Rural-urban differences in modifiable dementia risk factors among U.S. populations aged 45 years or older

Abstract

Background

Alzheimer's disease and related dementias (ADRD) have become a significant public health concern, and the burden is disproportionately concentrated in rural areas.

Objective

To examine rural-urban differences in the prevalence of modifiable dementia risk factors and their treatment among U.S. adults aged 45 years and older, and to investigate how these disparities vary by age group and geographic region.

Methods

This cross-sectional study analyzed nationally representative data from the 2023 National Health Interview Survey in 2025. Prevalence of 11 modifiable dementia risk factors (hypertension, high cholesterol, diabetes, obesity, hearing loss, visual impairment, traumatic brain injury, low education, depression, social isolation, smoking) and 7 corresponding treatments were assessed via self-report. Adjusted rate ratios (aRR) were estimated using robust Poisson regression models.

Results

The study population consisted of 16,981 individuals (mean age: 62.4, 51.6% female, 68.7% non-Hispanic White, 15.5% in rural areas). Rural residents had significantly higher prevalence of hypertension (aRR, 1.11; 95% CI, 1.06–1.17), obesity (aRR, 1.22; 95% CI, 1.15–1.30), diabetes (aRR, 1.29; 95% CI, 1.15–1.45), and hearing loss (aRR, 1.22; 95% CI, 1.12–1.34) compared to urban residents. Disparities were most significant among adults aged 45–64 years and in South/Midwest regions. Treatment rates for cardiometabolic conditions were high (>85%) and similar across regions, but treatment for sensory/behavioral risk factors remained low.

Conclusions

Rural U.S. adults face higher burden of modifiable dementia risk factors, particularly cardiometabolic and sensory impairments. Targeted public health strategies are needed to address structural inequities and improve dementia prevention in rural communities.

Keywords

Alzheimer's disease and related dementia middle aged and older adults modifiable risk factor rural

Introduction

Alzheimer's disease and related dementias (ADRD) have become a significant public health concern, with demographic projections indicating substantial growth in affected populations across diverse geographic regions of the United States (U.S.).^1,2 This burden is disproportionately concentrated in rural areas, where a complex interplay of socioeconomic determinants, healthcare infrastructure limitations, and environmental exposures increases vulnerability for cognitive decline.^1,2 More than 20% of rural residents are aged 65 years or older, compared to 16% in urban areas in the U.S.³ This demographic concentration, coupled with higher prevalence of chronic health conditions (e.g., diabetes, cardiovascular disease), positions rural populations at substantially increased risk of ADRD. Systematic disparities in diagnostic accuracy, access to specialists, and the implementation of evidence-based treatments have been consistently documented across the rural-urban continuum. Rural residents are more likely to report delayed diagnosis and limited access to neuropsychological assessment, specialty consultation, and multidisciplinary care coordination,^4–7 leading to higher mortality rates and poorer end-of-life care compared to their urban counterparts.^8–10

Given the current lack of curative pharmacologic treatments for ADRD, primary prevention efforts targeting modifiable risk factors have become critically important.^11,12 This approach also offers a promising strategy for addressing rural disparities in ADRD by identifying and mitigating risk factors that disproportionately contribute to the dementia burden in rural populations. The 2024 Lancet Commission on Dementia identified fourteen modifiable risk factors with strong causal evidence for contributing to dementia onset: low educational attainment, hearing loss, traumatic brain injury (TBI), hypertension, excessive alcohol use, obesity, smoking, depression, social isolation, physical inactivity, diabetes, vision impairment, high cholesterol, and air pollution.¹² It is estimated that up to 45% of dementia cases globally may be attributable to these risk factors.¹²

Previous research on rural-urban differences in ADRD has primarily focused on disparities in health service utilization patterns and clinical outcomes including diagnostic accuracy, emergency department visits, hospitalizations, and mortality.^13,14 While this body of work has effectively characterized the downstream consequences of ADRD in rural populations, it has insufficiently addressed the upstream determinants that may contribute to these observed disparities. Specifically, there remains a critical knowledge gap regarding how the prevalence and patterns of modifiable risk factors for ADRD vary systematically across the rural-urban continuum in the U.S. This knowledge gap limits evidence-based public health planning, development of geographically targeted prevention strategies, and efficient allocation of increasingly scarce healthcare resources to populations at greater risk for ADRD.

