Key Points
Question
What is the association between Asian American identity and financial toxicity among cancer survivors, and do differences emerge upon ethnic disaggregation?
Findings
In this cross-sectional study of 55 511 cancer survivors, financial toxicity differed across Asian American ethnic subgroups relative to non-Hispanic White survivors. Chinese cancer survivors had lower odds of cost-related nonadherence, and Vietnamese, Filipino, and Japanese survivors had higher odds of financial worry.
Meaning
Broad panethnic classifications obscure differences in subgroup financial toxicity, highlighting the need for disaggregated data to support at-risk cancer survivors.
This cross-sectional study examines the associations between disaggregated Asian American (compared with non-Hispanic White) identities and financial toxicity among cancer survivors.
Abstract
Importance
Panethnic categorization of Asian American individuals masks sociodemographic diversity. Whether this aggregation obscures heterogeneity in cancer-related financial toxicity remains unknown due to a lack of disaggregated data.
Objective
To evaluate associations between disaggregated Asian American identity and financial toxicity among cancer survivors compared with non-Hispanic White survivors.
Design, Setting, and Participants
This cross-sectional study analyzed National Institutes of Health All of Us Research Program data. Participants were Asian American and non-Hispanic White adults with an electronic health record–confirmed malignant neoplasm diagnosed before completing financial toxicity surveys. Surveys were completed between June 2018 and December 2024; analyses concluded July 2026.
Exposures
Self-reported Asian and non-Hispanic White race; self-reported Chinese, Filipino, Indian or Pakistani, Japanese, Korean, and Vietnamese ethnicity.
Main Outcomes and Measures
Three financial toxicity domains were assessed: cost-related nonadherence, foregone medical care, and financial worry. Adjusted odds ratios (AORs) were estimated using multivariable logistic regression adjusting for clinical covariates, with sequential adjustment for socioeconomic factors and nativity.
Results
The cohort included 55 511 survivors (median [IQR] age, 66.0 [56.0-73.0] years): 54 230 (97.7%) non-Hispanic White and 1281 (2.3%) Asian American individuals. Overall, 27 316 (49.2%) experienced at least 1 financial toxicity domain. In the clinical baseline model, Chinese survivors had lower odds of cost-related nonadherence than non-Hispanic White survivors (AOR, 0.68; 95% CI, 0.53-0.88). Vietnamese (AOR, 2.15; 95% CI, 1.30-3.56), Filipino (AOR, 1.58; 95% CI, 1.22-2.07), and Japanese (AOR, 1.44; 95% CI, 1.10-1.89) survivors had higher odds of financial worry than non-Hispanic White survivors. In aggregate, Asian American identity was associated with lower cost-related nonadherence odds (AOR, 0.79; 95% CI, 0.69-0.92) and higher financial worry odds (AOR, 1.31; 95% CI, 1.16-1.47). Worry associations attenuated after nativity adjustment.
Conclusions and Relevance
In this cross-sectional study, disaggregation revealed heterogeneity in financial toxicity across Asian American subgroups, which was obscured by panethnic classification. Elevated financial worry may reflect immigration factors and US wealth accumulation rather than income alone. Disaggregated data are needed to effectively target financial navigation.
Introduction
Financial toxicity is a multidimensional consequence of cancer care encompassing material hardship, behavioral coping, and psychosocial distress.1 With up to half of US cancer survivors experiencing financial toxicity, identifying and intervening for at-risk populations is a clinical priority.2,3,4
While racial and ethnic disparities in financial toxicity are well documented, prior studies have primarily focused on comparisons among non-Hispanic White, Black, and Hispanic populations.5,6,7 Although recent work highlights the need to disaggregate these broad categories—demonstrating, for example, differential economic hardship among Hispanic subgroups—Asian American patients are frequently analyzed as a single group.8,9,10,11 This panethnic classification masks significant heterogeneity; Asian American individuals represent origins spanning 48 countries and exhibit the greatest within-group income inequality of any US ethnic group.12 Furthermore, while disaggregated studies have revealed significant clinical disparities in cancer stage, treatment delays, and mortality among Asian American subgroups, whether similar heterogeneity exists in financial toxicity remains unexamined.13,14,15,16,17,18
This knowledge gap persists because many national datasets lack disaggregated racial categories, multidimensional financial metrics, or the ability to temporally align a cancer diagnosis with financial outcomes.19
The National Institutes of Health’s All of Us Research Program overcomes these limitations by integrating participant-reported survey data with linked electronic health records (EHRs).20,21 This infrastructure enables EHR-confirmation of cancer diagnoses alongside precise, temporally aligned assessments of postdiagnosis financial toxicity in populations historically underrepresented in biomedical research.
