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Correlates of colorectal cancer screening rates in primary care clinics serving low income, medically underserved populations

. Author manuscript; available in PMC: 2020 Sep 1.

Screening for colorectal cancer (CRC) is effective in reducing CRC burden. Primary care clinics have an important role in increasing screening. We investigated associations between clinic-level CRC screening rates of the clinics serving low income, medically underserved population, and clinic-level screening interventions, clinic characteristics and community contexts.

Using data (2015–16) from the Centers for Disease Control and Prevention's (CDC) Colorectal Cancer Control Program, we linked clinic-level data with county-level contextual data from external sources. Analysis variables included clinic-level CRC screening rates, four different evidence-based interventions (EBIs) intended to increase screening, clinic characteristics, and clinic contexts. In the analysis (2018), we used weighted ordinary least square multiple regression analyses to associate EBIs and other covariates with clinic-level screening rates.

Clinics (N = 581) had an average screening rate of 36.3% (weighted. Client reminders had the highest association (5.6 percentage points) with screening rates followed by reducing structural barriers (4.9 percentage points), provider assessment and feedback (3.2 percentage points), and provider reminders (< 1 percentage point). Increases in the number of EBIs was associated with steady increases in the screening rate (5.4 percentage points greater for one EBI). Screening rates were 16.4 percentage points higher in clinics with 4 EBIs vs. no EBI. Clinic characteristics, contexts (e.g. physician density), and context-EBI interactions were also associated with clinic screening rates.

These results may help clinics, especially those serving low income, medically underserved populations, select individual or combinations of EBIs suitable to their contexts while considering costs.

MeSH: Colorectal neoplasms, Community health centers, Evidence based practices, Early detection of cancer

Keywords: Colorectal cancer screening, Primary care clinics, Screening interventions, Evidence-based interventions (EBI), Clinic contexts

Colorectal cancer (CRC) is the third most prevalent cancer among men and women and the second leading cause of cancer related deaths (Centers for Disease Control and Prevention (CDC), 2018). In 2015, 140,788 men and women were diagnosed with CRC and 52,396 deaths were attributed to the disease (U.S. Cancer Statistics Data Visualizations Tool, Based on November 2017 Submission Data (1999–2015), 2018). Recent estimates in the United States (U.S.) indicate that 39% of CRC cases are found at localized stage compared with 21% at distant stage with 5-year survival rate of 89.9% and 13.9% (Howlader et al., n.d.), respectively.

These data highlight the importance of CRC prevention or early detection. Screening is the most effective prevention and early detection strategy to reduce CRC burden (Zauber et al., 2012). Increased use of screening would reduce overall mortality (Lin et al., 2016) and economic burden related to the disease (Etzioni et al., 2003). However, 2015 National Health Interview Survey data indicated only 62.4% of persons ages 50–75 received screening tests consistent with United States Preventive Services Task Force (USPSTF) recommendations (White et al., 2017). Screening rates are often associated with factors including patients' age, gender, ethnicity, insurance coverage, and routine clinic visits (Ioannou et al., 2003; Beydoun and Beydoun, 2008; Holden et al., 2010). For example, CRC screening use among average risk men and women aged 65 and older are 14 to 17 percentage points higher, respectively, than among those aged 50–64 years (Tessaro et al., 2006; Meissner et al., 2006). There are important disparities in CRC screening and outcomes attributed to socio-demographic factors, insurance coverage and geographic locations (Emmons et al., 2009; Wong, 2015; Siegel et al., 2015; Burnett-Hartman et al., 2016). Federally Qualified Health Centers (FQHCs), which traditionally serve low-income, medically underserved populations, have much lower screening rates than the national average. In 2015, FQHCs' average rate was 38.4% compared to the national average of 62.5% (White et al., 2017; 2016 National Health Center Data, 2016).

