https://orcid.org/0000-0003-2046-127X
Figures
Abstract
For a more comprehensive understanding of health disparities, consideration must be given not only to morbidity and mortality outcomes but also to more holistic health-related constructs, such as well-being. We assessed differences in well-being across sociodemographic subgroups in the United States (US). We used data from the Gallup National Health and Well-being Index (2014–2016, n = 530,920) to assess independent cross-sectional associations of self-reported race, ethnicity, educational attainment, and rural residence with measures of well-being for non-institutionalized residents from 50 US states and the District of Columbia. Our outcome measures are thriving, calculated from self-reported current and future life evaluation, and the composite Well-Being Index (WBI), calculated from 38 self-reported metrics spanning 5 domains of well-being (Career, Community, Physical, Financial, Social). We summarized these measures for race-education-urban subgroups and tested for interactions among these three factors, before and after adjustment for year of survey completion, age, sex, marital status, household size, and income. Among our sample, 55% were thriving. After adjustment, higher levels of education were associated with increased odds of thriving: compared with those with no high school degree, we found higher odds of thriving for respondents reporting completion of high school/technical school/some college (OR=1.61(1.57,1.66)), college (OR=2.77(2.69,2.85)), and postgraduate (OR=3.38 (3.29,3.49)) education. Compared with White respondents, Black (OR (95% CI)=1.15 (1.12,1.17)) and Hispanic (OR=1.26 (1.23,1.29)) respondents had higher odds of thriving; results were similar for WBI, except Asian respondents had also higher WBI after adjustment for all covariates (OR=2.54 (2.28,2.80)). Across race-education-urban subgroups, non-White respondents with a college degree living in urban and rural areas had the highest odds of thriving; White respondents with no college degree living in urban areas had the lowest odds of thriving (p < 0.001 for each), after adjustment. These findings raise questions about how well-being is mediated given the known worse clinical health measures among minoritized groups.
Citation: Roy B, Herrin J, Spatz ES, Kershaw KN, Witters D, Krumholz HM, et al. (2026) Describing well-being in the US by race/ethnicity, education level, and urbanicity: A cross-sectional study using the Gallup National Health and Well Being Index. PLoS One 21(9): e0353745. https://doi.org/10.1371/journal.pone.0353745
Editor: Angelina Wilson Fadiji, De Montfort University, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
Received: April 2, 2025; Accepted: June 26, 2026; Published: September 16, 2026
Copyright: © 2026 Roy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data underlying the results of this study are available from Harvard Dataverse (https://doi.org/10.7910/DVN/TG1GYA).
Funding: BR, CR The Nova Institute for Health https://novainstituteforhealth.org/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: BR and CR are Fellows of the Nova Institute for Health. DW is an employee of Gallup, Inc., the company that procured the population level data on well-being. In the past three years, HMK has received options for Element Science and Identifeye and payments from F-Prime for advisory roles. He is a co-founder of and holds equity in Hugo Health, Refactor Health, and ENSIGHT-AI. He is associated with research contracts through Yale University from Janssen, Kenvue, Novartis, and Pfizer. KNK is a paid member of the AHA DEIA Editorial Board. JH has received funding from Pfizer.
Introduction
Well-being is a holistic, positively framed, person-centered assessment of health and quality of life that describes “how people think, feel, and function—at a personal and social level—and how they evaluate their lives as a whole” [1]. It incorporates multiple important aspects of one’s life, including perceived health, social support, connection to and contribution to the community, opportunity for growth and advancement, liking what you do each day and having a sense of purpose in life [2,3]. It also can incorporate resilience.
At the population level, there is heterogeneity in well-being by where people live. Within the US, the percent population thriving by counties ranges from 38.3% to 69.6% [4]. The sociodemographic composition of these counties may be associated with its residents’ reported well-being. Prior studies of this topic are small and have not been able to account for the complex relationships between race, ethnicity, education levels, and rural/urban living contexts that exist due to the effects of longstanding structural racism and classism, and therefore, do not exist in isolation in the US. As such, evaluating associations of these sociodemographic characteristics and their potential interactions with respect to well-being may offer new insights on the health of populations.
