Association of Rurality With Risk of Heart Failure

Sarah E Turecamo; Meng Xu; Debra Dixon; Tiffany M Powell-Wiley; Michael T Mumma; Jungnam Joo; Deepak K Gupta; Loren Lipworth; Véronique L Roger

doi:10.1001/jamacardio.2022.5211

. 2023 Jan 25;8(3):231–239. doi: 10.1001/jamacardio.2022.5211

Association of Rurality With Risk of Heart Failure

Sarah E Turecamo ¹, Meng Xu ^2,³, Debra Dixon ^2,⁴, Tiffany M Powell-Wiley ^5,⁶, Michael T Mumma ⁷, Jungnam Joo ⁸, Deepak K Gupta ^2,⁴, Loren Lipworth ^2,^9,¹⁰, Véronique L Roger ^1,^✉

¹Division of Intramural Research, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland

²Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University Medical Center, Nashville, Tennessee

³Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee

⁴Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee

⁵Division of Intramural Research, Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland

⁶Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland

⁷Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee

⁸Office of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland

⁹Vanderbilt O’Brien Center for Kidney Disease, Nashville, Tennessee

¹⁰Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee

Accepted for Publication: November 23, 2022.

Published Online: January 25, 2023. doi:10.1001/jamacardio.2022.5211

^✉

Corresponding Author: Véronique L. Roger, MD, MPH, Division of Intramural Research, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, NIHBC 10 Center Dr, Clinical Center Room 5N311A, Bethesda, MD 20892 (veronique.roger@nih.gov).

Author Contributions: Dr Lipworth had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Turecamo, Gupta, Lipworth, Roger.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Turecamo, Roger.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Xu, Mumma, Joo.

Administrative, technical, or material support: Turecamo, Powell-Wiley, Roger.

Supervision: Gupta, Lipworth, Roger.

Conflict of Interest Disclosures: Dr Dixon reported grants from the National Institutes of Health (NIH) during the conduct of the study and grants from Bristol Myers Squibb outside the submitted work. Dr Powell-Wiley reported Intramural Investigator grants from the NIH outside the submitted work. Dr Gupta reported research support from Imara outside the submitted work and having a patent (11,079,394). No other disclosures were reported.

Funding/Support: This research was made possible through the National Institutes of Health (NIH) Medical Research Scholars Program (Ms Turecamo), a public-private partnership supported jointly by the NIH and contributions to the Foundation for the NIH from the Doris Duke Charitable Foundation, American Association for Dental Research, Colgate-Palmolive Company, and other private donors (complete list at http://fnih.org/what-we-do/current-education-and-training-programs/mrsp). This research was also supported by the Division of Intramural Research of the National Heart, Lung, and Blood Institute at NIH (Ms Turecamo and Drs Powell-Wiley, Joo, and Roger). Dr Powell-Wiley was also supported by the Intramural Research Program of the National Institute on Minority Health and Health Disparities. Dr Dixon was supported by Training Award in Cardiovascular Research (T32 HL007411). The Southern Community Cohort Study (SCCS) is supported by the National Cancer Institute (R01CA092447 and U01CA202979) and supplemental funding from the American Recovery and Reinvestment Act (3R01CA029447-0851). SCCS data collection was performed by the Survey and Biospecimen Shared Resource, which is supported in part by the Vanderbilt-Ingram Cancer Center (P30CA68485).

Role of the Funder/Sponsor: The funders 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 manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views expressed in this article are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; National Institute on Minority Health and Health Disparities; National Institutes of Health; or US Department of Health and Human Services.

Data Sharing Statement: See Supplement 2.

Additional Contributions: We thank Dera Tompkins, MLS, NIH Library Writing Center, for manuscript editing assistance. Additionally, we thank all the SCCS participants and the SCCS research team. No one was compensated by our research team for their assistance with the study.

^✉

Corresponding author.

PMCID: PMC9878434 PMID: 36696094

This cohort study analyzes data for participants from the Southern Community Cohort Study to determine whether rurality is associated with increased risk of heart failure and whether rurality-associated risk varies by race and sex.

Key Points

Question

Are rural populations at increased risk of heart failure?

Findings

In this cohort study of Black and White adults, rural participants had an increased risk of heart failure compared with urban participants. The risk of heart failure associated with rurality was independent of cardiovascular risk factors and socioeconomic status and varied across race-sex groups, and Black men had the highest risk.

Meaning

To address this association between rurality and higher risk of developing heart failure, particularly among Black men, interventions should focus on primary prevention of heart failure among these high-risk communities.

Abstract

Importance

Rural populations experience an increased burden of heart failure (HF) mortality compared with urban populations. Whether HF incidence is greater among rural individuals is less known. Additionally, the intersection between racial and rural health inequities is understudied.

Objective

To determine whether rurality is associated with increased risk of HF, independent of cardiovascular (CV) disease and socioeconomic status (SES), and whether rurality-associated HF risk varies by race and sex.

