individuals who engaged in fewer than 75 minutes of vigorous activity per week to those who engaged in at least 150 minutes of moderate activity or 75 minutes to 150 minutes of vigorous activity per week (0 = inactive, 1 = active). Alcohol consumption was based on the report of the number of drinks per day in the past 12 months. The responses were based on federal Dietary Guidelines for Americans, 2020–2025 (U.S. Department of Agriculture and U.S. Department of Health and Human Services, 2020), and categorized as: (0) None, (1) Moderate Consumption (1–2 drinks daily), and (2) Excessive Consumption (3+ drinks daily). Depressive symptoms were assessed and quantified through the use of the 20-item Center for Epidemiologic Studies Depression Scale based on self-reported values ranging from (0) not at all to (3) very much (Radloff, 1977). Questions included inquiry about feeling of depression, hope for future, and life enjoyment, among other depressive symptoms.

Socioeconomic status (SES) was assessed following an approach that has been previously used with these data (C. S. Thomas Tobin et al., 2022; 2022b). SES consists of the following three items: including educational attainment (0–20 years of education), annual household income (0 = $20,000; 1 = $20,000–$34,999; 2 = $35,000–$54,999; 3 = $55,000–$74,999; 4 = $75,000–$94,999; 5 = $95,000+), and occupational prestige (0–100) which was based on the 2000 Nam-Powers-Boyd occupational scores (Nam & Boyd, 2004) and derived for each respondent. Specifically, a SES score for each respondent was calculated by standardizing the three SES variables among Black respondents in the NSAHS, summing the scores, and dividing by the number of SES variables for which data were available. Standardization within the Black sample allows SES values to reflect respondents’ relative socioeconomic position within the broader distribution of Black participants rather than only within the older adult analytic subsample. This approach equally weighted education, income, and occupational prestige; as such, this affords the opportunity to capture individuals’ placement within a social hierarchy and provides a comprehensive assessment of SES while preserving data on individuals (Courtney S Thomas Tobin, Gutiérrez, et al., 2022, Thomas Tobin, Gutiérrez, et al., 2022; Erving & Thomas, 2018). In the present study, continuous SES scores represent the number of standard deviations above or below the sample’s mean SES score, such that scores above zero corresponded with above-average SES.

Analytic Strategy

Weighted percentages and means with standard deviations were used to summarize the total sample of older Black adults. Student’s t tests for continuous variables and chi-square tests for categorical variables were used to evaluate the mean and proportional differences by obesity status for the demographic characteristics, health behaviors, depressive symptoms, and SES. The prevalence of obesity (BMI ≥30) was greater than 10% in our project. Logistic regression is the standard statistical method for modeling binary outcomes, producing odds ratios (ORs) with 95% confidence intervals to quantify predictor–outcome associations (Bland & Altman, 2000). However, when outcome prevalence is common (exceeds 10%), the OR increasingly diverges from the risk ratio (McNutt et al., 2003; Thorpe et al., 2017; Zou, 2004). This often leads to an overestimation of the association between the outcome and independent variable. To address this, Zou (2004) introduced modified Poisson regression, which fits a Poisson model with a log link and employs robust (sandwich) variance estimators to correct standard errors (Zou, 2004). Exponentiating the resulting coefficients results in adjusted prevalence ratios that remain directly interpretable even with moderate sample sizes and reliably converge in scenarios where log-binomial models may fail to converge (McNutt et al., 2003; Thorpe et al., 2017; Zou, 2004). Four models were conducted to examine the impact of individual SES on the relationship between NRC and obesity. The first model tested the association between NRC and obesity. The second model tested the relationship between individual SES and obesity. The third model tested the association between NRC and obesity accounting for individual SES. The fourth model tested whether the relationship between NRC and obesity after accounting for individual SES, demographic and health behaviors, and depressive symptoms. This progressive modeling approach allows us to assess the extent to which adjustment for individual SES attenuates the NRC–obesity association (Thorpe et al., 2011; 2013; 2020). Because NRC, SES, and obesity were measured contemporaneously using cross-sectional data, we did not conduct a formal mediation analysis and instead used this progressive modeling approach to examine attenuation in the association between NRC and obesity. All tests were two tailed, and P values <0.05 were considered statistically significant. Analyses were conducted using STATA 17.0 (StataCorp LP, College Station, TX).

