David M. Mosen, PhD, MPH

 Examine association of comprehensiveness of colorectal cancer (CRC) screening discussion by primary care physicians (PCPs) with completion of CRC screening.

Observational study in Kaiser Permanente Northwest, a group-model health maintenance organization.

 A total of 883 participants overdue for CRC screening received an automated telephone call (ATC) between April and June 2009 encouraging CRC screening. Between January and March 2010, participants completed a survey on PCPs’ discussion of CRC screening and patient beliefs regarding screening. Primary outcome measure: receipt of CRC screening (assessed by electronic medical record [EMR], 9 months after ATC). Primary independent variable: comprehensiveness of CRC screening discussion by PCPs (7-item scale). Secondary independent variables: perceived benefits of screening (4-item scale assessing respondents’ agreement with benefits of timely screening) and primary care utilization (EMR; 9 months after ATC). The independent association of variables with CRC screening was assessed with logistic regression.

 Average scores for comprehensiveness of CRC discussion and perceived benefits were 0.4 (range 0-1) and 4.0 (range 1-5), respectively. A total of 28.2% (n = 249) completed screening, 84% of whom had survey assessments after their screening date. Of screeners, 95.2% completed the fecal immunochemical test. More comprehensive discussion of CRC screening was associated with increased screening (odds ratio [OR] = 1.51, 95% confidence interval [CI] = 1.03-2.21). Higher perceived benefits (OR = 1.46, 95% CI = 1.13-1.90) and 1 or more PCP visits (OR = 5.82, 95% CI = 3.87-8.74) were also associated with increased screening.

 More comprehensive discussion of CRC screening was independently associated with increased CRC screening. Primary care utilization was even more strongly associated with CRC screening, irrespective of discussion of CRC screening.

 More comprehensive discussion of CRC screening by PCPs was associated with higher screening rates among adults aged 51 to 80 years who were overdue for screening.

 Higher perceived benefits of CRC screening by patients was also associated with increased CRC screening.

 1 or more PCP visits was strongly associated with increased CRC screening, irrespective of discussion of CRC screening.

Colorectal cancer (CRC) is the third-most common cancer and the second-leading cause of cancer-related death in the United States.

 Recent estimates suggest that CRC will be newly diagnosed in about 112,340 people annually, resulting in over 52,180 deaths per year.

Total direct costs associated with treating CRC are $17 billion annually, with 9.8 million work days lost annually to hospitalization for colon cancer.

The early detection of high-risk lesions, or CRC itself, through appropriate screening is associated with decreased incidence of and mortality from CRC.

 The US Preventive Services Task Force recommends that men and women of average risk begin screening for CRC starting at age 50 years,

 and specialty care guidelines provide recommendations for higher-risk groups.

 Currently, recommended CRC screening modalities for average-risk patients include annual fecal occult blood testing (FOBT), colonoscopy every 10 years, or flexible sigmoidoscopy every 5 years, with or without interval FOBT.

 In addition, as a result of improved test performance and usability, in 2008 multiple professional societies endorsed the use of 4 types of fecal immunochemical tests (FITs) for CRC screening to replace FOBT.

Despite the proven benefits of CRC screening, screening rates remain suboptimal. Although more than half of adults 50 years and older in the United States have ever received a CRC screening test, less than half receive screening tests at recommended intervals.

and specific health-system characteristics are associated with receipt of CRC screening (having usual sources of care,

 and assistance with scheduling of colonoscopy),

 less is known about how providerpatient interactions and comprehensiveness of CRC discussion by primary care physicians (PCPs) influence CRC screening. Previous studies indicate that physician recommendation for screening

 may increase CRC screening. However, less is known about how the comprehensiveness of discussion by PCPs about testing options, screening intervals, and potential test complications may influence actual CRC screening. Study of the association of the comprehensiveness of CRC discussion by PCPs with receipt of CRC screening can inform the development of quality-improvement interventions.

The primary objective of this study was to determine the independent association of comprehensiveness of CRC screening discussion by PCPs, as reported by patients, with receipt of CRC screening.

The protocol for this study was approved by the institutional review board within the study health maintenance organization (HMO).

Study Site, Survey, and Electronic Data Sources

The study was conducted at Kaiser Permanente Northwest (KPNW), a not-for-profit HMO with about 485,000 members in southwest Washington and the Portland, Oregon, metro area. The members’ demographic characteristics (age, gender, race/ethnicity) are similar to those of the area population.

KPNW regional electronic databases provided data on patient membership, demographics, primary care assignment, clinical data (including weight and height, laboratory results, and other healthcare utilization, including CRC screening), and clinician data. These data capture over 95% of all medical care and pharmacy services members receive, and data are linked through each member’s health record number.

We included HMO members aged 50 to 80 years who were at average risk for developing CRC, were overdue for CRC screening, and received an automated telephone call as part of a CRC screening-outreach program between March and June 2009.

 The specifics of the automated call program, including the definition of “average risk,” have been described previously.

 Essentially, the average-risk population includes those who are overdue for CRC screening and do not meet any of the following

criteria: (1) active colon cancer/risk factors that would indicate need for non-routine screening, (2) ever diagnosed with adenomatous polyps or human immunodeficiency virus/acquired immunodeficiency syndrome, (3) referred for colonoscopy or sigmoidoscopy in previous 3 months, or (4) receipt of plavix or warfarin medications in previous 4 months that may increase risk of false-positive FIT. The brief automated telephone calls (ATCs) included information about the benefits of CRC screening, encouraged FOBT as a relatively simple and lowrisk method of screening, and allowed patients to request an FOBT by pushing a number on their telephone. The date of the telephone call was defined as the intervention index date.

Of the population receiving automated telephone calls, a random sample of 2000 adults were mailed a survey in January 2010. We chose to begin survey outreach efforts in January 2010 in order to have the most time for those efforts, while still ensuring that sufficient time elapsed (6-9 months) for patients who received ATCs between March and June 2009 to complete screening.

