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Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease logoLink to Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
. 2026 Jun 23;15(13):e045942. doi: 10.1161/JAHA.125.045942

Neighborhood‐Level Racial and Ethnic Residential Segregation and Incidence of Atrial Fibrillation: The Multi‐Ethnic Study of Atherosclerosis

Utibe R Essien 1,✉, Katharine Harrington 2, Shaina J Alexandria 2, Philip Greenland 2, Susan R Heckbert 3, Moyses Szklo 4, Mahasin S Mujahid 5, Mercedes R Carnethon 2, Karol E Watson 6, Jasmyn J Tang 1, Kiarri N Kershaw 2
PMCID: PMC13477282  PMID: 42333700

Abstract

Background

Atrial fibrillation (AF) is the most common arrhythmia, affecting up to 6 million in the United States, and is associated with significant morbidity and mortality. Despite higher rates of AF clinical and social risk factors, underrepresented racial and ethnic group individuals have lower AF incidence. Structural factors, such as neighborhood‐level racial and ethnic residential segregation, have been associated with incident cardiometabolic disease, particularly among underrepresented individuals. However, as data on segregation and incident AF are sparse our objective was to examine the association of segregation on AF.

Methods

Data from MESA (Multi‐Ethnic Study of Atherosclerosis; baseline 2000–2002) were used to identify those with a diagnosis of AF during follow‐up. Own‐group racial and ethnic segregation was defined by local Gi* statistic, which compares the percentage of each racial and ethnic group in a current census tract to the surrounding area. Racial‐ and ethnic‐stratified Cox proportional hazard models were used to estimate hazard ratios comparing across segregation levels. Models were adjusted for demographic, participant, and neighborhood level socioeconomic, and clinical factors.

Results

Our cohort comprised 5375 participants (31% Black, 25% Hispanic/Latino, 44% White). During a median follow‐up of 16.6 years, 1035 participants (19.3%) were diagnosed with AF. Black and Hispanic/Latino participants had the highest prevalence of AF risk factors at baseline and were more likely to reside in segregated neighborhoods than White participants. After adjusting for demographic, socioeconomic, and clinical factors, neighborhood segregation was not found to be associated with AF across racial and ethnic groups.

Conclusions

In this longitudinal analysis we did not observe an association between residential segregation and AF incidence for Black, Hispanic/Latino, or White participants in fully adjusted models. Further research is needed to understand how individual factors may intersect with clinical, health care, and structural factors to drive previously described differential risk of AF.

Keywords: atrial fibrillation, disparities, epidemiology, health equity, segregation

Subject Categories: Atrial Fibrillation, Race and Ethnicity, Risk Factors, Social Determinants of Health

Nonstandard Abbreviations and Acronyms

MESA

Multi‐Ethnic Study of Atherosclerosis

Research Perspective.

What Is New?

  • Examining MESA (Multi‐Ethnic Study of Atherosclerosis), our study is the first to our knowledge to assess longitudinal associations between residential segregation and incident atrial fibrillation and examine the potential influence of this relationship by race and ethnicity and individual and neighborhood‐level socioeconomic status, including poverty, social, and physical environment.

  • In a cohort of 5375 participants with median follow‐up of 16.6 years, there was no association observed between residential segregation and atrial fibrillation incidence across all racial and ethnic groups.

What Question Should Be Addressed Next?

  • Further research is needed to understand how individual clinical and sociodemographic factors may intersect with broader clinical, health care, and structural factors to influence the risk of atrial fibrillation among diverse groups.

Atrial fibrillation (AF) is the most common arrhythmia, affecting up to 60 million adults worldwide and 6 million in the United States. 1 , 2 AF is associated with increased all‐cause mortality along with high rates of cardiovascular hospitalization, morbidity (including from ischemic stroke), long‐term disability, and patient‐reported poor quality of life. 3 , 4 , 5 , 6 These AF‐associated complications have been shown to be more common among underrepresented racial and ethnic group individuals, suggesting the importance of early detection and awareness of AF in these populations. 7 , 8 , 9

Prior work has demonstrated that underrepresented racial and ethnic group individuals with AF have lower awareness of AF diagnosis. 10 This finding has previously been attributed to a “racial paradox,” in that despite higher rates of chronic risk factors, Black individuals, in particular, experience lower incidence of AF. Yet, recent data suggest AF rates are similar across racial and ethnic groups when using long‐term ambulatory monitoring. 11 These mixed findings from previous literature represent a knowledge gap in our understanding of the determinants of incident AF in historically underrepresented minoritized populations.

