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. Author manuscript; available in PMC: 2025 Sep 27.
Published in final edited form as: J Rural Health. 2025 Jun;41(3):e70045. doi: 10.1111/jrh.70045

Colorectal cancer survival disparities in persistent poverty areas

Peter DelNero 1,2, Mario Schootman 1,2, Cheng Peng 1,3, Mahima Saini 3, Emily Hallgren 4, Jonathan Laryea 1,5, Chenghui Li 1,3
PMCID: PMC12466537  NIHMSID: NIHMS2107178  PMID: 40629554

Abstract

Purpose:

We examined whether living in persistent poverty census tracts was associated with disparities in colorectal cancer (CRC) survival and whether the association varied between urban and rural settings.

Methods:

Using 2013–2019 state-wide cancer registry and 2013–2021 death records data, CRC patients were classified by tract-level persistent poverty and rural/urban status. Overall and CRC-specific survival were compared using Kaplan–Meier estimation and log-rank tests. Adjusted analyses were conducted using Cox proportional hazard and Fine-Gray competing risk models.

Findings:

During the study period, 558 (53%) of 1055 CRC patients died in persistent poverty tracts versus 3117 (45%) of 6938 patients in nonpersistent poverty tracts. Of the 3675 deaths, 2269 (61.7%) were from CRC-specific causes. In unadjusted analysis, CRC patients in persistent poverty areas had a higher risk of all-cause (HR, 95%CI: 1.28, 1.17–1.40) and CRC-specific (HR, 95% CI: 1.17, 1.04–1.31) mortality. After covariates adjustment, the relationship between persistent poverty and all-cause mortality (HR, 95% CI: 1.17, 1.06–1.29) and non-CRC-specific mortality (HR, 95% CI: 1.34, 1.15–1.57) remained significant, but CRC-specific mortality did not. In subgroup analyses, persistent poverty was associated with increased overall mortality among urban tracts (HR, 95% CI: 1.22, 1.08–1.38), but not rural tracts.

Conclusions:

After covariates adjustment, CRC patients in persistent poverty tracts are more likely to die of all causes and non-CRC causes but not CRC-specific causes than those in nonpersistent poverty areas, suggesting that differences in CRC-specific deaths may be partly attributed to demographics, geography, tumor characteristics, and treatment.

Keywords: catchment area health, colorectal neoplasms, poverty areas, rural health, survival analysis

INTRODUCTION

Persistent poverty refers to prolonged exposure to high levels of economic distress, defined as at least 20% of the population living below the federal poverty level for 30 years or more.1–4 Persistent poverty involves a constellation of structural and systemic challenges that impact health outcomes across the cancer continuum, including elevated carcinogenic exposures, lower health care quality, and unfavorable social determinants of health.5 The majority of communities that experience persistent poverty are located in rural areas in the South, with a higher proportion of residents from racial and ethnic minoritized groups.6,7

There is growing recognition that communities with continuous, long-term exposure to high-poverty experience different problems than intermittent high-poverty populations, including higher colorectal cancer (CRC) mortality.8,9 The complex, interrelated contextual factors that coexist in persistent poverty communities may influence the prognosis of CRC patients through various mechanisms such as higher prevalence of comorbidities, lower likelihood of tumor resection and adjuvant therapy, and greater burden of unmet social needs.5,10 Residents of persistent poverty areas are also more likely to experience food insecurity, lack adequate housing, and have poor access to transportation or medical care.11 As a result, social, economic, and environmental conditions may disproportionately impact cancer outcomes in communities characterized by decades of locally high poverty.12–15

Compared to urban counterparts, CRC patients who live in rural persistent poverty areas may face additional challenges in health and health care. Rurality is independently associated with worse survival outcomes for CRC and other diseases.16–18 Already, CRC mortality rates have been shown to be higher among rural persistent poverty counties compared to urban persistent poverty counties.19 In recent years, rural persistent poverty populations experienced higher CRC incidence rates and smaller improvements in CRC outcomes compared to those in urban and/or nonpersistent poverty areas.9

Given the extreme deprivation and distress of populations in persistent poverty, it is imperative to understand the factors that influence survival for CRC patients in both rural and urban persistent poverty areas. In this study, we analyzed the relationship between persistent poverty and risk of death among patients with CRC. First, we tested whether living in persistent poverty is associated with disparities in CRC survival before and after adjusting for demographics, tumor characteristics, and treatment factors. Second, we examined whether the association between persistent poverty and CRC survival varied between urban and rural settings. Findings may inform cancer prevention and control efforts aimed at reducing cancer health disparities in rural and persistent poverty areas.

METHODS

Data sources

State-wide cancer registry (2013–2019) and death records (2013–2021) data files from the Arkansas Department of Health were obtained through the Arkansas Center for Health Improvement. A hash algorithm consisting of a 44-character alias based on patient last name, sex, and date of birth was used to merge first primary CRC cases with death records through December 31, 2021.

Participant selection

Patients with a first primary CRC diagnosis in 2013–2019 were identified from the cancer registry data. We followed patients from the first CRC diagnosis in the registry until death or until December 31, 2021. Study exclusion criteria included patients with a prior history of cancer, patients with discrepancies between cancer registry and death records, and patients with missing information for census tract, sex, or date of diagnosis. Patients whose CRC diagnosis was ascertained from death certificate or autopsy, or whose death occurred on the same date as diagnosis, were excluded because survival time could not be calculated. Patients who reside in tracts where persistent poverty status is not available were also excluded.