Design, Setting, and Participants

This prospective cohort study analyzed data for Black and White participants of the Southern Community Cohort Study (SCCS) without HF at enrollment who receive care via Centers for Medicare & Medicaid Services (CMS). The SCCS is a population-based cohort of low-income, underserved participants from 12 states across the southeastern United States. Participants were enrolled between 2002 and 2009 and followed up until December 31, 2016. Data were analyzed from October 2021 to November 2022.

Exposures

Rurality as defined by Rural-Urban Commuting Area codes at the census-tract level.

Main Outcomes and Measures

Heart failure was defined using diagnosis codes via CMS linkage through 2016. Incidence of HF was calculated by person-years of follow-up and age-standardized. Sequentially adjusted Cox proportional hazards regression models tested the association between rurality and incident HF.

Results

Among 27 115 participants, the median (IQR) age was 54 years (47-65), 18 647 (68.8%) were Black, and 8468 (32.3%) were White; 5556 participants (20%) resided in rural areas. Over a median 13-year follow-up, age-adjusted HF incidence was 29.6 (95% CI, 28.9-30.5) per 1000 person-years for urban participants and 36.5 (95% CI, 34.9-38.3) per 1000 person-years for rural participants (P < .001). After adjustment for demographic information, CV risk factors, health behaviors, and SES, rural participants had a 19% greater risk of incident HF (hazard ratio [HR], 1.19; 95% CI, 1.13-1.26) compared with their urban counterparts. The rurality-associated risk of HF varied across race and sex and was greatest among Black men (HR, 1.34; 95% CI, 1.19-1.51), followed by White women (HR, 1.22; 95% CI, 1.07-1.39) and Black women (HR, 1.18; 95% CI, 1.08-1.28). Among White men, rurality was not associated with greater risk of incident HF (HR, 0.97; 95% CI, 0.81-1.16).

Conclusions and Relevance

Among predominantly low-income individuals in the southeastern United States, rurality was associated with an increased risk of HF among women and Black men, which persisted after adjustment for CV risk factors and SES. This inequity points to a need for additional emphasis on primary prevention of HF among rural populations.

Introduction

In the United States, rural populations have a greater burden of disease and worse health outcomes.¹ The American Heart Association’s Call to Action on Rural Health emphasized the urgency of addressing rural-urban health disparities related to cardiovascular (CV) disease.² Rural populations have a greater prevalence of smoking, obesity, physical inactivity, diabetes, hypertension, hyperlipidemia, stroke, and coronary heart disease.^2,3,4,5,6,7

Specifically for heart failure (HF), rural populations experience a disproportionate burden of HF mortality, compounded by racial inequities.^8,9 Black men living in rural areas, particularly the rural South, are more likely to die of HF compared with Black men in urban areas and White men in both rural and urban areas.⁹ Whether these inequities reflect higher HF incidence is less understood.¹⁰ Addressing this question is critical to develop interventions to reduce rural-urban disparities. Therefore, we examined the incidence of HF by rurality status across race and sex in a large community cohort of Black and White adults in the southeastern United States. We hypothesized that HF incidence would be higher among rural adults, that rurality would independently contribute to HF risk beyond CV risk factors and socioeconomic status (SES), and that rurality-associated HF incidence would vary by race and sex.

Methods

Study Population

The Southern Community Cohort Study (SCCS) is a prospective cohort with data on incidence of cancer and chronic disease, with focused recruitment in low-income, resource-limited, and Black populations historically underrepresented in research.^11,12 Participants in the SCCS provided written informed consent, and protocols were approved by the institutional review boards of Vanderbilt University Medical Center and Meharry Medical College.

Between March 2002 and September 2009, SCCS enrolled 84 797 participants across 12 states (Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia).¹³ Most participants (approximately 86%) were recruited from community health centers (CHCs) that care for medically underserved populations; around 14% were recruited via mail- and telephone-based population sampling.^11,12,14 After informed consent, participants completed a questionnaire (self-administered for participants recruited by mail or telephone or administered by CHC staff) and self-reported race, demographic data, SES, health behaviors, and medical history.

We identified 33 003 participants who either reported being covered by Centers for Medicare & Medicaid Services (CMS) at enrollment or did not report CMS coverage on the baseline questionnaire but had a claim within 90 days of enrollment.^12,14 Only participants whose self-reported race and ethnicity were African American or Black or non-Hispanic White were included because there were too few participants in other racial and ethnic groups (which included American Indian or Alaskan Native, Asian or Pacific Islander, Hispanic/Latino, multiracial, other, and unknown).¹² Participants with prevalent HF at baseline or study dropout and those missing rurality status were excluded, leaving 27 115 participants with data for analysis (eFigure and eTable 4 in Supplement 1). Data were analyzed from October 2021 to November 2022, and our reporting followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.¹⁵

Main Exposures and Covariates

Participant addresses were geocoded and linked to census tracts,^16,17 to assign Rural-Urban Commuting Area (RUCA) codes as developed by the US Department of Agriculture Economic Research Service (eTable 2 in Supplement 1).^18,19,20 Ten primary RUCA codes classified commuting flow into levels within metropolitan, micropolitan, small town, and rural areas.^18,20 Secondary codes provide further subdivisions based on secondary flows.^18,20 The University of Washington Rural Health Research Center provides code aggregation for use in health research. For this study, RUCA codes were grouped based on categorization C¹⁸ (eTable 2 in Supplement 1), consistent with prior research characterizing HF and rurality.¹⁰

