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3032296

Colorectal Cancer Characteristics and Mortality From Propensity Score-Matched Cohorts of Urban and Rural Veterans

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Article
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Colorectal Cancer Characteristics and Mortality From Propensity Score-Matched Cohorts of Urban and Rural Veterans

Author(s)
Minh Anh Le, MD
Po-Hong Liu, MD
Amar Mandalia, MD
Sergio Romero, PhD
Ishak A. Mansi, MD
Moheb Boktor, MD

Colorectal cancer (CRC) is the second-leading cause of cancer-related deaths in the United States, with an estimated 52,550 deaths in 2023.1 However, the disease burden varies among different segments of the population.2 While both CRC incidence and mortality have been decreasing due to screening and advances in treatment, there are disparities in incidence and mortality across the sociodemographic spectrum including race, ethnicity, education, and income.1-4 While CRC incidence is decreasing for older adults, it is increasing among those aged < 55 years.5 The incidence of CRC in adults aged 40 to 54 years has increased by 0.5% to 1.3% annually since the mid-1990s.6 The US Preventive Services Task Force now recommends starting CRC screening at age 45 years for asymptomatic adults with average risk.7

Disparities also exist across geographical boundaries and living environment. Rural Americans faces additional challenges in health and lifestyle that can affect CRC outcomes. Compared to their urban counterparts, rural residents are more likely to be older, have lower levels of education, higher levels of poverty, lack health insurance, and less access to health care practitioners (HCPs).8-10 Geographic proximity, defined as travel time or physical distance to a health facility, has been recognized as a predictor of inferior outcomes.11 These aspects of rural living may pose challenges for accessing care for CRC screening and treatment.11-13 National and local studies have shown disparities in CRC screening rates, incidence, and mortality between rural and urban populations.14-16

It is unclear whether rural/urban disparities persist under the Veterans Health Administration (VHA) health care delivery model. This study examined differences in baseline characteristics and mortality between rural and urban veterans newly diagnosed with CRC. We also focused on a subpopulation aged ≤ 45 years.

Methods

This study extracted national data from the US Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) hosted in the VA Informatics and Computing Infrastructure (VINCI) environment. VINCI is an initiative to improve access to VA data and facilitate the analysis of these data while ensuring veterans’ privacy and data security.17 CDW is the VHA business intelligence information repository, which extracts data from clinical and nonclinical sources following prescribed and validated protocols. Data extracted included demographics, diagnosis, and procedure codes for both inpatient and outpatient encounters, vital signs, and vital status. This study used data previously extracted from a national cohort of veterans that encompassed all patients who received a group of commonly prescribed medications, such as statins, proton pump inhibitors, histamine-2 blockers, acetaminophen-containing products, and hydrocortisone-containing skin applications. This cohort encompassed 8,648,754 veterans, from whom 2,460,727 had encounters during fiscal years (FY) 2016 to 2021 (study period). The cohort was used to ensure that subjects were VHA patients, allowing them to adequately capture their clinical profiles.

Patients were identified as rural or urban based on their residence address at the date of their first diagnosis of CRC. The Geospatial Service Support Center (GSSC) aggregates and updates veterans’ residence address records for all enrolled veterans from the National Change of Address database. The data contain 1 record per enrollee. GSSC Geocoded Enrollee File contains enrollee addresses and their rurality indicators, categorized as urban, rural, or highly rural.18 Rurality is defined by the Rural Urban Commuting Area (RUCA) categories developed by the Department of Agriculture and the Health Resources and Services Administration of the US Department of Health and Human Services.19 Urban areas had RUCA codes of 1.0 to 1.1, and highly rural areas had RUCA scores of 10.0. All other areas were classified as rural. Since the proportion of veterans from highly rural areas was small, we included residents from highly rural areas in the rural residents’ group.

Inclusion and Exclusion Criteria

All veterans newly diagnosed with CRC from FY 2016 to 2021 were included. We used the ninth and tenth clinical modification revisions of the International Classification of Diseases (ICD-9-CM and ICD-10-CM) to define CRC diagnosis (Supplemental materials).4,20 To ensure that patients were newly diagnosed with CRC, this study excluded patients with a previous ICD-9-CM code for CRC diagnosis since FY 2003.

Comorbidities were identified using diagnosis and procedure codes from inpatient and outpatient encounters, which were used to calculate the Charlson Comorbidity Index (CCI) at the time of CRC diagnosis using the weighted method described by Schneeweiss et al.21 We defined CRC high-risk conditions and CRC screening tests, including flexible sigmoidoscopy and stool tests, as described in previous studies (Supplemental materials).20

The main outcome was total mortality. The date of death was extracted from the VHA Death Ascertainment File, which contains mortality data from the Master Person Index file in CDW and the Social Security Administration Death Master File. We used the date of death from any cause, as cause of death was not available.

A propensity score (PS) was created to match rural (including highly rural) and urban residents at a ratio of 1:1. Using a standard procedure described in prior publications, multivariable logistic regression used all baseline characteristics to estimate the PS and perform nearest-number matching without replacement.22,23 A caliper of 0.01 maximized the matched cohort size and achieved balance (Supplemental materials). We then examined the balance of baseline characteristics between PS-matched groups.

Analyses

Cox proportional hazards regression analysis estimated the hazard ratio (HR) of death in rural residents compared to urban residents in the PS-matched cohort. The outcome event was the date of death during the study’s follow-up period (defined as period from first CRC diagnosis to death or study end), with censoring at the study’s end date (September 30, 2021). The proportional hazards assumption was assessed by inspecting the Kaplan-Meier curves. Multiple analyses examined the HR of total mortality in the PS-matched cohort, stratified by sex, race, and ethnicity. We also examined the HR of total mortality stratified by duration of follow-up.

Another PS-matching analysis among veterans aged ≤ 45 years was performed using the same techniques described earlier in this article. We performed a Cox proportional hazards regression analysis to compare mortality in PS-matched urban and rural veterans aged ≤ 45 years. The HR of death in all veterans aged ≤ 45 years (before PS-matching) was estimated using Cox proportional hazard regression analysis, adjusting for PS.

Dichotomous variables were compared using X2 tests and continuous variables were compared using t tests. Baseline characteristics with missing values were converted into categorical variables and the proportion of subjects with missing values was equalized between treatment groups after PS-matching. For subgroup analysis, we examined the HR of total mortality in each subgroup using separate Cox proportional hazards regression models similar to the primary analysis but adjusted for PS. Due to multiple comparisons in the subgroup analysis, the findings should be considered exploratory. Statistical tests were 2-tailed, and significance was defined as P < .05. Data management and statistical analyses were conducted from June 2022 to January 2023 using STATA, Version 17. The VA Orlando Healthcare System Institutional Review Board approved the study and waived requirements for informed consent because only deidentified data were used.

Results

After excluding 49 patients (Supplemental materials, available at doi:10.12788/fp.0560), we identified 30,219 veterans with newly diagnosed CRC between FY 2016 to 2021 (Table 1). Of these, 19,422 (64.3%) resided in urban areas and 10,797 (35.7%) resided in rural areas (Table 2). The mean (SD) duration from the first CRC diagnosis to death or study end was 832 (640) days, and the median (IQR) was 723 (246–1330) days. Overall, incident CRC diagnoses were numerically highest in FY 2016 and lowest in FY 2020 (Figure 1). Patients with CRC in rural areas vs urban areas were significantly older (mean, 71.2 years vs 70.8 years, respectively; P < .001), more likely to be male (96.7% vs 95.7%, respectively; P < .001), more likely to be White (83.6% vs 67.8%, respectively; P < .001) and more likely to be non-Hispanic (92.2% vs 87.5%, respectively; P < .001). In terms of general health, rural veterans with CRC were more likely to be overweight or obese (81.5% rural vs 78.5% urban; P < .001) but had fewer mean comorbidities as measured by CCI (5.66 rural vs 5.90 urban; P < .001). A higher proportion of rural veterans with CRC had received stool-based (fecal occult blood test or fecal immunochemical test) CRC screening tests (61.6% rural vs 57.2% urban; P < .001). Fewer rural patients presented with systemic symptoms or signs within 1 year of CRC diagnosis (54.4% rural vs 57.5% urban, P < .001). Among urban patients with CRC, 6959 (35.8%) deaths were observed, compared with 3766 (34.9%) among rural patients (P = .10).

0525FED-AVAHO-CRC_T10525FED-AVAHO-CRC_T20525FED-AVAHO-CRC_F1

There were 21,568 PS-matched veterans: 10,784 in each group. In the PS-matched cohort, baseline characteristics were similar between veterans in urban and rural communities, including age, sex, race/ethnicity, body mass index, and comorbidities. Among rural patients with CRC, 3763 deaths (34.9%) were observed compared with 3702 (34.3%) among urban veterans. There was no significant difference in the HR of mortality between rural and urban CRC residents (HR, 1.01; 95% CI, 0.97-1.06; P = .53) (Figure 2).

0525FED-AVAHO-CRC_F20525FED-AVAHO-CRC_F30525FED-AVAHO-CRC_F4

Among veterans aged ≤ 45 years, 551 were diagnosed with CRC (391 urban and 160 rural). We PS-matched 142 pairs of urban and rural veterans without residual differences in baseline characteristics (eAppendix 1). There was no significant difference in the HR of mortality between rural and urban veterans aged ≤ 45 years (HR, 0.97; 95% CI, 0.57-1.63; P = .90) (Figure 2). Similarly, no difference in mortality was observed adjusting for PS between all rural and urban veterans aged ≤ 45 years (HR, 1.03; 95% CI, 0.67-1.59; P = .88).

0525FED-AVAHO-CRC_eApp1

There was no difference in total mortality between rural and urban veterans in any subgroup except for American Indian or Alaska Native veterans (HR, 2.41; 95% CI, 1.29-4.50; P = .006) (eAppendix 2).

0525FED-AVAHO-CRC_eApp2

Discussion

This study examined characteristics of patients with CRC between urban and rural areas among veterans who were VHA patients. Similar to other studies, rural veterans with CRC were older, more likely to be White, and were obese, but exhibited fewer comorbidities (lower CCI and lower incidence of congestive heart failure, dementia, hemiplegia, kidney diseases, liver diseases and AIDS, but higher incidence of chronic obstructive lung disease).8,16 The incidence of CRC in this study population was lowest in FY 2020, which was reported by the Centers for Disease Control and Prevention and is attributed to COVID-19 pandemic disruption of health services.24 The overall mortality in this study was similar to rates reported in other studies from the VA Central Cancer Registry.4 In the PS-matched cohort, where baseline characteristics were similar between urban and rural patients with CRC, we found no disparities in CRC-specific mortality between veterans in rural and urban areas. Additionally, when analysis was restricted to veterans aged ≤ 45 years, the results remained consistent.

Subgroup analyses showed no significant difference in mortality between rural and urban areas by sex, race or ethnicity, except rural American Indian or Alaska Native veterans who had double the mortality of their urban counterparts (HR, 2.41; 95% CI, 1.29-4.50; P = .006). This finding is difficult to interpret due to the small number of events and the wide CI. While with a Bonferroni correction the adjusted P value was .08, which is not statistically significant, a previous study found that although CRC incidence was lower overall in American Indian or Alaska Native populations compared to non-Hispanic White populations, CRC incidence was higher among American Indian or Alaska Native individuals in some areas such as Alaska and the Northern Plains.25,26 Studies have noted that rural American Indian/Alaska Native populations experience greater poverty, less access to broadband internet, and limited access to care, contributing to poorer cancer outcomes and lower survival.27 Thus, the finding of disparity in mortality between rural and urban American Indian or Alaska Native veterans warrants further study.

Other studies have raised concerns that CRC disproportionately affects adults in rural areas with higher mortality rates.14-16 These disparities arise from sociodemographic factors and modifiable risk factors, including physical activity, dietary patterns, access to cancer screening, and gaps in quality treatment resources.16,28 These factors operate at multiple levels: from individual, local health system, to community and policy.2,27 For example, a South Carolina study (1996–2016) found that residents in rural areas were more likely to be diagnosed with advanced CRC, possibly indicating lower rates of CRC screening in rural areas. They also had higher likelihood of death from CRC.15 However, the study did not include any clinical parameters, such as comorbidities or obesity. A statewide, population-based study in Utah showed that rural men experienced a lower CRC survival in their unadjusted analysis.16 However, the study was small, with only 3948 urban and 712 rural residents. Additionally, there was no difference in total mortality in the whole cohort (HR, 0.96; 95% CI, 0.86-1.07) or in CRC-specific death (HR, 0.93; 95% CI, 0.81-1.08). A nationwide study also showed that CRC mortality rates were 8% higher in nonmetropolitan or rural areas than in the most urbanized areas containing large metropolitan counties.29 However, this study did not include descriptions of clinical confounders, such as comorbidities, making it difficult to ascertain whether the difference in CRC mortality was due to rurality or differences in baseline risk characteristics.

In this study, the lack of CRC-specific mortality disparities may be attributed to the structures and practices of VHA health care. Recent studies have noted that mortality of several chronic medical conditions treated at the VHA was lower than at non-VHA hospitals.30,31 One study that measured the quality of nonmetastatic CRC care based on National Comprehensive Cancer Network guidelines showed that > 72% of VHA patients received guideline-concordant care for each diagnostic and therapeutic measure, except for follow-up colonoscopy timing, which appear to be similar or superior to that of the private sector.30,32,33 Some of the VA initiative for CRC screening may bypass the urban-rurality divide such as the mailed fecal immunochemical test program for CRC. This program was implemented at the onset of the COVID-19 pandemic to avoid disruptions of medical care.34 Rural patients are more likely to undergo fecal immunochemical testing when compared to urban patients in this data. Beyond clinical care, the VHA uses processes to tackle social determinants of health such as housing, food security, and transportation, promoting equal access to health care, and promoting cultural competency among HCPs.35-37

The results suggest that solutions to CRC disparities between rural and urban areas need to consider known barriers to rural health care, including transportation, diminished rural health care workforce, and other social determinants of health.9,10,27,38 VHA makes considerable efforts to provide equitable care to all enrolled veterans, including specific programs for rural veterans, including ongoing outreach.39 This study demonstrated lack of disparity in CRC-specific mortality in veterans receiving VHA care, highlighting the importance of these efforts.

Strengths and Limitations

This study used the VHA cohort to compare patient characteristics and mortality between patients with CRC residing in rural and urban areas. The study provides nationwide perspectives on CRC across the geographical spectrum and used a longitudinal cohort with prolonged follow-up to account for comorbidities.

However, the study compared a cohort of rural and urban veterans enrolled in the VHA; hence, the results may not reflect CRC outcomes in veterans without access to VHA care. Rurality has been independently associated with decreased likelihood of meeting CRC screening guidelines among veterans and military service members.38 This study lacked sufficient information to compare CRC staging or treatment modalities among veterans. Although the data cannot identify CRC stage, the proportions of patients with metastatic CRC at diagnosis and CRC location were similar between groups. The study did not have information on their care outside of VHA setting.

This study could not ascertain whether disparities existed in CRC treatment modality since rural residence may result in referral to community-based CRC care, which did not appear in the data. To address these limitations, we used death from any cause as the primary outcome, since death is a hard outcome and is not subject to ascertainment bias. The relatively short follow-up time is another limitation, though subgroup analysis by follow-up did not show significant differences. Despite PS matching, residual unmeasured confounding may exist between urban and rural groups. The predominantly White, male VHA population with high CCI may limit the generalizability of the results.

Conclusions

Rural VHA enrollees had similar survival rates after CRC diagnosis compared to their urban counterparts in a PS-matched analysis. The VHA models of care—including mailed CRC screening tools, several socioeconomic determinants of health (housing, food security, and transportation), and promoting equal access to health care, as well as cultural competency among HCPs—HCPs—may help alleviate disparities across the rural-urban spectrum. The VHA should continue efforts to enroll veterans and provide comprehensive coordinated care in community partnerships.

References
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  4. Zullig LL, Smith VA, Jackson GL, et al. Colorectal cancer statistics from the Veterans Affairs central cancer registry. Clin Colorectal Cancer. 2016;15(4):e199-e204. doi:10.1016/j.clcc.2016.04.005
  5. Lin JS, Perdue LA, Henrikson NB, Bean SI, Blasi PR. Screening for Colorectal Cancer: An Evidence Update for the US Preventive Services Task Force. 2021. U.S. Preventive Services Task Force Evidence Syntheses, formerly Systematic Evidence Reviews:Chapter 1. Agency for Healthcare Research and Quality (US); 2021. Accessed February 18, 2025. https://www.ncbi.nlm.nih.gov/books/NBK570917/
  6. Siegel RL, Fedewa SA, Anderson WF, et al. Colorectal cancer incidence patterns in the United States, 1974-2013. J Natl Cancer Inst. 2017;109(8). doi:10.1093/jnci/djw322
  7. Davidson KW, Barry MJ, Mangione CM, et al. Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(19):1965-1977. doi:10.1001/jama.2021.6238
  8. Hines R, Markossian T, Johnson A, Dong F, Bayakly R. Geographic residency status and census tract socioeconomic status as determinants of colorectal cancer outcomes. Am J Public Health. 2014;104(3):e63-e71. doi:10.2105/AJPH.2013.301572
  9. Cauwels J. The many barriers to high-quality rural health care. 2022;(9):1-32. NEJM Catal Innov Care Deliv. Accessed April 24, 2025. https://catalyst.nejm.org/doi/pdf/10.1056/CAT.22.0254
  10. Gong G, Phillips SG, Hudson C, Curti D, Philips BU. Higher US rural mortality rates linked to socioeconomic status, physician shortages, and lack of health insurance. Health Aff (Millwood);38(12):2003-2010. doi:10.1377/hlthaff.2019.00722
  11. Aboagye JK, Kaiser HE, Hayanga AJ. Rural-urban differences in access to specialist providers of colorectal cancer care in the United States: a physician workforce issue. JAMA Surg. 2014;149(6):537-543. doi:10.1001/jamasurg.2013.5062
  12. Lyckholm LJ, Hackney MH, Smith TJ. Ethics of rural health care. Crit Rev Oncol Hematol. 2001;40(2):131-138. doi:10.1016/s1040-8428(01)00139-1
  13. Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: concepts, methodologies, and guidelines. Annu Rev Public Health. 1997;18:341-378. doi:10.1146/annurev.publhealth.18.1.341
  14. Singh GK, Jemal A. Socioeconomic and racial/ethnic disparities in cancer mortality, incidence, and survival in the United States, 1950-2014: over six decades of changing patterns and widening inequalities. J Environ Public Health. 2017;2017:2819372. doi:10.1155/2017/2819372
  15. Adams SA, Zahnd WE, Ranganathan R, et al. Rural and racial disparities in colorectal cancer incidence and mortality in South Carolina, 1996 - 2016. J Rural Health. 2022;38(1):34-39. doi:10.1111/jrh.12580
  16. Rogers CR, Blackburn BE, Huntington M, et al. Rural- urban disparities in colorectal cancer survival and risk among men in Utah: a statewide population-based study. Cancer Causes Control. 2020;31(3):241-253. doi:10.1007/s10552-020-01268-2
  17. US Department of Veterans Affairs. VA Informatics and Computing Infrastructure (VINCI), VA HSR RES 13-457. https://vincicentral.vinci.med.va.gov [Source not verified]
  18. US Department of Veterans Affairs Information Resource Center. VIReC Research User Guide: PSSG Geocoded Enrollee Files, 2015 Edition. US Department of Veterans Affairs, Health Services Research & Development Service, Information Resource Center; May. 2016. [source not verified]
  19. Goldsmith HF, Puskin DS, Stiles DJ. Improving the operational definition of “rural areas” for federal programs. US Department of Health and Human Services; 1993. Accessed February 27, 2025. https://www.ruralhealthinfo.org/pdf/improving-the-operational-definition-of-rural-areas.pdf
  20. Adams MA, Kerr EA, Dominitz JA, et al. Development and validation of a new ICD-10-based screening colonoscopy overuse measure in a large integrated healthcare system: a retrospective observational study. BMJ Qual Saf. 2023;32(7):414-424. doi:10.1136/bmjqs-2021-014236
  21. Schneeweiss S, Wang PS, Avorn J, Glynn RJ. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res. 2003;38(4):1103-1120. doi:10.1111/1475-6773.00165
  22. Becker S, Ichino A. Estimation of average treatment effects based on propensity scores. The Stata Journal. 2002;2(4):358-377.
  23. Leuven E, Sianesi B. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Statistical software components. Revised February 1, 2018. Accessed February 27, 2025. https://ideas.repec.org/c/boc/bocode/s432001.html.
  24. US Cancer Statistics Working Group. US cancer statistics data visualizations tool. Centers for Disease Control and Prevention. June 2024. Accessed February 27, 2025. https://www.cdc.gov/cancer/dataviz
  25. Cao J, Zhang S. Multiple Comparison Procedures. JAMA. 2014;312(5):543-544. doi:10.1001/jama.2014.9440
  26. Gopalani SV, Janitz AE, Martinez SA, et al. Trends in cancer incidence among American Indians and Alaska Natives and Non-Hispanic Whites in the United States, 1999-2015. Epidemiology. 2020;31(2):205-213. doi:10.1097/EDE.0000000000001140
  27. Zahnd WE, Murphy C, Knoll M, et al. The intersection of rural residence and minority race/ethnicity in cancer disparities in the United States. Int J Environ Res Public Health. 2021;18(4). doi:10.3390/ijerph18041384
  28. Blake KD, Moss JL, Gaysynsky A, Srinivasan S, Croyle RT. Making the case for investment in rural cancer control: an analysis of rural cancer incidence, mortality, and funding trends. Cancer Epidemiol Biomarkers Prev. 2017;26(7):992-997. doi:10.1158/1055-9965.EPI-17-0092
  29. Singh GK, Williams SD, Siahpush M, Mulhollen A. Socioeconomic, rural-urban, and racial inequalities in US cancer mortality: part i-all cancers and lung cancer and part iicolorectal, prostate, breast, and cervical cancers. J Cancer Epidemiol. 2011;2011:107497. doi:10.1155/2011/107497
  30. Jackson GL, Melton LD, Abbott DH, et al. Quality of nonmetastatic colorectal cancer care in the Department of Veterans Affairs. J Clin Oncol. 2010;28(19):3176-3181. doi:10.1200/JCO.2009.26.7948
  31. Yoon J, Phibbs CS, Ong MK, et al. Outcomes of veterans treated in Veterans Affairs hospitals vs non-Veterans Affairs hospitals. JAMA Netw Open. 2023;6(12):e2345898. doi:10.1001/jamanetworkopen.2023.45898
  32. Malin JL, Schneider EC, Epstein AM, Adams J, Emanuel EJ, Kahn KL. Results of the National Initiative for Cancer Care Quality: how can we improve the quality of cancer care in the United States? J Clin Oncol. 2006;24(4):626-634. doi:10.1200/JCO.2005.03.3365
  33. Levin B, Lieberman DA, McFarland B, et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. Gastroenterology. 2008;134(5):1570-1595. doi:10.1053/j.gastro.2008.02.002
  34. Deeds SA, Moore CB, Gunnink EJ, et al. Implementation of a mailed faecal immunochemical test programme for colorectal cancer screening among Veterans. BMJ Open Qual. 2022;11(4). doi:10.1136/bmjoq-2022-001927
  35. Yehia BR, Greenstone CL, Hosenfeld CB, Matthews KL, Zephyrin LC. The role of VA community care in addressing health and health care disparities. Med Care. 2017;55(Suppl 9 suppl 2):S4-S5. doi:10.1097/MLR.0000000000000768
  36. Wright BN, MacDermid Wadsworth S, Wellnitz A, Eicher- Miller HA. Reaching rural veterans: a new mechanism to connect rural, low-income US Veterans with resources and improve food security. J Public Health (Oxf). 2019;41(4):714-723. doi:10.1093/pubmed/fdy203
  37. Nelson RE, Byrne TH, Suo Y, et al. Association of temporary financial assistance with housing stability among US veterans in the supportive services for veteran families program. JAMA Netw Open. 2021;4(2):e2037047. doi:10.1001/jamanetworkopen.2020.37047
  38. McDaniel JT, Albright D, Lee HY, et al. Rural–urban disparities in colorectal cancer screening among military service members and Veterans. J Mil Veteran Fam Health. 2019;5(1):40-48. doi:10.3138/jmvfh.2018-0013
  39. US Department of Veterans Affairs, Office of Rural Health. The rural veteran outreach toolkit. Updated February 12, 2025. Accessed February 18, 2025. https://www.ruralhealth.va.gov/partners/toolkit.asp
Article PDF
0525FED AVAHO Colorectal Cancer_0.pdf
Author and Disclosure Information

Minh Anh Le, MDa; Po-Hong Liu, MDb; Amar Mandalia, MDc; Sergio Romero, MDd; Ishak A. Mansi, MDa,c; Moheb Boktor, MDb

Author affiliations: 
aUniversity of Central Florida, Orlando 
bUniversity of Texas Southwestern Medical Center, Dallas 
cOrlando Veterans Affairs Medical Center, Florida 
dNorth Florida/South Georgia Veterans Health System, Gainesville

Author disclosures: The authors report no actual or potential conflicts of interest with regard to this article.

Correspondence: Ishak Mansi (ishak.mansi@va.gov)

Fed Pract. 2025;42(suppl 2). Published online May 8. doi:10.12788/fp.0560

Issue
Federal Practitioner - 42(5)s
Publications
Federal Practitioner
AVAHO
Topics
Colorectal Cancer
Oncology
Page Number
S22-S31
Sections
Original Research
Author(s)
Minh Anh Le, MD
Po-Hong Liu, MD
Amar Mandalia, MD
Sergio Romero, PhD
Ishak A. Mansi, MD
Moheb Boktor, MD
Author(s)
Minh Anh Le, MD
Po-Hong Liu, MD
Amar Mandalia, MD
Sergio Romero, PhD
Ishak A. Mansi, MD
Moheb Boktor, MD
Author and Disclosure Information

Minh Anh Le, MDa; Po-Hong Liu, MDb; Amar Mandalia, MDc; Sergio Romero, MDd; Ishak A. Mansi, MDa,c; Moheb Boktor, MDb

Author affiliations: 
aUniversity of Central Florida, Orlando 
bUniversity of Texas Southwestern Medical Center, Dallas 
cOrlando Veterans Affairs Medical Center, Florida 
dNorth Florida/South Georgia Veterans Health System, Gainesville

Author disclosures: The authors report no actual or potential conflicts of interest with regard to this article.

