Neighborhood Factors, Air Pollution, and Mortality Among Kidney Failure Patients: Exploring Differences by Race and Ethnicity

Yiting Li; Gayathri Menon; Jane J Long; Malika Wilson; Byoungjun Kim; Mario P DeMarco; Babak J Orandi; Sunjae Bae; Wenbo Wu; Yijing Feng; Terry Gordon; George D Thurston; Dorry L Segev; Mara A McAdams-DeMarco

doi:10.1016/j.xkme.2025.101167

. 2025 Nov 1;8(1):101167. doi: 10.1016/j.xkme.2025.101167

Neighborhood Factors, Air Pollution, and Mortality Among Kidney Failure Patients: Exploring Differences by Race and Ethnicity

Yiting Li ¹, Gayathri Menon ¹, Jane J Long ¹, Malika Wilson ¹, Byoungjun Kim ¹, Mario P DeMarco ², Babak J Orandi ^1,³, Sunjae Bae ¹, Wenbo Wu ^3,⁴, Yijing Feng ¹, Terry Gordon ³, George D Thurston ³, Dorry L Segev ^1,⁴, Mara A McAdams-DeMarco ^1,^4,^∗

PMCID: PMC12768917 PMID: 41503187

Abstract

Rationale & Objective

Fine particulate matter (PM_2.5) is associated with increased mortality and disproportionately affects minoritized patients with kidney failure, particularly Black patients. Among patients with kidney failure, we tested whether neighborhood characteristics (racial and ethnic segregation, socioeconomic deprivation, and built environment) modified the association between PM_2.5 exposure and mortality, overall and by race and ethnicity.

Study Design

Cohort study (2003-2019).

Setting & Participants

National registry for patients with kidney failure.

Exposures

Annualized PM_2.5 concentrations (high, > 9 μg/m³), segregation scores (Theil’s H method), deprivation scores (American Community Survey), and built environment factors (medically underserved areas [MUA] and urbanicity) by patients’ residential ZIP code at dialysis initiation.

Outcome

All-cause mortality.

Analytical Approach

We used multivariable Cox regression with shared state-level frailty to quantify whether neighborhood factors modify the association between PM_2.5 and mortality, overall and stratified by race and ethnicity.

Results

High PM_2.5 (vs low) was differentially associated with mortality among patients with kidney failure residing in neighborhoods characterized by high segregation (adjusted hazard ratio [aHR], 1.17; 95% confidence interval [CI], 1.15-1.19; P_{interaction high vs low} < 0.001), high deprivation (aHR, 1.17; 95% CI, 1.15-1.19; P_{interaction high vs low} < 0.001), MUA (aHR, 1.15; 95% CI, 1.13-1.16; P_interaction = 0.005), and high-density urban (HDU) areas (aHR, 1.14; 95% CI, 1.12-1.15; P_interaction < 0.001). These differential associations were most prominent among Black patients ([high segregation: aHR, 1.25; 95% CI, 1.21-1.29; P_{interaction high vs low} = 0.006], [high deprivation: aHR, 1.26; 95% CI, 1.22-1.30; P_{interaction high vs low} < 0.001], [MUA: aHR, 1.22; 95% CI, 1.19-1.26; P_interaction = 0.02], [HDU areas: aHR, 1.23; 95% CI, 1.20-1.26; P_interaction < 0.001])

Limitations

Outdoor PM_2.5 may not reflect individual-level exposures.

Conclusions

High levels of PM_2.5 were associated with increased mortality risk among patients with kidney failure residing in neighborhoods characterized by high segregation, high deprivation, MUAs, and HDU areas, particularly among Black patients. Nephrologists should consider closer monitoring of patients, particularly those from minoritized groups who reside in these high-risk neighborhoods, to help mitigate the adverse effects of PM_2.5 and reduce related mortality.

Index Words: Residential neighborhood air pollution, segregation, deprivation, mortality, kidney failure

Plain-language Summary

Exposure to high levels of fine particulate matter (PM_2.5), a form of air pollution, disproportionately affects minoritized patients with kidney failure, particularly Black patients. Residential neighborhood characteristics may explain both the overall association between PM_2.5 exposure and mortality and the racial and ethnic differences in this association among patients with kidney failure in the United States. In this study, we found that high PM_2.5 levels were associated with increased mortality risk in neighborhoods characterized by high segregation and high deprivation, medically underserved areas, and HDU areas. These associations were most prominent among Black patients with kidney failure. Our findings underscore the need for clinicians to identify and monitor high-risk populations to reduce mortality risk related to PM_2.5, particularly among Black patients.

Exposure to elevated levels of fine particulate matter (aerodynamic diameter < 2.5 μm [PM_2.5]) is associated with increased mortality among patients with kidney failure.¹ One potential mechanistic pathway involves PM_2.5-induced systemic inflammation, which contributes to cardiovascular and respiratory comorbidities, ultimately increasing the risk of mortality in these individuals.¹^,²

Additionally, the mortality risk associated with PM_2.5 disproportionately affects minoritized patients with kidney failure, particularly Black patients.¹ These disparities are believed to be driven in part by variations in the distributions of pre-existing comorbidities and epigenetic aging.³^,⁴ However, these variations may not fully explain the deleterious association between PM_2.5 and mortality, nor the racial differences observed in this association.

Residential neighborhood characteristics may play a significant role in these associations, as they have in other health care disparities.⁵^,⁶ For instance, highly segregated neighborhoods often experience disinvestment,7, 8, 9, 10, 11 resulting in fewer resources,⁶^,¹² elevated PM_2.5 levels,13, 14, 15, 16, 17 and higher mortality risk, particularly among the Black population.¹⁸^,¹⁹ Neighborhood deprivation, reflected in indicators such as wealth, income, education, occupation, and housing, captures broader socioeconomic disadvantages and is associated with adverse health outcomes.20, 21, 22, 23, 24 Highly deprived neighborhoods may lack access to adequate health care,²³ while also being burdened with high PM_2.5 levels,²⁵ further increasing mortality risk,²² especially for Black individuals.²² Moreover, built environment factors, such as the presence of parks and high vehicle traffic, also influence PM_2.5 exposure.²⁶^,²⁷ Increased green spaces can promote physical activity and improve mental health,²⁸ whereas high traffic volumes contribute to vehicle emissions, deteriorating air quality.²⁷ Furthermore, individuals in medically underserved areas (MUAs) and urban settings may face additional challenges that heighten their susceptibility to the harmful effects of PM_2.5 on mortality.²⁹ Therefore, it is plausible that neighborhood factors interact with environmental exposure to PM_2.5, contributing to increased mortality. We hypothesize that these social and physical neighborhood characteristics modify the association between PM_2.5 and mortality, disproportionately impacting minoritized patients, particularly Black patients with kidney failure.

In this national study of patients with kidney failure, we (1) tested whether neighborhood factors (racial and ethnic segregation, neighborhood deprivation, and built environment factors [eg, parks, vehicle counts, MUA designation, and urbanicity types]) modify the associations between PM_2.5 and mortality and (2) stratified the aforementioned associations by race and ethnicity.

