Abstract
Background
Numerous studies examine the impact of cancer and disability on health-care use, financial toxicity, and health outcomes but not the relationship between cancer and disability. This study investigates whether and how cancer characteristics are associated with disability status (Social Security Disability Insurance) and timing of onset among prime working-age cancer survivors.
Methods
We used the 1999 to 2022 Surveillance, Epidemiology, and End Results file linked with Medicare data. Outcome was a categorical variable indicating no disability, disability onset ≥2 years and 0-<2 years before cancer diagnosis, and 0-<2 years and ≥2 years after diagnosis. Primary independent variables were 16 categories of cancer sites and stage at diagnosis. We employed a multivariable multinomial probit regression model, adjusting for patient and neighborhood characteristics, overall and by 5-year survival.
Results
Of the total sample (n = 97 642), 62.2%, 22.4%, and 15.4% had no, precancer, and postcancer disability, respectively. Likelihood of disability and timing of disability onset differed by cancer sites. Compared with prostate cancer survivors (lowest prevalence of disability), adjusted predicted probability of having disability ≥2 years before diagnosis was higher by 0.46 (95% CI = 0.43 to 0.49) among liver cancer survivors, whereas having disability <2 years after diagnosis was higher by 0.29 (95% CI = 0.27 to 0.32) among brain cancer survivors. Nearly all <5-year cancer survivors had disability, and the patterns of association varied significantly by 5-year survival.
Conclusions
Clinically assessed disability is strongly associated with specific cancer sites. These findings may point to strategies to improve efficiency of cancer prevention and care, reducing untoward impacts on survival.
Introduction
Cancer survivors with disability represent one of the most vulnerable groups facing health and financial hardship.1-4 Preexisting disability can impede preventive cancer screening,5,6 early-stage cancer diagnosis,7,8 and adequate treatment,1,6-9 and cancer can obstruct, complicate, or create disability care needs,10,11 resulting in greater need for health care, financial toxicity,11,12 and reduced well-being.4,13
A substantial literature documents the impact of preexisting disability on disparities in cancer care1,5,7-9,14 and the combined impact of cancer and disability on financial hardship1-3,11 and health outcomes.1,15-17 However, the relationship between cancer and the onset of disability is scarcely known,10,18-20 and no prior study has investigated how this relationship varied by length of disability. Further limiting our knowledge of this relationship, existing studies examined the relationship cross-sectionally18,19 or separately between pre-cancer20 or post-cancer10 periods using different cohorts. Notably, cross-sectional studies include only those alive at that point in time, which significantly biases estimates of the prevalence of disability and associations of explanatory factors.10,18-20 Analysis of separate periods with different cohorts prevents comparison of the relationship consistently over time. Moreover, the reports are limited in scope, considering only the 4 most common cancer sites (eg, breast, prostate),19,20 or use self-reported functional disability,18-20 preventing understanding of how disability may vary by cancer sites or different dimensions of disability.
Understanding of the sequence of precancer disability, cancer incidence, and postcancer disability in the population-based context and the difference by cancer site and survival status is necessary to guide timely intervention to improve efficiency of cancer treatment and minimize undesirable effects on health outcomes. Comprehension with clinically assessed disability is particularly important because clinical disability focuses on medical diagnosis and underlying pathology21 that directly influences cancer treatment, whereas functional disability refers to an individual’s practical inability to perform daily tasks.22
This study deepens the literature by leveraging Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data to identify the prevalence of disability and examine the association of cancer characteristics with disability status and timing of onset among a population of prime working-age individuals diagnosed with cancer.
Methods
Data sources
We used the SEER program (5% random sample; 1999 to 2021),23 the Medicare Master Beneficiary Summary File (MBSF; 1999 to 2022),24 and the Area Health Resources File (AHRF; 2000, 2005, 2010 to 2022).25 SEER is a population-based network of cancer registries covering approximately 45.9% of the US population.26 SEER constructs individual identifiers and cancer sequence numbers per patient. For each cancer occurrence, it collects age at diagnosis, date of diagnosis, primary tumor site, stage at diagnosis, date of death, and patient’s state and county Federal Information Processing Standards (FIPS) codes. Because date-related variables include only month and year, we constructed dates of diagnosis and death by using the 15th of the month for the main analysis.
The MBSF includes enrollment information for individuals. For each beneficiary, it provides unique individual identifiers, demographic characteristics, and the date and reason (age, Social Security Disability Insurance [SSDI], end-stage renal disease) for initial Medicare entitlement. SEER was linked with MBSF using individual identifier.
The AHRF includes county-level socioeconomic characteristics, health-care workforce and capacity measures, and rurality classifications. SEER-Medicare records were matched with AHRF data via state-county FIPS codes. We excluded 0.2% of study population due to nonmatched state-county FIPS codes between SEER-Medicare and AHRF.
Measurement of disability status and timing
Cancer survivors of prime working age who were enrolled in Medicare because of SSDI were classified as having disability.9,15 SSDI approval is determined by strict, multistep clinical assessment process, reflecting severe physical, cognitive, and/or mental impairments that prevent an individual from engaging in any gainful activity.21
The timing of disability onset was measured by the SSDI approval date,9,15 which was constructed using Medicare enrollment date. SSDI approval precedes Medicare enrollment by 29 months because individuals have a mandatory 5-month waiting period between SSDI approval and SSDI benefit entitlement, followed by a 24-month waiting period for Medicare eligibility.27
Study population
We applied 3 inclusion/exclusion criteria to define the study population. First, we restricted the study population to those who were diagnosed with first primary malignant cancer between 1999 and 2016 because we tracked timing of SSDI at least ≥2 years after cancer diagnosis and 2022 MBSF captured Medicare entitlement via SSDI through 2019 due to the 29-month waiting period. Second, we restricted ages to 25 to 61 at diagnosis (primary working age) to assess disability by SSDI. We excluded adults younger than 25 because SSDI eligibility requires a sufficient work history. We excluded those aged 62 or older because we track at least ≥2 years of SSDI after diagnosis and Medicare enrollment due to SSDI cannot be distinguished from age-based Medicare entitlement from age 65. Third, we excluded cancer survivors who were enrolled in Medicare because of end-stage renal disease.
For subsample analysis by 5-year survival status, we excluded individuals from 2 states (IL and MA) because SEER did not include date of death for these states and National Death Index data were not available to external Medicare data users as of 2022.28
Dependent variable
Outcome was a 5-category variable indicating disability status and its onset timing relative to cancer diagnosis: no disability (base outcome), disability onset ≥2 years before cancer diagnosis, disability onset 0 to <2 years before diagnosis, disability onset 0 to <2 years after diagnosis, and disability onset ≥2 years after diagnosis.
Primary independent variables
Primary independent variables were cancer site and stage of cancer at diagnosis (in situ, localized, regional, distant, unknown). Cancer site was categorized using International Classification of Diseases for Oncology (Third Edition)29 codes into the 15 most frequently observed types of cancer sites plus all other cancers. Prostate cancer was used as a reference because prevalence of disability was the lowest among them.
Other explanatory variables
Patient-level variables included age category at diagnosis (58 to 61, 54 to 57, 50 to 53, 25 to 49 years), sex, and race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Other). We adjusted for prevalence of 2 types of industries (manufacturing, construction) and poverty rate per county, which is known as a proxy for high-risk jobs and poor working environment30 because SEER-Medicare linked data do not provide information of individual socioeconomic status. For each county-level measure, we constructed nationwide quartiles in AHRF and then matched to counties in SEER data. Health-care access indicators included primary care physician shortage designations (none, partial county, whole county) and rurality (urban, rural).