Demographic characteristics and medical history included age, sex, race, annual household income, education, marital status, hypertension, diabetes, coronary disease, hyperlipidemia, stroke, depression, body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), smoking status, and insurance coverage. Using the healthy eating index (HEI), diet was assigned a score ranging from 0 to 100 based on alignment with 2010 US Dietary Guidelines.^21,22 Physical activity was quantified as standard metabolic-equivalent hours per day (calculated from the sum of light, moderate, and vigorous activities)^23,24 and total hours spent sitting per day.²⁴ Neighborhood deprivation index (NDI) was determined by a composite score including 11 census tract variables within 4 categories (education, employment, occupation, poverty), previously derived in the SCCS via principal components analysis.^13,17,25

Ascertainment of HF

Incident and prevalent HF was ascertained using diagnosis codes from the International Classification of Diseases, Ninth Revision, and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (hereafter ICD), via linkage of SCCS data to CMS Research Identifiable Files through December 31, 2016.¹² Incident HF was defined as the first ever occurrence of an HF code (428.x, I50x) documented in Medicare institutional, Part B carrier, or outpatient claims. Mortality was used as a censoring variable and was ascertained through the Social Security Administration vital status service and National Death Index through December 31, 2016.¹²

Statistical Analysis

Descriptive Statistics

Participants were analyzed by rural/urban status with a priori stratification by race-sex group (Black men, White men, Black women, White women), in concordance with prior research within SCCS.^12,14,17 Baseline characteristics by rural/urban status were summarized as frequency and percentage for categorical variables and median (IQR) for continuous variables; they were compared in univariate analyses with χ²/Fisher exact tests and Mann-Whitney U tests, respectively.

HF Incidence

Heart failure incidence was determined by calculating the duration of follow-up from enrollment to HF diagnosis, death, or December 31, 2016. Incidence rates were calculated by the number of HF cases by person-time of follow-up, presented per 1000 person-years, and were age-standardized to the eligible SCCS cohort.

The contribution of rurality to HF incidence was assessed in Cox proportional hazards regression models guided by the National Institute on Minority Health and Health Disparities (NIMHD) Minority Health and Health Disparities Research Framework and with a priori selected covariates sequentially added to models according to their domain of influence (Figure 1)²⁶ at the individual level. The first model was based on demographic information, including age, sex, and race and a priori defined race-sex groups. Next, biological factors (hypertension, diabetes, coronary disease, hyperlipidemia, stroke, depression, and BMI) were added. Thereafter, behavioral risk factors were included, represented by smoking status, HEI, physical activity, and sedentary (sitting) time. Sociocultural environment, including income, education, and marital status, was incorporated in the final model.

Figure 1. — BMI indicates body mass index. Permission to use the panel A diagram provided by the National Institute on Minority Health and Health Disparities.²⁶

Sensitivity analyses included addition of enrollment source, insurance coverage, and NDI to the final model, stratification by age younger than 65 years and 65 years and older, and inclusion of participants with self-reported race other than Black or White. In all models, continuous variables were included as restricted cubic splines with 4 knots, and proportional hazards assumption was assessed with a scaled Schoenfeld residual test. Stratification was used to treat the categorical predictors and time-dependent coefficient to treat continuous predictors that did not satisfy the proportional hazards assumption.²⁷ Complete case analysis was used because less than 7% of the covariates were missing. Multicollinearity was assessed with variance inflation factor greater than 5. Statistical analyses were performed using R version 3.6.3²⁸ with 2-sided tests and P < .05 interpreted as significant.

Results

Baseline Characteristics

Of the 27 115 participants, 5556 (20%) resided in rural areas (Table 1). A majority of participants (18 647 [68.8%]) were Black, without difference by rurality. Individuals in rural areas were older and more commonly identified as female. Rural participants had a slightly higher BMI and rates of hypertension, diabetes, coronary disease, and hyperlipidemia; similar rates of stroke; and a lower rate of depression. Diet and exercise were similar between urban and rural participants, but rural participants were less likely to be current smokers. Rural participants were more likely to be married and less educated, but income did not differ overall by rurality. Urban participants were slightly more likely to live in neighborhoods with a higher level of deprivation, as signified by a higher NDI quartile (50.6% of urban participants in Q3 or Q4 of NDI vs 47% of rural). Baseline characteristics by race-sex group are in eTable 1 in Supplement 1.

Table 1. Baseline Characteristics of the Study Cohort.

