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. 2026 Jul 20;8(10):101479. doi: 10.1016/j.xkme.2026.101479

Neighborhood Disadvantage and Dementia Risk Among Older Patients With Kidney Failure: Differences by Race/Ethnicity and Urbanicity

Yiting Li 1, Gayathri Menon 1, Jane J Long 1, Byoungjun Kim 1, Sunjae Bae 1, Babak J Orandi 1,2, Mario P DeMarco 3, Wenbo Wu 2,4, Deidra C Crews 5, Tanjala S Purnell 6,7,8, Roland J Thorpe Jr 8,9, Sarah L Szanton 10,11, Dorry L Segev 1,3, Mara A McAdams-DeMarco 1,3,∗
PMCID: PMC13625871  PMID: 42819435

Abstract

Rationale & Objective

Among older patients with kidney failure, those who are minoritized or residing in rural areas experience a disproportionate burden of Alzheimer’s disease and related dementias (ADRD). Neighborhood disadvantage drives health disparities, but few studies have directly examined its association with ADRD and whether this association varies by race/ethnicity and by urbanicity.

Study Design

Cohort study.

Setting & Participants

United States Renal Data System data; older patients (age ≥ 55 years) initiating dialysis in 2003-2021.

Exposures

Residential neighborhood disadvantage was measured using a ZIP-code level index across 9 domains (built environment, criminal justice, education, employment, housing, poverty, social fragmentation, transportation, and wealth).

Outcome

ADRD using diagnosis codes.

Analytical Approach

We used cause-specific hazards models to quantify the adjusted hazard ratios (aHR) of ADRD diagnoses. We then used interaction terms to quantify whether these associations differed by race/ethnicity and urbanicity.

Results

After adjustments, older patients in high-disadvantage neighborhoods had a higher risk of ADRD diagnoses (dementia: aHR = 1.09, 95% CI: 1.08-1.10; AD: aHR = 1.25, 95% CI: 1.22-1.27) compared with older patients in low-disadvantage neighborhoods; these associations differed by race/ethnicity (dementia: Pinteraction = 0.03; AD: Pinteraction = 0.002) and urbanicity (Pinteractions < 0.001 for both). Specifically, older Black patients in high-disadvantage neighborhoods had a higher risk of ADRD (dementia: aHR = 1.19, 95% CI: 1.17-1.21; AD: aHR = 1.31, 95% CI:1.28-1.35) compared with older Black patients in low-disadvantage neighborhoods. Within suburban, rural, and small-town areas, older patients residing in high-disadvantage neighborhoods had a higher risk of ADRD compared with those in low-disadvantage neighborhoods.

Limitations

ZIP codes as a proxy for neighborhoods.

Conclusions

Older patients with kidney failure residing in high-disadvantage neighborhoods, particularly Black patients and those in suburban, rural, or small-town areas, had a higher risk of ADRD compared with those in low-disadvantage neighborhoods. Multifaceted interventions (eg, cognitive screening, collaborative care) are needed to mitigate structural barriers associated with neighborhood disadvantage and preserve cognitive function in this population.

Index Words: Alzheimer’s disease, dementia, neighborhood disadvantage, older patients with kidney failure

Plain-Language Summary

In our study, we found that older patients with kidney failure who lived in neighborhoods with higher disadvantage (greater socioeconomic challenges and adverse built environments) had a higher risk of dementia and Alzheimer’s disease compared with those in less disadvantaged neighborhoods. This association differed by race/ethnicity, with older Black patients residing in higher-disadvantage neighborhoods having a higher risk. We also found that increasing levels of neighborhood disadvantage across domains such as policing intensity, housing instability, and poverty were associated with a higher dementia risk. Regular cognitive testing and coordinated care involving primary care providers, kidney specialists, and social workers may help reduce the risk of cognitive decline.

Dementia, a progressive neurocognitive disorder, is the leading cause of disability and dependence worldwide, compromising healthy longevity.1,2 In 2025, approximately 7.2 million people in the United States were living with Alzheimer’s disease and related dementias (ADRD), which is projected to increase to 12.7 million by 2050.3 Older patients with kidney failure are at a particularly high risk for ADRD because of the physiological demands of long-term dialysis and multiple comorbid conditions.1,2 Prior studies have shown that older patients residing in highly segregated neighborhoods have a higher risk of dementia diagnosis, with differences observed by race/ethnicity.1 These disparities suggest that, beyond individual-level characteristics, broader multidomain systemic factors, such as neighborhood disadvantage, may contribute to differences in ADRD diagnoses among older patients with kidney failure.

Neighborhood disadvantage is recognized as a major driver of health disparities across populations.4 However, limited research has directly examined whether residence in a disadvantaged neighborhood influences ADRD diagnoses among older patients with kidney failure or whether its impact differs by race and ethnicity and by urbanicity. Neighborhood disadvantage reflects how societal systems such as employment, housing, education, income, health care, and criminal justice shape access to resources and opportunities.4, 5, 6 Highly disadvantaged neighborhoods are often characterized by disinvestment, leading to limited access to quality health care and reduced social capital.1,5,7,8 These conditions may also contribute to social isolation and fewer supports for cognitive health.1,9,10 The impact of neighborhood disadvantage on ADRD risk may vary by race/ethnicity because unequal resource allocation and disinvestment disproportionately affect minoritized populations, increasing stress and reducing support for cognitive health.5,11 Similarly, this association may differ by urbanicity, given differences in health care infrastructure and social support services between rural and urban areas.12,13

In this national study, we (1) quantified the association between a composite index of neighborhood disadvantage and ADRD diagnoses among older patients with kidney failure; (2) tested whether these associations differed by race/ethnicity and urbanicity; and (3) determined which individual domains of the neighborhood disadvantage index were most strongly associated with ADRD risk.