Correspondence: Ishak Mansi (ishak.mansi@va.gov)

Fed Pract. 2025;42(suppl 2). Published online May 8. doi:10.12788/fp.0560

Author and Disclosure Information

Minh Anh Le, MDa; Po-Hong Liu, MDb; Amar Mandalia, MDc; Sergio Romero, MDd; Ishak A. Mansi, MDa,c; Moheb Boktor, MDb

Author affiliations: 
aUniversity of Central Florida, Orlando 
bUniversity of Texas Southwestern Medical Center, Dallas 
cOrlando Veterans Affairs Medical Center, Florida 
dNorth Florida/South Georgia Veterans Health System, Gainesville

Author disclosures: The authors report no actual or potential conflicts of interest with regard to this article.

Correspondence: Ishak Mansi (ishak.mansi@va.gov)

Fed Pract. 2025;42(suppl 2). Published online May 8. doi:10.12788/fp.0560

Article PDF
0525FED AVAHO Colorectal Cancer_0.pdf
Article PDF
0525FED AVAHO Colorectal Cancer_0.pdf

Colorectal cancer (CRC) is the second-leading cause of cancer-related deaths in the United States, with an estimated 52,550 deaths in 2023.1 However, the disease burden varies among different segments of the population.2 While both CRC incidence and mortality have been decreasing due to screening and advances in treatment, there are disparities in incidence and mortality across the sociodemographic spectrum including race, ethnicity, education, and income.1-4 While CRC incidence is decreasing for older adults, it is increasing among those aged < 55 years.5 The incidence of CRC in adults aged 40 to 54 years has increased by 0.5% to 1.3% annually since the mid-1990s.6 The US Preventive Services Task Force now recommends starting CRC screening at age 45 years for asymptomatic adults with average risk.7

Disparities also exist across geographical boundaries and living environment. Rural Americans faces additional challenges in health and lifestyle that can affect CRC outcomes. Compared to their urban counterparts, rural residents are more likely to be older, have lower levels of education, higher levels of poverty, lack health insurance, and less access to health care practitioners (HCPs).8-10 Geographic proximity, defined as travel time or physical distance to a health facility, has been recognized as a predictor of inferior outcomes.11 These aspects of rural living may pose challenges for accessing care for CRC screening and treatment.11-13 National and local studies have shown disparities in CRC screening rates, incidence, and mortality between rural and urban populations.14-16

It is unclear whether rural/urban disparities persist under the Veterans Health Administration (VHA) health care delivery model. This study examined differences in baseline characteristics and mortality between rural and urban veterans newly diagnosed with CRC. We also focused on a subpopulation aged ≤ 45 years.

Methods

This study extracted national data from the US Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) hosted in the VA Informatics and Computing Infrastructure (VINCI) environment. VINCI is an initiative to improve access to VA data and facilitate the analysis of these data while ensuring veterans’ privacy and data security.17 CDW is the VHA business intelligence information repository, which extracts data from clinical and nonclinical sources following prescribed and validated protocols. Data extracted included demographics, diagnosis, and procedure codes for both inpatient and outpatient encounters, vital signs, and vital status. This study used data previously extracted from a national cohort of veterans that encompassed all patients who received a group of commonly prescribed medications, such as statins, proton pump inhibitors, histamine-2 blockers, acetaminophen-containing products, and hydrocortisone-containing skin applications. This cohort encompassed 8,648,754 veterans, from whom 2,460,727 had encounters during fiscal years (FY) 2016 to 2021 (study period). The cohort was used to ensure that subjects were VHA patients, allowing them to adequately capture their clinical profiles.

Patients were identified as rural or urban based on their residence address at the date of their first diagnosis of CRC. The Geospatial Service Support Center (GSSC) aggregates and updates veterans’ residence address records for all enrolled veterans from the National Change of Address database. The data contain 1 record per enrollee. GSSC Geocoded Enrollee File contains enrollee addresses and their rurality indicators, categorized as urban, rural, or highly rural.18 Rurality is defined by the Rural Urban Commuting Area (RUCA) categories developed by the Department of Agriculture and the Health Resources and Services Administration of the US Department of Health and Human Services.19 Urban areas had RUCA codes of 1.0 to 1.1, and highly rural areas had RUCA scores of 10.0. All other areas were classified as rural. Since the proportion of veterans from highly rural areas was small, we included residents from highly rural areas in the rural residents’ group.

Inclusion and Exclusion Criteria

All veterans newly diagnosed with CRC from FY 2016 to 2021 were included. We used the ninth and tenth clinical modification revisions of the International Classification of Diseases (ICD-9-CM and ICD-10-CM) to define CRC diagnosis (Supplemental materials).4,20 To ensure that patients were newly diagnosed with CRC, this study excluded patients with a previous ICD-9-CM code for CRC diagnosis since FY 2003.

Comorbidities were identified using diagnosis and procedure codes from inpatient and outpatient encounters, which were used to calculate the Charlson Comorbidity Index (CCI) at the time of CRC diagnosis using the weighted method described by Schneeweiss et al.21 We defined CRC high-risk conditions and CRC screening tests, including flexible sigmoidoscopy and stool tests, as described in previous studies (Supplemental materials).20

The main outcome was total mortality. The date of death was extracted from the VHA Death Ascertainment File, which contains mortality data from the Master Person Index file in CDW and the Social Security Administration Death Master File. We used the date of death from any cause, as cause of death was not available.

A propensity score (PS) was created to match rural (including highly rural) and urban residents at a ratio of 1:1. Using a standard procedure described in prior publications, multivariable logistic regression used all baseline characteristics to estimate the PS and perform nearest-number matching without replacement.22,23 A caliper of 0.01 maximized the matched cohort size and achieved balance (Supplemental materials). We then examined the balance of baseline characteristics between PS-matched groups.

Analyses

Cox proportional hazards regression analysis estimated the hazard ratio (HR) of death in rural residents compared to urban residents in the PS-matched cohort. The outcome event was the date of death during the study’s follow-up period (defined as period from first CRC diagnosis to death or study end), with censoring at the study’s end date (September 30, 2021). The proportional hazards assumption was assessed by inspecting the Kaplan-Meier curves. Multiple analyses examined the HR of total mortality in the PS-matched cohort, stratified by sex, race, and ethnicity. We also examined the HR of total mortality stratified by duration of follow-up.

Another PS-matching analysis among veterans aged ≤ 45 years was performed using the same techniques described earlier in this article. We performed a Cox proportional hazards regression analysis to compare mortality in PS-matched urban and rural veterans aged ≤ 45 years. The HR of death in all veterans aged ≤ 45 years (before PS-matching) was estimated using Cox proportional hazard regression analysis, adjusting for PS.

Dichotomous variables were compared using X2 tests and continuous variables were compared using t tests. Baseline characteristics with missing values were converted into categorical variables and the proportion of subjects with missing values was equalized between treatment groups after PS-matching. For subgroup analysis, we examined the HR of total mortality in each subgroup using separate Cox proportional hazards regression models similar to the primary analysis but adjusted for PS. Due to multiple comparisons in the subgroup analysis, the findings should be considered exploratory. Statistical tests were 2-tailed, and significance was defined as P < .05. Data management and statistical analyses were conducted from June 2022 to January 2023 using STATA, Version 17. The VA Orlando Healthcare System Institutional Review Board approved the study and waived requirements for informed consent because only deidentified data were used.

Results

After excluding 49 patients (Supplemental materials, available at doi:10.12788/fp.0560), we identified 30,219 veterans with newly diagnosed CRC between FY 2016 to 2021 (Table 1). Of these, 19,422 (64.3%) resided in urban areas and 10,797 (35.7%) resided in rural areas (Table 2). The mean (SD) duration from the first CRC diagnosis to death or study end was 832 (640) days, and the median (IQR) was 723 (246–1330) days. Overall, incident CRC diagnoses were numerically highest in FY 2016 and lowest in FY 2020 (Figure 1). Patients with CRC in rural areas vs urban areas were significantly older (mean, 71.2 years vs 70.8 years, respectively; P < .001), more likely to be male (96.7% vs 95.7%, respectively; P < .001), more likely to be White (83.6% vs 67.8%, respectively; P < .001) and more likely to be non-Hispanic (92.2% vs 87.5%, respectively; P < .001). In terms of general health, rural veterans with CRC were more likely to be overweight or obese (81.5% rural vs 78.5% urban; P < .001) but had fewer mean comorbidities as measured by CCI (5.66 rural vs 5.90 urban; P < .001). A higher proportion of rural veterans with CRC had received stool-based (fecal occult blood test or fecal immunochemical test) CRC screening tests (61.6% rural vs 57.2% urban; P < .001). Fewer rural patients presented with systemic symptoms or signs within 1 year of CRC diagnosis (54.4% rural vs 57.5% urban, P < .001). Among urban patients with CRC, 6959 (35.8%) deaths were observed, compared with 3766 (34.9%) among rural patients (P = .10).

0525FED-AVAHO-CRC_T10525FED-AVAHO-CRC_T20525FED-AVAHO-CRC_F1

There were 21,568 PS-matched veterans: 10,784 in each group. In the PS-matched cohort, baseline characteristics were similar between veterans in urban and rural communities, including age, sex, race/ethnicity, body mass index, and comorbidities. Among rural patients with CRC, 3763 deaths (34.9%) were observed compared with 3702 (34.3%) among urban veterans. There was no significant difference in the HR of mortality between rural and urban CRC residents (HR, 1.01; 95% CI, 0.97-1.06; P = .53) (Figure 2).

0525FED-AVAHO-CRC_F20525FED-AVAHO-CRC_F30525FED-AVAHO-CRC_F4

Among veterans aged ≤ 45 years, 551 were diagnosed with CRC (391 urban and 160 rural). We PS-matched 142 pairs of urban and rural veterans without residual differences in baseline characteristics (eAppendix 1). There was no significant difference in the HR of mortality between rural and urban veterans aged ≤ 45 years (HR, 0.97; 95% CI, 0.57-1.63; P = .90) (Figure 2). Similarly, no difference in mortality was observed adjusting for PS between all rural and urban veterans aged ≤ 45 years (HR, 1.03; 95% CI, 0.67-1.59; P = .88).

0525FED-AVAHO-CRC_eApp1

There was no difference in total mortality between rural and urban veterans in any subgroup except for American Indian or Alaska Native veterans (HR, 2.41; 95% CI, 1.29-4.50; P = .006) (eAppendix 2).

0525FED-AVAHO-CRC_eApp2

Discussion

This study examined characteristics of patients with CRC between urban and rural areas among veterans who were VHA patients. Similar to other studies, rural veterans with CRC were older, more likely to be White, and were obese, but exhibited fewer comorbidities (lower CCI and lower incidence of congestive heart failure, dementia, hemiplegia, kidney diseases, liver diseases and AIDS, but higher incidence of chronic obstructive lung disease).8,16 The incidence of CRC in this study population was lowest in FY 2020, which was reported by the Centers for Disease Control and Prevention and is attributed to COVID-19 pandemic disruption of health services.24 The overall mortality in this study was similar to rates reported in other studies from the VA Central Cancer Registry.4 In the PS-matched cohort, where baseline characteristics were similar between urban and rural patients with CRC, we found no disparities in CRC-specific mortality between veterans in rural and urban areas. Additionally, when analysis was restricted to veterans aged ≤ 45 years, the results remained consistent.

Subgroup analyses showed no significant difference in mortality between rural and urban areas by sex, race or ethnicity, except rural American Indian or Alaska Native veterans who had double the mortality of their urban counterparts (HR, 2.41; 95% CI, 1.29-4.50; P = .006). This finding is difficult to interpret due to the small number of events and the wide CI. While with a Bonferroni correction the adjusted P value was .08, which is not statistically significant, a previous study found that although CRC incidence was lower overall in American Indian or Alaska Native populations compared to non-Hispanic White populations, CRC incidence was higher among American Indian or Alaska Native individuals in some areas such as Alaska and the Northern Plains.25,26 Studies have noted that rural American Indian/Alaska Native populations experience greater poverty, less access to broadband internet, and limited access to care, contributing to poorer cancer outcomes and lower survival.27 Thus, the finding of disparity in mortality between rural and urban American Indian or Alaska Native veterans warrants further study.

Other studies have raised concerns that CRC disproportionately affects adults in rural areas with higher mortality rates.14-16 These disparities arise from sociodemographic factors and modifiable risk factors, including physical activity, dietary patterns, access to cancer screening, and gaps in quality treatment resources.16,28 These factors operate at multiple levels: from individual, local health system, to community and policy.2,27 For example, a South Carolina study (1996–2016) found that residents in rural areas were more likely to be diagnosed with advanced CRC, possibly indicating lower rates of CRC screening in rural areas. They also had higher likelihood of death from CRC.15 However, the study did not include any clinical parameters, such as comorbidities or obesity. A statewide, population-based study in Utah showed that rural men experienced a lower CRC survival in their unadjusted analysis.16 However, the study was small, with only 3948 urban and 712 rural residents. Additionally, there was no difference in total mortality in the whole cohort (HR, 0.96; 95% CI, 0.86-1.07) or in CRC-specific death (HR, 0.93; 95% CI, 0.81-1.08). A nationwide study also showed that CRC mortality rates were 8% higher in nonmetropolitan or rural areas than in the most urbanized areas containing large metropolitan counties.29 However, this study did not include descriptions of clinical confounders, such as comorbidities, making it difficult to ascertain whether the difference in CRC mortality was due to rurality or differences in baseline risk characteristics.

In this study, the lack of CRC-specific mortality disparities may be attributed to the structures and practices of VHA health care. Recent studies have noted that mortality of several chronic medical conditions treated at the VHA was lower than at non-VHA hospitals.30,31 One study that measured the quality of nonmetastatic CRC care based on National Comprehensive Cancer Network guidelines showed that > 72% of VHA patients received guideline-concordant care for each diagnostic and therapeutic measure, except for follow-up colonoscopy timing, which appear to be similar or superior to that of the private sector.30,32,33 Some of the VA initiative for CRC screening may bypass the urban-rurality divide such as the mailed fecal immunochemical test program for CRC. This program was implemented at the onset of the COVID-19 pandemic to avoid disruptions of medical care.34 Rural patients are more likely to undergo fecal immunochemical testing when compared to urban patients in this data. Beyond clinical care, the VHA uses processes to tackle social determinants of health such as housing, food security, and transportation, promoting equal access to health care, and promoting cultural competency among HCPs.35-37

The results suggest that solutions to CRC disparities between rural and urban areas need to consider known barriers to rural health care, including transportation, diminished rural health care workforce, and other social determinants of health.9,10,27,38 VHA makes considerable efforts to provide equitable care to all enrolled veterans, including specific programs for rural veterans, including ongoing outreach.39 This study demonstrated lack of disparity in CRC-specific mortality in veterans receiving VHA care, highlighting the importance of these efforts.

Strengths and Limitations

This study used the VHA cohort to compare patient characteristics and mortality between patients with CRC residing in rural and urban areas. The study provides nationwide perspectives on CRC across the geographical spectrum and used a longitudinal cohort with prolonged follow-up to account for comorbidities.

However, the study compared a cohort of rural and urban veterans enrolled in the VHA; hence, the results may not reflect CRC outcomes in veterans without access to VHA care. Rurality has been independently associated with decreased likelihood of meeting CRC screening guidelines among veterans and military service members.38 This study lacked sufficient information to compare CRC staging or treatment modalities among veterans. Although the data cannot identify CRC stage, the proportions of patients with metastatic CRC at diagnosis and CRC location were similar between groups. The study did not have information on their care outside of VHA setting.

This study could not ascertain whether disparities existed in CRC treatment modality since rural residence may result in referral to community-based CRC care, which did not appear in the data. To address these limitations, we used death from any cause as the primary outcome, since death is a hard outcome and is not subject to ascertainment bias. The relatively short follow-up time is another limitation, though subgroup analysis by follow-up did not show significant differences. Despite PS matching, residual unmeasured confounding may exist between urban and rural groups. The predominantly White, male VHA population with high CCI may limit the generalizability of the results.

Conclusions

Rural VHA enrollees had similar survival rates after CRC diagnosis compared to their urban counterparts in a PS-matched analysis. The VHA models of care—including mailed CRC screening tools, several socioeconomic determinants of health (housing, food security, and transportation), and promoting equal access to health care, as well as cultural competency among HCPs—HCPs—may help alleviate disparities across the rural-urban spectrum. The VHA should continue efforts to enroll veterans and provide comprehensive coordinated care in community partnerships.

Colorectal cancer (CRC) is the second-leading cause of cancer-related deaths in the United States, with an estimated 52,550 deaths in 2023.1 However, the disease burden varies among different segments of the population.2 While both CRC incidence and mortality have been decreasing due to screening and advances in treatment, there are disparities in incidence and mortality across the sociodemographic spectrum including race, ethnicity, education, and income.1-4 While CRC incidence is decreasing for older adults, it is increasing among those aged < 55 years.5 The incidence of CRC in adults aged 40 to 54 years has increased by 0.5% to 1.3% annually since the mid-1990s.6 The US Preventive Services Task Force now recommends starting CRC screening at age 45 years for asymptomatic adults with average risk.7

Disparities also exist across geographical boundaries and living environment. Rural Americans faces additional challenges in health and lifestyle that can affect CRC outcomes. Compared to their urban counterparts, rural residents are more likely to be older, have lower levels of education, higher levels of poverty, lack health insurance, and less access to health care practitioners (HCPs).8-10 Geographic proximity, defined as travel time or physical distance to a health facility, has been recognized as a predictor of inferior outcomes.11 These aspects of rural living may pose challenges for accessing care for CRC screening and treatment.11-13 National and local studies have shown disparities in CRC screening rates, incidence, and mortality between rural and urban populations.14-16

It is unclear whether rural/urban disparities persist under the Veterans Health Administration (VHA) health care delivery model. This study examined differences in baseline characteristics and mortality between rural and urban veterans newly diagnosed with CRC. We also focused on a subpopulation aged ≤ 45 years.

Methods

This study extracted national data from the US Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) hosted in the VA Informatics and Computing Infrastructure (VINCI) environment. VINCI is an initiative to improve access to VA data and facilitate the analysis of these data while ensuring veterans’ privacy and data security.17 CDW is the VHA business intelligence information repository, which extracts data from clinical and nonclinical sources following prescribed and validated protocols. Data extracted included demographics, diagnosis, and procedure codes for both inpatient and outpatient encounters, vital signs, and vital status. This study used data previously extracted from a national cohort of veterans that encompassed all patients who received a group of commonly prescribed medications, such as statins, proton pump inhibitors, histamine-2 blockers, acetaminophen-containing products, and hydrocortisone-containing skin applications. This cohort encompassed 8,648,754 veterans, from whom 2,460,727 had encounters during fiscal years (FY) 2016 to 2021 (study period). The cohort was used to ensure that subjects were VHA patients, allowing them to adequately capture their clinical profiles.

Patients were identified as rural or urban based on their residence address at the date of their first diagnosis of CRC. The Geospatial Service Support Center (GSSC) aggregates and updates veterans’ residence address records for all enrolled veterans from the National Change of Address database. The data contain 1 record per enrollee. GSSC Geocoded Enrollee File contains enrollee addresses and their rurality indicators, categorized as urban, rural, or highly rural.18 Rurality is defined by the Rural Urban Commuting Area (RUCA) categories developed by the Department of Agriculture and the Health Resources and Services Administration of the US Department of Health and Human Services.19 Urban areas had RUCA codes of 1.0 to 1.1, and highly rural areas had RUCA scores of 10.0. All other areas were classified as rural. Since the proportion of veterans from highly rural areas was small, we included residents from highly rural areas in the rural residents’ group.

Inclusion and Exclusion Criteria

All veterans newly diagnosed with CRC from FY 2016 to 2021 were included. We used the ninth and tenth clinical modification revisions of the International Classification of Diseases (ICD-9-CM and ICD-10-CM) to define CRC diagnosis (Supplemental materials).4,20 To ensure that patients were newly diagnosed with CRC, this study excluded patients with a previous ICD-9-CM code for CRC diagnosis since FY 2003.

Comorbidities were identified using diagnosis and procedure codes from inpatient and outpatient encounters, which were used to calculate the Charlson Comorbidity Index (CCI) at the time of CRC diagnosis using the weighted method described by Schneeweiss et al.21 We defined CRC high-risk conditions and CRC screening tests, including flexible sigmoidoscopy and stool tests, as described in previous studies (Supplemental materials).20

The main outcome was total mortality. The date of death was extracted from the VHA Death Ascertainment File, which contains mortality data from the Master Person Index file in CDW and the Social Security Administration Death Master File. We used the date of death from any cause, as cause of death was not available.

A propensity score (PS) was created to match rural (including highly rural) and urban residents at a ratio of 1:1. Using a standard procedure described in prior publications, multivariable logistic regression used all baseline characteristics to estimate the PS and perform nearest-number matching without replacement.22,23 A caliper of 0.01 maximized the matched cohort size and achieved balance (Supplemental materials). We then examined the balance of baseline characteristics between PS-matched groups.

Analyses

Cox proportional hazards regression analysis estimated the hazard ratio (HR) of death in rural residents compared to urban residents in the PS-matched cohort. The outcome event was the date of death during the study’s follow-up period (defined as period from first CRC diagnosis to death or study end), with censoring at the study’s end date (September 30, 2021). The proportional hazards assumption was assessed by inspecting the Kaplan-Meier curves. Multiple analyses examined the HR of total mortality in the PS-matched cohort, stratified by sex, race, and ethnicity. We also examined the HR of total mortality stratified by duration of follow-up.

Another PS-matching analysis among veterans aged ≤ 45 years was performed using the same techniques described earlier in this article. We performed a Cox proportional hazards regression analysis to compare mortality in PS-matched urban and rural veterans aged ≤ 45 years. The HR of death in all veterans aged ≤ 45 years (before PS-matching) was estimated using Cox proportional hazard regression analysis, adjusting for PS.

Dichotomous variables were compared using X2 tests and continuous variables were compared using t tests. Baseline characteristics with missing values were converted into categorical variables and the proportion of subjects with missing values was equalized between treatment groups after PS-matching. For subgroup analysis, we examined the HR of total mortality in each subgroup using separate Cox proportional hazards regression models similar to the primary analysis but adjusted for PS. Due to multiple comparisons in the subgroup analysis, the findings should be considered exploratory. Statistical tests were 2-tailed, and significance was defined as P < .05. Data management and statistical analyses were conducted from June 2022 to January 2023 using STATA, Version 17. The VA Orlando Healthcare System Institutional Review Board approved the study and waived requirements for informed consent because only deidentified data were used.

Results

After excluding 49 patients (Supplemental materials, available at doi:10.12788/fp.0560), we identified 30,219 veterans with newly diagnosed CRC between FY 2016 to 2021 (Table 1). Of these, 19,422 (64.3%) resided in urban areas and 10,797 (35.7%) resided in rural areas (Table 2). The mean (SD) duration from the first CRC diagnosis to death or study end was 832 (640) days, and the median (IQR) was 723 (246–1330) days. Overall, incident CRC diagnoses were numerically highest in FY 2016 and lowest in FY 2020 (Figure 1). Patients with CRC in rural areas vs urban areas were significantly older (mean, 71.2 years vs 70.8 years, respectively; P < .001), more likely to be male (96.7% vs 95.7%, respectively; P < .001), more likely to be White (83.6% vs 67.8%, respectively; P < .001) and more likely to be non-Hispanic (92.2% vs 87.5%, respectively; P < .001). In terms of general health, rural veterans with CRC were more likely to be overweight or obese (81.5% rural vs 78.5% urban; P < .001) but had fewer mean comorbidities as measured by CCI (5.66 rural vs 5.90 urban; P < .001). A higher proportion of rural veterans with CRC had received stool-based (fecal occult blood test or fecal immunochemical test) CRC screening tests (61.6% rural vs 57.2% urban; P < .001). Fewer rural patients presented with systemic symptoms or signs within 1 year of CRC diagnosis (54.4% rural vs 57.5% urban, P < .001). Among urban patients with CRC, 6959 (35.8%) deaths were observed, compared with 3766 (34.9%) among rural patients (P = .10).

0525FED-AVAHO-CRC_T10525FED-AVAHO-CRC_T20525FED-AVAHO-CRC_F1

There were 21,568 PS-matched veterans: 10,784 in each group. In the PS-matched cohort, baseline characteristics were similar between veterans in urban and rural communities, including age, sex, race/ethnicity, body mass index, and comorbidities. Among rural patients with CRC, 3763 deaths (34.9%) were observed compared with 3702 (34.3%) among urban veterans. There was no significant difference in the HR of mortality between rural and urban CRC residents (HR, 1.01; 95% CI, 0.97-1.06; P = .53) (Figure 2).

0525FED-AVAHO-CRC_F20525FED-AVAHO-CRC_F30525FED-AVAHO-CRC_F4

Among veterans aged ≤ 45 years, 551 were diagnosed with CRC (391 urban and 160 rural). We PS-matched 142 pairs of urban and rural veterans without residual differences in baseline characteristics (eAppendix 1). There was no significant difference in the HR of mortality between rural and urban veterans aged ≤ 45 years (HR, 0.97; 95% CI, 0.57-1.63; P = .90) (Figure 2). Similarly, no difference in mortality was observed adjusting for PS between all rural and urban veterans aged ≤ 45 years (HR, 1.03; 95% CI, 0.67-1.59; P = .88).

0525FED-AVAHO-CRC_eApp1

There was no difference in total mortality between rural and urban veterans in any subgroup except for American Indian or Alaska Native veterans (HR, 2.41; 95% CI, 1.29-4.50; P = .006) (eAppendix 2).

0525FED-AVAHO-CRC_eApp2

Discussion

This study examined characteristics of patients with CRC between urban and rural areas among veterans who were VHA patients. Similar to other studies, rural veterans with CRC were older, more likely to be White, and were obese, but exhibited fewer comorbidities (lower CCI and lower incidence of congestive heart failure, dementia, hemiplegia, kidney diseases, liver diseases and AIDS, but higher incidence of chronic obstructive lung disease).8,16 The incidence of CRC in this study population was lowest in FY 2020, which was reported by the Centers for Disease Control and Prevention and is attributed to COVID-19 pandemic disruption of health services.24 The overall mortality in this study was similar to rates reported in other studies from the VA Central Cancer Registry.4 In the PS-matched cohort, where baseline characteristics were similar between urban and rural patients with CRC, we found no disparities in CRC-specific mortality between veterans in rural and urban areas. Additionally, when analysis was restricted to veterans aged ≤ 45 years, the results remained consistent.