The process flow of the survey administration process is described in the 

 We sent initial surveys to study respondents on January 15, 2010. Next, we sent a reminder postcard to non-responders 2 weeks after the initial mailing. Finally, we mailed the survey a second time to non-responders about 3 weeks after the reminder postcard. Survey enrollment was completed by March 31, 2010. Of the whole population, 1816 (90.8%) were contacted. A total of 184 participants were not contacted due to incorrect addresses. Of those contacted, 883 completed the survey, for a response rate of 48.6% (883/1816). Compared with non-respondents, respondents were more likely to be white (respondents = 95.3% vs non-respondents = 87.5%, 

<.0001), female (respondents = 58.3% vs non-respondents = 48.4%, 

<.0001), and taking more medications (respondents [mean ± standard deviation (SD)] = [3.1 ± 3.8]). Responders and non-responders did not differ in age or length of health plan membership.

This survey was designed to better understand the barriers and facilitators that patients encountered in their efforts to complete CRC screening. We focused on 2 patient-reported domains: (1) patients’ perceptions of clinician-patient counseling, such as comprehensiveness of CRC screening discussion by the PCP, and (2) patient beliefs; namely, perceived benefits of CRC screening. Comprehensiveness of CRC discussion was the primary independent variable, while primary care utilization and perceived benefits of CRC screening were the secondary independent variables.

Comprehensiveness of CRC Screening Discussion, Based on Patient Self-Report

Comprehensiveness of CRC screening discussion.

 These items were based on previous work from Braddock and colleagues

 that assessed levels of informed decision making occurring during patient-provider communications regarding CRC screening. Informed decision making, a means for facilitating patient participation in decision making, includes (1) providing relevant information about the clinical situation, alternatives, and risks and benefits; (2) assessing the patient’s understanding; and (3) giving the patient a clear opportunity to voice a preference.

 Seven yes/no items measured the content of patients’ discussions with their PCPs. We asked patients whether their PCPs did each of the following at any time in the 2 years prior to survey: (1) explained benefits of CRC screening, (2) discussed how often each screening test should be done, (3) provided information on how to do various tests, (4) explained the accuracy of each screening test, (5) explained potential test complications from screening tests, (6) asked if the patient understood or had any questions about CRC screening, and (7) asked if the patient had the information he or she wanted about CRC screening. These 7 items had high internal consistency (Cronbach’s alpha = 0.92). To calculate an average score, each of the 7 items was summed together (0.0 = no, 1.0 = yes) and divided by 7. A score of 1 indicates that patients answered “yes” to all 7 questions, while a score of 0 indicates a “no” answer to all 7 questions. Only the responses of patients who answered all 7 questions were included in the variable describing this scale.

Perceived Benefits of CRC Screening, Based on Patient Self-Report

Four items measured perceived benefits of CRC screening, scored on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Similar to the scale for comprehensiveness of CRC discussion, the perceived-benefits scale also had good internal consistency (Cronbach’s alpha = 0.80). To calculate an average score, each of the 4 items was summed together (1 = strongly disagree, 5 = strongly agree) and divided by 4. A score of 5 indicates that the respondent answered “strongly agree” to all 4 questions, and a score of 1 indicates an answer of “strongly disagree” to all 4 questions. Only the responses of patients who answered all 4 questions were included in the variable describing this scale. These questions were adapted from Myers and colleagues28-30 and measured general agreement with the following statements: (1) CRC screening is necessary even if no family history exists, (2) getting screening for CRC can protect health, (3) when colon polyps are found and removed, CRC can be prevented, and (4) when CRC is found early, it can be cured. The rationale for selecting perceived benefits is that beliefs about colon cancer have been identified in the literature as an important characteristic associated with CRC screening.

All patients were due for screening at the time of the ATC; the primary outcome measure was whether the patient completed CRC screening in the 9 months following the ATC. CRC screening included any of the following: 1 sample FIT, receipt of colonoscopy, and receipt of flexible sigmoidoscopy. The distribution of first completed CRC screening test for this study population was FIT (95.2%), colonoscopy (2.8%), and sigmoidoscopy (2.0%).

We identified patient age in relation to the intervention index date, and we identified gender from member eligibility files. We also determined length of KPNW membership. Individual racial categories were obtained from electronic databases for 267 (30.4%) participants, and missing data were imputed using the census tract block corresponding to each subject’s mailing address. Patient race was categorized as white or non-white (African American, Pacific Islander, Asian, and Native American). In addition, as a measure intended to reflect disease burden,

we determined the mean number (± SD) of unique generic drugs dispensed within the 6 months prior to index date for each participant. We also determined whether survey respondents had any primary care visits (internal medicine, family practice; >1 visits vs none) in the 9 months after the intervention index date.

We used logistic regression to model CRC screening during the 9 months following index date (receipt of ATC). Final variables were entered simultaneously and included age (continuous, by 10 years), gender (female vs male), number of medications (continuous), comprehensiveness of CRC discussion scale (continuous), perceived benefits scale (continuous), and having any primary care visit during the follow-up period. With the exception of age and gender, all variables with significant bivariate associations with CRC screening completion were included in the final multivariable model. Although not significant in bivariate analysis, age and gender were retained in final logistic models to adjust for demographics, given that age and gender have been associated with CRC screening in previous research. We lacked complete data on some of the central variables (ie, PCP discussion scale, perceived benefits scale), and we imputed missing data by multiple imputation (MI). Multiple imputation is considered a scientifically rigorous approach to handle missing data.

