Association Between Patient-Level, Clinic-Level, and Geographical-Level Factors and 1-Year Surveillance Colonoscopy Adherence

Abstract

INTRODUCTION: 

Surveillance colonoscopy 1 year after surgical resection for patients with stages I–III colorectal cancer (CRC) is suboptimal, and data on factors associated with lack of adherence are limited. Using surveillance colonoscopy data from Washington state, we aimed to determine the patient, clinic, and geographical factors associated with adherence.

METHODS: 

Using administrative insurance claims linked to Washington cancer registry data, we conducted a retrospective cohort study of adult patients diagnosed with stage I–III CRC between 2011 and 2018 with continuous insurance for at least 18 months after diagnosis. We determined the adherence rate to 1-year surveillance colonoscopy and conducted logistic regression analysis to identify factors associated with completion.

RESULTS: 

Of 4,481 patients with stage I–III CRC identified, 55.8% completed a 1-year surveillance colonoscopy. The median time to colonoscopy completion was 370 days. On multivariate analysis, older age, higher-stage CRC, Medicare insurance or multiple insurance carriers, higher Charlson Comorbidity Index score, and living without a partner were significantly associated with decreased adherence to 1-year surveillance colonoscopy. Among 29 eligible clinics, 51% (n = 15) reported lower-than-expected surveillance colonoscopy rates based on patient mix.

DISCUSSION: 

Surveillance colonoscopy 1 year after surgical resection is suboptimal in Washington state. Patient and clinic factors, but not geographic factors (Area Deprivation Index), were significantly associated with surveillance colonoscopy completion. These data will inform the development of patient-level and clinic-level interventions to address an important quality-of-care issue across Washington.

INTRODUCTION

Colorectal cancer (CRC) is the third most common cancer and second leading cause of cancer deaths in the United States and Washington (WA) state (¹). Among newly diagnosed individuals, 70%–80% will have locoregional disease (stages I–III) and undergo surgical resection with curative intent (²). However, up to 40% of patients will develop recurrent disease within 5 years (³). To prolong survival by detecting metachronous and recurrent cancers at an early stage, a surveillance colonoscopy 1 year after surgery is recommended by the National Comprehensive Cancer Network (⁴), the Association of Clinical Oncologists (⁵), and the US Multi-Society Task Force on Colorectal Cancer (^6–8). The National Comprehensive Cancer Network, for example, also recommends carcinoembryonic antigen tumor marker testing every 3–6 months and computed tomography imaging every year (⁴). While there is a debate among professional groups about the appropriate frequency and timing of carcinoembryonic antigen testing and computed tomography imaging, and resultant inconsistent adherence to these surveillance tests (^9–11), the 1-year postoperative colonoscopy is the most consistently recommended surveillance test for stage I–III CRC across professional groups. Despite strong evidence to support this recommendation, multiple studies have shown that adherence to surveillance colonoscopy recommendations rarely exceeds 60% (^11–14).

In previous studies, older age, male sex, Black race, Hispanic ethnicity, higher tumor stage, and increased comorbidities have been associated with decreased adherence to surveillance colonoscopy completion (^{9,11,12,14,15}). However, the factors associated with nonadherence to 1-year surveillance colonoscopy after surgery for CRC in WA are unknown. In addition, few studies have evaluated clinic-level or geographical-level factors associated with the lack of surveillance colonoscopy. With the goal of determining the proportion of individuals diagnosed with stage I–III CRC in WA state who completed a surveillance colonoscopy 1 year after surgery, we analyzed administrative claims linked to WA cancer registry data. We then used the data to determine the patient-level, clinic-level, and geographical-level factors associated with 1-year surveillance colonoscopy completion, with the goal of finding factors that are the most strongly associated with nonadherence. Ultimately, these findings will inform the development of actionable interventions that can be implemented in WA and beyond to improve surveillance colonoscopy completion.