Methods

Study Population and Data Source

We leveraged data from the United States Renal Data System (USRDS) to identify a cohort of 814,608 patients (age ≥ 18 years) diagnosed with kidney failure in the United States (US), who initiated dialysis between January 1, 2003, and December 31, 2019. The United States Renal Data System is a national registry that provides comprehensive information on all patients with kidney failure in the US, including information from the Center for Medicare & Medicaid Services (CMS) Medical Evidence form (CMS-2728) and Medicare billing claims.³⁰ Patient characteristics, comorbidities, and body mass index were extracted from CMS-2728 at dialysis initiation.³⁰ The study population was limited to patients with kidney failure whose primary payer was Medicare (Medicare Fee-for-Services) and whose race and ethnicity were categorized into one of the following categories: non-Hispanic (NH) Asian (Asian hereafter), NH Black (Black hereafter), Hispanic, or NH White (White hereafter).

The Institutional Review Board at the New York University Grossman School of Medicine reviewed our study and determined it to be exempt (s22-00535), because participants could not be identified.

Air Pollution

We obtained PM_2.5 levels (2003-2019) from the Centers for Disease Control and Prevention National Environmental Public Health Tracking Network.³¹ Daily PM_2.5 levels were annualized at the census tract level and averaged at the ZIP code level,³² which were then linked to the 5-digit residential ZIP code of patients at the time of dialysis initiation. We used the current annual National Ambient Air Quality standards set by the US Environmental Protection Agency (EPA) to define low (≤ 9 μg/m³) and high (> 9 μg/m³) PM_2.5 concentrations.³³ ZIP codes served as proxies for patients’ residential neighborhoods, consistent with prior research.⁵^,⁶

Neighborhood Factors

Residential racial and ethnic segregation

We used the Multigroup Entropy Index (Thiel’s H) method to quantify racial and ethnic segregation (segregation hereafter).⁵ This method is known for its robustness in measuring segregation across multiple racial and ethnic groups.34, 35, 36 The calculation included three steps: (1) extracting population-specific race and ethnicity counts within ZIP Code Tabulation Areas (ZCTAs) from American Community Survey 5-year estimates; (2) assessing the diversity within ZCTAs; and (3) examining the distribution of racial and ethnic populations within counties (Item S1: Calculations).34, 35, 36

We then assigned segregation scores to patients based on their 5-digit residential ZIP code at dialysis initiation. Segregation scores were categorized into tertiles, based on prior studies: low (< 3.4), medium (≥ 3.4 and < 11.6), and high (≥ 11.6).⁵^,⁶^,¹⁸^,37, 38, 39, 40, 41

Neighborhood deprivation index

We extracted census-tract-level neighborhood deprivation index (NDI) scores (deprivation hereafter), a composite measure derived from 10 American Community Survey variables, using the “ndi” R statistical software package.²⁰^,⁴²^,⁴³ NDI scores were weighted using population weights and aggregated at the ZIP code level,²⁰^,42, 43, 44 then linked with the residential 5-digit ZIP code of patients. They were then further divided into tertiles of low (≤ −0.11), medium (> −0.11 to ≤ 0.59), and high (> 0.59), based on prior literature.¹⁸^,37, 38, 39, 40, 41 A higher NDI score indicates a greater degree of socioeconomic deprivation.

Built Environment Factors

Parks and daily vehicle counts

The ZCTA-level number of parks (2018-2020) and daily vehicle count (1963-2019) were obtained from the National Neighborhood Data Archive of the Inter-University Consortium for Political and Social Research at the University of Michigan.⁴⁵^,⁴⁶ These data were categorized into low or high groups and then linked to each patient’s 5-digit residential ZIP code at dialysis initiation.

Medically underserved areas

We assessed geographic health care accessibility using the Health Resources and Services Administration’s medically underserved area (MUA) designation.⁴⁷ This designation identifies contiguous geographic areas with insufficient health care resources based on: the number of primary care physicians, the percentage of individuals living below the federal poverty level, the percentage of individuals aged ≥ 65 years, and infant mortality rates.⁴⁷ We spatially aligned the ZCTA centroids and MUAs to identify ZCTAs that fall within MUAs, and linked them to the residential 5-digit ZIP code of patients at dialysis initiation.

Urbanicity

We obtained urbanicity types from the modified 2017-2021 Rural-Urban Commuting Area Codes defined by the US Department of Agriculture.⁴⁸^,⁴⁹ These modified urbanicity types divided the metropolitan core into 2 subcategories based on the population and land area distribution: high-density urban (HDU) areas and suburban areas.⁴⁸ We then classified the residential ZIP codes of patients into four urbanicity types: HDU, suburban, rural, and small town.⁴⁸

Mortality

We obtained data on all-cause mortality from the USRDS, which captures information from multiple sources, including the CMS Medicare Enrollment Database, CMS forms 2746 (Death Notification form) and 2728, the Organ Procurement and Transplantation Network transplant follow-up form, the CROWN-Web database, and inpatient claims.¹ Patients with kidney failure were followed from the date of dialysis initiation until the earliest occurrence of any of the following events: death, loss of Medicare eligibility, or the end of the study (December 31, 2019).

Statistical Analyses

We used the Kaplan-Meier method to estimate the unadjusted cumulative incidence of mortality by PM_2.5 and race and ethnicity. We used complementary log-log plots and Schoenfeld residuals to test the proportional hazards assumption. We then used multivariable Cox proportional hazard models with shared state-level frailty to⁵⁰: (1) test whether neighborhood factors (segregation, NDI, and various built environmental factors) modify the association between PM_2.5 and mortality, using interaction terms and Wald; and (2) stratify the associations by race and ethnicity, using interaction terms and Wald tests to test differences by neighborhood factors within each racial and ethnic group. All models were adjusted for patient demographics other than race and ethnicity (age, sex, body mass index, and employment status), dialysis-related factors (pre-kidney failure nephrology care, cause of kidney failure), existing comorbidities (cancer, hypertension, diabetes, peripheral vascular disease, cerebrovascular disease, atherosclerotic heart disease, congestive heart failure, chronic obstructive pulmonary disease, illicit drug use, alcohol use, and tobacco use), assessments of functional status (ability to walk, residence in an institutionalized setting, and the need for assistance with daily activities), and neighborhood-level factors (% of residents with ≥ high school degree, % of residents with ≥ bachelor’s degree, and median household income). Variables were selected based on previous research.¹^,51, 52, 53, 54

Sensitivity Analysis

We assessed the robustness of our results through the following sensitivity analysis: (1) using Cox proportional hazards models without shared state-level frailty; (2) restricting analyses to individuals aged > 66 years; (3) adjusting for urbanicity types; (4) using a prior EPA cutoff of > 12 μg/m³ to define high PM_2.5 concentrations; and (5) examining whether neighborhood factors are associated with mortality among individuals exposed to low PM_2.5 levels.

All statistical analyses were conducted using SAS (v9.4 [SAS Institute]), Stata 17 MP (StataCorp LLC), R statistical software (version 4.0.3, R Foundation for Statistical Computing), and Quantum Geographic Information System (QGIS; v3.30).⁵⁵ Statistical significance was defined as a 2-sided P value < 0.05.