Methods

Data Source

We used data from the United States Renal Data System (USRDS), a national registry that contains detailed information on all patients with kidney failure in the United States and includes complete information from Medicare billing claims.14 Patient demographics, cause of kidney failure, comorbid conditions, and other patient characteristics (eg, body mass index [BMI]) were obtained from the patients dataset and Centers for Medicare & Medicaid Services (CMS) Medical Evidence form (CMS-2728) at dialysis initiation.14

Study Population

We identified 969,989 older patients with kidney failure who initiated dialysis from January 1, 2003 to December 31, 2021. This was a complete case analysis. Inclusion criteria included aged ≥55 years and Medicare as the primary payer (Part A and B).1,15, 16, 17 We excluded individuals with missing data on patient demographics and comorbid conditions (2.3%), dialysis factors (2.4%), ZIP code (2.9%), and neighborhood disadvantage scores (2.6%; Fig S1). We also excluded individuals (aged ≥66) with a prevalent diagnosis of dementia at dialysis initiation, based on a 1-year look-back using available Medicare claims.

This study was reviewed and deemed exempt by the Institutional Review Board of the New York University Grossman School of Medicine, waiving the requirement for informed consent because participants could not be identified, either directly or indirectly.

Exposure: Neighborhood Disadvantage Index and its Individual Components

A neighborhood disadvantage index, adapted from previously described measures of structural and socioeconomic differences across neighborhoods, is a published and validated composite measure.6 It is calculated using data from the American Community Survey (ACS; 2015-2019, 5-year estimates) and other publicly available data sources (eg, Department of Housing and Urban Development 2019, Healthcare Delivery Research Program 2012, etc.).6 This index consists of 9 standardized domains: built environment disadvantage, criminal injustice, education disadvantage, unemployment, housing instability, poverty, social fragmentation, transportation barrier, and wealth inequality at the census tract level (Table S1).6 A population-weighted average of the US census tract was then used to calculate index scores at the ZIP code level. Higher values of this index indicate greater neighborhood disadvantage.6

We assigned neighborhood disadvantage index scores to older patients based on their 5-digit ZIP code at the time of dialysis initiation. Based on previous research methods, the neighborhood disadvantage index scores were then divided into internal tertiles: low (≤–0.16), medium (>–0.16 and ≤0.68), and high (>0.68).18, 19, 20, 21, 22, 23

Outcome: ADRD

We identified incident ADRD diagnoses after dialysis initiation among older patients using International Classification of Diseases, Ninth and Tenth Revision (ICD-9/ICD-10) codes from Medicare billing claims, including inpatient, outpatient, skilled nursing facility, home health agency, hospice, and physician/supplier claims (Table S2). The ICD-9/ICD-10 codes were drawn from previously published literature and the CMS Chronic Conditions Data Warehouse.1,2,17,24,25 A prior study reported that Medicare claims had a sensitivity of 0.85 for dementia detection and 0.64 for Alzheimer’s disease, along with specificities of 0.89 for dementia and 0.95 for Alzheimer’s disease.26

Effect Modifiers: Race/Ethnicity and Urbanicity

Race/ethnicity information was obtained from the USRDS patient file. Race/ethnicity was categorized as non-Hispanic Asian (consisting of Asian American, Native Hawaiian, and Pacific Islander; Asian hereafter), non-Hispanic Black (Black hereafter), Hispanic/Latino (Hispanic hereafter), or non-Hispanic White (White hereafter). Urbanicity was defined using the modified Rural-Urban Commuting Area (RUCA) Codes from the US Department of Agriculture, and categorized as high-density urban, suburban, rural, or small town.27,28

Confounders: ADRD Predictors

We identified potential predictors of ADRD diagnoses based on prior research, which included patient demographics (age, sex [as recorded in the USRDS Patient File], race/ethnicity, BMI, and employment status), factors related to dialysis (year of dialysis initiation, prekidney failure nephrology care, cause of kidney failure), existing comorbid conditions (cancer, hypertension, diabetes, peripheral vascular disease, cerebrovascular disease, atherosclerotic heart disease, congestive heart failure, chronic obstructive pulmonary disease, drug dependence, alcohol dependence, and tobacco use), as well as assessments of functional status (ability to walk, residence in an institutionalized setting, and the need for assistance with daily activities), all of which were obtained from the patients and the CMS-2728 datasets.1,2

Statistical Analysis

We used the Kaplan–Meier method to estimate the unadjusted 5-year cumulative incidence of dementia diagnosis and Alzheimer’s disease diagnosis after dialysis initiation. For ease of interpretation, we omitted the medium neighborhood disadvantage index tertile from the unadjusted cumulative incidence figures. We then used cause-specific hazards models to quantify the following: (1) the association between tertiles of a neighborhood disadvantage index and the risk of dementia diagnosis and Alzheimer’s disease diagnosis; (2) the difference in this association by race/ethnicity and by urbanicity using interaction terms and Wald tests; and (3) the individual domains of the neighborhood disadvantage index that were most salient to the risk of dementia and Alzheimer’s disease. The domain scores were pre-standardized using population-weighted z-score standardization (mean = 0, standard deviation = 1) during index development and were used directly in all analyses.6 Additionally, dementia and Alzheimer's disease were analyzed separately in all analyses.

