INTRODUCTION
There has been a downward trend in colorectal cancer (CRC) incidence and mortality in the United States (U.S.) since the 1980s; however, racial, ethnic, and socioeconomic disparities persist.1 These disparities are the result of differences in CRC risk factors, screening, diagnosis, and quality of care.2,3 While many studies have demonstrated a role of race, ethnicity, and socioeconomic status (SES) in CRC screening and diagnosis, the impact of these factors on time to treatment after CRC diagnosis is understudied.
Time to treatment after CRC diagnosis impacts mortality, survival, and quality of life.4,5 Thus, we aimed to use national cancer registry data to study the impact of race, ethnicity, and SES on time to treatment among individuals with CRC in the U.S. As access to timely care is challenging for the medically underserved, exploring this relationship may help reduce disparities in CRC outcomes.
METHODS
We performed a cross-sectional analysis using data from the National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) database. Our study population included all individuals in SEER18 age 20 to 79 with a histologically confirmed diagnosis of colon or rectal cancer between January 1, 2000 and December 31, 2016. Individuals were excluded as detailed in Supplementary Figure 1.
The primary outcome was the diagnosis-to-treatment interval (DTI), defined as the number of months between CRC diagnosis and initiation of first CRC treatment (surgery, radiation, or chemotherapy). We created two DTI categories: 1) ≤ 1 month and 2) >1 month. DTI >1 month was considered prolonged DTI, consistent with data that a delay of over 30 days between a confirmed diagnosis of CRC and initiation of treatment is associated with 1.5-fold higher risk of death.4
We used multivariable logistic regression to determine factors associated with prolonged DTI and assessed the interaction between SES and race/ethnicity on DTI, controlling for relevant confounders. Additional details are provided in the Supplementary Materials.
RESULTS
Supplementary Table 1 summarizes the demographic and clinical characteristics of the 330,988 CRC cases included in the study. Average DTI was 0.6 months (s.d.=1.0).
The results of the multivariable logistic regression analyses are shown in Table 1A. There was a significant interaction between race/ethnicity and SES (p<0.001). Within each SES strata, racial/ethnic minority subgroups were significantly more likely to have prolonged DTI than non-Hispanic Whites (NHW) (p-values<0.05) (Table 1B, top panel). The only exceptions were medium and high SES non-Hispanic American Indian/Alaska Natives (NHAIAN). Differences from NHW were greatest in the lowest SES groups. For example, among Hispanics with low SES, the odds of prolonged DTI were 78% higher than in NHW with low SES (aOR=1.78; 95%CI=1.69–1.88). However, there was 26% higher odds of prolonged DTI for high SES Hispanics compared to high SES NHW (aOR=1.26; 95%CI=1.17–1.36). There was a similar pattern for Non-Hispanic Asian/Pacific Islanders (NHAPI) and NHAIAN but not for non-Hispanic Blacks (NHB).
Table 1:
Results of main effects model and interaction model evaluating associations with diagnosis-to-treatment interval, N=330,988.
|
A. Main effects model. | |||
| Unadjusted Odds Ratio (95% CI) | Adjusted Odds Ratio (95% CI) Main effects model | Adjusted Odds Ratio (95% CI) Interaction model* | |
| Sex | |||
| Male | 1.00 | 1.00 | 1.00 |
| Female | 0.81 (0.79, 0.83) | 0.81 (0.79, 0.83) | 0.81 (0.79, 0.83) |
| Age, years | |||
| 20–29 | 0.55 (0.48, 0.64) | 0.48 (0.41,0.56) | 0.48 (0.41,0.56) |
| 30–39 | 0.68 (0.63, 0.73) | 0.63 (0.59, 0.68) | 0.63 (0.59, 0.68) |
| 40–49 | 0.78 (0.75, 0.81) | 0.74 (0.71, 0.77) | 0.74 (0.71,0.77) |
| 50–59 | 0.96 (0.93, 0.98) | 0.93 (0.90, 0.96) | 0.93 (0.90, 0.96) |
| 60–69 (ref.) | 1.00 | 1.00 | 1.00 |
| 70–79 | 0.91 (0.88, 0.93) | 1.00 (0.97, 1.03) | 1.00 (0.97, 1.03) |
| Year of diagnosis | |||
| Per 5-year increments | 1.33 (1.32, 1.35) | 1.32 (1.30, 1.33) | 1.32 (1.30, 1.33) |
| Stage at diagnosis | |||
| Localized (ref.) | 1.00 | 1.00 | 1.00 |