Subgroup analyses showed no significant difference in mortality between rural and urban areas by sex, race or ethnicity, except rural American Indian or Alaska Native veterans who had double the mortality of their urban counterparts (HR, 2.41; 95% CI, 1.29-4.50; P = .006). This finding is difficult to interpret due to the small number of events and the wide CI. While with a Bonferroni correction the adjusted P value was .08, which is not statistically significant, a previous study found that although CRC incidence was lower overall in American Indian or Alaska Native populations compared to non-Hispanic White populations, CRC incidence was higher among American Indian or Alaska Native individuals in some areas such as Alaska and the Northern Plains.25,26 Studies have noted that rural American Indian/Alaska Native populations experience greater poverty, less access to broadband internet, and limited access to care, contributing to poorer cancer outcomes and lower survival.27 Thus, the finding of disparity in mortality between rural and urban American Indian or Alaska Native veterans warrants further study.

Other studies have raised concerns that CRC disproportionately affects adults in rural areas with higher mortality rates.14-16 These disparities arise from sociodemographic factors and modifiable risk factors, including physical activity, dietary patterns, access to cancer screening, and gaps in quality treatment resources.16,28 These factors operate at multiple levels: from individual, local health system, to community and policy.2,27 For example, a South Carolina study (1996–2016) found that residents in rural areas were more likely to be diagnosed with advanced CRC, possibly indicating lower rates of CRC screening in rural areas. They also had higher likelihood of death from CRC.15 However, the study did not include any clinical parameters, such as comorbidities or obesity. A statewide, population-based study in Utah showed that rural men experienced a lower CRC survival in their unadjusted analysis.16 However, the study was small, with only 3948 urban and 712 rural residents. Additionally, there was no difference in total mortality in the whole cohort (HR, 0.96; 95% CI, 0.86-1.07) or in CRC-specific death (HR, 0.93; 95% CI, 0.81-1.08). A nationwide study also showed that CRC mortality rates were 8% higher in nonmetropolitan or rural areas than in the most urbanized areas containing large metropolitan counties.29 However, this study did not include descriptions of clinical confounders, such as comorbidities, making it difficult to ascertain whether the difference in CRC mortality was due to rurality or differences in baseline risk characteristics.

In this study, the lack of CRC-specific mortality disparities may be attributed to the structures and practices of VHA health care. Recent studies have noted that mortality of several chronic medical conditions treated at the VHA was lower than at non-VHA hospitals.30,31 One study that measured the quality of nonmetastatic CRC care based on National Comprehensive Cancer Network guidelines showed that > 72% of VHA patients received guideline-concordant care for each diagnostic and therapeutic measure, except for follow-up colonoscopy timing, which appear to be similar or superior to that of the private sector.30,32,33 Some of the VA initiative for CRC screening may bypass the urban-rurality divide such as the mailed fecal immunochemical test program for CRC. This program was implemented at the onset of the COVID-19 pandemic to avoid disruptions of medical care.34 Rural patients are more likely to undergo fecal immunochemical testing when compared to urban patients in this data. Beyond clinical care, the VHA uses processes to tackle social determinants of health such as housing, food security, and transportation, promoting equal access to health care, and promoting cultural competency among HCPs.35-37

The results suggest that solutions to CRC disparities between rural and urban areas need to consider known barriers to rural health care, including transportation, diminished rural health care workforce, and other social determinants of health.9,10,27,38 VHA makes considerable efforts to provide equitable care to all enrolled veterans, including specific programs for rural veterans, including ongoing outreach.39 This study demonstrated lack of disparity in CRC-specific mortality in veterans receiving VHA care, highlighting the importance of these efforts.

Strengths and Limitations

This study used the VHA cohort to compare patient characteristics and mortality between patients with CRC residing in rural and urban areas. The study provides nationwide perspectives on CRC across the geographical spectrum and used a longitudinal cohort with prolonged follow-up to account for comorbidities.

However, the study compared a cohort of rural and urban veterans enrolled in the VHA; hence, the results may not reflect CRC outcomes in veterans without access to VHA care. Rurality has been independently associated with decreased likelihood of meeting CRC screening guidelines among veterans and military service members.38 This study lacked sufficient information to compare CRC staging or treatment modalities among veterans. Although the data cannot identify CRC stage, the proportions of patients with metastatic CRC at diagnosis and CRC location were similar between groups. The study did not have information on their care outside of VHA setting.

This study could not ascertain whether disparities existed in CRC treatment modality since rural residence may result in referral to community-based CRC care, which did not appear in the data. To address these limitations, we used death from any cause as the primary outcome, since death is a hard outcome and is not subject to ascertainment bias. The relatively short follow-up time is another limitation, though subgroup analysis by follow-up did not show significant differences. Despite PS matching, residual unmeasured confounding may exist between urban and rural groups. The predominantly White, male VHA population with high CCI may limit the generalizability of the results.

Conclusions

Rural VHA enrollees had similar survival rates after CRC diagnosis compared to their urban counterparts in a PS-matched analysis. The VHA models of care—including mailed CRC screening tools, several socioeconomic determinants of health (housing, food security, and transportation), and promoting equal access to health care, as well as cultural competency among HCPs—HCPs—may help alleviate disparities across the rural-urban spectrum. The VHA should continue efforts to enroll veterans and provide comprehensive coordinated care in community partnerships.

References
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  2. Carethers JM, Doubeni CA. Causes of socioeconomic disparities in colorectal cancer and intervention framework and strategies. Gastroenterology. 2020;158(2):354-367. doi:10.1053/j.gastro.2019.10.029
  3. Murphy G, Devesa SS, Cross AJ, Inskip PD, McGlynn KA, Cook MB. Sex disparities in colorectal cancer incidence by anatomic subsite, race and age. Int J Cancer. 2011;128(7):1668-75. doi:10.1002/ijc.25481
  4. Zullig LL, Smith VA, Jackson GL, et al. Colorectal cancer statistics from the Veterans Affairs central cancer registry. Clin Colorectal Cancer. 2016;15(4):e199-e204. doi:10.1016/j.clcc.2016.04.005
  5. Lin JS, Perdue LA, Henrikson NB, Bean SI, Blasi PR. Screening for Colorectal Cancer: An Evidence Update for the US Preventive Services Task Force. 2021. U.S. Preventive Services Task Force Evidence Syntheses, formerly Systematic Evidence Reviews:Chapter 1. Agency for Healthcare Research and Quality (US); 2021. Accessed February 18, 2025. https://www.ncbi.nlm.nih.gov/books/NBK570917/
  6. Siegel RL, Fedewa SA, Anderson WF, et al. Colorectal cancer incidence patterns in the United States, 1974-2013. J Natl Cancer Inst. 2017;109(8). doi:10.1093/jnci/djw322
  7. Davidson KW, Barry MJ, Mangione CM, et al. Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(19):1965-1977. doi:10.1001/jama.2021.6238
  8. Hines R, Markossian T, Johnson A, Dong F, Bayakly R. Geographic residency status and census tract socioeconomic status as determinants of colorectal cancer outcomes. Am J Public Health. 2014;104(3):e63-e71. doi:10.2105/AJPH.2013.301572
  9. Cauwels J. The many barriers to high-quality rural health care. 2022;(9):1-32. NEJM Catal Innov Care Deliv. Accessed April 24, 2025. https://catalyst.nejm.org/doi/pdf/10.1056/CAT.22.0254
  10. Gong G, Phillips SG, Hudson C, Curti D, Philips BU. Higher US rural mortality rates linked to socioeconomic status, physician shortages, and lack of health insurance. Health Aff (Millwood);38(12):2003-2010. doi:10.1377/hlthaff.2019.00722
  11. Aboagye JK, Kaiser HE, Hayanga AJ. Rural-urban differences in access to specialist providers of colorectal cancer care in the United States: a physician workforce issue. JAMA Surg. 2014;149(6):537-543. doi:10.1001/jamasurg.2013.5062
  12. Lyckholm LJ, Hackney MH, Smith TJ. Ethics of rural health care. Crit Rev Oncol Hematol. 2001;40(2):131-138. doi:10.1016/s1040-8428(01)00139-1
  13. Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: concepts, methodologies, and guidelines. Annu Rev Public Health. 1997;18:341-378. doi:10.1146/annurev.publhealth.18.1.341
  14. Singh GK, Jemal A. Socioeconomic and racial/ethnic disparities in cancer mortality, incidence, and survival in the United States, 1950-2014: over six decades of changing patterns and widening inequalities. J Environ Public Health. 2017;2017:2819372. doi:10.1155/2017/2819372
  15. Adams SA, Zahnd WE, Ranganathan R, et al. Rural and racial disparities in colorectal cancer incidence and mortality in South Carolina, 1996 - 2016. J Rural Health. 2022;38(1):34-39. doi:10.1111/jrh.12580
  16. Rogers CR, Blackburn BE, Huntington M, et al. Rural- urban disparities in colorectal cancer survival and risk among men in Utah: a statewide population-based study. Cancer Causes Control. 2020;31(3):241-253. doi:10.1007/s10552-020-01268-2
  17. US Department of Veterans Affairs. VA Informatics and Computing Infrastructure (VINCI), VA HSR RES 13-457. https://vincicentral.vinci.med.va.gov [Source not verified]
  18. US Department of Veterans Affairs Information Resource Center. VIReC Research User Guide: PSSG Geocoded Enrollee Files, 2015 Edition. US Department of Veterans Affairs, Health Services Research & Development Service, Information Resource Center; May. 2016. [source not verified]
  19. Goldsmith HF, Puskin DS, Stiles DJ. Improving the operational definition of “rural areas” for federal programs. US Department of Health and Human Services; 1993. Accessed February 27, 2025. https://www.ruralhealthinfo.org/pdf/improving-the-operational-definition-of-rural-areas.pdf
  20. Adams MA, Kerr EA, Dominitz JA, et al. Development and validation of a new ICD-10-based screening colonoscopy overuse measure in a large integrated healthcare system: a retrospective observational study. BMJ Qual Saf. 2023;32(7):414-424. doi:10.1136/bmjqs-2021-014236
  21. Schneeweiss S, Wang PS, Avorn J, Glynn RJ. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res. 2003;38(4):1103-1120. doi:10.1111/1475-6773.00165
  22. Becker S, Ichino A. Estimation of average treatment effects based on propensity scores. The Stata Journal. 2002;2(4):358-377.
  23. Leuven E, Sianesi B. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Statistical software components. Revised February 1, 2018. Accessed February 27, 2025. https://ideas.repec.org/c/boc/bocode/s432001.html.
  24. US Cancer Statistics Working Group. US cancer statistics data visualizations tool. Centers for Disease Control and Prevention. June 2024. Accessed February 27, 2025. https://www.cdc.gov/cancer/dataviz
  25. Cao J, Zhang S. Multiple Comparison Procedures. JAMA. 2014;312(5):543-544. doi:10.1001/jama.2014.9440
  26. Gopalani SV, Janitz AE, Martinez SA, et al. Trends in cancer incidence among American Indians and Alaska Natives and Non-Hispanic Whites in the United States, 1999-2015. Epidemiology. 2020;31(2):205-213. doi:10.1097/EDE.0000000000001140
  27. Zahnd WE, Murphy C, Knoll M, et al. The intersection of rural residence and minority race/ethnicity in cancer disparities in the United States. Int J Environ Res Public Health. 2021;18(4). doi:10.3390/ijerph18041384
  28. Blake KD, Moss JL, Gaysynsky A, Srinivasan S, Croyle RT. Making the case for investment in rural cancer control: an analysis of rural cancer incidence, mortality, and funding trends. Cancer Epidemiol Biomarkers Prev. 2017;26(7):992-997. doi:10.1158/1055-9965.EPI-17-0092
  29. Singh GK, Williams SD, Siahpush M, Mulhollen A. Socioeconomic, rural-urban, and racial inequalities in US cancer mortality: part i-all cancers and lung cancer and part iicolorectal, prostate, breast, and cervical cancers. J Cancer Epidemiol. 2011;2011:107497. doi:10.1155/2011/107497
  30. Jackson GL, Melton LD, Abbott DH, et al. Quality of nonmetastatic colorectal cancer care in the Department of Veterans Affairs. J Clin Oncol. 2010;28(19):3176-3181. doi:10.1200/JCO.2009.26.7948
  31. Yoon J, Phibbs CS, Ong MK, et al. Outcomes of veterans treated in Veterans Affairs hospitals vs non-Veterans Affairs hospitals. JAMA Netw Open. 2023;6(12):e2345898. doi:10.1001/jamanetworkopen.2023.45898
  32. Malin JL, Schneider EC, Epstein AM, Adams J, Emanuel EJ, Kahn KL. Results of the National Initiative for Cancer Care Quality: how can we improve the quality of cancer care in the United States? J Clin Oncol. 2006;24(4):626-634. doi:10.1200/JCO.2005.03.3365
  33. Levin B, Lieberman DA, McFarland B, et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. Gastroenterology. 2008;134(5):1570-1595. doi:10.1053/j.gastro.2008.02.002
  34. Deeds SA, Moore CB, Gunnink EJ, et al. Implementation of a mailed faecal immunochemical test programme for colorectal cancer screening among Veterans. BMJ Open Qual. 2022;11(4). doi:10.1136/bmjoq-2022-001927
  35. Yehia BR, Greenstone CL, Hosenfeld CB, Matthews KL, Zephyrin LC. The role of VA community care in addressing health and health care disparities. Med Care. 2017;55(Suppl 9 suppl 2):S4-S5. doi:10.1097/MLR.0000000000000768
  36. Wright BN, MacDermid Wadsworth S, Wellnitz A, Eicher- Miller HA. Reaching rural veterans: a new mechanism to connect rural, low-income US Veterans with resources and improve food security. J Public Health (Oxf). 2019;41(4):714-723. doi:10.1093/pubmed/fdy203
  37. Nelson RE, Byrne TH, Suo Y, et al. Association of temporary financial assistance with housing stability among US veterans in the supportive services for veteran families program. JAMA Netw Open. 2021;4(2):e2037047. doi:10.1001/jamanetworkopen.2020.37047
  38. McDaniel JT, Albright D, Lee HY, et al. Rural–urban disparities in colorectal cancer screening among military service members and Veterans. J Mil Veteran Fam Health. 2019;5(1):40-48. doi:10.3138/jmvfh.2018-0013
  39. US Department of Veterans Affairs, Office of Rural Health. The rural veteran outreach toolkit. Updated February 12, 2025. Accessed February 18, 2025. https://www.ruralhealth.va.gov/partners/toolkit.asp
References
  1. Siegel RL, Wagle NS, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2023. CA Cancer J Clin. 2023;73(3):233-254. doi:10.3322/caac.21772
  2. Carethers JM, Doubeni CA. Causes of socioeconomic disparities in colorectal cancer and intervention framework and strategies. Gastroenterology. 2020;158(2):354-367. doi:10.1053/j.gastro.2019.10.029
  3. Murphy G, Devesa SS, Cross AJ, Inskip PD, McGlynn KA, Cook MB. Sex disparities in colorectal cancer incidence by anatomic subsite, race and age. Int J Cancer. 2011;128(7):1668-75. doi:10.1002/ijc.25481
  4. Zullig LL, Smith VA, Jackson GL, et al. Colorectal cancer statistics from the Veterans Affairs central cancer registry. Clin Colorectal Cancer. 2016;15(4):e199-e204. doi:10.1016/j.clcc.2016.04.005
  5. Lin JS, Perdue LA, Henrikson NB, Bean SI, Blasi PR. Screening for Colorectal Cancer: An Evidence Update for the US Preventive Services Task Force. 2021. U.S. Preventive Services Task Force Evidence Syntheses, formerly Systematic Evidence Reviews:Chapter 1. Agency for Healthcare Research and Quality (US); 2021. Accessed February 18, 2025. https://www.ncbi.nlm.nih.gov/books/NBK570917/
  6. Siegel RL, Fedewa SA, Anderson WF, et al. Colorectal cancer incidence patterns in the United States, 1974-2013. J Natl Cancer Inst. 2017;109(8). doi:10.1093/jnci/djw322
  7. Davidson KW, Barry MJ, Mangione CM, et al. Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(19):1965-1977. doi:10.1001/jama.2021.6238
  8. Hines R, Markossian T, Johnson A, Dong F, Bayakly R. Geographic residency status and census tract socioeconomic status as determinants of colorectal cancer outcomes. Am J Public Health. 2014;104(3):e63-e71. doi:10.2105/AJPH.2013.301572
  9. Cauwels J. The many barriers to high-quality rural health care. 2022;(9):1-32. NEJM Catal Innov Care Deliv. Accessed April 24, 2025. https://catalyst.nejm.org/doi/pdf/10.1056/CAT.22.0254
  10. Gong G, Phillips SG, Hudson C, Curti D, Philips BU. Higher US rural mortality rates linked to socioeconomic status, physician shortages, and lack of health insurance. Health Aff (Millwood);38(12):2003-2010. doi:10.1377/hlthaff.2019.00722
  11. Aboagye JK, Kaiser HE, Hayanga AJ. Rural-urban differences in access to specialist providers of colorectal cancer care in the United States: a physician workforce issue. JAMA Surg. 2014;149(6):537-543. doi:10.1001/jamasurg.2013.5062
  12. Lyckholm LJ, Hackney MH, Smith TJ. Ethics of rural health care. Crit Rev Oncol Hematol. 2001;40(2):131-138. doi:10.1016/s1040-8428(01)00139-1
  13. Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: concepts, methodologies, and guidelines. Annu Rev Public Health. 1997;18:341-378. doi:10.1146/annurev.publhealth.18.1.341
  14. Singh GK, Jemal A. Socioeconomic and racial/ethnic disparities in cancer mortality, incidence, and survival in the United States, 1950-2014: over six decades of changing patterns and widening inequalities. J Environ Public Health. 2017;2017:2819372. doi:10.1155/2017/2819372
  15. Adams SA, Zahnd WE, Ranganathan R, et al. Rural and racial disparities in colorectal cancer incidence and mortality in South Carolina, 1996 - 2016. J Rural Health. 2022;38(1):34-39. doi:10.1111/jrh.12580
  16. Rogers CR, Blackburn BE, Huntington M, et al. Rural- urban disparities in colorectal cancer survival and risk among men in Utah: a statewide population-based study. Cancer Causes Control. 2020;31(3):241-253. doi:10.1007/s10552-020-01268-2
  17. US Department of Veterans Affairs. VA Informatics and Computing Infrastructure (VINCI), VA HSR RES 13-457. https://vincicentral.vinci.med.va.gov [Source not verified]
  18. US Department of Veterans Affairs Information Resource Center. VIReC Research User Guide: PSSG Geocoded Enrollee Files, 2015 Edition. US Department of Veterans Affairs, Health Services Research & Development Service, Information Resource Center; May. 2016. [source not verified]
  19. Goldsmith HF, Puskin DS, Stiles DJ. Improving the operational definition of “rural areas” for federal programs. US Department of Health and Human Services; 1993. Accessed February 27, 2025. https://www.ruralhealthinfo.org/pdf/improving-the-operational-definition-of-rural-areas.pdf
  20. Adams MA, Kerr EA, Dominitz JA, et al. Development and validation of a new ICD-10-based screening colonoscopy overuse measure in a large integrated healthcare system: a retrospective observational study. BMJ Qual Saf. 2023;32(7):414-424. doi:10.1136/bmjqs-2021-014236
  21. Schneeweiss S, Wang PS, Avorn J, Glynn RJ. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res. 2003;38(4):1103-1120. doi:10.1111/1475-6773.00165
  22. Becker S, Ichino A. Estimation of average treatment effects based on propensity scores. The Stata Journal. 2002;2(4):358-377.
  23. Leuven E, Sianesi B. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Statistical software components. Revised February 1, 2018. Accessed February 27, 2025. https://ideas.repec.org/c/boc/bocode/s432001.html.
  24. US Cancer Statistics Working Group. US cancer statistics data visualizations tool. Centers for Disease Control and Prevention. June 2024. Accessed February 27, 2025. https://www.cdc.gov/cancer/dataviz
  25. Cao J, Zhang S. Multiple Comparison Procedures. JAMA. 2014;312(5):543-544. doi:10.1001/jama.2014.9440
  26. Gopalani SV, Janitz AE, Martinez SA, et al. Trends in cancer incidence among American Indians and Alaska Natives and Non-Hispanic Whites in the United States, 1999-2015. Epidemiology. 2020;31(2):205-213. doi:10.1097/EDE.0000000000001140
  27. Zahnd WE, Murphy C, Knoll M, et al. The intersection of rural residence and minority race/ethnicity in cancer disparities in the United States. Int J Environ Res Public Health. 2021;18(4). doi:10.3390/ijerph18041384
  28. Blake KD, Moss JL, Gaysynsky A, Srinivasan S, Croyle RT. Making the case for investment in rural cancer control: an analysis of rural cancer incidence, mortality, and funding trends. Cancer Epidemiol Biomarkers Prev. 2017;26(7):992-997. doi:10.1158/1055-9965.EPI-17-0092
  29. Singh GK, Williams SD, Siahpush M, Mulhollen A. Socioeconomic, rural-urban, and racial inequalities in US cancer mortality: part i-all cancers and lung cancer and part iicolorectal, prostate, breast, and cervical cancers. J Cancer Epidemiol. 2011;2011:107497. doi:10.1155/2011/107497
  30. Jackson GL, Melton LD, Abbott DH, et al. Quality of nonmetastatic colorectal cancer care in the Department of Veterans Affairs. J Clin Oncol. 2010;28(19):3176-3181. doi:10.1200/JCO.2009.26.7948
  31. Yoon J, Phibbs CS, Ong MK, et al. Outcomes of veterans treated in Veterans Affairs hospitals vs non-Veterans Affairs hospitals. JAMA Netw Open. 2023;6(12):e2345898. doi:10.1001/jamanetworkopen.2023.45898
  32. Malin JL, Schneider EC, Epstein AM, Adams J, Emanuel EJ, Kahn KL. Results of the National Initiative for Cancer Care Quality: how can we improve the quality of cancer care in the United States? J Clin Oncol. 2006;24(4):626-634. doi:10.1200/JCO.2005.03.3365
  33. Levin B, Lieberman DA, McFarland B, et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. Gastroenterology. 2008;134(5):1570-1595. doi:10.1053/j.gastro.2008.02.002
  34. Deeds SA, Moore CB, Gunnink EJ, et al. Implementation of a mailed faecal immunochemical test programme for colorectal cancer screening among Veterans. BMJ Open Qual. 2022;11(4). doi:10.1136/bmjoq-2022-001927
  35. Yehia BR, Greenstone CL, Hosenfeld CB, Matthews KL, Zephyrin LC. The role of VA community care in addressing health and health care disparities. Med Care. 2017;55(Suppl 9 suppl 2):S4-S5. doi:10.1097/MLR.0000000000000768
  36. Wright BN, MacDermid Wadsworth S, Wellnitz A, Eicher- Miller HA. Reaching rural veterans: a new mechanism to connect rural, low-income US Veterans with resources and improve food security. J Public Health (Oxf). 2019;41(4):714-723. doi:10.1093/pubmed/fdy203
  37. Nelson RE, Byrne TH, Suo Y, et al. Association of temporary financial assistance with housing stability among US veterans in the supportive services for veteran families program. JAMA Netw Open. 2021;4(2):e2037047. doi:10.1001/jamanetworkopen.2020.37047
  38. McDaniel JT, Albright D, Lee HY, et al. Rural–urban disparities in colorectal cancer screening among military service members and Veterans. J Mil Veteran Fam Health. 2019;5(1):40-48. doi:10.3138/jmvfh.2018-0013
  39. US Department of Veterans Affairs, Office of Rural Health. The rural veteran outreach toolkit. Updated February 12, 2025. Accessed February 18, 2025. https://www.ruralhealth.va.gov/partners/toolkit.asp
Issue
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Colorectal Cancer Characteristics and Mortality From Propensity Score-Matched Cohorts of Urban and Rural Veterans

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Colorectal Cancer Characteristics and Mortality From Propensity Score-Matched Cohorts of Urban and Rural Veterans

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Cancer Data Trends 2025

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The annual issue of Cancer Data Trends, produced in collaboration with the Association of VA Hematology/Oncology (AVAHO), highlights the latest research in some of the top cancers impacting US veterans. 

In this issue: 

  1. Access, Race, and "Colon Age": Improving CRC Screening
  2. Lung Cancer: Mortality Trends in Veterans and New Treatments
  3. Racial Disparities, Germline Testing, and Improved Overall Survival in Prostate Cancer
  4. Breast and Uterine Cancer: Screening Guidelines, Genetic Testing, and Mortality Trends
  5. HCC Updates: Quality Care Framework and Risk Stratification Data
  6. Rising Kidney Cancer Cases and Emerging Treatments for Veterans
  7. Advances in Blood Cancer Care for Veterans
  8. AI-Based Risk Stratification for Oropharyngeal Carcinomas: AIROC
  9. Brain Cancer: Epidemiology, TBI, and New Treatments

 

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Colorectal Cancer
Lung Cancer
Prostate Cancer
Breast Cancer
Uterine Fibroids
Hepatocellular Carcinoma
Renal Cell Carcinoma
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Neuro-oncology
Sections
Supplements

The annual issue of Cancer Data Trends, produced in collaboration with the Association of VA Hematology/Oncology (AVAHO), highlights the latest research in some of the top cancers impacting US veterans. 

In this issue: 

  1. Access, Race, and "Colon Age": Improving CRC Screening
  2. Lung Cancer: Mortality Trends in Veterans and New Treatments
  3. Racial Disparities, Germline Testing, and Improved Overall Survival in Prostate Cancer
  4. Breast and Uterine Cancer: Screening Guidelines, Genetic Testing, and Mortality Trends
  5. HCC Updates: Quality Care Framework and Risk Stratification Data
  6. Rising Kidney Cancer Cases and Emerging Treatments for Veterans
  7. Advances in Blood Cancer Care for Veterans
  8. AI-Based Risk Stratification for Oropharyngeal Carcinomas: AIROC
  9. Brain Cancer: Epidemiology, TBI, and New Treatments

 

The annual issue of Cancer Data Trends, produced in collaboration with the Association of VA Hematology/Oncology (AVAHO), highlights the latest research in some of the top cancers impacting US veterans. 