For the outcome of dementia and Alzheimer’s disease, we followed older patients from the date of dialysis initiation to the date of their first diagnosis of dementia or Alzheimer’s disease, censoring at the earliest occurrence of the following: kidney transplant, end of Medicare coverage, death, or end of follow-up (December 31, 2021).

Sensitivity Analysis

We evaluated the robustness of our findings using the following sensitivity analysis: (1) using Fine and Gray proportional subdistribution hazards models (dementia: competing risk = death; Alzheimer’s disease: competing risks = death and other dementia diagnoses); (2) categorizing the neighborhood disadvantage index into tertiles based on the national average: low (≤-0.2), medium (>–0.2 and ≤0.52), and high (>0.52); (3) calculating E-values to assess potential unmeasured confounding; and (4) using a stricter definition of dementia and Alzheimer’s diagnoses, requiring 2 diagnosis codes on separate dates to define dementia and Alzheimer’s disease, treating the first date as the diagnosis date.6

All statistical analyses were conducted using SAS (v9.4 (SAS Institute, Cary, NC)) and Stata 17 MP (StataCorp LLC). Statistical significance was defined as a 2-sided P value < 0.05.

Results

Study Population

Among 969,989 older patients initiating dialysis, the mean age was 70.2 years (standard deviation [SD] = 9.3). In total, 43.9% were female, 4.2% were Asian, 23.9% were Black, 10.8% were Hispanic, and 61.1% were White. Additionally, 47.5% had diabetes and 30.9% had hypertension as the cause of kidney failure, 55.1% had prekidney failure nephrology care, and 38.0% resided in high-density urban areas (Table 1).

Table 1.

Characteristics of Older Patients (Aged ≥ 55) with Kidney Failure Initiating Dialysis Between 2003 and 2021, Stratified by Residential Neighborhood Disadvantage Index (N = 969,989).

Patient
Characteristics		 Total Patients
N = 969,989		 Neighborhood Disadvantage Indexa
Low N = 319,447 Medium N = 319,015 High N = 331,527
Age in years, mean (SD) 70.2 (9.3) 68.7 (8.9) 71.8 (9.5) 70.1 (9.2)
Age group, N (%)
 50-65 348,242 (35.9) 95,474 (29.9) 119,723 (36.1) 133,045 (41.7)
 66-75 325,964 (33.6) 105,394 (33.0) 112,790 (34.0) 107,780 (33.8)
 76-85 239,989 (24.7) 93,036 (29.1) 81,143 (24.5) 65,810 (20.6)
 >85 55,794 (5.8) 25,543 (8.0) 17,871 (5.4) 12,380 (3.9)
Female, N (%) 426,287 (43.9) 130,358 (40.8) 145,555 (43.9) 150,374 (47.1)
Race/ethnicity
 Asian 41,099 (4.2) 24,022 (7.5) 12,547 (3.8) 4,530 (1.4)
 Black 231,666 (23.9) 41,021 (12.8) 61,538 (18.6) 129,107 (40.5)
 Hispanic 104,970 (10.8) 21,554 (6.7) 36,805 (11.1) 46,611 (14.6)
 White 592,254 (61.1) 232,850 (72.9) 220,637 (66.6) 138,767 (43.5)
BMI in kg/m2, mean (SD) 28.9 (7.6) 29.2 (7.8) 28.4 (7.3) 29.2 (7.7)
BMI group, N (%)
 ≤25 328,495 (33.9) 116,440 (36.5) 107,920 (32.6) 104,135 (32.6)
 26-30 283,185 (29.2) 95,128 (29.8) 96,468 (29.1) 91,589 (28.7)
 >30 358,309 (36.9) 107,879 (33.8) 127,139 (38.3) 123,291 (38.6)
Employment status, N (%)
 Unemployed 140,601 (14.5) 35,256 (11.0) 45,688 (13.8) 59,657 (18.7)
 Employed 65,455 (6.7) 26,870 (8.4) 21,827 (6.6) 16,758 (5.3)
 Retired 712,561 (73.5) 240,971 (75.4) 245,977 (74.2) 225,613 (70.7)
 Other 51,372 (5.3) 16,350 (5.1) 18,035 (5.4) 16,987 (5.3)
Cause of kidney failure, N (%)
 Diabetes mellitus 460,540 (47.5) 138,727 (43.4) 160,818 (48.5) 160,995 (50.5)
 Hypertension 299,288 (30.9) 96,085 (30.1) 97,502 (29.4) 105,701 (33.1)
 Glomerulonephritis 54,598 (5.6) 22,352 (7.0) 18,567 (5.6) 13,679 (4.3)
 Other 155,563 (16.0) 62,283 (19.5) 54,640 (16.5) 38,640 (12.1)
Comorbid conditions, N (%)
 Cancer 87,711 (9.0) 33,262 (10.4) 30,268 (9.1) 24,181 (7.6)
 Peripheral vascular disease 139,821 (14.4) 45,910 (14.4) 49,599 (15.0) 44,312 (13.9)
 Cerebrovascular disease 102,061 (10.5) 31,391 (9.8) 35,238 (10.6) 35,432 (11.1)
 Atherosclerotic heart disease 180,394 (18.6) 65,610 (20.5) 62,916 (19.0) 51,868 (16.3)
 CHF 350,118 (36.1) 114,329 (35.8) 120,768 (36.4) 115,021 (36.1)
 COPD 111,877 (11.5) 33,139 (10.4) 41,749 (12.6) 36,989 (11.6)
 Drug use 5,118 (0.5) 1,131 (0.4) 1,527 (0.5) 2,460 (0.8)
 Alcohol use 11,320 (1.2) 3,195 (1.0) 3,792 (1.1) 4,333 (1.4)
 Tobacco use 52,077 (5.4) 12,227 (3.8) 18,805 (5.7) 21,045 (6.6)
 Functional Impairment 142,750 (14.7) 43,914 (13.7) 49,704 (15.0) 49,132 (15.4)
 Institutionalized 81,614 (8.4) 27,147 (8.5) 29,718 (9.0) 24,749 (7.8)
Pre-kidney failure nephrology care, N (%) 534,610 (55.1) 185,646 (58.1) 184,125 (55.5) 164,839 (51.7)
 Urbanicity, N (%)b
 High-density urban 368,461 (38.0) 144,848 (45.3) 115,451 (34.8) 108,162 (33.9)
 Suburban 332,217 (34.2) 132,030 (41.3) 113,347 (34.2) 86,840 (27.2)
 Rural 147,092 (15.2) 31,028 (9.7) 54,916 (16.6) 61,148 (19.2)
 Small town 122,219 (12.6) 11,541 (3.6) 47,813 (14.4) 62,865 (19.7)
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Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; CHF, congestive heart failure; SD, standard deviation.