Accordingly, this study aims to assess differences in well-being and its elements across various sociodemographic groups and to explore whether interactions among these sociodemographic characteristics exists. The Gallup National Health and Well-Being Index is an ideal data source, as the largest and most comprehensive assessment of well-being in the US, to address these aims. The survey includes a measure of overall thriving (reflecting current and anticipated high life evaluation) in addition to a multi-dimensional assessment of well-being and its elements, including Career (e.g., liking what you do), Community (e.g., satisfaction with living in one’s community), Physical (e.g., physical health and functioning), Financial (e.g., financial security), and Social (e.g., quality of social relationships). [5] In this study, we use survey data from more than one-half million Americans to describe thriving, well-being, and its elements among different sociodemographic subgroups, highlighting differences across historically disadvantaged groups defined by race/ethnicity, education, and urbanicity, as well as combinations of these characteristics.
Materials and methods
Data source
We used data from the Gallup National Health and Well-Being Index, previously known as the Gallup-Sharecare Well-Being Index, collected January 1st, 2014 to December 31st, 2016, to conduct this cross-sectional study assessing differences in levels of well-being associated with self-reported sociodemographic characteristics. Trained Gallup interviewers performed a computer-assisted telephone interview with a unique daily sample of 500–1000 adults over 18 years of age, each of 350 days per year. A structured, dual-frame sampling design (including random-digit-dialing) was used to recruit respondents from all 50 states and the District of Columbia. The 16-minute survey was administered in both English and Spanish, using both landlines and cellular phones. The resulting sample is estimated to represent more than 95% of the US population. The Yale University Human Subjects Committee approved this research and waived the need for consent.
Well-being measures
We include two primary measures of well-being from the survey in this study: thriving and a multi-dimensional well-being composite score (WBI composite). Cantril’s Self-Anchoring Striving Scale [6] is a well-validated measure of self-reported overall life evaluation that was used to calculate whether respondents were thriving. These items ask respondents to rate on a ladder scale from 0 to 10 their overall current life satisfaction as well as their anticipated life satisfaction in five years. Based on their responses, participants are categorized as thriving if their current life satisfaction is rated a seven or higher and their anticipated life satisfaction is rated an eight or higher [7].
To develop the WBI composite, survey items that aligned with prior research on well-being were compiled by experts in the field [8–10]. Based on the existing literature, items were selected so that the survey would include eudaimonic well-being (i.e., an individual’s judgments about the meaning and purpose in one’s life) and hedonic well-being (i.e., people’s feelings and their thoughts about their lives) [11]. The survey therefore includes items assessing meaning and purpose in life, daily emotional experience, and a wide variety of evaluative items, such as satisfaction with standard of living, community, work, relationships, and personal health. Data from a large, representative national sample was then used to perform factor analysis to determine the final set of questions. Criterion validity of geographically aggregated data was established by examining correlations with health and socioeconomic indicators [12]. Principal component and confirmatory factor analyses were then used to create an instrument valid for measuring individual well-being. The well-being measure has good reliability, internal and external validity [5].
From 2014–2016, the composite WBI was comprised of 38 scored metrics derived from five elements: Career, Community, Physical, Financial, and Social. The Career element assesses how much respondents like what they do, and includes the usage of natural talents and strengths, learning and growing, and setting and reaching goals. The Community element examines satisfaction with living in the community and factors that inform this condition including impactful volunteerism and personal safety. The Physical element includes measures of comorbid conditions, health behaviors, and physical functioning. The Financial element assesses managing one’s wealth and living within one’s means to build financial security. The Social element examines the presence of positive social relationships. As with the WBI composite score, each element is computed on a rebased scale from 0 to 100.
Sociodemographic characteristics
Participants were also asked about their sociodemographic characteristics, including age, sex, race, ethnicity, education, marital status, income, number of adults and children in the household, and zip code of residence. We grouped age into the following non-overlapping categories: <=25, 26–35, 36–45, 46–55, 56–65, 66–75, 76–85, and >=86 years. Sex was recorded as being either male or female. We categorized self-identified primary race and ethnicity into five mutually exclusive groups: Asian, Hispanic, non-Hispanic White (White), non-Hispanic Black (Black), or other, which included those who identified as American Indian, Native Hawaiian, or Pacific Islander. We categorized the highest level of education achieved as less than high school, high school/technologic degree/some college, college, and post-graduate degree. Marital status was recorded as being single/never been married, married, separated, divorced, widowed, or domestic partnership. We categorized annual household income into the following non-overlapping categories: < $24,000, $24,000 - $47,999, $48,000 - $119,999, >=$120,000, and refused. Number of adults in the household was recorded as 1, 2, 3, 4, or >=5. Number of children in the household was recorded as 0, 1, 2, 3, 4, or >=5. We used the 2010 Census Urban and Rural Classification and Urban Area Criteria to categorize participants’ reported residential zip code into one of three groups: rural area, small urban area, and large urban area [13].
