• primary prevention;
  • colorectal cancer;
  • cancer screening;
  • colonic neoplasms;
  • decision aid


  1. Top of page
  2. Abstract
  6. Acknowledgements


Colorectal cancer (CRC) screening reduces CRC incidence and mortality but is underused. Effective interventions to increase screening that can be implemented broadly are needed.


A controlled trial was conducted to evaluate a patient-level and practice-level intervention to increase the use of recommended CRC screening tests among health plan members. The patient-level intervention was a patient decision aid and included stage-targeted brochures that were mailed to health plan members. Intervention practices received academic detailing to prepare practices to facilitate CRC testing once patients were activated by the decision aid. We used patient surveys and claims data to assess CRC test completion.


Among 443 active participants, 75.8% were ages 52 to 59 years, 80.9% were white, 62.1% were women, and 46.4% had college degrees or greater education. Among 380 active participants with known screening status at 12 months based on survey results, 39% in the intervention group reported receiving CRC screening compared with 32.2% in the usual care group (unadjusted odds ratio [OR], 1.34; 95% confidence interval; [CI], 0.88-2.05; P = .17). After adjusting for baseline differences and accounting for clustering, the effect was somewhat larger (OR, 1.64; 95% CI, 0.98-2.73; P = .06). Claims analysis produced similar effects for active participants. The intervention was more effective in those who had incomes >$50,000 (OR, 2.16; 95% CI, 1.07-4.35) than in those who had lower incomes (OR, 1.25; 95% CI, 0.53-2.94; P = .03 for interaction).


Interventions combining a patient-directed decision aid and practice-directed academic detailing had a modest but statistically nonsignificant effect on CRC screening rates among active participants. Cancer 2011. © 2011 American Cancer Society.

Colorectal cancer (CRC) screening is effective, cost-effective, and a high priority among preventive services.1-3 Although the use of CRC screening has increased over the past 10 years, only 50% to 60% of age-eligible US adults were up-to-date with screening in 2006.4, 5 Effective and efficient methods are needed to increase CRC screening use.

Recent systematic reviews have identified several effective techniques for increasing CRC screening, including reminder systems, audit and feedback, and small media.6 Multicomponent interventions that target physicians' practices and patients and, thus, can address multiple barriers may be more effective than interventions that focus only on patients or physicians.7 Our team previously observed that a videotape decision aid delivered during routine primary care visits increased CRC screening test ordering and completion.8 Other research has demonstrated that practice-directed interventions, including academic detailing and organizational change interventions, could improve quality of care, including some studies that demonstrated increases in cancer screening rates.9-11

To bring the value of this research to larger populations, it is important to test whether interventions that are efficacious in controlled trials performed in selected environments can be implemented effectively in broader, less controlled settings, such as health plans and community practices. We sought to test whether an intervention that combined 2 effective techniques (patient decision aids and academic detailing) could improve CRC screening among health plan members in primary care practices.


  1. Top of page
  2. Abstract
  6. Acknowledgements

Communicating Health Options Through Information and Cancer Education (CHOICE) was a practice-level controlled trial to evaluate the effect of a patient-level intervention, which consisted of the provision of a mailed patient decision aid on CRC screening, combined with a practice-level intervention, ie, academic detailing. The study was conducted among members of a large health plan (Aetna's health maintenance organization [HMO] product) from selected metropolitan areas in Georgia and Florida. Details of the methods and baseline findings have been reported previously.12

Practice Recruitment

Potential practices for participation were identified from a list of primary care physicians in the Atlanta, Tampa, and Orlando areas who participated in the Aetna HMO product. Each medical practice that was recruited for the study had a minimum of 50 Aetna members between ages 52 and 75 years.

Enrolled practices were grouped into 3 waves of 10 practices each to facilitate timely entry into the trial. The first wave, which included only Georgia practices, was block-randomized into an intervention group and a usual care group based on 2 variables: the size of the study-eligible member population and rural versus urban location. The second wave was allocated randomly into pairs based on practice size and state (Georgia or Florida). When the third wave was recruited, we noted that the intervention and control groups from Waves 1 and 2 were unbalanced in practice size. Therefore, we used purposive assignment in Wave 3 to balance the intervention and control groups with respect to practice size. Two additional practices that originally were intended to be pilot sites (1 intervention and 1 control) also were included without randomization. Detailed information about practice recruitment and the characteristics of enrolled practices has been reported previously.12