To address these gaps, the present study used a recent nationally representative sample to estimate the prevalence of modifiable dementia risk factors among U.S. adults residing in rural areas and to assess their self-reported treatment for related conditions (e.g., diabetes and hypertension) in comparison to non-rural residents. Given that dementia risk and burden also vary by age,¹² race/ethnicity,^15,16 socioeconomic status,^17–19 and geographic region,²⁰ this study further aimed to explore disparities by stratifying analyses across these key demographic and contextual factors. We hypothesized that rural residents would report a higher burden of modifiable risk factors and lower treatment rates compared to their non-rural counterparts. We further expected that these disparities would be largely driven by differences in demographics, healthcare access, and socioeconomic factors.

Methods

Data source and study population

Data for this study were obtained from the 2023 National Health Interview Survey (NHIS), a nationally representative, cross-sectional household survey conducted annually by the National Center for Health Statistics (NCHS), part of the Centers for Disease Control and Prevention (CDC).²¹ For the 2023 sample, the conditional sample adult response rate was 87.6%.²¹ Our study included participants aged 45 years and older without self-reported ADRD (n = 18,511). We excluded those with missing data on study variables and covariates (n = 1530). As a result, 16,981 adults (Weighted N = 125,297,902) were included in the final analytic sample. As the NHIS is a publicly available, de-identified dataset, our university's Institutional Review Board (IRB) determined that this study was non-human subjects research. This study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.

Outcome variables

Based on the available variables in the 2023 NHIS, eleven self-reported ADRD risk factors were identified and categorized into three subgroups¹²: Cardiometabolic Health (hypertension, high cholesterol, diabetes, obesity), Sensory Impairments and Injury (hearing loss, visual impairment, TBI), and Psychosocial and Behavioral Factors (low education [<12 years of formal education], depression, social isolation, and smoking). In addition, seven treatment-related variables corresponding to these health condition-related risk factors were assessed: hypertension (medication use), high cholesterol (medication use), diabetes (use of medication pills or insulin), hearing loss (use of hearing aids), vision impairment (use of rehabilitation services or assistive devices), TBI (receipt of concussion evaluation), and depression (use of medication). Health condition-related diagnoses were determined based on self-reports of being informed of the diagnosis by a physician or other health professional. Lifestyle and treatment behaviors were self-reported using standardized NHIS measures. All outcomes were dichotomized for analysis (i.e., presence versus absence of the risk factor or treatment). Detailed operational definitions and variable specifications are provided in Supplemental Table 1.

Primary exposure variable

The primary exposure variable was rural-urban status, categorized based on the 2013 NCHS Urban-Rural Classification Scheme for Counties, which was the version implemented by NHIS for the 2023 data.^21,22 rural-urban status was classified into three groups: rural (micropolitan and non-core), small or medium metropolitan, and urban (large fringe metro and large central metro areas).

Covariates

Covariates included non-modifiable demographic factors: age (45–64, ≥ 65 years, reflecting common classifications of middle-aged and older adults in dementia and aging research),¹² sex (male, female), and race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian, and other). Healthcare-related variables included health insurance coverage (yes, no), having a usual source of care (yes, no), and any healthcare visits in the past 12 months (yes, no). Socioeconomic variables included federal poverty level (<200%, ≥ 200%), food insecurity (yes, no [derived from the 10-item U.S. Adult Food Security Survey Module]),²¹ home ownership (yes, no), marital status (married, not married), and U.S. census region (Northeast, Midwest, South, West).

Statistical analysis

We began by comparing baseline demographics, healthcare access, and socioeconomic characteristics across rural-urban categories using Rao-Scott chi-square tests. Weighted prevalence estimates and 95% confidence intervals (CI) were calculated for the eleven modifiable dementia risk factors and seven corresponding treatments. Prevalence patterns were further stratified by age group (45–64, ≥ 65 years) and census regions. To assess rural-urban differences in risk factor prevalence, we employed robust Poisson regression models to estimate both unadjusted and adjusted rate ratios (RR/aRR) for binary outcomes with non-rare events.²³ Sequential covariate adjustment models were implemented to assess the relative contribution of different factor domains to observed disparities. Model 1 adjusted for age, sex, and race/ethnicity. Model 2 was built upon Model 1 by additionally adjusting for health insurance coverage, healthcare visits in the past 12 months, and usual source of care. Model 3 extended Model 2 with further adjustments for federal poverty level, food insecurity, homeownership, and marital status. Given that socioeconomic factors may lie within the causal pathway linking rural residence to health risk factors, Model 3 should be interpreted as a fully adjusted descriptive model rather than a causal estimate, providing a conservative assessment of residual disparities after accounting for these related domains. All analyses were conducted in June and July 2025 using R (version 4.5.0) and SAS version 9.4 (SAS Institute), which accounted for the 2023 NHIS complex sampling design and incorporated weighted analyses based on weights provided with the dataset.²¹ Statistical significance was defined as a 95% CI that did not include the reference value of 1 or a two-tailed p-value <0.05.