	No./total No. (%)
	Overall (N = 27 115)	Urban (n = 21 559)	Rural (n = 5556)
Age, median (IQR), y	54 (47-65)	54 (46-64)	57 (48-66)
Age ≥65 y	7013/27 115 (25.9)	5280/21 559 (24.5)	1733/5556 (31.2)
Age at HF diagnosis, median (IQR), y	64 (55-72)	63 (55-71)	65 (56-73)
Sex
Female	16 957/27 115 (62.5)	13 404/21 559 (62.2)	3553/5556 (63.9)
Male	10 158/27 115 (37.5)	8155/21 559 (37.8)	2003/5556 (36.1)
Race^a
Black	18 647/27 115 (68.8)	14 862/21 559 (68.9)	3785/5556 (68.1)
White	8468/27 115 (32.2)	6697/21 559 (31.1)	1771/5556 (31.9)
Hypertension	16 914/27 067 (62.5)	13 187/21 518 (61.3)	3727/5549 (67.2)
Diabetes	7188/27 059 (26.6)	5575/21 512 (25.9)	1613/5547 (29.1)
Coronary disease^b	2333/27 044 (8.6)	1807/21 499 (8.4)	526/5545 (9.5)
Hyperlipidemia	10 678/26 996 (39.6)	8338/21 454 (38.9)	2340/5542 (42.2)
Stroke	2587/27 046 (9.6)	2040/21 501 (9.5)	547/5545 (9.9)
Depression	8621/27 046 (31.9)	7046/21 503 (32.8)	1575/5543 (28.4)
BMI, median (IQR)^c	29.1 (24.9-34.7)	29 (24.7-34.5)	29.7 (25.5-35.1)
Smoking status
Current	10 777/26 895 (40.1)	9042/21 402 (42.2)	1735/5493 (31.6)
Former	6804/26 895 (25.3)	5289/21 402 (24.7)	1515/5493 (27.6)
Never	9314/26 895 (34.6)	7071/21 402 (33.0)	2243/5493 (40.8)
Healthy eating index, median (IQR)	57.9 (49.5-66.5)	57.8 (49.5-66.4)	58.7 (49.6-66.6)
Physical activity, median (IQR)
Total activity, MET-h/d^d	12.9 (6.3-23)	12.9 (6.3-23.3)	12.6 (6.3-22.4)
Time sitting, h/d	8.0 (5.5-11.5)	8.0 (5.5-12)	7.71 (5-11)
Income, $
<15 000	18 603/26 697 (69.7)	14821/21 242 (69.8)	3782/5455 (69.3)
15 000-24 999	4800/26 697 (18.0)	3825/21 242 (18.0)	975/5455 (17.9)
≥25 000	3294/26 697 (12.3)	2596/21 242 (12.2)	698/5455 (12.8)
Education
<High school	10 411/27 095 (38.4)	8012/21 545 (37.2)	2399/5550 (43.2)
High school	14 394/27 095 (53.1)	11 725/21 545 (54.4)	2669/5550 (48.1)
College	2290/27 095 (8.5)	1808/21 545 (8.4)	482/5550 (8.7)
Marital status
Married	8024/26 930 (29.8)	5985/21 432 (27.9)	2039/5498 (37.1)
Divorced	9156/26 930 (34.0)	7595/21 432 (35.4)	1561/5498 (28.4)
Widowed	3894/26 930 (14.5)	2996/21 432 (14.0)	898/5498 (16.3)
Single	5856/26 930 (21.7)	4856/21 432 (22.7)	1000/5498 (18.2)
NDI quartile
1 (Least deprived)	6801/26 857 (25.3)	5902/21 370 (27.6)	899/5487 (16.4)
2	6657/26 857 (24.8)	4651/21 370 (21.8)	2006/5487 (36.6)
3	6773/26 857 (25.2)	5361/21 370 (25.1)	1412/5487 (25.7)
4 (Most deprived)	6626/26 857 (24.7)	5456/21 370 (25.5)	1170/5487 (21.3)
Insurance coverage^e
Medicaid	14 091/24 356 (57.9)	11 266/19 293 (58.4)	2825/5063 (55.8)
Medicare	13 765/24 356 (56.5)	10 569/19 293 (54.8)	3196/5063 (63.1)
Private insurance	3463/24 356 (14.2)	2639/19 293 (13.7)	824/5063 (16.3)
Enrollment source
CHC	24 188/27 115 (89.2)	19 448/21 559 (90.2)	4740/5556 (85.3)
Mail	2836/27 115 (10.5)	2026/21 559 (9.4)	810/5556 (14.6)
Telephone	91/27 115 (0.3)	85/21 559 (0.4)	6/5556 (0.1)

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Abbreviations: BMI, body mass index; CHC, community health center; MET, metabolic equivalent; NDI, neighborhood deprivation index.

^{^a}

Only data for participants whose self-reported race was Black or White were included in the primary analysis because there were too few participants in other racial and ethnic groups (which included American Indian or Alaskan Native, Asian or Pacific Islander, Hispanic/Latino, multiracial, other, and unknown).

^{^b}

Coronary disease defined as coronary artery bypass graft or myocardial infarction.

^{^c}

Calculated as weight in kilograms divided by height in meters squared.

^{^d}

Calculated from the sum of light, moderate, and vigorous activities.

^{^e}

Self-reported insurance coverage at enrollment.