The MI and MIANALYZE procedures in SAS used 5 replications. All analyses were conducted using SAS version 9.2 (SAS Institute, Cary, North Carolina).

 presents the population characteristics of patients who completed the patient survey. The mean age was 59 years, nearly 60% were female, 95% were white, and the average number of years with KPNW was 14. The average number of medications taken at baseline was 3.1. The average score on the comprehensiveness of CRC discussion scale was less than 0.5 (mean = 0.4, SD = 0.4, possible range 0 to 1), indicating that patients, on average, answered “yes” to fewer than 4 of the 7 screening discussion questions. In addition, the average score on the perceived benefits scale was high (mean = 4.0, SD = 0.7; possible range 1 to 5), suggesting that most believed in the benefits of CRC screening. Most (60%) reported 1 or more primary care visits in the 9 months after the intervention index date.

presents unadjusted and adjusted logistic regression results. Patients’ perceptions of the comprehensiveness of CRC screening discussions and perceived benefits had independent significant (

 <.05) associations with completion of CRC screening, after simultaneous adjustment. A higher score on the comprehensiveness of CRC screening discussion scale was associated with greater odds of completing CRC screening in both unadjusted (OR, 1.84; 95% CI, 1.20-2.81, 

 = .005) and adjusted (OR, 1.51; 95% CI, 1.03-2.21, 

 = .035) analyses. A higher score on the perceived benefits scale was associated with greater odds of completing CRC screening in both unadjusted (OR, 1.70; 95% CI, 1.32-2.18, 

<.0001) and adjusted (OR, 1.46; 95% CI, 1.13-1.90,

Of patient-demographic and clinical-characteristic covariates, 1 or more primary care visits in the follow-up period (9 months after ATC) was associated with greater odds of completing CRC screening in both unadjusted (OR, 6.16; 95% CI, 4.16-9.14, 

 <.0001) and adjusted (OR, 5.82; 95% CI, 3.87- 8.74, 

 <.0001) analyses. Having more medications (OR, 1.05; 95% CI, 1.01-1.09, 

= .012) was associated with greater odds of completing CRC screening in the unadjusted analysis; however, this finding did not remain significant in multivariable analysis.

Articles

More comprehensive discussion of colorectal screening by primary care physicians was associated with higher screening rates among adults aged 51 to 80 years who were overdue for screening.

More Comprehensive Discussion of CRC Screening Associated With Higher Screening

To estimate the cost-effectiveness of an automated telephone intervention for colorectal cancer screening from a managed care perspective, using data from a pragmatic randomized controlled trial.

Intervention patients received calls for fecal occult blood testing (FOBT) screening. We searched patients’ electronic medical records for any screening (defined as FOBT, flexible sigmoidoscopy, double-contrast barium enema, or colonoscopy) during follow-up. Intervention costs included project implementation and management, telephone calls, patient identification, and tracking. Screening costs included FOBT (kits, mailing, and processing) and any completed screening tests during follow-up. We estimated the incremental cost-effectiveness ratio (ICER) of the cost per additional screen.

 At 6 months, average costs for intervention and control patients were $37 (25% screened) and $34 (19% screened), respectively. The ICER at 6 months was $42 per additional screen, less than half what other studies have reported. Cost-effectiveness probability was 0.49, 0.84, and 0.99 for willingness-to-pay thresholds of $40, $100, and $200, respectively. Similar results were seen at 9 months. A greater increase in FOBT testing was seen for patients aged 

70 years (45/100 intervention, 33/100 control) compared with younger patients (25/100 intervention, 21/100 control). The intervention was dominant for patients aged 

70 years and was $73 per additional screen for younger patients. It increased screening rates by about 6% and costs by $3 per patient.

At willingness to pay of $100 or more per additional screening test, an automated telephone reminder intervention can be an optimal use of resources.

Colorectal cancer screening has been shown to be a cost-effective intervention, but uncertainty remains over the most efficient methods for increasing screening rates.

Automated phone calling increased screening rates by about 6% and costs by $3 per patient.

The incremental cost-effectiveness ratio per additional screen of $40 is less than half what other studies have reported.

At a willingness to pay of $100 or higher per additional screening test, our study suggests an automated telephone reminder intervention can be an optimal use of resources.

Colorectal cancer (CRC) is a common and often lethal disease with a lifetime risk of about 1 in 20; it causes more than 51,000 deaths annually in the United States. When diagnosed at late stages the 5-year survival rate is less than 12%, but patients with localized tumors have a 5-year survival rate greater than 90%.

 Early detection is clearly a key to improving survival, and screening has been shown to be cost-effective (eg, at a commonly used threshold of $50,000 per year of life gained), with some analyses even suggesting that screening is cost-saving.

 While CRC screening rates have increased in recent years, they are still below 60% for people aged 

Because the favorable cost-effectiveness of CRC screening is now established, attention has shifted to optimizing methods for increasing adherence with CRC screening. The cost-effectiveness of several intervention types has been reported, including mailing stool cards with and without targeted calls, where the cost per additional patient screened has ranged from about $100 to more than $6000 depending on the intensity of the intervention

 where the cost per additional patient screened has ranged from about $100 to $1000.

Automated telephone calls have been used to remind and inform patients about a wide range of preventive and therapeutic healthcare interventions.

 A recently reported pragmatic trial by our group showed that automated telephone reminders significantly improved CRC screening rates.

 Using data from that randomized control trial, this report focuses on the cost-effectiveness of telephone reminders compared with usual care per additional patient screened.

We used findings from a practical randomized control trial that enrolled patients due for CRC screening (as defined by the National Council for Quality Assurance) to estimate the cost-effectiveness of an automated telephone intervention for CRC screening relative to usual care. The cost analysis focused on replication costs

 from the perspective of a health maintenance organization (HMO) and included any method of screening rather than just fecal occult blood testing (FOBT). Our follow-up was less than 1 year, so costs and effects were not discounted; costs were inflated to reflect 2008 dollars.

A complete description of the trial has been reported elsewhere.