In this issue: 

  1. Access, Race, and "Colon Age": Improving CRC Screening
  2. Lung Cancer: Mortality Trends in Veterans and New Treatments
  3. Racial Disparities, Germline Testing, and Improved Overall Survival in Prostate Cancer
  4. Breast and Uterine Cancer: Screening Guidelines, Genetic Testing, and Mortality Trends
  5. HCC Updates: Quality Care Framework and Risk Stratification Data
  6. Rising Kidney Cancer Cases and Emerging Treatments for Veterans
  7. Advances in Blood Cancer Care for Veterans
  8. AI-Based Risk Stratification for Oropharyngeal Carcinomas: AIROC
  9. Brain Cancer: Epidemiology, TBI, and New Treatments

 

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Shifting Demographics: A Temporal Analysis of the Alarming Rise in Rectal Adenocarcinoma Among Young Adults

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Author(s)
Cinthiya Chander
John Paul Braun
Peter Silberstein, MD

Background

Rectal adenocarcinoma has long been associated with older adults, with routine screening typically beginning at age 45 or older. However, recent data reveal a concerning rise in rectal cancer incidence among adults under 40. These early-onset cases often present at later stages and may have distinct biological features. While some research attributes this trend to genetic or environmental factors, the contribution of socioeconomic disparities and healthcare access has not been fully explored. Identifying these influences is essential to shaping targeted prevention and early detection strategies for younger populations.

Objective

To evaluate temporal trends in rectal adenocarcinoma among young adults and assess demographic and socioeconomic predictors of early-onset diagnosis.

Methods

Data were drawn from the National Cancer Database (NCDB) for patients diagnosed with rectal adenocarcinoma from 2004 to 2022. Among 440,316 cases, 17,842 (4.1%) occurred in individuals under 40. Linear regression assessed temporal trends, while logistic regression evaluated associations between early-onset diagnosis and variables including sex, race, insurance status, income level, Charlson-Deyo comorbidity score, and tumor stage. Statistical significance was defined as α = 0.05.

Results

The number of young adults diagnosed rose from 424 in 2004 to 937 in 2022—an increase of over 120%. Each year was associated with a 1.7% rise in odds of early diagnosis (OR = 1.017, p < 0.001). Male patients had 24.7% higher odds (OR = 1.247, p < 0.001), and Black patients had 59.3% higher odds compared to White patients (OR = 1.593, p < 0.001). Non-private insurance was linked to a 41.6% decrease in early diagnosis (OR = 0.584, p < 0.001). Income level was not significant (p = 0.426). Lower Charlson-Deyo scores and higher tumor stages were also associated with early-onset cases.

Conclusions

Rectal adenocarcinoma is increasingly affecting younger adults, with significant associations across demographic and insurance variables. These findings call for improved awareness, early diagnostic strategies, and further research into underlying causes to mitigate this growing public health concern.

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Author(s)
Cinthiya Chander
John Paul Braun
Peter Silberstein, MD
Author(s)
Cinthiya Chander
John Paul Braun
Peter Silberstein, MD

Background

Rectal adenocarcinoma has long been associated with older adults, with routine screening typically beginning at age 45 or older. However, recent data reveal a concerning rise in rectal cancer incidence among adults under 40. These early-onset cases often present at later stages and may have distinct biological features. While some research attributes this trend to genetic or environmental factors, the contribution of socioeconomic disparities and healthcare access has not been fully explored. Identifying these influences is essential to shaping targeted prevention and early detection strategies for younger populations.

Objective

To evaluate temporal trends in rectal adenocarcinoma among young adults and assess demographic and socioeconomic predictors of early-onset diagnosis.

Methods

Data were drawn from the National Cancer Database (NCDB) for patients diagnosed with rectal adenocarcinoma from 2004 to 2022. Among 440,316 cases, 17,842 (4.1%) occurred in individuals under 40. Linear regression assessed temporal trends, while logistic regression evaluated associations between early-onset diagnosis and variables including sex, race, insurance status, income level, Charlson-Deyo comorbidity score, and tumor stage. Statistical significance was defined as α = 0.05.

Results

The number of young adults diagnosed rose from 424 in 2004 to 937 in 2022—an increase of over 120%. Each year was associated with a 1.7% rise in odds of early diagnosis (OR = 1.017, p < 0.001). Male patients had 24.7% higher odds (OR = 1.247, p < 0.001), and Black patients had 59.3% higher odds compared to White patients (OR = 1.593, p < 0.001). Non-private insurance was linked to a 41.6% decrease in early diagnosis (OR = 0.584, p < 0.001). Income level was not significant (p = 0.426). Lower Charlson-Deyo scores and higher tumor stages were also associated with early-onset cases.

Conclusions

Rectal adenocarcinoma is increasingly affecting younger adults, with significant associations across demographic and insurance variables. These findings call for improved awareness, early diagnostic strategies, and further research into underlying causes to mitigate this growing public health concern.

Background

Rectal adenocarcinoma has long been associated with older adults, with routine screening typically beginning at age 45 or older. However, recent data reveal a concerning rise in rectal cancer incidence among adults under 40. These early-onset cases often present at later stages and may have distinct biological features. While some research attributes this trend to genetic or environmental factors, the contribution of socioeconomic disparities and healthcare access has not been fully explored. Identifying these influences is essential to shaping targeted prevention and early detection strategies for younger populations.

Objective

To evaluate temporal trends in rectal adenocarcinoma among young adults and assess demographic and socioeconomic predictors of early-onset diagnosis.

Methods

Data were drawn from the National Cancer Database (NCDB) for patients diagnosed with rectal adenocarcinoma from 2004 to 2022. Among 440,316 cases, 17,842 (4.1%) occurred in individuals under 40. Linear regression assessed temporal trends, while logistic regression evaluated associations between early-onset diagnosis and variables including sex, race, insurance status, income level, Charlson-Deyo comorbidity score, and tumor stage. Statistical significance was defined as α = 0.05.

Results

The number of young adults diagnosed rose from 424 in 2004 to 937 in 2022—an increase of over 120%. Each year was associated with a 1.7% rise in odds of early diagnosis (OR = 1.017, p < 0.001). Male patients had 24.7% higher odds (OR = 1.247, p < 0.001), and Black patients had 59.3% higher odds compared to White patients (OR = 1.593, p < 0.001). Non-private insurance was linked to a 41.6% decrease in early diagnosis (OR = 0.584, p < 0.001). Income level was not significant (p = 0.426). Lower Charlson-Deyo scores and higher tumor stages were also associated with early-onset cases.

Conclusions

Rectal adenocarcinoma is increasingly affecting younger adults, with significant associations across demographic and insurance variables. These findings call for improved awareness, early diagnostic strategies, and further research into underlying causes to mitigate this growing public health concern.

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Expansion of an Intervention to Ensure Accuracy and Usefulness of a SQL Code Identifying Oncology Patients for VACCR

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Author(s)
Anna M. Rose, DNP, APRN-CNS
Sam Shepard, HSS
Preston Barrett

Purpose

The Veterans Affairs Central Cancer Registry (VACCR) is a data management system for cancer surveillance and epidemiologic-based efforts, seeking to reduce the overall cancer burden. In 2024, the local VACCR successfully implemented a Structured Query Language (SQL) code, created to identify documents in the electronic medical record (EMR) with associated ICD-10 codes matching reportable cancer cases in the Surveillance, Epidemiology, and End Results (SEER) list. In 2025, code application expansion began at four additional VISN9 sites.

Outcomes Studied

Accuracy and usefulness of SQL code application in a significantly larger population and a diagnosis-specific population.

Methods

Local Cancer Program leadership collaborated with VISN9 leadership to expand the SQL code to the four sites’ EMR, identifying the Veteran’s name, social security number, location by city/state/county, and visit-associated data including location, ICD-10 code, and visit year. Data validation focused on ICD- 10-specific data and quality replication.

Results

After SQL code application to Mt Home TN VACCR data, 750 unique, randomized charts from 2015-2025 were selected for accuracy review. Data validation found that 90.5% (679) had a reportable cancer; 14.9% (112) were not entered into VACCR. 9.5% (71) were not reportable. The SQL code was applied to Lexington data to identify colorectal cancer (CRC) (ICD-10 codes C17-C21.9). 746 charts from 2015-2025 were identified. 88.9% (663) had a reportable CRC; 14.9% (111) of those were not entered into VACCR, and 11% (83) were not reportable. Most cases not entered into VACCR at both sites were cases in which the majority of care was provided through Care in the Community (CITC). Historically, identification of CITC-provided oncologic care has been manual and notoriously difficult.

Conclusions

This study demonstrated the feasibility and accuracy of the SQL code in the identification of Veterans with diagnoses matching the SEER list in a large population and at a diagnosis-specific level. VISN-wide use of the report will increase efficiency and timeliness of data entry into VACCR, especially related to care provided through CITC. An improved understanding of oncologic care in the VISN would provide critical data to VISN executive leadership, enabling them to advocate for resources, targeted interventions, and access to care.

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Author(s)
Anna M. Rose, DNP, APRN-CNS
Sam Shepard, HSS
Preston Barrett
Author(s)
Anna M. Rose, DNP, APRN-CNS
Sam Shepard, HSS
Preston Barrett

Purpose

The Veterans Affairs Central Cancer Registry (VACCR) is a data management system for cancer surveillance and epidemiologic-based efforts, seeking to reduce the overall cancer burden. In 2024, the local VACCR successfully implemented a Structured Query Language (SQL) code, created to identify documents in the electronic medical record (EMR) with associated ICD-10 codes matching reportable cancer cases in the Surveillance, Epidemiology, and End Results (SEER) list. In 2025, code application expansion began at four additional VISN9 sites.

Outcomes Studied

Accuracy and usefulness of SQL code application in a significantly larger population and a diagnosis-specific population.

Methods

Local Cancer Program leadership collaborated with VISN9 leadership to expand the SQL code to the four sites’ EMR, identifying the Veteran’s name, social security number, location by city/state/county, and visit-associated data including location, ICD-10 code, and visit year. Data validation focused on ICD- 10-specific data and quality replication.

Results

After SQL code application to Mt Home TN VACCR data, 750 unique, randomized charts from 2015-2025 were selected for accuracy review. Data validation found that 90.5% (679) had a reportable cancer; 14.9% (112) were not entered into VACCR. 9.5% (71) were not reportable. The SQL code was applied to Lexington data to identify colorectal cancer (CRC) (ICD-10 codes C17-C21.9). 746 charts from 2015-2025 were identified. 88.9% (663) had a reportable CRC; 14.9% (111) of those were not entered into VACCR, and 11% (83) were not reportable. Most cases not entered into VACCR at both sites were cases in which the majority of care was provided through Care in the Community (CITC). Historically, identification of CITC-provided oncologic care has been manual and notoriously difficult.

Conclusions

This study demonstrated the feasibility and accuracy of the SQL code in the identification of Veterans with diagnoses matching the SEER list in a large population and at a diagnosis-specific level. VISN-wide use of the report will increase efficiency and timeliness of data entry into VACCR, especially related to care provided through CITC. An improved understanding of oncologic care in the VISN would provide critical data to VISN executive leadership, enabling them to advocate for resources, targeted interventions, and access to care.

Purpose

The Veterans Affairs Central Cancer Registry (VACCR) is a data management system for cancer surveillance and epidemiologic-based efforts, seeking to reduce the overall cancer burden. In 2024, the local VACCR successfully implemented a Structured Query Language (SQL) code, created to identify documents in the electronic medical record (EMR) with associated ICD-10 codes matching reportable cancer cases in the Surveillance, Epidemiology, and End Results (SEER) list. In 2025, code application expansion began at four additional VISN9 sites.

Outcomes Studied

Accuracy and usefulness of SQL code application in a significantly larger population and a diagnosis-specific population.

Methods

Local Cancer Program leadership collaborated with VISN9 leadership to expand the SQL code to the four sites’ EMR, identifying the Veteran’s name, social security number, location by city/state/county, and visit-associated data including location, ICD-10 code, and visit year. Data validation focused on ICD- 10-specific data and quality replication.

Results

After SQL code application to Mt Home TN VACCR data, 750 unique, randomized charts from 2015-2025 were selected for accuracy review. Data validation found that 90.5% (679) had a reportable cancer; 14.9% (112) were not entered into VACCR. 9.5% (71) were not reportable. The SQL code was applied to Lexington data to identify colorectal cancer (CRC) (ICD-10 codes C17-C21.9). 746 charts from 2015-2025 were identified. 88.9% (663) had a reportable CRC; 14.9% (111) of those were not entered into VACCR, and 11% (83) were not reportable. Most cases not entered into VACCR at both sites were cases in which the majority of care was provided through Care in the Community (CITC). Historically, identification of CITC-provided oncologic care has been manual and notoriously difficult.

Conclusions

This study demonstrated the feasibility and accuracy of the SQL code in the identification of Veterans with diagnoses matching the SEER list in a large population and at a diagnosis-specific level. VISN-wide use of the report will increase efficiency and timeliness of data entry into VACCR, especially related to care provided through CITC. An improved understanding of oncologic care in the VISN would provide critical data to VISN executive leadership, enabling them to advocate for resources, targeted interventions, and access to care.

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The Role of CDH1 Mutation in Colon Cancer Screening

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Tue, 09/02/2025 - 15:32
Author(s)
Jane Mathews, MD
Noor Humayun, MD, MBBS
Shan E. Zainab, MBBS
Syed Mehdi, MD

Background

Genetic testing can reveal inherited or acquired genetic changes that can help with identifying diagnosis, treatment, prognosis, and risk of the malignancy. CDH1 is a gene that prevents cancer by controlling cell growth. Mutated CDH1 gene can lead to specific malignancies including gastric and breast cancer.

Case Presentation

42 year old female with past medical history of ovarian cysts presented to the VA Emergency Department for right sided abdominal pain and red colored stool. Further workup showed ileocolonic intussusception with stranding. She underwent a colonoscopy which showed 4 centimeter mass at the ileocecal valve. Biopsy was done which showed invasive adenocarcinoma. She underwent laparoscopic hemicolectomy and was referred to oncology. Referral to genetic testing was positive for CDH1 gene mutation. She was advised that CDH1 mutation has a high risk of developing gastric and breast cancer with recommendations including possible total gastrectomy and bilateral mastectomies. The patient however, decided to decline gastrectomy and mastectomy and instead decided to be followed by frequent EGDs and mammograms.

Discussion

CDH1 mutations are found in only 3.8% of colorectal signet ring cell cancers, with limited data of their presence in typical adenocarcinomas. This case underscores the value of genetic testing in all colorectal adenocarcinomas for its prognostic significance and potential impact on other cancer screenings. CDH1 mutations can lead to an aggressive type of gastric cancer called hereditary diffuse gastric cancer in 56-70% of patients with the mutation. CDH1 mutations also have a 37-55% of having breast cancer compared to the 12% in the general population and patients tend to present with lobular breast cancer. Patients with positive CDH1 mutation should have regular screenings or in some cases, prophylactic surgery.

Conclusions

CDH1 mutation is an important tool in genetic testing because it allows physicians to tailor a treatment plan for their patients. It is important that patients who have a positive CDH1 mutation be advised of the risks of both gastric and breast cancer and should also be educated on treatment options including frequent screenings and prophylactic surgery.

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Jane Mathews, MD
Noor Humayun, MD, MBBS
Shan E. Zainab, MBBS
Syed Mehdi, MD
Author(s)
Jane Mathews, MD
Noor Humayun, MD, MBBS
Shan E. Zainab, MBBS
Syed Mehdi, MD

Background

Genetic testing can reveal inherited or acquired genetic changes that can help with identifying diagnosis, treatment, prognosis, and risk of the malignancy. CDH1 is a gene that prevents cancer by controlling cell growth. Mutated CDH1 gene can lead to specific malignancies including gastric and breast cancer.

Case Presentation

42 year old female with past medical history of ovarian cysts presented to the VA Emergency Department for right sided abdominal pain and red colored stool. Further workup showed ileocolonic intussusception with stranding. She underwent a colonoscopy which showed 4 centimeter mass at the ileocecal valve. Biopsy was done which showed invasive adenocarcinoma. She underwent laparoscopic hemicolectomy and was referred to oncology. Referral to genetic testing was positive for CDH1 gene mutation. She was advised that CDH1 mutation has a high risk of developing gastric and breast cancer with recommendations including possible total gastrectomy and bilateral mastectomies. The patient however, decided to decline gastrectomy and mastectomy and instead decided to be followed by frequent EGDs and mammograms.

Discussion

CDH1 mutations are found in only 3.8% of colorectal signet ring cell cancers, with limited data of their presence in typical adenocarcinomas. This case underscores the value of genetic testing in all colorectal adenocarcinomas for its prognostic significance and potential impact on other cancer screenings. CDH1 mutations can lead to an aggressive type of gastric cancer called hereditary diffuse gastric cancer in 56-70% of patients with the mutation. CDH1 mutations also have a 37-55% of having breast cancer compared to the 12% in the general population and patients tend to present with lobular breast cancer. Patients with positive CDH1 mutation should have regular screenings or in some cases, prophylactic surgery.

Conclusions

CDH1 mutation is an important tool in genetic testing because it allows physicians to tailor a treatment plan for their patients. It is important that patients who have a positive CDH1 mutation be advised of the risks of both gastric and breast cancer and should also be educated on treatment options including frequent screenings and prophylactic surgery.

Background

Genetic testing can reveal inherited or acquired genetic changes that can help with identifying diagnosis, treatment, prognosis, and risk of the malignancy. CDH1 is a gene that prevents cancer by controlling cell growth. Mutated CDH1 gene can lead to specific malignancies including gastric and breast cancer.

Case Presentation

42 year old female with past medical history of ovarian cysts presented to the VA Emergency Department for right sided abdominal pain and red colored stool. Further workup showed ileocolonic intussusception with stranding. She underwent a colonoscopy which showed 4 centimeter mass at the ileocecal valve. Biopsy was done which showed invasive adenocarcinoma. She underwent laparoscopic hemicolectomy and was referred to oncology. Referral to genetic testing was positive for CDH1 gene mutation. She was advised that CDH1 mutation has a high risk of developing gastric and breast cancer with recommendations including possible total gastrectomy and bilateral mastectomies. The patient however, decided to decline gastrectomy and mastectomy and instead decided to be followed by frequent EGDs and mammograms.

Discussion

CDH1 mutations are found in only 3.8% of colorectal signet ring cell cancers, with limited data of their presence in typical adenocarcinomas. This case underscores the value of genetic testing in all colorectal adenocarcinomas for its prognostic significance and potential impact on other cancer screenings. CDH1 mutations can lead to an aggressive type of gastric cancer called hereditary diffuse gastric cancer in 56-70% of patients with the mutation. CDH1 mutations also have a 37-55% of having breast cancer compared to the 12% in the general population and patients tend to present with lobular breast cancer. Patients with positive CDH1 mutation should have regular screenings or in some cases, prophylactic surgery.

Conclusions

CDH1 mutation is an important tool in genetic testing because it allows physicians to tailor a treatment plan for their patients. It is important that patients who have a positive CDH1 mutation be advised of the risks of both gastric and breast cancer and should also be educated on treatment options including frequent screenings and prophylactic surgery.

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Associations Between Prescreening Dietary Patterns and Longitudinal Colonoscopy Outcomes in Veterans

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Associations Between Prescreening Dietary Patterns and Longitudinal Colonoscopy Outcomes in Veterans

Author(s)
April R. Williams, PhD, MS
Thomas S. Redding IV, MS
Brian A. Sullivan, MD, MHS
Xuejun Qin, PhD
Belinda Ear, MPH
Kellie J. Sims, PhD
Elizabeth R. Hauser, PhD, MS
Christina D. Williams, PhD, MPH
Jason A. Dominitz, MD, MHS
David Lieberman, MD

Screening for colorectal cancer (CRC) with colonoscopy enables the identification and removal of CRC precursors (colonic adenomas) and has been associated with reduced risk of CRC incidence and mortality.1-3 Furthermore, there is consensus that diet and lifestyle may be associated with forestalling CRC pathogenesis at the intermediate adenoma stages.4-7 However, studies have shown that US veterans have poorer diet quality and a higher risk for neoplasia compared with nonveterans, reinforcing the need for tailored clinical approaches.8,9 Combining screening with conversations about modifiable environmental and lifestyle risk factors, such as poor diet, is a highly relevant and possibly easily leveraged prevention for those at high risk. However, there is limited evidence for any particular dietary patterns or dietary features that are most important over time.7

Several dietary components have been shown to be associated with CRC risk,10 either as potentially chemopreventive (fiber, fruits and vegetables,11 dairy,12 supplemental vitamin D,13 calcium,14 and multivitamins15) or carcinogenic (red meat16 and alcohol17). Previous studies of veterans have similarly shown that higher intake of fiber and vitamin D reduced risk, and red meat is associated with an increased risk for finding CRC precursors during colonoscopy.18 However, these dietary categories are often analyzed in isolation. Studying healthy dietary patterns in aggregate may be more clinically relevant and easier to implement for prevention of CRC and its precursors.19-21 Healthy dietary patterns, such as the US Dietary Guidelines for Americans represented by the Healthy Eating Index (HEI), the Mediterranean diet (MD), and the Dietary Approaches to Stop Hypertension (DASH) diet, have been associated with lower risk for chronic disease.22-24 Despite the extant literature, no known studies have compared these dietary patterns for associations with risk of CRC precursor or CRC development among US veterans undergoing long-term screening and follow-up after a baseline colonoscopy.

The objective of this study was to test for associations between baseline scores of healthy dietary patterns and the most severe colonoscopy findings (MSCFs) over ≥ 10 years following a baseline screening colonoscopy in veterans.

Methods

Participants in the Cooperative Studies Program (CSP) #380 cohort study included 3121 asymptomatic veterans aged 50 to 75 years at baseline who had consented to initial screening colonoscopy between 1994 and 1997, with subsequent follow-up and surveillance.25 Prior to their colonoscopy, all participants completed a baseline study survey that included questions about cancer risk factors including family history of CRC, diet, physical activity, and medication use.

Included in this cross-sectional analysis were data from a sample of veteran participants of the CSP #380 cohort with 1 baseline colonoscopy, follow-up surveillance through 2009, a cancer risk factor survey collected at baseline, and complete demographic and clinical indicator data. Excluded from the analysis were 67 participants with insufficient responses to the dietary food frequency questionnaire (FFQ) and 31 participants with missing body mass index (BMI), 3023 veterans.

Measures

MSCF. The outcome of interest in this study was the MSCF recorded across all participant colonoscopies during the study period. MSCF was categorized as either (1) no neoplasia; (2) < 2 nonadvanced adenomas, including small adenomas (diameter < 10 mm) with tubular histology; or (3) advanced neoplasia (AN), which is characterized by adenomas > 10 mm in diameter, with villous histology, with high-grade dysplasia, or CRC.

Dietary patterns. Dietary pattern scores representing dietary quality and calculated based on recommendations of the US Dietary Guidelines for Americans using the HEI, MD, and DASH diets were independent variables.26-28 These 3 dietary patterns were chosen for their hypothesized relationship with CRC risk, but each weighs food categories differently (Appendix 1).22-24,29 Dietary pattern scores were calculated using the CSP #380 self-reported responses to 129 baseline survey questions adapted from a well-established and previously validated semiquantitative FFQ.30 The form was administered by mail twice to a sample of 127 participants at baseline and at 1 year. During this interval, men completed 1-week diet records twice, spaced about 6 months apart. Mean values for intake of most nutrients assessed by the 2 methods were similar. Intraclass correlation coefficients for the baseline and 1-year FFQ-assessed nutrient intakes that ranged from 0.47 for vitamin E (without supplements) to 0.80 for vitamin C (with supplements). Correlation coefficients between the energy-adjusted nutrient intakes were measured by diet records and the 1-year FFQ, which asked about diet during the year encompassing the diet records. Higher raw and percent scores indicated better alignment with recommendations from each respective dietary pattern. Percent scores were calculated as a standardizing method and used in analyses for ease of comparing the dietary patterns. Scoring can be found in Appendix 2.

0825FED-AVAHO-COLON-A10825FED-AVAHO-COLON-A2

Demographic characteristics and clinical indicators. Demographic characteristics included age categories, sex, and race/ethnicity. Clinical indicators included BMI, the number of comorbid conditions used to calculate the Charlson Comorbidity Index, family history of CRC in first-degree relatives, number of follow-up colonoscopies across the study period, and food-based vitamin D intake.31 These variables were chosen for their applicability found in previous CSP #380 cohort studies.18,32,33 Self-reported race and ethnicity were collapsed due to small numbers in some groups. The authors acknowledge these are distinct concepts and the variable has limited utility other than for controlling for systemic racism in the model.

Statistical Analyses

Descriptive statistics were used to describe distributional assumptions for all variables, including demographics, clinical indicators, colonoscopy results, and dietary patterns. Pairwise correlations between the total dietary pattern scores and food category scores were calculated with Pearson correlation (r).

Multinomial logistic regression models were created using SAS procedure LOGISTIC with the outcome of the categorical MSCF (no neoplasia, nonadvanced adenoma, or AN).34 A model was created for each independent predictor variable of interest (ie, the HEI, MD, or DASH percentage-standardized dietary pattern score and each food category comprising each dietary pattern score). All models were adjusted for age, sex, race/ethnicity, BMI, number of comorbidities, family history of CRC, number of follow-up colonoscopies, and estimated daily food-derived vitamin D intake. The demographic and clinical indicators were included in the models as they are known to be associated with CRC risk.18 The number of colonoscopies was included to control for surveillance intensity presuming risk for AN is reduced as polyps are removed. Because colonoscopy findings from an initial screening have unique clinical implications compared with follow- up and surveillance, MSCF was observed in 2 ways in sensitivity analyses: (1) baseline and (2) aggregate follow-up and surveillance only, excluding baseline findings.

Adjusted odds ratios (aORs) and 95% CIs for each of the MSCF outcomes with a reference finding of no neoplasia for the models are presented. We chose not to adjust for multiple comparisons across the different dietary patterns given the correlation between dietary pattern total and category scores but did adjust for multiple comparisons for dietary categories within each dietary pattern. Tests for statistical significance used α= .05 for the dietary pattern total scores and P values for the dietary category scores for each dietary pattern controlled for false discovery rate using the MULTTEST SAS procedure.35 All data manipulations and analyses were performed using SAS version 9.4.

Results

The study included 3023 patients. All were aged 50 to 75 years, 2923 (96.7%) were male and 2532 (83.8%) were non-Hispanic White (Table 1). Most participants were overweight or obese (n = 2535 [83.8%]), 2024 (67.0%) had ≤ 2 comorbidities, and 2602 (86.1%) had no family history of CRC. The MSCF for 1628 patients (53.9%) was no neoplasia, 966 patients (32.0%) was nonadvanced adenoma, and 429 participants (14.2%) had AN.