Occurrence of HF

Over a median 13-year follow-up, 7542 incident HF events occurred: 1865 among rural and 5677 among urban participants (Table 2). The age-standardized HF incidence rate among rural participants was 36.5 (95% CI, 34.9-38.3) per 1000 person-years, and the urban incidence rate was 29.6 (95% CI, 28.9-30.5) per 1000 person-years (P < .001). Black men, Black women, and White women living in rural areas had higher age-adjusted HF incidence rates compared with their respective urban counterparts. Rural Black men had an age-adjusted incidence rate of 40.4 (95% CI, 36.8-44.3) per 1000 person-years, which was the highest incidence across all groups.

Table 2. Incident HF by Rurality Status, Overall and Stratified by Race-Sex Group.

	Overall		Black men		White men		Black women		White women
	Urban (n = 21 559)	Rural (n = 5556)	Urban (n = 5632)	Rural (n = 1318)	Urban (n = 2523)	Rural (n = 685)	Urban (n = 9230)	Rural (n = 2467)	Urban (n = 4174)	Rural (n = 1086)
Incident HF cases	5677	1865	1368	455	710	210	2433	846	1166	354
Person-years	193 931	49 286	50 324	11 140	20 479	5613	87 048	23 283	36 080	9249
Incident rate per 1000 PY (95% CI)	29.3 (28.5-30.0)	37.8 (36.1-39.6)	27.2 (25.7-28.6)	40.8 (37.1-44.6)	34.7 (32.1-37.2)	37.4 (32.4-42.5)	28.0 (26.8-29.1)	36.3 (33.9-38.8)	32.3 (30.5-34.2)	38.3 (34.3-42.3)
Age-adjusted incidence rate per 1000 PY (95% CI)	29.6 (28.9-30.5)^a	36.5 (34.9-38.3)^a	28.9 (27.4-30.6)^a	40.4 (36.8-44.3)^a	33.3 (30.8-35.9)	35.9 (30.5-41.5)	29.2 (28.1-30.4)^a	35.8 (33.5-38.3)^a	31.1 (29.3-33.0)^b	36.2 (32.3-40.3)^b

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Abbreviations: HF, heart failure; PY, person-years.

^{^a}

P < .001 for rural vs urban age-adjusted incidence rate.

^{^b}

P = .001 for rural vs urban age-adjusted incidence rate.

After adjustment for age, sex, and race, rurality was associated with a 21% greater risk for incident HF (hazard ratio [HR], 1.21; 95% CI, 1.15-1.28) (Table 3), which persisted after adjustment for biological factors, with rural residents experiencing 17% greater risk for incident HF (HR, 1.17; 95% CI, 1.11-1.24). The association between rurality and HF was attenuated neither by adding behavioral risk factors, nor by further addition of sociocultural environment covariates. In the final model, rurality was independently associated with a 19% greater risk of HF (HR, 1.19; 95% CI, 1.13-1.26). Addition of NDI, enrollment source, or insurance coverage to the final model or inclusion of other racial and ethnic groups did not attenuate the association between rurality and HF risk (eTable 3 in Supplement 1).

Table 3. Rurality-Associated Risk of Incident Heart Failure.

Model	HR (95% CI)
Model	Overall	Black men	White men	Black women	White women
1: Demographic data^a	1.21 (1.15-1.28)	1.38 (1.24-1.53)	1.04 (0.89-1.22)	1.22 (1.12-1.32)	1.13 (1.01-1.28)
2: Biological^b	1.17 (1.11-1.24)	1.30 (1.17-1.45)	0.98 (0.84-1.15)	1.18 (1.09-1.28)	1.13 (1.00-1.27)
3: Behavioral^c	1.20 (1.13-1.27)	1.35 (1.20-1.52)	0.96 (0.81-1.14)	1.20 (1.10-1.31)	1.19 (1.05-1.36)
4: Sociocultural environment^d	1.19 (1.13-1.26)	1.34 (1.19-1.51)	0.97 (0.81-1.16)	1.18 (1.08-1.28)	1.22 (1.07-1.39)

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Abbreviation: HR, hazard ratio.

^{^a}

Model 1 (demographic data): age, sex, and race and ethnicity.

^{^b}

Model 2 (biological): model 1 adjustments plus hypertension, diabetes, coronary disease, hyperlipidemia, stroke, depression, and body mass index.

^{^c}

Model 3 (behavioral): model 2 adjustments plus smoking status, healthy eating index, and physical activity (metabolic equivalent, h/d, and time sitting, h/d).

^{^d}

Model 4 (sociocultural environment): model 3 adjustments plus income, education, and marital status.

The risk of HF associated with rurality varied by race-sex group (Figure 2). Rural Black men had the highest risk of HF in every sequential model, including in the final model (HR, 1.34; 95% CI, 1.19-1.51). Black women (HR, 1.18; 95% CI, 1.08-1.28) and White women (HR, 1.22; 95% CI, 1.07-1.39) had a similar increased risk of HF attributable to rurality. In contrast, among White men, rurality was not associated with HF risk (HR, 0.97; 95% CI, 0.81-1.16).

Discussion

Among this large cohort of predominantly low-income Black and White individuals in the southeastern United States, the HF incidence was higher among adults living in rural areas and varied by race-sex group, with rural Black men experiencing the highest rate of incident HF. The substantial excess risk of HF associated with rurality persisted after adjustment for biological, behavioral, and sociocultural risk factors.