One known determinant of health disparities in the United States and marker of structural racism is neighborhood‐level racial and ethnic segregation. The spatial distribution of racial and ethnic groups in the United States is a consequence of complex social, cultural, and economic forces that have resulted in differential exposure to resources and opportunities. These resources may include access to appropriate health care and healthy lifestyles, which may influence development of conditions such as AF. Whereas a growing body of research has demonstrated a relation between segregation and other cardiovascular diseases, 12 , 13 , 14 to our knowledge, there have been no prior studies examining the role of neighborhood‐level racial and ethnic segregation, on AF incidence. Myriad neighborhood‐related factors, including education level, income and poverty level, access to insurance, and social and physical environment, may serve as either biologic or social mechanisms by which AF develops over time. Thus, using data from MESA (Multi‐Ethnic Study of Atherosclerosis), 15 an observational prospective cohort study, we examined whether AF incidence differs across neighborhood‐level segregation categories and its association within racial and ethnic groups.

METHODS

Data Source

The data and materials for the MESA study are available and can be requested at www.mesa‐nhlbi.org. MESA is an observational cohort study designed to examine determinants of subclinical and clinical cardiovascular disease in adults aged 45 to 84 years. 15 We used data from MESA to identify relevant study variables. Self‐identified Black, Chinese, Hispanic/Latino, and White participants free of clinical cardiovascular disease at baseline were recruited from 6 sites (New York, New York; Baltimore City and County, Maryland; Forsyth County, North Carolina; St. Paul, Minnesota; Chicago, Illinois; and Los Angeles County, California) between 2000 and 2002. Five additional examinations have been completed since baseline: Exam 2 (2002–2004), Exam 3 (2004–2005), Exam 4 (2005–2007), Exam 5 (2010–2012), and Exam 6 (2016–2018). Institutional review board approval was obtained at each site and all participants gave informed consent. The Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline was followed. 16

Study Population

Of the 6814 participants in MESA, 5375 (78.9%) were included in the study. We excluded participants if they were missing AF outcome and missing follow‐up time (N=75), or missing values for neighborhood segregation variables (N=639). Due to the low incidence of AF in Chinese participants within the MESA cohort, we also omitted Chinese participants from this analysis (N=725). Notably, prior MESA studies have shown that Chinese participants were the least populous group recruited into MESA and were least likely of other groups to be born in the United States (only 10%), which may limit representation to the broader Chinese population. 17 Additionally, this prior work suggests after adjustment for immigration history, positive associations with cardiovascular disease observed among Chinese participants no longer persist. 17 , 18 AF and other prevalent cardiovascular diseases had been excluded by design at baseline.

Study Outcome

Our primary study outcome was incident AF through 17 years of follow‐up. AF diagnosis status was evaluated in all MESA participants at all exams. At telephone contacts every 9 to 12 months during follow‐up, participants were asked to identify new hospitalizations and diagnoses, and medical records were obtained. 19 Medical records were obtained for all reported hospitalizations. We used the International Classification of Diseases, Ninth Revision (ICD‐9) diagnostic codes 427.31, 427.32 and Tenth Revision (ICD‐10) diagnostic code I.48 in any position from hospital discharges to identify patients with their first clinical AF diagnosis in the MESA cohort. For those enrolled in fee‐for‐service Medicare, AF was defined by an inpatient, outpatient, or physician claim with an AF diagnosis code. 11 Patients with AF were also identified using ECG Novacode criteria, a classification system for ECG abnormalities, among those who participated in an ancillary ECG monitoring study during Exam 6. 8 , 20

Independent Variable: Neighborhood‐Level Residential Segregation

Neighborhood‐level racial and ethnic residential segregation was calculated as the Getis‐Ord Gi* statistic based on geocoded addresses of MESA participants linked to 2000 US Census data from baseline Exam 1. The Gi* statistic returns a Z‐score for each neighborhood (census tract), indicating the extent to which the racial or ethnic composition in the tract of residence and neighboring tracts (ie, any that share an edge with the focal tract) deviates from the mean racial or ethnic composition of some larger areal unit surrounding the tract. 13 , 14 The Gi* statistic used for each participant is based on their race and ethnicity (ie, own‐group segregation). Consistent with previous studies, segregation levels were grouped into 3 categories: high (Gi*>1.96), medium (Gi*0–1.96), and low (Gi*<0). Operationalizing segregation using this approach allowed us to test both the ethnic density hypothesis, which would posit that there would be a protective association between segregation and incident AF, 21 as well as the fundamental cause hypothesis, which would posit that those living in more segregated neighborhoods would have higher incident AF. 22

