Background: Data on the number of individuals eligible for screening, and rates of screening, are necessary to assess national colorectal cancer screening efforts. Such data are sparse for safety-net health systems.

Methods: A retrospective cohort study of individuals ages 50 to 75 served by a safety-net health system in Tarrant County, TX was conducted to determine (a) the size of the potential screen-eligible population ages 50 to 75, (b) the rate of screening over 5 years among individuals ages 54 to 75, and (c) the potential predictors of screening, including sex, race/ethnicity, insurance status, frequency of outpatient visits, and socioeconomic status.

Results: Of 28,708 potential screen-eligible individuals, 20,416 were ages 54 to 75 and analyzed for screening; 22.0% were screened within the preceding 5 years. Female gender, Hispanic ethnicity, ages 65 to 75, insurance status, and two or more outpatient visits were independently associated with screening. Access to care was an important factor: adjusted odds ratio, 2.57 (95% confidence interval, 2.23-2.98) for any insurance; adjusted odds ratio, 3.53 (95% confidence interval, 3.15-3.97) for two or more outpatient visits.

Conclusions: The screen-eligible population served by our safety-net health system was large, and the projected deficit in screen rates was substantial. Access to care was the dominant predictor of screening participation. If our results are replicable in similar health systems, the data suggest that screening guidelines and policy efforts must take into account the feasibility of proposed interventions. Strong advocacy for more resources for colorectal cancer screening interventions (including research into the best manner to provide screening for large populations) is needed. (Cancer Epidemiol Biomarkers Prev 2009;18(9):2373–9)

Although screening has the potential to prevent colorectal cancer (CRC) mortality, colorectal cancer remains the second leading cause of cancer death (1). Nationwide, only 61% of individuals have had a colon screening test; rates are particularly low among the uninsured, African-Americans, and Hispanics (2-4). Safety-net hospitals provide service to these (and other) vulnerable groups (5). Thus, safety-net hospitals are in a unique position to affect the prevention of death from colorectal cancer if population screening in these settings can be implemented.

Because population screening in safety-net health systems is likely to require significant manpower and monetary resources, definition of the size of the screen target population and rate of baseline screening, as well as determination of screening completion-associated factors are required. In particular, identification of highly modifiable variables that contribute to likelihood of CRC screening, including those that enable access to care such as insurance and health provider availability, may allow for future efficient implementation and improvement in CRC screening programs (6). More broadly, data regarding the local and regional challenges to CRC screening are necessary to inform national policy regarding the best public health approach towards screening.

To this end, we conducted a retrospective cohort study in a large safety-net health system to determine the size of our potential screen-eligible population, ascertain rates of screening test completion, and identify predictors of screening.

### Study Setting and Materials

The Tarrant County Hospital District John Peter Smith Hospital Health Network (JPS) is a large county health system for Tarrant County, TX (including service to Fort Worth), serving an ethnically diverse population of >155,000 unique individuals through over 850,000 outpatient and inpatient encounters yearly.11

11John Peter Smith Health Network 2007 Report to the Community. Accessed at http://www.jpshealthnet.org/About-JPS/Annual-Reports.aspx on August 10, 2008.

JPS qualifies as a safety-net health system based on a commitment to deliver health care to uninsured, Medicaid, and other vulnerable patients (5). In addition to providing care for individuals with insurance and on a self-pay basis, JPS offers a tax-subsidized charity medical program called JPS Connection for uninsured Tarrant County residents, with premiums based on federal poverty income levels. The JPS Connection program includes access to a primary care provider, and affordable inpatient, outpatient, and specialty care, including cancer care.