 The trial was conducted at Kaiser Permanente Northwest, a not-for-profit, group-model HMO, and was approved by its institutional review board. 

 details how patients were selected in the randomized control trial; 6000 patients (out of 32,320) were randomly allocated to automated telephone contact or usual care. Consistent with US Preventive Services Task Force CRC screening guidelines in place at the time of trial initiation,

we selected average-risk patients aged 51 to 80 years who were eligible for screening (eg, had no evidence of relevant screening in the HMO’s records and did not have risk factors indicating need for disease monitoring).

Intervention group patients were exposed to calls prompting FOBT screening (call type 1) and FOBT return reminder calls (call type 2); while these calls were not personalized, they were directed toward specific individuals. 

details the timing of these calls. Call type 1 was brief (1 minute), provided information about the benefits of CRC screening, and encouraged FOBT as a relatively simple and low-risk method of cancer screening. Recipients who answered could immediately request a mailed FOBT kit by touch-tone. A detailed message that included a telephone number to call for FOBT kit requests was left via voice mail for unanswered calls. Patients who did not get CRC screening received up to 2 “general” automated reminder calls, 6 weeks apart. One additional reminder call (call type 2) was made to patients who had requested an FOBT kit but did not return it within 5 weeks from the date of request; those patients were given the opportunity to request an additional FOBT kit. All FOBT kits used the guaiac method (Hemoccult II; Beckman Coulter, Inc, Brea, California), which requires samples from 3 consecutive stools, and were delivered via standard mail.

Patients randomized to usual care did not receive the telephone contact intervention. Their CRC screening, if any, occurred via normal care processes. At the time of the study, clinicians had access to national guidelines, Intranetbased screening guidelines, and a “panel support tool” (an electronic prompt system embedded in the electronic medical record that informed clinicians which patients were due for preventive services, including CRC screening). For usual care patients, FOBT kits were obtained after a clinician order by picking the kit up either from the laboratory at the clinic where they received their care or from the HMO’s influenza immunization service, located in primary care clinics.

We used the HMO’s electronic data systems to identify patient outcomes. We specified 2 outcomes for the analysis: the cost per any completed screen and the cost per positive FOBT screen. Even though the intervention promoted FOBT, we defined a completed screen as a record of any US Preventive Services Task Force—recognized screening test (FOBT, flexible sigmoidoscopy, double-contrast barium enema [DCBE], or colonoscopy) during follow-up. For both outcomes, a patient was counted as screened only once, even if he or she had multiple tests; but as detailed below, costs for all screening tests during follow-up were included. We used the term “nonfecal tests” to collectively refer to those more invasive testing methods including flexible sigmoidoscopy, DCBE, and colonoscopy. We examined the effect of the intervention on both FOBT and nonfecal tests separately.

We estimated staff costs associated with the intervention from clinical trial records and study staff time estimates. These costs included project management time to oversee the entire process and computer programming efforts for patient identification, electronic file preparation for the automated telephone call vendor, and patient tracking (eg, for repeat calls to nonresponders). To improve generalizability to other systems, salary costs were taken from the US Bureau of Labor and Statistics.

 To fully allocate the costs we added a fringe benefit rate of 30% and overhead rate of 20%. The cost of the calls and their content was based on vendor charges. The cost of developing and maintaining the automated telephone system was embedded in the vendor charge to the HMO. FOBT kit mailing costs were estimated based on bulk mailing rates.

For patients in both groups, the costs associated with the FOBT testing kit, processing, and reporting was estimated from the HMO’s laboratory charges to nonmembers. Clinician time and cost associated with FOBT laboratory result review and follow-up with patients was estimated from a previous economic evaluation in laboratory medicine

 and from interviews with those doing the work. Costs for other types of testing, including DCBE, flexible sigmoidoscopy, and endoscopy, came from the HMO’s accounting database. While a patient was counted as screened only 1 time, all CRC testing-related costs during the follow-up were enumerated and assigned to that patient. We did this because the intervention may have had knock-on effects for other types of CRC testing, including both diagnostic (eg, following a positive FOBT) and screening (eg, encouraging a patient to request colonoscopy screening), and we wanted to more fully account for the economic consequences of the intervention. For example, if a patient was screened with FOBT at month 1 and then subsequently had a colonoscopy during month 8, the cost of both tests was counted in the 9-month analysis. We made no attempt to discern the clinical rationale for a given screening test (eg, a screening vs a diagnostic colonoscopy); rather, we conservatively included all tests for both outcomes and costs. Usual care patients may have gotten their screening advice at an office visit, but we assumed CRC screening advice was unlikely to be the chief reason for the visit, so we did not attempt to include those costs. Any resulting bias from that decision would make usual care appear less costly.

In the clinical trial, the mean age in both groups was about 60 years, with an even female/male split. There were 2943 patients in the intervention group, and 2962 patients in the usual care group. In the intervention group, 22.5% completed FOBT within 6 months, compared with 16.0% in the usual care group. Screening by any method was completed for 23.9% in the intervention versus 17.6% in the usual care group. In the intervention group, 2943 (100%) received the initial call type 1 (general reminder), 2406 (81.8%) received a second type 1 call, and 1632 (55.5%) received a total of 3 calls of type 1. Call type 2 (FOBT return reminder) was made to 18.3% of the intervention group.

We calculated the incremental cost-effectiveness ratio (ICER) as (cost

 = control, and effect = number of patients with a screening test. We used net benefit regression methods

 to estimate the intervention’s probability of being cost-effective. We report the probability of the intervention being cost-effective at several relevant thresholds of willingness to pay for an additional outcome (eg, additional screen) to aid decision makers’ translation of the results to their own setting and specific considerations. We also computed bootstrapped 95% confidence intervals on total costs using the percentile method.