Statistical analysis
We first conducted bivariate analyses using a Pearson χ2 test to examine differences in the disability status and timing of onset by patient, cancer, and neighborhood characteristics. For multivariable regression analyses, we employed a multinomial probit regression model,31 adjusting primary and other explanatory variables. We clustered observations within county to control potential intra-cluster correlation. We did not use a multilevel mixed-effect multinomial logistic model because the independence of irrelevant alternatives assumption was violated. The estimation coefficients from the multinomial probit model were converted into average marginal effects (AMEs) to improve interpretability. The AME represents the difference in the adjusted predicted probability of having disability relative to the reference group and is statistically significant if its 95% confidence interval (CI) does not include zero.
We repeated the same analyses stratified by 5-year cancer survival: <5 years vs ≥5 years. We conducted 5 sensitivity analyses. First, we restricted the sample to ages 32 to 61, because SSDI eligibility criteria differ for individuals aged 24 to 31.27 Second, in analysis for overall study population, we excluded observations from 2 states (IL and MA) that did not collect date of death and were not applicable for analysis by 5-year survival. Third, we recategorized the outcome to track back the disability onset timing every 2 years up to 6 years. Fourth, for dates of cancer diagnosis and death, we used days 1, 7, and 21 as opposed to day 15. Fifth, we clustered observations within cancer registry.
All statistical tests were 2-sided, and P-values ≤.05 were considered statistically significant. All analyses were performed by Stata MP v.18.0 (StataCorp LLC).
Results
Characteristics of patient, cancer, and neighborhood socioeconomic status
The total sample size was 97 642 (Table 1). Breast, prostate, colorectal, and skin cancer were the most prevalent sites. At diagnosis, 44.0% of patients had localized disease and 18.7% had regional disease.
Table 1.
Characteristics of patient, cancer, and neighborhood socioeconomic status among total study population: overall and by disability status and timing of onset.a,b
| Total | No disability | Onset of disability (approval of SSDI) |
P | ||||
|---|---|---|---|---|---|---|---|
| ≥2 years | ≥0, <2 years | >0, <2 years | ≥2 years | ||||
| before cancer | before cancer | after cancer | after cancer | ||||
| (n = 97 642) | (n = 60 693) | (n = 16 220) | (n = 5682) | (n = 8672) | (n = 6375) | ||
| n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | ||
| Age category at diagnosis | <.001 | ||||||
| 58 to 61 | 37 674 (38.6) | 27 952 (46.1) | 5799 (35.8) | 1585 (27.9) | 2091 (24.1) | 247 (3.9) | – |
| 54 to 57 | 26 891 (27.5) | 18 141 (29.9) | 4131 (25.5) | 1358 (23.9) | 2065 (23.8) | 1196 (18.8) | – |
| 50 to 53 | 16 689 (17.1) | 9903 (16.3) | 2600 (16.0) | 1048 (18.4) | 1519 (17.5) | 1619 (25.4) | – |
| 25 to 49 | 16 388 (16.8) | 4697 (7.7) | 3690 (22.7) | 1691 (29.8) | 2997 (34.6) | 3313 (52.0) | – |
| Women | 52 788 (54.1) | 33 702 (55.5) | 7658 (47.2) | 2814 (49.5) | 4736 (54.6) | 3878 (60.8) | <.001 |
| Race/ethnicity | <.001 | ||||||
| Non-Hispanic White | 70 762 (72.5) | 47 038 (77.5) | 10 540 (65.0) | 3482 (61.3) | 5417 (62.5) | 4285 (67.2) | – |
| Non-Hispanic Black | 11 833 (12.1) | 5126 (8.4) | 3342 (20.6) | 1023 (18.0) | 1425 (16.4) | 917 (14.4) | – |
| Hispanic | 9634 (9.9) | 4709 (7.8) | 1907 (11.8) | 890 (15.7) | 1270 (14.6) | 858 (13.5) | – |
| Non-Hispanic Other | 5413 (5.5) | 3820 (6.3) | 431 (2.7) | 287 (5.1) | 560 (6.5) | 315 (4.9) | – |
| Site of cancer | <.001 | ||||||
| Breast | 24 135 (24.7) | 17 519 (28.9) | 2332 (14.4) | 834 (14.7) | 1742 (20.1) | 1708 (26.8) | – |
| Prostate | 16 433 (16.8) | 13 209 (21.8) | 1552 (9.6) | 446 (7.8) | 595 (6.9) | 631 (9.9) | – |
| Colorectal | 8808 (9.0) | 4989 (8.2) | 1449 (8.9) | 621 (10.9) | 1142 (13.2) | 607 (9.5) | – |
| Skin | 7260 (7.4) | 5781 (9.5) | 703 (4.3) | 180 (3.2) | 203 (2.3) | 393 (6.2) | – |
| Uterus/ovary | 5735 (5.9) | 3843 (6.3) | 743 (4.6) | 319 (5.6) | 457 (5.3) | 373 (5.9) | – |
| Lung/bronchus | 4845 (5.0) | 1129 (1.9) | 2312 (14.3) | 525 (9.2) | 705 (8.1) | 174 (2.7) | – |
| Blood/BM/HRS | 4257 (4.4) | 1791 (3.0) | 864 (5.3) | 504 (8.9) | 776 (8.9) | 322 (5.1) | – |
| Kidney/renal pelvis | 3262 (3.3) | 1693 (2.8) | 820 (5.1) | 226 (4.0) | 224 (2.6) | 299 (4.7) | – |
| Thyroid gland | 3192 (3.3) | 2131 (3.5) | 449 (2.8) | 130 (2.3) | 160 (1.8) | 322 (5.1) | – |
| Lymph node | 2883 (3.0) | 1389 (2.3) | 525 (3.2) | 280 (4.9) | 429 (4.9) | 260 (4.1) | – |
| Bladder | 2864 (2.9) | 1965 (3.2) | 465 (2.9) | 114 (2.0) | 156 (1.8) | 164 (2.6) | – |
| Liver/IBD | 1206 (1.2) | 192 (0.3) | 692 (4.3) | 179 (3.2) | 110 (1.3) | 33 (0.5) | – |
| Stomach | 957 (1.0) | 356 (0.6) | 277 (1.7) | 96 (1.7) | 175 (2.0) | 53 (0.8) | – |
| Brain/CN/SC | 893 (0.9) | 76 (0.1) | 197 (1.2) | 189 (3.3) | 320 (3.7) | 111 (1.7) | – |
| Pancreas | 694 (0.7) | 115 (0.2) | 380 (2.3) | 81 (1.4) | 91 (1.0) | 27 (0.4) | – |
| All other | 10 218 (10.5) | 4515 (7.4) | 2460 (15.2) | 958 (16.9) | 1387 (16.0) | 898 (14.1) | – |
| Stage at diagnosis | <.001 | ||||||
| In situ | 10 301 (10.5) | 7978 (13.1) | 1253 (7.7) | 232 (4.1) | 280 (3.2) | 558 (8.8) | – |
| Localized | 42 969 (44.0) | 30 350 (50.0) | 5784 (35.7) | 1785 (31.4) | 2337 (26.9) | 2713 (42.6) | – |
| Regional | 18 218 (18.7) | 9792 (16.1) | 3105 (19.1) | 1412 (24.9) | 2630 (30.3) | 1279 (20.1) | – |
| Distant | 10 900 (11.2) | 3086 (5.1) | 3549 (21.9) | 1420 (25.0) | 2264 (26.1) | 581 (9.1) | – |
| Unknown | 15 254 (15.6) | 9487 (15.6) | 2529 (15.6) | 833 (14.7) | 1161 (13.4) | 1244 (19.5) | – |
| Proportion of employees in manufacturingc | <.001 | ||||||
| Bottom | 23 523 (24.1) | 13 929 (22.9) | 4247 (26.2) | 1482 (26.1) | 2217 (25.6) | 1648 (25.9) | – |
| Second | 48 426 (49.6) | 31 331 (51.6) | 7340 (45.3) | 2654 (46.7) | 4185 (48.3) | 2916 (45.7) | – |
| Third | 17 831 (18.3) | 10 983 (18.1) | 3303 (20.4) | 992 (17.5) | 1455 (16.8) | 1098 (17.2) | – |
| Top | 7862 (8.1) | 4450 (7.3) | 1330 (8.2) | 554 (9.8) | 815 (9.4) | 713 (11.2) | – |
| Proportion of employees in constructionc | <.001 | ||||||
| Bottom | 44 328 (45.4) | 28 763 (47.4) | 7301 (45.0) | 2275 (40.0) | 3578 (41.3) | 2411 (37.8) | – |