Results

The final analytic sample comprised 16,981 individuals aged 45 years or older (mean age [SE]: 62.4 [0.1], 51.6% female, 68.7% non-Hispanic White), with 2919 (15.5%) residing in rural areas, 5434 (30.7%) in small or medium metropolitan areas, and 8628 (53.8%) in urban areas. Table 1 presents the demographic, healthcare access, and socioeconomic characteristics of the study population stratified by rural-urban continuum. Compared to urban residents, individuals in rural areas were more likely to be 65 years or older (46.3% versus 38.8%, p < 0.001) and non-Hispanic White (85.0% versus 60.7%, p < 0.001). Rural residents were less likely to report having no healthcare visits in the past year compared to urban residents (3.5% versus 5.1%, p = 0.015), while insurance coverage and usual source of care remained comparable across geographic settings. Socioeconomic disparities were also evident, with rural residents reporting significantly higher levels of poverty (33.8% versus 23.2%, p < 0.001) despite having higher homeownership (84.8% versus 78.0%, p < 0.001).

Table 1.

Participants’ characteristics by rural-urban continuum.

Characteristic	Urban N = 8628		Medium or small metro N = 5434		Rural N = 2919		p
Characteristic	n	Weighted % (95% CI)	n	Weighted % (95% CI)	n	Weighted % (95% CI)	p
Demographic
Age (65+) years	4182	38.8 (37.6, 40.1)	2859	42.8 (41.1, 44.5)	1629	46.3 (44.1, 48.5)	<0.001
Female	4676	51.8 (50.4, 53.1)	2965	51.4 (49.8, 53.0)	1579	51.3 (49.3, 53.3)	0.9
Race/Ethnicity							<0.001
Non-Hispanic White	5630	60.7 (58.6, 62.8)	4227	74.3 (71.2, 77.1)	2484	85.0 (81.3, 88.1)
Non-Hispanic Black	1087	12.4 (11.3, 13.7)	485	9.1 (7.6, 10.8)	199	6.2 (4.5, 8.6)
Hispanic	1223	16.8 (15.1, 18.5)	495	12.0 (9.7, 14.8)	121	4.8 (3.1, 7.5)
Non-Hispanic Asian	561	8.6 (7.6, 9.7)	135	3.2 (2.3, 4.5)	19	0.7 (0.4, 1.4)
Other	127	1.5 (1.2, 1.8)	92	1.4 (1.0, 1.9)	96	3.2 (1.9, 5.2)
Health Care Access
Uninsured	337	4.8 (4.2, 5.4)	241	5.6 (4.7, 6.6)	145	5.7 (4.6, 7.0)	0.2
Having Usual Source of Care	8179	94.6 (94.0, 95.2)	5138	93.9 (93.0, 94.7)	2785	95.4 (94.3, 96.3)	0.1
No Health Care Visit in Past Year	404	5.1 (4.6, 5.6)	220	4.4 (3.7, 5.1)	92	3.5 (2.7, 4.4)	0.02
Socioeconomic Factor
Poverty (<200% FPL)	2105	23.2 (21.7, 24.6)	1540	26.8 (25.0, 28.7)	1105	33.8 (30.9, 36.7)	<0.001
Food Insecurity	607	7.1 (6.4, 7.9)	416	8.2 (7.1, 9.4)	238	7.8 (6.6, 9.2)	0.2
Homeownership (Own Homes)	6430	78.0 (76.6, 79.4)	4226	81.2 (79.5, 82.7)	2359	84.8 (83.0, 86.4)	<0.001
Unmarried	151	1.6 (1.3, 2.0)	61	1.0 (0.8, 1.3)	47	1.3 (0.9, 1.8)	0.1
Census Region							<.001
Northeast	1658	20.8 (18.5, 23.2)	791	15.0 (11.2, 18.9)	219	11.8 (9.7, 13.9)
Midwest	1739	18.8 (16.6, 21.0)	1119	19.9 (16.4, 23.5)	1007	32.6 (29.7, 35.4)
South	2967	36.2 (33.0, 39.4)	2082	40.5 (37.1, 43.6)	1253	40.3 (37.1, 43.6)
West	2264	24.2 (22.1, 26.3)	1442	15.2 (12.3, 18.2)	440	15.2 (12.3, 18.2)

CI: confidence interval; FPL: federal poverty level.

Bolded values indicate statistical significance.