In this study, we examined associations between race and ethnicity and 3 domains of financial toxicity (cost-related nonadherence, foregone medical care, and financial worry) among disaggregated Asian American cancer survivors compared with non-Hispanic White survivors. We hypothesized disaggregation would reveal differences across Asian American subgroups.
Methods
Data Source and Study Population
This cross-sectional study used data from the All of Us Research Program, Controlled Tier version 9. The All of Us institutional review board approved the program; all participants provided written informed consent. The All of Us resource access board permitted reporting participant counts under 20. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies.
Eligible participants completed requisite surveys and had an EHR-confirmed malignant neoplasm diagnosed before their index date (their most recent financial toxicity survey). Individuals meeting these criteria were classified as cancer survivors.22 Malignant neoplasms were identified using phecodes, which aggregate related International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis codes.23,24 Patients with exclusively keratinocyte carcinomas or benign neoplasms were excluded.
A parallel comparator cohort was constructed among participants without a qualifying EHR-confirmed malignant neoplasm diagnosis. Prevalence of each financial toxicity domain was compared between the cancer and noncancer cohorts by race and ethnicity.
Financial toxicity surveys were completed between June 2018 and December 2024. The All of Us data repository, version 9, had a data cutoff of January 2025; analyses concluded in July 2026.
Race and Ethnicity
The primary exposure was self-reported race and ethnicity. Participants reporting non-Hispanic White or Asian backgrounds were included; individuals of mixed heritage (reporting both Asian and non-Hispanic White backgrounds) were excluded. Asian participants were stratified by self-reported ethnicity (Chinese, Filipino, Indian or Pakistani, Japanese, Korean, and Vietnamese). For regression analyses, individuals reporting multiple Asian ethnicities received indicators for each corresponding subgroup and multiethnic Asian identity. For descriptive tables, these individuals were instead classified in a mutually exclusive multiethnic Asian category.
Financial Toxicity
Three binary domains of financial toxicity were derived from the Health Care Access and Utilization survey and modeled separately. Cost-related nonadherence was defined as reporting, within the past 12 months, skipping medication doses, taking less medication than prescribed, delaying filling a prescription, switching to a lower-cost medication, or purchasing medications from abroad to save money. Foregone medical care was defined as delayed or unmet health care needs due to cost, including inability to afford prescription medicines, mental health care, emergency care, dental care, specialist care, follow-up care, a regular doctor, or eyeglasses; delaying care due to copay, deductible, or other out-of-pocket costs; or using alternative therapies in place of conventional care to save money. Financial worry was defined as responding “somewhat worried” or “very worried” to the question: “If you get sick or have an accident, how worried are you that you will be able to pay your medical bills?”
Covariates
Covariates were selected a priori and organized into 3 sequential models.25 Model 1 included age, sex at birth, multiethnic Asian identity, cancer type (hematologic vs solid tumor), time since diagnosis, and comorbidity burden. Comorbidity burden was assessed via EHR-derived phecodes for 9 prespecified chronic conditions (hypertension, heart failure, ischemic heart disease, atrial fibrillation, diabetes, chronic obstructive pulmonary disease, asthma, major depressive disorder, and anxiety), dichotomized as fewer than 2 vs 2 or more conditions. Model 2 added socioeconomic measures: household income (<$50 000 vs ≥$50 000), education, employment status, insurance status, and marital status. Model 3 added nativity status (born in the US vs born outside the US). For patients with multiple qualifying cancers, the most recent diagnosis served as the index diagnosis.
Statistical Analysis
Continuous variables were summarized as medians with IQRs, and categorical variables as frequencies and proportions. Standardized mean differences (SMDs) were used to assess covariate balance.
Multivariable logistic regression evaluated associations between race and ethnicity and each financial toxicity outcome per model. Models compared aggregate Asian American participants and individual subgroups against non-Hispanic White participants.
Covariate missingness excluding household income (0.4%-0.9%) was imputed to the cohort mode. Full missingness by covariate is reported in eTable 1 in Supplement 1. Household income retained a distinct “unknown” indicator category. Maximum variance inflation factor across models was 1.38.
Percentage attenuation in excess log-odds was calculated between model 1 and each subsequent model for associations significant in model 1; attenuation was not calculated for null associations or odds ratios between 0.90 and 1.10. E-values were calculated for associations significant at the unadjusted threshold in the clinical baseline model.
Pairwise co-occurrence of the 3 financial toxicity domains was assessed using phi coefficients. Adjusted odds ratios (AORs) and 95% CIs were reported. False discovery rate–adjusted P values were calculated within each model and are reported alongside unadjusted values. Statistical significance was defined as 2-sided P < .05. Analyses were conducted using Python, version 3.12 (Python Software Foundation).