Results

Study Population

Among 814,608 patients with kidney failure who initiated dialysis, the mean age was 62.4 years (±SD, 15.1), 43.7% were female, 4.9% were Asian, 35.0% were Black, 14.8% were Hispanic, and 45.2% were White. Additionally, 46.3% had diabetes, 30.3% had hypertension as the cause of kidney failure, and 47.5% had pre-kidney failure nephrology care (Table 1).

Table 1.

Characteristics of Patients With Kidney Failure (Aged ≥ 18 years) Initiating Dialysis Between 2003 and 2019, Stratified By Race and Ethnicity and Residential Neighborhood PM_2.5 levels (N = 814,608).

Patient Characteristics	Patients N = 814,608	Asian N = 39,872		Black N = 285,399		Hispanic N = 120,798		White N = 368,539
		PM_2.5^a
		Low N = 10,225	High N = 29,647	Low N = 53,537	High N = 231,862	Low N = 41,436	High N = 79,362	Low N = 119,575	High N = 248,964
Age in y, mean (SD)	62.4 (15.1)	61.9 (15.3)	63.6 (15.6)	58.7 (14.7)	58.7 (14.9)	59.9 (14.8)	58.9 (14.5)	66.4 (14.4)	66.3 (14.4)
Age group, y, N (%)
18-34	39,931 (4.9)	589 (5.8)	1602 (5.4)	3,592 (6.7)	15,320 (6.6)	2,437 (5.9)	5,069 (6.4)	3,699 (3.1)	7,623 (3.1)
35-49	119,737 (14.7)	1,532 (15.0)	3,749 (12.6)	10,300 (19.2)	46,417 (20.0)	7,098 (17.1)	14,122 (17.8)	11,452 (9.6)	25,067 (10.1)
50-64	267,013 (32.8)	3,335 (32.6)	8,966 (30.2)	20,156 (37.6)	84,801 (36.6)	15,621 (37.7)	31,647 (39.9)	33,184 (27.8)	69,303 (27.8)
≥ 65	387,927 (47.6)	4,769 (46.6)	15,330 (51.7)	19,489 (36.4)	85,324 (36.8)	16,280 (39.3)	28,524 (35.9)	71,240 (59.6)	146,971 (59.0)
Female, N (%)	356,348 (43.7)	4,647 (45.4)	13,100 (44.2)	24,848 (46.4)	111,891 (48.3)	17,192 (41.5)	33,775 (42.6)	46,408 (38.8)	104,487 (42.0)
BMI in kg/m², mean (SD)	29.0 (7.9)	26.3 (6.4)	25.2 (6.0)	29.9 (8.2)	29.7 (8.3)	29.1 (7.3)	28.6 (7.1)	29.2 (7.7)	28.8 (7.8)
BMI group, N (%)
≤ 25	283,280 (34.8)	5,065 (49.5)	17,127 (57.8)	16,325 (30.5)	75,385 (32.5)	12,945 (31.2)	26,994 (34.0)	39,793 (33.3)	89,646 (36.0)
26-30	230,410 (28.3)	2,891 (28.3)	7,706 (26.0)	14,466 (27.0)	62,285 (26.9)	12,949 (31.3)	25,608 (32.3)	33,976 (28.4)	70,529 (28.3)
> 30	300,918 (36.9)	2,269 (22.2)	4,814 (16.2)	22,746 (42.5)	94,192 (40.6)	15,542 (37.5)	26,760 (33.7)	45,806 (38.3)	88,789 (35.7)
Employment status, N(%)
Unemployed	183,879 (22.6)	2,399 (23.5)	7,635 (25.8)	15,285 (28.6)	66,077 (28.5)	10,918 (26.3)	25,910 (32.6)	17,850 (14.9)	37,805 (15.2)
Employed	98,960 (12.1)	1,776 (17.4)	4,247 (14.3)	7,637 (14.3)	29,009 (12.5)	4,916 (11.9)	8,247 (10.4)	14,625 (12.2)	28,503 (11.4)
Retired	473,215 (58.1)	5,123 (50.1)	14,893 (50.2)	27,692 (51.7)	120,060 (51.8)	22,001 (53.1)	37,435 (47.2)	81,616 (68.3)	164,395 (66.0)
Other	58,554 (7.2)	927 (9.1)	2,872 (9.7)	2,923 (5.5)	16,716 (7.2)	3,601 (8.7)	77,70 (9.8)	5,484 (4.6)	18,261 (7.3)
Cause of Kidney Failure, N (%)
Diabetes mellitus	377,050 (46.3)	5,445 (53.3)	14,865 (50.1)	23,922 (44.7)	101,370 (43.7)	25,836 (62.4)	50,143 (63.2)	49,994 (41.8)	105,475 (42.4)
Hypertension	246,469 (30.3)	2,421 (23.7)	8,236 (27.8)	19,834 (37.0)	88,205 (38.0)	8,269 (20.0)	16,304 (20.5)	33,472 (28.0)	69,728 (28.0)
Glomerulonephritis	67,452 (8.3)	1,312 (12.8)	3,422 (11.5)	4,052 (7.6)	17,923 (7.7)	3,200 (7.7)	5,525 (7.0)	10,648 (8.9)	21,370 (8.6)
Other	123,637 (15.2)	1,047 (10.2)	3,124 (10.5)	5,729 (10.7)	24,364 (10.5)	4,131 (10.0)	7,390 (9.3)	25,461 (21.3)	52,391 (21.0)
Comorbidities, N (%)
Cancer	53,095 (6.5)	387 (3.8)	978 (3.3)	387 (3.8)	978 (3.3)	1,608 (3.9)	2,426 (3.1)	11,361 (9.5)	21,929 (8.8)
Peripheral vascular disease	93,318 (11.5)	607 (5.9)	1,744 (5.9)	607 (5.9)	1,744 (5.9)	4,867 (11.7)	7,799 (9.8)	15,605 (13.1)	37,146 (14.9)
Cerebrovascular disease	74,916 (9.2)	807 (7.9)	2,244 (7.6)	807 (7.9)	2,244 (7.6)	3,037 (7.3)	5,610 (7.1)	10,315 (8.6)	24,275 (9.8)
Atherosclerotic heart disease	99,450 (12.2)	1,347 (13.2)	3,069 (10.4)	1,347 (13.2)	3,069 (10.4)	5,420 (13.1)	7,250 (9.1)	20,690 (17.3)	36,311 (14.6)
CHF	247,297 (30.4)	2,275 (22.2)	7,210 (24.3)	2,275 (22.2)	7,210 (24.3)	11,060 (26.7)	20390 (25.7)	36,713 (30.7)	85,598 (34.4)
COPD	65,634 (8.1)	326 (3.2)	1,073 (3.6)	326 (3.2)	1,073 (3.6)	1,889 (4.6)	2,716 (3.4)	13,636 (11.4)	28,161 (11.3)
Drug use	10,663 (1.3)	27 (0.3)	50 (0.2)	27 (0.3)	50 (0.2)	349 (0.8)	646 (0.8)	1,046 (0.9)	1,660 (0.7)
Alcohol use	12,194 (1.5)	43 (0.4)	98 (0.3)	43 (0.4)	98 (0.3)	528 (1.3)	1,071 (1.3)	1,843 (1.5)	3,339 (1.3)
Tobacco use	47,793 (5.9)	255 (2.5)	628 (2.1)	255 (2.5)	628 (2.1)	1,357 (3.3)	2,094 (2.6)	7,591 (6.3)	15,647 (6.3)
