Elissa M. Ozanne

Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers

BackgroundMaking evidence-based decisions often requires comparison of two or more options. Research-based evidence may exist which quantifies how likely the outcomes are for each option. Understanding these numeric estimates improves patients’ risk perception and leads to better informed decision making. This paper summarises current “best practices” in communication of evidence-based numeric outcomes for developers of patient decision aids (PtDAs) and other health communication tools.MethodAn expert consensus group of fourteen researchers from North America, Europe, and Australasia identified eleven main issues in risk communication. Two experts for each issue wrote a “state of the art” summary of best evidence, drawing on the PtDA, health, psychological, and broader scientific literature. In addition, commonly used terms were defined and a set of guiding principles and key messages derived from the results.ResultsThe eleven key components of risk communication were: 1) Presenting the chance an event will occur; 2) Presenting changes in numeric outcomes; 3) Outcome estimates for test and screening decisions; 4) Numeric estimates in context and with evaluative labels; 5) Conveying uncertainty; 6) Visual formats; 7) Tailoring estimates; 8) Formats for understanding outcomes over time; 9) Narrative methods for conveying the chance of an event; 10) Important skills for understanding numerical estimates; and 11) Interactive web-based formats. Guiding principles from the evidence summaries advise that risk communication formats should reflect the task required of the user, should always define a relevant reference class (i.e., denominator) over time, should aim to use a consistent format throughout documents, should avoid “1 in x” formats and variable denominators, consider the magnitude of numbers used and the possibility of format bias, and should take into account the numeracy and graph literacy of the audience.ConclusionA substantial and rapidly expanding evidence base exists for risk communication. Developers of tools to facilitate evidence-based decision making should apply these principles to improve the quality of risk communication in practice.

Hypertension is an independent predictor of survival disparity between African-American and white breast cancer patients

The objective of this study was to determine whether comorbidity, or pre‐existing conditions, can account for some of the disparity in survival between African‐American and white breast cancer patients. A historical cohort study was conducted of 416 African‐American and 838 white women diagnosed with breast cancer between 1973 and 1986, and followed through 1999 in the Kaiser Permanente Northern California Medical Care Program. Information on comorbidity, tumor characteristics and breast cancer treatment was obtained from medical records, and Surveillance, Epidemiology and End Results, Northern California Cancer Center Registry. Associations between comorbidity and survival were analyzed with multiple Cox proportional hazards regression. Over a mean follow‐up of 9 years, African Americans had higher overall crude mortality than whites: 165 (39.7%) versus 279 (33.3%), respectively. When age, race, tumor characteristics and breast cancer treatment were controlled, the presence of hypertension was associated with all cause survival [hazard ratio (HR) = 1.33, 95% confidence intervals (CI) 1.07–1.67] and it accounted for 30% of racial disparity in this outcome. Hypertension‐augmented Charlson Comorbidity Index was a significant predictor of survival from all causes (HR = 1.32, 95%CI 1.18–1.49), competing causes (HR = 1.52, 95%CI 1.32–1.76) and breast cancer specific causes (HR = 1.18, 95%CI 1.03–1.35). In conclusion, hypertension has prognostic significance in relation to survival disparity between African‐American and white breast cancer patients. If our findings are replicated in contemporary cohorts, it may be necessary to include hypertension in the Charlson Comorbidity Index and other comorbidity measures.

Annual Screening Strategies in BRCA1 and BRCA2 Gene Mutation Carriers: A Comparative Effectiveness Analysis

Although breast cancer screening with mammography and magnetic resonance imaging (MRI) is recommended for breast cancer‐susceptibility gene (BRCA) mutation carriers, there is no current consensus on the optimal screening regimen.

The Psychometric Properties of CollaboRATE: A Fast and Frugal Patient-Reported Measure of the Shared Decision-Making Process