a

Neighborhood disadvantage index: 9 domains (built environment, criminal justice, education, employment, housing, income and poverty, social cohesion, transportation, and wealth. https://www.sreindex.com/

b

This classification modified the original 2010 Rural-Urban Commuting Area Codes (RUCA) Codes defined by United States Department of Agriculture (USDA).

Residential Neighborhood Disadvantage and Risk of Dementia

We found that 196,920 (20.3%) of older patients were diagnosed with dementia over a median follow-up of 2.4 years (interquartile range [IQR]: 0.8-4.6). The unadjusted 5-year cumulative incidence of dementia among older patients residing in high-disadvantage neighborhoods was higher than that among those in low-disadvantage neighborhoods (high=27.4% vs low=26.9%; PLog-rank < 0.001; Fig 1A).

Figure 1.

Figure 1

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Residential neighborhood disadvantage and time to (A) dementia diagnosis and (B) Alzheimer's disease diagnosis among older patients (aged ≥ 55) with kidney failure initiating dialysis (2003-2021). For ease of interpretation, we excluded the medium neighborhood disadvantage from the figure.

After adjusting for demographics and clinical factors, older patients residing in high-disadvantage neighborhoods had a higher risk of dementia diagnosis (adjusted hazard ratio [aHR] = 1.09, 95% confidence interval (CI): 1.08-1.10); this association significantly differed by race/ethnicity (Pinteraction = 0.03), and urbanicity (Pinteraction < 0.001; Table 2). Specifically, older Black patients residing in high-disadvantage neighborhoods had a higher risk of dementia diagnosis compared with their counterparts in low-disadvantage neighborhoods (aHR = 1.19, 95% CI: 1.17-1.21). However, older Asian patients residing in high-disadvantage neighborhoods had a lower risk of dementia diagnosis compared with their counterparts in low-disadvantage neighborhoods (aHR = 0.76, 95% CI: 0.71-0.81).

Table 2.

Residential Neighborhood Disadvantage on Time to Dementia and Alzheimer’s Disease Diagnoses Among Older Patients (Aged ≥ 55) with Kidney Failure Initiating Dialysis, Stratified by Race/Ethnicity and Urbanicity (2003-2021; N = 969,989).