Functional impairment	95,724 (11.8)	971 (9.5)	3,196 (10.8)	971 (9.5)	3,196 (10.8)	4,951 (11.9)	8,860 (11.2)	16,294 (13.6)	30,615 (12.3)
Institutionalized	50,788 (6.2)	350 (3.4)	12,62 (4.3)	350 (3.4)	1,262 (4.3)	1,844 (4.5)	3,096 (3.9)	9,785 (8.2)	17,799 (7.1)
Pre-kidney failure nephrology care, N (%)	386,985 (47.5)	6,959 (68.1)	13,998 (47.2)	6,959 (68.1)	13,998 (47.2)	21,643 (52.2)	31,051 (39.1)	76,291 (63.8)	111,242 (44.7)
Residential neighborhood Factors, N (%)^b
Percent with a high school degree or higher
≤ 84.2%	270,428 (33.2)	2,711 (26.5)	12,747 (43.0)	2,711 (26.5)	12,747 (43.0)	23,473 (56.7)	53,935 (68.0)	18,543 (15.5)	52,441 (21.1)
84.3%-91.5%	272,445 (33.5)	3,278 (32.1)	8,342 (28.1)	3,278 (32.1)	8,342 (28.1)	10,400 (25.1)	15,435 (19.4)	39,413 (33.0)	82,747 (33.2)
> 91.5%	271,542 (33.3)	4,236 (41.4)	8,558 (28.9)	4,236 (41.4)	8,558 (28.9)	7,534 (18.2)	9,990 (12.6)	61,585 (51.5)	113,724 (45.7)
Percent with bachelor’s degree or higher
≤ 22.1%	273,488 (33.6)	2,095 (20.5)	6,494 (21.9)	2,095 (20.5)	6,494 (21.9)	19,811 (47.8)	41,797 (52.7)	27,183 (22.7)	61,550 (24.7)
22.2%-36.7%	270,156 (33.2)	3,661 (35.8)	9,075 (30.6)	3,661 (35.8)	9,075 (30.6)	12,959 (31.3)	19,618 (24.7)	44,800 (37.5)	82,653 (33.2)
> 36.7%	270,771 (33.2)	4,469 (43.7)	14,078 (47.5)	4,469 (43.7)	14,078 (47.5)	8,637 (20.9)	17,945 (22.6)	47,558 (39.8)	104,709 (42.1)
Median Household Income ($)
≤ $53,197	269,700 (33.1)	1,180 (11.6)	3,318 (11.2)	1,180 (11.6)	3,318 (11.2)	15,522 (37.5)	27,382 (34.5)	23,482 (19.7)	54,976 (22.1)
$53,198-$74,434	272,377 (33.5)	2,957 (29.0)	8,053 (27.2)	2,957 (29.0)	8,053 (27.2)	15,817 (38.3)	25,459 (32.1)	48,556 (40.6)	87,661 (35.2)
> $74,434	272,062 (33.4)	6,076 (59.5)	18,268 (61.6)	6,076 (59.5)	18,268 (61.6)	10,009 (24.2)	26,505 (33.4)	47,436 (39.7)	106,208 (42.7)
Racial and ethnic segregation, N (%)^c
Low segregation	269,700 (33.1)	3,789 (37.1)	11,115 (37.5)	14,616 (27.3)	45,211 (19.5)	12,302 (29.7)	20953 (26.4)	58,626 (49.0)	104,829 (42.1)
Medium segregation	272,377 (33.5)	5,112 (50.0)	11,057 (37.3)	21,925 (41.0)	63,958 (27.6)	19,464 (47.0)	24455 (30.8)	45,103 (37.7)	79,003 (31.7)
High segregation	272,062 (33.4)	1,324 (12.9)	7,475 (25.2)	16,996 (31.7)	122,693 (52.9)	9,670 (23.3)	33954 (42.8)	15,846 (13.3)	65,132 (26.2)
Neighborhood deprivation, N (%)^d
Low deprivation	269,700 (33.1)	5,404 (52.9)	14,480 (48.8)	13,714 (25.6)	40,639 (17.5)	8,718 (21.0)	16,538 (20.8)	55,965 (46.8)	116,089 (46.6)
Medium deprivation	272,377 (33.5)	3,480 (34.0)	9,703 (32.7)	20,139 (37.6)	68,711 (29.6)	14,353 (34.6)	23,834 (30.0)	44,773 (37.4)	86,554 (34.8)
High deprivation	272,062 (33.4)	1,341 (13.1)	5,464 (18.4)	19,684 (36.8)	122,512 (52.8)	18,365 (44.3)	38,990 (49.1)	18,837 (15.8)	46,321 (18.6)
Number of parks, N (%)^e
1-11	409,532 (50.3)	4,055 (39.7)	13,470 (45.4)	23,888 (44.6)	114,964 (49.6)	17,580 (42.4)	38,724 (48.8)	62,097 (51.9)	134,754 (54.1)
≥ 12	405,076 (49.7)	6,170 (60.3)	16,177 (54.6)	29,649 (55.4)	116,898 (50.4)	23,856 (57.6)	40,638 (51.2)	57,478 (48.1)	114,210 (45.9)
Daily vehicle traffic, N (%)^f
Low (≤ 199,068)	433,077 (53.2)	5,124 (50.1)	16,298 (55.0)	22,620 (42.3)	127,521 (55.0)	18,344 (44.3)	43,113 (54.3)	58,974 (49.3)	141,083 (56.7)
High (> 199,068)	381,531 (46.8)	5,101 (49.9)	13,349 (45.0)	30,917 (57.7)	104,341 (45.0)	23,092 (55.7)	36,249 (45.7)	60,601 (50.7)	107,881 (43.3)
MUA, N (%)^g
Not MUA	488,156 (59.9)	7,370 (72.1)	22,102 (74.6)	26,180 (48.9)	117,661 (50.7)	19,314 (46.6)	417,38 (52.6)	72,076 (60.3)	181,715 (73.0)
MUA	326,452 (40.1)	2,855 (27.9)	7,545 (25.4)	27,357 (51.1)	114,201 (49.3)	22,122 (53.4)	37,624 (47.4)	47,499 (39.7)	672,49 (27.0)
Urbanicity, N (%)^h
High-density urban	467,378 (57.4)	7,077 (69.2)	24,145 (81.4)	32,037 (59.8)	142,227 (61.3)	23,128 (55.8)	62,241 (78.4)	50,986 (42.6)	125,537 (50.4)
Suburban	301,806 (37.0)	2,852 (27.9)	52,56 (17.7)	18,596 (34.7)	78,531 (33.9)	15,874 (38.3)	16,067 (20.2)	57,749 (48.3)	106,881 (42.9)
Rural	14,291 (1.8)	153 (1.5)	140 (0.5)	980 (1.8)	2,218 (1.0)	951 (2.3)	530 (0.7)	3,630 (3.0)	5,689 (2.3)
Small town	31,133 (3.8)	143 (1.4)	106 (0.4)	1,924 (3.6)	8,886 (3.8)	1,483 (3.6)	524 (0.7)	7,210 (6.0)	10,857 (4.4)

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Abbreviations: BMI = body mass index; CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease; MUA = medically underserved areas; SD = standard deviation.