Background Patient-centered health care is a central component of current health policy agendas. Shared decision making (SDM) is considered to be the pinnacle of patient engagement and methods to promote this are becoming commonplace. However, the measurement of SDM continues to prove challenging. Reviews have highlighted the need for a patient-reported measure of SDM that is practical, valid, and reliable to assist implementation efforts. In consultation with patients, we developed CollaboRATE, a 3-item measure of the SDM process. Objective There is a need for scalable patient-reported measure of the SDM process. In the current project, we assessed the psychometric properties of CollaboRATE. Methods A representative sample of the US population were recruited online and were randomly allocated to view 1 of 6 simulated doctor-patient encounters in January 2013. Three dimensions of SDM were manipulated in the encounters: (1) explanation of the health issue, (2) elicitation of patient preferences, and (3) integration of patient preferences. Participants then completed CollaboRATE (possible scores 0-100) in addition to 2 other patient-reported measures of SDM: the 9-item Shared Decision Decision Making Questionnaire (SDM-Q-9) and the Doctor Facilitation subscale of the Patient’s Perceived Involvement in Care Scale (PICS). A subsample of participants was resurveyed between 7 and 14 days after the initial survey. We assessed CollaboRATE’s discriminative, concurrent, and divergent validity, intrarater reliability, and sensitivity to change. Results The final sample consisted of 1341 participants. CollaboRATE demonstrated discriminative validity, with a significant increase in CollaboRATE score as the number of core dimensions of SDM increased from zero (mean score: 46.0, 95% CI 42.4-49.6) to 3 (mean score 85.8, 95% CI 83.2-88.4). CollaboRATE also demonstrated concurrent validity with other measures of SDM, excellent intrarater reliability, and sensitivity to change; however, divergent validity was not demonstrated. Conclusions The fast and frugal nature of CollaboRATE lends itself to routine clinical use. Further assessment of CollaboRATE in real-world settings is required.

Aggregate Cost of Mammography Screening in the United States: Comparison of Current Practice and Advocated Guidelines

Context The total cost of alternative programs that screen for breast cancer is unknown. Contribution The actual cost of breast cancer screening in the United States in 2010 was

Cost-effectiveness of Breast MR Imaging and Screen-Film Mammography for Screening BRCA1 Gene Mutation Carriers

7.8 billion. The cost would have been

Undetermined impact of patient decision support interventions on healthcare costs and savings: systematic review

10.1 billion for screening every year,

Implementing breast cancer decision aids in community sites: barriers and resources

2.6 billion for screening every 2 years, and

Identification and Management of Women at High Risk for Hereditary Breast/Ovarian Cancer Syndrome