Table 2 presents the prevalence and rate ratios of modifiable dementia risk factors across the rural-urban continuum using sequential robust Poisson regression models. Overall, adults residing in rural areas had significantly higher prevalence of several cardiometabolic, sensory, psychosocial, and behavioral risk factors compared to their urban counterparts. Specifically, rural residents had significantly higher rates of hypertension (aRR: 1.11; 95% CI: 1.06–1.17), obesity (aRR: 1.22; 95% CI: 1.15–1.30), diabetes (aRR: 1.29; 95% CI: 1.15–1.45), and hearing loss (aRR: 1.22; 95% CI: 1.12–1.34), even after controlling for demographics, healthcare access, and socioeconomic status. The differences in other dementia risk factors between rural and urban populations were attenuated and became nonsignificant after controlling for all covariates. Figure 1 illustrates that rural-urban differences were most pronounced among middle-aged adults aged 45–64 years, while Figure 2 highlights that the rural-urban gap was particularly significant in the Southern region of the U.S, closely followed by the Midwest region.

Figure 1.

Dementia risk factors by age group across rural and urban continuum.

Figure 2.

Dementia risk factors by census region across rural and urban continuum.

Table 2.

Factors contributing to rural-urban disparities in modifiable dementia risk factors using sequential robust Poisson regression analysis.

	Prevalence (95% CI)	Unadjusted RR (95% CI)	Adjusted RR (95% CI)
	Prevalence (95% CI)	Unadjusted RR (95% CI)	Model 1	Model 2	Model 3
MODIFIABLE DEMENTIA RISK FACTOR
CARDIOMETABOLIC RISK FACTORS
Hypertension
Urban	48.0 (46.7, 49.3)	Ref	Ref	Ref	Ref
Medium or small metro	48.7 (47.2, 50.3)	1.02 (0.97, 1.06)	1.01 (0.97, 1.06)	1.02 (0.98, 1.06)	1.01 (0.97, 1.05)
Rural	54.4 (52.3, 56.7)	1.14 (1.08, 1.19)	1.13 (1.07, 1.19)	1.13 (1.08, 1.19)	1.11 (1.06, 1.17)
High Cholesterol
Urban	43.4 (42.1, 44.7)	Ref	Ref	Ref	Ref
Medium or small metro	43.9 (42.4, 45.4)	1.01 (0.97, 1.06)	1.00 (0.95, 1.04)	1.00 (0.96, 1.05)	1.0 (0.95, 1.04)
Rural	47.3 (45.0, 49.6)	1.09 (1.03, 1.15)	1.06 (1.00, 1.12)	1.07 (1.01, 1.13)	1.04 (0.99, 1.11)
Diabetes
Urban	15.0 (14.1, 16.0)	Ref	Ref	Ref	Ref
Medium or small metro	15.7 (14.6, 16.8)	1.05 (0.95, 1.15)	1.11 (1.01, 1.21)	1.12 (1.02, 1.22)	1.09 (1.00, 1.20)