Study Covariates

We adjusted for individual‐level sociodemographic factors at baseline that included age at baseline (years), sex (female, male), study site, educational attainment (less than high school graduate, high school graduate, some college, and college degree or higher), family income in US dollars (categorized as <$20 000; $20 000–$50 000; >$50 000), and health insurance status (any insurance—private or public—and uninsured). We assessed clinical variables at baseline including body mass index (kg/m2), hypertension (defined as systolic blood pressure≥140 mm Hg or diastolic blood pressure≥90 mm Hg, self‐reported “yes” to having hypertension, or reported antihypertensive medication use, regardless of blood pressure reading), type 2 diabetes (fasting glucose ≥126 mg/dL, self‐reported “yes” to having diabetes, reported diabetes medication use), and hypercholesterolemia (low‐density lipoprotein ≥160 mg/dL). Additionally, we assessed lifestyle factors including alcohol and cigarette use (never, former, current), and physical activity (metabolic equivalents, hours/day). The MESA Typical Week Physical Activity Survey, adapted from the Cross‐Cultural Activity Participation Study, identified the time and frequency spent in various physical activities during a typical week in the past month. 12 , 23 We defined physical activity as total intentional exercise, measured in metabolic equivalent‐hours per day. Metabolic equivalent is the ratio of the rate of energy expended during an activity to the rate of energy expended at rest.

Neighborhood factors were also included in this analysis using data from MESA assessed at the baseline 2000 exam. These factors included neighborhood poverty (percentage of neighborhood residents who were below the US Census Bureau poverty level based on baseline address linked to 2020 US Census data). Survey‐based information on neighborhood social environment (eg, neighborhood safety, social cohesion, aesthetic quality score), and physical environment (eg, neighborhood walking and physical environment scores) were collected as part of the MESA Neighborhood Study. 15 This was an ancillary study designed to assess neighborhood conditions' impact on cardiovascular disease. Along with MESA participants, a sample of 5988 individuals (recruited between January and August 2004) residing in the same neighborhoods as MESA participants were asked to rate several aspects of their neighborhood via a telephone survey. 13 To capture these scores, study participants were asked to consider the area approximately 1 mile around their homes and indicate the extent to which they agreed with given statements about their neighborhood environment. 12 Neighborhood physical environment was characterized by survey‐based perception of healthy food environment (2 items about the availability of fruits and vegetables and low‐fat products) and walking environment (4 items about the pleasantness, ease, and frequency of walking in the neighborhood). Neighborhood social environment was measured by survey‐based perception of aesthetic quality (3 items about noise, litter, and neighborhood attractiveness), safety (3 items about violence and walking safety), and social cohesion (4 items about relationship with neighbors). Neighborhood physical and social environment scores were summarized from the scale‐based responses and categorized into race‐ and ethnicity‐specific quartiles. These scales have been used in prior studies and were shown to have good internal consistency and reliability (Cronbach's α=0.75 and 0.88 for physical and social environment scale, respectively). 24 , 25 , 26 , 27 A higher score reflected better neighborhood environment quality.

Statistical Analysis

Participant characteristics were first summarized by racial and ethnic groups. Participant characteristics were further summarized by racial ethnic group and presence of AF. Continuous measures are summarized as means±SD and categorical variables were summarized as counts (percentage). Cox proportional hazards models were used to estimate adjusted hazard ratios (HR) and 95% CIs for the association of neighborhood‐level racial and ethnic segregation and incident AF. A robust sandwich estimator was used to account for any residual correlations among individuals residing in the same census tract. 13 Participants were considered at risk of developing AF from baseline examination until the sooner of follow‐up time at which AF was identified or censoring time. All models were stratified by racial and ethnic group because neighborhood‐level racial and ethnic segregation measures were created for each racial ethnic group. Cox proportional hazards assumption was assessed by plotting Schoenfeld residuals against follow‐up time on the normal and log scale. The Cox proportional hazards assumption was violated by several covariates. To account for the violation of the assumption, variance estimation by bootstrap method was used to estimate variance and 95% CIs. 28

