Walton Sumner

The effect of an educational intervention on the perceived risk of breast cancer

AbstractOBJECTIVE: To appraise women’s perceived risk of developing breast cancer and the effects of a physician’s educational intervention on this perception. DESIGN: Longitudinal before-and-after study involving four measures of participants’ risk of developing breast cancer. Eligible women provided the data needed to calculate an objective estimate of their individual risk of developing breast cancer before age 80 using the Gail formula. They also provided a subjective estimate of their individual perceived risk. Then, each participant met with a general internal medicine physician who provided personalized information and education. Immediately after education, and again several months later, we reassessed each woman’s perceived risk. SETTING: Physician’s office. PARTICIPANTS: A convenience sample of 59 women participating in the Tamoxifen Breast Cancer Prevention Trial. Twenty-nine women returned for the follow-up risk assessment. MEASUREMENTS AND MAIN RESULTS: The median calculated risk of breast cancer before age 80 (by the Gail formula) was 15%, but the median perceived risk before educational intervention was 50%. The perceived risk after educational intervention fell to 25%. At late follow-up, the median perceived risk remained at 25%. The difference between the pre-educational perceptions and the calculated estimates was significant (p<.0001). After educational intervention, perceived risk measures shifted closer to the calculated value, but still remained significantly higher (p<.0001). CONCLUSIONS: Women often substantially overestimate their chances of getting breast cancer. Educational intervention by a physician, including explanation of an individual’s calculated risk, can reduce this error. The effect of education appears to persist at least for several months.

Preferences for Fractures and Other Glucocorticoid-Associated Adverse Effects among Rheumatoid Arthritis Patients

Objective. The objective of this study was to determine rheumatoid arthritis (RA) patients’ preferences for validated health state scenarios depicting glucocorticoid adverse events, predictors of these preferences, and psychometric properties of different preference techniques in this population. Methods. Preferences were elicited by rating scale and time trade-off methods. Time trade-offs included trading current health for either time spent alive in an adverse health state for chronic conditions (time trade-off) or time spent in a sleeplike state for acute conditions (sleep trade-off). Results. A total of 107 subjects with long-standing RA participated in the preference interviews. Mean preference values (rating scale/trade-off) were lowest for serious fracture adverse events, including hip fracture requiring a nursing home stay (0.55 ± 0.22/0.76 ± 0.36) and vertebral fracture with chronic pain (0.59 ± 0.23/0.67 ± 0.35), and highest for cataracts (0.84 ± 0.17/0.96 ± 0.09) and wrist fracture (0.82 ± 0.18/0.81 ± 0.29). Rating scales had a stronger correlation (r= 0.88) with physician ranking of scenarios than trade-off methods (r = 0.31). All methods were feasible and demonstrated good reliability, while rating scale method showed better construct validity than trade-off techniques. Conclusion. Relative to their current health, RA patients assigned low preference values to many glucocorticoid adverse events, particularly those associated with chronic fracture outcomes. Results varied with the preference measure used, indicating that methodological attributes of preference determinations must be considered in clinical decision making.

Choice-Matching Preference Reversals in Health Outcome Assessments

Background. Health outcome utility assessments generally assume procedural invariance. Preference reversals violating procedural invariance occur in economic scenarios when the assessment process shifts from a choiceto a fill-in-the-blanktask. Purpose. To determine if similar reversals occur in utility assessments. Methods. One hundred thirty-six volunteer subjects completed 6 preference assessments of 4 personal health scenarios. Patients responded to otherwise identical tasks using either choice or fill-in-the-blank processes in a randomized crossover design. The authors determined the percentage of subjects preferring, or inferred to prefer, a given choice. Results. Preference reversals occurred in all assessment scenarios. Conclusions. These preference reversals are a potential source of confusion for utility assessment and informed consent. They could be manipulated to achieve ends other than the best interest of patients. Anchoring or the prominence hypothesis may explain these findings.

A Formal Model of Family Medicine

Background: The American Board of Family Practice is developing a computer-based recertification process. An optimal implementation requires a formal model of family medicine, which will become the basis for a knowledge base. Design: The proposed model of family medicine contains six entities: Population, Record, Agents of Change, Health States, Findings, and Courses of Action. The model illustrates 15 important relations between entities. For instance: Health States Lead to Health States, and Findings Associate with Health States. These two relations describe natural history, manifestations of disease, and the effects of medical interventions and risk factors. Because time is such an important aspect of primary care, nearly all numeric data are represented as graphs of possible values over time, called Patterns, which include details about periodicity. Patterns and other aspects of the model provide a means of describing covariance between observations, such as the influence of height on weight. Results: The model reflects many family practice activities and suggests some formal descriptions of family practice. For instance, diagnostic activities focus largely on classifying early or short segments of Patterns in Findings. Most medical interventions attempt to alter either the probability distributions in a Lead-to relation or the impact of a Finding. Conclusion: The proposed model of family medicine could find uses in many applications, including computer-based tests, medical records, reference systems, and decision support tools.

Variation over time in preventable hospitalization rates across counties.

Background: The Agency for Health Care Research and Quality developed 14 prevention quality indicators (PQIs), including four PQIs related to preventable hospitalizations for diabetes and one to asthma. Quality indicators vary across counties, but variation over time has not been described. Methods: The Kentucky Cabinet for Health and Family Services published PQI data for each county in Kentucky in each of the 3 years from 2006 to 2008. Variation and correlations among PQI measures were calculated. Results: PQI rates often varied 10-fold between counties. Repeated measures of four PQIs were highly correlated, suggesting local health care processes that are stable over time. Some PQIs, such as PQI01—emergent complications of blood glucose control—correlated poorly with other measures. Other PQIs are correlated over geography and time, including PQI03 (long-term complications of diabetes); PQI14 (poorly controlled diabetes); and PQI15 (asthma). Conclusions: These county PQI measures were stable over time. Stability implies that PQI measures were not the result of random processes and did not rapidly shift. However, some health improvement needs varied between counties. Although tailoring health promotion interventions to each countys needs may be complex, stable needs afford time to undertake targeted quality improvement efforts.

“The End of the Beginning” for Clinical Simulation in the ABFM Self-Assessment Modules (SAMs)

“Now this is not the end. It is not even the beginning of the end. But it is, perhaps, the end of the beginning.”[1][1] With these words, Winston Churchill marked the Allied victory of Second El Alamein that represented a major turning point in the struggle against the Axis powers in World War