PM_2.5 obtained from the Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network; low ≤ 9 μg/m³, and high > 9 μg/m³ concentrations per US EPA cutoffs.

Neighborhood-level factors were extracted from the American Community Survey (ACS) 5-year estimates for every 5 years.

Segregation levels are calculated based on Iceland’s Multigroup Entropy Index, using patients’ 5-digit ZIP code as a proxy for neighborhoods. Tertiles of neighborhood segregation scores were used to identify high-, medium, and low-segregation neighborhoods.

Neighborhood deprivation scores (census tract level) were obtained from the “ndi” package in R, and averaged at the ZIP code level using population weights. Tertiles of neighborhood deprivation scores were used to identify high-, medium-, and low-deprivation neighborhoods.

National Neighborhood Data Archive (NaNDA): Parks by ZIP Code Tabulation Area, United States, 2018. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], December 7, 2020. https://doi.org/10.3886/E119803V1.

National Neighborhood Data Archive (NaNDA): Traffic Volume by ZIP Code Tabulation Area, United States, 1963-2019. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], January 20, 2022. https://doi.org/10.3886/E160261V1.

From the Health Resources & Services Administration (HRSA); https://data.hrsa.gov/data/download.

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

We identified 6,199 residential neighborhoods, of which 96% had ≥ 11 patients with kidney failure residing in them. Among Black, Hispanic, and White patients, those exposed to high PM_2.5 were more likely to be female and less likely to have received pre-kidney failure nephrology care compared with those exposed to low PM_2.5 (Table 1).

PM_2.5 and Mortality by Race and Ethnicity

The unadjusted 5-year cumulative incidence of mortality was higher for those exposed to high PM_2.5 for all racial and ethnic groups except for Hispanic patients (Fig 1A-D): Asian (high, 57.7%; low, 51.3%; P_Log-rank < 0.001), Black (high, 54.8%; low, 49.2%; P_Log-rank < 0.001), and White (high, 75.3%; low, 71.9%; P_Log-rank < 0.001).

Unadjusted cumulative incidence of mortality stratified by residential neighborhood PM_2.5 levels and race and ethnicity ([A] Asian, [B] Black, [C] Hispanic, [D] White] among patients with kidney failure (age ≥ 18 years) initiating dialysis (2003-2019).

After adjustment, the strength of the association between PM_2.5 and mortality among patients with kidney failure differed by race and ethnicity (P_interaction < 0.001). Among Asian (adjusted hazard ratio [aHR], 1.16; 95% confidence interval [CI]; 1.11-1.21); Black (aHR, 1.22; 95% CI, 1.20-1.24); Hispanic (aHR, 1.02; 95% CI, 1.00-1.04); and White (aHR, 1.12; 95% CI, 1.11-1.14) patients, exposure to high PM_2.5 was associated with increased mortality risk compared with individuals of the same racial and ethnic group exposed to low PM_2.5 (Table 2).

Table 2.

The Role of Race and Ethnicity and Neighborhood Characteristics in Modifying the Association Between Residential Neighborhood PM_2.5 and Mortality Among Patients With Kidney Failure (Aged ≥ 18 Years) Initiating Dialysis (2003-2019; N = 814,608)

Characteristic	Adjusted hazard ratio (aHR) (95% confidence interval) of Mortality
Characteristic	Low PM_2.5	High PM_2.5
Race and ethnicity^a
Asian	Reference	1.16 (1.11-1.21)
Black	Reference	1.22 (1.20-1.24)
Hispanic	Reference	1.02 (1.00-1.04)
White	Reference	1.12 (1.11-1.14)
P-value for interaction^h		< 0.001
Segregation Level^b
Low	Reference	1.10 (1.09-1.12)
Medium	Reference	1.13 (1.12-1.15)
High	Reference	1.17 (1.15-1.19)
P-value for interaction^h		<0.001
NDI^c
Low	Reference	1.11 (1.09-1.12)
Medium	Reference	1.12 (1.10-1.13)
High	Reference	1.17 (1.15-1.19)
P-value for interaction^h		<0.001
Number of Parks^d
1-11	Reference	1.13 (1.12-1.15)
≥ 12	Reference	1.13 (1.11-1.14)
P-value for interaction^h		0.58
Daily vehicle traffic^e
Low (≤ 199,068)	Reference	1.10 (1.09-1.12)
High (>199,068)	Reference	1.13 (1.11-1.14)
P-value for interaction^h		0.002
MUA^f
Not MUA	Reference	1.12 (1.11-1.13)
MUA	Reference	1.15 (1.13-1.16)
P-value for interaction^h		0.005
Urbanicity^g
High-density urban	Reference	1.14 (1.12-1.15)
Suburban	Reference	1.12 (1.10-1.13)
Rural	Reference	1.20 (1.14-1.26)
Small town	Reference	1.04 (1.00-1.07)
P-value for interaction^h		< 0.001

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Abbreviations: MUA = medically underserved areas; NDI = neighborhood deprivation index. Statistically significant associations (P < 0.05) are in bold.

Multilevel Cox models with state-level shared frailty. All models adjusted for age, sex, cause of kidney failure, employment status, body mass index (BMI), nephrology care, comorbidities (cancer, hypertension, diabetes, peripheral vascular disease, atherosclerotic heart disease, congestive heart failure, chronic obstructive pulmonary disease, drug use, alcohol use, tobacco use), functional status, and neighborhood factors (education and income). Neighborhood factors were excluded from models with NDI. Models included a clustering term at the patient’s state of residence.

PM_2.5 obtained from the Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network; low = ≤ 9 μg/m³, and high > 9 μg/m³ concentrations.

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

Health Resources & Services Administration (HRSA); https://data.hrsa.gov/data/download.

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

P-value for the interaction between PM_2.5 and race and ethnicity; PM_2.5 and segregation levels; PM_2.5 and deprivation levels; PM_2.5 and number of parks; PM_2.5 and daily vehicle traffic; PM_2.5 and MUA; PM_2.5 and urbanicity.

Neighborhood Factors and the Association Between PM_2.5 and Mortality

Residential racial and ethnic segregation

After adjustment, the strength of the association between PM_2.5 and mortality among patients with kidney failure differed by segregation level (P_interaction < 0.001; Table 2). Among patients with kidney failure residing in high-segregation neighborhoods, those exposed to high PM_2.5 had a 1.17-fold increased mortality risk (95% CI, 1.15-1.19), compared with individuals of the same neighborhood segregation level exposed to low PM_2.5. This association was strongest among those residing in high-segregation neighborhoods (Table 2).

NDI

After adjustment, the strength of the association between PM_2.5 and mortality among patients with kidney failure differed by deprivation levels (P_interaction < 0.001; Table 2). Among patients with kidney failure residing in high-deprivation neighborhoods, those exposed to high PM_2.5 had a 1.17-fold increased mortality risk (95% CI, 1.15-1.19) compared with individuals of the same neighborhood deprivation level exposed to low PM_2.5. This association was strongest among those residing in high-deprivation neighborhoods (Table 2).