Adjusted Hazard Ratio (aHR) (95% Confidence Interval)
Residential Neighborhood Disadvantage Indexa
Any Type Dementia
 Alzheimer’s Disease
Low Medium High Low Medium High
Overall Reference 0.98 (0.97-0.99) 1.09 (1.08-1.10) Reference 1.05 (1.02-1.07) 1.25 (1.22-1.27)
Race/Ethnicityb
 Asian Reference 0.82 (0.78-0.85) 0.76 (0.71-0.81) Reference 0.95 (0.88-1.03) 0.82 (0.72-0.94)
 Black Reference 1.18 (1.16-1.20) 1.19 (1.17-1.21) Reference 1.26 (1.22-1.31) 1.31 (1.28-1.35)
 Hispanic Reference 0.94 (0.92-0.96) 0.98 (0.96-1.01) Reference 1.19 (1.14-1.25) 1.36 (1.31-1.42)
 White Reference 0.93 (0.92-0.95) 1.05 (1.03-1.06) Reference 0.95 (0.93-0.98) 1.15 (1.12-1.19)
P value for interactionc <0.001 0.03 <0.001 0.002
Urbanicityd
 HDU Reference 1.02 (1.00-1.03) 0.99 (0.97-1.00) Reference 1.05 (1.01-1.09) 1.02 (0.99-1.06)
 Suburban Reference 0.99 (0.97-1.01) 1.08 (1.06-1.10) Reference 1.08 (1.03-1.12) 1.27 (1.22-1.32)
 Rural Reference 1.03 (0.99-1.06) 1.27 (1.23-1.31) Reference 1.09 (1.01-1.18) 1.43 (1.33-1.54)
 Small town Reference 1.05 (1.00-1.10) 1.31 (1.24-1.37) Reference 1.26 (1.12-1.42) 1.84 (1.64-2.07)
P value for interactionc 0.77 <0.001 <0.001 <0.001
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Associations that are statistically significant (P < 0.05) are in bold.

Abbreviation: HDU, high-density urban.

Cause-specific models adjusted for year of dialysis initiation, age, sex, cause of kidney failure, employment status, body mass index (BMI), nephrology care, comorbid conditions (cancer, hypertension, diabetes, peripheral vascular disease, atherosclerotic heart disease, congestive heart failure, chronic obstructive pulmonary disease, drug use, alcohol use, tobacco use), and functional status.

a

Neighborhood disadvantage index: nine domains (built environment disadvantage, criminal injustice, education disadvantage, unemployment, housing instability, poverty, social fragmentation, transportation barrier, and wealth inequality). As increasing values of the nine domains indicated more disadvantages, the naming convention was revised to reflect the appropriate interpretation of the domains and to improve clarity. https://www.sreindex.com/

b

Race/ethnicity: non-Hispanic White, non-Hispanic Black, Hispanic and non-Hispanic Asian (Asian American, Native Hawaiian, and Pacific Islander).

c

P value for the interaction between neighborhood disadvantage index and race/ethnicity; neighborhood disadvantage index and urbanicity.

d

Modified Rural-Urban Commuting Area [RUCA] Codes defined by US Department of Agriculture; high-density urban, suburban, rural, and small town. https://www.sciencedirect.com/science/article/pii/S2590291122000043

Additionally, older patients residing in high-disadvantage neighborhoods in suburban (aHR = 1.08, 95% CI: 1.06-1.10), rural (aHR = 1.27, 95% CI: 1.23-1.31), and small towns (aHR = 1.31, 95% CI: 1.24-1.37) had a higher risk of dementia diagnosis compared with their counterparts in low-disadvantage neighborhoods (Table 2).

Individual domains

A one standard deviation increase in disadvantage scores for criminal injustice was associated with a 2% higher risk of dementia diagnosis (aHR = 1.02, 95% CI: 1.01-1.04). Similarly, a one standard deviation increase in disadvantage scores for housing instability (aHR = 1.03, 95% CI: 1.01-1.06; 3% higher risk), and poverty (aHR = 1.02, 95% CI: 1.01-1.05; 2% higher risk) was also associated with increased risk (Table 3).

Table 3.

Components of residential neighborhood disadvantage index and time to dementia and Alzheimer's disease diagnoses among older patients (aged ≥55) with kidney failure initiating dialysis (2003-2021; N = 969,989).

Adjusted Hazard Ratio (aHR) (95% Confidence Interval)
Any Type Dementia Alzheimer’s Disease
Neighborhood Disadvantage Index Domain
 Built Environment Disadvantagea 1.00 (0.97-1.02) 1.10 (1.05-1.16)
 Criminal Injusticeb 1.02 (1.01-1.04) 1.05 (1.00-1.10)
 Education Disadvantagec 0.97 (0.93-1.01) 1.05 (1.00-1.11)
 Unemploymentd 0.96 (0.91-1.01) 1.01 (0.96-1.06)
 Housing Instabilitye 1.03 (1.01-1.06) 1.06 (1.00-1.13)
 Povertyf 1.02 (1.01-1.05) 1.11 (1.05-1.17)
 Social Fragmentationg 1.01 (0.98-1.04) 1.03 (0.97-1.09)
 Transportation Barrierh 0.94 (0.92-0.97) 1.04 (0.99-1.10)
 Wealth Inequalityi 0.97 (0.95-1.01) 1.05 (1.01-1.11)
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Notes: Neighborhood disadvantage index: nine domains (built environment disadvantage, criminal injustice, education disadvantage, unemployment, housing instability, poverty, social fragmentation, transportation barrier, and wealth inequality). As increasing values of the nine domains indicated increased disadvantages, the naming convention was revised to reflect the appropriate interpretation of the domains and to improve clarity. https://www.sreindex.com/. Each domain's measures were standardized.