METHODS

Study design and population

We conducted a retrospective cohort study of colon cancer survivors in the Western Washington Cancer Surveillance System (CSS) and the Washington State Cancer Registry (WSCR) from January 1, 2011 to December 31, 2018. Adults aged 18 years and older, diagnosed with American Joint Committee on Cancer (AJCC) stage I–III CRC that represented their first malignant disease, with continuous medical coverage for at least 18 months after diagnosis, and who received surgery within 1 year of diagnosis, were included. Individuals with a surveillance period, defined as no treatment for at least 90 days, with enrollment through the end of surveillance (maximum of 19 months), and an administrative claim during surveillance were also included. Patients with AJCC stage 0, IV, or unstaged disease were excluded. Patients with less than 18 months of continuous coverage after diagnosis or with missing sex, race, median household income, or Area Deprivation Index (ADI) were also excluded. ADI is a composite indicator of geographical socioeconomic position that is calculated using 17 indicators based on neighborhood-level census data (^16,17). Beyond urban and rural designations, ADI provides a measure of the material deprivation in a person's residence including factors such as income and income disparity, education, employment, and housing costs and quality.

Data sources

Data from the Hutchinson Institute for Cancer Outcomes Research (HICOR) were used to identify the patient population. The HICOR data repository consists of health plan enrollment and claims files linked to cancer registry records from CSS and WSCR and includes 2 large commercial insurers (Premera Blue Cross and Regence Blue Shield), Washington State Public Employees Benefits Board's Uniform Medical Plan, Medicaid, and Medicare. Enrollment and claims files were available for all insurers through December 31, 2018. This HICOR collective data asset represents approximately 70% of patients with cancer who received cancer care in Washington State during the study period (¹⁸). CSS collects comprehensive information on staging, treatment, and survival for those diagnosed with malignancies in 13 Western Washington counties, excluding nonmelanoma skin cancer. The CSS is part of the National Cancer Institute Surveillance, Epidemiology, and End Results program and provides data to WSCR. WSCR monitors cancer incidence and mortality in the entire state of Washington. We obtained clinic data from oncology clinics throughout Washington that could be rural, urban, community, or academic and contribute data to HICOR's data asset. For each measure, we attributed patients to 1 clinic. This enabled us to capture the clinic most likely to direct most of a patient's cancer care. Clinics are identified using Tax ID Numbers (TINs) or Centers for Medicare and Medicaid Services Certification Numbers (CCNs) on health insurance claims. Only clinics with data for at least 30 patients were included.

Ascertainment of covariates

Our dependent variable was 1-year surveillance colonoscopy completion, defined as a colonoscopy or similar endoscopic evaluation (see Supplemental Table 1, Supplementary Digital Content, https://links.lww.com/CTG/A946) within the surveillance period as described earlier. Independent variables were patient-level factors including demographics (age, sex, race, and ethnicity), median income according to zip code of residence using 2015 Census American Community Survey Data, AJCC stage, tumor location (colon or rectal), year of CRC diagnosis (2011–2014, 2015–2018), and an adaptation of the Charlson Comorbidity Index (CCI) developed for use with Surveillance, Epidemiology, and End Results-Medicare data (¹⁹). Other patient-level factors included insurance type (Commercial, Medicare, Medicaid, or Multiple) and living arrangement (living with a partner, living without a partner, or unknown). Covariates were selected a priori based on prior literature and what could be accessed through administrative claims data. Clinic-level and geographical-level factors included assigned oncology clinics with data for at least 30 patients and ADI, respectively.

Patient attribution to clinics

Clinic-level data were collected from clinics that contribute data to HICOR's data asset. The number of patients eligible for surveillance colonoscopy at each clinic were identified to assess for variations in adherence between clinics. To determine whether a clinic had a higher or lower 1-year surveillance colonoscopy completion rate than expected, given its patient mix (based on covariates outlined earlier), we calculated a clinic-level risk standardized rate using the following equation:$Clinic\hspace{0.17em}level\hspace{0.17em}risk\hspace{0.17em}standardized\hspace{0.17em}rate=\frac{Predicted\hspace{0.17em}rate}{Expected\hspace{0.17em}rate}\hspace{0.17em}x\hspace{0.17em}observed\hspace{0.17em}regional\hspace{0.17em}average$