| Second | 23 911 (24.5) | 14 785 (24.4) | 3635 (22.4) | 1428 (25.1) | 2239 (25.8) | 1824 (28.6) | – |
| Third | 19 043 (19.5) | 11 414 (18.8) | 3401 (21.0) | 1174 (20.7) | 1728 (19.9) | 1326 (20.8) | – |
| Top | 10 360 (10.6) | 5731 (9.4) | 1883 (11.6) | 805 (14.2) | 1127 (13.0) | 814 (12.8) | – |
| Proportion of povertyc | <.001 | ||||||
| Bottom (richest) | 33 136 (33.9) | 23 559 (38.8) | 3889 (24.0) | 1390 (24.5) | 2432 (28.0) | 1866 (29.3) | – |
| Second | 23 002 (23.6) | 13 698 (22.6) | 4368 (26.9) | 1349 (23.7) | 2054 (23.7) | 1533 (24.0) | – |
| Third | 26 189 (26.8) | 15 583 (25.7) | 4254 (26.2) | 1787 (31.5) | 2646 (30.5) | 1919 (30.1) | – |
| Top (poorest) | 15 315 (15.7) | 7853 (12.9) | 3709 (22.9) | 1156 (20.3) | 1540 (17.8) | 1057 (16.6) | – |
| Shortage of primary care physician | <.001 | ||||||
| None | 14 584 (14.9) | 8998 (14.8) | 3179 (19.6) | 714 (12.6) | 1082 (12.5) | 611 (9.6) | – |
| Partial county | 56 538 (57.9) | 34 360 (56.6) | 10 180 (62.8) | 3119 (54.9) | 4908 (56.6) | 3971 (62.3) | – |
| Whole county | 26 520 (27.2) | 17 335 (28.6) | 2861 (17.6) | 1849 (32.5) | 2682 (30.9) | 1793 (28.1) | – |
| Residential areas | <.001 | ||||||
| Urban | 86 678 (88.8) | 54 870 (90.4) | 13 714 (84.5) | 4898 (86.2) | 7577 (87.4) | 5619 (88.1) | – |
| Rural | 10 964 (11.2) | 5823 (9.6) | 2506 (15.5) | 784 (13.8) | 1095 (12.6) | 756 (11.9) | – |
Abbreviations: BM = bone marrow; CN = cranial nerves; HRS = hematopoietic-reticuloendothelial system; IBD = intrahepatic bile duct; SC = spinal cord; SSDI = Social Security Disability Insurance.
Percentages are column percentages.
Bivariate correlation coefficients are <0.3 for all variables.
For each county-level measure, we constructed nationwide quartiles in Area Health Resources File and then matched to counties in Surveillance, Epidemiology, and End Results data.
Sixty-two percent (62.2%) had no disability, whereas 22.4% had disability before cancer diagnosis and 15.4% had disability after diagnosis (Figure 1). Median durations between disability onset and pre- and post-cancer diagnosis were 5.9 years (SD 8.8 years) and 1.3 years (SD 3.6 years), respectively. Prevalence of disability was relatively low and spread out over onset timing for some cancer sites such as breast and prostate. However, liver or intrahepatic bile duct (IBD) (72.3%) and lung/bronchus cancer (58.5%) had higher prevalence of disability before cancer, especially ≥2 years before diagnosis. Postcancer disability was most frequently observed for cancers in brain, cranial nerve, or spinal cord (CN/SC) (48.2%) and blood, bone marrow, hematopoietic-reticuloendothelial system (BM/HRS; 25.8%), with the highest prevalence being disability onset <2 years after diagnosis.
Figure 1.
Percentages of cancer survivors by disability status and timing of onset among total study population: overall and by selected cancer sites. Abbreviations: BM = bone marrow; CN = cranial nerves; HRS = hematopoietic-reticuloendothelial system; IBD = intrahepatic bile duct; SC = spinal cord. P-value of χ2 test by cancer site was <.001.
Differences were significant by 5-year survival (Tables S1 and S2). Among <5-year cancer survivors, 96.1% had disability (Figure 2). Prevalence of disability was dominantly high ≥2 years before diagnosis in most cancer sites (eg, 81.7% and 77.3% in liver/IBD and lung/bronchus cancer, respectively). Although prevalence of disability <2 years after cancer varied by cancer sites, it was substantially high for some cancer sites (eg, 31.3% and 28.0% in brain/CN/SC and breast cancer, respectively). Disability onset ≥2 years after diagnosis occurred rarely, potentially because of mortality. Among ≥5-year cancer survivors, 30.7% had disability, and timing and prevalence of disability varied by cancer sites (Figure 3).
Figure 2.
Percentages of cancer survivors by disability status and timing of onset among <5-year cancer survivors: overall and by selected cancer sites. Sum of observations by 5-year survival was smaller than total sample size because 2 states (IL and MA) did not collect date of death. Abbreviations: BM = bone marrow; CN = cranial nerves; HRS = hematopoietic-reticuloendothelial system; IBD = intrahepatic bile duct; SC = spinal cord. P-value of χ2 test by cancer site was < .001. Percentages indicating numbers less than 11 (and another percentage, if necessary) were coarsened to comply with the Centers for Medicare and Medicaid Services cell size suppression policy.
Figure 3.
Percentages of cancer survivors by disability status and timing of onset among ≥5-year cancer survivors: overall and by selected cancer sites. Sum of observations by 5-year survival was smaller than total sample size because two states (IL and MA) did not collect date of death. Abbreviations: BM = bone marrow; CN = cranial nerves; HRS = hematopoietic-reticuloendothelial system; IBD = intrahepatic bile duct; SC = spinal cord. P-value of χ2 test by cancer site was < .001.
Compared with non-Hispanic White patients, non-Hispanic Black and Hispanic patients had markedly higher disability prevalence across all timing categories. Disability prevalence was higher among cancer survivors residing in counties with high rates of employees working in manufacturing and construction, poverty, and rurality (Table 1, Figure S1; P ≤ .05 for all comparisons).
Adjusted multivariable regression analyses
Variation in the association of cancer site and disability onset at different timing was persistent even after we adjusted for patient, cancer stage at diagnosis, and neighborhood characteristics (Table 2). For example, compared with prostate cancer survivors (with the lowest prevalence of disability), adjusted predicted probability of having disability ≥2 years before diagnosis was higher by 0.46 (AME 0.46, 95% CI = 0.43 to 0.49) among liver/IBD cancer survivors and by 0.32 (AME 0.32, 95% CI = 0.30 to 0.34) among lung/bronchus cancer survivors. On the other hand, adjusted predicted probability of having disability <2 years after diagnosis increased by 0.29 (AME 0.29, 95% CI = 0.27 to 0.32) among brain/CN/SC cancer survivors and by 0.09 (AME 0.09, 95% CI = 0.07 to 0.11) among stomach cancer survivors. Compared with patients who were diagnosed with cancer in situ, those who were diagnosed with distant stage cancer had increased adjusted predicted probability of disability at all timing periods except ≥2 years after diagnosis.