The primary data source for the study was electronic administrative records of patient visits. These records contain documentation of basic demographics, International Classification of Diseases 9th edition (ICD9) diagnoses, as well as coding for procedures done, and have been managed in a systematic fashion since 2002. Demographic characteristics, including age, gender, race/ethnicity, insurance status and type, zip code of residence, and number and type of health system encounters (e.g., inpatient, outpatient, or urgent care/emergency room visit) were retrieved, in addition to dates of completion of colorectal cancer screening tests. Records of screening test completion were based on internal administrative coding for fecal occult blood testing (FOBT), barium enema, flexible sigmoidoscopy, and colonoscopy. The year 2006 was used as the reference year for age, zip code of residence, and insurance type. Analyses were conducted to ascertain the following: (a) size of the potential screen-eligible population, (b) rate of CRC screening test completion over a 5-year period, and (c) predictors of CRC screening test completion.

### Identification of the Potential Screen-Eligible Population

We queried the administrative database for men and women ages 50 to 75 and alive in 2006, and retrieved all specified data for these individuals available in the database from January 1, 2002 to December 31, 2006. Individuals with ICD9 coding consistent with inflammatory bowel disease (e.g., Crohn's disease and ulcerative colitis), as well those with ICD9 coding consistent with colorectal polyps or cancer, were excluded to narrow the analysis to individuals most likely to receive testing for screening rather than disease work-up (see Supplementary Appendix 1 for ICD9 codes used). Thus, the screen-eligible population consisted of men and women ages 50 to 75 at the time of the health system encounter in 2006, without diagnostic coding consistent with inflammatory bowel disease, colorectal polyps, or colorectal cancer.

### Identification of CRC Screening Test Completion

From the potential screen-eligible population, we identified individuals ages 54 to 75 in 2006. An age range from 54 to 75 was chosen to allow data to reflect screening participation for individuals who turned 50 to 71 in 2002, and then had the potential for up to 5 y of follow-up in which screening could have occurred. Participation in screening was defined by administrative coding for one or more of the following: (a) FOBT in 2005 or 2006, or (b) any barium enema, any flexible sigmoidoscopy, or any colonoscopy from 2002 to 2006. FOBT in 2005 or 2006 was used as the primary measure of participation in FOBT. We also conducted sensitivity analyses to determine how estimates of participation changed with varying definitions of screening participation (e.g., any test at least once from 2002 to 2006, any FOBT, or any lower endoscopy/barium enema), and found no substantial differences with main analyses (data not shown). Distribution of screening test completed by test type was determined. Because the indication for the test (or tests) done was not available from administrative data, distinction between tests done for symptoms or screening was not made (3, 7).

### Ascertainment of Predictors of Screening Test Completion

We analyzed the relationship between likelihood of screening completion and several candidate predictors of screening, including age, gender, race/ethnicity, primary language (Spanish, English, or other), median household income (determined by zip code of residence), percentage of individuals living below the poverty line (determined by zip code of residence), presence of two or more outpatient visits in 2006, and insurance status (4). Distinction between individuals of Hispanic ethnicity by White or African-American race could not be made; therefore, Hispanics were categorized as one racial category.

### Statistical Analyses

The primary goals of the analyses were to estimate the size of the potential screen-eligible population, calculate the rate of CRC screening test completion over a 5-y period, and determine predictors of CRC screening test completion. Descriptive statistics, including proportions with 95% confidence intervals (95% CI) for estimates of rates of screening were presented. We categorized insurance status as no insurance, participation in the JPS Connection medical assistance program (hereafter referred to as “JPS Connection”), or other insurance. The “other insurance” category included Medicaid, Medicare, and private insurance plans. Estimates of median income and percentage of individuals living in poverty for the study group were derived from postal zip codes (8, 9). Postal zip codes for each patient were linked to median household income and percentage of individuals living in poverty by five-digit zip code tabulation areas from the U.S. Census 2000 Summary File 3.12

12U.S. Census Bureau. Census 2000 Summary File 3; Matrices P53, P77; generated by Samir Gupta; using American FactFinder at http://www.factfinder.census.gov/home on July 2, 2008.