Results
Study Population
Among 747 029 participants in the All of Us Research Program, 481 919 had linked EHR data and 267 038 completed the financial toxicity surveys. Of 66 795 participants with preceding EHR-confirmed cancer, 55 511 self-reporting as Asian American or non-Hispanic White were included in the cohort (Figure 1). The median time from diagnosis to survey completion was 3.1 years (IQR, 1.2-6.5 years).
Figure 1. Flow Chart of Participant Selection From the All of Us Research Program.
EHR indicates electronic health record.
Baseline Sociodemographic and Clinical Characteristics
The cohort included 54 230 (97.7%) non-Hispanic White and 1281 (2.3%) Asian American participants (Table 1). Among Asian American participants, 419 (32.7%) were Chinese, 227 (17.7%) Filipino, 203 (15.8%) Indian or Pakistani, 214 (16.7%) Japanese, 94 (7.3%) Korean, 64 (5.0%) multiethnic Asian, and 60 (4.7%) Vietnamese. Sociodemographic and clinical characteristics varied significantly across Asian American subgroups (Table 2), notably in age, the number born outside the US (from 56 [26.2%] Japanese to 155 [76.4%] Indian or Pakistani participants), and employment.
Table 1. Baseline Sociodemographic and Clinical Characteristics of the Overall Analytic Cohort, Stratified by Asian American and Non-Hispanic White Race and Ethnicity.
| Characteristic | No. (%)a | SMD | ||
|---|---|---|---|---|
| Overall (N = 55 511) | Asian American (aggregate) (n = 1281) | Non-Hispanic White (n = 54 230) | ||
| Age, median (IQR), y | 66.0 (56.0-73.0) | 55.0 (43.0-65.0) | 66.0 (57.0-73.0) | 0.70 |
| Sex at birth | ||||
| Female | 32 807 (59.1) | 833 (65.0) | 31 974 (59.0) | 0.13 |
| Male | 22 704 (40.9) | 448 (35.0) | 22 256 (41.0) | |
| Nativity | ||||
| Born outside the US | 3191 (5.7) | 712 (55.6) | 2479 (4.6) | 1.34 |
| Marital status | ||||
| Partnered | 36 685 (66.1) | 800 (62.5) | 35 885 (66.2) | 0.08 |
| Education | ||||
| Completed at least high school | 50 023 (90.1) | 1205 (94.1) | 48 818 (90.0) | 0.16 |
| Household income | ||||
| <$50 000 | 12 488 (22.5) | 233 (18.2) | 12 255 (22.6) | 0.13 |
| ≥$50 000 | 36 859 (66.4) | 873 (68.1) | 35 986 (66.4) | |
| Unknownb | 6164 (11.1) | 175 (13.7) | 5989 (11.0) | |
| Employment | ||||
| Working | 22 225 (40.0) | 734 (57.3) | 21 491 (39.6) | 0.37 |
| Insurance | ||||
| Insured | 54 567 (98.3) | 1252 (97.7) | 53 315 (98.3) | 0.05 |
| Tumor type | ||||
| Solid | 51 396 (92.6) | 1156 (90.2) | 50 240 (92.6) | 0.09 |
| Time since diagnosis, median (IQR), y | 3.1 (1.2-6.5) | 2.4 (1.0-5.6) | 3.1 (1.2-6.5) | 0.18 |
| Comorbid conditions | ||||
| 0 | 11 735 (21.1) | 401 (31.3) | 11 334 (20.9) | 0.31 |
| 1 | 12 815 (23.1) | 325 (25.4) | 12 490 (23.0) | |
| 2 | 11 692 (21.1) | 257 (20.1) | 11 435 (21.1) | |
| ≥3 | 19 269 (34.7) | 298 (23.3) | 18 971 (35.0) | |
Abbreviation: SMD, standardized mean difference.
Unless otherwise indicated.
Unknown responses for household income are reported but were excluded from SMD calculations.
Table 2. Baseline Sociodemographic and Clinical Characteristics of Asian American Participants, Stratified by Disaggregated Ethnic Subgroup.