Built Environment Factors

Parks and daily vehicle counts

The strength of the association between PM_2.5 and mortality among patients with kidney failure did not differ by park quantity, but it did differ by traffic level (P_interaction = 0.002; Table 2). Among patients with kidney failure residing in neighborhoods with heavy daily vehicle traffic, those exposed to high PM_2.5 had a 1.13-fold increased mortality risk (95% CI, 1.11-1.14; Table 2) compared with those exposed to low PM_2.5.

MUAs

The strength of the association between PM_2.5 and mortality among patients with kidney failure differed by MUA designation (P_interaction = 0.005). Among patients with kidney failure residing in MUAs, those exposed to high PM_2.5 (compared with those exposed to low PM_2.5) had a 1.15-fold increased mortality risk (95% CI, 1.13-1.16) compared with those exposed to low PM_2.5. This association was strongest among those residing in MUAs (Table 2).

Urbanicity

The strength of the association between PM_2.5 and mortality among patients with kidney failure differed by urbanicity (P_interaction < 0.001). Among patients with kidney failure residing in HDU and rural neighborhoods, those exposed to high PM_2.5 had a 1.14-fold (95% CI, 1.12-1.15) and 1.20-fold (95% CI, 1.14-1.26) increased mortality risk, respectively, compared with individuals of the same neighborhoods exposed to low PM_2.5 (Table 2).

Role of Neighborhood Factors on Racial and Ethnic Differences in PM_2.5 and Mortality

When stratified by race and ethnicity, the association between PM_2.5 and mortality differed across racial and ethnic groups (Table 3). Particularly, among Black patients with kidney failure, those exposed to high PM_2.5 had an increased risk of mortality when residing in neighborhoods characterized by high segregation (aHR, 1.25; 95% CI, 1.21-1.29; P_interaction = 0.006), high deprivation (aHR, 1.26; 95% CI, 1.22-1.30; P_interaction < 0.001), heavy daily traffic (aHR, 1.22; 95% CI, 1.18-1.26; P_interaction = 0.02), MUAs (aHR, 1.22; 95% CI, 1.19-1.26; P_interaction = 0.02), high density (aHR, 1.23; 95% CI, 1.20-1.26), and suburban neighborhoods (aHR, 1.17; 95% CI, 1.13-1.20; P_interaction < 0.001) compared with individuals of the same neighborhoods exposed to low PM_2.5. Similar associations between PM_2.5 and mortality were observed among Asian patients residing in rural neighborhoods (aHR, 1.48; 95% CI, 1.00-2.21; P_interaction = 0.19) and those in areas with fewer parks (aHR, 1.21; 95% CI, 1.12-1.30; P_interaction = 0.04), as well as among Hispanic (aHR, 1.21; 95% CI, 1.01-1.45; P_interaction = 0.007) and White patients (aHR, 1.13; 95% CI, 1.07-1.20; P_interaction = 0.02) residing in rural neighborhoods compared with individuals of the same neighborhoods exposed to low PM_2.5 (Table 3).

Table 3.

The Role of Residential Neighborhood Characteristics in Modifying the Association Between Residential Neighborhood PM_2.5 and Mortality Among Patients With Kidney Failure (Aged ≥ 18 Years) Initiating Dialysis (2003-2019), Stratified by Race and Ethnicity (N = 814,608)

Charcateristic	Adjusted hazard ratio (aHR) (95% confidence interval) of mortality
	Asian N = 39,872		Black N = 285,399		Hispanic N = 120,798		White N = 368,539
	PM_2.5^a
	Low	High	Low	High	Low	High	Low	High
Segregation level^b
Low	Reference	1.08 (1.01-1.16)	Reference	1.16 (1.12-1.20)	Reference	0.99 (0.95-1.03)	Reference	1.13 (1.11-1.14)
Medium	Reference	1.18 (1.10-1.26)	Reference	1.18 (1.15-1.22)	Reference	1.08 (1.05-1.12)	Reference	1.13 (1.11-1.15)
High	Reference	1.20 (1.07-1.34)	Reference	1.25 (1.21-1.29)	Reference	1.03 (0.99-1.08)	Reference	1.16 (1.13-1.19)
P-value for interaction^h		0.11		0.006		0.08		0.12
NDI^c
Low	Reference	1.10 (1.04-1.17)	Reference	1.18 (1.14-1.23)	Reference	1.08 (1.03-1.13)	Reference	1.11 (1.09-1.12)
Medium	Reference	1.18 (1.09-1.27)	Reference	1.13 (1.10-1.17)	Reference	1.03 (0.99-1.07)	Reference	1.14 (1.12-1.16)
High	Reference	1.19 (1.06-1.33)	Reference	1.26 (1.22-1.30)	Reference	1.02 (0.99-1.05)	Reference	1.20 (1.17-1.23)
P-value for interaction^h		0.19		<0.001		0.16		< 0.001
Number of Parks^d
1-11	Reference	1.21 (1.12-1.30)	Reference	1.19 (1.16-1.22)	Reference	1.06 (1.03-1.10)	Reference	1.13 (1.11-1.15)
≥ 12	Reference	1.11 (1.04-1.17)	Reference	1.20 (1.17-1.23)	Reference	1.02 (0.99-1.05)	Reference	1.14 (1.12-1.16)
P-value for interaction^h		0.04		0.55		0.07		0.52
Daily Vehicle Traffic^e
Low (≤ 199,068)	Reference	1.14 (1.06-1.22)	Reference	1.17 (1.14-1.20)	Reference	1.07 (1.04-1.11)	Reference	1.12 (1.10-1.14)
High (> 199,068)	Reference	1.12 (1.05-1.19)	Reference	1.22 (1.18-1.26)	Reference	0.98 (0.95-1.01)	Reference	1.12 (1.10-1.14)
P-value for interaction^h		0.68		0.02		<0.001		0.94
MUA^f
Not MUA	Reference	1.14 (1.08-1.20)	Reference	1.17 (1.14-1.20)	Reference	1.06 (1.02-1.09)	Reference	1.13 (1.11-1.15)
MUA	Reference	1.16 (1.07-1.26)	Reference	1.22 (1.19-1.26)	Reference	1.02 (0.99-1.05)	Reference	1.14 (1.12-1.16)
P-value for interaction^h		0.68		0.02		0.08		0.51
Urbanicity^g
High-density urban	Reference	1.16 (1.10-1.23)	Reference	1.23 (1.20-1.26)	Reference	1.05 (1.01-1.08)	Reference	1.15 (1.13-1.16)
Suburban	Reference	1.10 (1.01-1.20)	Reference	1.17 (1.13-1.20)	Reference	1.03 (1.00-1.07)	Reference	1.13 (1.12-1.15)
Rural	Reference	1.48 (1.00-2.21)	Reference	1.13 (0.99-1.29)	Reference	1.21 (1.01-1.45)	Reference	1.13 (1.07-1.20)
Small town	Reference	0.88 (0.58-1.36)	Reference	1.06 (0.97-1.15)	Reference	0.81 (0.68-0.95)	Reference	1.06 (1.02-1.11)
P-value for interaction^h		0.19		< 0.001		0.007		0.02

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Abbreviations: MUA = medically underserved areas; NDI = neighborhood deprivation index. Statistically significant associations (P < 0.05) are in bold.