Rural	18.4 (16.7, 20.3)	1.23 (1.09, 1.38)	1.36 (1.21, 1.53)	1.37 (1.22, 1.54)	1.29 (1.15, 1.45)
Obesity
Urban	33.6 (32.2, 34.9)	Ref	Ref	Ref	Ref
Medium or small metro	34.9 (33.4, 36.4)	1.04 (0.98, 1.10)	1.04 (0.98, 1.10)	1.04 (0.98, 1.10)	1.03 (0.98, 1.09)
Rural	41.1 (39.1, 43.1)	1.22 (1.15, 1.31)	1.24 (1.16, 1.32)	1.24 (1.16, 1.32)	1.22 (1.15, 1.30)
SENSORY IMPAIRMENTS AND INJURY RISK FACTORS
Hearing Loss
Urban	18.4 (17.4, 19.4)	Ref	Ref	Ref	Ref
Medium or small metro	22.6 (21.2, 24.1)	1.23 (1.13, 1.34)	1.12 (1.03, 1.22)	1.13 (1.03, 1.22)	1.11 (1.02, 1.21)
Rural	27.1 (25.2, 29.1)	1.48 (1.35, 1.62)	1.25 (1.15, 1.37)	1.26 (1.15, 1.37)	1.22 (1.12, 1.34)
Visual Impairment
Urban	21.8 (20.7, 22.9)	Ref	Ref	Ref	Ref
Medium or small metro	23.2 (21.7, 24.7)	1.06 (0.98, 1.16)	1.06 (0.97, 1.15)	1.05 (0.97, 1.14)	1.03 (0.95, 1.11)
Rural	24.8 (22.8, 26.8)	1.14 (1.03, 1.25)	1.13 (1.03, 1.25)	1.13 (1.02, 1.24)	1.07 (0.97, 1.18)
Traumatic Brain Injury
Urban	1.5 (1.2, 1.8)	Ref	Ref	Ref	Ref
Medium or small metro	1.9 (1.5, 2.3)	1.25 (0.93, 1.67)	1.17 (0.87, 1.57)	1.17 (0.88, 1.57)	1.12 (0.83, 1.51)
Rural	2.3 (1.7, 3.0)	1.52 (1.10, 2.12)	1.37 (0.98, 1.90)	1.36 (0.98, 1.90)	1.26 (0.91, 1.74)
PSYCHOSOCIAL AND BEHAVIORAL RISK FACTORS
Low education (<12 years)
Urban	13.5 (12.4, 14.8)	Ref	Ref	Ref	Ref
Medium or small metro	14.2 (12.5, 16.0)	1.05 (0.90, 1.22)	1.21 (1.07, 1.37)	1.17 (1.04, 1.32)	1.12 (0.99, 1.25)
Rural	15.9 (13.7, 18.4)	1.17 (0.99, 1.39)	1.71 (1.47, 1.99)	1.65 (1.42, 1.90)	1.42 (1.24, 1.63)
Depression
Urban	15.0 (14.1, 16.0)	Ref	Ref	Ref	Ref
Medium or small metro	18.6 (17.3, 19.9)	1.24 (1.13, 1.36)	1.17 (1.07, 1.28)	1.17 (1.07, 1.28)	1.12 (1.03, 1.23)
Rural	19.4 (17.6, 21.3)	1.29 (1.15, 1.44)	1.17 (1.04, 1.31)	1.17 (1.04, 1.31)	1.08 (0.96, 1.21)
Social Isolation
Urban	9.5 (89.7, 91.2)	Ref	Ref	Ref	Ref
Medium or small metro	10.8 (9.9, 11.8)	1.14 (1.01, 1.27)	1.12 (1.00, 1.25)	1.13 (1.00, 1.26)	1.04 (0.93, 1.17)
Rural	11.6 (10.1, 13.4)	1.22 (1.04, 1.43)	1.18 (1.01, 1.39)	1.19 (1.01, 1.40)	1.00 (0.85, 1.16)
Smoking
Urban	10.2 (9.4, 11.1)	Ref	Ref	Ref	Ref
Medium or small metro	12.4 (11.3, 13.6)	1.21 (1.07, 1.37)	1.19 (1.05, 1.34)	1.17 (1.04, 1.32)	1.10 (0.98, 1.24)
Rural	15.4 (13.6, 17.2)	1.50 (1.30, 1.73)	1.45 (1.25, 1.67)	1.43 (1.24, 1.66)	1.28 (1.11, 1.48)