We created categories that combined the three characteristics of race/ethnicity, education, and urban/rural residence designation (race-education-urban subgroups) to assess whether the interaction of these sociodemographic characteristics was associated with thriving, the WBI composite and its elements. For these subgroups, we recategorized each sociodemographic characteristic into a binary variable. We stratified race/ethnicity by whether a participant identified as White, because White was the largest racial group in the sample and because the other racial groups experience minoritization. We stratified education by whether a college degree or higher was achieved because prior literature reports higher well-being among college-educated individuals. We collapsed urban designation into either living inside an urbanized area or an urban cluster because we hypothesized these groups would be more similar than rural areas.
Analysis
We first summarized thriving (percent) and WBI scores (mean and standard deviation) for each sociodemographic characteristic: age, sex, race/ethnicity, education, urban/rural, income, and marital status. We used logistic and linear regression to test for differences in thriving status and WBI across characteristics, respectively, reporting overall Wald P-values for each characteristic. We then used a sequential approach to identify the independent associations of these sociodemographic characteristics with the measures of well-being. We first examined bivariate associations between race/ethnicity, education, income, urban/rural categories with thriving and the WBI using logistic or linear models to test for differences among groups. Retaining all significant characteristics, we used singular decomposition to identify collinear variables and exclude those that were redundant [14]. We entered the retained characteristics in a series of sequential multivariable models to assess independent associations of race/ethnicity, education, and urban/rural designation with thriving and WBI composite and its elements: the base model (model 1) was adjusted for race/ethnicity, education, and/or urbanicity, accordingly; model 2 further adjusted model 1 for age and sex; model 3 further adjusted model 2 for marital status and household size; and model 4 further adjusted model 3 for income. Finally, we expanded the sequential multivariable models to include interactions for race/education/urbanicity. To avoid excessive hypothesis testing and spurious findings, we report confidence intervals for all effects, and P-values only for overall tests that all effects for given categorical characteristics are null.
All models accounted for correlation of measurements within counties by incorporating random intercepts across counties. Missing values were accounted for using multiple imputation, with 5 imputation sets. Level of statistical significance was set at ɑ = 0.05. All analyses were performed using Stata version 16.1 (StataCorp, College Station, TX).
Results
We included data from 530,920 participants of the Gallup National Health and Well-Being Index (Table 1).
Participants were 50% female and 75% White. Fifty-eight percent of participants had less than a college degree and 35% had lower than $48,000 annual household income. Eleven percent of participants lived in areas designated as rural. Fifty-five percent of participants were categorized as thriving. Characteristics associated with thriving were younger age, female sex, Asian race/ethnicity, higher educational attainment, having higher annual income, urbanicity, and being married. The mean WBI composite score among all participants was 62.9 (SD 14.4). Characteristics associated with higher WBI composite included older age, female sex, Asian race/ethnicity, higher educational attainment, having higher annual household income, living in rural area, and living with a spouse/significant other or being widowed.
We found differences in odds of thriving and WBI composite scores among education, urban/rural and race/ethnic groups, in our base and fully adjusted models (Table 2). Singular decomposition analysis found no collinearity, so all bivariate factors were carried forward to multivariable models.
Education
Higher levels of educational achievement were associated with higher odds of thriving and higher WBI scores in a graded fashion (p-trends <0.001). These relationships were slightly attenuated but persisted after adjustment for all covariates (p-trends <0.001). Each additional level of education was associated with higher odds of thriving and higher WBI scores (all 95% CIs > 1 for thriving and > 0 for WBI) with one exception: those with a high-school diploma, technical degree, or some college had similar WBI scores to those without a high school diploma (b = 0.01 (−0.16,0.17)).
Rural/urban
Compared with living in rural areas, living in large urban areas was associated with higher odds of thriving in our base model and after adjustment for all covariates (overall p-values <0.001; Table 2). However, those living in large urban areas had lower WBI composite scores compared with those living in rural areas, even in fully adjusted models (b = −0.62(−0.76,-0.47)).