The parent clinical trial revealed a significant intervention effect that depended on patient age, so we undertook a similar subgroup analysis. As was done in the clinical trial, we followed patients for 6 months in our primary analysis. Since fecal screening is recommended annually, the health plan encourages screening on that basis; therefore, we also wanted to examine a time period more compatible with that interval. To accomplish this goal and to avoid the potential overlapping effects from the next annual screening efforts, we undertook a secondary analysis and followed patients for completed tests up to 9 months after their index date. SAS version 9.1 (SAS Institute, Cary, North Carolina) and STATA versions 10 and 11 (STATA Corporation, College Station, Texas) were used for all analyses.

enumerates the resources necessary to implement and sustain the automated calling program in the intervention arm and to carry out all types of CRC screening tests in both arms. 

 shows the resource use and average cost per person in both groups at 6 and 9 months and by age. Accounting for the staff time associated with project management, computer programming, patient tracking and reporting, and the calls themselves, the intervention cost was about $4.20 per person.

For patients of all ages, the average cost per patient for FOBT testing (kits and their processing) was slightly higher in the intervention group, while the per patient cost associated with nonfecal tests (endoscopy, DCBE, and flexible sigmoidoscopy) was slightly higher in the usual care group. We found that the number of FOBT tests completed at 9 months was markedly higher in the intervention group (ie, 812 vs 669). On the other hand, we observed similar overall proportions of patients with nonfecal tests (3.2% intervention and 3.0% usual care, data not shown). But there were differences by age; intervention patients aged 70 years or older had 5.6 nonfecal tests per 100 persons compared with 7.8 per 100 persons in usual care. However, for patients younger than 70 years, nonfecal test use was similar in both groups (7.9 and 8.0 nonfecal tests per 100 persons in the intervention and usual care groups, respectively). FOBT testing increased in both age groups, but the FOBT increase was greater in the older age group (45/100 intervention patients and 33/100 control patients) than in the younger age group (25/100 intervention patients and 21/100 control patients). As shown in Table 2, these observed differences by age in the use of FOBT and nonfecal tests translated into notable differences in costs at both 6 and 9 months. For example, at 9 months the average costs of FOBT and nonfecal tests were similar for patients younger than 70 years in the intervention and usual care groups. However, at 9 months, for patients 70 years or older, the costs of FOBT were 25% higher in the intervention group ($1.50 vs $1.20), while the costs for nonfecal tests were more than 40%

 in the intervention group ($34.60 vs $48.70). While these component costs showed some variation, total costs were similar in both groups, as evidenced by the overlapping confidence limits.

 shows the cost-effectiveness at 6 and 9 months by age using each of the 2 measures of effectiveness: any completed screen and the positive FOBT screen. Overall, we found that the cost for an additional screened patient was $42 at 6 months and $40 at 9 months. At 6 months, the chance of the intervention being cost-effective was about 50% at a willingness to pay of $40 for an additional patient screened. It increased to 84% for a willingness to pay of $100 and 99% for a willingness to pay of $200.

The intervention was dominant (ie, lower costs with a greater proportion of patients screened) at 6 months for patients 70 years or older, but it had an ICER of $73 for younger patients. In both age groups the intervention had a high probability (

95%) of being cost-effective at a willingness to pay of $200 for an additional screened patient at 6 months, but cost-effectiveness was markedly different at willingness-to-pay thresholds of $100 (0.97 and 0.64 for older and younger patients, respectively) and $40 (0.75 and 0.35 for older and younger patients, respectively). We found a similar pattern of results at 9 months.

The cost per additional positive FOBT screen was $1156 at 9 months. The probability of cost-effectiveness was 0.47 at a willingness to pay of $1000 and increased to 0.70 at a willingness to pay of $7000.

Depending on one’s willingness to pay for an additional screen, we found that automated telephone calls can be costeffective. For example, at a willingness to pay of $100 per additional screen, the probability of cost-effectiveness is 0.84 and increases to near-certainty (0.99) at a willingness to pay of $200 per additional screen. But at lower willingness-to-pay levels (ie, $40 per additional screen), there is considerable uncertainty about the cost-effectiveness (probability of being cost-effective was 0.49).

A recent systematic review noted that, compared with no screening, all types of CRC screening are cost-effective at common thresholds (eg, $50,000 per year of life gained) and some are even estimated to be cost-saving.

 Our study focused on the cost and cost-effectiveness of using automated telephone calling to promote FOBT screening relative to usual care. We found that automated calls were low cost and compared favorably with other reported CRC screening programs at about $40 per additional patient screened with any test. For example, a recent report reviewed several studies that analyzed the cost-effectiveness of comparable interventions to increase CRC screening.

 Those authors noted that the cost per additional patient screened ranged from more than $6000 to $94 for a range of interventions including physician communication training and patient education, physician feedback, and patient mailings.

Our intervention differed from previous interventions in several important ways that may have had a direct effect on the differential program costs. First, we did not send stool cards unless the patient requested the kit. By requiring a patient request before mailing a kit (and thus before incurring associated costs), a relatively costly component of the intervention was avoided by presumably targeting those patients most likely to complete testing. Additionally, by requiring a patient commitment (ie, a positive response to the call), the intervention potentially further enhanced patient follow-through. Our results suggest that future reviews on this topic might usefully compare the costs and cost-effectiveness of patient-directed versus provider-directed interventions and consider whether automated systems are more cost-effective, perhaps in a stepped approach.

Automated telephone calls can increase colorectal cancer screening rates at a cost of about $40 per additional screen.

Automated Telephone Calls to Enhance Colorectal Cancer Screening: Economic Analysis

To compare completion rates of colorectal cancer screening tests within a health maintenance organization before and after widespread adoption of the fecal immunochemical test (FIT).