Historically, cancer screening in the U.S. has been predominantly opportunistic, i.e. based on individual's decision or health care provider recommendation during health encounters (Miles et al., 2004). Recently, public health researchers have promoted more organized approaches to screening (Plescia et al., 2012), including maximizing the role of primary care in prevention and early detection efforts (Rubin et al., 2015). Primary care clinics are uniquely positioned to implement interventions shown to be effective in increasing CRC screening rates (Sarfaty et al., 2013). In 2015, the Centers for Disease Control and Prevention (CDC) funded the Colorectal Cancer Control Program (CRCCP), which includes 30 state, tribal, and university grantees, for five years with the aim to increase CRC screening rates and reduce disparities among high need populations. The program prioritized populations who were low income, medically underserved and who also had low screening rates. CRCCP grantees partner with health systems such as FQHCs and clinics serving the priority populations to implement up to four priority evidence-based interventions (EBIs) recommended in The Community Guide (Force CPST, 2017) and up to four supporting activities (SAs) (Table 1).

Clinic-level evidence-based interventions and supporting activities.

To evaluate the CRCCP, the CDC collects a baseline record, including clinic-level screening rates, at the time of clinic recruitment followed by an annual record thereafter (Satsangi and DeGroff, 2016). Analysis of these clinic data completed after one year of program implementation found that grantees were working with the intended population - over 70% of clinics were FQHCs and 30% of clinics had populations with 20% or greater uninsured patients. Additionally, the average baseline CRC screening rate for clinics was 43%, far lower than national rates. Previous research suggests individual-level screening decisions are related to clinic factors including size, distance to endoscopy centers, clinical support arrangements (Weiss et al., 2013; Yano et al., 2007; Pruitt et al., 2014; Wheeler et al., 2014), and contextual factors such as physician density, rural/urban status, and other aspects contributing to socio-economic deprivation (Anderson et al., 2013; Calo et al., 2015; Davis et al., 2017; Doubeni et al., 2012; Shariff-Marco et al., 2013). CRCCP data allows us to explore similar relationships at the clinic level, where little research exists. In this paper, we investigated associations between clinic-level screening rates among clinics serving low income, medically underserved populations and clinic-level EBI use at baseline, controlling for the effects of clinic and contextual factors.

All clinics recruited through the CRCCP's second program year with a baseline CRC screening rate reported were included in the analysis. Clinic data included clinic characteristics (e.g., size, type, preferred CRC test, proportion of uninsured patients), implementation status of EBIs/SAs, and CRC screening rate. Information about the clinic data, including related collection and reporting processes, have been previously published (Satsangi and DeGroff, 2016; DeGroff et al., 2018). Clinic data are self-reported. The baseline screening rates represent the 12-month period prior to program participation spanning from 2015 to 2016. We used county-level contextual data from external sources: Spatial Impact Factor database (2015), United States Department of Agriculture (USDA) – Rural Atlas (2013), and United States Cancer Statistics (USCS) (2015). External data were linked with CRCCP clinic data using county identifiers. Contextual data provided county level socio-demographic and other health-related information for counties where CRCCP clinics were located. The clinic data were collected in 2015–2016 and analysis was conducted in 2018.

The outcome/dependent variable was the clinic CRC screening rate, defined as the percentage of 50–75 year old clinic patients up-to-date with CRC screening according to USPSTF guidelines. Explanatory/independent variables included four dichotomous (0, 1) variables for the clinic level EBIs, representing whether the respective EBIs were in place or not in place. Other explanatory variables included ones for clinic characteristics and clinic contexts. Variables for clinic characteristics included number of SAs, clinic size and type, percentage of patients uninsured, preferred screening test type by the clinic, and availability of free fecal tests such as fecal immunochemical tests (FIT). All clinic characteristics were categorical except the number of SAs which was a count variable. Clinic contextual variables (county-level) were all continuous. They included average distance to an endoscopy suite in miles from Special Impact Factor database; number of primary care providers (PCPs) per 100,000 population, poverty rate (percent in 2014), percent of people with college education or higher, percent of Hispanic, Black and Asian residents from Rural Atlas – USDA database; and CRC incidence rate (per 100,000 population in 2010–2014) from uses.

