Change to FIT Increased CRC Screening Rates: Evaluation of a US Screening Outreach Program | Page 2

Survey Sample: Patients eligible to receive the survey about their experiences with both gFOBT and FIT included HMO members who had received an ATC for CRC screening between March and June 2009, had a PCP, and had no diagnosis of dementia in their electronic medical record (EMR). Of a total eligible population of 8077, we mailed the survey to 2000 randomly selected adults; of this population, 1816 (90.8%) were contacted. Reasons for non-contact included incorrect phone numbers and/or addresses; 48.6% (N = 883) responded. We analyzed responses from 192 patients who had previously received both types of fecal tests and answered questions about barriers and facilitators of fecal test completion.

Survey Design. We included questions both from known validated prior questionnaires and questions that we designed in order to improve understanding of issues that emerged from 4 individual patient interviews. Interviewed patients were selected from lists of patients of PCPs at KPNW who had either the highest screening rates or the lowest screening rates. All 4 patients who agreed to be interviewed had completed screening. Two had higher screening rate PCPs and 2 had lower screening rate PCPs. Interviewees shared their beliefs about and knowledge of colorectal cancer and their perceived individual risk for cancer. Domains of the questionnaire included validated questions about beliefs, worries, and knowledge about CRC screening,^31-36 experiences with specific CRC-screening tests, experiences with healthcare providers and members of the healthcare team,^37,38 and perceived barriers and facilitators to CRC screening completion.^13,39-42

The subset of survey respondents (N = 192) who answered specific questions about both gFOBT and FIT used a Likert scale (1 indicating strong agreement and 5 indicating strong disagreement) to answer questions about specific test perceptions and experiences of gFOBT and FIT (Table 1).

Study Variables for Cox Proportional Hazards Regression. We extracted the following variables from the EMR: The primary outcome of CRC screening completion (any of gFOBT, FIT, flexible sigmoidoscopy, colonoscopy, or dual contrast barium enema) within 9 months of an ATC, demographic variables (age at the time the ATC was received, gender, race/ethnicity—derived from electronic databases, with missing data geocoded using the census tract block corresponding to each patient’s mailing address), health characteristics (BMI, number of active medications at the time of ATC receipt), “era” (whether they received the gFOBT or FIT as part of the ATC outreach program), and, lastly, the variables describing encounters with the healthcare system. These latter variables included length of KP membership (by 3 years), whether the participant had a PCP (vs none), and whether they had visited their PCP (vs no PCP visit) or a different PCP (vs no “other” PCP visit) within 9 months of the ATC. Healthcare encounters also included visits with medical specialists (vs no visit) or with “other” specialists (eg, orthopedic surgery, neurosurgery, optometry) within 9 months of the ATC (vs no “other” specialty visit).

Analysis Approach—Cohort Analysis. Cox proportional hazard models were used to assess the association between factor  that may be predictive of completing screening, and to assess whether those factors were associated with FIT or FOBT. Factors related to screening completion (using any screening method) were first tested with bivariate models and significant factors were carried forward into the multivariable model. We entered variables into the multivariable model in steps in the following order: 1) “Screening era” was the first variable examined (FIT vs gFOBT, with the latter as the reference level); 2) Next, we added demographic and health characteristics of the patient (eg, age, gender, number of medications—as a measure of disease burden) and patient healthcare utilization factors (eg, recent visit to PCP); and 3) significant interaction terms (screening era by patient characteristic/utilization). To aid in interpretation of the interactions, we stratified the data by screening era, and estimated separate multivariable Cox proportional hazard models for the FIT and gFOBT eras (data not shown).

Analysis Approach—Survey Analysis. We assessed the proportions of patients answering either “agree” or “strongly agree” to each question of the 4-part questions about gFOBT and FIT (Table 1). We compared the proportions within each question between the answers for gFOBT and for FIT using a χ2 test. In all analyses we considered a P <.05 to be statistically significant.

RESULTS

Table 2 compares patient demographic and utilization characteristics between those in the gFOBT era and the FIT era. The mean values and standard deviations in the table demonstrate the cohorts to be similar. In the gFOBT era, 28.3% completed stool testing, 4.1% flexible sigmoidoscopy, and 4.9% colonoscopy. In the FIT era, 37.2% completed stool testing, 1.9% flexible sigmoidoscopy, and 5.9% colonoscopy.

Table 3 displays the results of the Cox proportional hazards regression, comparing the association of fecal test (FIT vs gFOBT) with CRC screening completion within 9 months of an ATC. First, we considered only the association of fecal test era with screening completion; those in the FIT era were more likely to complete screening than those in the gFOBT era (hazard ratio [HR] = 1.33; 95% confidence interval [CI] 1.30-1.36, P <.0001). Results of step 2 of the regression model demonstrate that offering FIT was associated with increased screening completion, even after adjusting for any differences in patient characteristics and utilization variables (HR = 1.40; 95% CI 1.37-1.43, P <.0001). Patient age, gender, length of KP membership, number of medications, and BMI were each bivariately associated with screening completion using a fecal test; however, race was not significantly related to screening completion and thus race was not included in the multivariable model. Being older, male, having a lower BMI, having longer length of KP membership, having an assigned PCP, visiting a PCP other than one’s assigned PCP, or having a medical specialty visit or any other type of specialty visit within 9 months following the call were all associated with increased screening completion.