For all analyses, 4 varying levels of model adjustment were used, informed by available data along with a health equity fundamental causes conceptual framework. 22 Model 1 adjusted for individual demographic factors as potential confounders, including age, sex, and study site. Model 2 additionally adjusted for individual socioeconomic factors which include education, family income, and health insurance status. These factors may dictate where a participant could live and at the same time could be influenced by segregation so could be considered potential confounders or mediators. Model 3 additionally adjusted for neighborhood socioeconomic factors through which segregation could affect AF, including neighborhood poverty, social environment, and physical environment. Model 4 additionally adjusted for clinical factors that may be on the causal pathway, including cigarette use, alcohol use, physical activity, body mass index, high cholesterol, hypertension, and diabetes. Multiple imputation by chained equations was used to impute data for missing covariates. 29 A total of 10 imputed data sets were created, and results were pooled using Rubin's rules. All statistical analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC). All figures were generated using R (version 4.3.2, R Program for Statistical Computing). A 2‐sided P value of <0.05 was considered statistically significant. For HRs, statistical significance was inferred when the 95% CI did not include 1.0.

RESULTS

Baseline Cohort Characteristics

Our final cohort comprised of 5375 participants including 1671 (31.1%) Black, 1325 (24.7%) Hispanic/Latino, and 2379 (44.3%) White individuals with an overall average of 16.6 years of follow‐up (Table 1). The mean age at baseline ranged from 61.2 years in Hispanic/Latino individuals to 62.2 years in White individuals. Table 1 displays the distribution of participant characteristics by race and ethnicity at Exam 1. At Exam 1, 58.6% of Black, 63.8% of Hispanic/Latino, and 12.9% of White individuals resided in highly segregated neighborhoods. Individuals who did not identify as White had relatively lower household income and were less likely to be insured. Black and Hispanic/Latino individuals had higher prevalence of chronic conditions including hyperlipidemia, hypertension, and diabetes compared with White individuals.

Table 1.

Cohort Characteristics by Race and Ethnicity, Multi‐Ethnic Study of Atherosclerosis

Black participants Hispanic/Latino participants White participants
No. (%) 1671 (31.1) 1325 (24.7) 2379 (44.3)
Follow‐up time, y, median (interquartile range) 16.5 (5.5) 16.6 (5.5) 16.7 (5.1)
Sociodemographic characteristics
Age, y, mean±SD 61.8±9.9 61.2±10.3 62.2±10.1
Sex, n (%)
Female 915 (54.8) 684 (51.6) 1234 (51.9)
Male 756 (45.2) 641 (48.4) 1145 (48.1)
Education, n (%)*
Less than high school 187 (11.2) 566 (42.7) 112 (4.7)
High school/general educational development/some college/tech/associate 897 (53.7) 620 (46.8) 1045 (43.9)
College/graduate/professional 578 (34.6) 139 (10.5) 1215 (51.1)
Household income, n (%)*
<$20,000 307 (18.4) 490 (37.0) 238 (10.0)
$20 000 to <$50 000 633 (37.9) 573 (43.2) 734 (30.9)
$50 000+ 598 (35.8) 234 (17.7) 1350 (56.7)
Health insurance status, n (%)*
Any 1551 (92.8) 1102 (83.2) 2308 (97.0)
None 103 (6.2) 219 (16.5) 59 (2.5)
Neighborhood‐level racial and ethnic residential segregation level, n (%)
High 979 (58.6) 846 (63.8) 306 (12.9)
Medium 401 (24.0) 231 (17.4) 1024 (43.0)
Low 291 (17.4) 248 (18.7) 1049 (44.1)
Neighborhood poverty (%), mean±SD 0.2±0.1 0.2±0.1 0.1±0.1
Neighborhood social environment score, mean±SD 10.7±1.0 10.3±0.8 11.2±0.8
Q1 range 8.00–9.95 8.05–9.76 8.78–10.5
Q2 range 9.96–10.7 9.76–10.3 10.5–11.2
Q3 range 10.7–11.3 10.3–10.8 11.2–11.7
Q4 range 11.3–13.0 10.8–12.9 11.7–13.0
Neighborhood physical environment score, mean±SD 7.1±0.7 7.4±0.4 7.5±0.9
Q1 range 5.82–6.63 6.48–7.15 5.82–6.82
Q2 range 6.63–7.19 7.15–7.38 6.83–7.32
Q3 range 7.20–7.55 7.38–7.58 7.32–8.11
Q4 range 7.55–9.23 7.59–8.92 8.11–9.23
Clinical characteristics
Physical activity (metabolic equivalents h/d Monday–Sunday), mean±SD 15.6±16.2 14.7±14.7 13.7±12.8
Smoking status, n (%)*
Nonsmoker 748 (44.8) 710 (53.6) 1062 (44.6)
Former smoker 615 (36.8) 437 (33.0) 1052 (44.2)
Current smoker 299 (17.9) 178 (13.4) 259 (10.9)
Alcohol use, n (%)*
Nondrinker 277 (16.6) 329 (24.8) 220 (9.2)
Former drinker 544 (32.6) 349 (26.3) 429 (18.0)
Current drinker 833 (49.9) 646 (48.8) 1712 (72.0)
Body mass index (kg/m2), mean±SD 30.1±5.7 29.5±5.1 27.7±5.0
Hyperlipidemia, n (%) 155 (9.3) 135 (10.2) 201 (8.4)
Hypertension, n (%) 926 (55.4) 481 (36.3) 764 (32.1)
Diabetes, n (%) 291 (17.4) 240 (18.1) 149 (6.3)
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Q indicates quartile.