We used bivariate and multiple regression analyses to associate the clinic-level interventions with the dependent variable. We used an ordinary least square (OLS) regression model where observations (i.e., clinics) were weighted by the clinic's number of screening eligible patients. A P-value p < .05 was used to determine the statistical significance of estimated regression coefficients. Bonferroni correction was applied a posteriori to avoid the risk of making a type-I error because the study involved simultaneous testing of several hypotheses. For Bonferroni correction, we used a p-value of 0.05/34 = 0.0015 where 34 was the total number of independent variables including constant tested in these regression models.

We used different OLS models to determine associations between clinic level EBIs and clinic screening rates. First, we used bivariate models to examine the association between each independent variable and the outcome variable. Next, we used the EBIs + SAs Model with covariates limited to EBIs and SAs. The Partial Model extended the EBIs + SAs Model by adding clinic characteristic variables as covariates. The Full Model extended the Partial Model to include all covariates (Partial Model covariates plus contextual variables).

Additionally, we estimated several regression models using screening rate as the dependent variable and different sets of independent variables. We used EBI count (0–4, with 0 EBIs as the referent) as the independent variable with no covariates (Bivariate EBI Count Model) and with all covariates (Full EBI Count Model). Next, we used every combination of the 4 EBIs without (Partial Multi-Component Model) and with covariates (Full Multi-Component Model). The Multi-Component Models used 15 EBI interaction terms – indicators for each combination of EBI categories (0 and 1). Given a large number of covariates used in the analysis, we conducted a post-hoc power analysis to detect an effect size of 3% with α = 0.01 on a sample of 581 clinics. The Full Multi-Component Model had the largest number of covariates – a total of 34 predictors. Power analysis showed that with the sample size of 581 clinics (unweighted), we had at least 94.4% chance of detecting a moderation effect size of 3%, if the effect truly is present. All statistical analyses were conducted using STATA (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC). Power analysis was conducted using G*Power software by Heinrich Heine University. CDC determined this study to be public health practice and exempt from human subjects review.

Analysis included 581 clinics in total. All results presented were weighted unless specified as unweighted. A large proportion (69%, unweighted) of clinics were FQHCs, while other types comprised 31% (unweighted) or less (Table 2). After weighting, the share of FQHCs was only 54%. More than one fifth of the clinics (22%) reported serving a patient population with over 20% uninsured. At least one EBI was used in 87% of clinics with provider reminders most often in place (66%). Among clinics, 67% had at least one SA in place. The average distance to an endoscopy suite in clinic counties was about 14 miles and there were an average of 81 primary care providers (PCPs) per 100,000 population. The average population with a college degree or higher was 30%. The average CRC incidence rate was 46 per 100,000 population (2010–2014).

Summary statistics of all study variables (categorical and continuous).

Regression results are presented in Tables 3-4. All four priority EBIs were positively associated with clinic screening rates with statistical significance (Table 3). Results were generally consistent across all four models. In the Full Model, client reminders had the most substantial association with screening rates (5.6 percentage points) followed by reducing structural barriers (4.9 percentage points) and provider assessment and feedback (3.2 percentage points). Provider reminders were also positively associated with clinic screening rates, but the association (< 1 percentage point) was the weakest of all EBIs. The strength of the association between EBIs and the outcome was sensitive to model specification. The magnitude of the regression coefficients on EBIs were larger in bivariate models compared to all other models suggesting their correlations with the added covariates. SAs, although statistically significant, were weakly associated with the outcome.

Results (estimated coefficients of associations) from different ordinary least square regression models^a.

Regression results (estimated coefficients of associations) for evidence based interventions (EBIs) counts and EBI combinations^a.