| Regional | 1.15 (1.12, 1.18) | 1.16 (1.13, 1.19) | 1.16 (1.13, 1.19) |
| Distant | 1.30 (1.26, 1.33) | 1.30 (1.26, 1.34) | 1.30 (1.26, 1.34) |
| Setting of Residence | |||
| Rural | 1.00 | 1.00 | 1.00 |
| Urban | 1.42 (1.36, 1.48) | 1.35 (1.29, 1.41) | 1.33 (1.27, 1.39) |
| Race/Ethnicity | |||
| Non-Hispanic White (ref.) | 1.00 | 1.00 | ---- |
| Non-Hispanic Black | 1.39 (1.35, 1.44) | 1.31 (1.26, 1.35) | |
| Hispanic (All Races) | 1.66 (1.61, 1.71) | 1.55 (1.50, 1.60) | |
| Non-Hispanic Asian or Pacific Islander | 1.32 (1.28, 1.38) | 1.29 (1.24, 1.34) | |
| Non-Hispanic American Indian/Alaska Native | 1.62 (1.41, 1.85) | 1.39 (1.18, 1.65) | |
| SES | |||
| Low (ref.) | 1.00 | 1.00 | ---- |
| Medium | 0.86 (0.84, 0.89) | 0.90 (0.87–0.92) | |
| High | 0.82 (0.80, 0.84) | 0.87 (0.84–0.89) | |
|
B. Interaction model for SES and race/ethnicity. | |||
| Low SES | Medium SES | High SES | |
| Odds ratios for race/ethnicity within strata of SES | |||
| NHW | Ref | Ref | Ref |
| NHB | 1.35 (1.29, 1.42) | 1.31 (1.22, 1.40) | 1.37 (1.25, 1.49) |
| Hispanic | 1.78 (1.69, 1.88) | 1.50 (1.42, 1.59) | 1.26 (1.17, 1.36) |
| NHAPI | 1.43 (1.31, 1.56) | 1.38 (1.29, 1.48) | 1.17 (1.10, 1.24) |
| NHAIAN | 1.68 (1.31,2.16) | 1.30 (0.98, 1.73) | 1.11 (0.75, 1.64) |
| Odds ratios for SES within strata of race/ethnicity | |||
| NHW | Ref | 0.93 (0.90, 0.97) | 0.94 (0.90, 0.97) |
| NHB | Ref | 0.91 (0.84, 0.97) | 0.95 (0.87, 1.04) |
| Hispanic | Ref | 0.79 (0.74, 0.84) | 0.66 (0.61,0.72) |
| NHAPI | Ref | 0.90 (0.81, 1.00) | 0.77 (0.69, 0.84) |
| NHAIAN | Ref | 0.72 (0.49, 1.06) | 0.62 (0.39, 0.98) |
The multivariable main effects model and interaction models were controlled for sex, age, year of diagnosis, stage at diagnosis, urban/rural location, race/ethnicity, SES, and race/ethnicity x SES.
Values were adjusted ORs (95%CI).
ORs that are statistically different than 1 have p<0.05.
Socioeconomic status (SES) level is defined as YOST, a composite score provided by NCI/SEER that is constructed from seven variables (median household income, median house value, median rent, percent below 150% of poverty line, education index, percent working class, and percent unemployed) to measure different aspects of the SES of a census tract. Then census tracts are categorized into SES tertiles (low, medium, and high) based on the YOST score
NHW, non-Hispanic White; NHB, non-Hispanic Black; NHAPI, non-Hispanic Asian/Pacific Islander; NHAIAN, non-Hispanic American Indian/Alaska Native
When we examined the association between SES and DTI stratified by race/ethnicity (Table 1B, bottom panel), findings were similar for Hispanics, NHAPI, and NHAIAN—as SES increased, likelihood of prolonged DTI decreased. High SES Hispanic and NHAPI subjects had 34% and 23% lower odds, respectively, of prolonged DTI compared to low SES Hispanics and NHAPI. For NHB, SES tier did not significantly impact DTI. Overall, differences in prolonged DTI by race/ethnicity and SES were most pronounced among NHAIAN, Hispanics, and NHAPI. Older age, male sex, advanced stage at diagnosis, later year of diagnosis, and urban residence were also associated with prolonged DTI (Table 1A).
DISCUSSION
We used national cancer registry data to demonstrate that race/ethnicity and SES are significantly associated with time to CRC treatment in the U.S. Our findings are consistent with a well-documented synergistic relationship between race/ethnicity and SES in health outcomes.6 When racial/ethnic disparities in health are adjusted for SES, differences often diminish, indicating that SES accounts for some of the differences by race/ethnicity.6 There is also an independent role of race/ethnicity on the primary outcome, however. Even at the same SES level, minorities received treatment later than NHW. This interaction was strongest for Hispanics, NHAPI, and NHAIAN, suggesting that improving SES disparities (e.g., income, education) in these groups may improve time to treatment. In contrast, race/ethnicity seems to be more influential than SES for NHB, suggesting we must focus on something inherent to how NHB are medically managed to close the time to treatment gap between NHB and NHW.