Area-level characteristics

Patients residing census tracts were classified based on their residential address reported at the time of diagnosis using the Texas A&M, NAACCR, NCI Geocoding Service.20,21 This geocoder has a reported address match rate of 99.9%, indicating a high level of accuracy.22 We used census tracts for area-level analysis because they provide the smallest geographic unit for which persistent poverty and urban/rural classifications are available. Compared to counties, tracts also offer a broader representation of people living in persistent poverty settings.9 Tracts from the 2010 decennial census were identified by the US Department of Agriculture (USDA) as having poverty rates of at least 20.0% for four consecutive measurement periods spanning 30 years (decennial census 1-year estimates for 1990 and 2000 and American Community Survey 5-year estimates for 2007–2011 and 2015–2019).2 Persistent poverty status was considered not available if poverty rate estimates were missing for one or more measurement period, or if the estimated margin of error included 20.0% and the reliability index was low.2 Of Arkansas’s 686 tracts, 111 tracts (16%) were indicated as persistent poverty areas in the 30-year measurement period ending in 2015–2019 (Figure S2). Persistent poverty status was not available for 29 tracts (4%). Rurality of a patient’s residing census tract at the time of diagnosis was defined using the secondary USDA Rural–Urban Commuting Area (RUCA) codes, based on the 2010 decennial census and 2006–2010 American Community Survey. Tracts were classified as urban if located in metropolitan areas (RUCA codes 1–3) or if the commuting flow to urban areas was at least 30% (RUCA codes 4.1, 5.1, 7.1, 8.1, and 10.1).20 All other micropolitan (RUCA codes 4–6), small town (RUCA codes 7–9), and rural areas (RUCA code 10) were classified as rural, as specified in the cancer registry datafile.

Covariates

Based on previous literature and expert opinion, covariates were selected that included patient and tumor characteristics at diagnosis that were predictive of overall survival and/or CRC-specific survival among CRC patients.23,24 Individual attributes were age, sex, race, marital status, and insurance type. Tumor information included anatomical site, stage at diagnosis (localized, regional, and distant disease), tumor grade, tumor size, lymph node involvement, lymph vascular invasion, and perineural invasion.25,26 First course of treatment was coded as surgery, radiation, and chemotherapy. Number of lymph nodes examined was included as a quality-of-care measure with at least 12 lymph nodes examined considered to be high-quality care.27 All patient and clinical characteristics were from the cancer registry datafile.

Outcomes

The primary outcome was overall survival, defined as the time from CRC diagnosis to death from any cause. We used underlying cause of death to further classify the cause of death as CRC-specific or non-CRC-specific.

Statistical analysis

Univariate comparisons of demographics, tumor characteristics, and treatment type were performed using a chi-square or Fisher’s exact test for categorical variables. Due to different clinical practice guidelines, treatment type and adequate lymphadenectomy were stratified by anatomical site for univariate analyses (i.e., colon, rectum, and rectosigmoid junction). Kaplan–Meier estimation and log-rank tests were used to compare all-cause and CRC-specific survival.

Adjusted analysis was conducted using Cox’s proportional hazards models for all cause of death. Fine-Gray subdistribution hazard models were used to analyze competing risk of CRC-specific and non-CRC-specific causes of death. Assessment of the proportional hazard assumption was conducted via visual inspection of the Kaplan–Meier curves. No violations of the proportional hazard assumption were detected. A subgroup analysis was conducted to assess differences among rural and urban settings. Point estimates, 95% confidence intervals, and p-values for statistical tests were reported. p < 0.05 were considered statistically significant. Statistical analyses were conducted using SAS version 9.4.

RESULTS

We identified 8658 CRC patients who were newly diagnosed in 2013–2019 (Figure S1). In total, 665 patients were removed based on the exclusion criteria, with 7993 patients remaining in the analytical dataset. Patients were classified as 56.6% urban nonpersistent poverty (n = 4522 patients from 320 tracts), 30.2% rural nonpersistent poverty (n = 2416 patients from 226 tracts), 7.9% urban persistent poverty (n = 633 patients from 62 tracts), and 5.3% rural persistent poverty (n = 422 patients from 49 tracts). Persistent poverty status was not available for 29 tracts (n = 222 patients), which were excluded from the study.

Compared to patients from nonpersistent poverty tracts, patients living in persistent poverty tracts were more likely to be Black, unmarried, with Medicaid coverage at the time of diagnosis, diagnosed at a more advanced stage, and reside in rural areas (all p-values < 0.001; Table 1). There were no statistically significant differences by age, sex, or anatomical site. Patients from persistent poverty tracts with rectosigmoid and colon cancers were less likely to receive surgery and less likely to have adequate lymphadenectomy compared to patients from nonpersistent poverty tracts (Table 2). There were no statistically significant differences for receipt of radiation or chemotherapy between patients from persistent poverty versus other tracts.

TABLE 1.

Characteristics of patients with colorectal cancer (CRC) by tract-level persistent poverty status.