3.5 billion for screening according to recommendations from the U.S. Preventive Services Task Force. Caution These results depend on assumptions, not all of which are equally well-supported. Implication Dollars saved under less expensive programs could be used to screen women who are not being screened. The Editors The frequency and appropriate age to start mammography screening for the detection of breast cancer have been debated in the United States for decades. Controversy intensified after the U.S. Preventive Services Task Force (USPSTF) recommended a change to biennial mammography on the basis that both annual and biennial screening reduce mortality rates, but biennial screening also decreases the negative effects (1). However, in the United States, there has been resistance to reducing frequency or modifying the age range for mammography. The USPSTF guidelines conflict with professional organizations, such as the American Cancer Society, which recommend annual screening from age 40 years and continued regardless of a womans age as long as she does not have serious, chronic health problems. Given the broad population that mammography serves, it is important to consider the economic effect of the conflicting guidelines. In 2009, the USPSTF recommended biennial screening for women between the ages of 50 and 74 years, with consideration of screening women aged 40 to 49 years on a riskbenefit decision (2). The USPSTF recommendations are based on a rigorous review of screening trials and work from the Cancer Intervention and Surveillance Modeling Network investigators that demonstrated that there is little net benefit in increasing the frequency of mammography (3). The Cancer Intervention and Surveillance Modeling Network modeling is corroborated by evidence from the Breast Cancer Surveillance Consortium, showing that false-positive recall and biopsy rates are significantly lower in the setting of biennial screening but without a significant increase in detected later-stage cancer (4, 5). The USPSTF recommendations on frequency are now in alignment with most European countries, where many of the defining mammography trials were conducted, with the exceptions of the United Kingdom and Finland, which screen every 3 years (611). Screening in the United States is delivered locally or regionally and covered by myriad payer and health plan organizations. Thus, the total resources required or the cost-tradeoffs of different recommendations are currently unknown. This study was designed to inform the debate by estimating the lower bound of the aggregate annual cost of mammography screening in the U.S. population when current (2010) screening practices are compared with guideline-recommended screening strategies. Our findings should be valuable to women, clinicians, and health policymakers alike who are aware of the many conflicting guidelines. Methods Study Design To estimate the cost of mammography in the United States, we created a simulation model using mammography screening in 2010 as our base case. We then simulated 3 strategies (annual, biennial, and USPSTF) from the payer perspective. Analyses were done using R (R Foundation for Statistical Computing, Vienna, Austria). Table 1 shows the 4 screening strategies, 1 of which is an estimate of actual practice (18, 19). The other 3 standardize on the population screened (85%) but differ on the age at which to start and stop and the frequency at which to screen. The biennial strategy represents the European approach, the annual strategy reflects the American Cancer Society (20) (among others) recommendations, and the USPSTF strategy (17) represents a risk-based strategy for screening those younger than 50 years and older than 75 years on the basis of their 2009 recommendations. Table 1. Model Inputs and Formulas The final output of the model was the aggregate cost of mammography screening per year. The summation included the costs of mammography, computer-aided detection (CAD), and recalls and biopsies. A description of the modeling methods is available in the Supplement. Supplement. Detailed Description of the Cost Models and the Sensitivity Analyses Inputs and Variables Model inputs were attained from several sources, including the Breast Cancer Surveillance Consortium (21), an observational data set designed to reflect mammography practice as it is done in the community and to reflect the distribution of women in the United States who have mammography, and they are listed in Table 1(13, 15, 16, 2224). All input variables except costs were age-specific. The number of mammography screenings was calculated by determining the population of women at risk by using census data. To focus on screening as opposed to diagnostic or surveillance mammography, we limited the population of women at risk to those between the ages of 40 and 85 years and excluded the number of women diagnosed with breast cancer in the past 5 years, who should be receiving surveillance mammography. We used data from the Behavioral Risk Factor Surveillance System 2010 Survey, a telephone health survey conducted by the Centers for Disease Control and Prevention, to determine the frequency and percentage of women receiving mammography. We corrected for survey bias by using the correction suggested by Rothman and colleagues (25). Although the survey does not distinguish between screening and diagnostic, we excluded women younger than 40 years and older than 85 years and those with recent history of breast cancer to best estimate screening as opposed to diagnostic mammography screenings (25). In our base-case model of actual practice, we included women who reported receiving mammography in the past 1 to 5 years and estimated the number who would have been screened in 1 given year; otherwise, the simulated strategies only simulated women receiving mammography every 1 or 2 years. The simulated strategies modeled a targeted participation of 85%, a screening participation achieved in the past for cervical cancer screening (26). For the USPSTF strategy, we modeled 20% of women aged 40 to 50 years as high-risk. We then simulated biennial screening for this cohort on the basis of evidence that women aged 40 to 49 years with a 2-fold increased risk have similar harmsbenefit ratios from biennial screening as women aged 50 to 74 years with average risk (27). The USPSTF strategy also modeled screening women between the ages of 70 and 85 years who are healthy, defined as having fewer than 3 self-reported chronic conditions as reported by Medicare (28). The percentage of recalled mammograms was obtained from the Breast Cancer Surveillance Consortium, using mammography screening performance data from 1908447 examinations for 749597 women screened from 2001 to 2007. Following the Breast ImagingReporting and Data System manual (29), a recall was defined as an initial Breast ImagingReporting and Data System assessment of 0 (needs additional imaging evaluation), 4 (suspicious abnormality), 5 (highly suggestive of malignancy), or 3 (probably benign finding) if it was accompanied by a recommendation for immediate work-up. Separate estimates were computed for recall rates at first and subsequent mammography screenings (that is, prevalent and incident screenings) as well as stratified by frequency of screening, digital versus film mammography, and a womans age. The estimated costs of the modeled strategies include the cost of screening mammography and the subsequent recall costs. Costs for mammography and CAD were determined using 2010 national Medicare reimbursements rates (16). Recall costs were calculated from the DMIST (Digital Mammographic Imaging Screening Trial) results of work-up costs, including additional imaging and biopsies from false-positive and true-positive examination results (24). We adjusted DMIST recall costs proportional to the use of digital versus film mammography in 2010. We adjusted all cost data to 2010 U.S. dollars on the basis of inflation as estimated by the medical portion of the Consumer Price Index (30). Sensitivity Analysis We used Monte Carlo simulations to estimate the uncertainty of our total cost estimates and quantify the sensitivity of the output (total cost) to the model inputs (Supplement) (31). In the sensitivity analysis, all terms in the formulas were assumed to be independent and follow -distributions as detailed in the Supplement. Role of the Funding Source This study was funded by the University of California and Safeway Foundation. The funding source had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. Results Our model simulated screening practices in 2010 and estimated the aggregate U.S. population cost of mammography per year. Three mammography screening strategies advocated by various professional societies with targeted participation rates were also simulated and yielded costs that ranged from

Pilot trial of a computerized decision aid for breast cancer prevention.

10.1 billion for the most to