Using electronic medical records of 113,901 patients eligible for colorectal cancer screening, we examined test completion during 2 successive time periods among those who received an automated screening outreach call. The time periods were: 1) the “guaiac fecal occult blood test (gFOBT) era,” a 15-month period during which only gFOBT was routinely offered, and 2) a 9-month “FIT era,” when only a new FIT was routinely offered. In addition to analyzing completion rates, we analyzed the impact of practice-level variables and patient-level variables on overall screening completion during the 2 different observation periods.

 The change from gFOBT to FIT in an integrated care delivery system increased the likelihood of screening completion by 7.7% overall, and the likelihood of screening with a fecal test by 8.9%. The greatest gains in screening completion using FIT were among women and elderly patients. Completion of FIT was not as strongly associated with medical office visits or with having a primary care provider as was screening with gFOBT.

Adoption of FIT within an integrated care system increased completion of colon cancer screening tests within a 9-month assessment period,

compared with a previous 15-month gFOBT era. Higher completion rates of the FIT may allow for more effective dissemination of programs to increase colorectal cancer screening through centralized outreach programs.

Dissemination of the fecal immunochemical test (FIT) resulted in higher colorectal cancer screening completion rates than were observed using the guaiac fecal occult blood test (gFOBT).

		Visiting a healthcare provider may be less important for completion of screening with the use of FIT than with the use of gFOBT.

		The FIT may enable broader adoption of centralized outreach programs for CRC screening.

		Populations less inclined to screen with fecal tests, including women, the elderly, and those taking more medications may more readily complete screening when offered FIT than when offered gFOBT.

Colorectal cancer (CRC) is the second-leading cause of cancer death in the United States, and affects men and women almost equally.

 The US Preventive Services Task Force (USPSTF) recommends screening with any of 3 options, including fecal testing, flexible sigmoidoscopy, or colonoscopy. Screening for CRC with fecal occult blood testing done annually or biennially has been shown to decrease mortality from colorectal cancer by 15% to 33%, primarily through detection of early-stage cancers.

 The guaiac fecal occult blood test (gFOBT) has a known positive balance of benefit and risk in screening populations, is the least expensive screening method, and is preferred over endoscopy in 30% to 55% of patients.

However, gFOBT has limitations in the areas of test adherence and test performance because testing requires dietary and medication restrictions during the 3 days that 3 separate stool samples are collected—a cumbersome protocol that can interfere with test completion.

While adherence to test completion in the initial round of screening with gFOBT in 3 large randomized trials was 59% to 67%,

smallerscale studies have demonstrated lower 1-time screening completion rates using gFOBT by 25% to 30%.

Retaining patients in annual or biennial gFOBT screening programs has proved challenging, with observed rescreen rates of approximately 50% on a second round.

The fecal immunochemical test (FIT) may improve upon these rates. Previous randomized studies have shown that adherence to 1-time completion of a 1-sample or 2-sample FIT is 10% to 12% greater than adherence to gFOBT, and that the sensitivity of FIT is equal to or greater  than FOBT.

A single (3-sample) gFOBT detects about 12% to 38% of cancers,20-22 whereas a 1-sample FIT detects 25% to 69% of cancers,

 and a 3-sample FIT detects 66% to 92% of cancers.

As a result, in 2008, multiple professional societies endorsed the use of 4 types of FITs for colorectal cancer screening as a replacement for gFOBT in the United States.

 However, it remains unclear to what extent a transition from gFOBT to FIT will improve screening test completion in large community-based populations and which specific populations may benefit the most. We capitalized on a natural experiment by analyzing completion rates before and after the change from gFOBT to FIT.

The study was conducted at Kaiser Permanente Northwest (KPNW), a not-for-profit HMO in the Pacific Northwest with about 485,000 members. The membership of KPNW is similar to the local insured community.

 Electronic records and a patient survey described below provided clinician and patient data.

KPNW maintains a CRC screening clinical practice guideline based upon the recommendations of the USPSTF. Each of the USPSTF-recommended CRC screening modalities (ie, fecal testing, flexible sigmoidoscopy, or colonoscopy) is a covered benefit and available to patients, although fecal testing is encouraged through systemwide outreach efforts in lower-risk individuals. The study site has had an automated call CRC reminder program in place since January 2008; details of the patient selection process for outreach and of the automated call system have been published. The system targets averagerisk individuals who are not being actively treated for major diseases (eg, cancer), or receiving nursing home or hospice care.

 Each month, approximately 5000 eligible HMO members receive a telephone call with an offer for a fecal test to be sent to their home. Included in the mailed packets are the test, instructions, and a card stock envelope addressed to the KPNW laboratory for return. Those who request the test but do not complete it within 6 weeks receive up to 2 reminder phone calls, 6 weeks apart.

In April 2009, KPNW switched from sending the 3-sample gFOBT to sending a single-sample FIT that required no dietary or medication restrictions—the OC -Micro FIT (Polymedco, Cortland Manor, New York).

The retrospective cohort study examined colorectal cancer screening test completion among those receiving an automated telephone call (ATC) during 2 successive time periods: 1) The “gFOBT era,” a 15-month period during which the gFOBT was routinely offered through ATC outreach, and 2) a 9-month “FIT era.” We also analyzed the impact of practicelevel variables (eg, primary care provider [PCP] assignment, primary care utilization, and specialty care utilization) and patient-level variables (eg, age, gender, number of medications, body mass index [BMI], and length of HMO membership) on overall screening completion during the 2 different observation periods.

Additionally, we mailed a survey to 2000 patients who received an ATC during 1 or both time periods. This survey was designed to understand the barriers and facilitators that patients encountered in their efforts to complete colorectal cancer screening. For the purposes of this analysis, we discuss the specific answers among only those respondents who answered questions about 

 tests, because they had had prior experience with each type of fecal test.

This retrospective cohort study was conducted in 2 phases. The 

The cohort consisted of HMO members aged 50 to 80 years who were overdue for CRC screening at the beginning of each month of an observation period, and who received an ATC from the CRC screening outreach program at KPNW.