Table 2.
Association of cancer characteristics with disability status and timing of onset: among total study population (n = 97 642).a
| Onset of disability (approval of SSDI) |
||||
|---|---|---|---|---|
| ≥2 years | ≥0, <2 years | >0, <2 years | ≥2 years | |
| before cancer | before cancer | after cancer | after cancer | |
| AME (95% CI) | AME (95% CI) | AME (95% CI) | AME (95% CI) | |
| Age category at diagnosis | ||||
| 58 to 61 | Referent | Referent | Referent | Referent |
| 54 to 57 | 0.00 (−0.01, 0.00) | 0.01 (0.00, 0.01) | 0.02 (0.02, 0.03) | 0.04 (0.04, 0.04) |
| 50 to 53 | 0.00 (−0.01, 0.00) | 0.02 (0.01, 0.02) | 0.03 (0.03, 0.04) | 0.09 (0.09, 0.10) |
| 25 to 49 | 0.04 (0.04, 0.05) | 0.05 (0.04, 0.05) | 0.10 (0.09, 0.11) | 0.20 (0.19, 0.21) |
| Women | −0.03 (−0.04, −0.03) | −0.01 (−0.01, −0.01) | −0.01 (−0.01, 0.00) | 0.00 (0.00, 0.01) |
| Race/ethnicity | ||||
| Non-Hispanic White | Referent | Referent | Referent | Referent |
| Non-Hispanic Black | 0.11 (0.10, 0.13) | 0.03 (0.02, 0.03) | 0.03 (0.02, 0.04) | 0.01 (0.00, 0.01) |
| Hispanic | 0.04 (0.03, 0.05) | 0.03 (0.02, 0.03) | 0.03 (0.02, 0.04) | 0.01 (0.00, 0.02) |
| Non-Hispanic Other | −0.06 (−0.07, −0.05) | 0.01 (0.00, 0.01) | 0.02 (0.01, 0.04) | 0.00 (−0.01, 0.00) |
| Site of cancer | ||||
| Breast | 0.02 (0.01, 0.03) | 0.01 (0.00, 0.01) | 0.03 (0.03, 0.04) | 0.00 (−0.01, 0.01) |
| Prostate | Referent | Referent | Referent | Referent |
| Colorectal | 0.05 (0.05, 0.06) | 0.03 (0.02, 0.04) | 0.06 (0.05, 0.07) | 0.00 (−0.01, 0.01) |
| Skin | 0.03 (0.02, 0.04) | 0.01 (0.00, 0.01) | 0.00 (−0.01, 0.00) | −0.01 (−0.02, 0.00) |
| Uterus/ovary | 0.05 (0.04, 0.07) | 0.03 (0.02, 0.04) | 0.03 (0.02, 0.04) | 0.00 (−0.01, 0.01) |
| Lung/bronchus | 0.32 (0.30, 0.34) | 0.06 (0.05, 0.07) | 0.07 (0.06, 0.08) | −0.02 (−0.03, −0.01) |
| Blood/BM/HRS | 0.01 (0.00, 0.02) | 0.03 (0.02, 0.03) | 0.03 (0.02, 0.04) | 0.02 (0.00, 0.03) |
| Kidney/renal pelvis | 0.14 (0.13, 0.16) | 0.03 (0.02, 0.04) | 0.02 (0.01, 0.03) | 0.01 (0.00, 0.03) |
| Thyroid gland | 0.06 (0.05, 0.08) | 0.01 (0.00, 0.02) | 0.00 (−0.01, 0.01) | 0.00 (−0.01, 0.01) |
| Lymph node | 0.02 (0.01, 0.04) | 0.03 (0.02, 0.03) | 0.03 (0.02, 0.04) | 0.01 (0.00, 0.02) |
| Bladder | 0.08 (0.06, 0.09) | 0.03 (0.02, 0.04) | 0.05 (0.03, 0.07) | 0.01 (0.00, 0.02) |
| Liver/IBD | 0.46 (0.43, 0.49) | 0.10 (0.08, 0.12) | 0.03 (0.02, 0.05) | −0.04 (−0.05, −0.03) |
| Stomach | 0.17 (0.14, 0.20) | 0.05 (0.04, 0.06) | 0.09 (0.07, 0.11) | −0.01 (−0.03, 0.00) |
| Brain/CN/SC | 0.15 (0.12, 0.18) | 0.18 (0.15, 0.21) | 0.29 (0.27, 0.32) | 0.00 (−0.01, 0.02) |
| Pancreas | 0.38 (0.34, 0.41) | 0.06 (0.05, 0.08) | 0.05 (0.03, 0.07) | −0.02 (−0.04, 0.00) |
| All other | 0.13 (0.12, 0.14) | 0.05 (0.04, 0.06) | 0.07 (0.06, 0.08) | 0.00 (0.00, 0.01) |
| Stage at diagnosis | ||||
| In situ | Referent | Referent | Referent | Referent |
| Localized | −0.02 (−0.03, −0.01) | 0.02 (0.01, 0.02) | 0.03 (0.03, 0.03) | 0.01 (0.01, 0.02) |
| Regional | −0.01 (−0.02, 0.00) | 0.04 (0.03, 0.05) | 0.10 (0.09, 0.10) | 0.01 (0.00, 0.01) |
| Distant | 0.12 (0.10, 0.13) | 0.08 (0.07, 0.09) | 0.15 (0.14, 0.16) | −0.02 (−0.02, −0.01) |
| Unknown | −0.03 (−0.05, −0.01) | 0.02 (0.01, 0.02) | 0.03 (0.03, 0.04) | 0.00 (0.00, 0.01) |
| Proportion of employees in manufacturing | ||||
| Bottom | Referent | Referent | Referent | Referent |
| Second | −0.02 (−0.03, 0.00) | 0.00 (−0.01, 0.00) | 0.00 (−0.01, 0.01) | −0.01 (−0.02, 0.00) |
| Third | 0.00 (−0.02, 0.02) | 0.00 (0.00, 0.01) | 0.00 (−0.01, 0.00) | 0.00 (−0.01, 0.01) |
| Top | −0.01 (−0.03, 0.00) | 0.01 (0.01, 0.02) | 0.01 (0.00, 0.03) | 0.03 (0.02, 0.05) |
| Proportion of employees in construction | ||||
| Bottom | Referent | Referent | Referent | Referent |
| Second | −0.01 (−0.03, 0.01) | 0.01 (0.00, 0.01) | 0.01 (0.00, 0.02) | 0.02 (0.01, 0.04) |
| Third | 0.01 (0.00, 0.03) | 0.01 (0.00, 0.02) | 0.01 (0.00, 0.02) | 0.02 (0.00, 0.03) |
| Top | 0.01 (−0.01, 0.04) | 0.02 (0.01, 0.02) | 0.02 (0.01, 0.03) | 0.02 (0.01, 0.04) |
| Proportion of poverty | ||||
| Bottom (richest) | Referent | Referent | Referent | Referent |
| Second | 0.07 (0.05, 0.09) | 0.01 (0.00, 0.01) | 0.00 (0.00, 0.01) | 0.00 (−0.01, 0.01) |
| Third | 0.05 (0.03, 0.07) | 0.01 (0.01, 0.02) | 0.01 (0.00, 0.02) | 0.00 (0.00, 0.01) |
| Top (poorest) | 0.10 (0.08, 0.12) | 0.02 (0.01, 0.02) | 0.00 (−0.01, 0.01) | 0.00 (−0.02, 0.01) |
| Shortage of primary care physician | ||||
| None | Referent | Referent | Referent | Referent |
| Partial county | −0.06 (−0.08, −0.04) | 0.00 (0.00, 0.01) | 0.01 (0.00, 0.02) | 0.03 (0.02, 0.04) |
| Whole county | −0.14 (−0.16, −0.12) | 0.01 (0.01, 0.02) | 0.02 (0.01, 0.03) | 0.04 (0.03, 0.04) |
| Residential areas | ||||
| Urban | Referent | Referent | Referent | Referent |
| Rural | 0.07 (0.04, 0.11) | 0.04 (0.01, 0.07) | 0.03 (0.02, 0.04) | 0.00 (−0.01, 0.00) |
Abbreviations: AME = average marginal effect; BM = bone marrow; CI = confidence interval; CN = cranial nerves; HRS = hematopoietic-reticuloendothelial system; IBD = intrahepatic bile duct; SC = spinal cord; SSDI = social security disability insurance.