This ratio was then rescaled by the regional average for interpretation with respect to the average outcome for the region. Risk standardization accounted for differences in the numbers of patients per clinic, differences in patient characteristics across clinics, and outliers in the data. The summary quality score represents a clinic's overall quality relative to the regional average and is calculated by first measuring the difference between a clinic's risk standardized rate and the regional average for each individual metric within the measure and then summing the differences for each quality metric. A (predicted/expected) ratio <1 indicates that the clinic is performing worse than expected (²⁰).

The technique of calculating the clinic-level risk standardization rate uses hierarchical modeling to explicitly model the variation in care at each level (i.e., patients, clinics, etc). This approach is used to account for chance by quantifying and reducing statistical “noise” and is increasingly used in the measurement of quality for inpatient and outpatient medical conditions (²¹).

Statistical analysis

Patient-level, clinic-level, and geographical-level factors and our primary analysis of 1-year surveillance colonoscopy completion was described as proportions or medians and interquartile ranges (IQRs). Differences between groups were analyzed with univariate logistic regression, χ², and nonparametric median 2-sample tests, as appropriate. For our secondary analysis, we used multivariate logistic regression modeling, adjusted for age, sex, race, ethnicity, diagnostic year, cancer site, cancer stage, insurance, living arrangement, CCI, median income, and ADI with 1-year surveillance colonoscopy completion as the outcome of interest to determine the patient-level and geographical-level factors associated with 1-year surveillance colonoscopy completion. We then used a hierarchical generalized linear model that included all the covariates listed and clinics to determine adherence to 1-year surveillance colonoscopy by clinic. Accompanying odds ratios (ORs), 95% confidence interval (CI), and P values were reported, and P values <0.05 were considered statistically significant. We used SAS (Enterprise Guide 7.15, SAS Institute Inc., Cary, NC) and Stata (version 17, StataCorp, College Station, TX (for hierarchical generalized linear model with clinic effect) statistical software for all analyses.

RESULTS

Based on inclusion and exclusion criteria, 4,481 patients were eligible for the study (Figure 1). The median age was 72 (IQR [64–80]) years, 51% (n = 2,290) were female, 90% White, and 57% (n = 2,545) were diagnosed between 2011 and 2014. In the population that met inclusion criteria, 84% (n = 3,745) were diagnosed with colon cancer and the remaining with rectal cancer, and 60% (n = 2,689) were insured through Medicare. Most of them (71% (n = 3,171) had AJCC stage II–III disease, and 79% (n = 3,532) had a CCI score of 0 (Table 1).

Figure 1.: Flow chart of patients included and excluded in the study.

Table 1. - Characteristics of patient population by 1-year surveillance colonoscopy completion