Study Population and Recruitment

Potentially eligible patient participants at participating practices were Aetna members between ages 52 and 80 years who were not current with CRC screening based on claims data. We excluded individuals who were at increased risk of CRC (personal history of CRC or polyps, known history of CRC or polyps in a first-degree relative, or personal history of inflammatory bowel disease), individuals who had medical conditions that could have limited their study participation or that could have indicated that they were not appropriate candidates for screening (eg, dementia, heart failure), individuals who were unable to communicate effectively in English, and individuals who no longer were insured by Aetna or who no longer were receiving care in 1 of the participating practices. Members were considered up-to-date with screening and, thus, ineligible if claims data indicated that they had received fecal occult blood testing (FOBT) within the last year or had undergone a sigmoidoscopy, colonoscopy, or barium enema during the period covered by claims data.

Potentially eligible members were sent a brief eligibility survey to obtain information about whether they had received CRC screening that was not identified from in claims data and to identify individuals who were at increased risk for CRC for whom the decision aid would not be appropriate (eg, those who had a first-degree relative with a history of CRC). Results from the eligibility survey and agreement with previous claims data have been reported previously.13 Patients who were deemed eligible after review of claims and completion of the eligibility survey were mailed the baseline survey. Those who agreed to participate and completed the baseline survey were enrolled and considered active participants in the trial.

This study received approval from Emory University and the University of North Carolina Committees on the Protection of Human Subjects. A partial Health Insurance Portability and Accountability Act (HIPAA) waiver was granted to allow access to information in Aetna's claims data repository to identify eligible health plan members. In addition, a full HIPAA waiver was obtained to permit access to claims data for survey nonresponders in both study arms (n = 638) to allow us to test, in a separate substudy, the effect of sending the decision aid mailing to the nonresponders in intervention practices.

Practice-Level Intervention: Academic Detailing

The academic detailing intervention was modeled after previously successful interventions to improve the use of appropriate clinical services.9 The goal of detailing was to prepare practices to facilitate CRC screening once patients were activated by the decision aid. Because practices also saw patients who were covered by multiple other health plans (and, thus, were not eligible for the CHOICE trial), we did not focus on changing the overall approach to cancer screening in the practice, as also reported in the use of previous, more intensive interventions.11

The detailing sessions have been described elsewhere.12 Briefly, 2 physician detailers (drawn from a group of 4 trained physician detailers) conducted 2 sessions for each practice that included information about colon cancer and screening tests, practice-specific screening rates, clips of the decision aid, and the development of practice-specific plans to address requests for screening.

Member-Level Intervention: Decision Aid

The decision aid that was used in the CHOICE trial was a modified version of a previously tested decision aid that was effective at increasing CRC screening rates in previous practice-based trials.8, 14 The updated version's total duration is 22 minutes. It contains 1) general information on CRC and the benefits of screening; 2) information about specific screening tests (FOBT, sigmoidoscopy, colonoscopy, combination of FOBT and sigmoidoscopy, and double-contrast barium enema); 3) comparative information on screening tests (efficacy, frequency of testing, preparation, discomfort, likelihood of major complications, and costs); and 4) color-coded, stage-targeted brochures.

Decision aid mailing

The intervention mailing contained a personalized letter, the decision aid in DVD and VHS formats with instructions for viewing, stage-targeted brochures, Aetna-specific copayment and referral information, CRC screening options chart, and the decision aid survey. The survey assessed use of and reactions to the materials and evaluated changes in knowledge as a result of viewing the decision aid.

Usual care condition

Usual care practices received no academic detailing; participants in these practices did not receive the decision aid. All Aetna members (including those in our study's intervention and usual care groups) annually received brief mailed reminders from Aetna encouraging them to obtain CRC screening.

Subtrial for initial nonresponders

We performed a separate subtrial for members who did not respond to eligibility or baseline surveys and, thus, were not considered active participants for the main analysis. Those from intervention practices were mailed the decision aid materials. The effect of the decision aid mailing on screening among these nonrespondents was assessed with claims data (see below).


Our primary outcome was self-reported completion of any CRC screening test at 12 months. Screening status at 12 months for active participants was determined by response to a mailed survey at 12 months after the baseline survey or by screening reported on the baseline survey.

For secondary analyses of active participants and to analyze the effect of the decision aid in the subtrial of survey nonrespondents, we used claims data to assess CRC screening test completion. Receipt of CRC screening was defined as having a claim with a procedure code or diagnosis code for FOBT, sigmoidoscopy, double-contrast barium enema, or colonoscopy.