Rurality and HF

Our study population reflected the demographics of rural America, namely older age, lower educational attainment, and greater burden of CV disease and comorbidities.² Rural participants had a higher rate of incident HF, which persisted after adjustment for biological and behavioral risk factors, suggesting that the effect of rurality is not simply a consequence of higher CV risk among rural residents. The rich data in SCCS enabled extensive adjustment, which indicated the association between rurality and incident HF was independent of sociocultural factors, including education, income, and marital status. Further, addition of NDI, an indicator of overall neighborhood social environment,¹⁷ did not attenuate the association between HF and rurality. Therefore, the association between rurality and incident HF is not merely a manifestation of neighborhood deprivation, but rather suggests that the place-based effect of a rural environment uniquely contributes to risk for HF above and beyond NDI.

Prior research on HF within rural communities has focused mainly on mortality outcomes.^8,9,10,29 However, research on HF incidence among rural residents within the United States is relatively limited. Some international studies reported a higher prevalence and incidence of HF among rural residents.^30,31 However, no population-based study examined HF incidence by rurality in the United States. Further, our study included both inpatient and outpatient HF visits and evaluated risk of HF while focusing on the intersection with race and sex.

Our results suggest that, beyond individual-level risk indicators and SES, the excess risk of HF associated with rurality is driven by societal, community, and interpersonal factors.²⁶ Access to health care is a societal driver of rural-urban disparities and is singled out as a top priority in the Rural Healthy People 2020 report.³² Differing access to care in rural areas can negatively influence health outcomes; specifically within HF after diagnosis, rural patients have a longer delay in care and reduced emergency department visits, hospitalizations, and hospice use.^10,33,34 In addition to affecting outcomes, access to care can affect disease incidence because of inequities in levels of preventive care and risk factor management. Future research should examine the impact of preventive care utilization on risk of HF and chronic disease among rural residents.

Race-Sex Differences

Our results underscore the importance of examining the effect of rurality by race-sex group, as we observed that both White and Black women had an increased risk of HF associated with rurality, in contrast to White men, who had no rurality-associated risk of HF. The association between rurality and HF risk among women was previously reported in a cohort study of more than 2.5 million women with preeclampsia or eclampsia that showed an increased risk of hospitalization for incident HF (HR, 1.63; 95% CI, 1.50-1.76) associated with living in a nonmetropolitan or micropolitan area vs a large metropolitan area.³⁵ However, the generalizability of these data is limited as it only included postpartum individuals in 2 US states and did not stratify by race.

We observed striking differences in HF incidence by rurality across race-sex groups. Rural Black men had the highest age-adjusted HF incidence of all groups (40.4/1000 person-years; 95% CI, 36.8-44.3), which is markedly higher than both the HF incidence among urban Black men (28.9/1000 person-years) and the previously reported overall HF incidence among Black men in SCCS (34.9/1000 person-years).¹² Further, the incidence of HF for rural Black men is higher than estimates of HF incidence for Black men in other cohorts, including 4.6 per 1000 person-years for Black men in the Multi-Ethnic Study of Atherosclerosis³⁶ and 9.1 per 1000 person-years for Black men in the Atherosclerosis Risk in Communities study.³⁷ In the fully adjusted model, Black men experienced a more than 30% increased risk of HF associated with rurality, an excess risk not shared by White men within this study. We cannot exclude a rurality-associated risk of HF among White men potentially masked by unmeasured confounding, a limitation common to all observational studies.

Prior research demonstrated higher mortality among rural Black adults across several diseases, including diabetes, cancer, stroke, and HF.^9,38,39,40 However, the effect of race and rurality on disease incidence remains understudied. Data from the Behavioral Risk Factor Surveillance System showed that compared with White adults, Black adults in rural areas self-reported worse health status, higher physical distress, greater chronic condition burden, and obesity⁴¹; however, this cross-sectional study could not examine disease incidence. In our analysis, we leveraged the extensive amount of individual-level information in the SCCS survey to adjust for biological, behavioral, and sociocultural factors that may affect disease incidence, yet the risk of HF associated with rurality among Black men persisted. This observation underscores the importance of considering other domains beyond the individual level²⁶ to understand how rurality differentially affects Black and White men. One such domain is structural racism, which creates racial inequities through multiple mechanisms, including political participation, treatment within the criminal justice system, housing stability and accessibility, wealth and property accumulation, food environment, neighborhood segregation, and discrimination within the health care system.^{42,43,44,45,46,47} There is a need to develop robust measures of structural racism within both rural and urban communities. The next steps in the investigation of rurality among Black adults must focus on health equity to examine the effect of sociopolitical and economic context on CV health at the population and societal level.⁴⁵

Public Health Implications

Prior research identified that HF mortality was increased among rural residents^8,9,10,29; our results bring new knowledge to this domain by demonstrating that rural residents have a higher rate of HF incidence, with race-sex variation. Our results resonate with the call for primary prevention as a strategy to reduce HF health inequities put forth by Youmans et al⁴⁸ and provide important practical strategies to do so. Our hypothesis-generating results suggest a need to personalize prevention,⁴⁸ focusing on rural women and rural Black men as key groups, and to elucidate the mechanism by which rurality is associated with HF risk.