Only participants with zip code tabulation areas partially or fully included in Texas were included for linkage to census data. Zip codes for 98% of participants in the data set are linked to five-digit zip code tabulation areas. For estimation of screening participation, age was categorized as 54 to 64 and 65 to 75 to account for an expected increase in access to Medicare health insurance at age 65. As current United Services Preventive Task Force Guidelines recommend against screening for individuals older than age 75, individuals older than age 75 were not included in our primary analysis; in analyses including individuals up to age 85, the results were not substantially different (data not shown). Univariate and multivariate logistic regression were conducted to identify any association between potential predictors of screening participation, and the primary outcome, the presence or absence of colorectal cancer screening completion. Cochran-Armitage trend test was conducted to investigate if there is an increasing trend towards screening with increasing years of insurance coverage. Associations with two-sided P < 0.05 were considered statistically significant; no adjustment for multiple comparisons was made. The final “best fit” model for independent predictors of colorectal cancer screening test completion is presented. Statistical analyses were done with SAS version 9.1.

### Human Subjects Protection and Role of the Funding Source

This study was approved by the Institutional Review Boards of the JPS, as well as the University of Texas Southwestern Medical Center.

### Size of the Potential Screen-Eligible Population and Rate of Screening Completion

Figure 1 summarizes the study flow, size of the potential screen-eligible population, and rate of screening. The characteristics of the study population are provided in Table 1. In 2006, >28,000 potentially screen-eligible individuals ages 50 to 75 were identified. The median age of potential screen-eligible individuals was 57 years; the majority was female, with a significant representation of African-Americans and Hispanics. Twenty-five percent had no insurance, 41% had JPS insurance, and 34% had other insurance (Medicare, Medicaid, private, or other) in 2006. Of potential screen-eligible individuals, 20,416 were ages 54 to 75 and comprised the population analyzed for screening completion. Within this group, 22% had a record of CRC screening completion (defined as FOBT in 2005 or 2006, or any colonoscopy, flexible sigmoidoscopy, or barium enema from 2002 to 2006; Fig. 1). The distribution of CRC screening completion by test type among test completers is summarized in Fig. 2. Exclusive screening with FOBT was noted for 56%, and with colonoscopy for 17%.

Figure 1.

Study Flow. Screening completion defined as FOBT in 2005 or 2006 (A), or any barium enema, flexible sigmoidoscopy, or colonoscopy from 2002 to 2006 (B). IBD, inflammatory bowel disease.

Figure 1.

Study Flow. Screening completion defined as FOBT in 2005 or 2006 (A), or any barium enema, flexible sigmoidoscopy, or colonoscopy from 2002 to 2006 (B). IBD, inflammatory bowel disease.

Close modal
Table 1.

Demographic characteristics of potential screen-eligible individuals and analysis of screening populations

Potential screen-eligible population, ages 50 to 75 (n = 28,708)Analysis of screening population, ages 54 to 75 (n = 20,416)
Characteristic
Median age, IQR (y) 57 (53-63) 60 (57-65)
Women (%) 57.5 59.1
Race/ethnicity (%)
White 44.2 43.7
African-American 28.7 27.7
Hispanic 22.5 23.4
Other 4.6 5.2
Primary language spoken (%)
English 83.4 82
Spanish 11.7 12.5
Other 4.9 5.6
Insurance in 2006 (%)
None 25.1 20.5
JPS Connection 41.4 39.9
Medicare, Medicaid, private, or other 33.5 39.6
Median no. of individuals living below the poverty line, IQR (%) 15.2 (7.9-21.4) 15.2 (7.9-21.4)
Median household income, IQR ($) 35,419 (30,510-46,091) 35,419 (30,510-46,091) Potential screen-eligible population, ages 50 to 75 (n = 28,708)Analysis of screening population, ages 54 to 75 (n = 20,416) Characteristic Median age, IQR (y) 57 (53-63) 60 (57-65) Women (%) 57.5 59.1 Race/ethnicity (%) White 44.2 43.7 African-American 28.7 27.7 Hispanic 22.5 23.4 Other 4.6 5.2 Primary language spoken (%) English 83.4 82 Spanish 11.7 12.5 Other 4.9 5.6 Insurance in 2006 (%) None 25.1 20.5 JPS Connection 41.4 39.9 Medicare, Medicaid, private, or other 33.5 39.6 Median no. of individuals living below the poverty line, IQR (%) 15.2 (7.9-21.4) 15.2 (7.9-21.4) Median household income, IQR ($) 35,419 (30,510-46,091) 35,419 (30,510-46,091)

Abbreviation: IQR, interquartile range.