*

Denotes categorical covariates within which there were missing data within at least 1 racial or ethnic group.

Incidence of Atrial Fibrillation by Race, Ethnicity, and Neighborhood Segregation

Overall, 1035 (19.3%) of participants developed incident AF over the study period. By the end of study follow‐up, 5.2% of Black, 4.1% of Hispanic/Latino, and 9.9% of White participants developed AF. Table 2 provides the distribution of baseline participant characteristics by AF status. Generally, participants who developed AF were more likely to be older and have higher rates of diabetes and hypertension. AF incidence per 1000 person‐years was lowest in Hispanic/Latino (2.97), followed by Black (3.74) and White (7.13) individuals (Table 3). Black participants had the highest cumulative incidence (lowest probability of remaining free of AF) in high segregation neighborhoods with similar rates within medium and low segregation (FigureFigure1A). Comparatively, Hispanic/Latino participants had a higher cumulative incidence within medium segregation neighborhoods and similar rates in high and low segregation (FigureFigure1B). White participants had the lowest incidence rate of AF (highest probability of remaining free of AF) in low segregation neighborhoods and similar rates in areas with high and medium segregation (FigureFigure1C). In multivariate models, adjusting for demographic, participant, and neighborhood socioeconomic and clinical factors, we did not observe significant differences in AF incidence for those residing in medium and high racially and ethnically segregated neighborhoods compared with low segregated neighborhood in any racial and ethnic group (Table 3).

Table 2.

Cohort Characteristics by Race and Ethnicity and Presence of Atrial Fibrillation, Multi‐Ethnic Study of Atherosclerosis