| Characteristic | No. (%)a | P value | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Overall (N = 1281) | Chinese (n = 419) | Filipino (n = 227) | Indian or Pakistani (n = 203) | Japanese (n = 214) | Korean (n = 94) | Multiethnic Asian (n = 64) | Vietnamese (n = 60) | ||
| Age, median (IQR), y | 55.0 (43.0-65.0) | 57.0 (43.0-67.0) | 54.0 (44.0-63.5) | 52.0 (41.0-66.0) | 59.0 (49.0-66.8) | 48.0 (39.2-55.0) | 46.5 (34.8-60.2) | 50.5 (40.0-64.0) | <.001 |
| Sex at birth | |||||||||
| Female | 833 (65.0) | 284 (67.8) | 153 (67.4) | 102 (50.2) | 148 (69.2) | 66 (70.2) | 43 (67.2) | 37 (61.7) | <.001 |
| Male | 448 (35.0) | 135 (32.2) | 74 (32.6) | 101 (49.8) | 66 (30.8) | 28 (29.8) | 21 (32.8) | 23 (38.3) | |
| Nativity | |||||||||
| Born outside the US | 712 (55.6) | 254 (60.6) | 128 (56.4) | 155 (76.4) | 56 (26.2) | 57 (60.6) | 17 (26.6) | 45 (75.0) | <.001 |
| Marital status | |||||||||
| Partnered | 800 (62.5) | 256 (61.1) | 148 (65.2) | 140 (69.0) | 128 (59.8) | 56 (59.6) | 38 (59.4) | 34 (56.7) | .43 |
| Education | |||||||||
| Completed at least high school | 1205 (94.1) | 400 (95.5) | 209 (92.1) | 196 (96.6) | 203 (94.9) | 92 (97.9) | 55 (85.9) | 50 (83.3) | <.001 |
| Household income | |||||||||
| <$50 000 | 233 (18.2) | 72 (17.2) | 46 (20.3) | 34 (16.7) | 44 (20.6) | 12 (12.8) | 15 (23.4) | 10 (16.7) | .01 |
| ≥$50 000 | 873 (68.1) | 295 (70.4) | 146 (64.3) | 146 (71.9) | 145 (67.8) | 70 (74.5) | 40 (62.5) | 31 (51.7) | |
| Unknown | 175 (13.7) | 52 (12.4) | 35 (15.4) | 23 (11.3) | 25 (11.7) | 12 (12.8) | 9 (14.1) | 19 (31.7) | |
| Employment | |||||||||
| Working | 734 (57.3) | 245 (58.5) | 119 (52.4) | 124 (61.1) | 104 (48.6) | 66 (70.2) | 38 (59.4) | 38 (63.3) | <.001 |
| Insurance | |||||||||
| Insured | 1252 (97.7) | 416 (99.3) | 220 (96.9) | 198 (97.5) | 208 (97.2) | 92 (97.9) | 58 (90.6) | 60 (100.0) | .02 |
| Tumor type | |||||||||
| Solid | 1156 (90.2) | 389 (92.8) | 203 (89.4) | 180 (88.7) | 191 (89.3) | 87 (92.6) | 58 (90.6) | 48 (80.0) | .06 |
| Time since diagnosis, median (IQR), y | 2.4 (1.0-5.6) | 2.6 (1.0-5.4) | 2.5 (0.9-6.0) | 2.5 (1.0-5.8) | 2.4 (1.1-5.9) | 1.9 (0.8-5.0) | 2.1 (1.2-3.8) | 3.0 (0.6-6.9) | .73 |
| Comorbid conditions | |||||||||
| 0 | 401 (31.3) | 154 (36.8) | 49 (21.6) | 65 (32.0) | 59 (27.6) | 32 (34.0) | 20 (31.2) | 22 (36.7) | .05 |
| 1 | 325 (25.4) | 106 (25.3) | 60 (26.4) | 49 (24.1) | 57 (26.6) | 27 (28.7) | 12 (18.8) | 14 (23.3) | |
| 2 | 257 (20.1) | 81 (19.3) | 51 (22.5) | 40 (19.7) | 38 (17.8) | 18 (19.1) | 15 (23.4) | 14 (23.3) | |
| ≥3 | 298 (23.3) | 78 (18.6) | 67 (29.5) | 49 (24.1) | 60 (28.0) | 17 (18.1) | 17 (26.6) | 10 (16.7) | |
Unless otherwise indicated.
Prevalence of Financial Toxicity
Overall, 27 316 participants (49.2%) experienced at least 1 financial toxicity domain, with moderate pairwise co-occurrence (phi coefficients, 0.27-0.37). Aggregate Asian American individuals reported lower cost-related nonadherence (266 [20.8%] compared with 12 148 [22.4%]), but higher foregone medical care (418 [32.6%] compared with 13 503 [24.9%]) and financial worry (614 [47.9%] compared with 18 167 [33.5%]) than non-Hispanic White individuals, varying significantly across subgroups (eTable 5 in Supplement 1). A contemporaneous noncancer comparison cohort exhibited higher financial toxicity across most domains (eTable 5 in Supplement 1); this cohort was substantially younger (median age, 53 vs 66 years; SMD = 0.78) and had lower income (eTable 6 in Supplement 1).