Screening test completion: Active participants with survey data

Our main analysis included participants who had known screening status based on their response to the 12-month survey or who reported completing a screening test on the baseline survey. In our main analysis, we included those who reported screening on the baseline survey as screened at Year 1, because screening at baseline may have been the result of earlier study contact. We also performed a sensitivity analysis to exclude those who reported screening on the baseline survey.

We compared screening outcomes in the intervention group and the usual care group using mixed effects logistic regression models. Because the assignment of practices did not completely balance patient characteristics at the individual level,12 the analysis controlled for characteristics that differed between the intervention and usual care groups at baseline, including state, race, and general beliefs about CRC screening. The analysis also controlled for sex, age category (<60 years vs ≥60 years), and education. We accounted for clustering of patients within practices by including a random intercept for practice in the models. The intracluster correlation coefficient (ICC) for screening outcomes at 12 months within practices was estimated as 0.03 using a version of the analysis of variance ICC estimator adapted for use with binary variables.15

To assess whether the intervention had different effectiveness among different demographic groups, we considered models that included terms for the interaction between the intervention and common demographic variables, including age, sex, education, income, and race. Interaction with each of these factors was assessed individually in separate models. For the analysis of interaction with income, we conducted statistical analysis with and without imputation of missing values for income only. Imputation was done 35 times using the logistic regression method, and the imputation model included all variables in the analytical model. The SAS PROC MI function in the SAS software package (SAS Institute, Inc., Cary, NC) was used to impute income values, PROC NLMIXED was used to conduct analysis of the resulting datasets with income imputation, and PROC MIANALYZE was used to combine the results of the analyses of the imputations. Interaction analyses controlled for baseline differences and other demographic characteristics and included a random intercept for practice. Statistical significance of the set of interaction terms was assessed using the likelihood-ratio test.

Claims analysis for screening test completion

We used mixed effects logistic regression models for all claims analyses. We conservatively assumed that health plan members who had incomplete follow-up claims data because of leaving Aetna coverage did not receive any additional screening after the end of their claims data availability. All claims analyses accounted for clustering of patients within practices through inclusion of a random intercept for practice in the models.

Because the nature of our study design made it difficult to set a “time zero” for assessing the effect of the intervention among active participants, we conducted claims analyses with 2 different time frames. First, for active participants only, we identified receipt of CRC screening through examination of any claims with dates of service from 6 months before to 12 months after the receipt of baseline surveys. This time frame was chosen to be as comparable as possible to results from the 12-month surveys, which asked about screening within the previous 18 months. The random effects logistic regression models for this analysis controlled for the same variables that were used in the survey analysis (see above).

For our second claims analysis, receipt of CRC screening was identified through examination of any claims for dates of service within 30 months (912 days) after the mailing date for the eligibility survey. Patients who were identified as having received screening based on claims data before the date when their eligibility surveys were sent were not included in these analyses. Random effects logistic regression models for this analysis controlled for age category (<60 years vs ≥60 years), sex, state, and response to the baseline survey (as a marker of participation in the survey portion of the study).

Agreement between survey and claims data was good for those participants who had both sources of data available (n = 374): Simple agreement was 82.6%, and the kappa statistic was 0.58. For the separate subtrial of survey nonrespondents, for claims analysis, we used the second method of identifying any claims within 30 months of the mailing of the eligibility survey.

All analyses were conducted using SAS software (version 9.2; SAS Institute Inc.). For random effects models, we used the SAS PROC NLMIXED function with degrees of freedom based on the number of practices and with the assumption of normally distributed random effects.


  1. Top of page
  2. Abstract
  6. Acknowledgements

The study flow is illustrated in Figure 1. Characteristics of the 443 active study participants (211 in the intervention group and 232 in the usual care group) have been described previously in detail.12 Most participants (75.8%) were ages 52 to 59 years, 62.1% were women, 80.9% were white, 15.2% were African American, 37.1% reported an annual income <$50,000, and 46.4% had graduated from college or had an advanced degree. There were few demographic differences between the intervention and usual care groups except for race (there was a smaller proportion of whites/non-Hispanics in the intervention group).

thumbnail image

Figure 1. This is a Consolidated Standards of Reporting Trials (CONSORT) diagram of the Communicating Health Options Through Information and Cancer Education (CHOICE) trial. DA indicates decision aid.