Figure 2.

Distribution of CRC test type among screening test completers (n = 4,496) Screening tests include FOBT, colonoscopy, flexible sigmoidoscopy, and barium enema.

Figure 2.

Distribution of CRC test type among screening test completers (n = 4,496) Screening tests include FOBT, colonoscopy, flexible sigmoidoscopy, and barium enema.

Close modal

### Predictors of Screening Completion

History of screening completion characterized by demographic group is summarized in Table 2. Univariate analyses revealed that the likelihood of screening completion was higher for women, individuals ages 65 to 75, African-Americans and Hispanics, primary Spanish language speakers, individuals seen as an outpatient two or more times, and the insured (see Supplementary Appendix 2).

Table 2.

Screening completion by demographic characteristic in screen analysis population

CharacteristicsHistory of screening completion within group, % (95% CI)
Median age
Age 54-64 (n = 14,724) 20.8 (20.3-21.4)
Age 65-75 (n = 5,692) 25.1 (24.5-25.7)
Gender
Women (n = 12,048) 25.1 (24.5-25.7)
Men (n = 8,333) 17.5 (17.0-18.0)
Race/ethnicity
White (n = 8,618) 20.1 (19.5-20.6)
Black (n = 5,475) 21.7 (21.1-22.2)
Hispanic (n = 4,613) 24.6 (24.0-25.2)
Other (n = 1,029) 23.5 (22.9-24.1)
Primary language spoken
English (n = 16,730) 21.4 (20.8-21.9)
Spanish (n = 2,550) 24.2 (23.6-24.8)
Other (n = 1,136) 26.7 (26.1-27.3)
Insurance in 2006
None (n = 4,358) 6.7 (6.4-7.0)
JPS Connection (n = 8,356) 26.9 (26.3-27.5)
Medicare, Medicaid, private, or other (n = 9,292) 25.1 (24.5-25.7)
Individuals living in poverty (n = 4,445) 15.9 (15.6-16.2)
CharacteristicsHistory of screening completion within group, % (95% CI)
Median age
Age 54-64 (n = 14,724) 20.8 (20.3-21.4)
Age 65-75 (n = 5,692) 25.1 (24.5-25.7)
Gender
Women (n = 12,048) 25.1 (24.5-25.7)
Men (n = 8,333) 17.5 (17.0-18.0)
Race/ethnicity
White (n = 8,618) 20.1 (19.5-20.6)
Black (n = 5,475) 21.7 (21.1-22.2)
Hispanic (n = 4,613) 24.6 (24.0-25.2)
Other (n = 1,029) 23.5 (22.9-24.1)
Primary language spoken
English (n = 16,730) 21.4 (20.8-21.9)
Spanish (n = 2,550) 24.2 (23.6-24.8)
Other (n = 1,136) 26.7 (26.1-27.3)
Insurance in 2006
None (n = 4,358) 6.7 (6.4-7.0)
JPS Connection (n = 8,356) 26.9 (26.3-27.5)
Medicare, Medicaid, private, or other (n = 9,292) 25.1 (24.5-25.7)
Individuals living in poverty (n = 4,445) 15.9 (15.6-16.2)

Independent predictors of screening completion used in a multiple logistic regression analysis were age, gender, race/ethnicity, primary language, measures of socioeconomic status, insurance status, and presence of two or more outpatient visits in 2006. Only gender, ages 65 to 75, Hispanic ethnicity, the presence of two or more outpatient visits in 2006, and insurance status remained clear independent predictors of screening completion; lower median household income and percentage of individuals living below the poverty line approached statistical significance but confidence intervals included 1 (Table 3).