Model 1: adjusted for age, sex, and race/ethnicity.

Model 2: adjusted for age, sex, race/ethnicity, health insurance coverage, healthcare visits in the past 12 months, and usual source of care.

Model 3: adjusted for age, sex, race/ethnicity, health insurance coverage, healthcare visits in the past 12 months, usual source of care, federal poverty level, food insecurity, home ownership, and marital status.

RR: rate ratio; CI: confidence interval; Ref: reference.

Bolded values indicate statistical significance.

Table 3 shows the prevalence and relative likelihood of receiving treatment for health condition-related modifiable dementia risk factors across the rural-urban continuum. In general, treatment rates for cardiometabolic conditions were high and relatively similar across geographic locations. For example, medication use for hypertension and diabetes exceeded 85% in all regions, with no significant differences between rural and urban areas in fully adjusted models. Treatment for sensory and behavioral risk factors remained relatively low across all settings, with no significant rural-urban differences after adjusting for covariates.

Table 3.

Factors contributing to rural-urban disparities in health condition-related modifiable dementia risk factor treatment using sequential robust Poisson regression analysis.

	Prevalence (95% CI)	Unadjusted RR (95% CI)	Adjusted RR (95%CI)
	Prevalence (95% CI)	Unadjusted RR (95% CI)	Model 1	Model 2	Model 3
TREATMENT
CARDIOMETABOLIC RISK FACTORS
Hypertension (Medication)
Urban	86.8 (85.4, 88.0)	Ref	Ref	Ref	Ref
Medium or small metro	85.9 (84.0, 87.6)	0.99 (0.97, 1.02)	0.99 (0.96, 1.01)	0.99 (0.97, 1.02)	0.99 (0.97, 1.02)
Rural	89.0 (86.8, 90.8)	1.03 (1.00, 1.05)	1.03 (1.00, 1.05)	1.03 (1.00, 1.05)	1.02 (1.00, 1.05)
High Cholesterol (Medication)
Urban	70.9 (69.2, 72.6)	Ref	Ref	Ref	Ref
Medium or small metro	72.3 (70.2, 74.4)	1.02 (0.98, 1.06)	1.02 (0.98, 1.05)	1.01 (0.98, 1.05)	1.02 (0.98, 1.05)
Rural	75.2 (72.7, 77.5)	1.06 (1.02, 1.10)	1.06 (1.01, 1.10)	1.04 (1.00, 1.09)	1.04 (1.00, 1.08)
Diabetes (Medication and Insulin)
Urban	88.2 (86.0, 90.1)	Ref	Ref	Ref	Ref
Medium or small metro	87.8 (84.7, 90.3)	1.00 (0.96, 1.03)	1.01 (0.97, 1.05)	1.01 (0.97, 1.05)	1.01 (0.97, 1.05)
Rural	88.1 (84.3, 91.0)	1.00 (0.96, 1.04)	1.03 (0.98, 1.08)	1.03 (0.98, 1.08)	1.03 (0.98, 1.08)
SENSORY IMPAIRMENTS AND INJURY RISK FACTORS
Hearing Loss (Hearing Aids)
Urban	17.9 (15.8, 20.1)	Ref	Ref	Ref	Ref
Medium or small metro	18.8 (16.6, 21.3)	1.05 (0.88, 1.25)	0.99 (0.84, 1.17)	1.00 (0.84, 1.17)	1.00 (0.85, 1.18)
Rural	21.3 (18.3, 24.7)	1.19 (0.98, 1.45)	1.07 (0.88, 1.29)	1.07 (0.88, 1.29)	1.08 (0.89, 1.31)
Visual Impairment (Rehabilitation Services/ Assistive Devices)
Urban	19.3 (17.2, 21.6)	Ref	Ref	Ref	Ref
Medium or small metro	20.2 (17.7, 22.9)	1.05 (0.88, 1.25)	1.00 (0.84, 1.19)	1.00 (0.84, 1.19)	0.99 (0.83, 1.18)
Rural	19.6 (16.3, 23.4)	1.02 (0.82, 1.26)	0.93 (0.75, 1.15)	0.93 (0.75, 1.15)	0.92 (0.74, 1.15)
Traumatic Brain Injury (Concussion Evaluation)
Urban	55.9 (47.0, 64.4)	Ref	Ref	Ref	Ref
Medium or small metro	57.1 (46.0, 67.4)	1.02 (0.80, 1.31)	0.93 (0.73, 1.17)	0.92 (0.73, 1.16)	0.93 (0.73, 1.17)
Rural	46.1 (33.8, 58.9)	0.83 (0.60, 1.14)	0.76 (0.56, 1.03)	0.76 (0.56, 1.02)	0.78 (0.58, 1.04)
PSYCHOSOCIAL AND BEHAVIORAL RISK FACTORS
Depression (Medication)
Urban	56.2 (53.1, 59.2)	Ref	Ref	Ref	Ref
Medium or small metro	63.2 (59.7, 66.5)	1.12 (1.04, 1.21)	1.09 (1.01, 1.18)	1.09 (1.02, 1.18)	1.09 (1.01, 1.17)
Rural	62.7 (57.7, 67.4)	1.12 (1.02, 1.23)	1.07 (0.98, 1.18)	1.07 (0.97, 1.18)	1.07 (0.97, 1.17)

Model 1: adjusted for age, sex, and race/ethnicity.

Model 2: adjusted for age, sex, race/ethnicity, health insurance coverage, healthcare visits in the past 12 months, and usual source of care.

RR: rate ratio; CI: confidence interval; Ref: reference.

Bolded values indicate statistical significance.

Discussion

In this nationally representative study, U.S. adults living in rural areas had a significantly higher burden of modifiable dementia risk factors compared to their non-rural counterparts. Rural-urban disparities were especially pronounced among middle-aged adults aged 45–64 years and those residing in the South and Midwest regions, across multiple risk domains. While our sequential models showed that adjusting for demographic, healthcare access, and socioeconomic factors modestly reduced the observed rural-urban differences, these disparities largely persisted. This pattern suggests that although such factors contribute to the variation in modifiable dementia risk factors, they do not fully explain it, indicating that additional contextual, behavioral, or structural determinants may underlie the remaining geographic gaps. Given that socioeconomic and healthcare access variables may function as intermediates in the causal pathway linking rural residence to dementia risk, the fully adjusted estimates from Model 3 should be interpreted as conservative associations. Accordingly, the interpretation emphasizes the coherence of findings across models, providing a nuanced understanding of both the direct and indirect influences of rurality on dementia risk profiles. Critically, differences in treatment for health-related risk factors across the rural-urban continuum were not statistically significant, suggesting that disparities in ADRD risk burden, rather than treatment access, represent the primary driver of rural health disparities. This highlights the pressing need to improve preventive care efforts, particularly given the disproportionately high burden of risk among rural populations.