The associations between clinic characteristics and the outcome were mixed. Compared to the clinics with less than a 5% uninsured patient population, clinics with 5–20% and above 20% uninsured patients had lower screening rates (−3.8 and −5.5 percentage points, respectively). Both medium and large size clinics were associated with higher clinic screening rates compared with small size clinics. Likewise, when compared to FQHCs, all other clinic types were associated with higher screening rates. The association ranged from 9.4 percentage points for health department clinics to 16.2 percentage points for health system/hospital owned clinics. Clinics using FIT as the primary CRC screening test and providing free fecal test kits were both associated with lower screening rates than clinics primarily referring for colonoscopy (−2.5 and −6.3 percentage points respectively).

The average distance to endoscopy suites and the number of PCPs per 100,000 population were negatively associated with clinic screening rates. The percent of county population with a college degree and higher had the largest positive association with clinic screening rates (0.7 percentage points) followed by percent of the non-Hispanic Asian population (0.4) and CRC incidence rate (0.3). Poverty rates (0.2) and percent of the Hispanic population percentage (0.1) in counties were also associated positively while the percentage of non-Hispanic Black populations was associated negatively with clinic screening rates.

Overall, when controlling for clinic and contextual factors, higher numbers of EBIs were associated with a steady increase in clinic screening rates from 5.4 percentage points for any one EBI to 15.1 percentage points for all 4 EBIs (Table 4, Full EBI Count Model). However, compared to no EBIs, different combinations of two or more EBIs did not have similarly steady associations with the outcome. Some inconsistencies were likely due to the small number of clinics in the group. However, a consistent pattern emerged when clinics had any of three or all four EBIs in place. Clinics with three or four EBIs in place had 9.1 to 16.4 percentage points higher screening rates compared to those with none in place (Table 4, Full Multi-Component Model). Almost all estimated statistically significant coefficients in Table 3 and Table 4 remained significant at 5% level after Bonferroni correction. Boldface indicates statistical significance at 5% level even after Bonferroni correction.

In this exploratory work, we used a unique dataset from CDC's CRCCP to measure associations between EBIs recommended in The Community Guide and baseline clinic-level CRC screening rates. The study was not designed to measure causal impact, therefore, the estimated associations do not necessarily indicate causal links between covariates and CRC screening rates.

These results contribute to the literature by showing that the four priority EBIs used in the CRCCP are positively associated with screening use in low-resourced, community-based settings including FQHCs and local health department clinics serving low income, medically underserved populations. Given that all EBIs and almost all EBI combinations were associated with higher mean clinic screening rates, clinics may be able to choose to implement EBIs most suitable and least costly to them. Findings indicate that all individual EBIs, all numbers of EBIs, and all combinations of EBIs, except one, were associated with increased screening use. Thus, clinics may be able to select EBIs or combinations of EBIs that they believe best address their local needs. However, findings also indicate that some EBIs and combinations of EBIs may be associated with greater screening use than others. For example, larger numbers of EBIs were associated with higher screening use than a single EBI.

Our results are consistent with past studies, and, therefore, further strengthen the body of evidence tying these interventions to increased screening use. A systematic review (Holden et al., 2010) of past studies found that the influence of client reminders on CRC screening rates ranged from 5 to 15 percentage points and no influence of provider reminder, consistent with our estimates. However, the same study found that eliminating structural barriers increased CRC screening rates by 15.0 to 42.0 percentage points, much higher than our finding of association between reducing structural barriers and clinic level screening rates. A meta-analysis of evidence on CRC screening interventions examined the effect of provider assessment and feedback, client reminders, and provider reminders (Stone et al., 2002). Among the three EBIs, client reminders had the greatest effect, followed by provider reminders and provider assessment and feedback. Our results found client reminders and reducing structural barriers to have greater associations with screening rates, followed by provider assessment and feedback and provider reminders. Our finding that supporting activities were weakly, although positively, associated with screening rates are also compatible with results from a past study (Holden et al., 2010). However, we should note that, unlike the past systematic reviews/meta analyses cited above, our unit of analysis was the clinic, not patients. This implies that using clinics as the unit of analysis also allows us to reach similar conclusions about the relationships between interventions and outcomes.