Variable	All (N = 4,481)		Patients w/o colonoscopy (N = 1,979)		Patients w/colonoscopy (N = 2,502)		OR	CI (95%)	P value
	N	%	N	%	N	%
Age
<65	1,143	25.5%	340	29.7%	803	70.3%	ref.	ref.	ref
65+	3,338	74.5%	1,639	49.1%	1,699	50.9%	0.44	0.38–0.51	<0.01
Sex
Male	2,191	48.9%	952	43.5%	1,239	56.5%	ref.	ref.	ref
Female	2,290	51.1%	1,027	44.8%	1,263	55.2%	0.95	0.84–1.06	0.35
Race
White	4,032	90.0%	1,773	44.0%	2,259	56.0%	ref.	ref.	ref
African American	93	2.1%	47	50.5%	46	49.5%	0.77	0.51–1.16	0.21
AI/AN	41	0.9%	26	63.4%	15	36.6%	0.45	0.24–0.86	0.02
Asian/PI	260	5.8%	108	41.5%	152	58.5%	1.11	0.86–1.43	0.44
Other/multi	55	1.2%	25	45.5%	30	54.5%	0.94	0.55–1.61	0.83
Hispanic
No	4,349	97.1%	1,924	44.2%	2,425	55.8%	ref.	ref.	ref
Yes	132	2.9%	55	41.7%	77	58.3%	1.11	0.78–1.58	0.56
Dx year
2011–2014	2,545	56.8%	1,171	46.0%	1,374	54.0%	ref.	ref.	ref
2015–2018	1,936	43.2%	808	41.7%	1,128	58.3%	1.19	1.06–1.34	<0.01
Cancer site
Colon	3,745	83.6%	1,664	44.4%	2,081	55.6%	ref.	ref.	ref
Rectum	736	16.4%	315	42.8%	421	57.2%	1.07	0.91–1.25	0.42
AJCC stage
I	1,310	29.2%	493	37.6%	817	62.4%	ref.	ref.	ref
II	1,530	34.1%	721	47.1%	809	52.9%	0.68	0.58–0.79	<0.01
III	1,641	36.6%	765	46.6%	876	53.4%	0.69	0.60–0.80	<0.01
Payer
Commercial	814	18.2%	201	24.7%	613	75.3%	ref.	ref.	ref
Medicaid	200	4.5%	65	32.5%	135	67.5%	0.68	0.49–0.95	0.03
Medicare	2,689	60.0%	1,369	50.9%	1,320	49.1%	0.32	0.27–0.38	<0.01
Multiple	778	17.4%	344	44.2%	434	55.8%	0.41	0.33–0.51	<0.01
Comorbidity score
0	3,532	78.8%	1,355	38.4%	2,177	61.6%	ref.	ref.	ref
1	401	8.9%	228	56.9%	173	43.1%	0.47	0.38–0.58	<0.01
2+	548	12.2%	396	72.3%	152	27.7%	0.24	0.20–0.29	<0.01
Marital status at diagnosis
Living w/o partner	1,653	36.9%	876	53.0%	777	47.0%	ref.	ref.	ref
Living w/partner	2,292	51.1%	834	36.4%	1,458	63.6%	1.97	1.73–2.24	<0.01
Unknown	536	12.0%	269	50.2%	267	49.8%	1.11	0.92–1.36	0.26
Median household income
Q1	1,098	24.5%	537	48.9%	561	51.1%	ref.	ref.	ref
Q2	1,127	25.2%	495	43.9%	632	56.1%	1.22	1.03–1.44	0.02
Q3	1,127	25.2%	487	43.2%	640	56.8%	1.26	1.06–1.49	0.01
Q4	1,129	25.2%	460	40.7%	669	59.3%	1.39	1.18–1.65	<0.01
ADI
1–3	1,319	29.4%	529	40.1%	790	59.9%	ref.	ref.	ref
4–6	1,437	32.1%	613	42.7%	824	57.3%	0.90	0.77–1.05	0.17
7–10	1725	38.5%	837	48.5%	888	51.2%	0.71	0.61–0.82	<0.01

ADI, Area Deprivation Index; AI/AN, American Indian/Alaska Native; Asian/PI, Asian/Pacific Islander; AJCC, American Joint Committee on Cancer; CI, confidence interval; OR, odds ratio.

Adherence to 1-year surveillance colonoscopy in our population was 55.8%, and the median time to surveillance colonoscopy was 370 days (IQR 322–431). Univariate logistic regression revealed that patients older than 65 years were less likely to complete a 1-year surveillance colonoscopy than patients aged 65 years or younger (51% vs 70%, OR 0.44, CI 0.38–0.51, P < 0.01) as were patients with a CCI score of +2 compared with those with a CCI score of 0 (28% vs 62%, OR 0.24, CI 0.20–0.29, P < 0.01). American Indian/Alaska Native (AI/AN) patients were less likely to obtain 1-year surveillance colonoscopy compared with White patients (37% vs 56%, OR 0.45, CI 0.24–0.86, P = 0.02). When grouped by tertials, patients in the highest tertial group (ADI scores of 7–10 representing those who live in areas of highest disadvantage) were less likely to obtain a 1-year surveillance colonoscopy compared with those in the lowest tertial group (ADI of 1–3 representing those who live in areas of lowest disadvantage) (Table 1). CRC stage, insurance type, living arrangement, and median household income were also significant factors associated with surveillance colonoscopy completion in the univariate analysis. There were no significant differences in surveillance colonoscopy completion by sex, ethnicity, or cancer site (Table 1).