Results

The distribution of demographic characteristics, health behaviors, depressive symptoms, and SES for the total sample and by obesity status is displayed in Table 1. In the sample, older Black adults classified as obese resided, on average, in neighborhoods with greater White concentration compared to those who were not obese. The average age for adults included in this study is 56.4 ± 6 years old and 55.2% of the study participants reported being a woman. Marital status was reported by participants as married (39.0%), never married (15.4%), or other (45.4%). Approximately a third of individuals reported being current smokers and physically active (34.3% and 34.4%, respectively). Over half (57.4%) of the study participants reported no alcohol consumption. About a fourth (24.0%) of individuals reported consuming 1–2 alcoholic drinks daily and 18.4% of individuals disclosed drinking 3 or more alcoholic drinks daily. The mean depressive symptoms score was value 12.2 ± 12.8, while for SES score was −0.57 ± 1.0. Over half of the sample (61.9%) were considered obese.

Table 1.

Distribution of Select Characteristics for the Total Sample and by Obesity Status for Older Black Adults in the Nashville Stress and Health Study 2010–2014

Characteristic	Total	Not obese	Obese	p-value
Neighborhood racial composition	−0.16 ± 0.8	−0.02 ± 0.8	−0.24 ± 0.7	.007
Age, mean ± SD	56.4 ± 6.3	56.4 ± 6.9	56.3 ± 5.9	.862
Women, %	55.2	35.0	67.6	<.001
Marital status, %				.637
Married	39.0	42.5	36.9
Never married	15.4	12.0	17.6
Other	45.4	45.4	45.4
Current smoker, %	34.3	53.8	22.3	.001
Physically active, %	34.4	44.0	28.6	.010
Alcohol consumption, %				.586
None	57.4	51.2	61.2
Moderate	24.0	28.6	21.2
Excessive	18.4	20.0	17.4
Depressive symptoms, mean ± SD	12.2 ± 12.8	13.8 ± 14.3	11.27 ± 11.7	.079
Socioeconomic status, mean ± SD	−0.57 ± 1.0	−0.78 ± 0.9	−0.44 ± 1.0	.011
Obesity (BMI >30), %	61.9

Note. SD = standard deviation, BMI = body mass index.

Socioeconomic Status score calculated by standardizing the educational attainment, annual household income, and occupational prestige variables, summing the scores, and dividing by the number of SES variables for which data were available.

Index of Concentration at the Extremes for race, the degree of racial concentration in respondents’ neighborhoods, serves as a proxy for structural racial segregation. Values ranged from −1 to +1. Higher (positive) values reflect a greater concentration of Black residents relative to White residents in the census tract, while lower (more negative) values indicate a higher concentration of White residents. A value of zero indicates equal numbers of Black and White residents in the census tract.

When examining NRC, demographic characteristics, health behaviors, and depressive symptoms by obesity status, we observed that older Black adults classified as obese resided, on average, in neighborhoods with greater White concentration compared to those who were not obese. A greater proportion of women were classified as obese compared to men. Compared to non-obese respondents, obese respondents had higher mean SES and were less likely to be current smokers or physically active. No statistically significant differences in age, marital status, alcohol consumption, or depressive symptoms were observed between obese and non-obese older Black adults.