Download figure to PowerPoint

Of 443 active participants, 374 (84.4%) completed 12-month surveys, including 168 in the intervention group and 206 in the usual care group. In addition, the 12-month analysis included 6 participants (4 in the intervention group and 2 in the usual care group) who did not respond to the Year-1 survey but reported having been screened on the baseline survey, for a total of 380 active participants in the analysis. Those without known screening status at 12 months (n = 63) were statistically more likely to be older, nonwhite, and not to have graduated from college than those who completed 12-month surveys. Intervention group members, those with no physician's visits in the past year, those with low perceived risk for CRC at baseline, and those who expressed agreement with statements about colon cancer and CRC screening that would denote less belief in the benefits of CRC screening also were more likely to have an unknown screening status at 12 months (data not shown).

Main Outcome: Screening Test Completion at 12 Months

Among 380 active participants (172 in the intervention group and 208 in the usual care group) for whom 12-month outcome data were available, screening rates were 39% for the intervention group compared with 32.2% for the usual care group, resulting in an unadjusted difference in the percentage screened between the groups of 6.7 percentage points (unadjusted 95% confidence interval [CI], from −3.5% to +16.9%; unadjusted odds ratio [OR], 1.34; 95% CI, 0.88-2.05; P = .17). After adjusting for baseline differences and accounting for clustering of patients by practice, the overall effect was somewhat larger (OR, 1.64; 95% CI, 0.98-2.73; P = .06).

Excluding those participants who reported screening on the baseline survey (whether or not they completed 12-month surveys), screening rates among the 336 remaining participants (150 in the intervention group and 186 in the usual care group) were 30% for the intervention group compared with 24.2% for the usual care group (adjusted OR, 1.82; 95% CI, 1.02-3.25; P = .04). Colonoscopy was the most frequently reported screening test followed by FOBT. The type of screening test completed did not differ appreciably between the intervention and usual care groups (data not shown).

Differences in Effect by Demographic Characteristics

The unadjusted percentages screened, stratified by demographic group and intervention status, are illustrated in Figure 2, and the adjusted ORs are provided in Table 1. Individuals with income >$50,000 per year had a larger effect from the intervention (adjusted OR, 2.16; 95% CI, 1.07-4.35) than individuals with lower income (adjusted OR, 1.25; 95% CI, 0.53-2.94). Interactions with sex, age, race, and education were not statistically significant, although the effect of the intervention in the highest education group (those with a postgraduate or professional degree or higher) was substantially larger than the effect in other educational groups.

thumbnail image

Figure 2. This chart illustrates an analysis of interactions between intervention and demographic variables for screening at 12 months.

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Table 1. Analysis of Interaction Between Intervention and Demographic Variables (n=380)
VariableOverall No.Screened at Year 1 or at BaselineOR for Screening at Year 1 Survey Intervention vs Usual CareaLR Test P
No.%OR95% CIP
  • OR indicates odds ratio; CI, confidence interval; LR, likelihood ratio; GED, General Education Degree.

  • a

    Adjusted for baseline differences between groups (state, race, and general beliefs) and other demographic factors (sex, age category, and education) and accounting for clustering by practice.

Sex (missing=0)
 Women      .44
  Usual care1304131.5    
  Usual care782633.3    
Age, y (missing=0)
 52-59      .53
  Usual care1585132.3    
  Usual care501632    
Race (missing=3)
  White non-Hispanic      .45
   Usual care1765631.8    
   Usual care20525    
   Usual care9444.4    
Education (missing=1)       
 High school, GED, or less      .32
  Usual care391128.2    
 Some college or technical school
  Usual care611727.9    
 College graduate
  Usual care582237.9    
 Postgraduate or professional degree
  Usual care491734.7    
Income (missing=4)
 Up to $50,000      .03
  Usual care772329.9    
  Usual care933335.5    
 Prefer not to answer
  Usual care35925.7    

Decision Aid Use in the Intervention Group

Among 205 active participants in the intervention group, 149 (72.7%) completed decision aid surveys, 83.2% reported watching at least some or all of the video, 77.9% reported reading any of the readiness stage-targeted brochures, and 69% reported reading the copayment and referral information sheets. Eleven respondents (7.4%) reported no use of the materials. Knowledge was greater among those who reported watching the decision aid than among those who reported not watching it (Table 2).

Table 2. Decision Aid Use and Knowledge About Colorectal Cancer Screening Among Decision Aid Survey Respondents in the Intervention Group
Question/ResponseWatched Video (n=124)Did Not Watch Video (n=25)PRead Brochures (n=116)Did Not Read Brochures (n=33)P
No.Column %No.Column %No.Column %No.Column %
  1. FOBT indicates fecal occult blood test.