Race/ethnicity
Odds of thriving and WBI composite scores differed among race/ethnic subgroups (overall p-values <0.001 in all models), and our sequentially adjusted models revealed differential effects of confounding by other sociodemographic covariates on these relationships. Comparing Asian respondents with White respondents, Asian respondents had higher odds of thriving in Model 1 (OR (95% CI)=1.13 (1.08,1.17)), but after adjustment for all covariates they had lower odds of thriving (OR=0.92 (0.88,0.95)). For the WBI composite, compared with White respondents, Asian respondents had similar WBI scores (b = 0.79 (0.52,1.07)), but after adjustment for age, sex, and all other covariates, Asian respondents had higher WBI (b = 2.54 (2.28,2.80)). Compared with White respondents, Hispanic respondents had higher odds of thriving in all models (all 95% CIs above 1). Negative confounding with WBI scores was observed among this subgroup as well: compared with White respondents, Hispanic respondents had higher WBI scores in Model 1 (b = 1.34 (1.19,1.49)), and the association was stronger after adjustment for all covariates (b = 4.02 (3.87,4.16)).
When comparing Black respondents with White respondents, Black respondents had higher odds of thriving in the base model (OR=1.08 (1.05,1.10)) but lower odds of thriving in Model 2, after adjusting for age, sex, education, and urbanicity (Table 2). However, after further adjustment for marital status, household size, and income, Black respondents had higher odds of thriving (OR=1.15 (1.12,1.17)) (Table 2, Model 4). Similarly, the direction of associations with the WBI composite changed with sequential adjustment for sociodemographic covariates. In our base model, Black respondents had lower WBI scores than White respondents (b = −1.58 (−1.72,-1.43)). After adjustment for age and sex, Black respondents still had lower WBI scores than White respondents (b = −0.68 (−0.82,-0.54)), but after further adjustment for marital status, household size, and income, a higher relative association emerged between Black race and the WBI composite score (b = 1.04 (0.90–1.17)).
To further elucidate these changes in the directionality of the relative relationships between Black and White race and WBI composite across our sequential models, we assessed the effects of adjusting for each covariate associated with Black race separately. Black race was associated with younger age, having more children in the home, and lower income among the sample. When adjusting for each of these covariates separately, no single covariate changed the direction of the relative relationship with WBI composite. However, the combination of adjusting for age and income changed the direction of the relative relationship with WBI among Black respondents compared with White respondents, with further adjustment for marital status and household size having little additional impact on this relationship.
WBI elements
Relative associations between sociodemographic subgroup categories and WBI element scores varied (Table 3).
In the base models, non-White groups generally fared poorly in the Financial element compared with Whites (Black: −9.88(−10.12,-9.64); Asian: −0.20(−0.67,0.26); Hispanic: −8.10(−8.34,-7.86); Other: −7.81(−8.36,-7.26)), but these differences were attenuated after adjusting for income and other covariates. Compared with White respondents, Asian respondents on average scored 3.4 points higher in the Physical element (95%CI: 3.13,3.75) and 3.5 points higher in the Financial element (95%CI: 3.02,3.92), after adjusting for all covariates; Hispanic respondents scored a mean 5.3 points higher on the Career element (95%CI: 5.14,5.53) and 4.3 points higher on the Community element (95%CI: 4.09,4.50) in fully adjusted models; and Black respondents scored a mean 2.4 points higher on the Career element (95%CI: 2.19,2.57) and 2.3 points higher on the Social element (95%CI: 2.13,2.54). Generally, higher levels of education were associated with higher scores in all WBI elements with the exception of the Community element, after adjustment for income and other covariates. Relationships among element scores and categories of urbanicity were mixed, with lower Career and Community element scores and higher Physical, Financial, and Social element scores among large urban areas compared with rural areas.
Race-education-urban intersectional subgroups
Finally, we observed differences in thriving as well as in the WBI and its elements among race-education-urban subgroups across sequentially adjusted models, with overall test of an interaction having P < 0.001 in all models (Tables 4 and 5).