The impact of SES on the association between NRC and obesity prevalence is displayed in Table 2. In Model 1, older Black adults living in more Black concentrated neighborhoods have lower prevalence of obesity (prevalence ratio (PR) = 0.76, 95% confidence interval (CI) 0.63–0.93) compared to those older Black adults living in more White concentrated neighborhoods. In Model 2 that includes only individual SES, for every standard deviation above the sample mean SES score older Black adults had a higher prevalence of obesity (PR = 1.27, 95% CI 1.07–1.51). In Model 3 which adjusts for individual SES, older Black adults living in more Black concentrated neighborhoods have a similar prevalence of obesity (PR = 0.82, 95% CI 0.64–1.05) compared to those older Black adults living in more White concentrated neighborhoods. Accounting for demographic, health behaviors, and SES, the relationship remained such that older Black adults living in more Black concentrated neighborhoods have a similar prevalence of obesity (PR = 0.83, 95% CI 0.68–1.01) compared to those older Black adults living in more White concentrated neighborhoods.

Table 2.

Association Between Neighborhood Racial Composition and Obesity Prevalence in Older Adults from Nashville Stress and Health Study and American Community Survey, 2010–2014

	PR (95% CI)
Characteristic	Model 1	Model 2	Model 3	Model 4
Neighborhood racial composition	0.76 (0.63–0.93)		0.82 (0.64–1.05)	0.83 (0.68–1.01)
Age				0.97 (0.95–1.00)
Gender
Women				1.44 (1.18–1.76)
Marital status
Never married				1.32 (0.97–1.78)
Other				1.23 (0.83–1.82)
Smoking status
Current smoker				0.68 (0.42–1.10)
Physical activity
Active				0.78 (0.64–0.95)
Alcohol consumption
Moderate				0.88 (0.61–1.25)
Excessive				1.04 (0.77–1.40)
Depressive symptoms				0.98 (0.96–0.99)
Socioeconomic status		1.27 (1.07–1.51)	1.24 (1.02–1.50)	1.16 (0.99–1.36)

Note. PR = prevalence ratio; CI = confidence interval; alcohol consumption: moderate = 1–2 daily, excessive = 3+ daily. The reference group used was male, married, non-smoker, physically inactive, and non-drinker individuals.

Discussion

This study sought to examine whether individual SES influences the association between NRC and obesity among older Black adults. Our analysis found that NRC was significantly associated with obesity in unadjusted models. However, the association was attenuated and no longer statistically significant after adjusting for individual SES. Understanding which specific indicators of individual SES drive the relationship between NRC and obesity is a key next step in this scientific inquiry.

After adjusting for individual SES, the association between NRC and obesity was no longer statistically significant. This finding suggests that SES may account for the observed relationship and could represent a pathway through which residential segregation influences obesity risk. These results build on existing literature identifying SES as a key determinant of obesity (Anekwe et al., 2020), but few studies (Conklin et al., 2013) have examined this relationship in the context of NRC or specifically among older Black adults.

Although higher SES is often associated with lower obesity risk in the general United States population, we found that among older Black adults, higher SES was associated with a greater prevalence of obesity. Moreover, adjusting for SES attenuated the association between NRC and obesity to below the threshold for statistical significance, suggesting that individual SES may account for a meaningful portion of the observed relationship between NRC and obesity. This pattern is consistent with the diminishing health returns framework, which posits that Black Americans do not experience the same health benefits from socioeconomic advancement as their White counterparts (Assari, 2018; Boen, 2016; Colen et al., 2018). Structural racism and chronic exposure to discrimination may constrain the protective effects of socioeconomic attainment, such that upward mobility does not uniformly translate into improved health outcomes. Existing evidence further suggests that neighborhood context may shape how SES operates for Black adults. Using data from the Nashville Stress and Health Study, DeAngelis (2022) found that Black adults residing in higher-status and predominantly White neighborhoods reported greater perceived discrimination, which was associated with elevated stress biomarkers and chronic pain (DeAngelis, 2022). After accounting for racism-related stressors, Black adults exhibited comparable physiological distress regardless of neighborhood context, indicating that the health benefits of residential mobility may be offset by discrimination-related stress. In this context, higher SES may not protect against obesity as expected but instead may reflect greater exposure to stressors that arise within racially unequal social environments. Future research should explore whether and how SES mediates the effects of neighborhood segregation, ideally through formal mediation analysis or longitudinal designs that can better assess causal pathways.