Base outcome of a multivariable multinomial probit model is patients with no disability (results not shown).
Among <5-year survivors, compared with prostate cancer survivors, patients with most cancer sites had higher adjusted predicted probability of disability ≥2 before diagnosis as opposed to other categories of disability onset, implying disability with long-term history is strongly associated with mortality (Table 3). However, among ≥5-year survivors, patients had increased adjusted predicted probability of disability in close timing to cancer diagnosis (ie, either <2 years before or after cancer diagnosis) as well as distant timing (ie, ≥2 years before cancer diagnosis), with substantially varying magnitudes over cancer site.
Table 3.
Association of cancer characteristics with disability status and timing of onset: by 5-year cancer survival.
| Onset of disability (approval of SSDI) |
||||
|---|---|---|---|---|
| ≥2 years | ≥0, <2 years | >0, <2 years | ≥2 years | |
| before cancer | before cancer | after cancer | after cancer | |
| AME (95% CI) | AME (95% CI) | AME (95% CI) | AME (95% CI) | |
| I. <5-year cancer survivors (n = 9343)a,b,c | ||||
| Site of cancer | ||||
| Breast | 0.02 (−0.04, 0.08) | −0.04 (−0.10, 0.01) | 0.05 (−0.02, 0.11) | 0.00 (−0.02, 0.02) |
| Prostate | Referent | Referent | Referent | Referent |
| Colorectal | 0.03 (−0.03, 0.10) | −0.03 (−0.08, 0.02) | 0.04 (−0.01, 0.10) | 0.00 (−0.02, 0.01) |
| Skin | 0.19 (0.10, 0.27) | −0.06 (−0.13, 0.00) | −0.08 (−0.15, −0.02) | −0.01 (−0.02, 0.01) |
| Uterus/ovary | 0.05 (−0.03, 0.13) | −0.04 (−0.11, 0.03) | 0.05 (−0.02, 0.11) | −0.01 (−0.02, 0.01) |
| Lung/bronchus | 0.27 (0.21, 0.33) | −0.10 (−0.15, −0.05) | −0.08 (−0.13, −0.03) | −0.01 (−0.02, 0.01) |
| Blood/BM/HRS | 0.09 (0.02, 0.16) | −0.02 (−0.07, 0.04) | −0.01 (−0.06, 0.05) | −0.01 (−0.02, 0.01) |
| Kidney/renal pelvis | 0.20 (0.12, 0.28) | −0.09 (−0.14, −0.03) | −0.06 (−0.13, 0.01) | 0.00 (−0.02, 0.01) |
| Thyroid gland | 0.18 (0.06, 0.30) | −0.01 (−0.10, 0.09) | −0.14 (−0.21, −0.06) | −0.01 (−0.02, 0.00) |
| Lymph node | 0.23 (0.16, 0.30) | −0.08 (−0.14, −0.02) | −0.08 (−0.13, −0.02) | – |
| Bladder | 0.23 (0.14, 0.31) | −0.10 (−0.17, −0.03) | −0.07 (−0.15, 0.02) | – |
| Liver/IBD | 0.33 (0.27, 0.39) | −0.10 (−0.15, −0.04) | −0.14 (−0.19, −0.09) | −0.01 (−0.02, 0.01) |
| Stomach | 0.16 (0.08, 0.25) | −0.06 (−0.12, 0.00) | 0.00 (−0.07, 0.08) | −0.01 (−0.02, 0.00) |
| Brain/CN/SC | 0.00 (−0.09, 0.08) | 0.00 (−0.07, 0.06) | 0.10 (0.03, 0.18) | – |
| Pancreas | 0.28 (0.21, 0.35) | −0.11 (−0.17, −0.06) | −0.09 (−0.16, −0.03) | −0.01 (−0.02, 0.00) |
| All other | 0.17 (0.11, 0.23) | −0.06 (−0.11, −0.01) | −0.03 (−0.09, 0.02) | – |
| Stage at diagnosis | ||||
| In situ | Referent | Referent | Referent | Referent |
| Localized | −0.05 (−0.13, 0.02) | 0.04 (−0.01, 0.09) | 0.04 (−0.02, 0.09) | 0.00 (−0.01, 0.02) |
| Regional | −0.14 (−0.21, −0.07) | 0.06 (0.02, 0.11) | 0.10 (0.05, 0.15) | 0.00 (−0.01, 0.01) |
| Distant | −0.08 (−0.15, 0.00) | 0.05 (0.00, 0.09) | 0.08 (0.03, 0.12) | 0.00 (−0.01, 0.01) |
| Unknown | −0.04 (−0.10, 0.03) | 0.03 (−0.02, 0.07) | 0.03 (−0.02, 0.08) | 0.00 (−0.01, 0.01) |
| II. ≥5-year cancer survivors (n = 74 598)a,b,c | ||||
| Site of cancer | ||||
| Breast | 0.00 (−0.01, 0.01) | 0.01 (0.00, 0.01) | 0.03 (0.03, 0.04) | 0.00 (−0.01, 0.01) |
| Prostate | Referent | Referent | Referent | Referent |
| Colorectal | 0.02 (0.01, 0.03) | 0.02 (0.01, 0.02) | 0.05 (0.04, 0.06) | 0.01 (0.00, 0.02) |
| Skin | 0.01 (0.00, 0.02) | 0.01 (0.00, 0.01) | 0.00 (−0.01, 0.01) | 0.00 (−0.01, 0.01) |
| Uterus/ovary | 0.03 (0.02, 0.04) | 0.02 (0.02, 0.03) | 0.02 (0.01, 0.03) | 0.01 (0.00, 0.02) |
| Lung/bronchus | 0.09 (0.07, 0.11) | 0.09 (0.07, 0.10) | 0.11 (0.09, 0.12) | 0.01 (−0.01, 0.02) |
| Blood/BM/HRS | 0.06 (0.04, 0.08) | 0.03 (0.02, 0.03) | 0.03 (0.02, 0.04) | 0.01 (0.00, 0.02) |
| Kidney/renal pelvis | 0.11 (0.09, 0.12) | 0.03 (0.02, 0.04) | 0.02 (0.01, 0.03) | 0.02 (0.01, 0.04) |
| Thyroid gland | 0.04 (0.03, 0.06) | 0.01 (0.00, 0.02) | 0.01 (0.00, 0.02) | 0.01 (0.00, 0.02) |
| Lymph node | 0.04 (0.02, 0.05) | 0.03 (0.02, 0.04) | 0.03 (0.02, 0.04) | 0.01 (0.00, 0.03) |
| Bladder | 0.03 (0.02, 0.05) | 0.03 (0.02, 0.05) | 0.05 (0.03, 0.07) | 0.02 (0.00, 0.03) |
| Liver/IBD | 0.16 (0.12, 0.21) | 0.15 (0.12, 0.19) | 0.09 (0.06, 0.12) | −0.01 (−0.03, 0.01) |
| Stomach | 0.06 (0.03, 0.09) | 0.04 (0.03, 0.06) | 0.09 (0.07, 0.12) | 0.00 (−0.02, 0.02) |
| Brain/CN/SC | 0.01 (−0.02, 0.03) | 0.17 (0.13, 0.21) | 0.33 (0.30, 0.37) | 0.03 (0.01, 0.06) |
| Pancreas | 0.05 (0.01, 0.10) | 0.10 (0.06, 0.15) | 0.10 (0.06, 0.14) | 0.04 (0.00, 0.07) |
| All other | 0.06 (0.05, 0.07) | 0.05 (0.04, 0.05) | 0.07 (0.06, 0.08) | 0.01 (0.00, 0.02) |
| Stage at diagnosis | ||||
| In situ | Referent | Referent | Referent | Referent |
| Localized | −0.02 (−0.03, −0.01) | 0.01 (0.01, 0.02) | 0.03 (0.02, 0.03) | 0.01 (0.01, 0.02) |
| Regional | −0.03 (−0.04, −0.02) | 0.03 (0.03, 0.04) | 0.09 (0.08, 0.09) | 0.02 (0.01, 0.02) |
| Distant | −0.03 (−0.04, −0.01) | 0.07 (0.06, 0.08) | 0.17 (0.15, 0.18) | 0.01 (0.00, 0.02) |
| Unknown | −0.06 (−0.08, −0.05) | 0.01 (0.01, 0.02) | 0.03 (0.02, 0.04) | 0.01 (0.00, 0.02) |
Abbreviations: AME = average marginal effect; BM = bone marrow; CI = confidence interval; CN = cranial nerves; HRS = hematopoietic-reticuloendothelial system; IBD = intrahepatic bile duct; SC = spinal cord; SSDI = social security disability insurance.
Sum of observations by 5-year survival is smaller than total sample size because 2 states (IL and MA) did not collect date of death.
Base outcome of a multivariable multinomial probit model is patients with no disability (results not shown).