Table 3.

Multiple logistic regression analysis of candidate predictors of CRC screening completion

CharacteristicOdds ratio (95% CI)
Age (65-75 vs. 54-64) 1.16 (1.06-1.26)
Women vs. men 1.25 (1.16-1.35)
Hispanic vs. White 1.2 (1.07-1.34)
Black vs. White 1.05 (0.96-1.15)
Primary language Spanish vs. English 0.95 (0.83-1.09)
Lower median household income (in $1,000 increments) 0.99 (0.99-1.00) Increasing proportion of individuals living in poverty (in 5% point increments) 0.97 (0.94-1.00) Any health insurance* vs. none 2.57 (2.23-2.98) JPS insurance vs. none 2.55 (2.21-2.95) Seen as an outpatient two or more times in 2006 3.53 (3.15-3.97) CharacteristicOdds ratio (95% CI) Age (65-75 vs. 54-64) 1.16 (1.06-1.26) Women vs. men 1.25 (1.16-1.35) Hispanic vs. White 1.2 (1.07-1.34) Black vs. White 1.05 (0.96-1.15) Primary language Spanish vs. English 0.95 (0.83-1.09) Lower median household income (in$1,000 increments) 0.99 (0.99-1.00)
Increasing proportion of individuals living in poverty (in 5% point increments) 0.97 (0.94-1.00)
Any health insurance* vs. none 2.57 (2.23-2.98)
JPS insurance vs. none 2.55 (2.21-2.95)
Seen as an outpatient two or more times in 2006 3.53 (3.15-3.97)

NOTE: Model adjusts for age category, race, primary language, gender, insurance status, presence of two or more outpatient visits in 2006, household income by \$1,000 increments, proportion living in poverty in 5 percentage-point increments.

*Any insurance is defined by Medicaid, Medicare, JPS medical assistance program, private insurance, or other insurance.

Although the observed associations were mild in strength for other variables, insurance and frequency of outpatient visits were more strongly associated with likelihood of completion of a screening test. Adjusted odds ratios were as follows: 2.57 (95% CI, 2.23-2.98) for any insurance, 2.55 (95% CI, 2.21-2.95) for JPS Connection, and 3.53 (95% CI, 3.15-3.97) for two or more outpatient visits in 2006. A trend for increasing test completion with increasing years of insurance coverage was observed (Ptrend < 0.001). The proportion of individuals with screening varied by insurance status and frequency of outpatient visits (Table 4). For example, screening was done in 4.1% individuals without insurance seen less than twice as an outpatient compared with 29.5% of individuals with both insurance and two or more outpatient visits. Overall, of 4,496 individuals who completed a screening test, 87% had a record of both insurance and two or more outpatient visits in 2006.

Table 4.

Screening completion by insurance status and frequency of outpatient visits

Presence of any insurance*Two or more outpatient visits in 2006Frequency of screeningProportion with screening (%)
No No 121 4.1
No Yes 159 13.2
Yes No 303 10.3
Yes Yes 3,913 29.5
Presence of any insurance*Two or more outpatient visits in 2006Frequency of screeningProportion with screening (%)
No No 121 4.1
No Yes 159 13.2
Yes No 303 10.3
Yes Yes 3,913 29.5

*Any insurance is defined by Medicaid, Medicare, JPS Connection medical assistance program, private, or other health insurance.

Our results highlight the challenges facing safety-net health systems seeking to institute systematic programs for colorectal cancer screening. The size of the potential screen-eligible population at our health network is large—more than 28,000. The historical rate of participation in a colorectal cancer screening test was low, at fewer than one in four individuals. Although comparison of the CRC screening rate reported here to that of other safety-net health systems is limited by a lack of published literature on similar populations, our findings suggest that access to care (defined by having health insurance or two or more outpatient visits in 2006) plays a critical role in determining whether individuals complete CRC screening in this setting.