Adjusted Associations of Race and Ethnicity With Financial Toxicity
In the clinical baseline model (model 1), aggregate Asian American identity was associated with lower odds of cost-related nonadherence (AOR, 0.79; 95% CI, 0.69-0.92; P = .001) and higher odds of financial worry (AOR, 1.31; 95% CI, 1.16-1.47; P < .001) compared with non-Hispanic White identity. Foregone medical care did not differ (AOR, 1.03; 95% CI, 0.90-1.17; P = .69) (Table 3 and Figure 2; eTable 2 in Supplement 1).
Table 3. Association of Aggregate and Disaggregated Asian American Ethnicity With Financial Toxicity Outcomes Across Stepwise Multivariable Models Among Cancer Survivors.
| Variablea | Model 1 (clinical baseline)b | Model 2 (+ socioeconomic)c | Model 3 (+ nativity)d | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| AOR (95% CI) | P value | AOR (95% CI) | Attenuation, %e | P value | AOR (95% CI) | Attenuation, %e | P value | ||||
| Raw | FDR | Raw | FDR | Raw | FDR | ||||||
| Cost-related nonadherence | |||||||||||
| All Asian American | 0.79 (0.69-0.92) | .001 | .009 | 0.80 (0.70-0.93) | 6.3 | .003 | .02 | 0.81 (0.70-0.95) | 10.3 | .007 | .02 |
| Filipino | 0.78 (0.57-1.06) | .12 | .28 | 0.79 (0.57-1.07) | NA | .13 | .30 | 0.79 (0.58-1.09) | NA | .15 | .35 |
| Vietnamese | 0.97 (0.56-1.67) | .92 | .92 | 0.95 (0.55-1.65) | NA | .86 | .94 | 0.97 (0.56-1.67) | NA | .90 | .97 |
| Indian or Pakistani | 0.92 (0.67-1.27) | .62 | .92 | 0.92 (0.67-1.28) | NA | .64 | .94 | 0.94 (0.67-1.31) | NA | .71 | .97 |
| Korean | 0.92 (0.58-1.45) | .71 | .92 | 0.96 (0.61-1.52) | NA | .87 | .94 | 0.97 (0.62-1.54) | NA | .90 | .97 |
| Chinese | 0.68 (0.53-0.88) | .003 | .009 | 0.70 (0.54-0.89) | 4.9 | .004 | .02 | 0.70 (0.55-0.91) | 8.1 | .007 | .02 |
| Japanese | 0.99 (0.72-1.34) | .92 | .92 | 0.99 (0.73-1.35) | NA | .94 | .94 | 0.99 (0.73-1.35) | NA | .97 | .97 |
| Financial worry | |||||||||||
| All Asian American | 1.31 (1.16-1.47) | <.001 | <.001 | 1.34 (1.19-1.52) | −10.2 | <.001 | <.001 | 1.16 (1.02-1.32) | 43.0 | .02 | .05 |
| Filipino | 1.58 (1.22-2.07) | <.001 | .002 | 1.65 (1.26-2.16) | −8.6 | <.001 | <.001 | 1.43 (1.09-1.87) | 22.4 | .01 | .05 |
| Vietnamese | 2.15 (1.30-3.56) | .003 | .007 | 2.05 (1.24-3.41) | 5.8 | .005 | .01 | 1.71 (1.03-2.84) | 29.6 | .04 | .06 |
| Indian or Pakistani | 1.10 (0.82-1.46) | .52 | .61 | 1.12 (0.84-1.50) | NA | .44 | .51 | 0.93 (0.69-1.24) | NA | .61 | .61 |
| Korean | 1.28 (0.85-1.91) | .23 | .33 | 1.32 (0.88-1.98) | NA | .19 | .26 | 1.14 (0.76-1.72) | NA | .52 | .60 |
| Chinese | 1.05 (0.86-1.28) | .64 | .64 | 1.07 (0.87-1.31) | NA | .52 | .52 | 0.91 (0.74-1.12) | NA | .38 | .53 |
| Japanese | 1.44 (1.10-1.89) | .009 | .02 | 1.47 (1.11-1.93) | −5.3 | .007 | .01 | 1.38 (1.05-1.82) | 11.3 | .02 | .05 |
| Foregone care | |||||||||||
| All Asian American | 1.03 (0.90-1.17) | .69 | .80 | 1.07 (0.93-1.22) | NA | .34 | .60 | 1.08 (0.94-1.24) | NA | .31 | .60 |
| Filipino | 1.07 (0.81-1.42) | .61 | .80 | 1.11 (0.83-1.47) | NA | .48 | .60 | 1.12 (0.84-1.50) | NA | .43 | .60 |
| Vietnamese | 1.56 (0.95-2.55) | .08 | .27 | 1.47 (0.89-2.44) | NA | .13 | .46 | 1.50 (0.90-2.49) | NA | .12 | .41 |
| Indian or Pakistani | 1.32 (0.98-1.76) | .07 | .27 | 1.37 (1.02-1.86) | NA | .04 | .28 | 1.40 (1.03-1.91) | NA | .03 | .23 |
| Korean | 1.11 (0.74-1.68) | .61 | .80 | 1.19 (0.78-1.82) | NA- | .42 | .60 | 1.21 (0.79-1.85) | NA | .38 | .60 |
| Chinese | 0.90 (0.72-1.12) | .33 | .78 | 0.93 (0.74-1.16) | NA | .52 | .60 | 0.94 (0.75-1.19) | NA | .62 | .72 |
| Japanese | 0.97 (0.72-1.30) | .82 | .82 | 0.97 (0.71-1.32) | NA | .85 | .85 | 0.98 (0.72-1.33) | NA | .87 | .87 |
Abbreviations: AOR, adjusted odds ratio; FDR, false discovery rate; NA, not applicable.
