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Data on the number of individuals eligible for screening, and rates of screening, are necessary to assess national colorectal cancer (CRC) screening efforts. Such data are sparse for safety-net health systems.
A retrospective cohort study of individuals aged 50 to 75 served by a safety-net health system in Tarrant County, Texas was conducted to determine: (a) Size of the potential screen-eligible population aged 50–75, (b) Rate of screening over 5 years among individuals aged 54–75, and (c) Potential predictors of screening, including sex, race/ethnicity, insurance status, frequency of outpatient visits, and socioeconomic status.
Of 28,708 potential screen-eligible individuals, 20,416 were aged 54–75 and analyzed for screening; 22.0% were screened within the preceding 5 years. Female gender, Hispanic ethnicity, age 65–75, insurance status, and ≥2 outpatient visits were independently associated with screening. Access to care was an important factor: adjusted(aj) OR 2.57 (95% CI 2.23–2.98) for any insurance, ajOR 3.53 (95% CI 3.15–3.97) for ≥2 outpatient visits.
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 CRC screening interventions (including research into the best manner to provide screening to large populations) is needed.
Though screening has the potential to prevent colorectal cancer 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 impact prevention of death from CRC 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.
The Tarrant County Hospital District John Peter Smith Hospital Health Network (JPS) is a large county health system for Tarrant County, Texas(including service to Fort Worth), serving an ethnically diverse population of over 155,000 unique individuals through over 850,000 outpatient and inpatient encounters yearly1. 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 performed, 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. Record of screening test completion was based on internal administrative coding for FOBT, barium enema, flexible sigmoidoscopy, and colonoscopy. Year 2006 was used as the reference year for age, zip code of residence, and insurance type. Analyses were conducted to ascertain the following: 1) Size of the potential screen-eligible population, 2) Rate of CRC screening test completion over a 5-year period, and 3) Predictors of CRC screening test completion.
We queried the administrative database for men and women aged 50 to 75 and alive in 2006, and retrieved all specified data for these individuals available in the database from January 1, 2002 through 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 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 age 50 to 75 at time of a health system encounter in 2006, without diagnostic coding consistent with inflammatory bowel disease, colorectal polyps, or colorectal cancer.
From the potential screen-eligible population, we identified individuals aged 54 to 75 in 2006. 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 years 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 2002 through 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 2002 through 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) performed was not available from administrative data, distinction between tests done for symptoms or screening was not made(3, 7).
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), percent of individuals living below poverty (determined by zip code of residence), presence of ≥2 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.
The primary goals of the analyses were to estimate the size of the potential screen-eligible population, calculate rate of CRC screening test completion over a 5-year period, and determine predictors of CRC screening test completion. Descriptive statistics, including proportions with 95% confidence intervals for estimates of rates of screening are 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 percent 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 percent of individuals living in poverty by 5 digit zip code tabulation areas (ZCTA) from the United States Census 2000 Summary File 32. Only participants with ZCTAs partially or fully included in Texas were included for linkage to census data. Zip codes for 98% of participants in the dataset are linked to 5 digit ZCTAs. For estimation of screening participation, age was categorized as 54–64 and 65–75 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: presence or absence 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 values < 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 performed with SAS version 9.1, Cary, N.C.
This study was approved by the Institutional Review Boards of the John Peter Smith Health Network, as well as the University of Texas Southwestern Medical Center.
Figure 1 summarizes the study flow, size of the potential screen-eligible population, and rate of screening. Characteristics of the study population are provided in Table 1. In 2006, over 28,000 potentially screen-eligible individuals aged 50 to 75 were identified. Median age of potential screen-eligible individuals was 57 years; the majority was female, with 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 aged 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 2002–2006—Figure 1). The distribution of CRC screening completion by test type among test completers is summarized by Figure 2. Exclusive screening with FOBT was noted for 56%, and with colonoscopy for 17%.
History of screening completion characterized by demographic group is summarized in Table 2. Univariate analyses revealed that likelihood of screening completion was higher for women, individuals age 65–74, African-,Americans Hispanics, primary Spanish language speakers, individuals seen as an outpatient ≥2 times, and the insured (see Supplementary Appendix 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 ≥2 outpatient visits in 2006. Only gender, age 65–75, Hispanic ethnicity, presence of ≥2 outpatient visits in 2006, and insurance status remained clear independent predictors of screening completion; lower median household income and percent of individuals living below poverty reached statistical significance but confidence intervals included 1 (Table 3).