We utilized 2 observation periods: 1) The “gFOBT era”—a 15-month period during which the gFOBT was routinely offered through ATC outreach from January 1, 2008, through March 31, 2009 (n = 59,876); and 2) a corresponding “FIT era” from April 1, 2009, through December 31, 2009 (n = 32,601), excluding a single month (September 2009) in which KPNW was piloting a different type of ATC vendor.

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.

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,

 experiences with specific CRC-screening tests, experiences with healthcare providers and members of the healthcare team,

and perceived barriers and facilitators to CRC screening completion.

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 (

Study Variables for Cox Proportional Hazards Regression.

We extracted the following variables from the EMR: The 

 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).

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).

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

 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.

Fecal immunochemical testing resulted in higher colorectal cancer screening rates than did guaiac fecal occult blood tests, with less dependence on office visits.

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

 To determine whether adding selfreported health and functional status data to a diagnostic risk-score model explains additional variance in predicting inpatient admissions and costs.

 We used data from a Health Status Questionnaire (HSQ), completed by 6407 Kaiser Permanente Northwest Medicare patients between December 2006 and October 2008. We used answers from 3 items on the HSQ: (1) General Self-rated Health score, (2) needing help with 1 or more activities of daily living, and (3) having a bothersome health condition. We calculated a DxCG relative risk score from utilization information in the year prior to the survey, using electronic medical records. We compared: (1) DxCG as the sole independent variable and (2) DxCG plus the 3 items as independent variables. We estimated area under the curve (AUC) for each model. Any inpatient admission (yes/no) and being in the top 10% of costs (in the year after survey) were the dependent variables for the first and second logistic regression models, respectively.

The 3 items explained an additional 2.8% and 4.0% of variance for inpatient admissions and top 10% of costs, respectively, in addition to the variance explained by the DxCG score alone. For DxCG alone, the AUC was 0.686 (95% confidence interval [CI] 0.663-0.710) and 0.741 (95% CI 0.719- 0.764), respectively, for inpatient admissions and top 10% of costs and improved to 0.709 (95% CI 0.687-0.730) and 0.770 (95% CI 0.749-0.790) when the 3 self-reported items were added.

 Using self-reported health information improved the predictive power of a DxCG model to forecast inpatient admissions and patient cost-tier.

	In a Medicare population, self-reported information about being in poorer health was associated with higher inpatient admissions and being in the top tier for costs. Items associated with these outcomes were: (1) lower score on the General Self-rated Health score item, (2) answering yes to “do you need help with 1 or more activities of daily living?” and, (3) answering yes to “do you have a bothersome health condition?” These items:

		Explained an additional 2.8% and 4.0% of admission and cost variance, respectively.

		Were independently predictive of future inpatient admissions and being in the top 10% cost group.

	Predictive healthcare models are valuable tools for identifying individuals at risk for adverse outcomes, including high healthcare utilization and mortality. Predictive models are typically based on diagnostic history from administrative data or sometimes on patient-reported outcomes, but rarely both. The purpose of our analyses was to determine whether combining these 2 types of data in an integrated predictive model improved upon the predictive power compared with models based on administrative data alone.

	Claims-based predictive models are the most common because of the ubiquitous availability of claims data. There are a variety of commercially available claims-based predictive models, and their validity is well documented.

 The Society of Actuaries has conducted periodic comparisons of the predictive power of these models for following-year costs.

 In these models, health diagnoses and prescriptions, in addition to prior expenses, are the variables with most predictive accuracy. Laboratory results have also been shown to add predictive power.

	Predictive models based on patient-reported outcomes are less common because such measures are not routinely collected and reported. However, patient-reported outcomes have been shown to have strong predictive value for forecasting costs, utilization, and mortality. In particular, the predictive value of a single General Self-rated Health (GSRH) question is well documented. DeSalvo (2005) concluded in a meta-analysis that people with “poor” self-rated health had double the mortality risk of people with “excellent” self-rated health, even after adjustment for key factors such as functional status, depression, and comorbidity.

 GSRH has also been shown to be responsive to change. In a study of older adults, self-reported health declined steeply prior to adverse events, such as death or stroke.

 A study of repeated administrations of 2 GSRH questions showed good reliability, reproducibility, and discriminate scale performance.

 Although the single-question GSRH measure is seldom used in clinical practice settings, it is widely used in medical and economic research.

 In an investigation of the causal relationship between self-reported health and subsequent outcomes, Jylha suggests that aspects of self-reported health may reflect important physiological dysregulations, such as increases in tissue inflammation.

	Similarly, DeSalvo (2009) notes that respondents provide composite assessments of their physical and mental health and social status when answering questions about their overall health, which helps explain the strong performance of GSRH in predicting future risk.

 The literature includes several examples of patientreported outcomes used in predictive models. DeSalvo (2009) compared predictive ability of single- and multi-item assessments of self-reported health to a comorbidity index and diagnostic history in predicting next year’s costs. They found that the single-item self-reported health question predicted costs as well as the multi-item assessment and the comorbidity index, but not as well as the diagnostic history model.

 Using data from a survey of Medicare members, Brody et al developed highly sensitive indices of frailty and advanced illness.

 The models use survey items about self-reported health and function, such as poor general health and assistance needed for medications, bathing, or dressing, and were shown to be superior to clinical judgment in predicting frailty and advanced illness.

	There are, however, few examples of predictive models that combine diagnostic history and patient-reported outcomes. Fleishman et al found that measures of health status improved prediction of subsequent medical expenditures in a model that included demographics, chronic conditions, and previous expenditures in a nationally representative sample.

 Fleishman and Cohen later found that measures of self-rated health and function improved prediction of high expenditures beyond more detailed information on medical conditions for a nationally representative sample.