Persistent poverty tract
 p-value
Total No Yes
Total (n) 7993 6938 1055
Sociodemographic characteristics
Age group 0.25
<50 years 926 (11.5%) 815 (11.7%) 111 (10.5%)
50–64 years 2773 (34.6%) 2381 (34.3%) 392 (37.1%)
65–75 years 2389 (29.8%) 2075 (29.9%) 314 (29.7%)
75+ years 1905 (23.8%) 1667 (24.0%) 238 (22.5%)
Sex 0.51
Female 3864 (48.3%) 3344 (48.1%) 520 (49.2%)
Male 4129 (51.6%) 3594 (51.8%) 535 (50.7%)
Race <0.001
White 6612 (82.7%) 6058 (87.3%) 554 (52.5%)
Black 1124 (14.0%) 677 (9.7%) 447 (42.3%)
Multiracial/Other 208 (2.6%) 167 (2.4%) 41 (3.8%)
Unknown/Missing 49 (0.6%) 36 (0.5%) 13 (1.2%)
Marital status <0.001
Not married/Unknown 4184 (52.3%) 3515 (50.6%) 669 (63.4%)
Married 3809 (47.6%) 3423 (49.3%) 386 (36.5%)
Payer at diagnosis <0.001
Uninsured/Self pay 192 (2.4%) 162 (2.3%) 30 (2.8%)
Medicaid 490 (6.1%) 384 (5.5%) 106 (10.0%)
Medicare 4123 (51.5%) 3578 (51.5%) 545 (51.6%)
Private 2782 (34.8%) 2480 (35.7%) 302 (28.6%)
Other/Unknown 406 (5.0%) 334 (4.8%) 72 (6.8%)
Rural tract 0.001
No 5155 (64.4%) 4522 (65.1%) 633 (60.0%)
Yes 2838 (35.5%) 2416 (34.8%) 422 (40.0%)
Tumor characteristics
Stage <0.001
Local 2493 (31.1%) 2172 (31.3%) 321 (30.4%)
Regional 3096 (38.7%) 2733 (39.3%) 363 (34.4%)
Distant 1844 (23.0%) 1556 (22.4%) 288 (27.2%)
Unknown/Missing 560 (7.0%) 477 (6.8%) 83 (7.8%)
Grade <0.001
Well-differentiated 723 (9.0%) 601 (8.6%) 122 (11.5%)
Moderately 4842 (60.5%) 4220 (60.8%) 622 (58.9%)
Poorly/Undifferentiated 1205 (15.0%) 1073 (15.4%) 132 (12.5%)
Unknown/Missing 1223 (15.3%) 1044 (15.0%) 179 (16.9%)
Tumor size <0.001
<2 cm 795 (9.9%) 700 (10.0%) 95 (9.0%)
2–5 cm 3126 (39.1%) 2757 (39.7%) 369 (34.9%)
>5–10 cm 1955 (24.4%) 1698 (24.4%) 257 (24.3%)
>10 cm 192 (2.4%) 164 (2.3%) 28 (2.6%)
Unknown/Missing 1925 (24.0%) 1619 (23.3%) 306 (29.0%)
Number of positive lymph nodes 0.003
0 3351 (41.9%) 2942 (42.4%) 409 (38.7%)
1–3 1392 (17.4%) 1224 (17.6%) 168 (15.9%)
>3 954 (11.9%) 830 (11.9%) 124 (11.7%)
Unknown/Missing 2296 (28.7%) 1942 (27.9%) 354 (33.5%)
Lymph vascular invasion 0.002
No 3476 (43.4%) 3028 (43.6%) 448 (42.4%)
Yes 1870 (23.3%) 1657 (23.8%) 213 (20.1%)
Unknown/Missing 2647 (33.1%) 2253 (32.4%) 394 (37.3%)
Perineural invasion 0.81
No 998 (12.4%) 862 (12.4%) 136 (12.8%)
Yes 216 (2.7%) 190 (2.7%) 26 (2.4%)
Unknown/Missing 6779 (84.8%) 5886 (84.8%) 893 (84.6%)
CRC site 0.24
Right colon 2581 (32.2%) 2240 (32.2%) 341 (32.3%)
Transverse 551 (6.8%) 462 (6.6%) 89 (8.4%)
Left colon 2140 (26.7%) 1859 (26.7%) 281 (26.6%)
Rectosigmoid junction 583 (7.2%) 519 (7.4%) 64 (6.0%)
Rectum 1779 (22.2%) 1546 (22.2%) 233 (22.0%)
Unknown/Missing 359 (4.4%) 312 (4.4%) 47 (4.4%)
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TABLE 2.

Treatment type and care quality characteristics for patients with colorectal cancer (CRC) by tumor location and persistent poverty status.