It is also worth noting that in unadjusted and partially adjusted models’ older Black adults living in more Black-concentrated neighborhoods had a lower prevalence of obesity. This challenges dominant narratives that portray predominantly Black neighborhoods are uniformly detrimental to health. The diminished prevalence in more Black-concentrated areas indicates that being surrounded by those of a similar racial background may be a protective factor for preventing obesity. These findings are partially consistent with previous research (e.g., Lim and Harris), though they differ in direction. One possible explanation is that living in neighborhoods with racial composition similar to one’s own may offer social and psychological benefits. Higher levels of social cohesion, shared cultural norms, and reduced exposure to interpersonal discrimination may support healthier behaviors. The utilization of ICE_race as a measure for NRC captures the level of physical separation of racial groups (Atere-Roberts et al., 2024), but does not explain why this separation may exist, whether from intentional choice of residence or the accumulation of historical and modern racially restrictive policies. While the value does not consider mobility of residents nor contains component for residence history (Feldman et al., 2015), ICE_race is still acknowledged in literature to uniquely capture racialized segregation (Krieger et al., 2016). Additionally, the presence of community members with similar lived experiences may foster a sense of belonging that mitigates stress. Future research should investigate protective mechanisms in Black-concentrated neighborhoods, including psychosocial resources, social networks, and potential stress-buffering effects. Several limitations must be considered when interpreting these findings. The cross-sectional design limits the ability to determine causality or directionality. The use of BMI to define obesity, while widely accepted, may not capture body composition accurately in older adults. Although BMI remains a standard metric, it has well-documented limitations, particularly in aging populations. The sample was restricted to older Black American adults residing in Nashville, which may limit generalizability to other racial groups, younger populations, or individuals living in rural areas. Older Black adults living in more white concentrated areas may have access to better healthcare, healthier foods, and more exercise options (Franco et al., 2008). However, these measures were not collected in NSAHS. Future work should consider these measures when examining the relationship between NRC and obesity. Additionally, while ICE is a validated measure of racial concentration, it does not capture other dimensions of segregation, such as isolation, interaction, or historical redlining. Further, although ICE captures the relative concentration of racial groups across neighborhoods, it cannot distinguish whether residential patterns reflect structural constraints such as segregation or individual residential preferences. As a result, the measure reflects racialized residential context rather than the mechanisms that produce those patterns. Unmeasured confounders, including perceived discrimination, healthcare access, and characteristics of the built environment, may also influence the observed relationships and should be considered in future research. Gender differences pose an important consideration and direction for future research in this field. Stratifying by both gender and NRC would yield small subgroup sizes, reducing statistical power and compromising the interpretability of results for this data. While gender is an important axis of health inequity, examining gendered patterns in the NRC–obesity relationship is beyond the scope of the current study. We acknowledge this as a limitation of our study and encourage future work with larger samples to build on this line of inquiry.

Despite these limitations, the study offers several important strengths. It addresses a critical gap in the literature by focusing on an understudied and high-risk population: older Black adults. Obesity rates in Nashville exceed those in comparable cities, such as Charlotte and Austin, making the focus on this geographic region especially relevant (Metro Public Health Department). The use of the Index of Concentration at the Extremes (ICE) allows for a structural analysis of racial segregation, providing a validated measure of spatial privilege and deprivation. Beyond structural racial discrimination, racial segregation, as measured through various indicators, can reflect disparities in incarceration rates, educational attainment, economic status, and employment (Bailey et al., 2017). It is also associated with poorly resourced education systems, food deserts, high crime rates, limited green space, all of which have been associated with obesity (Kim et al., 2021; Tu et al., 2024). Racial segregation can restrict access to affordable healthy foods, safe spaces for physical activity, adequate health insurance, and necessary medications (Lofton et al., 2023). Therefore, a greater understanding of racial segregation’s association with health yields greater insight into numerous disparities. The quantification of individual SES using a composite score presents further strength to the study. Our progressive modeling approach sheds light on the role of individual SES in shaping health disparities linked to neighborhood context. This study contributes to a growing body of work on how structural racism and socioeconomic inequality intersect to shape health outcomes over the life course. These findings underscore the significance of both neighborhood context and socioeconomic position in influencing long-term health and well-being.