While 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 (aj) odds ratios (OR) 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, 3.53 (95% CI 3.15–3.97) for ≥2 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 performed in 4.1% individuals without insurance seen less than 2 times as an outpatient compared to 29.5% of individuals with both insurance and two or more outpatient visits. Overall, of 4496 individuals who completed a screening test, 87% had a record of both insurance and ≥2 outpatient visits in 2006.
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 less than 1 in 4 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 ≥2 outpatient visits in 2006) plays a critical role in determining whether individuals complete CRC screening in this setting.
When placed in context of national screening recommendations from the US Multisociety Task Force on Colorectal Cancer(10) and the US Preventive Services Task Force (USPSTF)(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 FIT). USPSTF guidelines do not make these distinctions, and suggest screening with highly sensitive FOBT, flexible sigmoidoscopy, or colonoscopy based on modeling that demonstrated similar life-years gained for all three approaches compared to 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 approximate 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 USPSTF 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 impact 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 for 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).
There are several potential limitations to our study. First our estimates and conclusions are based on administrative data, thus misclassifications in completion of a screening test and estimates of predictors of screening could have occurred(17, 18). However, preliminary results from an ongoing ancillary study, in which the paper and electronic medical charts for a random sample of 100 individuals each with administrative record of FOBT, colonoscopy, flexible sigmoidoscopy, barium enema, or “no screening,” have been systematically reviewed suggest that the agreement of administrative coding with individual chart record is substantial for all administrative coding overall (kappa=0.63, 95%CI: 0.55–0.71), and almost perfect for the most common tests completed (colonoscopy and FOBT). Further, just 4% of individuals with no administrative coding for screening had record of screening on chart review, the sensitivity of a positive administrative code for test completion ranged 91 to 99%, and the specificity ranged 55 to 92%. Thus, imprecise administrative coding may have had limited impact on our estimates of screening prevalence. Second, JPS is not a closed health system; therefore some individuals, particularly those with insurance, may have had screening performed at other health facilities, leading to underestimates of screening participation. Third, only colonoscopy data for 5 rather than 10 years were available. Though an individual who had a colonoscopy between 1997 and 2006 would be guideline adherent to CRC screening based on current definitions(10), lack of electronic record of colonoscopy procedures prior to 2002 precluded analysis based on this criterion. Colonoscopy was not routinely recommended for primary screening in our system prior to this period, perhaps minimizing underestimation of screening completion based on this factor. 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 performed 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 demonstrated 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 screening because indications were not abstracted (3, 7). Fifth, zip code linkage to ZCTA 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 are 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 ≥2 times, with at least one non-urgent care/non-emgency 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). Further, 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 fo 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 demonstrated 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. Further, short and long term screening guidelines and policy efforts must take into account the feasibility and potential costs of proposed interventions. Substantial resources for near 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.
*This project was supported by National Institutes of Health grant number 1 KL2 RR024983-01, titled, “North and Central Texas Clinical and Translational Science Initiative” (Milton Packer, M.D., PI) from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH) and NIH Roadmap for Medical Research, and its contents are solely the responsibility of the authors and do not necessarily represent the official view of the NCRR or NIH. Information on NCRR is available at http://www.ncrr.nih.gov/. Information on Re-engineering the Clinical Research Enterprise can be obtained from http://nihroadmap.nih.gov/clinicalresearch/overview-translational.asp.
‡Acknowledgements: We would like to thank Susan Crabtree for help with dataset extraction, and Bonnie Rose, RN for help with chart review, Dr. Jay Haynes for supporting our collaboration and logistical support, as well as Dr. Anna Schenck for providing a framework tool for our ongoing validation study of administrative data
1John Peter Smith Health Network 2007 Report to the Community. Accessed at www.jpshealthnet.org/About-JPS/Annual-Reports.aspx on 10 August 2008.
2U.S. Census Bureau. Census 2000 Summary File 3; Matrices P53, P77; generated by Samir Gupta; using American FactFinder at www.factfinder.census.gov on 2 July 2008.