In our multivariate analysis, older age, AI/AN race, higher AJCC stage, Medicare insurance or multiple insurance carriers, higher CCI score, and living without a partner remained significantly associated with a decreased likelihood of completing surveillance colonoscopy while median household income and ADI did not remain significant factors (Table 2).

Table 2. - Multivariate logistic regression of patient characteristics associated with colonoscopy completion within the surveillance period

	aORa	95% CI	P value
Age
<65	Ref	—	—
65+	0.74	0.60–0.92	<0.01
Sex
Male	Ref	—	—
Female	1.11	0.97–1.26	0.13
Race
White	Ref	—	—
African American	0.71	0.46–1.10	0.13
AI/AN	0.44	0.22–0.86	0.02
Asian/PI	0.95	0.73–1.25	0.73
Other/Multi	0.86	0.50–1.49	0.59
Hispanic
No	Ref	—	—
Yes	1.04	0.71–1.51	0.85
Dx year
2011–2014	Ref	—	—
2015–2018	1.11	0.98–1.27	0.10
Cancer site
Colon	Ref	—	—
Rectum	0.77	0.64–0.91	<0.01
AJCC stage
I	Ref	—	—
II	0.77	0.66–0.91	<0.01
III	0.71	0.61–0.84	<0.01
Payer
Commercial	Ref	—	—
Medicaid	0.95	0.66–1.36	0.77
Medicare	0.52	0.40–0.66	<0.01
Multiple	0.61	0.47–0.79	<0.01
Comorbidity score
0	Ref	—	—
1	0.55	0.44–0.68	<0.01
2+	0.29	0.23–0.35	<0.01
Marital status at diagnosis
Living w/o partner	Ref	—	—
Living w/partner	1.81	1.57–2.08	<0.01
Unknown	1.19	0.97–1.47	0.10
Median household income
Q1	Ref	—	—
Q2	1.1	0.91–1.31	0.36
Q3	1.01	0.82–1.25	0.91
Q4	0.96	0.76–1.22	0.75
ADI
1–3	Ref	—	—
4–6	1.00	0.84–1.21	0.95
7–10	0.90	0.72–1.11	0.31

ADI, Area Deprivation Index; AI/AN, American Indian/Alaska Native; AJCC, American Joint Committee on Cancer; CI, confidence interval; aOR, adjusted odds ratio.

^aAdjusted for age, sex, race, ethnicity, diagnostic year, cancer site, cancer stage, insurance, living arrangement, Charlson comorbidity, median income, and ADI.

In the analysis of clinic-level data, among 4,481 patients, surveillance colonoscopy completion ranged from 37% to 88% (see Supplemental Table 2, Supplementary Digital Content, https://links.lww.com/CTG/A947). There is a 14-percentage point difference between the highest performing clinic and the lowest performing clinic (data not shown), showing a moderate difference in surveillance colonoscopy. Among the 29 clinics eligible for inclusion in the surveillance colonoscopy risk standardized rate, 51% (n = 15) had lower-than-expected 1-year surveillance colonoscopy rate based on patient mix with the lowest ratio of 0.88 (Table 3). These data from clinic are risk adjusted for factors analyzed earlier, including age, sex, race, AJCC stage, CCI, cancer site, payer, median household income, and ADI.