Associations that are statistically significant (P < 0.05) are in bold.

a

Built environment disadvantage: building vacancy rate, mobile home, no internet access (American Community Survey 5-year estimates 2015-2019); Cancer risk (Environmental Protection Agency, Air Toxics Screening Assessment), low food access for SNAP recipients (Department of Agriculture, Food Access Research Atlas 2021).

b

Criminal injustice: Pretrial jail rate, total jail rate, law enforcement personnel per capita (municipality).

c

Education disadvantage: Bachelor’s degree or higher, high school diploma (American Community Survey 5-year estimates 2015-2019); per pupil spending (school district)

d

Unemployment: Unemployed, white-collar occupation; Retail job availability (stratify by age).

e

Housing instability: Housing units without telephone, housing units without plumbing, crowding, group quarters; foreclosure risk; eviction rate.

f

Poverty: Below 100% FPL, below 200% FPL, public assistance, family income, per capita income; supplemental poverty measure.

g

Social fragmentation: Changed address in last year, single-parent households, income gap; residential segregation.

h

Transportation barrier: Carpooled to work, no access to a motor vehicle, took public transit to work, biked to work, walked to work; Transportation cost burden, median income family.

i

Wealth inequality: Aggregate home value, median real estate taxes paid, median home value, median gross rent, median monthly mortgage, owner-occupied homes.

Cause-specific hazards models adjusted for year of dialysis initiation, age, sex, cause of kidney failure, employment status, body mass index (BMI), nephrology care, comorbid conditions (cancer, hypertension, diabetes, peripheral vascular disease, atherosclerotic heart disease, congestive heart failure, chronic obstructive pulmonary disease, drug use, alcohol use, tobacco use), and functional status.

Residential Neighborhood Disadvantage and Risk of Alzheimer’s Disease

The unadjusted 5-year cumulative incidence of Alzheimer’s disease was higher among older patients residing in high-disadvantage neighborhoods compared with those in low-disadvantage neighborhoods (high = 7.8% vs low = 6.7%; PLog-rank < 0.001; Fig 1B).

After adjustment, older patients residing in high-disadvantage neighborhoods had a higher risk of Alzheimer’s disease diagnosis (aHR = 1.25, 95% CI: 1.22-1.27); this association significantly differed by race/ethnicity (Pinteraction = 0.002) and urbanicity (Pinteraction < 0.001; Table 2). Specifically, older Black (aHR = 1.31, 95% CI: 1.28-1.35) and Hispanic (aHR = 1.36, 95% CI: 1.31-1.42) patients residing in high-disadvantage neighborhoods had a higher risk of Alzheimer’s disease diagnosis compared with their counterparts in low-disadvantage neighborhoods. However, older Asian patients residing in high-disadvantage neighborhoods had a lower risk of Alzheimer’s disease diagnosis compared with their counterparts in low-disadvantage neighborhoods (aHR=0.82, 95% CI: 0.72-0.94).

Additionally, older patients residing in high-disadvantage neighborhoods in suburban (aHR=1.27, 95% CI: 1.22-1.32), rural (aHR=1.43, 95% CI: 1.33-1.54), and small towns (aHR=1.84, 95% CI: 1.64-2.07) had a higher risk of Alzheimer’s disease diagnosis compared with their counterparts in low-disadvantage neighborhoods (Table 2).

Individual domains

A one standard deviation increase in disadvantage scores for built environment was associated with a 10% higher risk of Alzheimer’s disease diagnosis (aHR = 1.10, 95% CI: 1.05-1.16). Similar associations were observed for disadvantage scores for criminal injustice (aHR = 1.05, 95% CI: 1.00-1.10; 5% higher risk), education (aHR = 1.05, 95% CI: 1.00-1.11; 5% higher risk), housing instability (aHR = 1.06, 95% CI: 1.00-1.13; 6% higher risk), poverty (aHR = 1.11, 95% CI: 1.05-1.17; 11% higher risk), and wealth inequality (aHR = 1.05, 95% CI: 1.01-1.11; 5% higher risk; Table 3).

Sensitivity Analysis

Our results were robust in the following sensitivity analyses: (1) using Fine and Gray proportional subdistribution hazards models (Table S3); (2) splitting the neighborhood disadvantage index into tertiles based on the national average (Table S4); (3) assessing potential unmeasured confounding using E-values (Table S5); and (4) requiring 2 diagnosis codes to define dementia and Alzheimer’s disease (Table S6).

Discussion

In this national study of 969,989 older patients (2003-2021), we found that residing in a high-disadvantage neighborhood was associated with a 1.09-fold higher risk of dementia and a 1.25-fold higher risk of Alzheimer’s disease diagnoses. We also observed that the association between neighborhood disadvantage and dementia and Alzheimer’s disease varied across racial and ethnic groups and urbanicity types. Among older Black patients, those residing in high-disadvantage neighborhoods had a 1.19-fold higher risk of dementia and a 1.31-fold higher risk of Alzheimer’s disease compared with those in low-disadvantage neighborhoods. Additionally, those residing in high-disadvantage neighborhoods in suburban, rural, and small towns had a higher risk of dementia and Alzheimer’s disease diagnoses compared with those in low-disadvantage neighborhoods. Increasing levels of criminal injustice, housing instability, and poverty at the neighborhood level were domains that were associated with a higher risk of dementia and Alzheimer’s disease diagnoses.