Colon cancer is the second leading cause of cancer deaths in the United States
  Incorrect, don't know, or missing1512.11248 1613.81133.3 
For most people, colon cancer screening should start at age 60 y
  Incorrect, don't know, or missing75.6728 97.8515.2 
Colon cancer is most curable when it is found early by a screening test
  Incorrect, don't know, or missing21.600 10.913 
In general, once someone starts doing home stool blood tests (FOBT), about how often should they do them?
  Incorrect, don't know, or missing1612.91144 1412.11339.4 
If someone has a colonoscopy, and the results show no signs of colon cancer, when should they have another colon cancer screening test?
  Incorrect, don't know, or missing2822.61768 2521.62060.6 
You can prevent colon cancer with regular screening tests
  Incorrect, don't know, or missing4032.31560 4034.51545.5 
You should be tested for colon cancer even if you don't have any symptoms
  Incorrect, don't know, or missing21.614 10.926.1 

Among 140 decision aid survey respondents with known screening status at 12 months, screening rates did not differ between those who reported viewing or not viewing the decision aid (39.5% vs 38.1%; P = .90) or between those who reported reading or not reading the brochures (38.2% vs 43.3%; P = .61). These results were unchanged when the analysis was repeated considering decision aid survey nonrespondents with known screening status at 12 months (n = 32) who did not watch the decision aid and did not read the brochures.

Screening Test Completion Based on Claims Analysis—Active Participants

Among active study participants (n = 443), we identified claims for CRC screening within 6 months before or 12 months after the date when baseline surveys were received for 27.5% in the intervention group and 24.6% in the control group (adjusted OR, 1.50; 95% CI 0.91-2.48; P = .11). Among active study participants (n = 433; 10 were excluded because they were screened by claims on or before the date when the eligibility survey was sent), the percentage screened within 30 months of the eligibility survey mailing date was 34.3% (71 of 207 participants) in the intervention group and 31% (70 of 226 participants) in the usual care group (OR [adjusted for age category, sex, and state and accounting for clustering by practice], 1.60; 95% CI, 1.01-2.51; P = .04). Among those in the substudy of eligibility and baseline survey nonrespondents (n = 638), the percentage screened within 30 months of the eligibility survey mailing date was 28.6% (94 of 329 participants) in the intervention group and 27.8% (86 of 309 participants) in the usual care group (OR [adjusted for age category, sex, and state and accounting for clustering by practice], 1.08; 95% CI, 0.75-1.55; P = .67).


  1. Top of page
  2. Abstract
  6. Acknowledgements

In the current study, we observed that a combined intervention, which included a patient-directed decision aid and practice-directed academic detailing, had modest but statistically nonsignificant overall effects on CRC screening test completion at 12 months of follow-up among active trial participants who completed surveys. The observed effect appeared to be present mainly for those with higher levels of education and income. Claims-based analyses of active participants produced similar effect sizes. Screening test completion did not differ based on self-reported use of intervention materials. Among those who completed the surveys that addressed intervention materials, decision aid use was high, and those who reported viewing the decision aid had greater knowledge than those that did not. Our subtrial of mailing decision aid materials to survey nonresponders, however, had no effect on screening rates.

Our observed effect on CRC screening rates was similar to that reported from other recent interventions that sought to increase screening through patient-directed and provider-directed interventions.6 In a previous trial of mailing decision aids to patients in 1 academic internal medicine practice, we observed an 11 percentage point increase in screening at an estimated cost of $94 per additional patient screened.16 Sequist and colleagues reported that mailed reminders to patients increased CRC screening by 6 percentage points and no additional effect from a physician reminder.17 Ganz and colleagues reported no difference in CRC screening rates with a multimodal intervention directed to health plan members.18

More intensive interventions that include telephone-based counseling may have somewhat greater effects. Ling and colleagues reported that enhanced office management (including telephone-based motivational interviewing by a health educator who also facilitated test ordering and implementation of office systems) increased the odds of participants completing colonoscopy or flexible sigmoidoscopy (OR, 1.63; 95% CI, 1.11-2.41; P = .01). In the same trial, however, tailored reminder letters that were sent to patients did not increase screening (OR, 1.08; 95% CI, 0.72-1.62; P = .71).19 Lasser and colleagues demonstrated that mailed reminder letters about the need for screening, followed by telephone-based counseling from a patient navigator, increased screening (31% vs 9%) among community health center patients.20 However, Myers and colleagues observed that, although a targeted, mailed intervention increased screening by 13% points, the addition of tailored messages or a telephone reminder did not increase screening more than the mailed intervention.21