Cardiometabolic health (hypertension, high cholesterol, diabetes, obesity)

Our findings revealed that adults residing in rural areas had significantly higher rates for all four cardiometabolic risk factors examined—diabetes, hypertension, high cholesterol, and obesity—compared to their urban counterparts, which is consistent with previous studies.²²^,24–26 These disparities may be partly attributed to slower improvements in managing these conditions in rural communities, alongside more substantial progress in urban areas and widening gaps in physical and social environments in recent years.^24,26 Notably, these rural-urban disparities were most pronounced among middle-aged adults aged 45–64 years. This age range appears to be a critical period during which health disadvantages begin to accumulate and disparities become more apparent. Several factors may contribute to this pattern, including the cumulative impact of disadvantages experienced during childhood and adolescence,^27–28 and socioeconomic stressors²⁹ that may disproportionately affect rural residents in midlife.

In addition, our results demonstrated that hypertension and diabetes were particularly relevant cardiometabolic risk factors for rural adults residing in the South census region of the U.S. These findings correspond with previous studies indicating that adults in the South experience the greatest burden of hypertension^30,31 and diabetes,³² impacting the development of other chronic conditions and disabilities that result in both decreased quality of life and reduced overall life expectancy.^33,34 Moreover, a recent cohort study among U.S. veterans indicated that the risk of dementia was 25% higher for adults residing in the Southeast, potentially due to cultural and social factors that affect education and impact the development of co-morbid conditions.³⁵

Despite the elevated burden of cardiometabolic risk factors among rural adults, our findings also showed that treatment rates for these conditions were not significantly higher among rural residents, suggesting that rural adults may not be receiving adequate management or care for their cardiometabolic conditions. This gap between disease burden and treatment access highlights a serious missed opportunity for intervention and may contribute to worse long-term outcomes, including cardiovascular events and cognitive decline.^1,2,22,24,25 Together, these findings underscore the urgent need for targeted, age-specific public health strategies that not only prevent the onset of these conditions but also improve chronic disease management in rural communities, particularly among middle-aged adults who may represent a highly vulnerable yet underserved population.

Sensory impairments and injury (hearing loss, visual loss/impairment, TBI)

Disparities in sensory impairments and TBI were also observed among rural residents. Even after adjusting for demographics, healthcare access, and socioeconomic factors, hearing loss remained significantly elevated. This persistent disparity suggests that structural factors^36,37 beyond healthcare access and socioeconomic status may contribute to the elevated burden of hearing loss in rural communities, potentially including environmental exposures (e.g., agricultural noise),^38,39 choice of leisure activities (e.g., recreational firearms, all-terrain vehicles),^40,41 delayed screening, or limited availability of audiologic services.⁴² This finding is particularly important given the well-established link between hearing loss and cognitive decline, underscoring the need for rural-specific public health efforts to promote earlier detection and treatment of hearing loss as a strategy to reduce dementia risk. For example, to reduce delays in the diagnosis of hearing loss in rural communities, telehealth services could be utilized at a greater capacity to increase healthcare provider availability for hearing-related issues.⁴³

In contrast, while visual impairment and TBI were more prevalent in rural areas compared to urban areas, their associations with rurality weakened after full adjustment. These findings suggest that disparities in TBI and visual impairment may be largely explained by differences in demographic, healthcare, and socioeconomic factors across geographic settings. For example, vision loss is impacted by factors such as hypertension^44–45 conditions which were both elevated and not sufficiently controlled in our study sample of rural adults. Nevertheless, the initial disparities observed reinforce the importance of improving access to injury prevention, eye care services, and follow-up treatment in rural communities, particularly for vulnerable subgroups with limited healthcare engagement or socioeconomic resources.

Psychosocial and behavioral factors (low education, depression, social isolation, smoking)

Our findings highlight persistent rural-urban disparities in low educational attainment, depression, social isolation, and smoking. Among these, rural residents consistently exhibited higher prevalence rates, with smoking and low education showing particularly significant differences. These findings suggest that disparities in education and tobacco use are not fully explained by individual socioeconomic or healthcare access factors alone and may instead reflect deeper structural inequities such as long-standing underinvestment in rural schools and tobacco control efforts.^46,47 Our results are in alignment with prior evidence showing that rural adults have a higher smoking prevalence and lower odds of quit attempts compared to adults who reside in urban areas of the U.S.^48–49 Since our study findings suggested that, when compared with urban adults, rural adults were more likely to report having a health care visit in the last year, this point of contact with a healthcare professional could signal a critical opportunity for provider-to-patient education about the harms of smoking and the promotion of quit strategies. Given the known links between smoking, educational attainment, and later-life cognitive outcomes, addressing these disparities is crucial for dementia prevention in rural populations.