Multilevel Cox models with state-level shared frailty. All models adjusted for age, sex, cause of kidney failure, employment status, body mass index (BMI), nephrology care, comorbidities (cancer, hypertension, diabetes, peripheral vascular disease, atherosclerotic heart disease, congestive heart failure, chronic obstructive pulmonary disease, drug use, alcohol use, tobacco use), functional status, and neighborhood factors (education & income).). Neighborhood factors were excluded from models with NDI. Models included a clustering term at the patient’s state of residence.

PM_2.5 obtained from the Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network; low = ≤ 9 μg/m³, and high > 9 μg/m³ concentrations.

Health Resources & Services Administration (HRSA); https://data.hrsa.gov/data/download.

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

P-value for the interaction between PM_2.5 and segregation levels; PM_2.5 and deprivation levels; PM_2.5 and number of parks; PM_2.5 and daily vehicle traffic; PM_2.5 and MUA; PM_2.5 and urbanicity.

Sensitivity Analysis

Our findings were robust to the following sensitivity analyses: (1) using Cox proportional hazards models without shared state-level frailty (Tables S1, 2); (2) restricting the cohort to individuals aged > 66 years (Tables S3, 4); (3) adjusting for urbanicity (Table S5); (4) defining high PM_2.5 using the previous EPA threshold (Tables S6, 7); and (5) examining whether residence in MUAs, suburban, rural, and small town areas was associated with increased mortality risk when exposed to low PM_2.5 levels (Table S8).

Discussion

In this national study involving 814,608 patients with kidney failure, exposure to higher PM_2.5 was associated with an increased mortality risk among those residing in neighborhoods characterized by high segregation (1.17-fold), high deprivation (1.17-fold), MUA designation (1.15-fold), and HDU areas (1.14-fold), compared with those exposed to lower PM_2.5 levels. There was a differential impact of PM_2.5 exposure by race and ethnicity, where Black patients exposed to high (vs low) PM_2.5 experienced the greatest mortality risk when residing in neighborhoods characterized by high segregation (1.25-fold), high deprivation (1.26-fold), neighborhoods characterized as MUAs (1.22-fold), and HDU areas (1.23-fold).

Our findings of increased mortality risk among residents of high-segregation neighborhoods exposed to high PM_2.5 levels are consistent with prior research.¹¹^,⁵⁶^,⁵⁷ Patients residing in these neighborhoods often experience elevated PM_2.5 exposure,⁵⁸^,⁵⁹ which contributes to increased mortality risk.⁶⁰^,⁶¹ This association could be attributed to the upstream causes of segregation, including discriminatory zoning and redlining,⁶² which creates an uneven distribution of resources and the formation of neighborhoods characterized by poor social conditions,⁶² including a high concentration of polluting industries.⁶¹ Additionally, living near major roads or areas with heavy vehicle traffic can reduce air quality because of increased vehicle emissions.²⁷ Long-term exposure to vehicle emissions has been associated with higher rates of emergency visits, hospital admissions, and increased mortality in the literature.²⁷ However, built environments with more green spaces, such as a higher number of parks, have been linked to a reduced mortality risk associated with exposure to high levels of PM_2.5.²⁶ In our study, we observed this association between heavy vehicle traffic and mortality, but not with the number of parks. This could be attributed to other unexplored factors at play, such as the synergistic impact of green spaces with neighborhood urbanicity, which was beyond the scope of our study to test.

Moreover, it is well established that residing in MUAs is associated with inadequate health care resources,²⁹ and a limited availability of qualified health care providers and facilities.⁶³ However, prior research has highlighted the role of urbanization in mitigating the adverse effects of elevated PM_2.5 by improving the quality and accessibility of health services and increasing awareness of healthy lifestyles.⁶⁴ Therefore, it is plausible that the observed association between increased PM_2.5 levels and mortality in MUA neighborhoods could be, in part, driven by rurality. Rurality may impede access to and utilization of health care services, which is associated with poorer health outcomes and increased mortality.⁶⁵^,⁶⁶ Interestingly, we also found that among patients with kidney failure, particularly Black patients, the mortality risk was higher among those exposed to high PM_2.5 and residing in high-density or suburban areas compared with those exposed to low PM_2.5. This could be because of increasing environmental risk factors or behavioral changes with increasing urbanization, independent of health care access, which may also amplify the deleterious effects of PM_2.5 on mortality.⁶⁴ However, further research is required to disentangle the role of health care access and environmental risk factors by means of neighborhood urbanicity.

Additionally, when stratified by race and ethnicity, there was a differential impact such that Black patients exposed to high levels of PM_2.5 experienced increased mortality, especially in neighborhoods characterized by high segregation, high deprivation, and poor built environments. Historically, segregation has perpetuated racial hierarchies and exclusions,¹⁷ particularly affecting Black populations.⁵ Prior studies noted that high-segregation neighborhoods that are predominantly Black had a higher concentration of polluting industries,⁶⁷ which may contribute to increased mortality risk. Additionally, Black patients residing in deprived neighborhoods are often exposed to a range of socioeconomic stressors,⁶⁸^,⁶⁹ which, compounded by exposure to elevated PM_2.5,⁷⁰ can lead to adverse health conditions,71, 72, 73 affecting psychosocial well-being, quality of life, and mortality.⁷⁴ We observed similar associations between exposure to high PM_2.5 and increased mortality among Asian and White patients, possibly because of reasons similar to those affecting Black patients.

Surprisingly, we found no associations between high PM_2.5 exposure and increased mortality among Hispanic patients, except in HDU, suburban, and rural neighborhoods. Moreover, increasing levels of neighborhood deprivation were associated with increased mortality among Asian, Black, and White patients exposed to high PM_2.5, which was not observed for Hispanic patients. The Hispanic population is highly heterogeneous (eg, US-born vs foreign-born),⁷⁵ which may also contribute to variation in observed effects. The results could also reflect the “Hispanic paradox,”⁷⁶ potentially driven by underreporting of comorbid conditions and the healthy migrant effect.⁷⁶ A previous study has shown that foreign-born Mexican Americans have a lower risk of mortality compared with US-born non-Hispanic White individuals.⁷⁷ However, more research is warranted to better understand these associations.