Rectum
 Rectosigmoid
 Colon
Persistent poverty
 Persistent poverty
 Persistent poverty
No Yes p-value No Yes p-value No Yes p-value
Total (n) 1546 233 519 64 4873 758
Surgery 0.84 0.001 0.03
No 442 (28.5%) 72 (30.9%) 71 (13.6%) 18 (28.1%) 638 (13.0%) 126 (16.6%)
Local excision 241 (15.5%) 36 (15.4%) 17 (3.2%) 3 (4.6%) 120 (2.4%) 25 (3.2%)
Colectomy 739 (47.8%) 110 (47.2%) 410 (78.9%) 36 (56.2%) 3944 (80.9%) 581 (76.6%)
Surgery, NOS 26 (1.6%) 2 (0.8%) 4 (0.7%) 2 (3.1%) 31 (0.6%) 2 (0.2%)
Unknown 98 (6.3%) 13 (5.5%) 17 (3.2%) 5 (7.8%) 140 (2.8%) 24 (3.1%)
Radiation 0.57 0.15 0.69
No 683 (44.1%) 98 (42.0%) 372 (71.6%) 42 (65.6%) 4453 (91.3%) 686 (90.5%)
Yes 723 (46.7%) 117 (50.2%) 110 (21.1%) 13 (20.3%) 94 (1.9%) 15 (1.9%)
Unknown 140 (9.0%) 18 (7.7%) 37 (7.1%) 9 (14.0%) 326 (6.6%) 57 (7.5%)
Chemotherapy 0.57 0.64 0.32
No 507 (32.7%) 79 (33.9%) 206 (39.6%) 28 (43.7%) 2554 (52.4%) 393 (51.8%)
Yes 893 (57.7%) 137 (58.7%) 259 (49.9%) 28 (43.7%) 1796 (36.8%) 270 (35.6%)
Unknown 146 (9.4%) 17 (7.2%) 54 (10.4%) 8 (12.5%) 523 (10.7%) 95 (12.5%)
Number of lymph nodes examined 0.35 <0.001 0.03
0 703 (45.4%) 120 (51.5%) 94 (18.1%) 26 (40.6%) 771 (15.8%) 150 (19.7%)
<12 241 (15.5%) 35 (15.0%) 94 (18.1%) 8 (12.5%) 709 (14.5%) 116 (15.3%)
≥12 471 (30.4%) 62 (26.6%) 305 (58.7%) 27 (42.1%) 3146 (64.5%) 451 (59.4%)
Unknown 131 (8.4%) 16 (6.8%) 26 (5.0%) 3 (4.6%) 247 (5.0%) 41 (5.4%)
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Abbreviation: NOS, not otherwise specified.

Among those in persistent poverty areas, 558 (52.9%) of 1055 CRC patients died versus 3117 (44.9%) of 6938 CRC patients in nonpersistent poverty areas from January 2013 to December 2021. The difference in median overall survival time was 2.2 years (median survival, 95% CI: 4.50, 3.87–5.57 years for persistent poverty; 6.69, 6.30–7.10 years for nonpersistent poverty; Figure 1). In an unadjusted model, CRC patients in persistent poverty areas were more likely to die from all causes than those in nonpersistent poverty areas (HR, 95% CI: 1.28, 1.17–1.40; Table 3). When examining competing risks, CRC-specific mortality was higher in persistent poverty areas compared to nonpersistent poverty areas in the unadjusted analysis (HR, 95% CI: 1.17, 1.04–1.31).

FIGURE 1.

FIGURE 1

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Kaplan–Meier curves with overall (i) and CRC-specific (ii) survival by tract-level persistent poverty designation (A) and stratified by rural/urban status (B). CRC, colorectal cancer; non-PP, nonpersistent poverty; PP, persistent poverty.

TABLE 3.

Association between persistent poverty and death among patients diagnosed with colorectal cancer (CRC) in Arkansas from 2013 to 2019.