When placed in the context of national screening recommendations from the U.S. Multisociety Task Force on Colorectal Cancer (10) and the U.S. Preventive Services Task Force (11), our findings raise several important questions. Multisociety guidelines emphasize that the goal of cancer screening should be to “diagnose and prevent” cancer, and recommend use of tests that mainly identify polyps and cancer (e.g., colonoscopy, flexible sigmoidoscopy, barium enema, and computed tomographic colonography) over those it claims mainly identify cancers (such as guaiac FOBT and fecal immunochemical test (FIT)). U.S. Preventive Services Task Force guidelines do not make these distinctions, and suggest screening with highly sensitive FOBT, flexible sigmoidoscopy, or colonoscopy based on modeling which showed similar life-years gained for all three approaches compared with no screening (11, 12).

For our health system, taking the Multisociety approach would greatly strain both our financial and manpower resources. We estimate employing this approach to try to screen the ∼16,000 individuals who have not had screening would require an estimated 800% increase in the number of combined colonoscopies, sigmoidoscopies, and barium enemas performed, assuming the number of unscreened individuals has remained constant. A primary FOBT-based screening approach, as would be acceptable under U.S. Preventive Services Task Force guidelines, would also require a substantial increase in resources. If every unscreened individual were to receive a FOBT even once, assuming a 5% to 10% positivity rate, 100% adherence to follow-up colonoscopy for positive FOBT, and no increase in the population in need of screening, we would require a 150% increase in the capacity to provide colonoscopy over 5 years. Thus, the sheer scale of need and associated resources required for screening mandate that we determine whether more CRC-associated death will be prevented by programmatically offering a resource-expensive test such as colonoscopy to a subset of our target population versus a more economical test such as guaiac FOBT or FIT, that is less sensitive for cancer or polyps to a larger group. Similar considerations are important for other safety-net health systems. Indeed, if the goal is to maximize the public health effect of screening, the question of whether offering programmatic screening with invasive tests will achieve greater population benefits than offering programmatic screening with noninvasive tests (e.g., FIT) should be addressed by future comparative effectiveness research.

Beyond raising questions as to the optimal test(s) to use for programmatic screening, our data support the concept that access to care (i.e., having health insurance and being able to see a health care provider on a regular basis) may be the most important requisite for permitting preventive care such as CRC screening (2, 4, 13, 14). In our analysis, participation in the local county health medical assistance program was associated with rates of screening comparable to that observed for individuals with other insurance such as Medicare. It is significant that a medical assistance program supported by local taxpayers can be associated with rates of completion of a preventive service such as colon cancer screening similar to other types of insurance. Our observation, if replicable, suggests that even if the scope of national health care initiatives were limited, substantial benefits with respect to CRC prevention could be achieved.

It is important to note that in this study population, neither African-American race nor Hispanic ethnicity was associated with decreased rates of participation. Indeed, screening participation among Hispanic participants was higher than for Whites. This is in contrast to prior reports, which have observed disparities in screening rates for African-Americans and Hispanics even after adjusting for factors such as socioeconomic status and insurance status (3, 4, 13). We speculate that race and ethnic-based disparities were not observed because of otherwise similar geography, insurance status, socioeconomic status, and access to care in this study population. Our findings may complement other observations that racial and ethnic disparities in U.S. health delivery may be surmountable when access to care is enabled (14-16).