In contrast, disparities in other psychosocial risk factors, such as depression and social isolation, appeared to be attenuated after adjustment. These results suggest that disparities in depression and social isolation are largely driven by demographic and structural determinants such as poverty, lack of healthcare engagement, and social disadvantage. Interventions aimed at improving mental health care access and social connectedness in rural communities may help mitigate these risks. Collectively, these findings underscore the importance of a comprehensive approach targeting education, smoking cessation, mental health, and social support in efforts to reduce dementia risk among rural populations.

Limitations

The strength of this study lies in its examination of disparities in established modifiable dementia risk factors and related treatments using sequential modeling based on a recent, nationally representative sample. However, this study is not without limitations. First, due to data constraints, we were unable to examine all fourteen established modifiable dementia risk factors and their corresponding treatments. In addition, all outcome measures were based on self-report, which may be subject to recall bias and social desirability bias. Second, although we adjusted for a range of individual-level covariates, we were unable to account for broader contextual factors such as neighborhood-level social and physical environments, which are known to influence health and may differ substantially between rural and urban settings. Future research should integrate these contextual measures and consider developing comprehensive comorbidity indices (e.g., Rockwood frailty index) to more accurately reflect the complex interplay between aging, multimorbidity, and dementia risk. Third, the data do not allow for tracking individuals’ residential histories; thus, participants classified as rural residents may have previously lived in urban areas, and vice versa. This potential misclassification could lead to underestimation or overestimation of rural-urban differences. Fourth, although we adjusted for a comprehensive set of covariates informed by prior literature, certain measures such as healthcare visits in the past year did not distinguish the type of encounter (e.g., specialist, primary care, or annual wellness visit). Given that these visit types differ in opportunities for risk factor detection and treatment, residual confounding remains possible. Similarly, our prevalence estimates likely underestimate the true subclinical and undiagnosed burden of disease. Finally, the cross-sectional nature of the data limits our ability to infer causal relationships; findings should be interpreted as associations rather than causal effects.

Conclusions

The burden of modifiable ADRD risk factors was significantly higher among middle-aged and older adults residing in rural areas compared to their urban counterparts. However, treatment rates for these conditions were largely comparable across geographic settings, highlighting a critical need to improve healthcare access and address deeper structural inequities in rural communities. Notably, the most pronounced rural-urban disparities were observed among middle-aged adults and individuals living in the Southern region across multiple risk factors. Targeted public health strategies should prioritize cardiometabolic health management, smoking cessation, and hearing health services in rural communities, with particular attention to middle-aged adults who represent a critical intervention window for dementia prevention.

Supplemental Material

sj-docx-1-alr-10.1177_25424823251395318 - Supplemental material for Rural-urban differences in modifiable dementia risk factors among U.S. populations aged 45 years or older

Supplemental material, sj-docx-1-alr-10.1177_25424823251395318 for Rural-urban differences in modifiable dementia risk factors among U.S. populations aged 45 years or older by Zhigang Xie, Jiamin Hu, Sericea Stallings-Smith, Ambar Kulshreshtha and Young-Rock Hong in Journal of Alzheimer's Disease Reports

Footnotes

Acknowledgements

The authors have no acknowledgments to report.

ORCID iDs

Zhigang Xie

Jiamin Hu

Sericea Stallings-Smith

Ambar Kulshreshtha

Young-Rock Hong

Ethical considerations

As the NHIS is a publicly available, de-identified dataset, the Institutional Review Board (IRB) at the first author's university determined that this study was non-human subjects research.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Author contribution(s)

Zhigang Xie: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Visualization; Writing – original draft.

Jiamin Hu: Conceptualization; Data curation; Formal analysis; Methodology; Writing – review & editing.

Sericea Stallings-Smith: Investigation; Writing – original draft; Writing – review & editing.

Ambar Kulshreshtha: Investigation; Writing – review & editing.

Young-Rock Hong: Conceptualization; Data curation; Investigation; Methodology; Resources; Supervision; Writing – original draft; Writing – review & editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

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

Data availability statement

The data supporting the findings of this study are openly available in Centers for Disease Control and Prevention These data were derived from the following resources available in the public domain: .

Supplemental material

Supplemental material for this article is available online.

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