Model 1: Unadjusted
 Model 2: Adjusted, all tracts
 Model 3: Adjusted, stratified by urban/rural tracts
All cause of death CRC death Non-CRC death All cause of death CRC death Non-CRC death All cause of death CRC death Non-CRC death
HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI)
Persistent poverty 1.28 (1.17, 1.40)*** 1.17 (1.04, 1.31)* 1.30 (1.13, 1.50)*** 1.17 (1.06, 1.29)** 1.00 (0.88, 1.15) 1.34 (1.15, 1.57)***
Urban strata 1.36 (1.21, 1.52)*** 1.22 (1.05, 1.41) 1.38 (1.15, 1.65)*** 1.22 (1.08, 1.38)** 1.01 (0.86, 1.20) 1.40 (1.15, 1.70)***
Rural strata 1.16 (1.00, 1.34)* 1.08 (0.90, 1.30) 1.20 (0.95, 1.52) 1.10 (0.95, 1.28) 0.98 (0.79, 1.21) 1.25 (1.01, 1.63)*
Rural tract
No (ref)
Yes 1.00 (0.94, 1.07) 1.08 (0.99, 1.18) 0.90 (0.81, 1.01)
Age group
<50 years (ref)
50–64 years 1.34 (1.18, 1.52)*** 1.20 (1.04, 1.38)* 1.32 (1.03, 1.70)* 1.33 (1.17, 1.51)*** 1.20 (1.04, 1.38)* 1.32 (1.02, 1.69)*
65–74 years 1.69 (1.46, 1.96)*** 1.34 (1.12, 1.60)** 1.94 (1.46, 2.59)*** 1.68 (1.45, 1.95)*** 1.33 (1.12, 1.59)** 1.94 (1.46, 2.58)***
≥75 years 3.25 (2.80, 3.78)*** 1.98 (1.65, 2.38)*** 3.37 (2.53, 4.48)*** 3.24 (2.79, 3.76)*** 1.98 (1.64, 2.38)*** 3.36 (2.52, 4.47)***
Sex
Female (ref)
Male 1.14 (1.07, 1.22)*** 1.07 (0.98, 1.17) 1.22 (1.09, 1.36)*** 1.14 (1.06, 1.21)*** 1.06 (0.97, 1.16) 1.21 (1.09, 1.35)***
Race
White (ref)
Black 1.03 (0.93, 1.13) 1.13 (0.99, 1.29) 0.84 (0.70, 1.00)* 1.02 (0.92, 1.13) 1.13 (0.99, 1.29) 0.83 (0.70, 0.99)*
Multiracial/Other 1.01 (0.82, 1.24) 1.09 (0.84, 1.42) 0.97 (0.68, 1.38) 1.00 (0.81, 1.23) 1.09 (0.83, 1.42) 0.96 (0.67, 1.37)
Unknown/Missing 0.17 (0.06, 0.45)*** 0.09 (0.01, 0.62)* 0.38 (0.12, 1.18) 0.16 (0.06, 0.45)*** 0.08 (0.01, 0.61)* 0.37 (0.11, 1.17)
Marital status
Not married/Unknown (ref)
Married 0.86 (0.81, 0.93)*** 0.92 (0.84, 1.01) 0.84 (0.75, 0.95)** 0.86 (0.80, 0.92)*** 0.91 (0.83, 1.00) 0.84 (0.75, 0.94)**
Payer at diagnosis
Uninsured/Self pay
Medicaid 1.26 (1.03, 1.55)* 1.53 (1.19, 1.96)*** 0.79 (0.49, 1.29) 1.26 (1.02, 1.55)* 1.52 (1.19, 1.95)*** 0.79 (0.48, 1.28)
Medicare 1.27 (1.10, 1.47)** 1.01 (0.84, 1.22) 1.69 (1.32, 2.17)*** 1.26 (1.09, 1.46)** 1.01 (0.83, 1.22) 1.69 (1.32, 2.17)***
Private (ref) 1.28 (1.16, 1.42)*** 1.13 (0.99, 1.29) 1.36 (1.13, 1.63)** 1.28 (1.15, 1.42)*** 1.13 (0.98, 1.29) 1.35 (1.12, 1.63)**
Other/Unknown 1.15 (0.96, 1.37) 1.12 (0.89, 1.41) 1.14 (0.85, 1.51) 1.14 (0.96, 1.36) 1.11 (0.89, 1.40) 1.13 (0.85, 1.51)
Stage
Local (ref)
Regional 2.27 (1.88, 2.74)*** 2.27 (1.88, 2.74)*** 0.90 (0.78, 1.05) 1.38 (1.23, 1.54)*** 2.26 (1.88, 2.73)*** 0.90 (0.77, 1.04)
Distant 6.88 (5.67, 8.36)*** 6.88 (5.67, 8.36)*** 0.85 (0.70, 1.03) 4.09 (3.63, 4.60)*** 6.88 (5.66, 8.35)*** 0.85 (0.70, 1.02)
Unknown/Missing 2.17 (1.65, 2.85)*** 2.17 (1.65, 2.85)*** 0.91 (0.68, 1.20) 1.30 (1.08, 1.56)** 2.16 (1.65, 2.85)*** 0.90 (0.68, 1.20)
Grade
Well differentiated (ref)
Moderately 1.32 (1.08, 1.63)** 1.32 (1.08, 1.63)** 0.97 (0.80, 1.18) 1.19 (1.03, 1.37)* 1.32 (1.07, 1.62)** 0.97 (0.80, 1.17)