Our finding of the significant and negative association between the percent uninsured in a clinic and screening rate was expected, corroborating findings from previous studies (Beydoun and Beydoun, 2008; Holden et al., 2010). That non-FQHC clinics had higher screening rates than FQHCs may highlight the fact that the latter serve patients who are more disadvantaged with lower screening rates than other populations included in this study. Similarly, our finding that clinics with larger patient populations had higher screening rates suggests availability of resources and clinic capacity may underlie their performance. Programmatically, because EBIs can be resource-intensive to implement irrespective of the clinic size, our results support targeting larger clinics, when feasible and appropriate, where greater impact can be achieved. Our finding that clinics primarily using FIT tests or providing free fecal kits were associated with lower screening rates might represent a relationship between clinics preferring FIT tests and clinics with lower screening rates (such as FQHCs). Additionally, assuring annual FIT testing may be more difficult in contrast to colonoscopy which is required only once each 10 years.

Importantly, findings indicate stronger associations with higher screening rates when multiple EBIs were implemented. Additionally, the association between the number of EBIs and clinic screening rates increased steadily as the number of EBIs increased. Unlike the combinations of two EBIs, any combination of three or all four EBIs had greater and more consistent associations with higher screening rates. We also observed that having provider reminders in the EBI combination was associated with a smaller increase in screening rates than not having it. This reconfirms the weaker association of provider reminders we observed as an independent EBI in the base model. Together, these results for two or more EBI combinations are consistent with evidence supporting multi-component interventions reported in the past by the Community Preventive Services Task Force (Weiss et al., 2013) and other studies (Power et al, 2009; Community-Preventive-Services-Task-Force, 2016). Further, the effectiveness of these EBIs can substantially vary by context. Future work in this area can evaluate the association between EBIs and screening rate in different contexts.

Several limitations are noted. First, the clinic data are self-reported by CRCCP awardees and we lacked information on the quality or intensity of EBI implementation. Consequently, the effectiveness of EBIs likely varied across clinics. Moreover, grantees follow recommendations detailed in The Community Guide about these strategies which could be different than the way they have been implemented in studies that established their effectiveness. Second, the data do not capture all variables likely to influence and explain variation in clinic-level screening rates. This implies our results may suffer from omitted variable bias, similar to most regression models. Some important omitted variables in this study include those that capture characteristics of individual patients (e.g. age, education, insurance coverage and other barriers) and the ability of clinics to implement EBIs including EBI quality and intensity. Also, the use of county-level information cannot sufficiently capture the variations in contexts with potential ecological biases in results. With county-level variables, we can only interpret those effects as change in outcomes associated with change in clinic contexts (such as socio-demographic characteristics of the population served) represented by those variables. Making inferences on clinic-level relationships using aggregated county-level data could potentially suffer from ecological fallacy (i.e. individual level relationships can be eclipsed due to data aggregation). Further, as associations are not causal, other factors may have confounded relationships with EBIs and clinic screening rates. For instance, a clinic's ability to implement multiple EBIs might also be influenced by its culture, priorities, and supporting infrastructure. Existence of these factors might affect screening use through avenues other than the implementation of EBIs. Finally, this study was conducted primarily among clinics serving low income, medically underserved populations in community clinics such as FQHCs. The generalizability of these results, therefore, is limited.

Results from our analysis provide new insights on how clinic-level interventions including EBIs and SAs, clinic characteristics, and contextual factors are associated with clinic-level CRC screening rates. Such insights, combined with other practical considerations, can inform the design and implementation of clinic-focused, organized approaches to increase CRC screening rates.

Financial disclosure

No financial disclosures were reported by the authors of this paper.

Declaration of Competing Interest

None of the authors has any conflicts of interest to report.

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