Characteristics of patient (age, sex, race/ethnicity) and neighborhood (2 types of industries, poverty, primary care physician shortage, rurality) were adjusted in a multivariable multinomial probit model.
Sensitivity analyses showed similar results both in magnitudes and statistical significance in most cases (results not shown). When we tracked over timing of disability onset ≥6 years before cancer diagnosis, the patterns were similar but the difference by 5-year cancer survival was greater.
Discussion
In this report, we sought to clarify the relationship of cancer incidence relative to onset timing of disability, adjusting patient, cancer, and neighborhood characteristics among prime working-age cancer survivors. We found significant associations of cancer site and stage at diagnosis with prevalence and onset timing of disability, overall and by 5-year cancer survival.
SEER-Medicare data collect the dates of cancer diagnosis and Medicare enrollment and enable us to identify the time sequence of preexisting disability, cancer incidence by site, and postcancer disability. In multivariable regression analyses, we examined the association of 16 different cancer sites with disability to make the comparison of the patterns of association consistent before and after cancer incidence. We also conducted analysis stratified by 5-year survival because survival status would be influenced by both cancer sites and stage, and by unobserved confounders correlated with both cancer and disability.
Our study makes several novel contributions to the literature that have important practice or policy implications. First, to our knowledge, this study is the first to examine the association of cancer characteristics with time of onset and length of disability using a population-based network of cancer registries. Only a few studies investigated the relationship between cancer and disability. These focused on the 4 most prevalent cancer sites (breast, prostate, colorectal, lung) with the strength of the association relatively less for all but lung cancer,10,18-20 and no prior study has focused on the length of disability. Second, SEER-Medicare data enabled us to include all cancer survivors and conduct analysis by 5-year survival. Prior studies used cross-sectional survey data, which could not provide any information on those deceased at that point in time18-20,32 nor make comparison by survival status applicable. Third, we focused on prime working-age cancer patients and employed clinically assessed disability, whereas the previous literature focused on the elderly and self-reported functional disability.18,32
Our findings may have important implications for cancer care. For patients who present with long-lasting disability, past history of disability is a strong risk factor for cancer incidence for specific sites and worse prognosis than those who never become disabled or who have disability after treatment. Studies need to assess those aspects of the nature and course of treatment that are associated with worse (and alternatively, better) prognosis and do so for specific cancer sites. For example, whereas 5.1% of cancer survivors without disability were diagnosed at distant stage, 21.9% of cancer survivors with long-lasting prior disability were, resulting in high mortality. The cancer sites strongly associated with long-lasting prior disability (eg, lung/bronchus, liver/IBD) are related to health risk factors (eg, smoking, drinking, obesity), physical and financial stress, and social isolation from peers.33-35 From a prevention standpoint, the US cancer societies do not differentiate recommendation for preventive cancer screening by disability status.36-39 However, our findings suggest the need of future research to investigate long-lasting disability as a predictor of higher risk and to incorporate evidences into cancer screening guidelines, if any.
Similarly, new-onset disability with cancer incidence at specific sites indicates the need to investigate aspects of cancer care that are associated with onset of disability after diagnosis. For example, brain tumors directly destroy, compress, or disrupt critical neurological networks and immediately compromise essential functions such as communication, memory, and behavior.40 There is some limited evidence suggesting that intensive and specialized prehabilitation (eg, structured exercise, nutrition counseling) and rehabilitation (eg, physical/occupational therapy) programs could improve functional capability and reduce complications, thereby reducing chemotherapy or surgery-induced disability.10,41,42 Future studies should examine whether proactively targeted and enhanced prehabilitation and/or rehabilitation for those cancer sites that experience higher prevalence of disability make such interventions more effective and efficient. The goal, of course, is to improve cancer care to maximize the patient’s responsiveness to treatment while minimizing side effects, prolonging survival.
Patients in prime working age deserve particular attention, because cancer incidence has increased among this group in recent decades17,43,44 and the implications of disability for their economic and social stability (eg, disruptions to career, loss of productivity and employer-sponsored health insurance) differ markedly from those of older adults.45,46 Although International Classification of Functional Disability is the commonly accepted model of disability in public health,47 this may not explicitly reflect the concerns of the disabled non-elderly or clinically assessed health conditions. Furthermore, our study showed that younger adults (ages 25 to 49) had increased prevalence of disability at all timing in adjusted multivariable regression analysis, indicating a need to explore the reason(s) for this occurrence.