Non-Hispanic White served as the reference group for all models.
Model 1 adjusted for age, sex, comorbidity count, cancer type, time since diagnosis, and multiethnic identity.
Model 2 added household income, education, employment, insurance coverage, and partnership status.
Model 3 added nativity (born in the US or born outside the US).
The percentage attenuation of excess log-odds (β) relative to model 1 (clinical baseline).
Figure 2. Dot Plot of Adjusted Odds Ratios for Financial Toxicity Outcomes Among Asian American Cancer Survivors.
Adjusted odds ratios and 95% CIs are displayed for cost-related nonadherence, foregone care, and financial worry among aggregate and disaggregated Asian American subgroups compared with non-Hispanic White cancer survivors. The vertical dotted line at 1.00 indicates the null effect. Square markers represent point estimates, and horizontal bars represent 95% CIs. Estimates were derived from multivariable logistic regression models adjusted for baseline clinical and demographic characteristics: age, sex, comorbidity count, cancer type (solid vs hematologic), time since diagnosis, and multiethnic Asian identity.
aP < .05.
When ethnicity was disaggregated, these associations were concentrated in specific subgroups (Table 3 and Figure 2). For cost-related nonadherence, only Chinese participants—the largest subgroup—had lower odds than non-Hispanic White participants (75 [17.9%]; AOR, 0.68; 95% CI, 0.53-0.88; P = .003). For financial worry, Vietnamese (39 [65.0%]; AOR, 2.15; 95% CI, 1.30-3.56; P = .003), Filipino (121 [53.3%]; AOR, 1.58; 95% CI, 1.22-2.07; P < .001), and Japanese (102 [47.7%]; AOR, 1.44; 95% CI, 1.10-1.89; P = .009) participants had higher odds than non-Hispanic White participants. No subgroup differed significantly for foregone medical care.
After false discovery rate (FDR) correction, the aggregate and Chinese associations with cost-related nonadherence and the aggregate, Vietnamese, Filipino, and Japanese associations with financial worry persisted (Table 3). Adjustment for socioeconomic factors (model 2) did not materially alter associations; the Indian or Pakistani association with foregone medical care reached nominal significance (73 [36.0%]; AOR, 1.37; 95% CI, 1.02-1.86; P = .04) but did not survive FDR correction (FDR-adjusted P = .28).
Attenuation of Financial Worry With Nativity Adjustment
Adjustment for nativity (model 3) attenuated associations with financial worry. The excess odds of financial worry were attenuated by 43.0% for aggregate Asian American identity (AOR, 1.16; 95% CI, 1.02-1.32), 29.6% for Vietnamese (AOR, 1.71; 95% CI, 1.03-2.84), 22.4% for Filipino (AOR, 1.43; 95% CI, 1.09-1.87), and 11.3% for Japanese (AOR, 1.38; 95% CI, 1.05-1.82); corresponding FDR-adjusted P values were .05, .06, .05, and .05, respectively. Conversely, the protective cost-related nonadherence associations attenuated minimally and remained significant after full adjustment (aggregate AOR, 0.81; 95% CI, 0.70-0.95; Chinese AOR, 0.70; 95% CI, 0.55-0.91; both FDR-adjusted P = .02). Being born outside the US was independently associated with financial worry (AOR, 1.33; 95% CI, 1.23-1.45; P < .001) but not with cost-related nonadherence or foregone medical care (eTable 4 in Supplement 1).