Variables Black participants Hispanic/Latino participants White participants
Atrial fibrillation No atrial fibrillation Atrial fibrillation No atrial fibrillation Atrial fibrillation No atrial fibrillation
No. (%) 280 (5.2) 1391 (25.9) 222 (4.1) 1103 (20.5) 533 (9.9) 1846 (34.3)
Neighborhood‐level racial and ethnic residential segregation level, n (%)
High 179 (63.9) 800 (57.5) 134 (60.4) 712 (64.6) 74 (13.9) 232 (12.6)
Medium 58 (20.7) 343 (24.7) 48 (21.6) 183 (16.6) 252 (47.3) 772 (41.8)
Low 43 (15.4) 248 (17.8) 40 (18.0) 208 (18.9) 207 (38.8) 842 (45.6)
Age, y, mean±SD 66.6±9.0 60.8±9.8 66.9±8.6 60.1±10.2 68.4±7.9 60.4±9.9
Sex, n (%)
Female 130 (46.4) 785 (56.4) 98 (44.1) 586 (53.1) 236 (44.3) 998 (54.1)
Male 150 (53.6) 606 (43.6) 124 (55.9) 517 (46.9) 297 (55.7) 848 (45.9)
Education, n (%)*
Less than high school 41 (14.6) 146 (10.5) 101 (45.5) 465 (42.2) 32 (6.0) 80 (4.3)
High school/general educational development/some college/tech/associate 142 (50.7) 755 (54.3) 98 (44.1) 522 (47.3) 244 (45.8) 801 (43.4)
College/graduate/professional 97 (34.6) 481 (34.6) 23 (10.4) 116 (10.5) 255 (47.8) 960 (52.0)
Household income, n (%)*
<$20 000 68 (24.3) 239 (17.2) 105 (47.3) 385 (34.9) 67 (12.6) 171 (9.3)
$20 000 to <$50 000 103 (36.8) 530 (38.1) 78 (35.1) 495 (44.9) 188 (35.3) 546 (29.6)
$50 000+ 89 (31.8) 509 (36.6) 35 (15.8) 199 (18.0) 265 (49.7) 1085 (58.8)
Health insurance status, n (%)*
Any 268 (95.7) 1283 (92.2) 197 (88.7) 905 (82.0) 518 (97.2) 1790 (97.0)
None 11 (3.9) 92 (6.6) 25 (11.3) 194 (17.6) 13 (2.4) 46 (2.5)
Neighborhood poverty (%), mean±SD 0.2±0.1 0.2±0.1 0.2±0.1 0.2±0.1 0.1±0.1 0.1±0.1
Neighborhood social environment score (quartiles), n (%)
Q1 60 (21.4) 339 (24.4) 48 (21.6) 291 (26.4) 126 (23.6) 489 (26.5)
Q2 80 (28.6) 318 (22.9) 49 (22.1) 254 (23.0) 145 (27.2) 439 (23.8)
Q3 73 (26.1) 341 (24.5) 67 (30.2) 253 (22.9) 126 (23.6) 454 (24.6)
Q4 58 (20.7) 378 (27.2) 56 (25.2) 282 (25.6) 134 (25.1) 453 (24.5)
Neighborhood physical environment score (quartiles), n (%)
Q1 72 (25.7) 334 (24.0) 53 (23.9) 284 (25.7) 109 (20.5) 424 (23.0)
Q2 66 (23.6) 357 (25.7) 56 (25.2) 241 (21.8) 146 (27.4) 530 (28.7)
Q3 60 (21.4) 350 (25.2) 62 (27.9) 274 (24.8) 135 (25.3) 431 (23.3)
Q4 76 (27.1) 338 (24.3) 48 (21.6) 273 (24.8) 141 (26.5) 453 (24.5)
Physical activity (metabolic equivalents h/d Monday–Sunday), mean±SD 13.8±12.1 15.9±16.9 11.7±12.9 15.3±15.0 13.1±12.6 13.8±12.9
Smoking status, n (%)*
Non‐smoker 119 (42.5) 629 (45.2) 117 (52.7) 593 (53.8) 221 (41.5) 841 (45.6)
Former smoker 115 (41.1) 500 (35.9) 78 (35.1) 359 (32.5) 258 (48.4) 794 (43.0)
Current smoker 46 (16.4) 253 (18.2) 27 (12.2) 151 (13.7) 52 (9.8) 207 (11.2)
Alcohol use, n (%)*
Non‐drinker 47 (16.8) 230 (16.5) 61 (27.5) 268 (24.3) 53 (9.9) 167 (9.0)
Former drinker 101 (36.1) 443 (31.8) 70 (31.5) 279 (25.3) 85 (15.9) 344 (18.6)
Current drinker 132 (47.1) 701 (50.4) 91 (41.0) 555 (50.3) 390 (73.2) 1322 (71.6)
Body mass index, kg/m2, mean±SD 30.1±5.7 30.1±5.7 30.3±5.5 29.3±5.0 27.9±4.9 27.6±5.1
Hyperlipidemia, n (%) 24 (8.6) 131 (9.4) 17 (7.7) 118 (10.7) 42 (7.9) 159 (8.6)
Hypertension, n (%) 184 (65.7) 742 (53.3) 121 (54.5) 360 (32.6) 218 (40.9) 546 (29.6)
Diabetes, n (%) 63 (22.5) 228 (16.4) 63 (28.4) 177 (16.0) 36 (6.8) 113 (6.1)
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Q indicates quartile.

*

Denotes categorical covariates within which there were missing data within at least 1 racial or ethnic group.

Table 3.

Association of Neighborhood‐Level Racial and Ethnic Residential Segregation With Incident Atrial Fibrillation for Each Racial and Ethnic Group