Ultimately, addressing the racial and ethnic differences in the impact of PM_2.5 on mortality among patients with kidney failure may require multifaceted interventions and environmental policies. At the national level, efforts should focus on greater investment in segregated neighborhoods, improving the built environment (including green spaces and health care access),²⁶^,²⁹ and reducing vehicle traffic.²⁷ At the clinician level, this involves closely monitoring patients from high-risk neighborhoods to mitigate PM_2.5-related mortality. Clinicians concerned about their patients’ exposure to air pollution might consider implementing air pollution screening tools to assess indoor and outdoor exposure levels and provide targeted interventions.⁷⁸ These could include home air pollution monitors, air purifiers, upgraded ventilation systems, regular mask usage, provision of educational materials,⁶³^,⁷⁹ and forming partnerships with local community organizations and policymakers to reduce the observed racial and ethnic differences in mortality.⁷⁹ Additionally, increasing access to primary health care providers in MUAs and maintaining engagement with current providers may help reduce mortality risk even in areas with high PM_2.5 levels.⁸⁰

Our study has several notable strengths, including a large cohort using national registry data spanning over 17 years to quantify the association between PM_2.5 and mortality with linkage to neighborhood characteristics. However, our study must be considered in light of its limitations. First, we used residential ZIP codes as proxies for neighborhoods because of data availability. Despite the possibility of spatial misclassification and systematic biases,⁷⁸ ZIP codes are frequently used to define residential neighborhoods.⁸¹^,⁸² Second, we relied on outdoor PM_2.5 measurements, which may not capture the total PM_2.5 exposure experienced by patients from other sources, such as smoking, cooking, and workplace environments. Nevertheless, outdoor PM_2.5 remains a major contributor to overall exposure.⁸³ Third, we only considered PM_2.5 and various neighborhood characteristics at the initiation of dialysis and assumed them to remain constant over time. However, this assumption could be challenged if patients relocated to new neighborhoods with differing PM_2.5 levels and neighborhood characteristics.

In conclusion, we found that high PM_2.5 exposure was associated with an increased mortality risk in neighborhoods characterized by high segregation, high socioeconomic deprivation, and those that are medically underserved and HDU areas. These associations were observed among Asian, Hispanic, and White patients, but were most prominent among Black patients. Clinicians should consider closer monitoring of patients residing in these high-risk neighborhoods to reduce PM_2.5-related mortality risk, particularly among Black patients with kidney failure.

Article Information

Authors’ Full Names and Academic Degrees

Yiting Li, MPH, Gayathri Menon, MHS, Jane J. Long, MD, Malika Wilson, MD, MBA, Byoungjun Kim, PhD, Mario P. DeMarco, MD, MPH, Babak J. Orandi MD, PhD, MSc, Sunjae Bae MD, PhD, Wenbo Wu PhD, Yijing Feng, MHS, Terry Gordon, PhD, George D. Thurston, ScD, Dorry L. Segev MD, PhD, and Mara A. McAdams-DeMarco, PhD

Authors’ Contributions

Concept and design: MMD, YL; acquisition, analysis, and interpretation of data: MMD, YL, GH, JL, BK, MMD, BO, SB, WW, TG, GT; statistical analysis: LY; obtained funding: MMD, DS; administrative, technical, or material support: LY, GM; supervision: MDM, 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

None.

Financial Disclosure

The 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 US government.

Data Sharing

The datasets used and/or analyzed during the current study are available from the United States Renal Data System (USRDS) upon 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 December 21, 2024. Evaluated by 1 external peer reviewer, with direct editorial input from an Associate Editor and the Editor-in-Chief. Accepted in revised form July 14, 2025.

Footnotes

Complete author and article information provided before references.

Supplementary File (PDF)

Item S1. Calculations: Racial and Ethnic Segregation Score Models Based on Iceland’s Entropy Approach

Table S1. Cox Proportional Hazards Model—without Shared State-Level Frailty.

The role of race and ethnicity and neighborhood characteristics in modifying the association between residential neighborhood PM_2.5 and mortality among patients with kidney failure (aged ≥ 18 years) initiating dialysis (2003-2019; N = 814,608)

Table S2: Cox Proportional Hazards Model—Without Shared State-Level Frailty.

The role of residential neighborhood characteristics in modifying the association between residential neighborhood PM_2.5 and mortality among patients with kidney failure (aged ≥18) initiating dialysis (2003-2019; N = 814,608)

Table S3: Sensitivity Analysis—Aged > 66 years.

The role of race and ethnicity and neighborhood characteristics in modifying the association between residential neighborhood PM_2.5 and mortality among patients with kidney failure (aged > 66 years) initiating dialysis (2003-2019; N = 345,718)

Table S4: Sensitivity Analysis—Aged >66 years.

Table S5: Sensitivity Analysis—Adjusting for Urbanicity.

Table S6: Sensitivity Analysis—Previous Environmental Protection Agency (EPA) Cutoff for High PM_2.5 > 12 μg/m³.

The role of race and ethnicity and neighborhood characteristics in modifying the association between residential neighborhood PM_2.5 and mortality among patients with kidney failure (aged ≥18 years) initiating dialysis (2003-2019; N = 814,608)

Table S7: Sensitivity Analysis—Previous Environmental Protection Agency (EPA) Cutoff for high PM_2.5 > 12 μg/m³.

Table S8: Cox Proportional Hazards Model—Low PM_2.5 Neighborhoods.

The role of residential neighborhood characteristics in modifying the association between residential neighborhood low PM_2.5 and mortality among patients with kidney failure (aged ≥ 18 years) initiating dialysis (2003-2019; N = 224,773)

Supplementary Materials

Supplementary File (PDF)

/Item S1, Tables S1-S8.

mmc1.pdf^{(402.1KB, 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

Supplementary File (PDF)

/Item S1, Tables S1-S8.

mmc1.pdf^{(402.1KB, pdf)}

[bib1] 1.Feng Y., Jones M.R., Chu N.M., Segev D.L., McAdams-DeMarco M. Ambient air pollution and mortality among older patients initiating maintenance dialysis. Am J Nephrol. 2021;52(3):217–227. doi: 10.1159/000514233. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Neighborhood Factors, Air Pollution, and Mortality Among Kidney Failure Patients: Exploring Differences by Race and Ethnicity

Yiting Li

Gayathri Menon

Jane J Long

Malika Wilson

Byoungjun Kim

Mario P DeMarco

Babak J Orandi

Sunjae Bae

Wenbo Wu

Yijing Feng

Terry Gordon

George D Thurston

Dorry L Segev

Mara A McAdams-DeMarco

Abstract

Rationale & Objective

Study Design

Setting & Participants

Exposures

Outcome

Analytical Approach

Results

Limitations

Conclusions

Plain-language Summary

Methods

Study Population and Data Source

Air Pollution

Neighborhood Factors

Residential racial and ethnic segregation

Neighborhood deprivation index

Built Environment Factors

Parks and daily vehicle counts

Medically underserved areas

Urbanicity

Mortality

Statistical Analyses

Sensitivity Analysis

Results

Study Population

Table 1.

PM2.5 and Mortality by Race and Ethnicity

Figure 1.

Table 2.

Neighborhood Factors and the Association Between PM2.5 and Mortality

Residential racial and ethnic segregation

NDI

Built Environment Factors

Parks and daily vehicle counts

MUAs

Urbanicity

Role of Neighborhood Factors on Racial and Ethnic Differences in PM2.5 and Mortality

Table 3.

Sensitivity Analysis

Discussion

Article Information

Authors’ Full Names and Academic Degrees

Authors’ Contributions

Support

Financial Disclosure

Disclaimer

Data Sharing

Peer Review

Footnotes

Supplementary Materials

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

PM_2.5 and Mortality by Race and Ethnicity

Neighborhood Factors and the Association Between PM_2.5 and Mortality

Role of Neighborhood Factors on Racial and Ethnic Differences in PM_2.5 and Mortality