Poorly/Undifferentiated 1.75 (1.40, 2.19)*** 1.75 (1.40, 2.19)*** 1.04 (0.83, 1.31) 1.66 (1.42, 1.94)*** 1.75 (1.40, 2.19)*** 1.04 (0.82, 1.31)
Unknown/Missing 1.26 (1.00, 1.58) 1.26 (1.00, 1.58) 1.10 (0.87, 1.38) 1.35 (1.16, 1.58)*** 1.25 (1.00, 1.57)* 1.09 (0.87, 1.38)
Tumor size
<2 cm (ref)
2–5 cm 1.49 (1.16, 1.91)** 1.49 (1.16, 1.91)** 1.27 (1.03, 1.57)* 1.34 (1.14, 1.57)*** 1.48 (1.15, 1.90)** 1.26 (1.02, 1.56)*
>5–10 cm 1.99 (1.54, 2.56)*** 1.99 (1.54, 2.56)*** 1.23 (0.98, 1.55) 1.63 (1.38, 1.93)*** 1.98 (1.54, 2.56)*** 1.23 (0.98, 1.54)
>10 cm 1.82 (1.26, 2.64)*** 1.82 (1.26, 2.64)*** 1.32 (0.90, 1.93) 1.56 (1.21, 2.00)*** 1.82 (1.26, 2.63)** 1.31 (0.89, 1.92)
Unknown/Missing 1.58 (1.22, 2.05)*** 1.58 (1.22, 2.05)*** 1.28 (1.01, 1.61)* 1.53 (1.29, 1.81)*** 1.58 (1.22, 2.04)*** 1.27 (1.00, 1.60)*
Number of positive lymph nodes
0 (ref)
1–3 1.45 (1.24, 1.69)*** 1.45 (1.24, 1.69)*** 0.96 (0.80, 1.16) 1.19 (1.06, 1.34)** 1.44 (1.23, 1.69)*** 0.96 (0.80, 1.15)
>3 2.27 (1.92, 2.68)*** 2.27 (1.92, 2.68)*** 0.89 (0.72, 1.12) 1.80 (1.59, 2.04)*** 2.26 (1.92, 2.67)*** 0.89 (0.71, 1.11)
Unknown/Missing 0.87 (0.53, 1.40) 0.87 (0.53, 1.40) 1.51 (0.89, 2.57) 1.19 (0.86, 1.65) 0.86 (0.53, 1.40) 1.50 (0.88, 2.57)
Lymph vascular invasion
No (ref)
Yes 1.31 (1.15, 1.50)*** 1.31 (1.15, 1.50)*** 1.03 (0.87, 1.21) 1.22 (1.10, 1.35)*** 1.31 (1.14, 1.50)*** 1.02 (0.87, 1.21)
Unknown/Missing 1.07 (0.92, 1.23) 1.07 (0.92, 1.23) 1.11 (0.96, 1.29) 1.13 (1.02, 1.25)* 1.06 (0.92, 1.22) 1.11 (0.95, 1.28)
CRC site
Right colon
Transverse 1.10 (0.96, 1.26) 1.10 (0.96, 1.26) 0.96 (0.82, 1.13) 1.16 (1.04, 1.28)** 1.10 (0.96, 1.26) 0.96 (0.81, 1.13)
Left colon 1.00 (0.82, 1.22) 1.00 (0.82, 1.22) 1.12 (0.89, 1.41) 1.18 (1.02, 1.37)* 0.99 (0.81, 1.21) 1.11 (0.88, 1.41)
Rectosigmoid 1.15 (1.01, 1.31)* 1.15 (1.01, 1.31)* 0.97 (0.82, 1.15) 1.18 (1.06, 1.30)** 1.15 (1.01, 1.31) 0.96 (0.81, 1.14)
Rectum (ref) 0.96 (0.80, 1.17) 0.96 (0.80, 1.17) 1.05 (0.83, 1.33) 1.07 (0.93, 1.24) 0.96 (0.79, 1.16) 1.04 (0.82, 1.32)
Unknown/Missing 0.89 (0.71, 1.11) 0.89 (0.71, 1.11) 1.55 (1.20, 1.99)*** 1.43 (1.23, 1.67)*** 0.88 (0.70, 1.10) 1.54 (1.19, 1.99)***
Surgery
No (ref)
Local excision 0.35 (0.24, 0.50)*** 0.35 (0.24, 0.50)*** 0.70 (0.51, 0.97)* 0.32 (0.26, 0.41)*** 0.34 (0.24, 0.50)*** 0.69 (0.50, 0.96)*
Colectomy 0.53 (0.41, 0.69)*** 0.53 (0.41, 0.69)*** 0.94 (0.69, 1.27) 0.45 (0.38, 0.55)*** 0.53 (0.41, 0.69)*** 0.93 (0.69, 1.26)
Unknown/NOS 0.64 (0.49, 0.83)*** 0.64 (0.49, 0.83)*** 1.00 (0.72, 1.39) 0.56 (0.46, 0.68)*** 0.64 (0.49, 0.83)*** 1.00 (0.72, 1.39)
Lymph nodes examined
0 (ref)
<12 2.35 (1.34, 4.15)** 2.35 (1.34, 4.15)** 0.69 (0.38, 1.25) 1.26 (0.86, 1.83) 2.35 (1.33, 4.15)** 0.69 (0.38, 1.25)
≥12 1.29 (1.13, 1.48)*** 1.29 (1.13, 1.48)*** 1.08 (0.93, 1.26) 1.23 (1.11, 1.36)*** 1.29 (1.13, 1.48)*** 1.07 (0.92, 1.25)
Unknown 2.44 (1.49, 3.99)*** 2.44 (1.49, 3.99)*** 0.54 (0.30, 0.97)* 1.18 (0.84, 1.65) 2.43 (1.48, 3.99)*** 0.54 (0.30, 0.96)*
Open in a new tab