Table 3. - Comparison of expected colonoscopy completion in clinics across Washington state and adjusted predicted rates–based patient populations

Clinic	Na	Predicted rate based on patient and clinic impact, %	Expected rate based on patient demographics, %	Clinic performance ratio
OO	105	70.50	62.80	1.12
KD	76	53.70	48.50	1.11
CC	229	66.30	60.20	1.1
CJ	44	76.40	70.80	1.08
BI	44	55.90	52.90	1.06
DD	74	58.80	55.80	1.05
S	49	65.90	62.70	1.05
AE	61	60.30	57.40	1.05
BM	50	56.90	55.10	1.03
AA	429	63.40	61.90	1.02
CI	65	67.50	66.20	1.02
KK	116	55.80	55.50	1.01
HH	59	62.70	62.60	1.00
WB	30	56.60	56.50	1.00
CK	31	69.80	70.40	0.99
BJ	48	57.80	58.70	0.99
D	75	57.00	57.80	0.99
CM	33	64.70	66.30	0.98
O	136	63.60	65.10	0.98
BB	338	59.30	62.00	0.96
Y	176	54.60	57.20	0.96
W	219	54.40	57.10	0.95
A	37	54.50	57.20	0.95
E	99	59.30	62.30	0.95
CO	85	57.20	60.40	0.95
BG	133	53.80	56.80	0.95
R	292	52.50	56.60	0.93
G	35	51.10	57.20	0.89
T	261	49.30	56.20	0.88

^aIncludes clinics with at least 30 patients (N patients = 3,429).

DISCUSSION

While numerous medical societies recommend a surveillance colonoscopy for patients with stage I–III CRC 1 year after surgical resection with curative intent, in a retrospective analysis of insured individuals in WA state, only 55.8% of patients adhered to this recommendation. We found that older age, high comorbidity scores, higher stage CRC, Medicare insurance, and living without a partner were important factors associated with lack of adherence to this recommendation. When evaluating race, only AI/AN patients were less likely to obtain surveillance colonoscopy compared with White patients. In a clinic-level analysis, more than half of examined clinics reported lower-than-expected 1-year surveillance colonoscopy rates based on patient mix.

The proportion of patients in our study who completed a 1-year surveillance colonoscopy is consistent with the existing literature (^{13–15,22–24}). We found multiple demographic factors associated with decreased adherence to 1-year surveillance colonoscopy that have been previously reported, including increasing age, higher CCI, later stage disease, and Medicare insurance (^13,14). Adding to the existing literature, we found that patients who identified as AI/AN were less likely to complete surveillance colonoscopy after curative treatment compared with other races. Prior studies have investigated differences in care obtained by AI/AN, mainly related to CRC screening (^25–27), but data on AI/AN populations and adherence to surveillance colonoscopy completion are sparse. Consistent with existing literature, we also found that patients with Medicare were less likely to complete a 1-year surveillance colonoscopy compared with patients with private/commercial insurance (¹³). There is a robust literature on the impact of health insurance type on the receipt of healthcare services. Specific to receipt of surveillance colonoscopy, this association is likely influenced by older age of Medicare beneficiaries and more comorbid conditions leading to higher CCI (^28,29).

A higher ADI (higher deprivation) has been associated with poorer outcomes in cancer incidence, clinical trial effectiveness, and cancer survival (^30,31). While we found a linear negative correlation between higher ADI and the proportion of patients who completed a 1-year surveillance coloscopy, this effect did not persist in multivariable analysis. This suggests that while the direction of a high ADI on cancer outcomes is consistent, the magnitude of this effect likely varies in the context of other patient-level, clinic-level, and geographical-level factors (³²). It is also possible that ADI alone is an inadequate measure for testing the association between geographical-level factors and surveillance colonoscopy completion, and other measures (e.g., measures that incorporate residential segregation) are needed.

We observed a large variation in surveillance colonoscopy completion between WA state clinics, even when adjusting for differences in patient populations and the likelihood of completing surveillance. These differences are likely due to clinic-level factors leading to higher-performing or lower-performing clinics and could be due to provider practice differences or ease of access to endoscopy sites. Prior work evaluating higher-performing versus lower-performing clinics have been performed in CRC screening (^33–36) but not surveillance after treatment. These studies found that higher-performing clinics (i) use registries to track patients with abnormal screening results until colonoscopy completion, (ii) have a clear chain of command of individuals or teams who own abnormal results, and (iii) team members who own these abnormal results consistently include nurses and medical assistants but not physicians. Similar patterns may be applicable in higher-performing vs lower-performing clinics as it pertains to surveillance colonoscopy completion, but this has yet to be investigated. Mixed-methods inquiry of clinic-level practices through patient and provider surveys and interviews could yield insight that is missing from our quantitative analysis and inform interventions to improve 1-year surveillance colonoscopy completion.