Compared with the group that identified as non-White, had less than a college degree, and lived in a rural area, groups that achieved a college degree were associated with approximately twice the odds of thriving in age- and sex-adjusted models (Table 4, Model 2). After adjusting for all covariates, including income, the group with the highest odds of thriving and the highest WBI score was the group identifying as non-White, with a college degree or higher, living in rural areas (Thriving OR: 1.54 (1.34,1.76); WBI b = 1.27 (0.41,2.14)) (Table 4, Model 4). The higher mean WBI score of this group was achieved through higher Financial (b = 7.22 (5.62,8.83)), Physical (b = 2.80 (1.81,3.80)), and Social (b = 1.70 (0.49,2.91)) well-being elements compared with the reference race-education-urban subgroup (Table 5). The subgroup with the lowest odds of thriving in fully adjusted models were respondents who identified as White, had less than a college degree, and were living in rural areas (OR=0.84 (0.79,0.90)) (Table 4, Model 4). The subgroup with the lowest WBI composite identified as White, had less than a college degree, and lived in urban areas (b = −2.74 (−3.16,-2.33)), which was driven primarily by relatively lower scores in the Community (b = −4.43 (−5.00,-3.86)) and Career (b = −3.90 (−4.45,-3.35)) elements (Table 5).
Discussion
Findings from this study reveal complex relationships among sociodemographic characteristics and well-being that are reflective of longstanding structural racism that have limited opportunities for educational attainment, income, and location of residence based on one’s race/ethnicity. Though higher levels of educational achievement were consistently associated with higher odds of thriving and higher levels of well-being, its impact varied among race/ethnic subgroups. After adjusting for education and rural/urban residence, we revealed differences in odds of thriving and levels of well-being across racial/ethnic subgroups that varied in direction and strength depending upon additional adjustments for age, sex, marital status, household size, and income. Indeed, differences in well-being, as measured by the WBI, were driven primarily by differences in the Financial, Physical, and Career well-being elements. When assessing well-being across combination groups of race/ethnicity, educational attainment, and urban/rural residence, we found that the group who self-identified as non-White, with at least a college degree, and living in rural areas had the highest odds of thriving and reported the highest well-being.
Our findings suggest that structurally racist policies that have limited financial opportunity among non-White individuals compared with White individuals in the US with the same educational attainment for decades has had deleterious effects on their well-being. This is aligned with Keyes’ (2009) report using data from a nationally representative sample of middle-aged adults that Black individuals had higher rates of flourishing than White individuals after controlling for education and income—and this was higher after controlling for perceived discrimination [15]. The fact that in our study, the relative associations between Black and White race with measures of well-being changed in direction before and after adjustment for income and age has important implications for understanding the effects of structural racism. After adjusting for education and urbanicity, we found that participants who identified as Black had higher odds of thriving but lower levels of well-being. The financial return on investment is different for Black and White individuals, which manifests in a lower level of financial well-being among Blacks at a given education level. However, after further adjustment for income, Black participants of the same age as their White counterparts reported higher well-being. Our findings suggest that mitigating racial inequities in access to financial opportunity may be a mechanism to improve well-being.
Our study further highlights the complexity of relationships and interactions between race/ethnicity, education level, and rural or urban living environments. Most prior reports compare well-being between urban and rural residents, but we further assessed well-being among White and non-White residents with more or less than a college degree within these residential areas. The college-educated, non-White individuals living in rural areas that reported the highest rates of thriving and well-being comprised a very small portion of our sample, approximately 1500 participants (0.3%). However, this group was from all states and was evenly distributed among the non-White race/ethnicity categories. It is possible that these individuals report higher well-being in part because they are relatively better off than their parents and in part because they are relatively better off than others in their community. It is plausible that this subgroup of college-educated, non-White individuals were doing better than their parents, which may foster higher levels of optimism and their level of satisfaction with daily activities. It is also possible that this group, due to higher levels of optimism over the life course, invested more in their education and were more likely to succeed [16,17]. In addition, because this subgroup had higher education while living in rural areas with low cost of living, it is likely that they have higher relative income than most others in their community, supporting their experience of higher well-being [18,19]. This theory is supported by this groups’ higher relative scores in the Financial element.