Abbreviations: CI, confidence interval; CRC, colorectal cancer; HR, hazard ratio; NOS, not otherwise specified.

*

p < 0.05.

**

p < 0.01.

***

p < 0.001.

The difference in overall mortality remained statistically significant between patients in persistent poverty tracts and other tracts after adjusting for sociodemographic and clinical characteristics (HR, 95% CI: 1.17, 1.06–1.29). However, in the adjusted model, persistent poverty was not associated with CRC-specific mortality among CRC patients (HR, 95% CI: 1.00, 0.88–1.15). Full parameter estimates for the adjusted models are provided in Table 3.

Rural–urban difference

In the unadjusted model, there was a significant difference in overall mortality and CRC-specific mortality among CRC patients from rural versus urban tracks (HR, 95% CI: 1.07, 1.00–1.15 for overall mortality; 1.11, 1.02–1.21 for CRC-specific mortality). However, after adjusting for covariates and persistent poverty status, rural/urban residence was not a significant predictor of overall death (HR, 95% CI: 1.00, 0.94–1.07) or CRC-specific death (HR, 95% CI: 1.08, 0.99–1.18) (Table 3).

When stratified by rural/urban location, CRC patients in persistent poverty areas were at increased risk of death in both urban (HR, 95% CI: 1.36, 1.21–1.52) and rural (HR, 95% CI: 1.16, 1.00–1.34) census tracts (Table 3). Compared to nonpersistent poverty areas, the median overall survival time among patients in persistent poverty areas was 2.4 years lower in urban areas (median survival, 95% CI: 4.50, 3.34–5.59 years for urban persistent poverty; 6.91, 6.39–7.56 years for urban nonpersistent poverty) and 1.6 years lower in rural areas (median survival, 95% CI: 4.47, 3.69–6.19 years for rural persistent poverty; 6.33, 5.62–6.99 years for rural nonpersistent poverty) (Figure 1). In the adjusted models, the relationship between persistent poverty and overall mortality remained significant in urban tracts (HR, 95% CI: 1.22, 1.08–1.38) but not in rural tracts (HR, 95% CI: 1.10, 0.95–1.28). After adjusting for the covariates, there were no significant differences in hazard for CRC-specific deaths associated with persistent poverty among patients in rural (HR, 95% CI: 0.98, 0.79–1.21) or urban areas (HR, 95% CI: 1.01, 0.86–1.20). However, CRC patients from persistent poverty areas were more likely to die from non-CRC causes in both urban (HR, 95% CI: 1.40, 1.15–1.70) and rural settings (HR, 95% CI: 1.28, 1.01–1.63) in the adjusted models.

DISCUSSION

This study found that living in persistent poverty census tracts is associated with decreased overall survival among CRC patients. Both CRC-specific deaths and non-CRC deaths were higher among CRC patients who lived in persistent poverty tracts compared to those who did not. After adjusting for patients’ sociodemographics, tumor characteristics, and CRC treatment factors, there was no longer a statistically significant difference in CRC-specific death, but overall death and non-CRC-specific death remained elevated among residents in persistent poverty tracts compared to other tracts. After adjusting for the covariates, persistent poverty was associated with elevated risk of all-cause mortality among CRC patients in urban, but not rural, persistent poverty areas. In the adjusted model, persistent poverty was associated with a heightened risk of non-CRC-specific deaths among CRC patients in both rural and urban tracts.

Consistent with previous literature, we observed worse overall survival among CRC patients who live in persistent poverty tracts versus nonpersistent poverty tracts.9 For decades, reductions in CRC incidence and mortality rates have been greatest among metropolitan and affluent populations.18,28 Compared to areas with lower poverty rates, residents of persistent poverty communities were more likely to have late-stage diagnosis, less likely to receive surgery or adjuvant chemotherapy, and more likely to die from their cancer.29–34 As a result, estimates of CRC mortality rates are currently between 12% and 18% higher among persistent poverty populations compared to nonpersistent poverty areas.8,35 Adding to this evidence, our results show that living in persistent poverty tracts is associated with 17% higher overall risk of death among CRC patients in our study population. Adjusting for demographic and clinical variables did not eliminate the observed association between living in persistent poverty tracts and worse overall survival among CRC patients.

In our data, several modifiable factors were significantly associated with CRC deaths, including insurance status, stage at diagnosis, type of surgery, and number of lymph nodes examined. In addition, patients from persistent poverty tracts were less likely to receive surgery than counterparts in nonpersistent poverty tracts. Reasons for the differences in surgical care may include patients’ treatment decisions, physicians’ recommendations for treatment, or prevalence of comorbidities.36,37 Patient navigation has been proposed as an evidence-based strategy to increase the likelihood for all patients to receive timely, appropriate treatment,37,38 but barriers to CRC health care access and quality remain.39 Our findings reinforce the evidence that health care access, screening, treatment, and quality of care are associated with CRC survival disparities.36,39–41 These factors operate at individual, institutional, and systemic levels to exacerbate CRC disparities across the socioeconomic gradient.39,42

Our results add to the growing body of evidence for adverse cancer-related outcomes associated with persistent poverty residence. A recent study by Bhattacharya and colleagues found that patients in persistent poverty census tracts had lower 5-year survival rates compared to those in nonpersistent poverty tracts for eight different cancer sites, including CRC.9 Other studies found associations of persistent poverty residence with higher cancer mortality rates for patients with breast cancer,29,43 hepatopancreatobiliary cancer,30,44 melanoma,45 CRC,29,46 non-small cell lung cancer,29 liver cancer,47 and oral and pharyngeal cancers.48 For oral and pharyngeal cancers, the relationship between persistent poverty and survival was modified by urban–rural geography.48 Compared to previous studies of cancer survival in persistent poverty areas, our analysis used different geographic boundaries (tract- vs. county-level analysis), sample selection (CRC patients vs. general population), and choice of study area (Arkansas vs. United States).9,29,31,43–48

Based on previous literature, CRC patients in rural areas were expected to have worse survival outcomes than their urban counterparts.49,50 Consistent with this hypothesis, rural residence at diagnosis was associated with higher overall and CRC-specific mortality among CRC patients in the unadjusted model. However, rural/urban geography was not associated with overall or CRC-specific deaths after adjusting for covariates and persistent poverty status. This result may be due in part to collinearity, as persistent poverty was independently associated with rural residence in the univariate analysis. In addition, county-based classifications tend to highlight rural disadvantage,8 whereas tract-based classifications provide finer geographic detail to understand persistent poverty across the rural–urban continuum.9,51 In the subgroup analysis, persistent poverty was associated with worse overall survival among patients from urban tracts, but not those from rural tracts. Further research is needed to determine whether rural geography modifies the association between persistent poverty and CRC survival.