Sociodemographic and Clinical Covariates
Among covariates in the socioeconomic model (model 2; eTable 3 in Supplement1), household income below $50 000 annually was associated with higher odds of all 3 outcomes (AORs, 1.80 [95% CI, 1.71-1.90] for cost-related nonadherence, 2.99 [95% CI, 2.83-3.15] for foregone medical care, and 2.38 [95% CI, 2.26-2.50] for financial worry; all P < .001), and uninsured status with higher odds of foregone medical care (AOR, 1.71; 95% CI, 1.41-2.08; P < .001). Comorbidity burden (≥2 conditions) and hematologic malignant neoplasm were associated with higher odds across all 3 domains, whereas older age, male sex, and longer time since diagnosis were associated with modestly lower odds. E-values for the clinical baseline (model 1) associations significant at P < .05 ranged from 1.49 (aggregate Asian American identity and cost-related nonadherence) to 2.29 (Vietnamese identity and financial worry).
Discussion
In this study of 55 511 cancer survivors in the All of Us Research Program, disaggregating Asian American survivors into ethnic subgroups revealed heterogeneity in financial toxicity. Compared with non-Hispanic White survivors, Chinese survivors had lower odds of cost-related nonadherence, whereas Vietnamese, Filipino, and Japanese survivors reported elevated financial worry. These findings underscore the limitations of panethnic classification, which collapses culturally distinct populations and obscures divergent financial toxicity patterns.
Notably, subgroup differences persisted after adjustment for socioeconomic status, suggesting that standard metrics incompletely capture the multidimensional nature of financial toxicity. Despite high aggregate median incomes, Asian American households exhibit high wealth inequality and the highest median debt-to-income ratios nationally.26 Competing obligations such as mortgages, loans, and remittances sent by roughly 1 in 4 households to family abroad further constrain liquidity to absorb new medical expenses.27,28 Ultimately, financial toxicity may reflect these differences in financial flexibility and liquidity rather than income alone.
Qualitative research further suggests that family-centered norms can inform how health care costs are interpreted and managed in some Asian American communities. For instance, self-perceived burden—the documented distress of believing one’s care imposes a financial and practical strain on family—is heightened in communities with strong family-obligation norms, and manifests as internalized worry rather than changes to one’s own care.29 Relatedly, some older Vietnamese immigrants regard nonsymptomatic care as “looking for trouble.”30 Concepts such as utang na loob, often translated as a “debt of gratitude,” reflect enduring expectations of reciprocity and family responsibility in Filipino communities.31,32 Viewed through this lens, individuals may defer nonurgent care or internalize worry to mitigate family strain.33,34,35(p2)36 This dynamic aligns with our finding that certain subgroups exhibit elevated financial worry without a corresponding increase in cost-related nonadherence.
Chinese participants exhibited lower odds of cost-related nonadherence that persisted after adjustment for socioeconomic factors and nativity, suggesting protective influences not fully captured by measured covariates. The financial-worry associations among Vietnamese, Filipino, and Japanese survivors attenuated after adjustment for nativity, and being born outside the US was independently associated with financial worry. This pattern suggests that immigration-related factors—including duration of US residence, acculturation, and accumulated wealth—may partly underlie the elevated financial worry observed in these groups, beyond household income alone.
Beyond ethnicity, clinical factors were associated with financial toxicity. Comorbidity burden demonstrated a graded association with cost-related nonadherence and foregone care, reflecting how multimorbidity increases out-of-pocket spending.37,38 Hematologic malignant neoplasms elevated odds across domains, likely due to sustained costs of chronic therapies.39,40 Increasing time since diagnosis lowered odds, mirroring the known U-shaped curve of survivorship costs.41
These persistent demographic and clinical disparities underscore the urgent need for health systems to systematically identify and mitigate financial toxicity. While more than half of major oncology networks report routine financial distress screening, these estimates selectively reflect well-resourced academic centers.42,43 Furthermore, passive screening frequently fails vulnerable patients: 55.4% of breast cancer patients desiring financial counseling do not receive it, and up to 81% screening positive for financial toxicity receive no documented follow-up.44,45
Bridging this gap requires standardized measurement coupled with proactive care team integration. Validated patient-reported outcome measures, such as the Comprehensive Score for Financial Toxicity (COST) and its easily embedded 2-item variant, are essential for accurate risk stratification and cross-institutional comparability.46,47 For example, in gynecologic oncology, baseline COST risk stratification identified 90% of patients who later developed financial hardship.48 Critically, the efficacy of these tools depends on structured clinical workflows. In the cluster-randomized PRO-TECT trial,49 integrating monthly financial toxicity into routine digital monitoring—with automated care-team alerts for elevated scores—significantly reduced the development or worsening of financial difficulties compared with usual care (30.2% vs 39.0%; P = .004), without a mandated intervention protocol.