Our results should be interpreted with several limitations in mind. Because we used a hybrid allocation procedure performed at the practice level, we could not completely control confounders at the individual patient level. We accounted for this situation by performing adjustment for key variables that were not distributed equally at baseline (eg, race), and we also accounted for clustering of patients within practices and heterogeneity of screening rates between practices through inclusion of a random intercept for practice. However, it is possible that incomplete adjustment, unmeasured confounding, or misspecification of the distribution of random effects may have affected our results.

Our recruitment and data collection procedures required repeated contacts with active participants in both the intervention group and the control group. Such contact may have acted as an “intervention” to the control group and, thus, reduced potential differences between groups. Despite the selected nature of our relatively highly educated and insured survey population, overall screening rates were not particularly high, suggesting that we were not limited by a ceiling effect.

Another important limitation is that our practice-level intervention was not strong enough to produce important changes in practice behavior. We designed our practice intervention with the limited goal of helping to ensure that it was easy for patients who requested screening to have tests scheduled and ordered; however, because of our limited contact (2 visits) and because only a small proportion of the practice's patients would be health plan members and, thus, eligible to receive the intervention, the approach may have limited practice engagement and reduced our ability to facilitate screening. Successful cancer screening practice change interventions have used more frequent and intensive practice contact.6

The “real-world” nature of our research also posed some analytic challenges. We had to determine eligibility and baseline characteristics through separate questionnaires and could not easily define a “time zero” for analyses of test completion. Our main analysis of screening outcome included those who reported screening on the baseline survey (but who were unscreened at eligibility assessment) to recognize that screening at baseline may have been the result of the study contact for either intervention group or control group participants. Secondary analyses excluding those screened at baseline, however, produced similar results.

We also did not have complete 12-month survey results for all active participants, and the loss to follow-up differed by intervention status. Our claims analyses, however, did not suffer from this limitation and produced results (OR, 1.5-1.6) similar to those produced by the survey-based analyses of active participants. The availability of both claims and survey data is a particular strength of our study.

In conclusion, we observed that our combined intervention to increase CRC screening among health plan members likely had a modest, statistically nonsignificant overall effect on screening test completion but produced larger effects for those with higher incomes and advanced education. We designed and pretested our decision aid so that it would be accessible to a wide range of users; however, our results suggest that, in its current form of dissemination, it may be more effective for individuals with fewer socioeconomic barriers. Additional, larger studies will be required to determine whether these observed differences in effect by income and education were real or were caused by chance.

Even a small improvement in the screening rate (a net increase ≥5 percentage points), if applied broadly, could translate into a considerable reduction in deaths from CRC.22 It appears that the increase in screening observed in the current study came mainly from the mailed intervention serving as a cue to action among active participants. Based on the experience of our detailing teams, it is unlikely that the practice intervention produced meaningful changes in screening itself. In addition, the practice-level intervention was labor intensive and expensive and, thus, would not be a good candidate for broad implementation. Our results and those from other recent studies suggest that a reasonable next step might be to study the effect of an inexpensive mailed reminder (with access to a decision aid for those who want more information) followed by telephone-based counseling and navigation for those who do not respond to the reminder or who need more help in facilitating test scheduling and completion.


  1. Top of page
  2. Abstract
  6. Acknowledgements

We thank the following for their assistance with the study: Chris DeLeon, Raquel Vazquez Ludwig, Lauren Taglialatela, Jennifer Griffith, Alison Brenner, Renata Hilson, Murtaza Cassoobhoy, and Lisa Bernstein.


  1. Top of page
  2. Abstract
  6. Acknowledgements

This research was supported by grant PH000018 from the Centers for Disease Control and Prevention and is registered as NCT00134589 at Dr. Pignone also was supported by a National Cancer Institute Established Investigator Award (K05 CA129166) and by the Foundation for Informed Medical Decision Making. Dr. Glanz also was supported by a Georgia Cancer Coalition Distinguished Scholar Award. Dr. Lewis was supported by a K07 Mentored Career Development Award (5K07CA104128) from the National Cancer Institute.


  1. Top of page
  2. Abstract
  6. Acknowledgements
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