Our findings were consistent with earlier studies on racial and ethnic segregation, which, along with other forms of neighborhood disadvantage (eg, structural disadvantages), was associated with an elevated risk of dementia and Alzheimer’s disease, especially among older minoritized populations with kidney failure.1,29,30 Expanding on previous research, we examined multiple domains of neighborhood disadvantage to better capture the interconnected factors and identify potential mechanisms contributing to elevated ADRD risk. We found that greater neighborhood-level disadvantages in criminal injustice, housing instability, and poverty were associated with an increased risk of ADRD. This may be attributed to the chronic stress experienced by older patients who reside in such neighborhoods which, over time, can lead to a sustained endocrine response and increased production of proinflammatory cytokines, and ultimately neurological decline.11,31 Furthermore, individuals residing in these neighborhoods may face limited access to health care, healthy food, and green spaces, all of which are essential for maintaining cognitive health, preventing ADRD, and promoting healthy longevity.32, 33, 34

In our study, we found that residence in a high-disadvantage neighborhood was associated with an increased risk of dementia and Alzheimer’s disease, and these associations differed across racial/ethnic groups, highlighting potential effect modification by race/ethnicity. Specifically, older Black patients who reside in high-disadvantage neighborhoods had an increased risk of ADRD compared with their counterparts in low-disadvantage neighborhoods. This might be because high-disadvantage neighborhoods often expose residents to multiple stressors, such as segregation, limited resources, fewer educational and employment opportunities, and a lack of recreational activities.1,29,35, 36, 37, 38, 39 The synergistic impact of these stressors may eventually impair cognitive function.29 Moreover, Black and Hispanic individuals living in these neighborhoods often have a higher prevalence of hypertension, diabetes, and cardiovascular diseases, all of which are known risk factors for cognitive decline and dementia.21,40, 41, 42 Furthermore, prolonged exposure to high-disadvantage neighborhoods during early adulthood or midlife has been linked to cognitive decline in later years, emphasizing the significant influence these neighborhoods may have on cognitive health trajectories, especially among older Black and Hispanic patients.43 However, among older Asian patients, residing in high-disadvantage neighborhoods was associated with a lower risk of ADRD. This could be due to the effect of acculturation among Asian individuals.44 Older Asian patients who migrated at a later age are less likely to be acculturated and less exposed to the effects of neighborhood disadvantage.30,44 They may also receive stronger social support and face less discrimination.30 Further research is warranted to better understand how early-life exposures to structural racism affect brain health in these populations and to support long-term cognitive health.

Moreover, we found that older patients residing in high-disadvantage neighborhoods in suburban, rural, and small towns had a significantly increased risk of ADRD. Health services resources are unequally distributed within and across counties, with particularly limited availability in rural areas and small towns, creating significant barriers to care.12,45 These barriers, such as delays in treatment, limited follow-up care, and transportation challenges, can prevent timely diagnosis and management of chronic conditions that elevate the risk for cognitive decline.12,45, 46, 47, 48 In addition, socioeconomic factors such as social isolation, low educational attainment, and a fragmented health care system further restrict access to quality care, increasing the likelihood of undetected or poorly managed health issues that may ultimately lead to cognitive decline.42,49, 50, 51, 52 To support healthy longevity among older patients living in rural areas, resilience factors such as strong community networks and social support may help mitigate the risk of ADRD.49,53

Our study highlighted a critical barrier to achieving healthy longevity in this population. Addressing the impact of neighborhood disadvantage on the risk of ADRD will require collaborative efforts and multifaceted interventions at both local and national levels. At the local level, clinicians can implement routine cognitive screening for individuals residing in high-risk neighborhoods and design collaborative care plans that support guided decision making, offer recommendations for daily activities, and enhance social support, particularly for individuals from minoritized populations.54,55 At the national level, clinicians, researchers, and policymakers should advocate for policies that address criminal justice reform, promote income equality, expand access to education, and support fair housing practices.54

Our study has several notable strengths, including the use of a large, nationally representative cohort and 17 years of registry data to examine the association between neighborhood disadvantage and the risk of ADRD. However, we recognize several limitations. First, because of data constraints, we used ZIP codes as a proxy for neighborhoods, which may introduce spatial misclassification and systemic biases.56 Nevertheless, ZIP codes are one of the most commonly used proxies for defining neighborhoods in research.57,58 Second, the duration of individuals’ residence in these neighborhoods is unknown, which may affect their cumulative exposure to stressors associated with high levels of disadvantage. Third, we identified ADRD cases using ICD-9/ICD-10 codes in Medicare claims. Using a single code may have led to misclassification because codes can be assigned for purposes other than confirming a diagnosis and may under-detect mild cognitive impairment. However, previous research has demonstrated that these diagnosis codes have comparable sensitivity and specificity, supporting the validity of our approach.26,59 Fourth, the neighborhood disadvantage index reflects the most recent available data rather than historical neighborhood conditions; however, this validated measure has been used in previous studies, with findings supporting its validity.60,61 Fifth, because of data limitations, information on individual-level education and income was not available, and Medicare claims were only available after Medicare eligibility (following dialysis initiation, at age 65, or because of disability), limiting capture of earlier health care utilization. Lastly, selection bias may have occurred because of missing data or exclusions from the study sample. Given the large sample size, some statistically significant findings may reflect small differences that are not necessarily clinically meaningful.