Combining screening with EHR-integrated pathways is a proven workflow to translate identification into outcomes. At Memorial Sloan Kettering, embedding a financial toxicity referral order set into the EMR increased connection to assistance resources, generating more than $850 000 in copayment aid within the first year.50
Such interventions require time and workforce capacity that many underresourced oncology clinics lack. Programs like Patient COUNTS address this gap by recruiting community members to deliver language-concordant, culturally tailored navigation, achieving high patient approval and treatment completion rates.51
Building on this model, institutions can mitigate financial toxicity by recruiting culturally concordant navigators to administer COST assessments, triage at-risk patients, and connect them to localized resources, such as transit aid or region-specific charitable funds, that are often invisible to centralized systems and not generalizable nationally. For example, one study found that culturally trained navigators secured insurance and transit aid for over 90% of patients, reducing median treatment times.52,53 Prior work in a national cancer center found that a financial navigation program led to an average of $2500 in financial benefits secured per participant.54 Culturally concordant navigators may be positioned to uncover hidden financial distress stemming from cultural norms, remittances, and family obligations that may not manifest overtly in standard screenings.
Nevertheless, patient navigation alone cannot offset the structural drivers of financial toxicity.55,56 Clinicians and health systems must concurrently minimize avoidable financial hardship. When treatment options offer comparable efficacy, cost must be weighed alongside toxicity and quality-of-life outcomes. This includes eliminating low-value care—for instance, limiting surveillance positron emission tomography or computed tomography imaging in asymptomatic low-risk patients, an independent driver of financial toxicity.57 Further clinical stewardship includes using evidence-based shorter radiation fractionation schedules, selecting systemic therapies that minimize both physical toxicity and travel time, and reserving targeted therapies for biomarker-selected populations.58
Strengths and Limitations
This study has several important strengths. It represents the first analysis of financial toxicity among disaggregated Asian American cancer survivors. By using linked electronic health records and restricting the cohort to survivors whose cancer diagnosis preceded their financial toxicity assessment, the study establishes a clear temporal sequence, minimizes recall bias, and reduces the misclassification of preexisting financial strain. Furthermore, multidimensional measures allowed for differentiation between behavioral, access-related, and psychosocial domains.
Limitations should be considered. First, the All of Us cohort is not nationally representative; therefore, descriptive estimates may not generalize nationally. Asian American individuals constitute 2.3% of our sample, compared with national estimates of 7% to 8%, suggesting a potential sampling bias toward Asian American individuals who are engaged with the health care system.59 This modest size limits statistical power, precluding analysis of smaller groups (eg, Hmong, Cambodian). Point estimates in small subgroups should be interpreted as hypothesis-generating. Furthermore, aggregating Indian and Pakistani participants to maintain power may mask disparities between these distinct communities. Categorized ethnicities (eg, Chinese, Filipino) also represent broad nationalities with distinct internal cultures we could not further disaggregate.
Additionally, because financial toxicity domains were self-reported, individuals may internalize or report worry differently at comparable levels of objective financial hardship; thus, these findings reflect the subjective psychological burden of care rather than strict material deficits. Finally, the cross-sectional design precludes causal inferences.
Conclusions
In this cross-sectional study of 55 511 cancer survivors, aggregate analyses identified broad differences between Asian American and non-Hispanic White groups, whereas ethnic disaggregation revealed substantial heterogeneity across Asian American subgroups. These findings suggest that more granular collection and reporting of ethnicity data may improve identification of subgroup-specific disparities that are not apparent in panethnic analyses.
eTable 1. Pre-Imputation Missingness Across Model Covariates
eTable 2. Full Multivariable Regression Output for Model 1 (Clinical Baseline)
eTable 3. Full Multivariable Regression Output for Model 2 (+ Socioeconomic)
eTable 4. Full Multivariable Regression Output for Model 3 (+ Nativity)
eTable 5. Financial Toxicity Prevalence by Race and Ethnicity: Cancer vs Non-Cancer Cohort
eTable 6. Baseline Composition: Cancer (n=55,511) vs Non-Cancer (n=218,926)
Data Sharing Statement
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
eTable 1. Pre-Imputation Missingness Across Model Covariates
eTable 2. Full Multivariable Regression Output for Model 1 (Clinical Baseline)
eTable 3. Full Multivariable Regression Output for Model 2 (+ Socioeconomic)
eTable 4. Full Multivariable Regression Output for Model 3 (+ Nativity)
eTable 5. Financial Toxicity Prevalence by Race and Ethnicity: Cancer vs Non-Cancer Cohort
eTable 6. Baseline Composition: Cancer (n=55,511) vs Non-Cancer (n=218,926)
Data Sharing Statement