Arkansas is a rural, Southern state with a high percentage of people living in persistent poverty areas. In the 5-year period of 2015–2019, 31.6% of Arkansans lived in high-poverty tracts, which ranked 5th highest among US states.52 Since the 1990s, persistent poverty areas in Arkansas have suffered among the highest CRC mortality rates in the United States.53 In particular, counties in the Lower Mississippi Delta region report high CRC burden,53,54 and they are twice as likely to be designated persistent poverty areas compared to other parts of the state (28.9% in the Delta vs. 13.1% outside the Delta).2,55 The heightened CRC burden in the Delta may reflect a combination of health care system challenges,56 environmental exposures,57 geographic access,56 and individual risk factors such as epigenetic aging58 that accompany persistent poverty. Our results suggest that a heightened risk of non-CRC-specific mortality may contribute to disparities faced by CRC patients in persistent poverty areas, including communities in the Delta.

For decades, the US government has used place-based approaches to invest in regions affected by persistent poverty, such as the Delta Regional Authority, Appalachian Regional Commission, Southwest Border Commission, and other federal opportunity zones.59 Since 2009, Congress has specifically defined persistent poverty as a provision for federal appropriations related to education, employment, health care, food systems, transportation, housing, climate resilience, and aid to underserved groups.60 For example, the US Economic Development Administration directed 10% of Public Works and Build to Scale toward persistent poverty areas.3 Likewise, the USDA allocated at least 10% of rural development funds to areas of persistent poverty.1 These place-based policies are intended to optimize public investments within and across persistent poverty communities to reduce social and economic inequities.59 In the meantime, persistent poverty populations should be prioritized for additional support to implement recommended practices for CRC prevention,61 screening,61 and treatment.62 Likewise, insurance coverage for ancillary services to address health-related social needs of CRC patients could potentially mitigate the adverse consequences of living in persistent poverty areas.63

This study has several strengths. To our knowledge, it is the first study to test the association between persistent poverty and CRC survival in a rural Southern state, where persistent poverty is more prevalent. Second, we used tract-level classification for persistent poverty, which reduces the risk of misclassification of exposure compared to county-level analyses because of higher granularity. In addition, the finer spatial scale captures more nuance for how persistent poverty impacts urban and rural communities.9

As an observational study, our results are subject to the limitations that not all covariates in the model may be correctly recorded in the database, and there may be potential unmeasured confounders between persistent poverty and CRC outcomes. For instance, our data lacked information about residential mobility and duration of exposure to persistent poverty conditions before and after diagnosis. Likewise, comorbidities were not recorded in the cancer registry. If CRC patients in persistent poverty areas experienced higher comorbidities, then our model may underestimate the independent association between persistent poverty and CRC survival. Moreover, we adjusted for CRC treatment characteristics because they were associated with living in persistent poverty tracts and are predictive of CRC survival. However, they could also be a result of living in persistent poverty areas. Therefore, our model may have overadjusted for treatment characteristics and care quality measures, which may unduly weaken the association between persistent poverty and CRC-specific death. Further research is needed to determine whether these variables are mediators on a causal pathway between persistent poverty and CRC survival.

CONCLUSION

Living in persistent poverty tracts was associated with increased risk of overall and CRC-specific death among CRC patients. After adjusting for differences in demographics, tumor characteristics, and treatment, CRC patients who lived in persistent poverty tracts were not more likely to die from CRC compared to those from nonpersistent poverty tracts. However, this population experienced higher risk of death from other causes. Together, the study provides further evidence of unmet disparities for patients with CRC who reside in persistent poverty areas. Strategies to eliminate persistent poverty may have a beneficial effect on cancer health equity.

Supplementary Material

jrh70045-sup-0001-SuppMat
jrh70045-sup-0002-FigS1
jrh70045-sup-0003-FigS2

Additional supporting information can be found online in the Supporting Information section at the end of this article.

ACKNOWLEDGMENTS

Research reported in this publication was supported by the Winthrop P. Rockefeller Cancer Institute Rural Research Award Program and the National Center for Advancing Translational Sciences of the National Institutes of Health under award numbers UL1 TR003107 and UM1 TR004909. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Chenghui Li is supported by American Cancer Society (RSGI-23–1039245-01-HOPS). Access to the Arkansas Central Cancer Registry and Vital Records database was provided by support from the Arkansas Biosciences Institute/Arkansas Insurance Department/Arkansas Healthcare Transparency Initiative Collaboration. The funding organizations had no role in the design and conduct of the study; the collection, analysis, and interpretation of data; or the preparation of the manuscript. The manuscript was reviewed and approved by the Arkansas Insurance Department and the Arkansas Healthcare Transparency Initiative.

Funding information

National Institutes of Health, Grant/Award Numbers: UL1 TR003107, UM1 TR004909; Arkansas Biosciences Institute; /Arkansas Insurance Department/Arkansas Healthcare Transparency Initiative Collaboration; Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences; National Center for Advancing Translational Sciences; American Cancer Society, Grant/Award Number: RSGI-23–1039245-01-HOPS

Footnotes

DISCLOSURES

Chenghui Li received research support for unrelated projects sponsored by University of Utah and AstraZeneca.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

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jrh70045-sup-0002-FigS1
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