Other sociodemographic characteristics were associated with different patterns of disability onset. Compared with non-Hispanic White patients, prevalence of disability ≥2 years before cancer diagnosis was nearly twice among non-Hispanic Black patients. However, among non-Hispanic Other, disability ≥2 years before cancer was even lower than non-Hispanic White, but their disability <2 after cancer diagnosis was 1.5 times higher, implying disparities in cancer treatment and rehabilitation in this racial/ethnic group. Disparities in poor and rural areas were also substantial ≥2 years before cancer incidence, potentially because of physically demanding industries, limited availability for specialized care, and long distance to care.48
Some limitations of this study deserve discussion. First, SSDI is applicable for individuals with sufficient work history only and does not capture non-clinically severe disability, type and severity of disability, or delayed claims. This could influence the magnitudes of associations and 5-year survival status, but the direction of bias in estimates is unclear because SSDI excludes those with both better (eg, moderate disability) and potentially worse (eg, insufficient work history because of poor health49) health status. Second, cancer diagnoses were restricted to 2016 because we followed at least ≥2 years of SSDI after diagnosis and the latest MBSF identified date of SSDI through 2019. Regular updates with more recent data are recommended. Third, we have a longer observation period for precancer than postcancer because individuals become eligible for Medicare at age 65 and the date of SSDI was right censored. However, we designed our study to observe at least ≥2 years both before and after cancer incidence. Fourth, we did not adjust comorbid conditions (eg, diabetes) and risk factors of cancer and disability (eg, health behavior), which could modify the associations.
In summary, our study suggests that cancer incidence is strongly associated with disability, and the impact varies substantially by cancer site, length of disability, and 5-year survival status. These findings may provide insights to policymakers and medical practitioners by which to guide their approach to enhanced preventive cancer care among disabled patients and to reduce the likelihood of disability onset in those who enter care without disability.
Supplementary Material
Acknowledgments
The reporting and interpretation of these data are the sole responsibility of the authors.
Contributor Information
Hyo Jung Tak, Department of Health Services Research and Administration, University of Nebraska Medical Center, Omaha, NE, United States.
Ya-Chen Tina Shih, Department of Radiation Oncology, UCLA, Los Angeles, CA, United States.
Brandon L Grimm, Department of Health Services Research and Administration, University of Nebraska Medical Center, Omaha, NE, United States.
Andrew M Goldsweig, Department of Cardiovascular Medicine, Baystate Medical Center and Division of Cardiovascular Medicine, University of Massachusetts-Baystate, Springfield, MA, United States.
Min Sok Lee, Department of Economics, University of Chicago, Chicago, IL, United States.
Ronnie D Horner, Department of Health Services Research and Administration, University of Nebraska Medical Center, Omaha, NE, United States.
Author contributions
Hyo Jung Tak (Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Writing—original draft), Ya-Chen Tina Shih (Conceptualization, Methodology, Writing—review & editing), Brandon L. Grimm (Conceptualization, Investigation, Resources, Writing—review & editing), Andrew M. Goldsweig (Conceptualization, Investigation, Methodology, Writing—review & editing), Min Sok Lee (Conceptualization, Methodology, Writing—review & editing), and Ronnie D. Horner (Conceptualization, Funding acquisition, Investigation, Methodology, Writing—original draft)
Supplementary material
Supplementary material is available at JNCI Cancer Spectrum Online.
Funding
This work was partly supported by the National Cancer Institute at the National Institutes of Health (grant number: 3P30CA036727-37S2).
Conflicts of interest
All authors have no conflicts of interest to disclose on the subject of this article.
Data availability
Data underlying this article were provided by the National Cancer Institute and the Centers for Medicare & Medicaid Services by permission. The datasets used to conduct this study are available upon approval of a research protocol from the National Cancer Institute. Instructions for obtaining these data are available at https://healthcaredelivery.cancer.gov/seermedicare/obtain.
Role of the funder
The funder had no role in the design of the study; the collection, analysis, or interpretation of the data; or the writing of the manuscript and decision to submit it for publication.
References
- 1. Yang Y, Afshar N, Butchart J, et al. Cancer inequalities experienced by people with disability: a systematic review. Disabil Health J. 2025;18:101851. 10.1016/j.dhjo.2025.101851 [DOI] [PubMed] [Google Scholar]
- 2. Tak HJ, Horner RD, Lee MS, Shih YT. Impact of functional disability on health-care use and medical costs among cancer survivors. JNCI Cancer Spectr. 2023;7:pkad059. 10.1093/jncics/pkad059 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Altice CK, Banegas MP, Tucker-Seeley RD, Yabroff KR. Financial hardships experienced by cancer survivors: a systematic review. J Natl Cancer Inst. 2016;109:djw205. 10.1093/jnci/djw205 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Lathan CS, Cronin A, Tucker-Seeley R, Zafar SY, Ayanian JZ, Schrag D. Association of financial strain with symptom burden and quality of life for patients with lung or colorectal cancer. J Clin Oncol. 2016;34:1732-1740. 10.1200/JCO.2015.63.2232 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Iezzoni LI, Kurtz SG, Rao SR. Trends in colorectal cancer screening over time for persons with and without chronic disability. Disabil Health J. 2016;9:498-509. 10.1016/j.dhjo.2016.02.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Horner-Johnson W, Dobbertin K, Lee JC, Andresen EM. Rural disparities in receipt of colorectal cancer screening among adults ages 50-64 with disabilities. Disabil Health J. 2014;7:394-401. 10.1016/j.dhjo.2014.06.001 [DOI] [PubMed] [Google Scholar]
- 7. Iglay K, Santorelli ML, Hirshfield KM, et al. Diagnosis and treatment delays among elderly breast cancer patients with pre-existing mental illness. Breast Cancer Res Treat. 2017;166:267-275. 10.1007/s10549-017-4399-x [DOI] [PubMed] [Google Scholar]
- 8. Iezzoni LI. Cancer detection, diagnosis, and treatment for adults with disabilities. Lancet Oncol. 2022;23:e164-e173. 10.1016/S1470-2045(22)00018-3 [DOI] [PubMed] [Google Scholar]
- 9. Iezzoni LI, Ngo LH, Li D, Roetzheim RG, Drews RE, McCarthy EP. Treatment disparities for disabled Medicare beneficiaries with stage I non-small cell lung cancer. Arch Phys Med Rehabil. 2008;89:595-601. 10.1016/j.apmr.2007.09.042 [DOI] [PubMed] [Google Scholar]
- 10. Turrina S, Gibelli F, De Leo D. Chemotherapy-induced cognitive impairment from the forensic medicine perspective: a review of the updated literature. J Forensic Leg Med. 2020;76:102070. 10.1016/j.jflm.2020.102070 [DOI] [PubMed] [Google Scholar]
- 11. Zheng Z, Yabroff KR, Guy GP Jr., et al. Annual medical expenditure and productivity loss among colorectal, female breast, and prostate cancer survivors in the United States. J Natl Cancer Inst. 2015;108:djv382. 10.1093/jnci/djv382 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Zafar SY, Peppercorn J. Reflections on the financial toxicity of cancer: 10 years later. Oncologist. 2023;28:654-656. 10.1093/oncolo/oyad179 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Zafar SY, McNeil RB, Thomas CM, Lathan CS, Ayanian JZ, Provenzale D. Population-based assessment of cancer survivors’ financial burden and quality of life: a prospective cohort study. J Oncol Pract. 2015;11:145-150. 10.1200/JOP.2014.001542 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Edwards DJ, Sakellariou D, Anstey S. Barriers to, and facilitators of, access to cancer services and experiences of cancer care for adults with a physical disability: a mixed methods systematic review. Disabil Health J. 2020;13:100844. 10.1016/j.dhjo.2019.100844 [DOI] [PubMed] [Google Scholar]