 These results are consistent with previous studies based on nonrepresentative samples. In a study of Veterans Affairs (VA) beneficiaries, self-reported physical and mental health improved mortality prediction when combined with a diagnostic history model.

Similarly in a previous study of VA beneficiaries, a multidomain health assessment added predictive power to a diagnostic history model.

	In this study, we examined whether inclusion of answers to a few brief self-reported items improved the prediction of future inpatient admissions and medical care costs compared with using an administrative risk score alone to predict the same outcomes. This study is unique in that it is the first of which we are aware to conduct this analysis in a Medicare population. Further, the Medicare population represents a unique population, given the large proportion with chronic health conditions, which contributes to increased inpatient admissions and costs—as the US population ages. We selected the well-validated, single-item general health question, a question about activities of daily living,

 and a question indicating whether or not the person currently has a physical condition that bothers him or her. These 3 items were selected because they are questions that could be quickly administered during an office visit, over the phone, by mail, or the Internet and are not tailored to a specific population (eg, those with chronic conditions) and capture information not readily attainable through administrative data.

	Study subjects were members of Kaiser Permanente Northwest (KPNW) and were aged 65 years and older. To be included in the study, members needed to complete a Health Status Questionnaire (HSQ) between December 2006 and October 2008, and have 1 year of membership eligibility prior to and after the completion of the HSQ. This time frame allowed us to compute the DxCG scores. When the member had completed more than 1 HSQ in the time frame of interest, 1 HSQ was selected at random for inclusion in the study.

	During the period of study, the HSQ was mailed to new Medicare members aged 65 years and older. This population included those newly enrolled to KPNW and current members who recently became Medicare eligible. In addition, it was mailed annually to a Medicare expanded-care population. The general response rate for the HSQ is 76%.

	The HSQ is a 45-item survey that includes measures of physical function, activities of daily living, health conditions, self-care deficits, and general health. For this study we used data from 3 items. The self-reported general health measure is scored in a range of 1 (excellent) to 4 (poor) by answering the question “compared to other persons your age, would you say your health is…?” The second item asked “because of a disability or health problem, do you need or receive help from another person for any of the following everyday activities…?” The activities listed include preparing meals, shopping for groceries, doing routine household chores, managing money, doing laundry, taking medications, getting to places not within walking distance, and using the telephone. This item was scored 0 if no items were checked and if 1 or more items were checked. The third item was “Is there any physical condition, illness, or health problem that bothers you now?” and was scored 0 for “no” and 1 for “yes.”

 is based on an individual’s total predicted cost in the next year relative to the population mean and is used to assign individuals to a Diagnostic Cost Group (DCG). DxCG Risk Solutions software

 is used to calculate the DxCG relative risk score. DCG cost estimates are calculated using 1 year of inpatient and outpatient 

International Classification of Diseases, Ninth Revision, Clinical Modification 

) codes to classify patients into groups with similar cost patterns. The diagnoses are classified into mutually exclusive DCGs, based on predicting future inpatient and outpatient costs. The software algorithm for DCG classifications was developed using 3 different populations: a privately insured population, a Medicaid population, and a Medicare population. Diagnostic Cost Groups for this study were based on the Medicare-derived DCG grouping. The range of Medicare DxCG scores ranged from 0.04 (minimum) to 15.59 (maximum). Individuals with a score of 1.0 have the relative risk equivalent to the national Medicare reference population. This reference population includes any of the following (excluding those with end-stage renal disease): (1) individuals 65 years or older, (2) disabled (under age 65 years) Medicare eligible, or (3) Medicare/Medicaid dual eligible. Previous research has used the DxCG relative risk score to predict future costs and mortality estimates.

	In the year following the date of the HSQ, we used electronic medical records to determine if the subject had any inpatient hospital admissions. Actual health plan costs for each person for this same period were extracted from administrative data and presented in 2008 dollars.

	Prior to logistic regression analysis, we tested for collinearity among the self-reported health items and DxCG risk score by calculating a Pearson product-moment correlation coefficient. We used 2 sets of logistic regressions, with 2 models in each set: DxCG as the sole independent variable and DxCG plus the 3 self-reported items as the independent variables. We calculated sensitivity, specificity, receiver operating characteristic (ROC) curves, and the area under the curve (AUC) for each model. Any inpatient admission (yes/no) was the dependent variable for the first set of regression models and being in the top 10% of costs (based on the observed study population) was the dependent variable for the second set of regression models. Prior to conducting the main analyses, those with and without complete data were compared using t tests and χ

	A total of 7545 HSQs were returned between December 2006 and October 2008 from 7496 unique individuals. Of those, 6407 people met the inclusion criteria of 1 year of eligibility prior to and after the HSQ date. Of those, 5815 (90.8%) had complete data on the 3 self-report items of interest and the DxCG. Those with complete data were younger (mean = 68.55 vs 70.71, 

 <.001), more likely to be male (44.9% vs 39.6%, 

 = .016), more likely to have attended college (67.0% vs 59.7%, 

 <.001), more likely to be married (69.0% vs 59.5%, 

 <.001), and to be white (93.1% vs 88.0%, 

 <.001). There were no significant differences between those with and without complete data on the likelihood of being in the top 10% of costs (

 = .680) or of having an inpatient admission (

Among a Medicare population, use of 3 self-reported health items improves predicted inpatient admissions and healthcare costs when used with risk-prediction model.

David M. Mosen, PhD, MPH

Articles

More Comprehensive Discussion of CRC Screening Associated With Higher Screening

Automated Telephone Calls to Enhance Colorectal Cancer Screening: Economic Analysis

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

Self-Reported Health and Functional Status Information Improves Prediction of Inpatient Admissions and Costs

Medication Use, Emergency Hospital Care Utilization, and Quality-of-Life Outcome Disparities by Race/Ethnicity Among Adults With Asthma

How Well Do the HEDIS Asthma Inclusion Criteria Identify Persistent Asthma?