Conclusions

In summary, findings indicate that one’s racial composition of their neighborhood impacted their obesity prevalence. However, in the presence of individual SES, this relationship was eliminated. A more nuanced understanding of the relationship between SES and NRC is needed among older Black adults.

Footnotes

ORCID iDs

Corina Mills

Ángela Gutiérrez

Roland J. Thorpe

Courtney S. Thomas Tobin

Author Contributions

Conceptualization: C.T.T. and R.J.T.Jr.

Writing—original draft preparations: C.M. and R.J.T.Jr.

Writing—review and editing: C.M., C.T.T., R.J.T.Jr., A.G., and J.L.

Supervision: C.M., C.T.T., and R.J.T.Jr.

Project administration: C.M., C.T.T., and A.G.

Funding acquisition: C.T.T. and R.J.T.Jr.

All authors have read and agreed to the published version of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study used data from the Nashville Stress and Health Study (NSAHS), which was funded by the Office of Behavioral and Social Sciences Research and the National Institute on Aging (R01AG034067; PI: R. Jay Turner). Dr Thorpe was funded by U54MD000214 and P30AG059298.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data that was used to generate results in this paper can be made available per request to C.T.T.*

Institutional Review Board Statement

The NSAHS and all study procedures were approved by the Vanderbilt University Institutional Review Board. Data from both sources were deidentified or public use.

References

Anekwe

C. V.

Jarrell

A. R.

Townsend

M. J.

Gaudier

G. I.

Hiserodt

J. M.

Stanford

F. C.

(2020). Socioeconomics of obesity. Current Obesity Reports, 9(3), 272–279. https://doi.org/10.1007/s13679-020-00398-7

Assari

(2018). Health disparities due to diminished return among Black Americans: Public policy solutions. Social Issues and Policy Review, 12(1), 112–145. https://doi.org/10.1111/sipr.12042

Atere-Roberts

Delamater

P. L.

Robinson

W. R.

Aiello

A. E.

Hargrove

T. W.

Martin

C. L.

(2024). Indicators of inequity: Exploring the complexities of operationalizing area-level structural racism. SSM - Population Health, 27, 101701. https://doi.org/10.1016/j.ssmph.2024.101701

Bae

J. P.

Nelson

D. R.

Boye

K. S.

Mather

K. J.

(2025). Prevalence of complications and comorbidities associated with obesity: A health insurance claims analysis. BMC Public Health, 25(1), 273. https://doi.org/10.1186/s12889-024-21061-z

Bailey

Z. D.

Krieger

Agénor

Graves

Linos

Bassett

M. T.

(2017). Structural racism and health inequities in the USA: Evidence and interventions. The Lancet, 389(10077), 1453–1463. https://doi.org/10.1016/s0140-6736(17)30569-x

Bland

J. M.

Altman

D. G.

(2000). Statistics notes. The odds ratio. BMJ, 320(7247), 1468. https://doi.org/10.1136/bmj.320.7247.1468

Boardman

J. D.

Saint Onge

J. M.

Rogers

R. G.

Denney

J. T.

(2005). Race differentials in obesity: The impact of place. Journal of Health and Social Behavior, 46(3), 229–243. https://doi.org/10.1177/002214650504600302

Boen

(2016). The role of socioeconomic factors in black-white health inequities across the life course: Point-in-time measures, long-term exposures, and differential health returns. Soc Sci Med, 170, 63–76. https://doi.org/10.1016/j.socscimed.2016.10.008

Brown

T. N.

Turner

R. J.

Moore

T. R.

(2016). The multidimensionality of health: Associations between allostatic load and self-report health measures in a community epidemiologic study [article]. Health Sociology Review, 25(3), 272–287. https://doi.org/10.1080/14461242.2016.1184989

10.