Neighborhood segregation NAF AF cases/1000 person‐years Model 1 Model 2 Model 3 Model 4
HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI)
Black residents 280 3.74
Low 43 10.12 Reference Reference Reference Reference
Medium 58 10.19 0.98 (0.82–1.17) 1.00 (0.83–1.20) 1.09 (0.87–1.36) 1.07 (0.86–1.33)
High 179 13.66 0.93 (0.71–1.23) 0.97 (0.73–1.29) 1.07 (0.77–1.49) 1.04 (0.74–1.46)
Hispanic/Latino residents 222 2.97
Low 40 11.46 Reference Reference Reference Reference
Medium 48 15.51 0.91 (0.60–1.37) 0.88 (0.58–1.34) 0.77 (0.51–1.17) 0.76 (0.51–1.14)
High 134 11.41 1.00 (0.69–1.46) 0.98 (0.66–1.44) 0.78 (0.52–1.15) 0.75 (0.51–1.11)
White residents 533 7.13
Low 207 13.78 Reference Reference Reference Reference
Medium 252 17.79 1.28 (0.83–1.97) 1.32 (0.86–2.03) 1.30 (0.83–2.03) 1.31 (0.84–2.04)
High 74 17.45 1.00 (0.70–1.43) 0.99 (0.68–1.43) 0.97 (0.64–1.49) 0.99 (0.64–1.53)
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Model 1. Adjusted for demographic factors that include age, sex, and site. Model 2. Adjusted for model 1 and participant socioeconomic factors that include education, family income, and health insurance status. Model 3. Adjusted for model 2 and neighborhood socioeconomic factors that include neighborhood poverty, social environment, and physical environment. Model 4. Adjusted for model 3 and clinical factors that include smoke status, alcohol use, physical activity, body mass index, hypercholesterolemia, hypertension, and diabetes. AF indicates atrial fibrillation;and HR, hazard ratio.

Figure 1. Kaplan–Meier curve of AF cumulative incidence by race, ethnicity, and neighborhood segregation.

Figure 1

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The survival probability for participants was based on 3 levels of segregation: high, medium, and low is demonstrated. Kaplan–Meier curves are stratified by race and ethnicity with (A) Black participants, (B) Hispanic/Latino participants, (C) White participants. AF indicates atrial fibrillation.

DISCUSSION

This study examined the association between neighborhood‐level racial and ethnic residential segregation—an important mechanism of structural racism that results in unequal access to resources across neighborhoods—and development of incident AF. Using data from a cohort of middle‐aged and older adults across the United States with nearly 17 years of follow‐up, we did not observe a significant association between neighborhood residential segregation and AF incidence in either Black, Hispanic/Latino, or White participants. This finding persisted even after controlling for individual sociodemographic and clinical characteristics as well as neighborhood factors such as poverty level, physical, and social environment scores.

Our study is the first, to our knowledge, to assess longitudinal associations between residential segregation and incident AF and to examine the potential influence of this relationship by race and ethnicity and individual and neighborhood‐level socioeconomic status, including poverty, and social and physical environment. Notably, our findings ran counter to previous investigations documenting the negative impact of neighborhood segregation on cardiovascular outcomes, including risk factors for AF. In a previous study of the MESA cohort, researchers observed a 20% to 33% increased hazards of developing hypertension among individuals residing in segregated neighborhoods compared with nonsegregated neighborhoods. 12 Another study of the MESA cohort demonstrated that for Black participants, residing in a racially segregated neighborhood was associated with 12% higher hazards of developing cardiovascular disease, such as stroke or myocardial infarction, even after controlling for neighborhood‐level characteristics. 13 Furthermore, before the development of cardiovascular disease, neighborhood segregation has been shown to be associated with coronary artery calcification incidence; using data from the Coronary Artery Risk Development in Young Adults, researchers found that Black participants residing in low segregation neighborhoods had a 0.52 times lower risk of developing coronary artery calcification compared with those residing in higher segregation neighborhoods. 14 Nonetheless, our findings add to the existing literature by assessing AF incidence and using models adjusted for multiple factors, not only including demographic and clinical characteristics but also socioeconomic factors, neighborhood poverty, social environment, and physical environment.

By examining the association of neighborhood segregation on AF incidence, we sought to extend our understanding of the growing, but limited, evidence on the influence of social determinants of health on AF. 30 , 31 To date, there have been few prior studies demonstrating the association between AF incidence and individual socioeconomic factors such as education level and household income 32 along with neighborhood‐level factors such as deprivation and poverty. 33 , 34 However, these analyses have been limited in their description of the mechanism of these associations with AF as well as an assessment of differential incidence between racial and ethnic groups, who may be disproportionately impacted by neighborhood disadvantage. One potential mechanism explaining neighborhood and AF outcomes, we hypothesized, was racial and ethnic neighborhood segregation, a hypothesis founded on the theory of fundamental causes. 35 This theory places an individuals' social condition as the fundamental cause of disease through the influence of multiple factors including social and physical environment, lifestyle and behavior, and access to resources, all of which may influence downstream AF incidence. For example, exposure to pollution, 36 which is often higher in segregated neighborhoods, is associated with higher incidence of AF. 37 Although limited green space access has been associated with lower prevalence of cardiovascular disease, 38 research examining its role in AF incidence is limited, represented a critical knowledge gap. Conditions associated with unhealthy lifestyle including low physical activity and cigarette and alcohol use are all associated with higher rates of AF and can track closely with neighborhood segregation. 39 Lastly, access to medical care, including primary care clinical specialists, varies by neighborhood and may influence exposure to early preventive care to avoid detection of AF. 40 , 41