0825FED-AVAHO-COLON-T1

Mean percent scores were 58.5% for HEI, 38.2% for MD, and 63.1% for the DASH diet, with higher percentages indicating greater alignment with the recommendations for each diet (Table 2). All 3 dietary patterns scores standardized to percentages were strongly and significantly correlated in pairwise comparisons: HEI:MD, r = 0.62 (P < .001); HEI:DASH, r = 0.60 (P < .001); and MD:DASH, r = 0.72 (P < .001). Likewise, food category scores were significantly correlated across dietary patterns. For example, whole grain and fiber values from each dietary score were strongly correlated in pairwise comparisons: HEI Whole Grain:MD Grain, r = 0.64 (P < .001); HEI Whole Grain:DASH Fiber, r = 0.71 (P < .001); and MD Grain:DASH Fiber, r = 0.70 (P < .001).

0825FED-AVAHO-COLON-T2

Associations between individual participants' dietary pattern scores and the outcome of their pooled MSCF from baseline screening and ≥ 10 years of surveillance are presented in Table 3. For each single-point increases in dietary pattern scores (reflecting better dietary quality), aORs for nonadvanced adenoma vs no neoplasia were slightly lower but not statistically significantly: HEI, aOR, 1.00 (95% CI, 0.99-1.01); MD, aOR, 0.98 (95% CI, 0.94-1.02); and DASH, aOR, 0.99 (95% CI, 0.99-1.00). aORs for AN vs no neoplasia were slightly lower for each dietary pattern assessed, and only the MD and DASH scores were significantly different from 1.00: HEI, aOR, 1.00 (95% CI, 0.99-1.01); MD, aOR, 0.95 (95% CI, 0.90-1.00); and DASH, aOR, 0.99 (95% CI, 0.98-1.00).

0825FED-AVAHO-COLON-T3

We observed lower odds for nonadvanced adenoma and AN among all these dietary patterns when there was greater alignment with the recommended intake of whole grains and fiber. In separate models conducted using food categories comprising the dietary patterns as independent variables and after correcting for multiple tests, higher scores for the HEI Refined Grain category were associated with higher odds for nonadvanced adenoma (aOR, 1.03 [95% CI, 1.01-1.05]; P = .01) and AN (aOR, 1.05 [95% CI, 1.02-1.08]; P < .001). Higher scores for the HEI Whole Grain category were associated with lower odds for nonadvanced adenoma (aOR, 0.97 [95% CI, 0.95-0.99]; P = .01) and AN (aOR, 0.96 [95% CI, 0.93-0.99]; P = .01). Higher scores for the MD Grain category were significantly associated with lower odds for nonadvanced adenoma (aOR, 0.44 [95% CI, 0.26-0.75]; P = .002) and AN (aOR, 0.29 [95% CI, 0.14-0.62]; P = .001). The DASH Grains category also was significantly associated with lower odds for AN (aOR, 0.86 [95% CI, 0.78-0.95]; P = .002).

Discussion

In this study of 3023 veterans undergoing first-time screening colonoscopy and ≥ 10 years of surveillance, we found that healthy dietary patterns, as assessed by the MD and DASH diet, were significantly associated with lower risk of AN. Additionally, we identified lower odds for AN and nonadvanced adenoma compared with no neoplasia for higher grain scores for all the dietary patterns studied. Other food categories that comprise the dietary pattern scores had mixed associations with the MSCF outcomes. Several other studies have examined associations between dietary patterns and risk for CRC but to our knowledge, no studies have explored these associations among US veterans.

These results also indicate study participants had better than average (based on a 50% threshold) dietary quality according to the HEI and DASH diet scoring methods we used, but poor dietary quality according to the MD scoring method. The mean HEI scores for the present study were higher than a US Department of Agriculture study by Dong et al that compared dietary quality between veterans and nonveterans using the HEI, for which veterans’ expected HEI score was 45.6 of 100.8 This could be explained by the fact that the participants needed to be healthy to be eligible and those with healthier behaviors overall may have self-selected into the study due to motivation for screening during a time when screening was not yet commonplace. 36 Similarly, participants of the present study had higher adherence to the DASH diet (63.1%) than adolescents with diabetes in a study by Günther et al. Conversely, firefighters who were coached to use a Mediterranean-style dietary pattern and dietary had higher adherence to MD than did participants in this study.27

A closer examination of specific food category component scores that comprise the 3 distinct dietary patterns revealed mixed results from the multinomial modeling, which may have to do with the guideline thresholds used to calculate the dietary scores. When analyzed separately in the logistic regression models for their associations with nonadvanced adenomas and AN compared with no neoplasia, higher MD and DASH fruit scores (but not HEI fruit scores) were found to be significant. Other studies have had mixed findings when attempting to test for associations of fruit intake with adenoma recurrence.10,37

This study had some unexpected findings. Vegetable intake was not associated with nonadvanced adenomas or AN risk. Studies of food categories have consistently found vegetable (specifically cruciferous ones) intake to be linked with lower odds for cancers.38 Likewise, the red meat category, which was only a unique food category in the MD score, was not associated with nonadvanced adenomas or AN. Despite consistent literature suggesting higher intake of red meat and processed meats increases CRC risk, in 2019 the Nutritional Recommendations Consortium indicated that the evidence was weak.39,40 This study showed higher DASH diet scores for low-fat dairy, which were maximized when participants reported at least 50% of their dairy servings per day as being low-fat, had lower odds for AN. Yet, the MD scores for low-fat dairy had no association with either outcome; their calculation was based on total number of servings per week. This difference in findings suggests the fat intake ratio may be more relevant to CRC risk than intake quantity.

The literature is mixed regarding fatty acid intake and CRC risk, which may be relevant to both dairy and meat intake. One systematic review and meta-analysis found dietary fat and types of fatty acid intake had no association with CRC risk.41 However, a more recent meta-analysis that assessed both dietary intake and plasma levels of fatty acids did find some statistically significant differences for various types of fatty acids and CRC risk.42

The findings in the present study that grain intake is associated with lower odds for more severe colonoscopy findings among veterans are notable.43 Lieberman et al, using the CSP #380 data, found that cereal fiber intake was associated with a lower odds for AN compared with hyperplastic polyps (OR, 0.98 [95% CI, 0.96- 1.00]).18 Similarly, Hullings et al determined that older adults in the highest quintile of cereal fiber intake had significantly lower odds of CRC than those in lower odds for CRC when compared with lowest quintile (OR, 0.89 [95% CI, 0.83- 0.96]; P < .001).44 These findings support existing guidance that prioritizes whole grains as a key source of dietary fiber for CRC prevention.

A recent literature review on fiber, fat, and CRC risk suggested a consensus regarding one protective mechanism: dietary fiber from grains modulates the gut microbiota by promoting butyrate synthesis.45 Butyrate is a short-chain fatty acid that supports energy production in colonocytes and has tumor-suppressing properties.46 Our findings suggest there could be more to learn about the relationship between butyrate production and reduction of CRC risk through metabolomic studies that use measurements of plasma butyrate. These studies may examine associations between not just a singular food or food category, but rather food patterns that include fruits, vegetables, nuts and seeds, and whole grains known to promote butyrate production and plasma butyrate.47

Improved understanding of mechanisms and risk-modifying lifestyle factors such as dietary patterns may enhance prevention strategies. Identifying the collective chemopreventive characteristics of a specific dietary pattern (eg, MD) will be helpful to clinicians and health care staff to promote healthy eating to reduce cancer risk. More studies are needed to understand whether such promotion is more clinically applicable and effective for patients, as compared with eating more or less of specific foods (eg, more whole grains, less red meat). Furthermore, considering important environmental factors collectively beyond dietary patterns may offer a way to better tailor screening and implement a variety of lifestyle interventions. In the literature, this is often referred to as a teachable moment when patients’ attentions are captured and may position them to be more receptive to guidance.48

Limitations

This study has several important limitations and leaves opportunities for future studies that explore the role of dietary patterns and AN or CRC risk. First, the FFQ data used to calculate dietary pattern scores used in analysis were only captured at baseline, and there are nearly 3 decades across the study period. However, it is widely assumed that the diets of older adults, like those included in this study, remain stable over time which is appropriate given our sample population was aged 50 to 75 years when the baseline FFQ data were collected.49-51 Additionally, while the HEI is a well-documented, standard scoring method for dietary quality, there are multitudes of dietary pattern scoring approaches for MD and DASH.23,52,53 Finally, findings from this study using the sample of veterans may not be generalizable to a broader population. Future longitudinal studies that test for a clinically significant change threshold are warranted.

Conclusion

Results of this study suggest future research should further explore the effects of dietary patterns, particularly intake of specific food groups in combination, as opposed to individual nutrients or food items, on AN and CRC risk. Possible studies might explore these dietary patterns for their mechanistic role in altering the microbiome metabolism, which may influence CRC outcomes or include diet in a more comprehensive, holistic risk score that could be used to predict colonic neoplasia risk or in intervention studies that assess the effects of dietary changes on long-term CRC prevention. We suggest there are differences in people’s dietary intake patterns that might be important to consider when implementing tailored approaches to CRC risk mitigation.

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  30. Rimm EB, Giovannucci EL, Stampfer MJ, Colditz GA, Litin LB, Willett WC. Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals. Am J Epidemiol. 1992;135(10):1114-1126. doi:10.1093/oxfordjournals.aje.a116211
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  32. Lieberman DA, Weiss DG, Harford WV, et al. Fiveyear colon surveillance after screening colonoscopy. Gastroenterology. 2007;133(4):1077-1085. doi:10.1053/j.gastro.2007.07.006
  33. Lieberman D, Sullivan BA, Hauser ER, et al. Baseline colonoscopy findings associated with 10-year outcomes in a screening cohort undergoing colonoscopy surveillance. Gastroenterology. 2020;158(4):862-874.e8. doi:10.1053/j.gastro.2019.07.052
  34. PROC LOGISTIC: PROC LOGISTIC Statement : SAS/STAT(R) 9.22 User’s Guide. Accessed July 22, 2025. https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_logistic_sect004.htm
  35. PROC MULTTEST: PROC MULTTEST Statement : SAS/ STAT(R) 9.22 User’s Guide. Accessed July 22, 2025. https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_multtest_sect005.htm
  36. Elston DM. Participation bias, self-selection bias, and response bias. J Am Acad Dermatol. Published online June 18, 2021. doi:10.1016/j.jaad.2021.06.025
  37. Sansbury LB, Wanke K, Albert PS, et al. The effect of strict adherence to a high-fiber, high-fruit and -vegetable, and low-fat eating pattern on adenoma recurrence. Am J Epidemiol. 2009;170(5):576-584. doi:10.1093/aje/kwp169
  38. Borgas P, Gonzalez G, Veselkov K, Mirnezami R. Phytochemically rich dietary components and the risk of colorectal cancer: A systematic review and meta-analysis of observational studies. World J Clin Oncol. 2021;12(6):482- 499. doi:10.5306/wjco.v12.i6.482
  39. Papadimitriou N, Markozannes G, Kanellopoulou A, et al. An umbrella review of the evidence associating diet and cancer risk at 11 anatomical sites. Nat Commun. 2021;12(1):4579. doi:10.1038/s41467-021-24861-8
  40. Johnston BC, Zeraatkar D, Han MA, et al. Unprocessed red meat and processed meat consumption: dietary guideline recommendations from the nutritional recommendations (NutriRECS) Consortium. Ann Intern Med. 2019;171(10):756-764. doi:10.7326/M19-1621
  41. Kim M, Park K. Dietary fat intake and risk of colorectal cancer: a systematic review and meta-analysis of prospective studies. Nutrients. 2018;10(12):1963. doi:10.3390/nu10121963
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  43. Gherasim A, Arhire LI, Ni.a O, Popa AD, Graur M, Mihalache L. The relationship between lifestyle components and dietary patterns. Proc Nutr Soc. 2020;79(3):311-323. doi:10.1017/S0029665120006898
  44. Hullings AG, Sinha R, Liao LM, Freedman ND, Graubard BI, Loftfield E. Whole grain and dietary fiber intake and risk of colorectal cancer in the NIH-AARP Diet and Health Study cohort. Am J Clin Nutr. 2020;112(3):603- 612. doi:10.1093/ajcn/nqaa161
  45. Ocvirk S, Wilson AS, Appolonia CN, Thomas TK, O’Keefe SJD. Fiber, fat, and colorectal cancer: new insight into modifiable dietary risk factors. Curr Gastroenterol Rep. 2019;21(11):62. doi:10.1007/s11894-019-0725-2
  46. O’Keefe SJD. Diet, microorganisms and their metabolites, and colon cancer. Nat Rev Gastroenterol Hepatol. 2016;13(12):691-706. doi:10.1038/nrgastro.2016.165
  47. The health benefits and side effects of Butyrate Cleveland Clinic. July 11, 2022. Accessed July 22, 2025. https://health.clevelandclinic.org/butyrate-benefits/
  48. Knudsen MD, Wang L, Wang K, et al. Changes in lifestyle factors after endoscopic screening: a prospective study in the United States. Clin Gastroenterol Hepatol Off ClinPract J Am Gastroenterol Assoc. 2022;20(6):e1240-e1249. doi:10.1016/j.cgh.2021.07.014
  49. Thorpe MG, Milte CM, Crawford D, McNaughton SA. Education and lifestyle predict change in dietary patterns and diet quality of adults 55 years and over. Nutr J. 2019;18(1):67. doi:10.1186/s12937-019-0495-6
  50. Chapman K, Ogden J. How do people change their diet?: an exploration into mechanisms of dietary change. J Health Psychol. 2009;14(8):1229-1242. doi:10.1177/1359105309342289
  51. Djoussé L, Petrone AB, Weir NL, et al. Repeated versus single measurement of plasma omega-3 fatty acids and risk of heart failure. Eur J Nutr. 2014;53(6):1403-1408. doi:10.1007/s00394-013-0642-3
  52. Bach-Faig A, Berry EM, Lairon D, et al. Mediterranean diet pyramid today. Science and cultural updates. Public Health Nutr. 2011;14(12A):2274-2284. doi:10.1017/S1368980011002515
  53. Miller PE, Cross AJ, Subar AF, et al. Comparison of 4 established DASH diet indexes: examining associations of index scores and colorectal cancer123. Am J Clin Nutr. 2013;98(3):794-803. doi:10.3945/ajcn.113.063602
  54. Krebs-Smith SM, Pannucci TE, Subar AF, et al. Update of the Healthy Eating Index: HEI-2015. J Acad Nutr Diet. 2018;118(9):1591-1602. doi:10.1016/j.jand.2018.05.021
  55. P.R. Pehrsson, Cutrufelli RL, Gebhardt SE, et al. USDA Database for the Added Sugars Content of Selected Foods. USDA; 2005. www.ars.usda.gov/nutrientdata
Article PDF
0825FED AVAHO Colonoscopy.pdf
Author and Disclosure Information

April R. Williams, PhD, MSa; Thomas S. Redding IV, MSb; Brian A. Sullivan, MD, MHSb,c; Xuejun Qin, PhDb,c; Belinda Ear, MPHb; Kellie J. Sims, PhDb; Elizabeth R. Hauser, PhD, MSb,c; Christina D. Williams, PhD, MPHb,c; Jason A. Dominitz, MD, MHSd,e; David Lieberman, MDf,g

Author affiliations
aVeterans Affairs Boston Healthcare System, Massachusetts
bVeterans Affairs Durham Health Care System, North Carolina
cDuke University, Durham, North Carolina
dUniversity of Washington, Seattle
eVeterans Health Administration, Washington, DC
fVeterans Affairs Portland Health Care System, Oregon
gOregon Health & Science University, Portland

Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.

Correspondence: April Williams (april.williams9@va.gov)

Fed Pract. 2025;42(suppl 3). Published online August 15. doi:10.12788/fp.0609

Issue
Federal Practitioner - 42(suppl 3)
Publications
AVAHO
Topics
Colorectal Cancer
Colon and Rectal
Oncology
Page Number
S30-S39
Sections
Original Research
Author(s)
April R. Williams, PhD, MS
Thomas S. Redding IV, MS
Brian A. Sullivan, MD, MHS
Xuejun Qin, PhD
Belinda Ear, MPH
Kellie J. Sims, PhD
Elizabeth R. Hauser, PhD, MS
Christina D. Williams, PhD, MPH
Jason A. Dominitz, MD, MHS
David Lieberman, MD
Author(s)
April R. Williams, PhD, MS
Thomas S. Redding IV, MS
Brian A. Sullivan, MD, MHS
Xuejun Qin, PhD
Belinda Ear, MPH
Kellie J. Sims, PhD
Elizabeth R. Hauser, PhD, MS
Christina D. Williams, PhD, MPH
Jason A. Dominitz, MD, MHS
David Lieberman, MD
Author and Disclosure Information

April R. Williams, PhD, MSa; Thomas S. Redding IV, MSb; Brian A. Sullivan, MD, MHSb,c; Xuejun Qin, PhDb,c; Belinda Ear, MPHb; Kellie J. Sims, PhDb; Elizabeth R. Hauser, PhD, MSb,c; Christina D. Williams, PhD, MPHb,c; Jason A. Dominitz, MD, MHSd,e; David Lieberman, MDf,g

Author affiliations
aVeterans Affairs Boston Healthcare System, Massachusetts
bVeterans Affairs Durham Health Care System, North Carolina
cDuke University, Durham, North Carolina
dUniversity of Washington, Seattle
eVeterans Health Administration, Washington, DC
fVeterans Affairs Portland Health Care System, Oregon
gOregon Health & Science University, Portland

Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.

Correspondence: April Williams (april.williams9@va.gov)

Fed Pract. 2025;42(suppl 3). Published online August 15. doi:10.12788/fp.0609

Author and Disclosure Information

April R. Williams, PhD, MSa; Thomas S. Redding IV, MSb; Brian A. Sullivan, MD, MHSb,c; Xuejun Qin, PhDb,c; Belinda Ear, MPHb; Kellie J. Sims, PhDb; Elizabeth R. Hauser, PhD, MSb,c; Christina D. Williams, PhD, MPHb,c; Jason A. Dominitz, MD, MHSd,e; David Lieberman, MDf,g

Author affiliations
aVeterans Affairs Boston Healthcare System, Massachusetts
bVeterans Affairs Durham Health Care System, North Carolina
cDuke University, Durham, North Carolina
dUniversity of Washington, Seattle
eVeterans Health Administration, Washington, DC
fVeterans Affairs Portland Health Care System, Oregon
gOregon Health & Science University, Portland

Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.

Correspondence: April Williams (april.williams9@va.gov)

Fed Pract. 2025;42(suppl 3). Published online August 15. doi:10.12788/fp.0609

Article PDF
0825FED AVAHO Colonoscopy.pdf
Article PDF
0825FED AVAHO Colonoscopy.pdf

Screening for colorectal cancer (CRC) with colonoscopy enables the identification and removal of CRC precursors (colonic adenomas) and has been associated with reduced risk of CRC incidence and mortality.1-3 Furthermore, there is consensus that diet and lifestyle may be associated with forestalling CRC pathogenesis at the intermediate adenoma stages.4-7 However, studies have shown that US veterans have poorer diet quality and a higher risk for neoplasia compared with nonveterans, reinforcing the need for tailored clinical approaches.8,9 Combining screening with conversations about modifiable environmental and lifestyle risk factors, such as poor diet, is a highly relevant and possibly easily leveraged prevention for those at high risk. However, there is limited evidence for any particular dietary patterns or dietary features that are most important over time.7

Several dietary components have been shown to be associated with CRC risk,10 either as potentially chemopreventive (fiber, fruits and vegetables,11 dairy,12 supplemental vitamin D,13 calcium,14 and multivitamins15) or carcinogenic (red meat16 and alcohol17). Previous studies of veterans have similarly shown that higher intake of fiber and vitamin D reduced risk, and red meat is associated with an increased risk for finding CRC precursors during colonoscopy.18 However, these dietary categories are often analyzed in isolation. Studying healthy dietary patterns in aggregate may be more clinically relevant and easier to implement for prevention of CRC and its precursors.19-21 Healthy dietary patterns, such as the US Dietary Guidelines for Americans represented by the Healthy Eating Index (HEI), the Mediterranean diet (MD), and the Dietary Approaches to Stop Hypertension (DASH) diet, have been associated with lower risk for chronic disease.22-24 Despite the extant literature, no known studies have compared these dietary patterns for associations with risk of CRC precursor or CRC development among US veterans undergoing long-term screening and follow-up after a baseline colonoscopy.

The objective of this study was to test for associations between baseline scores of healthy dietary patterns and the most severe colonoscopy findings (MSCFs) over ≥ 10 years following a baseline screening colonoscopy in veterans.

Methods

Participants in the Cooperative Studies Program (CSP) #380 cohort study included 3121 asymptomatic veterans aged 50 to 75 years at baseline who had consented to initial screening colonoscopy between 1994 and 1997, with subsequent follow-up and surveillance.25 Prior to their colonoscopy, all participants completed a baseline study survey that included questions about cancer risk factors including family history of CRC, diet, physical activity, and medication use.

Included in this cross-sectional analysis were data from a sample of veteran participants of the CSP #380 cohort with 1 baseline colonoscopy, follow-up surveillance through 2009, a cancer risk factor survey collected at baseline, and complete demographic and clinical indicator data. Excluded from the analysis were 67 participants with insufficient responses to the dietary food frequency questionnaire (FFQ) and 31 participants with missing body mass index (BMI), 3023 veterans.

Measures

MSCF. The outcome of interest in this study was the MSCF recorded across all participant colonoscopies during the study period. MSCF was categorized as either (1) no neoplasia; (2) < 2 nonadvanced adenomas, including small adenomas (diameter < 10 mm) with tubular histology; or (3) advanced neoplasia (AN), which is characterized by adenomas > 10 mm in diameter, with villous histology, with high-grade dysplasia, or CRC.

Dietary patterns. Dietary pattern scores representing dietary quality and calculated based on recommendations of the US Dietary Guidelines for Americans using the HEI, MD, and DASH diets were independent variables.26-28 These 3 dietary patterns were chosen for their hypothesized relationship with CRC risk, but each weighs food categories differently (Appendix 1).22-24,29 Dietary pattern scores were calculated using the CSP #380 self-reported responses to 129 baseline survey questions adapted from a well-established and previously validated semiquantitative FFQ.30 The form was administered by mail twice to a sample of 127 participants at baseline and at 1 year. During this interval, men completed 1-week diet records twice, spaced about 6 months apart. Mean values for intake of most nutrients assessed by the 2 methods were similar. Intraclass correlation coefficients for the baseline and 1-year FFQ-assessed nutrient intakes that ranged from 0.47 for vitamin E (without supplements) to 0.80 for vitamin C (with supplements). Correlation coefficients between the energy-adjusted nutrient intakes were measured by diet records and the 1-year FFQ, which asked about diet during the year encompassing the diet records. Higher raw and percent scores indicated better alignment with recommendations from each respective dietary pattern. Percent scores were calculated as a standardizing method and used in analyses for ease of comparing the dietary patterns. Scoring can be found in Appendix 2.

0825FED-AVAHO-COLON-A10825FED-AVAHO-COLON-A2

Demographic characteristics and clinical indicators. Demographic characteristics included age categories, sex, and race/ethnicity. Clinical indicators included BMI, the number of comorbid conditions used to calculate the Charlson Comorbidity Index, family history of CRC in first-degree relatives, number of follow-up colonoscopies across the study period, and food-based vitamin D intake.31 These variables were chosen for their applicability found in previous CSP #380 cohort studies.18,32,33 Self-reported race and ethnicity were collapsed due to small numbers in some groups. The authors acknowledge these are distinct concepts and the variable has limited utility other than for controlling for systemic racism in the model.

Statistical Analyses

Descriptive statistics were used to describe distributional assumptions for all variables, including demographics, clinical indicators, colonoscopy results, and dietary patterns. Pairwise correlations between the total dietary pattern scores and food category scores were calculated with Pearson correlation (r).

Multinomial logistic regression models were created using SAS procedure LOGISTIC with the outcome of the categorical MSCF (no neoplasia, nonadvanced adenoma, or AN).34 A model was created for each independent predictor variable of interest (ie, the HEI, MD, or DASH percentage-standardized dietary pattern score and each food category comprising each dietary pattern score). All models were adjusted for age, sex, race/ethnicity, BMI, number of comorbidities, family history of CRC, number of follow-up colonoscopies, and estimated daily food-derived vitamin D intake. The demographic and clinical indicators were included in the models as they are known to be associated with CRC risk.18 The number of colonoscopies was included to control for surveillance intensity presuming risk for AN is reduced as polyps are removed. Because colonoscopy findings from an initial screening have unique clinical implications compared with follow- up and surveillance, MSCF was observed in 2 ways in sensitivity analyses: (1) baseline and (2) aggregate follow-up and surveillance only, excluding baseline findings.

Adjusted odds ratios (aORs) and 95% CIs for each of the MSCF outcomes with a reference finding of no neoplasia for the models are presented. We chose not to adjust for multiple comparisons across the different dietary patterns given the correlation between dietary pattern total and category scores but did adjust for multiple comparisons for dietary categories within each dietary pattern. Tests for statistical significance used α= .05 for the dietary pattern total scores and P values for the dietary category scores for each dietary pattern controlled for false discovery rate using the MULTTEST SAS procedure.35 All data manipulations and analyses were performed using SAS version 9.4.

Results

The study included 3023 patients. All were aged 50 to 75 years, 2923 (96.7%) were male and 2532 (83.8%) were non-Hispanic White (Table 1). Most participants were overweight or obese (n = 2535 [83.8%]), 2024 (67.0%) had ≤ 2 comorbidities, and 2602 (86.1%) had no family history of CRC. The MSCF for 1628 patients (53.9%) was no neoplasia, 966 patients (32.0%) was nonadvanced adenoma, and 429 participants (14.2%) had AN.

0825FED-AVAHO-COLON-T1

Mean percent scores were 58.5% for HEI, 38.2% for MD, and 63.1% for the DASH diet, with higher percentages indicating greater alignment with the recommendations for each diet (Table 2). All 3 dietary patterns scores standardized to percentages were strongly and significantly correlated in pairwise comparisons: HEI:MD, r = 0.62 (P < .001); HEI:DASH, r = 0.60 (P < .001); and MD:DASH, r = 0.72 (P < .001). Likewise, food category scores were significantly correlated across dietary patterns. For example, whole grain and fiber values from each dietary score were strongly correlated in pairwise comparisons: HEI Whole Grain:MD Grain, r = 0.64 (P < .001); HEI Whole Grain:DASH Fiber, r = 0.71 (P < .001); and MD Grain:DASH Fiber, r = 0.70 (P < .001).