Of note, these college-educated, non-White individuals living in rural and urban areas also reported better Physical well-being, which includes self-reported overall health and engagement in healthy behaviors, each of which have been linked to lower morbidity and mortality [20,21]. In this, our findings may at first appear to contradict what we might expect based on disparities in health outcomes well-described for some non-White groups and for rural populations [22–24]. For example, prior studies have shown that Black Americans have worse health outcomes than White Americans at all levels of income [25]. Importantly though, most of these prior analyses grouped all Black Americans together, including those living in urban and rural areas and with all levels of education. Though majority of this literature limits comparisons to people identifying as Black to those identifying as White, a recent study using data from the Multi-Ethnic Study of Atherosclerosis reports lower hazard of mortality from cardiovascular disease among Chinese and Hispanic participants compared with White participants [26]. We combined participants from these other race/ethnic groups together with Black participants, so we cannot determine whether a certain race/ethnic group drove the observed association. As an additional note, these prior studies did not explore whether well-being influenced associations with health outcomes. Our analyses may reveal weaknesses and some sources of residual confounding among prior studies, thereby highlighting areas for future research.
Our findings have notable public health implications. Assessment of well-being of these subgroups provides complementary and additional insights into the drivers of physical and mental health outcomes [27]. Well-being, an outcome with high intrinsic value, is also important to health outcomes of significance, such as longevity [28]. Efforts to increase length of life and mitigate disparities in life expectancy, though numerous, are unlikely to succeed if they do not also address well-being, as well-being is both a determinant and an outcome of health – that is, health contributes to well-being, and well-being contributes to health [29]. Perceived overall health alone is alone a strong predictor of mortality [20], and other positive psychosocial constructs related to well-being such as overall life satisfaction, social support, optimism, and emotion regulation have been linked prospectively with better health outcomes [16,30–33]. In addition, a recent report described low-income Americans experiencing the greatest race-based disparities in optimism: low-income Black Americans were most optimistic and low-income White Americans least optimistic, perhaps accounting for opposing trends in life expectancy gains among these two groups prior to the COVID-19 pandemic [34]. As such, it is plausible that identifying sociodemographic differences in a holistic measure of well-being may offer novel targets to address disparities in clinical health outcomes by strengthening positive elements of well-being rather than mitigating negative attributes associated with health outcomes [35].
Our study has several limitations to consider when interpreting results. First, because these data are collected by telephone survey, our results may be subject to response bias. The Gallup National Health and Well-Being survey has a twelve percent overall response rate. However, data from their sample has been successfully validated against other national surveys, such as the Behavioral Risk Factor Surveillance System [4]. Second, when we collapsed sociodemographic categories into dichotomous groups for the analyses on interactions, we lost the ability to potentially find differences among more granular subgroups. For example, we were unable to elicit any potential differences on returns on education in terms of well-being between Black, Hispanic, and Asian subgroups living in urban or rural environments. Future work should disentangle these effects. Third, we performed analyses using unweighted estimates of data collected using a sampling method designed to produce weighted population-based estimates. As such, our sample was more educated, had more respondents identifying as White, and fewer rural residents compared with US population averages. However, while this means our estimates of effect may be biased, findings of association remain valid because we are not extrapolating them to the population level. It is, however, possible that non-White, rural-dwelling respondents were more likely to experience higher well-being than non-White, rural-dwelling non-respondents. Finally, the years during which our data were collected (2014–2016) may have influenced respondents’ sense of optimism and well-being. Life evaluation can be shaped by the elected U.S. President at that time, and the data used in our analyses were collected during the second half of the Obama administration. While Black Americans’ life evaluation increased during Obama’s first term, it returned to typical levels in the second term [36,37]. Additionally, these years preceded the rise in well-being among residents of rural areas during the initial years of the Trump administration. As such, the data collected during 2014–2016 were not strongly influenced by politics at that time. Of note, our findings are also consistent with a multi-city sample of US community dwelling residents sampled during Winter 2016–2017 that reported higher odds of self-reported thriving among Black, Hispanic, and Asian populations compared with Whites, further suggesting that timing of our study may not have influenced results [38].
Conclusion
Our findings reveal complex differences in well-being measures across race/ethnic subgroups in the US after controlling for education, which is the strongest predictor of thriving and level of well-being in the US. Several findings suggest that historical and current structural or systemic factors contribute to the observed disparities in well-being outcomes. The study also shows the importance of urban versus rural place of residence. These findings should prompt not only further study to understand more deeply and track these inequities but also community and societal level action to improve well-being and eliminate related inequities.
Acknowledgments
We would like to thank The Nova Institute for supporting our team’s effort to produce this manuscript. We would also like to acknowledge Brent Hamar, DDS, for his contributions to this work. An abstract summarizing the main findings from this manuscript was presented at the Interdisciplinary Association of Population Health Sciences in Seattle, Washington, in September 2019.
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