There are multiple potential reasons for a lack of an observed association between neighborhood segregation and AF incidence in our analysis. First, it is possible we may not have had a sufficient sample or time in follow‐up to detect statistically significant differences between groups at the neighborhood level. Second, given the many drivers of AF incidence at the patient, provider, system, and social level, there may be other factors on the pathway between neighborhood segregation and AF incidence that require further exploration. Lastly, as prior research has shown, there may be underdetection or underawareness of AF among individuals from racial and ethnic minoritized groups that may have affected our findings, including in neighborhoods within which there is poorer access to care. 10

Our study has limitations to consider. First, we examined only own‐group segregation, which did not capture the experience of a racially and ethnically minoritized participant living in a neighborhood with a high proportion of minoritized populations not sharing their racial or ethnic background. Second, residential history information was not available, therefore, we did not know whether participants grew up in the neighborhoods they lived in during their baseline examination. Third, although we adjusted for a multitude of covariates, residual confounding may remain because of measurement error as well as unavailability of other key confounders influencing AF incidence. Furthermore, MESA is limited in its assessment of family history of or genetic predisposition to AF as well as capture of all comorbidities that may be associated with AF and as such these were not included in our analyses. Fourth, lead time bias, similar to that which may occur in screening evaluation, may have occurred in our study, in that individuals with greater access to health may have had their AF diagnosed earlier, resulting in distortions of survival curves and HRs. We have attempted to attenuate this potential bias by adjusting for indirect markers of health care access, including education, income, health insurance status, and comorbidities. However, adjustment may not have been sufficient because, for example, MESA does not have information on supplemental insurance programs. Finally, residential segregation may operate differently depending on the metropolitan region which the population is residing; thus, findings based on this study may not be generalizable to the regions outside of individual study sites. Similarly, MESA participants were enrolled at each study site with the intent of having specific distributions across strata defined by race, ethnicity, sex, and age group and not by random sample. Thus, due to this sampling strategy, MESA participants might not be representative of the race and ethnicity of the populations at each site. 15

CONCLUSIONS

In a longitudinal analysis of the MESA cohort, we examined the association of residential segregation with incidence of AF, finding no significant association across racial and ethnic groups. Improving our understanding of how social factors may influence the development of this increasingly common condition can improve risk factor control, screening, treatment, and downstream health outcomes for millions of adults in the United States.

Sources of Funding

MESA and work in this article was supported by the National Heart, Lung, and Blood Institute through the following grants and contracts: 75N92020D00001, HHSN268201500003I, N01‐HC‐95159, 75N92020D00005, N01‐HC‐95160, 75N92020D00002, N01‐HC‐95161, 75N92020D00003, N01‐HC‐95162, 75N92020D00006, N01‐HC‐95163, 75N92020D00004, N01‐HC‐95164, 75N92020D00007, N01‐HC‐95165, N01‐HC‐95166, N01‐HC‐95167, N01‐HC‐95168 and N01‐HC‐95169 from the National Heart, Lung, and Blood Institute, grants UL1‐TR‐000040 and UL1‐TR‐001420 from National Center for Advancing Translational Science, and UL1‐RR‐025005 from National Center for Research Resources. Funding for MESA Family is provided by grants R01‐HL‐071051, R01‐HL‐071205, R01‐HL‐071250, R01‐HL‐071251, R01‐HL‐071252, R01‐HL‐071258, and R01‐HL‐071259 from the National Heart, Lung, and Blood Institute, UL1‐TR‐001079 and UL1‐TR‐001881 from National Center for Advancing Translational Science, and grant UL1‐RR‐033176 from National Center for Research Resources. The authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa‐nhlbi.org. Funding was supported in part by the Robert Wood Johnson/American Heart Association Amos Medical Faculty Development Program Award (Essien). The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the article; and decision to submit the article for publication.

Disclosures

The authors report no conflicts of interest.

Supporting information

Strobe Checklist

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Acknowledgments

Dr Essien, Dr Alexandria, and Katharine Harrington had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

This article was sent to Jong‐Ho Park, MD, PhD, Guest Editor, for review by expert referees, editorial decision, and final disposition.

Presented in part at the 2025 American Heart Association EPI|Lifestyle Scientific Sessions in New Orleans, Louisiana, March 6–8, 2025.

For Sources of Funding and Disclosures, see page 9.

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