An important strength of our study included a cohort of persons with comprehensive health plan enrollment and claims files linked to cancer registry records from Washington state. This data asset represents approximately 70% of patients with cancer who received care in WA state during the study period. In addition, our extended observation time after surgery enabled us to capture a maximal number of endoscopic procedures. Our study also has limitations. First, due to the use of administrative claims data, our study included only insured patients and did not capture patterns that might be associated with the lack of insurance and our outcome of interest. Second, patients with supplemental insurance plans not through Premera Blue Cross, Regence Blue Shield, WA Medicaid, or Medicare may not have been completely captured in our cohort. Third, it is possible that excluding individuals with AJCC stage 0, IV, or unstaged disease and those without continuous insurance coverage for 18 months, a priori, might have introduced selection bias. If present, it is unlikely that this substantively biased our results because the 1-year surveillance colonoscopy completion rate in this analysis is consistent with those in other published literature. Our methods followed the best practices for reporting by excluding patients with AJCC stage 0 or IV disease because surveillance is not consistently recommended. In addition, because lack of continuous insurance coverage could introduce unmeasured confounders, our approach allowed for a more accurate examination of factors associated with surveillance colonoscopy completion. Finally, it is also possible that our population reflects a healthier cohort than other publications because 79% had a CCI of 0. This likely reflects our study design because patients needed to remain alive during the surveillance window to be included in the cohort.

In conclusion, our retrospective analysis of patients with stage I–III CRC treated with curative intent in Washington state revealed that only 56% completed a 1-year surveillance colonoscopy. This is suboptimal, given the increased risk for recurrent and metachronous cancer in this population. We identified patient-level and clinic-level factors but not geographical-level factors (i.e., ADI) that were significantly associated with the lack of adherence to surveillance colonoscopy completion. Qualitative studies including patient and provider interviews and outlining surveillance practices in higher-performing vs lower-performing clinics could help identify additional modifiable barriers to surveillance colonoscopy completion in this population. Research that incorporates learnings from quantitative and qualitative analyses could lead to interventions that effectively increase 1-year surveillance colonoscopy completion rates in Washington state and beyond.

CONFLICTS OF INTEREST

Guarantor of the article: Rachel Issaka, MD, MAS.

Specific author contributions: R.B.I.: study concept and design. T.S., Q.S., R.B.I.: acquisition, analysis, and interpretation of data. T.S., R.B.I.: drafting of the manuscript. All authors: critical revision of the manuscript for important intellectual content, approval of the final manuscript: R.B.I.: obtained funding.

Financial support: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award numbers K08CA241296 (R.B.I.) and P30 CA015704 (R.B.I.).

Potential competing interests: R.B.I. reported receiving grants from National Institutes of Health/National Cancer Institute award numbers K08 CA241296 and P30 CA015704 for the conduct of this study. No other disclosures were reported.

Role of the Funder/Sponsor: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: The datasets generated during and/or analyzed during this study are not publicly available due to protected health information.

Study Highlights

WHAT IS KNOWN

✓ Surveillance colonoscopy 1 year after surgical resection for patients with stages I-III colorectal cancer is suboptimal—Prior analyses have been limited to patient-level factors associated with 1-year surveillance colonoscopy completion.

WHAT IS NEW HERE

✓ Adherence to 1-year surveillance colonoscopy in Washington state is 56%, similar to nationally reported rates—In addition to patient-level factors, our analysis revealed that clinic-level factors are also associated with the lack of adherence to surveillance colonoscopy. In a multivariable analysis, geography evaluated by the Area Deprivation Index was not associated with surveillance colonoscopy completion.