Centers for Disease Control and Prevention . (2023). Overweight and obesity: Defining adult overweight & obesity. https://www.cdc.gov/obesity/basics/adult-defining.html#:∼:text=If%20your%20BMI%20is%20less,falls%20within%20the%20obesity%20range

11.

Cockerham

W. C.

Bauldry

Sims

(2022). Obesity-related health lifestyles of late-middle age black Americans: The Jackson heart study. American Journal of Preventive Medicine, 63(1), S47–S55. https://doi.org/10.1016/j.amepre.2022.02.014

12.

Colen

C. G.

Ramey

D. M.

Cooksey

E. C.

Williams

D. R.

(2018). Racial disparities in health among nonpoor African Americans and Hispanics: The role of acute and chronic discrimination. Soc Sci Med, 199, 167–180. https://doi.org/10.1016/j.socscimed.2017.04.051

13.

Conklin

A. I.

Forouhi

N. G.

Suhrcke

Surtees

Wareham

N. J.

Monsivais

(2013). Socioeconomic status, financial hardship and measured obesity in older adults: A cross-sectional study of the EPIC-norfolk cohort. BMC Public Health, 13, 1039. https://doi.org/10.1186/1471-2458-13-1039

14.

DeAngelis

R. T.

(2022). Moving on up? Neighborhood status and racism-related distress among black Americans. Soc Forces, 100(4), 1503–1532. https://doi.org/10.1093/sf/soab075

15.

Emmerich

S. D.

Fryar

C. D.

Stierman

Ogden

C. L.

(2024). Obesity and severe obesity prevalence in adults: United States, August 2021-August 2023. NCHS Data Brief, (508). https://doi.org/10.15620/cdc/159281

16.

Erving

C. L.

Thomas

C. S.

(2018). Race, emotional reliance, and mental health. Society and Mental Health, 8(1), 69–83. https://doi.org/10.1177/2156869317713552

17.

Ezendu

Pohl

Lee

C. J.

Wang

Dunn

J. P.

(2025). Prevalence of obesity-related multimorbidity and its health care costs among adults in the United States. Journal of Managed Care & Specialty Pharmacy, 31(2), 179–188. https://doi.org/10.18553/jmcp.2025.31.2.179

18.

Feldman

J. M.

Waterman

P. D.

Coull

B. A.

Krieger

(2015). Spatial social polarisation: Using the index of concentration at the extremes jointly for income and race/ethnicity to analyse risk of hypertension. J Epidemiol Community Health, 69(12), 1199–1207. https://doi.org/10.1136/jech-2015-205728

19.

Fjeldstad

A. S.

Acree

L. S.

Nickel

K. J.

Gardner

A. W.

(2008). The influence of obesity on falls and quality of life. Dynamic Medicine, 7(1), 4. https://doi.org/10.1186/1476-5918-7-4

20.

Franco

Diez Roux

A. V.

Glass

T. A.

Caballero

Brancati

F. L.

(2008). Neighborhood characteristics and availability of healthy foods in Baltimore. Am J Prev Med, 35(6), 561–567. https://doi.org/10.1016/j.amepre.2008.07.003

21.

Gaskin

D. J.

Thorpe

R. J.

McGinty

E. E.

Bower

Rohde

Young

J. H.

Laveist

T. A.

Dubay

(2014). Disparities in diabetes: The nexus of race, poverty, and place. American Journal of Public Health, 104(11), 2147–2155. https://doi.org/10.2105/ajph.2013.301420

22.

Geronimus

A. T.

Hicken

Keene

Bound

(2006). “Weathering” and age patterns of allostatic load scores among blacks and whites in the United States. American Journal of Public Health, 96(5), 826–833. https://doi.org/10.2105/ajph.2004.060749

23.

Kim

Lee

Ramos

(2021). Investigating the relationship between accessibility of green space and adult obesity rates: A secondary data analysis in the United States. Journal of Preventive Medicine and Public Health, 54(3), 208–217. https://doi.org/10.3961/jpmph.20.625

24.