There are several limitations to our study. First, DTI values are not exact and capped due to limitations of the SEER database. However, overestimations or underestimations of DTI would be uniform across all groups and unlikely affect model results. Second, SEER does not include patient-level data on comorbidities, distance to referral center, healthcare utilization, provider recommendations, or prior screening that might impact DTI.
Despite these limitations, the study has many strengths. We used a large population-level dataset with broad representation of racial/ethnic and SES groups to address an important question about cancer care and outcomes. Our study validates the results of prior small studies regarding the relationship between race/ethnicity and DTI but demonstrates these disparities on a national level.7 Finally, our findings go beyond focusing on either race/ethnicity or SES to explore the interaction between these two factors on DTI, which is done infrequently but is important given the correlation between race/ethnicity and SES.
In conclusion, multiple factors are associated with prolonged time to treatment for CRC, including a complex interaction between race/ethnicity and SES. Demographic groups in our study that were more likely to experience prolonged DTI, including NHB and NHAIAN, also have high CRC mortality. Differences in screening and treatment explain a large proportion of racial/ethnic CRC disparities in mortality.3 As screening gaps narrow, CRC treatments improve, and wait times for cancer care in the U.S. lengthen, minimizing differences in time to treatment becomes increasingly important to reduce disparities.2 Thus, our findings suggest that physicians must be more vigilant about timely care for minority and low-income patients diagnosed with CRC. Strategies include first developing an infrastructure to identify patients at high risk of care delays and patient-level barriers to receiving timely care. Then, implementing patient navigation to assist with overcoming barriers has shown promise in reducing disparities in cancer care.8 Finally, action should be taken to address discriminatory practices or structural racism that contribute to differences in care recommendations or care receipt in underserved populations.
Supplementary Material
Acknowledgments
Grant support: Dr. May receives funding from National Institutes of Health/National Cancer Institute award number R03CA230947, the Tobacco-Related Disease Research Program award number TRDRP587791, the Vatche and Tamar Manoukian Division of Digestive Diseases, and the Jonsson Comprehensive Cancer Center at UCLA.
Abbreviations:
- CRC
colorectal cancer
- DTI
diagnosis-to-treatment interval
- NCI
National Cancer Institute
- NHAIAN
non-Hispanic American Indian/Alaska Natives
- NHAPI
non-Hispanic Asian/Pacific Islanders
- NHB
non-Hispanic Blacks
- NHW
non-Hispanic Whites
- SES
socioeconomic status
- SEER
Surveillance, Epidemiology, and End Results
- U.S.
United States
Footnotes
Conflict of interest statement: There are no conflicts to report for A. Bui, L. Yang, A. Myint, or F. May.
Disclosures: There are no disclosures to report for A. Bui, L. Yang, A. Myint, or F. May.
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
REFERENCES
- 1.Siegel RL, Miller KD, Goding Sauer A, et al. Colorectal cancer statistics, 2020. CA: A Cancer Journal for Clinicians. 2020;70(3):145–164. [DOI] [PubMed] [Google Scholar]
- 2.Edwards BK, Ward E, Kohler BA, et al. Annual report to the nation on the status of cancer, 1975–2006, featuring colorectal cancer trends and impact of interventions (risk factors, screening, and treatment) to reduce future rates. Cancer. 2010;116(3):544–573. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Lansdorp-Vogelaar I, Kuntz KM, Knudsen AB, et al. Contribution of screening and survival differences to racial disparities in colorectal cancer rates. Cancer Epidemiol Biomarkers Prev. 2012;21(5):728–736. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Lee YH, Kung PT, Wang YH, et al. Effect of length of time from diagnosis to treatment on colorectal cancer survival: A population-based study. PLoS One. 2019;14(1):e0210465. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Visser MR, van Lanschot JJ, van der Velden J, et al. Quality of life in newly diagnosed cancer patients waiting for surgery is seriously impaired. J Surg Oncol. 2006;93(7):571–577. [DOI] [PubMed] [Google Scholar]
- 6.Williams DR. Race, socioeconomic status, and health. The added effects of racism and discrimination. Ann N Y Acad Sci. 1999;896:173–188. [DOI] [PubMed] [Google Scholar]
- 7.Jones LA, Ferrans CE, Polite BN, et al. Examining racial disparities in colon cancer clinical delay in the Colon Cancer Patterns of Care in Chicago study. Ann Epidemiol. 2017;27(11):731–738.e731. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Rodday AM, Parsons SK, Snyder F, et al. Impact of patient navigation in eliminating economic disparities in cancer care. Cancer. 2015;121(22):4025–4034. [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.