0825FED-AVAHO-COLON-T2

Associations between individual participants' dietary pattern scores and the outcome of their pooled MSCF from baseline screening and ≥ 10 years of surveillance are presented in Table 3. For each single-point increases in dietary pattern scores (reflecting better dietary quality), aORs for nonadvanced adenoma vs no neoplasia were slightly lower but not statistically significantly: HEI, aOR, 1.00 (95% CI, 0.99-1.01); MD, aOR, 0.98 (95% CI, 0.94-1.02); and DASH, aOR, 0.99 (95% CI, 0.99-1.00). aORs for AN vs no neoplasia were slightly lower for each dietary pattern assessed, and only the MD and DASH scores were significantly different from 1.00: HEI, aOR, 1.00 (95% CI, 0.99-1.01); MD, aOR, 0.95 (95% CI, 0.90-1.00); and DASH, aOR, 0.99 (95% CI, 0.98-1.00).

0825FED-AVAHO-COLON-T3

We observed lower odds for nonadvanced adenoma and AN among all these dietary patterns when there was greater alignment with the recommended intake of whole grains and fiber. In separate models conducted using food categories comprising the dietary patterns as independent variables and after correcting for multiple tests, higher scores for the HEI Refined Grain category were associated with higher odds for nonadvanced adenoma (aOR, 1.03 [95% CI, 1.01-1.05]; P = .01) and AN (aOR, 1.05 [95% CI, 1.02-1.08]; P < .001). Higher scores for the HEI Whole Grain category were associated with lower odds for nonadvanced adenoma (aOR, 0.97 [95% CI, 0.95-0.99]; P = .01) and AN (aOR, 0.96 [95% CI, 0.93-0.99]; P = .01). Higher scores for the MD Grain category were significantly associated with lower odds for nonadvanced adenoma (aOR, 0.44 [95% CI, 0.26-0.75]; P = .002) and AN (aOR, 0.29 [95% CI, 0.14-0.62]; P = .001). The DASH Grains category also was significantly associated with lower odds for AN (aOR, 0.86 [95% CI, 0.78-0.95]; P = .002).

Discussion

In this study of 3023 veterans undergoing first-time screening colonoscopy and ≥ 10 years of surveillance, we found that healthy dietary patterns, as assessed by the MD and DASH diet, were significantly associated with lower risk of AN. Additionally, we identified lower odds for AN and nonadvanced adenoma compared with no neoplasia for higher grain scores for all the dietary patterns studied. Other food categories that comprise the dietary pattern scores had mixed associations with the MSCF outcomes. Several other studies have examined associations between dietary patterns and risk for CRC but to our knowledge, no studies have explored these associations among US veterans.

These results also indicate study participants had better than average (based on a 50% threshold) dietary quality according to the HEI and DASH diet scoring methods we used, but poor dietary quality according to the MD scoring method. The mean HEI scores for the present study were higher than a US Department of Agriculture study by Dong et al that compared dietary quality between veterans and nonveterans using the HEI, for which veterans’ expected HEI score was 45.6 of 100.8 This could be explained by the fact that the participants needed to be healthy to be eligible and those with healthier behaviors overall may have self-selected into the study due to motivation for screening during a time when screening was not yet commonplace. 36 Similarly, participants of the present study had higher adherence to the DASH diet (63.1%) than adolescents with diabetes in a study by Günther et al. Conversely, firefighters who were coached to use a Mediterranean-style dietary pattern and dietary had higher adherence to MD than did participants in this study.27

A closer examination of specific food category component scores that comprise the 3 distinct dietary patterns revealed mixed results from the multinomial modeling, which may have to do with the guideline thresholds used to calculate the dietary scores. When analyzed separately in the logistic regression models for their associations with nonadvanced adenomas and AN compared with no neoplasia, higher MD and DASH fruit scores (but not HEI fruit scores) were found to be significant. Other studies have had mixed findings when attempting to test for associations of fruit intake with adenoma recurrence.10,37

This study had some unexpected findings. Vegetable intake was not associated with nonadvanced adenomas or AN risk. Studies of food categories have consistently found vegetable (specifically cruciferous ones) intake to be linked with lower odds for cancers.38 Likewise, the red meat category, which was only a unique food category in the MD score, was not associated with nonadvanced adenomas or AN. Despite consistent literature suggesting higher intake of red meat and processed meats increases CRC risk, in 2019 the Nutritional Recommendations Consortium indicated that the evidence was weak.39,40 This study showed higher DASH diet scores for low-fat dairy, which were maximized when participants reported at least 50% of their dairy servings per day as being low-fat, had lower odds for AN. Yet, the MD scores for low-fat dairy had no association with either outcome; their calculation was based on total number of servings per week. This difference in findings suggests the fat intake ratio may be more relevant to CRC risk than intake quantity.

The literature is mixed regarding fatty acid intake and CRC risk, which may be relevant to both dairy and meat intake. One systematic review and meta-analysis found dietary fat and types of fatty acid intake had no association with CRC risk.41 However, a more recent meta-analysis that assessed both dietary intake and plasma levels of fatty acids did find some statistically significant differences for various types of fatty acids and CRC risk.42

The findings in the present study that grain intake is associated with lower odds for more severe colonoscopy findings among veterans are notable.43 Lieberman et al, using the CSP #380 data, found that cereal fiber intake was associated with a lower odds for AN compared with hyperplastic polyps (OR, 0.98 [95% CI, 0.96- 1.00]).18 Similarly, Hullings et al determined that older adults in the highest quintile of cereal fiber intake had significantly lower odds of CRC than those in lower odds for CRC when compared with lowest quintile (OR, 0.89 [95% CI, 0.83- 0.96]; P < .001).44 These findings support existing guidance that prioritizes whole grains as a key source of dietary fiber for CRC prevention.

A recent literature review on fiber, fat, and CRC risk suggested a consensus regarding one protective mechanism: dietary fiber from grains modulates the gut microbiota by promoting butyrate synthesis.45 Butyrate is a short-chain fatty acid that supports energy production in colonocytes and has tumor-suppressing properties.46 Our findings suggest there could be more to learn about the relationship between butyrate production and reduction of CRC risk through metabolomic studies that use measurements of plasma butyrate. These studies may examine associations between not just a singular food or food category, but rather food patterns that include fruits, vegetables, nuts and seeds, and whole grains known to promote butyrate production and plasma butyrate.47

Improved understanding of mechanisms and risk-modifying lifestyle factors such as dietary patterns may enhance prevention strategies. Identifying the collective chemopreventive characteristics of a specific dietary pattern (eg, MD) will be helpful to clinicians and health care staff to promote healthy eating to reduce cancer risk. More studies are needed to understand whether such promotion is more clinically applicable and effective for patients, as compared with eating more or less of specific foods (eg, more whole grains, less red meat). Furthermore, considering important environmental factors collectively beyond dietary patterns may offer a way to better tailor screening and implement a variety of lifestyle interventions. In the literature, this is often referred to as a teachable moment when patients’ attentions are captured and may position them to be more receptive to guidance.48

Limitations

This study has several important limitations and leaves opportunities for future studies that explore the role of dietary patterns and AN or CRC risk. First, the FFQ data used to calculate dietary pattern scores used in analysis were only captured at baseline, and there are nearly 3 decades across the study period. However, it is widely assumed that the diets of older adults, like those included in this study, remain stable over time which is appropriate given our sample population was aged 50 to 75 years when the baseline FFQ data were collected.49-51 Additionally, while the HEI is a well-documented, standard scoring method for dietary quality, there are multitudes of dietary pattern scoring approaches for MD and DASH.23,52,53 Finally, findings from this study using the sample of veterans may not be generalizable to a broader population. Future longitudinal studies that test for a clinically significant change threshold are warranted.

Conclusion

Results of this study suggest future research should further explore the effects of dietary patterns, particularly intake of specific food groups in combination, as opposed to individual nutrients or food items, on AN and CRC risk. Possible studies might explore these dietary patterns for their mechanistic role in altering the microbiome metabolism, which may influence CRC outcomes or include diet in a more comprehensive, holistic risk score that could be used to predict colonic neoplasia risk or in intervention studies that assess the effects of dietary changes on long-term CRC prevention. We suggest there are differences in people’s dietary intake patterns that might be important to consider when implementing tailored approaches to CRC risk mitigation.

Screening for colorectal cancer (CRC) with colonoscopy enables the identification and removal of CRC precursors (colonic adenomas) and has been associated with reduced risk of CRC incidence and mortality.1-3 Furthermore, there is consensus that diet and lifestyle may be associated with forestalling CRC pathogenesis at the intermediate adenoma stages.4-7 However, studies have shown that US veterans have poorer diet quality and a higher risk for neoplasia compared with nonveterans, reinforcing the need for tailored clinical approaches.8,9 Combining screening with conversations about modifiable environmental and lifestyle risk factors, such as poor diet, is a highly relevant and possibly easily leveraged prevention for those at high risk. However, there is limited evidence for any particular dietary patterns or dietary features that are most important over time.7

Several dietary components have been shown to be associated with CRC risk,10 either as potentially chemopreventive (fiber, fruits and vegetables,11 dairy,12 supplemental vitamin D,13 calcium,14 and multivitamins15) or carcinogenic (red meat16 and alcohol17). Previous studies of veterans have similarly shown that higher intake of fiber and vitamin D reduced risk, and red meat is associated with an increased risk for finding CRC precursors during colonoscopy.18 However, these dietary categories are often analyzed in isolation. Studying healthy dietary patterns in aggregate may be more clinically relevant and easier to implement for prevention of CRC and its precursors.19-21 Healthy dietary patterns, such as the US Dietary Guidelines for Americans represented by the Healthy Eating Index (HEI), the Mediterranean diet (MD), and the Dietary Approaches to Stop Hypertension (DASH) diet, have been associated with lower risk for chronic disease.22-24 Despite the extant literature, no known studies have compared these dietary patterns for associations with risk of CRC precursor or CRC development among US veterans undergoing long-term screening and follow-up after a baseline colonoscopy.

The objective of this study was to test for associations between baseline scores of healthy dietary patterns and the most severe colonoscopy findings (MSCFs) over ≥ 10 years following a baseline screening colonoscopy in veterans.

Methods

Participants in the Cooperative Studies Program (CSP) #380 cohort study included 3121 asymptomatic veterans aged 50 to 75 years at baseline who had consented to initial screening colonoscopy between 1994 and 1997, with subsequent follow-up and surveillance.25 Prior to their colonoscopy, all participants completed a baseline study survey that included questions about cancer risk factors including family history of CRC, diet, physical activity, and medication use.

Included in this cross-sectional analysis were data from a sample of veteran participants of the CSP #380 cohort with 1 baseline colonoscopy, follow-up surveillance through 2009, a cancer risk factor survey collected at baseline, and complete demographic and clinical indicator data. Excluded from the analysis were 67 participants with insufficient responses to the dietary food frequency questionnaire (FFQ) and 31 participants with missing body mass index (BMI), 3023 veterans.

Measures

MSCF. The outcome of interest in this study was the MSCF recorded across all participant colonoscopies during the study period. MSCF was categorized as either (1) no neoplasia; (2) < 2 nonadvanced adenomas, including small adenomas (diameter < 10 mm) with tubular histology; or (3) advanced neoplasia (AN), which is characterized by adenomas > 10 mm in diameter, with villous histology, with high-grade dysplasia, or CRC.

Dietary patterns. Dietary pattern scores representing dietary quality and calculated based on recommendations of the US Dietary Guidelines for Americans using the HEI, MD, and DASH diets were independent variables.26-28 These 3 dietary patterns were chosen for their hypothesized relationship with CRC risk, but each weighs food categories differently (Appendix 1).22-24,29 Dietary pattern scores were calculated using the CSP #380 self-reported responses to 129 baseline survey questions adapted from a well-established and previously validated semiquantitative FFQ.30 The form was administered by mail twice to a sample of 127 participants at baseline and at 1 year. During this interval, men completed 1-week diet records twice, spaced about 6 months apart. Mean values for intake of most nutrients assessed by the 2 methods were similar. Intraclass correlation coefficients for the baseline and 1-year FFQ-assessed nutrient intakes that ranged from 0.47 for vitamin E (without supplements) to 0.80 for vitamin C (with supplements). Correlation coefficients between the energy-adjusted nutrient intakes were measured by diet records and the 1-year FFQ, which asked about diet during the year encompassing the diet records. Higher raw and percent scores indicated better alignment with recommendations from each respective dietary pattern. Percent scores were calculated as a standardizing method and used in analyses for ease of comparing the dietary patterns. Scoring can be found in Appendix 2.

0825FED-AVAHO-COLON-A10825FED-AVAHO-COLON-A2

Demographic characteristics and clinical indicators. Demographic characteristics included age categories, sex, and race/ethnicity. Clinical indicators included BMI, the number of comorbid conditions used to calculate the Charlson Comorbidity Index, family history of CRC in first-degree relatives, number of follow-up colonoscopies across the study period, and food-based vitamin D intake.31 These variables were chosen for their applicability found in previous CSP #380 cohort studies.18,32,33 Self-reported race and ethnicity were collapsed due to small numbers in some groups. The authors acknowledge these are distinct concepts and the variable has limited utility other than for controlling for systemic racism in the model.

Statistical Analyses

Descriptive statistics were used to describe distributional assumptions for all variables, including demographics, clinical indicators, colonoscopy results, and dietary patterns. Pairwise correlations between the total dietary pattern scores and food category scores were calculated with Pearson correlation (r).

Multinomial logistic regression models were created using SAS procedure LOGISTIC with the outcome of the categorical MSCF (no neoplasia, nonadvanced adenoma, or AN).34 A model was created for each independent predictor variable of interest (ie, the HEI, MD, or DASH percentage-standardized dietary pattern score and each food category comprising each dietary pattern score). All models were adjusted for age, sex, race/ethnicity, BMI, number of comorbidities, family history of CRC, number of follow-up colonoscopies, and estimated daily food-derived vitamin D intake. The demographic and clinical indicators were included in the models as they are known to be associated with CRC risk.18 The number of colonoscopies was included to control for surveillance intensity presuming risk for AN is reduced as polyps are removed. Because colonoscopy findings from an initial screening have unique clinical implications compared with follow- up and surveillance, MSCF was observed in 2 ways in sensitivity analyses: (1) baseline and (2) aggregate follow-up and surveillance only, excluding baseline findings.

Adjusted odds ratios (aORs) and 95% CIs for each of the MSCF outcomes with a reference finding of no neoplasia for the models are presented. We chose not to adjust for multiple comparisons across the different dietary patterns given the correlation between dietary pattern total and category scores but did adjust for multiple comparisons for dietary categories within each dietary pattern. Tests for statistical significance used α= .05 for the dietary pattern total scores and P values for the dietary category scores for each dietary pattern controlled for false discovery rate using the MULTTEST SAS procedure.35 All data manipulations and analyses were performed using SAS version 9.4.

Results

The study included 3023 patients. All were aged 50 to 75 years, 2923 (96.7%) were male and 2532 (83.8%) were non-Hispanic White (Table 1). Most participants were overweight or obese (n = 2535 [83.8%]), 2024 (67.0%) had ≤ 2 comorbidities, and 2602 (86.1%) had no family history of CRC. The MSCF for 1628 patients (53.9%) was no neoplasia, 966 patients (32.0%) was nonadvanced adenoma, and 429 participants (14.2%) had AN.

0825FED-AVAHO-COLON-T1

Mean percent scores were 58.5% for HEI, 38.2% for MD, and 63.1% for the DASH diet, with higher percentages indicating greater alignment with the recommendations for each diet (Table 2). All 3 dietary patterns scores standardized to percentages were strongly and significantly correlated in pairwise comparisons: HEI:MD, r = 0.62 (P < .001); HEI:DASH, r = 0.60 (P < .001); and MD:DASH, r = 0.72 (P < .001). Likewise, food category scores were significantly correlated across dietary patterns. For example, whole grain and fiber values from each dietary score were strongly correlated in pairwise comparisons: HEI Whole Grain:MD Grain, r = 0.64 (P < .001); HEI Whole Grain:DASH Fiber, r = 0.71 (P < .001); and MD Grain:DASH Fiber, r = 0.70 (P < .001).

0825FED-AVAHO-COLON-T2

Associations between individual participants' dietary pattern scores and the outcome of their pooled MSCF from baseline screening and ≥ 10 years of surveillance are presented in Table 3. For each single-point increases in dietary pattern scores (reflecting better dietary quality), aORs for nonadvanced adenoma vs no neoplasia were slightly lower but not statistically significantly: HEI, aOR, 1.00 (95% CI, 0.99-1.01); MD, aOR, 0.98 (95% CI, 0.94-1.02); and DASH, aOR, 0.99 (95% CI, 0.99-1.00). aORs for AN vs no neoplasia were slightly lower for each dietary pattern assessed, and only the MD and DASH scores were significantly different from 1.00: HEI, aOR, 1.00 (95% CI, 0.99-1.01); MD, aOR, 0.95 (95% CI, 0.90-1.00); and DASH, aOR, 0.99 (95% CI, 0.98-1.00).

0825FED-AVAHO-COLON-T3

We observed lower odds for nonadvanced adenoma and AN among all these dietary patterns when there was greater alignment with the recommended intake of whole grains and fiber. In separate models conducted using food categories comprising the dietary patterns as independent variables and after correcting for multiple tests, higher scores for the HEI Refined Grain category were associated with higher odds for nonadvanced adenoma (aOR, 1.03 [95% CI, 1.01-1.05]; P = .01) and AN (aOR, 1.05 [95% CI, 1.02-1.08]; P < .001). Higher scores for the HEI Whole Grain category were associated with lower odds for nonadvanced adenoma (aOR, 0.97 [95% CI, 0.95-0.99]; P = .01) and AN (aOR, 0.96 [95% CI, 0.93-0.99]; P = .01). Higher scores for the MD Grain category were significantly associated with lower odds for nonadvanced adenoma (aOR, 0.44 [95% CI, 0.26-0.75]; P = .002) and AN (aOR, 0.29 [95% CI, 0.14-0.62]; P = .001). The DASH Grains category also was significantly associated with lower odds for AN (aOR, 0.86 [95% CI, 0.78-0.95]; P = .002).

Discussion

In this study of 3023 veterans undergoing first-time screening colonoscopy and ≥ 10 years of surveillance, we found that healthy dietary patterns, as assessed by the MD and DASH diet, were significantly associated with lower risk of AN. Additionally, we identified lower odds for AN and nonadvanced adenoma compared with no neoplasia for higher grain scores for all the dietary patterns studied. Other food categories that comprise the dietary pattern scores had mixed associations with the MSCF outcomes. Several other studies have examined associations between dietary patterns and risk for CRC but to our knowledge, no studies have explored these associations among US veterans.

These results also indicate study participants had better than average (based on a 50% threshold) dietary quality according to the HEI and DASH diet scoring methods we used, but poor dietary quality according to the MD scoring method. The mean HEI scores for the present study were higher than a US Department of Agriculture study by Dong et al that compared dietary quality between veterans and nonveterans using the HEI, for which veterans’ expected HEI score was 45.6 of 100.8 This could be explained by the fact that the participants needed to be healthy to be eligible and those with healthier behaviors overall may have self-selected into the study due to motivation for screening during a time when screening was not yet commonplace. 36 Similarly, participants of the present study had higher adherence to the DASH diet (63.1%) than adolescents with diabetes in a study by Günther et al. Conversely, firefighters who were coached to use a Mediterranean-style dietary pattern and dietary had higher adherence to MD than did participants in this study.27

A closer examination of specific food category component scores that comprise the 3 distinct dietary patterns revealed mixed results from the multinomial modeling, which may have to do with the guideline thresholds used to calculate the dietary scores. When analyzed separately in the logistic regression models for their associations with nonadvanced adenomas and AN compared with no neoplasia, higher MD and DASH fruit scores (but not HEI fruit scores) were found to be significant. Other studies have had mixed findings when attempting to test for associations of fruit intake with adenoma recurrence.10,37

This study had some unexpected findings. Vegetable intake was not associated with nonadvanced adenomas or AN risk. Studies of food categories have consistently found vegetable (specifically cruciferous ones) intake to be linked with lower odds for cancers.38 Likewise, the red meat category, which was only a unique food category in the MD score, was not associated with nonadvanced adenomas or AN. Despite consistent literature suggesting higher intake of red meat and processed meats increases CRC risk, in 2019 the Nutritional Recommendations Consortium indicated that the evidence was weak.39,40 This study showed higher DASH diet scores for low-fat dairy, which were maximized when participants reported at least 50% of their dairy servings per day as being low-fat, had lower odds for AN. Yet, the MD scores for low-fat dairy had no association with either outcome; their calculation was based on total number of servings per week. This difference in findings suggests the fat intake ratio may be more relevant to CRC risk than intake quantity.

The literature is mixed regarding fatty acid intake and CRC risk, which may be relevant to both dairy and meat intake. One systematic review and meta-analysis found dietary fat and types of fatty acid intake had no association with CRC risk.41 However, a more recent meta-analysis that assessed both dietary intake and plasma levels of fatty acids did find some statistically significant differences for various types of fatty acids and CRC risk.42

The findings in the present study that grain intake is associated with lower odds for more severe colonoscopy findings among veterans are notable.43 Lieberman et al, using the CSP #380 data, found that cereal fiber intake was associated with a lower odds for AN compared with hyperplastic polyps (OR, 0.98 [95% CI, 0.96- 1.00]).18 Similarly, Hullings et al determined that older adults in the highest quintile of cereal fiber intake had significantly lower odds of CRC than those in lower odds for CRC when compared with lowest quintile (OR, 0.89 [95% CI, 0.83- 0.96]; P < .001).44 These findings support existing guidance that prioritizes whole grains as a key source of dietary fiber for CRC prevention.

A recent literature review on fiber, fat, and CRC risk suggested a consensus regarding one protective mechanism: dietary fiber from grains modulates the gut microbiota by promoting butyrate synthesis.45 Butyrate is a short-chain fatty acid that supports energy production in colonocytes and has tumor-suppressing properties.46 Our findings suggest there could be more to learn about the relationship between butyrate production and reduction of CRC risk through metabolomic studies that use measurements of plasma butyrate. These studies may examine associations between not just a singular food or food category, but rather food patterns that include fruits, vegetables, nuts and seeds, and whole grains known to promote butyrate production and plasma butyrate.47

Improved understanding of mechanisms and risk-modifying lifestyle factors such as dietary patterns may enhance prevention strategies. Identifying the collective chemopreventive characteristics of a specific dietary pattern (eg, MD) will be helpful to clinicians and health care staff to promote healthy eating to reduce cancer risk. More studies are needed to understand whether such promotion is more clinically applicable and effective for patients, as compared with eating more or less of specific foods (eg, more whole grains, less red meat). Furthermore, considering important environmental factors collectively beyond dietary patterns may offer a way to better tailor screening and implement a variety of lifestyle interventions. In the literature, this is often referred to as a teachable moment when patients’ attentions are captured and may position them to be more receptive to guidance.48

Limitations

This study has several important limitations and leaves opportunities for future studies that explore the role of dietary patterns and AN or CRC risk. First, the FFQ data used to calculate dietary pattern scores used in analysis were only captured at baseline, and there are nearly 3 decades across the study period. However, it is widely assumed that the diets of older adults, like those included in this study, remain stable over time which is appropriate given our sample population was aged 50 to 75 years when the baseline FFQ data were collected.49-51 Additionally, while the HEI is a well-documented, standard scoring method for dietary quality, there are multitudes of dietary pattern scoring approaches for MD and DASH.23,52,53 Finally, findings from this study using the sample of veterans may not be generalizable to a broader population. Future longitudinal studies that test for a clinically significant change threshold are warranted.

Conclusion

Results of this study suggest future research should further explore the effects of dietary patterns, particularly intake of specific food groups in combination, as opposed to individual nutrients or food items, on AN and CRC risk. Possible studies might explore these dietary patterns for their mechanistic role in altering the microbiome metabolism, which may influence CRC outcomes or include diet in a more comprehensive, holistic risk score that could be used to predict colonic neoplasia risk or in intervention studies that assess the effects of dietary changes on long-term CRC prevention. We suggest there are differences in people’s dietary intake patterns that might be important to consider when implementing tailored approaches to CRC risk mitigation.