Krieger

Waterman

P. D.

Spasojevic

Maduro

Van Wye

(2016). Public health monitoring of privilege and deprivation with the index of concentration at the extremes. Am J Public Health, 106(2), 256–263. https://doi.org/10.2105/ajph.2015.302955

25.

Lim

Harris

T. G.

(2015). Neighborhood contributions to racial and ethnic disparities in obesity among New York city adults. Am J Public Health, 105(1), 159–165. https://doi.org/10.2105/ajph.2013.301782

26.

Lincoln

K. D.

(2020). Race, obesity, and mental health among older adults in the United States: A literature review. Innov Aging, 4(5), igaa031. https://doi.org/10.1093/geroni/igaa031

27.

Lincoln

K. D.

Abdou

C. M.

Lloyd

(2014). Race and socioeconomic differences in obesity and depression among black and Non-Hispanic white Americans. J Health Care Poor Underserved, 25(1), 257–275. https://doi.org/10.1353/hpu.2014.0038

28.

Lofton

Ard

J. D.

Hunt

R. R.

Knight

M. G.

(2023). Obesity among African American people in the United States: A review. Obesity, 31(2), 306–315. https://doi.org/10.1002/oby.23640

29.

McNutt

L. A.

Xue

Hafner

J. P.

(2003). Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol, 157(10), 940–943. https://doi.org/10.1093/aje/kwg074

30.

Nam

C. B.

Boyd

(2004). Occupational status in 2000; over a century of census-based measurement. Population Research and Policy Review, 23(4), 327–358. https://doi.org/10.1023/b:popu.0000040045.51228.34

31.

National Institutes of Health . (2020). Health information for the public: Assessing your weight and health risk. https://www.nhlbi.nih.gov/health/educational/lose_wt/risk.htm

32.

Radloff

L. S.

(1977). The CES-D scale:a self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. https://doi.org/10.1177/014662167700100306

33.

Rejeski

W. J.

Marsh

A. P.

Chmelo

Rejeski

J. J.

(2010). Obesity, intentional weight loss and physical disability in older adults. Obes Rev, 11(9), 671–685. https://doi.org/10.1111/j.1467-789X.2009.00679.x

34.

Ruel

Reither

E. N.

Robert

S. A.

Lantz

P. M.

(2010). Neighborhood effects on BMI trends: Examining BMI trajectories for black and white women. Health & Place, 16(2), 191–198. https://doi.org/10.1016/j.healthplace.2009.09.009

35.

Rundle

Neckerman

K. M.

Freeman

Lovasi

G. S.

Purciel

Quinn

Richards

Sircar

Weiss

(2009). Neighborhood food environment and walkability predict obesity in New York city. Environ Health Perspect, 117(3), 442–447. https://doi.org/10.1289/ehp.11590

36.

Scommegna

(2021). Key factors underlying racial disparities in health between black and white older Americans. Population Reference Bureau. https://www.prb.org/wp-content/uploads/2021/05/tra-41.pdf

37.

Suglia

S. F.

Shelton

R. C.

Hsiao

Wang

Y. C.

Rundle

Link

B. G.

(2016). Why the neighborhood social environment is critical in obesity prevention. J Urban Health, 93(1), 206–212. https://doi.org/10.1007/s11524-015-0017-6

38.

Thomas Tobin

C. S.

Gutiérrez

Á.

Bell

C. N.

Thorpe

R. J.

(2022). Early life racial discrimination, racial centrality, and allostatic load among African American older adults. The Gerontologist, 62(5), 721–731. https://doi.org/10.1093/geront/gnab185

39.

Thomas Tobin

C. S.

Huynh

Farmer

H. R.

Israel Cross

Barve

Robinson

Leslie

E. P.

Thorpe

R. J.

(2022). Perceived neighborhood racial composition and depressive symptoms among black Americans across adulthood: Evaluating the role of psychosocial risks and resources. J Aging Health, 35(9), 8982643221100789. https://doi.org/10.1177/08982643221100789

40.