In conclusion, residing in high-disadvantage neighborhoods was associated with an increased risk of ADRD, particularly among older Black patients and those living in suburban, rural, and small towns. Specifically, higher levels of neighborhood criminal injustice, housing instability, and poverty were associated with a greater risk of ADRD. Targeted, multifaceted interventions (eg, routine cognitive screening and collaborative care) are critical for addressing the structural barriers associated with neighborhood disadvantage and reducing the risk of cognitive decline. These interventions also play an important role in advancing healthy longevity among older patients. Future research and policy efforts should prioritize identifying and addressing the upstream social and structural determinants that drive neighborhood disadvantage and contribute to the elevated risk of ADRD.

Article Information

Authors’ Full Names and Academic Degrees

Yiting Li, MPH, Gayathri Menon, MHS, Jane J. Long, MD, Byoungjun Kim, PhD, Sunjae Bae, MD, PhD, Babak J. Orandi, MD, PhD, MSc, Mario P. DeMarco, MD, MPH, Wenbo Wu, PhD, Deidra C. Crews, MD, ScM, Tanjala S. Purnell, PhD, Roland J. Thorpe, Jr. PhD, Sarah L. Szanton, PhD, RN, Dorry L. Segev MD, PhD, Mara A. McAdams-DeMarco, PhD.

Authors’ Contributions

Concept and design: MMD, YL; acquisition, analysis, and interpretation of data: MMD, YL, GM, JL, BK, SB, BO, MD, WW, DC, TP, RT, SS, DS; granted access to data and verified it: YL and GM; Statistical analysis: YL; obtained funding: MMD, DS; administrative, technical or material support: YL, GM; supervision: MMD, DS. Each author made significant intellectual contributions to the drafting or revision of the manuscript and takes responsibility for the work as a whole, ensuring that any questions regarding the accuracy or integrity of any part of the work are thoroughly investigated and resolved.

Support

This work was supported by grant number R01AG077888 (PI: McAdams-DeMarco) from the National Institute on Aging (NIA). In addition, coauthors are also supported by the following grant numbers: F32AG082486 (PI: Long), DP1AG069874 (PI: Szanton, Co-I: Thorpe), K02AG059140 (PI: Thorpe), and P30AG059298 (PI: Thorpe) from the National Institute on Aging (NIA); K01DK139420 (PI: Kim) from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). The funding organizations 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; or the decision to submit the manuscript for publication.

Financial Disclosure

DL Segev receives consulting fees from AstraZeneca, CareDx, Moderna Therapeutics, Medscape, Novavax, Regeneron, Springer Publishing, Hansa, and Roche. DL Segev also receives honoraria from AstraZeneca, CareDx, Houston Methodist, Northwell Health, Optum Health Education, Sanofi, WebMD, and ASN. DL Segev is the editor of and receives payment from Springer. BJ Orandi served on an advisory board for Boehringer Ingelheim. DC Crews receives research funding from Somatus. The remaining authors declare that they have no relevant financial interests.

Disclaimer

The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the U.S. government.

Data sharing

The datasets used and/or analyzed during the current study are available from the United States Renal Data System (USRDS) on Data Use Agreement (DUA) approval. Per the DUA between the authors and USRDS, the rerelease of the data or the deposition of data into publicly available repositories or to individuals is not allowed.

Peer Review

Received August 26, 2025. Evaluated by 1 external peer reviewer with direct editorial input from an Associate Editor and the Editor-in-Chief. Accepted in revised form April 24, 2026.

Footnotes

Complete article and author information provided before references.

Figure S1. Cohort derivation flowchart.

Table S1. The Domains/Components of the Neighborhood Disadvantage Effect Index.

Table S2. ICD-9 and ICD-10 Codes for Identifying Diagnosed Dementia and Alzheimer’s Disease in Medicare Claims.

Table S3. [Sensitivity Analysis – Competing Risk Model] Residential Neighborhood Disadvantage on Time to Dementia and Alzheimer’s Disease Diagnoses Among Older Patients (Aged ≥ 55) with Kidney Failure Initiating Dialysis, Stratified by Race/Ethnicity and Urbanicity (2003-2021; N = 969,989).

Table S4. [Sensitivity Analysis – National Tertiles] Residential Neighborhood Disadvantage on Time to Dementia and Alzheimer’s Disease Diagnoses Among Older Patients (Aged ≥ 55) with Kidney Failure Initiating Dialysis, Stratified by Race/Ethnicity and Urbanicity (2003-2021; N = 969,989).

Table S5. [Sensitivity Analysis – E-values] Hazard Ratios and E-values for Dementia and Alzheimer’s Disease.

Table S6. [Sensitivity Analysis – 2 Dx Codes to Identify Dementia and Alzheimer’s Disease] Residential Neighborhood Disadvantage on Time to Dementia and Alzheimer’s Disease Diagnoses Among Older Patients (Aged ≥ 55) with Kidney Failure Initiating Dialysis, Stratified by Race/Ethnicity and Urbanicity (2003-2021; N = 969,989).

Supplementary Material

Supplementary File (PDF)

Figure S1; Tables S1-S6

mmc1.pdf (426.6KB, pdf)

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Supplementary Materials

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Figure S1; Tables S1-S6

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