Strengths and Limitations

Some limitations must be acknowledged to aid in the interpretation of our results. Most participants had low incomes and were covered by CMS; therefore, these findings may not be generalizable to individuals with a higher income or private insurance and may overrepresent older individuals covered by CMS.¹⁴ The findings pertain to the southeastern United States and may not be generalizable to other regions. Our analysis focused on participants who identified as White/Black and male/female, so we were unable to examine the influence of rurality on individuals of other racial, ethnic, and gender identities. We examined race as a social construct and could not examine risk factors for HF related to ancestry. While demographic information, health behavior, comorbidity, and SES were self-reported, an independent study demonstrated the validity of this information in this cohort.^11,24 Although prevalent coronary heart disease was adjusted for in this analysis, incident coronary heart disease was not included and may have occurred prior to the onset of HF. Adjustment for process-related measures was not possible, such as health care utilization and longitudinal risk factor control (blood pressure medication and control, statin use, lipid levels, and hemoglobin A_1c control), which may explain the observed differences. Incident HF was ascertained using ICD codes, without adjudication. Rurality status was ascertained by enrollment addresses and did not capture movement of participants throughout the study. As in any observational study, we cannot exclude residual confounding, and caution must be exerted when making causal inferences. This analysis was limited to individual-level domains of influence,²⁶ but future work should investigate interpersonal, community, or societal factors that could contribute to rural-urban disparities, such as access to care, a domain that includes both care-seeking behaviors and logistical access to care.

Our study also has several important strengths. We included more than 27 000 participants across an entire region of the United States (southeast), and most participants had low SES and a majority identified as Black, both characteristics typically underrepresented in epidemiologic cohorts. In contrast to other studies examining rurality on a population-wide level, we examined HF risk on the individual level and adjusted for a multitude of risk factors, enabling us to use the NIMHD Minority Health and Health Disparities Research Framework to examine the effect of multiple domains of influence. By defining rurality at the census tract level, we captured greater detail than by using county-level categorizations of rurality, which may span both rural and urban areas.

Conclusions

In this large southeastern US population, rural populations had an increased incidence of HF, which was particularly striking among women and Black men. The rurality-associated risk of HF was independent of individual-level biological, behavioral, and sociocultural risk factors. These inequities highlight the intersectionality of race and sex and rurality and the need for further investigation into the rurality-associated risk of HF to guide public health efforts aimed at HF prevention among rural populations.

Supplement 1.

eTable 1. Baseline characteristics across race-sex groups by rural-urban status

eTable 2. Rural-Urban Commuting Area (RUCA) code definitions

eTable 3. Rurality associated risk of heart failure sensitivity analyses

eTable 4. Baseline characteristics of 5,883 participants excluded from analysis

eFigure. Study sample flow diagram

eReferences

Click here for additional data file.^{(274.8KB, pdf)}

Supplement 2.

Data sharing statement

Click here for additional data file.^{(12.5KB, pdf)}

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eTable 1. Baseline characteristics across race-sex groups by rural-urban status

eTable 2. Rural-Urban Commuting Area (RUCA) code definitions

eTable 3. Rurality associated risk of heart failure sensitivity analyses

eTable 4. Baseline characteristics of 5,883 participants excluded from analysis

eFigure. Study sample flow diagram

eReferences

Click here for additional data file.^{(274.8KB, pdf)}

Supplement 2.

Data sharing statement

Click here for additional data file.^{(12.5KB, pdf)}

[hoi220084r1] 1.Michael Meit A, Alana Knudson M, Gilbert T, et al. The 2014 Update of the rural-urban chartbook. Published online 2014. Accessed February 1, 2022. https://www.ruralhealthresearch.org/

[hoi220084r2] 2.Harrington RA, Califf RM, Balamurugan A, et al. Call to action: rural health: a presidential advisory from the American Heart Association and American Stroke Association. Circulation. 2020;141(10):e615-e644. doi: 10.1161/CIR.0000000000000753 [DOI] [PubMed] [Google Scholar]

[hoi220084r3] 3.Howard G, Kleindorfer DO, Cushman M, et al. Contributors to the excess stroke mortality in rural areas in the United States. Stroke. 2017;48(7):1773-1778. doi: 10.1161/STROKEAHA.117.017089 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r4] 4.Kamin Mukaz D, Dawson E, Howard VJ, et al. Rural/urban differences in the prevalence of stroke risk factors: a cross-sectional analysis from the REGARDS study. J Rural Health. 2022;38(3):668-673. doi: 10.1111/jrh.12608 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r5] 5.O’Connor A, Wellenius G. Rural-urban disparities in the prevalence of diabetes and coronary heart disease. Public Health. 2012;126(10):813-820. doi: 10.1016/j.puhe.2012.05.029 [DOI] [PubMed] [Google Scholar]