- 15. McCarthy EP, Ngo LH, Chirikos TN, et al. Cancer stage at diagnosis and survival among persons with Social Security Disability Insurance on Medicare. Health Serv Res. 2007;42:611-628. 10.1111/j.1475-6773.2006.00619.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Lawrence WR, Kuliszewski MG, Hosler AS, et al. Association between preexisting mental illnesses and mortality among Medicaid-insured women diagnosed with breast cancer. Soc Sci Med. 2021;270:113643. 10.1016/j.socscimed.2020.113643 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Shiels MS, Haque AT, Berrington de González A, et al. Trends in cancer incidence and mortality rates in early-onset and older-onset age groups in the United States, 2010-2019. Cancer Discov. 2025;15:1363-1376. 10.1158/2159-8290.CD-24-1678 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Cao C, Yang L, Schmitz KH, Ligibel JA. Prevalence and cancer-specific patterns of functional disability among US cancer survivors, 2017-2022. J Clin Oncol. 2024;42:2257-2270. 10.1200/JCO.23.02536 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Iezzoni LI, Rao SR, Agaronnik ND, El-Jawahri A. Cross-sectional analysis of the associations between four common cancers and disability. J Natl Compr Canc Netw. 2020;18:1031-1044. 10.6004/jnccn.2020.7551 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Iezzoni LI, Rao SR, Agaronnik ND, El-Jawahri A. Associations between disability and breast or cervical cancers, accounting for screening disparities. Med Care. 2021;59:139-147. 10.1097/MLR.0000000000001449 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. US Social Security Administration. How Do We Define Disability? Accessed September 30, 2025. https://www.ssa.gov/redbook/eng/definedisability.htm? tl=0
- 22. Dorsey R, Graham G. New HHS data standards for race, ethnicity, sex, primary language, and disability status. JAMA. 2011;306:2378-2379. 10.1001/jama.2011.1789 [DOI] [PubMed] [Google Scholar]
- 23. National Cancer Institute. SEER-Medicare Linked Data Resource. Accessed July 29, 2025. https://healthcaredelivery.cancer.gov/seermedicare/
- 24. Research Data Assistance Center. Medicare Beneficiary Summery File A/B/C/D. Accessed September 24, 2025. https://resdac.org/cms-data/files/mbsf-base
- 25. Bureau of Health Workforce. Area Health Resources File. US Department of Health and Human Services, Health Resources and Service Administration. Accessed February 1, 2026. https://data.hrsa.gov/topics/health-workforce/nchwa/ahrf
- 26. Overview of the SEER Program. Accessed March 1, 2026. https://seer.cancer.gov/about/overview.html
- 27. Social Security Administration: Disability Benefits. Accessed September 24, 2025. https://www.ssa.gov/benefits/disability/approval.html
- 28. Research Data Assistance Center. Updated National Death Index (NDI) Data Available in the Virtual Research Data Center. Accessed March 1, 2026. https://resdac.org/cms-news/updated-national-death-index-ndi-data-available-vrdc
- 29. National Cancer Institute. International Classification of Diseases for Oncology, Third Edition (ICD-O-3) Guidelines. Accessed July 29, 2025. https://seer.cancer.gov/icd-o-3/
- 30. US Bureau of Labor Statistics. A look at falls, slips, and trips in the construction industry. Accessed September 30, 2025. https://www.bls.gov/opub/ted/2024/a-look-at-falls-slips-and-trips-in-the-construction-industry.htm
- 31. Cameron A, Trivedi P. Microeconometrics: Methods and Applications. Cambridge University Press; 2005. [Google Scholar]
- 32. Neo J, Fettes L, Gao W, Higginson IJ, Maddocks M. Disability in activities of daily living among adults with cancer: a systematic review and meta-analysis. Cancer Treat Rev. 2017;61:94-106. 10.1016/j.ctrv.2017.10.006 [DOI] [PubMed] [Google Scholar]
- 33. Aram J, Slopen N, Arria AM, Liu H, Dallal CM. Drug and alcohol use disorders among adults with select disabilities: the national survey on drug use and health. Disabil Health J. 2023;16:101467. 10.1016/j.dhjo.2023.101467 [DOI] [PubMed] [Google Scholar]
- 34. Jonas DE, Reuland DS, Reddy SM, et al. Screening for lung cancer with low-dose computed tomography: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2021;325:971-987. 10.1001/jama.2021.0377 [DOI] [PubMed] [Google Scholar]
- 35. Townsend MJ, Claridy MD, Bajaj SS, Tu L, Stanford FC. Obesity and eligibility for obesity treatments among adults with disabilities in the U.S. Am J Prev Med. 2022;63:513-520. 10.1016/j.amepre.2022.04.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Nicholson WK, Silverstein M, Wong JB, et al. Screening for breast cancer: US Preventive Services Task Force recommendation statement. JAMA. 2024;30. [Google Scholar]
- 37. Curry SJ, Krist AH, Owens DK, et al. ; US Preventive Services Task Force. Screening for cervical cancer: US Preventive Services Task Force recommendation statement. JAMA. 2018;320:674-686. 10.1001/jama.2018.10897 [DOI] [Google Scholar]
- 38. Davidson KW, Barry MJ, Mangione CM, et al. ; US Preventive Services Task Force. Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. JAMA, 2021;325:1965-1977. 10.1001/jama.2021.6238 [DOI] [Google Scholar]
- 39. U.S. Preventive Services Task Force. Screening for lung cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325:962-970. 10.1001/jama.2021.1117 [DOI] [PubMed] [Google Scholar]
- 40. Bou Dargham T, Winter SF, Batich K, et al. Neurocognitive outcomes in patients with brain metastases: a systematic review. Lancet Oncol. 2025;26:e671-e682. 10.1016/S1470-2045(25)00525-X [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Kos N, Kos B, Benedicic M. Early medical rehabilitation after neurosurgical treatment of malignant brain tumours in Slovenia. Radiol Oncol. 2016;50:139-144. 10.1515/raon-2015-0004 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Yu J, Jung Y, Park J, et al. Intensive rehabilitation therapy following brain tumor surgery: a pilot study of effectiveness and long-term satisfaction. Ann Rehabil Med. 2019;43:129-141. 10.5535/arm.2019.43.2.129 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Zhao J, Xu L, Sun J, et al. Global trends in incidence, death, burden and risk factors of early-onset cancer from 1990 to 2019. BMJ Oncol. 2023;2:e000049. 10.1136/bmjonc-2023-000049 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. CA Cancer J Clin. 2025;75:10-45. 10.3322/caac.21871 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Schwartz LF, Shubeck SP, Danielson EC. Young people with cancer: a growing population in need of united attention. Curr Oncol Rep. 2025;27:333-335. 10.1007/s11912-025-01661-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Yabroff KR, Doran JF, Zhao J, et al. Cancer diagnosis and treatment in working-age adults: implications for employment, health insurance coverage, and financial hardship in the United States. CA Cancer J Clin. 2024;74:341-358. 10.3322/caac.21837 [DOI] [PubMed] [Google Scholar]
- 47. Krahn GL, Walker DK, Correa-De-Araujo R. Persons with disabilities as an unrecognized health disparity population. Am J Public Health. 2015;105 Suppl 2:S198-206. 10.2105/AJPH.2014.302182 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Theiss K, Carpenter B, Hanass-Hancock J, et al. Is disability prevalence higher in rural areas? Evidence from functional difficulty and nightlight data in 15 low- and middle-income countries. Disabil Health J. 2026;19:101964. 10.1016/j.dhjo.2025.101964 [DOI] [PubMed] [Google Scholar]
- 49. Silver SR, Li J, Quay B. Employment status, unemployment duration, and health-related metrics among US adults of prime working age: behavioral Risk Factor Surveillance System, 2018-2019. Am J Ind Med. 2022;65:59-71. 10.1002/ajim.23308 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data underlying this article were provided by the National Cancer Institute and the Centers for Medicare & Medicaid Services by permission. The datasets used to conduct this study are available upon approval of a research protocol from the National Cancer Institute. Instructions for obtaining these data are available at https://healthcaredelivery.cancer.gov/seermedicare/obtain.