Nonetheless, we estimate that even taking into account a possible 4% rate of false-negative assessment of screening by administrative data, and a doubling of the colonoscopy rate due to unmeasured examinations occurring more than 5 years remote to cohort inception would increase our estimate of the prevalence of screening to only 29% (data not shown). Fourth, examinations done for symptoms such as hematochezia are reflexive actions rather than preventive measures, and may not optimize the goal of finding early-stage cancerous or polypoid lesions in asymptomatic patients, whereas benefits of screening have been most clearly shown in randomized controlled trials of asymptomatic patients (10, 19-23). Indeed, some investigators may characterize the present analysis as one of colorectal cancer testing or test use rather than screening because indications were not abstracted (3, 7). Fifth, zip code linkage to zip code tabulation area–associated census data may be an imprecise estimate of the relationship of measures of socioeconomic deprivation and health outcomes (24, 25). Future investigation of any relationship between measures of socioeconomic deprivation and screening outcomes using more precise measures employing census tract or block measures is warranted. Sixth, some potential confounding factors for predictors of screening completion, such as potential confounding of the association between frequency of outpatient visits and screening completion by burden of comorbid illness, were not studied. Lastly, individuals seen in urgent care and emergency room settings in our analysis were generally not recruited for screening, and some might suggest that these individuals should not be included in our study. On sensitivity analyses, restriction of the study population to individuals seen two or more times, with at least one non–urgent care/non–emergency room visit in 2006, resulted in a modest increase in the estimate of prevalence of screening to 27%, and did not substantially change estimates for predictors of screening on multivariate analyses (see Supplementary Appendix 3). Furthermore, from the perspective of a safety-net health system, our true study base and target population includes all individuals in Tarrant County, TX, who, if symptomatic colorectal cancer developed due to lack of screening, would present to our emergency department and clinics for treatment of later stage disease. From our local public health perspective, it is this population that requires identification and specific interventions.

In conclusion, we have shown that the size of the screen-eligible population, and the number who go unscreened, pose significant challenges to our safety-net health system. If our data are representative of other safety-net systems, specific and potentially modifiable variables (such as insurance status and access to a medical provider) deserve further study in order to overcome the challenges posed. Furthermore, short and long-term screening guidelines and policy efforts must take into account the feasibility and potential costs of proposed interventions. Substantial resources for short-term and long-term population-based screening (including comparative effectiveness research into the best manner to provide screening to large populations, improving access to care, and promoting screening outside of traditional health visit settings) may be required to provide the immense potential benefit of CRC screening to individuals served by safety-net systems.

No potential conflicts of interest were disclosed.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

We thank Susan Crabtree for help with data set extraction, Bonnie Rose, RN, for help with chart review, Dr. Jay Haynes for supporting our collaboration and logistical support, and Dr. Anna Schenck for providing a framework tool for our ongoing validation study of administrative data.