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  35. PROC MULTTEST: PROC MULTTEST Statement : SAS/ STAT(R) 9.22 User’s Guide. Accessed July 22, 2025. https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_multtest_sect005.htm
  36. Elston DM. Participation bias, self-selection bias, and response bias. J Am Acad Dermatol. Published online June 18, 2021. doi:10.1016/j.jaad.2021.06.025
  37. Sansbury LB, Wanke K, Albert PS, et al. The effect of strict adherence to a high-fiber, high-fruit and -vegetable, and low-fat eating pattern on adenoma recurrence. Am J Epidemiol. 2009;170(5):576-584. doi:10.1093/aje/kwp169
  38. Borgas P, Gonzalez G, Veselkov K, Mirnezami R. Phytochemically rich dietary components and the risk of colorectal cancer: A systematic review and meta-analysis of observational studies. World J Clin Oncol. 2021;12(6):482- 499. doi:10.5306/wjco.v12.i6.482
  39. Papadimitriou N, Markozannes G, Kanellopoulou A, et al. An umbrella review of the evidence associating diet and cancer risk at 11 anatomical sites. Nat Commun. 2021;12(1):4579. doi:10.1038/s41467-021-24861-8
  40. Johnston BC, Zeraatkar D, Han MA, et al. Unprocessed red meat and processed meat consumption: dietary guideline recommendations from the nutritional recommendations (NutriRECS) Consortium. Ann Intern Med. 2019;171(10):756-764. doi:10.7326/M19-1621
  41. Kim M, Park K. Dietary fat intake and risk of colorectal cancer: a systematic review and meta-analysis of prospective studies. Nutrients. 2018;10(12):1963. doi:10.3390/nu10121963
  42. Lu Y, Li D, Wang L, et al. Comprehensive investigation on associations between dietary intake and blood levels of fatty acids and colorectal cancer risk. Nutrients. 2023;15(3):730. doi:10.3390/nu15030730
  43. Gherasim A, Arhire LI, Ni.a O, Popa AD, Graur M, Mihalache L. The relationship between lifestyle components and dietary patterns. Proc Nutr Soc. 2020;79(3):311-323. doi:10.1017/S0029665120006898
  44. Hullings AG, Sinha R, Liao LM, Freedman ND, Graubard BI, Loftfield E. Whole grain and dietary fiber intake and risk of colorectal cancer in the NIH-AARP Diet and Health Study cohort. Am J Clin Nutr. 2020;112(3):603- 612. doi:10.1093/ajcn/nqaa161
  45. Ocvirk S, Wilson AS, Appolonia CN, Thomas TK, O’Keefe SJD. Fiber, fat, and colorectal cancer: new insight into modifiable dietary risk factors. Curr Gastroenterol Rep. 2019;21(11):62. doi:10.1007/s11894-019-0725-2
  46. O’Keefe SJD. Diet, microorganisms and their metabolites, and colon cancer. Nat Rev Gastroenterol Hepatol. 2016;13(12):691-706. doi:10.1038/nrgastro.2016.165
  47. The health benefits and side effects of Butyrate Cleveland Clinic. July 11, 2022. Accessed July 22, 2025. https://health.clevelandclinic.org/butyrate-benefits/
  48. Knudsen MD, Wang L, Wang K, et al. Changes in lifestyle factors after endoscopic screening: a prospective study in the United States. Clin Gastroenterol Hepatol Off ClinPract J Am Gastroenterol Assoc. 2022;20(6):e1240-e1249. doi:10.1016/j.cgh.2021.07.014
  49. Thorpe MG, Milte CM, Crawford D, McNaughton SA. Education and lifestyle predict change in dietary patterns and diet quality of adults 55 years and over. Nutr J. 2019;18(1):67. doi:10.1186/s12937-019-0495-6
  50. Chapman K, Ogden J. How do people change their diet?: an exploration into mechanisms of dietary change. J Health Psychol. 2009;14(8):1229-1242. doi:10.1177/1359105309342289
  51. Djoussé L, Petrone AB, Weir NL, et al. Repeated versus single measurement of plasma omega-3 fatty acids and risk of heart failure. Eur J Nutr. 2014;53(6):1403-1408. doi:10.1007/s00394-013-0642-3
  52. Bach-Faig A, Berry EM, Lairon D, et al. Mediterranean diet pyramid today. Science and cultural updates. Public Health Nutr. 2011;14(12A):2274-2284. doi:10.1017/S1368980011002515
  53. Miller PE, Cross AJ, Subar AF, et al. Comparison of 4 established DASH diet indexes: examining associations of index scores and colorectal cancer123. Am J Clin Nutr. 2013;98(3):794-803. doi:10.3945/ajcn.113.063602
  54. Krebs-Smith SM, Pannucci TE, Subar AF, et al. Update of the Healthy Eating Index: HEI-2015. J Acad Nutr Diet. 2018;118(9):1591-1602. doi:10.1016/j.jand.2018.05.021
  55. P.R. Pehrsson, Cutrufelli RL, Gebhardt SE, et al. USDA Database for the Added Sugars Content of Selected Foods. USDA; 2005. www.ars.usda.gov/nutrientdata
References
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  6. Godos J, Bella F, Torrisi A, Sciacca S, Galvano F, Grosso G. Dietary patterns and risk of colorectal adenoma: a systematic review and meta-analysis of observational studies. J Hum Nutr Diet Off J Br Diet Assoc. 2016;29(6):757-767. doi:10.1111/jhn.12395
  7. Haggar FA, Boushey RP. Colorectal cancer epidemiology: incidence, mortality, survival, and risk factors. Clin Colon Rectal Surg. 2009;22(4):191-197. doi:10.1055/s-0029-1242458
  8. Dong D, Stewart H, Carlson AC. An Examination of Veterans’ Diet Quality. U.S. Department of Agriculture, Economic Research Service; 2019:32.
  9. El-Halabi MM, Rex DK, Saito A, Eckert GJ, Kahi CJ. Defining adenoma detection rate benchmarks in average-risk male veterans. Gastrointest Endosc. 2019;89(1):137-143. doi:10.1016/j.gie.2018.08.021
  10. Alberts DS, Hess LM, eds. Fundamentals of Cancer Prevention. Springer International Publishing; 2019. doi:10.1007/978-3-030-15935-1
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  14. Carroll C, Cooper K, Papaioannou D, Hind D, Pilgrim H, Tappenden P. Supplemental calcium in the chemoprevention of colorectal cancer: a systematic review and meta-analysis. Clin Ther. 2010;32(5):789-803. doi:10.1016/j.clinthera.2010.04.024
  15. Park Y, Spiegelman D, Hunter DJ, et al. Intakes of vitamins A, C, and E and use of multiple vitamin supplements and risk of colon cancer: a pooled analysis of prospective cohort studies. Cancer Causes Control CCC. 2010;21(11):1745- 1757. doi:10.1007/s10552-010-9549-y
  16. Alexander DD, Weed DL, Miller PE, Mohamed MA. Red meat and colorectal cancer: a quantitative update on the state of the epidemiologic science. J Am Coll Nutr. 2015;34(6):521-543. doi:10.1080/07315724.2014.992553
  17. Park SY, Wilkens LR, Setiawan VW, Monroe KR, Haiman CA, Le Marchand L. Alcohol intake and colorectal cancer risk in the multiethnic cohort study. Am J Epidemiol. 2019;188(1):67-76. doi:10.1093/aje/kwy208
  18. Lieberman DA. Risk Factors for advanced colonic neoplasia and hyperplastic polyps in asymptomatic individuals. JAMA. 2003;290(22):2959. doi:10.1001/jama.290.22.2959
  19. Archambault AN, Jeon J, Lin Y, et al. Risk stratification for early-onset colorectal cancer using a combination of genetic and environmental risk scores: an international multi-center study. J Natl Cancer Inst. 2022;114(4):528-539. doi:10.1093/jnci/djac003
  20. Carr PR, Weigl K, Edelmann D, et al. Estimation of absolute risk of colorectal cancer based on healthy lifestyle, genetic risk, and colonoscopy status in a populationbased study. Gastroenterology. 2020;159(1):129-138.e9. doi:10.1053/j.gastro.2020.03.016
  21. Sullivan BA, Qin X, Miller C, et al. Screening colonoscopy findings are associated with noncolorectal cancer mortality. Clin Transl Gastroenterol. 2022;13(4):e00479. doi:10.14309/ctg.0000000000000479
  22. Erben V, Carr PR, Holleczek B, Stegmaier C, Hoffmeister M, Brenner H. Dietary patterns and risk of advanced colorectal neoplasms: A large population based screening study in Germany. Prev Med. 2018;111:101-109. doi:10.1016/j.ypmed.2018.02.025
  23. Donovan MG, Selmin OI, Doetschman TC, Romagnolo DF. Mediterranean diet: prevention of colorectal cancer. Front Nutr. 2017;4:59. doi:10.3389/fnut.2017.00059
  24. Mohseni R, Mohseni F, Alizadeh S, Abbasi S. The Association of Dietary Approaches to Stop Hypertension (DASH) diet with the risk of colorectal cancer: a meta-analysis of observational studies.Nutr Cancer. 2020;72(5):778-790. doi:10.1080/01635581.2019.1651880
  25. Lieberman DA, Weiss DG, Bond JH, Ahnen DJ, Garewal H, Chejfec G. Use of colonoscopy to screen asymptomatic adults for colorectal cancer. Veterans Affairs Cooperative Study Group 380. N Engl J Med. 2000;343(3):162-168. doi:10.1056/NEJM200007203430301
  26. Developing the Healthy Eating Index (HEI) | EGRP/ DCCPS/NCI/NIH. Accessed July 22, 2025. https://epi.grants.cancer.gov/hei/developing.html#2015c
  27. Reeve E, Piccici F, Feairheller DL. Validation of a Mediterranean diet scoring system for intervention based research. J Nutr Med Diet Care. 2021;7(1):053. doi:10.23937/2572-3278/1510053
  28. Günther AL, Liese AD, Bell RA, et al. ASSOCIATION BETWEEN THE DIETARY APPROACHES TO HYPERTENSION (DASH) DIET AND HYPERTENSION IN YOUTH WITH DIABETES. Hypertens Dallas Tex 1979. 2009;53(1):6-12. doi:10.1161/HYPERTENSIONAHA.108.116665
  29. Buckland G, Agudo A, Luján L, et al. Adherence to a Mediterranean diet and risk of gastric adenocarcinoma within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort study. Am J Clin Nutr. 2010;91(2):381- 390. doi:10.3945/ajcn.2009.28209
  30. Rimm EB, Giovannucci EL, Stampfer MJ, Colditz GA, Litin LB, Willett WC. Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals. Am J Epidemiol. 1992;135(10):1114-1126. doi:10.1093/oxfordjournals.aje.a116211
  31. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383. doi:10.1016/0021-9681(87)90171-8
  32. Lieberman DA, Weiss DG, Harford WV, et al. Fiveyear colon surveillance after screening colonoscopy. Gastroenterology. 2007;133(4):1077-1085. doi:10.1053/j.gastro.2007.07.006
  33. Lieberman D, Sullivan BA, Hauser ER, et al. Baseline colonoscopy findings associated with 10-year outcomes in a screening cohort undergoing colonoscopy surveillance. Gastroenterology. 2020;158(4):862-874.e8. doi:10.1053/j.gastro.2019.07.052
  34. PROC LOGISTIC: PROC LOGISTIC Statement : SAS/STAT(R) 9.22 User’s Guide. Accessed July 22, 2025. https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_logistic_sect004.htm
  35. PROC MULTTEST: PROC MULTTEST Statement : SAS/ STAT(R) 9.22 User’s Guide. Accessed July 22, 2025. https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_multtest_sect005.htm
  36. Elston DM. Participation bias, self-selection bias, and response bias. J Am Acad Dermatol. Published online June 18, 2021. doi:10.1016/j.jaad.2021.06.025
  37. Sansbury LB, Wanke K, Albert PS, et al. The effect of strict adherence to a high-fiber, high-fruit and -vegetable, and low-fat eating pattern on adenoma recurrence. Am J Epidemiol. 2009;170(5):576-584. doi:10.1093/aje/kwp169
  38. Borgas P, Gonzalez G, Veselkov K, Mirnezami R. Phytochemically rich dietary components and the risk of colorectal cancer: A systematic review and meta-analysis of observational studies. World J Clin Oncol. 2021;12(6):482- 499. doi:10.5306/wjco.v12.i6.482
  39. Papadimitriou N, Markozannes G, Kanellopoulou A, et al. An umbrella review of the evidence associating diet and cancer risk at 11 anatomical sites. Nat Commun. 2021;12(1):4579. doi:10.1038/s41467-021-24861-8
  40. Johnston BC, Zeraatkar D, Han MA, et al. Unprocessed red meat and processed meat consumption: dietary guideline recommendations from the nutritional recommendations (NutriRECS) Consortium. Ann Intern Med. 2019;171(10):756-764. doi:10.7326/M19-1621
  41. Kim M, Park K. Dietary fat intake and risk of colorectal cancer: a systematic review and meta-analysis of prospective studies. Nutrients. 2018;10(12):1963. doi:10.3390/nu10121963
  42. Lu Y, Li D, Wang L, et al. Comprehensive investigation on associations between dietary intake and blood levels of fatty acids and colorectal cancer risk. Nutrients. 2023;15(3):730. doi:10.3390/nu15030730
  43. Gherasim A, Arhire LI, Ni.a O, Popa AD, Graur M, Mihalache L. The relationship between lifestyle components and dietary patterns. Proc Nutr Soc. 2020;79(3):311-323. doi:10.1017/S0029665120006898
  44. Hullings AG, Sinha R, Liao LM, Freedman ND, Graubard BI, Loftfield E. Whole grain and dietary fiber intake and risk of colorectal cancer in the NIH-AARP Diet and Health Study cohort. Am J Clin Nutr. 2020;112(3):603- 612. doi:10.1093/ajcn/nqaa161
  45. Ocvirk S, Wilson AS, Appolonia CN, Thomas TK, O’Keefe SJD. Fiber, fat, and colorectal cancer: new insight into modifiable dietary risk factors. Curr Gastroenterol Rep. 2019;21(11):62. doi:10.1007/s11894-019-0725-2
  46. O’Keefe SJD. Diet, microorganisms and their metabolites, and colon cancer. Nat Rev Gastroenterol Hepatol. 2016;13(12):691-706. doi:10.1038/nrgastro.2016.165
  47. The health benefits and side effects of Butyrate Cleveland Clinic. July 11, 2022. Accessed July 22, 2025. https://health.clevelandclinic.org/butyrate-benefits/
  48. Knudsen MD, Wang L, Wang K, et al. Changes in lifestyle factors after endoscopic screening: a prospective study in the United States. Clin Gastroenterol Hepatol Off ClinPract J Am Gastroenterol Assoc. 2022;20(6):e1240-e1249. doi:10.1016/j.cgh.2021.07.014
  49. Thorpe MG, Milte CM, Crawford D, McNaughton SA. Education and lifestyle predict change in dietary patterns and diet quality of adults 55 years and over. Nutr J. 2019;18(1):67. doi:10.1186/s12937-019-0495-6
  50. Chapman K, Ogden J. How do people change their diet?: an exploration into mechanisms of dietary change. J Health Psychol. 2009;14(8):1229-1242. doi:10.1177/1359105309342289
  51. Djoussé L, Petrone AB, Weir NL, et al. Repeated versus single measurement of plasma omega-3 fatty acids and risk of heart failure. Eur J Nutr. 2014;53(6):1403-1408. doi:10.1007/s00394-013-0642-3
  52. Bach-Faig A, Berry EM, Lairon D, et al. Mediterranean diet pyramid today. Science and cultural updates. Public Health Nutr. 2011;14(12A):2274-2284. doi:10.1017/S1368980011002515
  53. Miller PE, Cross AJ, Subar AF, et al. Comparison of 4 established DASH diet indexes: examining associations of index scores and colorectal cancer123. Am J Clin Nutr. 2013;98(3):794-803. doi:10.3945/ajcn.113.063602
  54. Krebs-Smith SM, Pannucci TE, Subar AF, et al. Update of the Healthy Eating Index: HEI-2015. J Acad Nutr Diet. 2018;118(9):1591-1602. doi:10.1016/j.jand.2018.05.021
  55. P.R. Pehrsson, Cutrufelli RL, Gebhardt SE, et al. USDA Database for the Added Sugars Content of Selected Foods. USDA; 2005. www.ars.usda.gov/nutrientdata
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Associations Between Prescreening Dietary Patterns and Longitudinal Colonoscopy Outcomes in Veterans

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Associations Between Prescreening Dietary Patterns and Longitudinal Colonoscopy Outcomes in Veterans

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Access, Race, and "Colon Age": Improving CRC Screening

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Divya B. Bhatt, MD, MA

Click here to view more from Cancer Data Trends 2025

References

1. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74:12-49. doi: 10.3322/caac.21820. 

2. Riviere P, Morgan KM, Deshler LN, et al. Racial disparities in colorectal cancer outcomes and access to care: a multi-cohort analysis. Front Public Health. 2024;12:1414361. doi:10.3389/fpubh.2024.1414361

3. Imperiale TF, Myers LJ, Barker BC, Stump TE, Daggy JK. Colon Age: A metric for whether and how to screen male veterans for early-onset colorectal cancer. Cancer Prev Res. 2024:17:377-384.  doi:10.1158/1940-6207.CAPR-23-0544

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Colorectal Cancer
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Divya B. Bhatt, MD, MA
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Divya B. Bhatt, MD, MA

Click here to view more from Cancer Data Trends 2025

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References

1. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74:12-49. doi: 10.3322/caac.21820. 

2. Riviere P, Morgan KM, Deshler LN, et al. Racial disparities in colorectal cancer outcomes and access to care: a multi-cohort analysis. Front Public Health. 2024;12:1414361. doi:10.3389/fpubh.2024.1414361

3. Imperiale TF, Myers LJ, Barker BC, Stump TE, Daggy JK. Colon Age: A metric for whether and how to screen male veterans for early-onset colorectal cancer. Cancer Prev Res. 2024:17:377-384.  doi:10.1158/1940-6207.CAPR-23-0544

References

1. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74:12-49. doi: 10.3322/caac.21820. 

2. Riviere P, Morgan KM, Deshler LN, et al. Racial disparities in colorectal cancer outcomes and access to care: a multi-cohort analysis. Front Public Health. 2024;12:1414361. doi:10.3389/fpubh.2024.1414361

3. Imperiale TF, Myers LJ, Barker BC, Stump TE, Daggy JK. Colon Age: A metric for whether and how to screen male veterans for early-onset colorectal cancer. Cancer Prev Res. 2024:17:377-384.  doi:10.1158/1940-6207.CAPR-23-0544

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Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the United States, with an estimated 53,010 deaths and 152,810 new diagnoses in 2024.1 

Incidence of CRC is higher in Black patients than in White patients, and racial disparities in survival persist in the general population1 until individuals reach Medicare eligibility.2 Interestingly, data published in 2024 have shown that this trend does not appear in the VHA system, indicating that access to care may play a more crucial role than racial contributions in influencing outcomes among non-Hispanic Black and White individuals.2

CRC rates and deaths are steadily decreasing among those aged 50 years or older but are rising in individuals under age 50. Early-onset colorectal cancer (EOCRC)—cases diagnosed before age 50—now represent 10% to 11% of all CRC. Of these cases, 75% occur in people aged 40 to 49, whereas about 50% affect individuals younger than 45. A novel measure, “colon age,” quantifies EOCRC risk by taking biological factors into account. This metric can help VHA providers clarify CRC risk and help patients better grasp their screening options before age 45 or 50.3
 

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AI-Aided Colonoscopy’s ‘Intelligent’ Module Ups Polyp Detection

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Diana Swift

Results from the British COLO-DETECT trial add to the growing body of evidence supporting the use of artificial intelligence (AI)–aided colonoscopy to increase premalignant colorectal polyp detection in routine colonoscopy practice.

Colin J. Rees, a professor of gastroenterology in the Faculty of Medical Sciences at Newcastle University in Newcastle upon Tyne, England, and colleagues compared the real-world clinical effectiveness of computer-aided detection (CADe)–assisted colonoscopy using an “intelligent” module with that of standard colonoscopy in a study in The Lancet Gastroenterology & Hepatology.

They found the GI Genius Intelligent Endoscopy Module (Medtronic) increased the mean number of adenomas detected per procedure and the adenoma detection rate, especially for small, flat (type 0-IIa) polyps, and sessile serrated lesions, which are more likely to be missed.

“Missed sessile serrated lesions disproportionately increase the risk of post-colonoscopy colorectal cancer, thus the adoption of GI Genius into routine colonoscopy practice could not only increase polyp detection but also reduce the incidence of post-colonoscopy colorectal cancer,” the investigators wrote.

“AI is going to have a major impact upon most aspects of healthcare. Some areas of medical practice are now well established, and some are still in evolution,” Rees, who is also president of the British Society of Gastroenterology, said in an interview. “Within gastroenterology, the role of AI in endoscopic diagnostics is also evolving. The COLO-DETECT trial demonstrates that AI increases detection of lesions, and work is ongoing to see how AI might help with characterization and other elements of endoscopic practice.”

 

Study Details

The multicenter, open-label, parallel-arm, pragmatic randomized controlled trial was conducted at 12 National Health Service hospitals in England. The study cohort consisted of adults ≥ 18 years undergoing colorectal cancer (CRC) screening or colonoscopy for gastrointestinal symptom surveillance owing to personal or family history.

Recruiting staff, participants, and colonoscopists were unmasked to allocation, whereas histopathologists, cochief investigators, and trial statisticians were masked.

CADe-assisted colonoscopy consisted of standard colonoscopy plus the GI Genius module active for at least the entire inspection phase of colonoscope withdrawal.

The primary outcome was mean adenomas per procedure (total number of adenomas detected divided by total number of procedures). The key secondary outcome was adenoma detection rate (proportion of colonoscopies with at least one adenoma).

From March 2021 to April 2023, the investigators recruited 2032 participants, 55.7% men, with a mean cohort age of 62.4 years and randomly assigned them to CADe-assisted colonoscopy (n = 1015) or to standard colonoscopy (n = 1017). Of these, 60.6% were undergoing screening and 39.4% had symptomatic indications.

Mean adenomas per procedure were 1.56 (SD, 2.82; n = 1001 participants with data) in the CADe-assisted group vs 1.21 (n = 1009) in the standard group, for an adjusted mean difference of 0.36 (95% CI, 0.14-0.57; adjusted incidence rate ratio, 1.30; 95% CI, 1.15-1.47; P < .0001).

Adenomas were detected in 555 (56.6%) of 980 participants in the CADe-assisted group vs 477 (48.4%) of 986 in the standard group, representing a proportion difference of 8.3% (95% CI, 3.9-12.7; adjusted odds ratio, 1.47; 95% CI, 1.21-1.78; P < .0001).

As to safety, adverse events were numerically comparable in both the intervention and control groups, with overall events 25 vs 19 and serious events 4 vs 6. On independent review, no adverse events in the CADe-assisted colonoscopy group were related to GI Genius.

 

Dr. Nabil M. Mansour

Offering a US perspective on the study, Nabil M. Mansour, MD, an associate professor and director of the McNair General GI Clinic at Baylor College of Medicine in Houston, Texas, said GI Genius and other CADe systems represent a significant advance over standard colonoscopy for identifying premalignant polyps. “While the data have been mixed, most studies, particularly randomized controlled trials have shown significant improvements with CADe in detection both terms of in adenomas per colonoscopy and reductions in adenoma miss rate,” he said in an interview.

He added that the main utility of CADe is for asymptomatic patients undergoing average-risk screening and surveillance colonoscopy for CRC screening and prevention, as well as for those with positive stool-based screening tests, “though there is no downside to using it in symptomatic patients as well.” Though AI colonoscopy likely still stands at < 50% of endoscopy centers overall, and is used mainly at academic centers, his clinic has been using it for the past year.

The main question, Mansour cautioned, is whether increased detection of small polyps will actually reduce CRC incidence or mortality, and it will likely be several years before clear, concrete data can answer that.

“Most studies have shown the improvement in adenoma detection is mainly for diminutive polyps < 5 mm in diameter, but whether that will actually translate to substantive improvements in hard outcomes is as yet unknown,” he said. “But if gastroenterologists are interested in doing everything they can today to help improve detection rates and lower miss rates of premalignant polyps, serious consideration should be given to adopting the use of CADe in practice.”

This study was supported by Medtronic. Rees reported receiving grant funding from ARC Medical, Norgine, Medtronic, 3-D Matrix, and Olympus Medical, and has been an expert witness for ARC Medical. Other authors disclosed receiving research funding, honoraria, or travel expenses from Medtronic or other private companies. Mansour had no competing interests to declare.

A version of this article appeared on Medscape.com.

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Results from the British COLO-DETECT trial add to the growing body of evidence supporting the use of artificial intelligence (AI)–aided colonoscopy to increase premalignant colorectal polyp detection in routine colonoscopy practice.

Colin J. Rees, a professor of gastroenterology in the Faculty of Medical Sciences at Newcastle University in Newcastle upon Tyne, England, and colleagues compared the real-world clinical effectiveness of computer-aided detection (CADe)–assisted colonoscopy using an “intelligent” module with that of standard colonoscopy in a study in The Lancet Gastroenterology & Hepatology.

They found the GI Genius Intelligent Endoscopy Module (Medtronic) increased the mean number of adenomas detected per procedure and the adenoma detection rate, especially for small, flat (type 0-IIa) polyps, and sessile serrated lesions, which are more likely to be missed.

“Missed sessile serrated lesions disproportionately increase the risk of post-colonoscopy colorectal cancer, thus the adoption of GI Genius into routine colonoscopy practice could not only increase polyp detection but also reduce the incidence of post-colonoscopy colorectal cancer,” the investigators wrote.

“AI is going to have a major impact upon most aspects of healthcare. Some areas of medical practice are now well established, and some are still in evolution,” Rees, who is also president of the British Society of Gastroenterology, said in an interview. “Within gastroenterology, the role of AI in endoscopic diagnostics is also evolving. The COLO-DETECT trial demonstrates that AI increases detection of lesions, and work is ongoing to see how AI might help with characterization and other elements of endoscopic practice.”

 

Study Details

The multicenter, open-label, parallel-arm, pragmatic randomized controlled trial was conducted at 12 National Health Service hospitals in England. The study cohort consisted of adults ≥ 18 years undergoing colorectal cancer (CRC) screening or colonoscopy for gastrointestinal symptom surveillance owing to personal or family history.

Recruiting staff, participants, and colonoscopists were unmasked to allocation, whereas histopathologists, cochief investigators, and trial statisticians were masked.

CADe-assisted colonoscopy consisted of standard colonoscopy plus the GI Genius module active for at least the entire inspection phase of colonoscope withdrawal.

The primary outcome was mean adenomas per procedure (total number of adenomas detected divided by total number of procedures). The key secondary outcome was adenoma detection rate (proportion of colonoscopies with at least one adenoma).

From March 2021 to April 2023, the investigators recruited 2032 participants, 55.7% men, with a mean cohort age of 62.4 years and randomly assigned them to CADe-assisted colonoscopy (n = 1015) or to standard colonoscopy (n = 1017). Of these, 60.6% were undergoing screening and 39.4% had symptomatic indications.

Mean adenomas per procedure were 1.56 (SD, 2.82; n = 1001 participants with data) in the CADe-assisted group vs 1.21 (n = 1009) in the standard group, for an adjusted mean difference of 0.36 (95% CI, 0.14-0.57; adjusted incidence rate ratio, 1.30; 95% CI, 1.15-1.47; P < .0001).

Adenomas were detected in 555 (56.6%) of 980 participants in the CADe-assisted group vs 477 (48.4%) of 986 in the standard group, representing a proportion difference of 8.3% (95% CI, 3.9-12.7; adjusted odds ratio, 1.47; 95% CI, 1.21-1.78; P < .0001).

As to safety, adverse events were numerically comparable in both the intervention and control groups, with overall events 25 vs 19 and serious events 4 vs 6. On independent review, no adverse events in the CADe-assisted colonoscopy group were related to GI Genius.

 

Dr. Nabil M. Mansour

Offering a US perspective on the study, Nabil M. Mansour, MD, an associate professor and director of the McNair General GI Clinic at Baylor College of Medicine in Houston, Texas, said GI Genius and other CADe systems represent a significant advance over standard colonoscopy for identifying premalignant polyps. “While the data have been mixed, most studies, particularly randomized controlled trials have shown significant improvements with CADe in detection both terms of in adenomas per colonoscopy and reductions in adenoma miss rate,” he said in an interview.