Thorpe

R. J.

Jr. Bruce

M. A.

Howard

D. L.

LaVeist

T. A.

(2020). Race differences in mobility status among prostate cancer survivors: The role of socioeconomic status. Advances in Cancer Research, 146, 103–114. https://doi.org/10.1016/bs.acr.2020.01.006

41.

Thorpe

R. J.

Jr Koster

Kritchevsky

S. B.

Newman

A. B.

Harris

Ayonayon

H. N.

Perry

Rooks

R. N.

Simonsick

E. M.

Health, Aging, and Body Composition Study . (2011). Race, socioeconomic resources, and late-life mobility and decline: Findings from the health, aging, and body composition study. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 66(10), 1114–1123. https://doi.org/10.1093/gerona/glr102

42.

Thorpe

R. J.

Jr. Szanton

S. L.

Bell

C. N.

Whitfield

K. E.

(2013). Education, income and disability in African Americans. Ethnicity & Disease, 23(1), 12–17. https://www.jstor.org/stable/48667788.

43.

Thorpe

R. J.

Parker

L. J.

Cobb

R. J.

Dillard

Bowie

(2017). Association between discrimination and obesity in African-American men. Biodemography and Social Biology, 63(3), 253–261. https://doi.org/10.1080/19485565.2017.1353406

44.

Marzouk

Dowdell

K. N.

Stanford

F. C.

(2024). Reimagining urban spaces: Green spaces, obesity, and health resilience in an era of extreme heat. Journal of Urban Health, 101(2), 344–348. https://doi.org/10.1007/s11524-024-00834-2

45.

United States Census Bureau . (2024a). American community survey (ACS). https://www.census.gov/programs-surveys/acs

46.

United States Census Bureau . (2024b). American community survey information guide. https://www.census.gov/programs-surveys/acs/about/information-guide.html

47.

United States Census Bureau . (2024c). Using 1-Year or 5-Year American community survey data. https://www.census.gov/programs-surveys/acs/guidance/estimates.html

48.

U.S. Department of Agriculture and U.S. Department of Health and Human Services . (December 2020). Dietary guidelines for Americans, 2020–2025. (9th ed). USDA.gov, Available at https://www.dietaryguidelines.gov/sites/default/files/2020-12/Dietary_Guidelines_for_Americans_2020-2025.pdf

49.

U.S. Department of Health and Human Services . (2018). Physical activity guidelines for Americans. U. S. D. o. H. a. H. Services.

50.

Wang

M. L.

Narcisse

M. R.

McElfish

P. A.

(2023). Higher walkability associated with increased physical activity and reduced obesity among United States adults. Obesity (Silver Spring), 31(2), 553–564. https://doi.org/10.1002/oby.23634

51.

Ward

Z. J.

Bleich

S. N.

Cradock

A. L.

Barrett

J. L.

Giles

C. M.

Flax

Long

M. W.

Gortmaker

S. L.

(2019). Projected U.S. state-level prevalence of adult obesity and severe obesity. New England Journal of Medicine, 381(25), 2440–2450. https://doi.org/10.1056/nejmsa1909301

52.

Westrick

A. C.

Bailey

Z. D.

Schlumbrecht

Hlaing

W. M.

Kobetz

E. E.

Feaster

D. J.

Balise

R. R.

(2020). Residential segregation and overall survival of women with epithelial ovarian cancer. Cancer, 126(16), 3698–3707. https://doi.org/10.1002/cncr.32989

53.

Yang

Cho

Nguyen

Nsoesie

E. O.

(2023). Association of neighborhood racial and ethnic composition and historical redlining with built environment indicators derived from street view images in the US. JAMA Netw Open, 6(1), e2251201. https://doi.org/10.1001/jamanetworkopen.2022.51201

54.

Zare

Gilmore

D. R.

Creighton

Azadi

Gaskin

D. J.

Thorpe

R. J.

Jr. (2021). How income inequality and race/ethnicity drive obesity in U.S. adults: 1999-2016. Healthcare (Basel), 9(11), 1442. https://doi.org/10.3390/healthcare9111442

55.

Zou

(2004). A modified poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702–706. https://doi.org/10.1093/aje/kwh090