1
Jemal
A
,
Siegel
R
,
Ward
E
, et al
.
Cancer statistics, 2008
.
CA Cancer J Clin
2008
;
58
:
71
96
.
2
Ward
E
,
Halpern
M
,
Schrag
N
, et al
.
Association of insurance with cancer care utilization and outcomes
.
CA Cancer J Clin
2008
;
58
:
9
31
.
3
Use of colorectal cancer tests—United States, 2002, 2004, and 2006
.
MMWR Morb Mortal Wkly Rep
2008
;
57
:
253
8
.
4
Beydoun
HA
,
Beydoun
MA
.
Predictors of colorectal cancer screening behaviors among average-risk older adults in the United States
.
Cancer Causes Control
2008
;
19
:
339
59
.
5
America's health care safety net: intact but endangered
.
Washington, D.C.
:
;
2000
.
6
Andersen
RM
.
Revisiting the behavioral model and access to medical care: does it matter?
J Health Soc Behav
1995
;
36
:
1
10
.
7
Pollack
LA
,
Blackman
DK
,
Wilson
KM
,
Seeff
LC
,
MR
.
Colorectal cancer test use among Hispanic and non-Hispanic U.S. populations
.
Prev Chronic Dis
2006
;
3
:
A50
.
8
Elston Lafata
J
,
Cole Johnson
C
,
Ben-Menachem
T
,
Morlock
RJ
.
Sociodemographic differences in the receipt of colorectal cancer surveillance care following treatment with curative intent
.
Med Care
2001
;
39
:
361
72
.
9
Cooper
GS
,
Doug Kou
T
.
Underuse of colorectal cancer screening in a cohort of Medicare beneficiaries
.
Cancer
2008
;
112
:
293
9
.
10
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
:
1570
95
.
11
Screening for colorectal cancer: U.S. Preventive Services Task Force recommendation statement
.
Ann Intern Med
2008
;
149
:
627
37
.
12
Zauber
AG
,
Lansdorp-Vogelaar
I
,
Knudsen
AB
, et al
.
Evaluating test strategies for colorectal cancer screening: a decision analysis for the U.S. Preventive Services Task Force
.
Ann Intern Med
2008
;
149
:
659
69
.
13
Trivers
KF
,
Shaw
KM
,
Sabatino
SA
,
Shapiro
JA
,
Coates
RJ
.
Trends in colorectal cancer screening disparities in people aged 50-64 years, 2000-2005
.
Am J Prev Med
2008
;
185
93
.
14
Cardarelli
R
,
Thomas
JE
.
Having a personal health care provider and receipt of colorectal cancer testing
.
Ann Fam Med
2009
;
7
:
5
10
.
15
Jha
AK
,
Shlipak
MG
,
Hosmer
W
,
Frances
CD
,
Browner
WS
.
Racial differences in mortality among men hospitalized in the Veterans Affairs health care system
.
JAMA
2001
;
285
:
297
303
.
16
Rabeneck
L
,
Souchek
J
,
El-Serag
HB
.
Survival of colorectal cancer patients hospitalized in the Veterans Affairs Health Care System
.
Am J Gastroenterol
2003
;
98
:
1186
92
.
17
Schenck
AP
,
Klabunde
CN
,
Warren
JL
, et al
.
Data sources for measuring colorectal endoscopy use among Medicare enrollees
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
2118
27
.
18
Schenck
AP
,
Klabunde
CN
,
Warren
JL
, et al
.
Evaluation of claims, medical records, and self-report for measuring fecal occult blood testing among Medicare enrollees in fee for service
.
Cancer Epidemiol Biomarkers Prev
2008
;
17
:
799
804
.
19
Hardcastle
JD
,
Thomas
WM
,
Chamberlain
J
, et al
.
Randomised, controlled trial of faecal occult blood screening for colorectal cancer. Results for first 107,349 subjects
.
Lancet
1989
;
1
:
1160
4
.
20
Mandel
JS
,
Church
TR
,
Ederer
F
,
Bond
JH
.
Colorectal cancer mortality: effectiveness of biennial screening for fecal occult blood
.
J Natl Cancer Inst
1999
;
91
:
434
7
.
21
Mandel
JS
,
Bond
JH
,
Church
TR
, et al
.
Reducing mortality from colorectal cancer by screening for fecal occult blood. Minnesota Colon Cancer Control Study
.
N Engl J Med
1993
;
328
:
1365
71
.
22
Kronborg
O
,
Fenger
C
,
Olsen
J
,
Jorgensen
OD
,
Sondergaard
O
.
Randomised study of screening for colorectal cancer with faecal-occult-blood test
.
Lancet
1996
;
348
:
1467
71
.
23
Faivre
J
,
Dancourt
V
,
Lejeune
C
, et al
.
Reduction in colorectal cancer mortality by fecal occult blood screening in a French controlled study
.
Gastroenterology
2004
;
126
:
1674
80
.
24
Krieger
N
,
Chen
JT
,
Waterman
PD
, et al
.
Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter? the Public Health Disparities Geocoding Project
.
Am J Epidemiol
2002
;
156
:
471
82
.
25
Krieger
N
,
Waterman
P
,
Chen
JT
, et al
.
Zip code caveat: bias due to spatiotemporal mismatches between zip codes and US census-defined geographic areas—the Public Health Disparities Geocoding Project
.
Am J Public Health
2002
;
92
:
1100
2
.