He added that the main utility of CADe is for asymptomatic patients undergoing average-risk screening and surveillance colonoscopy for CRC screening and prevention, as well as for those with positive stool-based screening tests, “though there is no downside to using it in symptomatic patients as well.” Though AI colonoscopy likely still stands at < 50% of endoscopy centers overall, and is used mainly at academic centers, his clinic has been using it for the past year.

The main question, Mansour cautioned, is whether increased detection of small polyps will actually reduce CRC incidence or mortality, and it will likely be several years before clear, concrete data can answer that.

“Most studies have shown the improvement in adenoma detection is mainly for diminutive polyps < 5 mm in diameter, but whether that will actually translate to substantive improvements in hard outcomes is as yet unknown,” he said. “But if gastroenterologists are interested in doing everything they can today to help improve detection rates and lower miss rates of premalignant polyps, serious consideration should be given to adopting the use of CADe in practice.”

This study was supported by Medtronic. Rees reported receiving grant funding from ARC Medical, Norgine, Medtronic, 3-D Matrix, and Olympus Medical, and has been an expert witness for ARC Medical. Other authors disclosed receiving research funding, honoraria, or travel expenses from Medtronic or other private companies. Mansour had no competing interests to declare.

A version of this article appeared on Medscape.com.

Results from the British COLO-DETECT trial add to the growing body of evidence supporting the use of artificial intelligence (AI)–aided colonoscopy to increase premalignant colorectal polyp detection in routine colonoscopy practice.

Colin J. Rees, a professor of gastroenterology in the Faculty of Medical Sciences at Newcastle University in Newcastle upon Tyne, England, and colleagues compared the real-world clinical effectiveness of computer-aided detection (CADe)–assisted colonoscopy using an “intelligent” module with that of standard colonoscopy in a study in The Lancet Gastroenterology & Hepatology.

They found the GI Genius Intelligent Endoscopy Module (Medtronic) increased the mean number of adenomas detected per procedure and the adenoma detection rate, especially for small, flat (type 0-IIa) polyps, and sessile serrated lesions, which are more likely to be missed.

“Missed sessile serrated lesions disproportionately increase the risk of post-colonoscopy colorectal cancer, thus the adoption of GI Genius into routine colonoscopy practice could not only increase polyp detection but also reduce the incidence of post-colonoscopy colorectal cancer,” the investigators wrote.

“AI is going to have a major impact upon most aspects of healthcare. Some areas of medical practice are now well established, and some are still in evolution,” Rees, who is also president of the British Society of Gastroenterology, said in an interview. “Within gastroenterology, the role of AI in endoscopic diagnostics is also evolving. The COLO-DETECT trial demonstrates that AI increases detection of lesions, and work is ongoing to see how AI might help with characterization and other elements of endoscopic practice.”

 

Study Details

The multicenter, open-label, parallel-arm, pragmatic randomized controlled trial was conducted at 12 National Health Service hospitals in England. The study cohort consisted of adults ≥ 18 years undergoing colorectal cancer (CRC) screening or colonoscopy for gastrointestinal symptom surveillance owing to personal or family history.

Recruiting staff, participants, and colonoscopists were unmasked to allocation, whereas histopathologists, cochief investigators, and trial statisticians were masked.

CADe-assisted colonoscopy consisted of standard colonoscopy plus the GI Genius module active for at least the entire inspection phase of colonoscope withdrawal.

The primary outcome was mean adenomas per procedure (total number of adenomas detected divided by total number of procedures). The key secondary outcome was adenoma detection rate (proportion of colonoscopies with at least one adenoma).

From March 2021 to April 2023, the investigators recruited 2032 participants, 55.7% men, with a mean cohort age of 62.4 years and randomly assigned them to CADe-assisted colonoscopy (n = 1015) or to standard colonoscopy (n = 1017). Of these, 60.6% were undergoing screening and 39.4% had symptomatic indications.

Mean adenomas per procedure were 1.56 (SD, 2.82; n = 1001 participants with data) in the CADe-assisted group vs 1.21 (n = 1009) in the standard group, for an adjusted mean difference of 0.36 (95% CI, 0.14-0.57; adjusted incidence rate ratio, 1.30; 95% CI, 1.15-1.47; P < .0001).

Adenomas were detected in 555 (56.6%) of 980 participants in the CADe-assisted group vs 477 (48.4%) of 986 in the standard group, representing a proportion difference of 8.3% (95% CI, 3.9-12.7; adjusted odds ratio, 1.47; 95% CI, 1.21-1.78; P < .0001).

As to safety, adverse events were numerically comparable in both the intervention and control groups, with overall events 25 vs 19 and serious events 4 vs 6. On independent review, no adverse events in the CADe-assisted colonoscopy group were related to GI Genius.

 

Dr. Nabil M. Mansour

Offering a US perspective on the study, Nabil M. Mansour, MD, an associate professor and director of the McNair General GI Clinic at Baylor College of Medicine in Houston, Texas, said GI Genius and other CADe systems represent a significant advance over standard colonoscopy for identifying premalignant polyps. “While the data have been mixed, most studies, particularly randomized controlled trials have shown significant improvements with CADe in detection both terms of in adenomas per colonoscopy and reductions in adenoma miss rate,” he said in an interview.

He added that the main utility of CADe is for asymptomatic patients undergoing average-risk screening and surveillance colonoscopy for CRC screening and prevention, as well as for those with positive stool-based screening tests, “though there is no downside to using it in symptomatic patients as well.” Though AI colonoscopy likely still stands at < 50% of endoscopy centers overall, and is used mainly at academic centers, his clinic has been using it for the past year.

The main question, Mansour cautioned, is whether increased detection of small polyps will actually reduce CRC incidence or mortality, and it will likely be several years before clear, concrete data can answer that.

“Most studies have shown the improvement in adenoma detection is mainly for diminutive polyps < 5 mm in diameter, but whether that will actually translate to substantive improvements in hard outcomes is as yet unknown,” he said. “But if gastroenterologists are interested in doing everything they can today to help improve detection rates and lower miss rates of premalignant polyps, serious consideration should be given to adopting the use of CADe in practice.”

This study was supported by Medtronic. Rees reported receiving grant funding from ARC Medical, Norgine, Medtronic, 3-D Matrix, and Olympus Medical, and has been an expert witness for ARC Medical. Other authors disclosed receiving research funding, honoraria, or travel expenses from Medtronic or other private companies. Mansour had no competing interests to declare.

A version of this article appeared on Medscape.com.

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Does Intensive Follow-Up Testing Improve Survival in CRC?

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Author(s)
Gargi Mukherjee

TOPLINE:

In patients with stage II or III colorectal cancer (CRC), frequent follow-up testing with CT scans and serum carcinoembryonic antigen (CEA) screening provides no significant overall or cancer-specific survival benefits at 10 years, according to findings from a secondary analysis.

METHODOLOGY:

  • After curative surgery for CRC, intensive patient follow-up is common in clinical practice. However, there’s limited evidence to suggest that more frequent testing provides a long-term survival benefit.
  • In the COLOFOL trial, patients with stage II or III CRC who had undergone curative resection were randomly assigned to either high-frequency follow-up (CT scans and CEA screening at 6, 12, 18, 24, and 36 months) or low-frequency follow-up (testing at 12 and 36 months) after surgery.
  • This secondary analysis of the COLOFOL trial included 2456 patients (median age, 65 years), 1227 of whom received high-frequency follow-up and 1229 of whom received low-frequency follow-up.
  • The main outcome of the secondary analysis was 10-year overall mortality and CRC–specific mortality rates.
  • The analysis included both intention-to-treat and per-protocol approaches, with outcomes measured through December 2020.

TAKEAWAY:

  • In the intention-to-treat analysis, the 10-year overall mortality rates were similar between the high- and low-frequency follow-up groups — 27.1% and 28.4%, respectively (risk difference, 1.3%; P = .46).
  • A per-protocol analysis confirmed these findings: The 10-year overall mortality risk was 26.4% in the high-frequency group and 27.8% in the low-frequency group.
  • The 10-year CRC–specific mortality rate was also similar between the high-frequency and low-frequency groups — 15.6% and 16.0%, respectively — (risk difference, 0.4%; P = .72). The same pattern was seen in the per-protocol analysis, which found a 10-year CRC–specific mortality risk of 15.6% in the high-frequency group and 15.9% in the low-frequency group.
  • Subgroup analyses by cancer stage and location (rectal and colon) also revealed no significant differences in mortality outcomes between the two follow-up groups.

IN PRACTICE:

“This secondary analysis of the COLOFOL randomized clinical trial found that, among patients with stage II or III colorectal cancer, more frequent follow-up testing with CT scan and CEA screening, compared with less frequent follow-up, did not result in a significant rate reduction in 10-year overall mortality or colorectal cancer-specific mortality,” the authors concluded. “The results of this trial should be considered as the evidence base for updating clinical guidelines.”

SOURCE:

The study, led by Henrik Toft Sørensen, MD, PhD, DMSc, DSc, Aarhus University Hospital and Aarhus University, Aarhus, Denmark, was published online in JAMA Network Open.

LIMITATIONS:

The staff turnover at recruitment centers potentially affected protocol adherence. The inability to blind patients and physicians to the follow-up frequency was another limitation. The low-frequency follow-up protocol was less intensive than that recommended in the current guidelines by the National Comprehensive Cancer Network and the American Society of Clinical Oncology, potentially limiting comparisons to current standard practices.

DISCLOSURES:

The initial trial received unrestricted grants from multiple organizations including the Nordic Cancer Union, A.P. Møller Foundation, Beckett Foundation, Danish Cancer Society, and Swedish Cancer Foundation project. The authors reported no relevant conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article first appeared on Medscape.com.

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Gargi Mukherjee
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Gargi Mukherjee

TOPLINE:

In patients with stage II or III colorectal cancer (CRC), frequent follow-up testing with CT scans and serum carcinoembryonic antigen (CEA) screening provides no significant overall or cancer-specific survival benefits at 10 years, according to findings from a secondary analysis.

METHODOLOGY:

  • After curative surgery for CRC, intensive patient follow-up is common in clinical practice. However, there’s limited evidence to suggest that more frequent testing provides a long-term survival benefit.
  • In the COLOFOL trial, patients with stage II or III CRC who had undergone curative resection were randomly assigned to either high-frequency follow-up (CT scans and CEA screening at 6, 12, 18, 24, and 36 months) or low-frequency follow-up (testing at 12 and 36 months) after surgery.
  • This secondary analysis of the COLOFOL trial included 2456 patients (median age, 65 years), 1227 of whom received high-frequency follow-up and 1229 of whom received low-frequency follow-up.
  • The main outcome of the secondary analysis was 10-year overall mortality and CRC–specific mortality rates.
  • The analysis included both intention-to-treat and per-protocol approaches, with outcomes measured through December 2020.

TAKEAWAY:

  • In the intention-to-treat analysis, the 10-year overall mortality rates were similar between the high- and low-frequency follow-up groups — 27.1% and 28.4%, respectively (risk difference, 1.3%; P = .46).
  • A per-protocol analysis confirmed these findings: The 10-year overall mortality risk was 26.4% in the high-frequency group and 27.8% in the low-frequency group.
  • The 10-year CRC–specific mortality rate was also similar between the high-frequency and low-frequency groups — 15.6% and 16.0%, respectively — (risk difference, 0.4%; P = .72). The same pattern was seen in the per-protocol analysis, which found a 10-year CRC–specific mortality risk of 15.6% in the high-frequency group and 15.9% in the low-frequency group.
  • Subgroup analyses by cancer stage and location (rectal and colon) also revealed no significant differences in mortality outcomes between the two follow-up groups.

IN PRACTICE:

“This secondary analysis of the COLOFOL randomized clinical trial found that, among patients with stage II or III colorectal cancer, more frequent follow-up testing with CT scan and CEA screening, compared with less frequent follow-up, did not result in a significant rate reduction in 10-year overall mortality or colorectal cancer-specific mortality,” the authors concluded. “The results of this trial should be considered as the evidence base for updating clinical guidelines.”

SOURCE:

The study, led by Henrik Toft Sørensen, MD, PhD, DMSc, DSc, Aarhus University Hospital and Aarhus University, Aarhus, Denmark, was published online in JAMA Network Open.

LIMITATIONS:

The staff turnover at recruitment centers potentially affected protocol adherence. The inability to blind patients and physicians to the follow-up frequency was another limitation. The low-frequency follow-up protocol was less intensive than that recommended in the current guidelines by the National Comprehensive Cancer Network and the American Society of Clinical Oncology, potentially limiting comparisons to current standard practices.

DISCLOSURES:

The initial trial received unrestricted grants from multiple organizations including the Nordic Cancer Union, A.P. Møller Foundation, Beckett Foundation, Danish Cancer Society, and Swedish Cancer Foundation project. The authors reported no relevant conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article first appeared on Medscape.com.

TOPLINE:

In patients with stage II or III colorectal cancer (CRC), frequent follow-up testing with CT scans and serum carcinoembryonic antigen (CEA) screening provides no significant overall or cancer-specific survival benefits at 10 years, according to findings from a secondary analysis.

METHODOLOGY:

  • After curative surgery for CRC, intensive patient follow-up is common in clinical practice. However, there’s limited evidence to suggest that more frequent testing provides a long-term survival benefit.
  • In the COLOFOL trial, patients with stage II or III CRC who had undergone curative resection were randomly assigned to either high-frequency follow-up (CT scans and CEA screening at 6, 12, 18, 24, and 36 months) or low-frequency follow-up (testing at 12 and 36 months) after surgery.
  • This secondary analysis of the COLOFOL trial included 2456 patients (median age, 65 years), 1227 of whom received high-frequency follow-up and 1229 of whom received low-frequency follow-up.
  • The main outcome of the secondary analysis was 10-year overall mortality and CRC–specific mortality rates.
  • The analysis included both intention-to-treat and per-protocol approaches, with outcomes measured through December 2020.

TAKEAWAY:

  • In the intention-to-treat analysis, the 10-year overall mortality rates were similar between the high- and low-frequency follow-up groups — 27.1% and 28.4%, respectively (risk difference, 1.3%; P = .46).
  • A per-protocol analysis confirmed these findings: The 10-year overall mortality risk was 26.4% in the high-frequency group and 27.8% in the low-frequency group.
  • The 10-year CRC–specific mortality rate was also similar between the high-frequency and low-frequency groups — 15.6% and 16.0%, respectively — (risk difference, 0.4%; P = .72). The same pattern was seen in the per-protocol analysis, which found a 10-year CRC–specific mortality risk of 15.6% in the high-frequency group and 15.9% in the low-frequency group.
  • Subgroup analyses by cancer stage and location (rectal and colon) also revealed no significant differences in mortality outcomes between the two follow-up groups.

IN PRACTICE:

“This secondary analysis of the COLOFOL randomized clinical trial found that, among patients with stage II or III colorectal cancer, more frequent follow-up testing with CT scan and CEA screening, compared with less frequent follow-up, did not result in a significant rate reduction in 10-year overall mortality or colorectal cancer-specific mortality,” the authors concluded. “The results of this trial should be considered as the evidence base for updating clinical guidelines.”

SOURCE:

The study, led by Henrik Toft Sørensen, MD, PhD, DMSc, DSc, Aarhus University Hospital and Aarhus University, Aarhus, Denmark, was published online in JAMA Network Open.

LIMITATIONS:

The staff turnover at recruitment centers potentially affected protocol adherence. The inability to blind patients and physicians to the follow-up frequency was another limitation. The low-frequency follow-up protocol was less intensive than that recommended in the current guidelines by the National Comprehensive Cancer Network and the American Society of Clinical Oncology, potentially limiting comparisons to current standard practices.

DISCLOSURES:

The initial trial received unrestricted grants from multiple organizations including the Nordic Cancer Union, A.P. Møller Foundation, Beckett Foundation, Danish Cancer Society, and Swedish Cancer Foundation project. The authors reported no relevant conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article first appeared on Medscape.com.

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Could Diet and Gut Bacteria Be Fueling Early CRC?

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Author(s)
David J. Kerr, CBE, MD, DSc

This transcript has been edited for clarity. 

I’d like to reflect a little on the ever-rising incidence of early-onset colorectal cancer. I saw two patients in the clinic on Friday, both in their early thirties, presenting with stage IV disease. Both had young families — a disaster.

This is an issue that we must address, I think, epidemiologically. We know that early-onset colorectal cancer is defined as a disease arising in those under the age of 50. There’s been a very sharp increase globally over the past 20-30 years, and currently, around 200,000 such cases are diagnosed every year, but it is said to increase unquestionably.

The epidemiologists, I think, correctly have identified that this sharp, rapid increase does imply that there is a new environmental change that is underpinning or underscoring this rise in early-onset disease. 

There’s a fantastic team that has been put together by Paul Brennan, Mike Stratton, and colleagues, a collaborative group of epidemiologists, geneticists, and bioinformaticians, who are looking at a global study to try to understand the basis of early-onset colorectal cancer. Their approach is to combine conventional epidemiology, genomics, and fantastic computational support to try to unpick the mutational signatures involved.

The dominant hypothesis is that, over the past 20-25 years or so, there has been a change in diet that has allowed an alteration in the gut microbiome such that we now harbor, in some cases, more bacteria capable of manufacturing, synthesizing, and releasing mutagenic chemicals. There’s a subtype of Escherichia coli which manufactures one such mutagen called colibactin.

Again, through some of the painstaking, extraordinary work that Mike Stratton and colleagues have done at the Sanger Institute, they have managed to, using a variety of different techniques — in vitro, observational, and so on — relate exposure to the mutagen colibactin to a particular mutational signature.

They plan to do a large global study — one of the strengths — involving many different countries around the globe, collect material from older colorectal cancer patients and early-onset colorectal cancer patients, and undertake a staggeringly large mutational study to see if the mutational signature associated with colibactin is more highly represented in these early-onset cases. The hypothesis is that, if you’re exposed to this mutagen in childhood, then it increases the tumor mutational burden and therefore the likelihood of developing cancer at an earlier age. 

All of us believe that converting a normal cell into a tumor cell usually requires five or six or seven separate mutational events occurring at random. The earlier these occur, the greater the tumor, the greater the normal single-cellular mutational burden, and the more likely it is to develop cancer sooner rather than later. 

This is a fantastically interesting study, and it’s the way ahead with modern genetic epidemiology, one would say. We wish them well. This will be a 3- to 5-year truly international effort, bringing together a genuinely internationally outstanding research team. We hope that they are able to shed more light on the epidemiology of this early-onset disease, because only by understanding can we deflect and deal with it. 

Knowledge is power, as I’ve said many times before. If we understand the underlying epidemiology, that will allow us to intervene, one would hope, and avoid the chaotic disaster of my clinic on Friday, with these two young patients with an extremely limited lifespan and large families who will be left bereft in having lost a parent.

More power to the team. We wish them well with the study, but again, this is a pointer to the future, one would hope, of modern genetic computational epidemiology. 

I’d be really interested in any ideas or comments that you might have. Are you in the field? Are you seeing more young patients? Do you have any ideas or hypotheses of your own around the microbiome and what bugs might be involved and so on?

Dr. Kerr, Professor, Nuffield Department of Clinical Laboratory Science, University of Oxford, England; Professor of Cancer Medicine, Oxford Cancer Centre, Oxford, United Kingdom, has disclosed relevant financial relationships with Celleron Therapeutics, Oxford Cancer Biomarkers, Afrox, GlaxoSmithKline, Bayer, Genomic Health, Merck Serono, and Roche. 

A version of this article appeared on Medscape.com.

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This transcript has been edited for clarity. 

I’d like to reflect a little on the ever-rising incidence of early-onset colorectal cancer. I saw two patients in the clinic on Friday, both in their early thirties, presenting with stage IV disease. Both had young families — a disaster.

This is an issue that we must address, I think, epidemiologically. We know that early-onset colorectal cancer is defined as a disease arising in those under the age of 50. There’s been a very sharp increase globally over the past 20-30 years, and currently, around 200,000 such cases are diagnosed every year, but it is said to increase unquestionably.

The epidemiologists, I think, correctly have identified that this sharp, rapid increase does imply that there is a new environmental change that is underpinning or underscoring this rise in early-onset disease. 

There’s a fantastic team that has been put together by Paul Brennan, Mike Stratton, and colleagues, a collaborative group of epidemiologists, geneticists, and bioinformaticians, who are looking at a global study to try to understand the basis of early-onset colorectal cancer. Their approach is to combine conventional epidemiology, genomics, and fantastic computational support to try to unpick the mutational signatures involved.

The dominant hypothesis is that, over the past 20-25 years or so, there has been a change in diet that has allowed an alteration in the gut microbiome such that we now harbor, in some cases, more bacteria capable of manufacturing, synthesizing, and releasing mutagenic chemicals. There’s a subtype of Escherichia coli which manufactures one such mutagen called colibactin.

Again, through some of the painstaking, extraordinary work that Mike Stratton and colleagues have done at the Sanger Institute, they have managed to, using a variety of different techniques — in vitro, observational, and so on — relate exposure to the mutagen colibactin to a particular mutational signature.

They plan to do a large global study — one of the strengths — involving many different countries around the globe, collect material from older colorectal cancer patients and early-onset colorectal cancer patients, and undertake a staggeringly large mutational study to see if the mutational signature associated with colibactin is more highly represented in these early-onset cases. The hypothesis is that, if you’re exposed to this mutagen in childhood, then it increases the tumor mutational burden and therefore the likelihood of developing cancer at an earlier age. 

All of us believe that converting a normal cell into a tumor cell usually requires five or six or seven separate mutational events occurring at random. The earlier these occur, the greater the tumor, the greater the normal single-cellular mutational burden, and the more likely it is to develop cancer sooner rather than later. 

This is a fantastically interesting study, and it’s the way ahead with modern genetic epidemiology, one would say. We wish them well. This will be a 3- to 5-year truly international effort, bringing together a genuinely internationally outstanding research team. We hope that they are able to shed more light on the epidemiology of this early-onset disease, because only by understanding can we deflect and deal with it. 

Knowledge is power, as I’ve said many times before. If we understand the underlying epidemiology, that will allow us to intervene, one would hope, and avoid the chaotic disaster of my clinic on Friday, with these two young patients with an extremely limited lifespan and large families who will be left bereft in having lost a parent.

More power to the team. We wish them well with the study, but again, this is a pointer to the future, one would hope, of modern genetic computational epidemiology. 

I’d be really interested in any ideas or comments that you might have. Are you in the field? Are you seeing more young patients? Do you have any ideas or hypotheses of your own around the microbiome and what bugs might be involved and so on?

Dr. Kerr, Professor, Nuffield Department of Clinical Laboratory Science, University of Oxford, England; Professor of Cancer Medicine, Oxford Cancer Centre, Oxford, United Kingdom, has disclosed relevant financial relationships with Celleron Therapeutics, Oxford Cancer Biomarkers, Afrox, GlaxoSmithKline, Bayer, Genomic Health, Merck Serono, and Roche. 

A version of this article appeared on Medscape.com.

This transcript has been edited for clarity. 

I’d like to reflect a little on the ever-rising incidence of early-onset colorectal cancer. I saw two patients in the clinic on Friday, both in their early thirties, presenting with stage IV disease. Both had young families — a disaster.

This is an issue that we must address, I think, epidemiologically. We know that early-onset colorectal cancer is defined as a disease arising in those under the age of 50. There’s been a very sharp increase globally over the past 20-30 years, and currently, around 200,000 such cases are diagnosed every year, but it is said to increase unquestionably.

The epidemiologists, I think, correctly have identified that this sharp, rapid increase does imply that there is a new environmental change that is underpinning or underscoring this rise in early-onset disease. 

There’s a fantastic team that has been put together by Paul Brennan, Mike Stratton, and colleagues, a collaborative group of epidemiologists, geneticists, and bioinformaticians, who are looking at a global study to try to understand the basis of early-onset colorectal cancer. Their approach is to combine conventional epidemiology, genomics, and fantastic computational support to try to unpick the mutational signatures involved.

The dominant hypothesis is that, over the past 20-25 years or so, there has been a change in diet that has allowed an alteration in the gut microbiome such that we now harbor, in some cases, more bacteria capable of manufacturing, synthesizing, and releasing mutagenic chemicals. There’s a subtype of Escherichia coli which manufactures one such mutagen called colibactin.

Again, through some of the painstaking, extraordinary work that Mike Stratton and colleagues have done at the Sanger Institute, they have managed to, using a variety of different techniques — in vitro, observational, and so on — relate exposure to the mutagen colibactin to a particular mutational signature.

They plan to do a large global study — one of the strengths — involving many different countries around the globe, collect material from older colorectal cancer patients and early-onset colorectal cancer patients, and undertake a staggeringly large mutational study to see if the mutational signature associated with colibactin is more highly represented in these early-onset cases. The hypothesis is that, if you’re exposed to this mutagen in childhood, then it increases the tumor mutational burden and therefore the likelihood of developing cancer at an earlier age. 

All of us believe that converting a normal cell into a tumor cell usually requires five or six or seven separate mutational events occurring at random. The earlier these occur, the greater the tumor, the greater the normal single-cellular mutational burden, and the more likely it is to develop cancer sooner rather than later. 

This is a fantastically interesting study, and it’s the way ahead with modern genetic epidemiology, one would say. We wish them well. This will be a 3- to 5-year truly international effort, bringing together a genuinely internationally outstanding research team. We hope that they are able to shed more light on the epidemiology of this early-onset disease, because only by understanding can we deflect and deal with it. 

Knowledge is power, as I’ve said many times before. If we understand the underlying epidemiology, that will allow us to intervene, one would hope, and avoid the chaotic disaster of my clinic on Friday, with these two young patients with an extremely limited lifespan and large families who will be left bereft in having lost a parent.

More power to the team. We wish them well with the study, but again, this is a pointer to the future, one would hope, of modern genetic computational epidemiology. 

I’d be really interested in any ideas or comments that you might have. Are you in the field? Are you seeing more young patients? Do you have any ideas or hypotheses of your own around the microbiome and what bugs might be involved and so on?

Dr. Kerr, Professor, Nuffield Department of Clinical Laboratory Science, University of Oxford, England; Professor of Cancer Medicine, Oxford Cancer Centre, Oxford, United Kingdom, has disclosed relevant financial relationships with Celleron Therapeutics, Oxford Cancer Biomarkers, Afrox, GlaxoSmithKline, Bayer, Genomic Health, Merck Serono, and Roche. 

A version of this article appeared on Medscape.com.

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