[hoi220084r6] 6.Matthews KA, Croft JB, Liu Y, et al. Health-related behaviors by urban-rural county classification - United States, 2013. MMWR Surveill Summ. 2017;66(5):1-8. doi: 10.15585/mmwr.ss6605a1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r7] 7.Lundeen EA, Park S, Pan L, O’Toole T, Matthews K, Blanck HM. Obesity prevalence among adults living in metropolitan and nonmetropolitan counties - United States, 2016. MMWR Morb Mortal Wkly Rep. 2018;67(23):653-658. doi: 10.15585/mmwr.mm6723a1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r8] 8.Khan MS, Kumar P, Sreenivasan J, et al. Rural-urban differences in mortality from ischemic heart disease, heart failure, and stroke in the United States. Circ Cardiovasc Qual Outcomes. 2021;14(4):e007341. doi: 10.1161/CIRCOUTCOMES.120.007341 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r9] 9.Pierce JB, Shah NS, Petito LC, et al. Trends in heart failure-related cardiovascular mortality in rural versus urban United States counties, 2011-2018: a cross-sectional study. PLoS One. 2021;16(3):e0246813. doi: 10.1371/journal.pone.0246813 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r10] 10.Manemann SM, St Sauver J, Henning-Smith C, et al. Rurality, death, and healthcare utilization in heart failure in the community. J Am Heart Assoc. 2021;10(4):e018026. doi: 10.1161/JAHA.120.018026 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r11] 11.Signorello LB, Hargreaves MK, Blot WJ. The Southern Community Cohort Study: investigating health disparities. J Health Care Poor Underserved. 2010;21(1)(suppl):26-37. doi: 10.1353/hpu.0.0245 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r12] 12.Akwo EA, Kabagambe EK, Wang TJ, et al. Heart failure incidence and mortality in the Southern Community Cohort Study. Circ Heart Fail. 2017;10(3):e003553. doi: 10.1161/CIRCHEARTFAILURE.116.003553 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r13] 13.Signorello LB, Cohen SS, Williams DR, Munro HM, Hargreaves MK, Blot WJ. Socioeconomic status, race, and mortality: a prospective cohort study. Am J Public Health. 2014;104(12):e98-e107. doi: 10.2105/AJPH.2014.302156 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r14] 14.Kubicki DM, Xu M, Akwo EA, et al. Race and sex differences in modifiable risk factors and incident heart failure. JACC Heart Fail. 2020;8(2):122-130. doi: 10.1016/j.jchf.2019.11.001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r15] 15.von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative . The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med. 2007;147(8):573-577. doi: 10.7326/0003-4819-147-8-200710160-00010 [DOI] [PubMed] [Google Scholar]

[hoi220084r16] 16.Sonderman JS, Mumma MT, Cohen SS, Cope EL, Blot WJ, Signorello LB. A multi-stage approach to maximizing geocoding success in a large population-based cohort study through automated and interactive processes. Geospat Health. 2012;6(2):273-284. doi: 10.4081/gh.2012.145 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r17] 17.Akwo EA, Kabagambe EK, Harrell FE Jr, et al. Neighborhood deprivation predicts heart failure risk in a low-income population of Blacks and Whites in the southeastern United States. Circ Cardiovasc Qual Outcomes. 2018;11(1):e004052. doi: 10.1161/CIRCOUTCOMES.117.004052 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r18] 18.Washington, Wyoming, Alaska, Montana, Idaho (WWAMI) Rural Health Research Center. Rural urban commuting area codes data. Accessed August 3, 2021. http://depts.washington.edu/uwruca/ruca-uses.php

[hoi220084r19] 19.Hall SA, Kaufman JS, Ricketts TC. Defining urban and rural areas in U.S. epidemiologic studies. J Urban Health. 2006;83(2):162-175. doi: 10.1007/s11524-005-9016-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi220084r20] 20.USDA Economic Research Service . Rural-urban commuting area codes. Accessed August 3, 2021. https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes.aspx

[hoi220084r21] 21.National Cancer Institute . Overview and background of the healthy eating index (HEI). Accessed November 30, 2021. https://epi.grants.cancer.gov/hei/

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PERMALINK

Association of Rurality With Risk of Heart Failure

Sarah E Turecamo, BA

Meng Xu, MS

Debra Dixon, MD, MS

Tiffany M Powell-Wiley, MD, MPH

Michael T Mumma, MS

Jungnam Joo, PhD

Deepak K Gupta, MD, MSCI

Loren Lipworth, ScD

Véronique L Roger, MD, MPH

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Study Population

Main Exposures and Covariates

Ascertainment of HF

Statistical Analysis

Descriptive Statistics

HF Incidence

Figure 1. Application of the National Institute of Minority Health and Health Disparities (NIMHD) Research Framework.

Results

Baseline Characteristics

Table 1. Baseline Characteristics of the Study Cohort.

Occurrence of HF

Table 2. Incident HF by Rurality Status, Overall and Stratified by Race-Sex Group.

Table 3. Rurality-Associated Risk of Incident Heart Failure.

Figure 2. Rurality-Associated Risk of Incident Heart Failure.

Discussion

Rurality and HF

Race-Sex Differences

Public Health Implications

Strengths and Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases