Robert F. Nease

Patient preferences for location of care: implications for regionalization.

BACKGROUND Regionalization of high-risk surgical procedures to selected high-volume centers has been proposed as a way to reduce operative mortality. For patients, however, travel to regional centers may be undesirable despite the expected mortality benefit. OBJECTIVE To determine the strength of patient preferences for local care. DESIGN Using a scenario of potentially resectable pancreatic cancer and a modification of the standard gamble utility assessment technique, we determined the level of additional operative mortality risk patients would accept to undergo surgery at a local rather than at a distant regional hospital in which operative mortality was assumed to be 3%. We used multiple logistic regression to identify predictors of willingness to accept additional risk. SUBJECTS One hundred consecutive patients (95% male, median age 65) awaiting elective surgery at the Veterans Affairs Medical Center in White River Jct., VT. MAIN OUTCOME MEASURE Additional operative mortality risk patients would accept to keep care local. RESULTS All patients preferred local surgery if the operative mortality risk at the local hospital were the same as the regional hospital (3%). If local operative mortality risk were 6%, which is twice the regional risk, 45 of 100 patients would still prefer local surgery. If local risk were 12%, 23 of 100 patients would prefer local surgery. If local risk were 18%, 18 of 100 patients would prefer local surgery. Further increases in local risk did not result in large changes in the proportion of patients preferring local care. CONCLUSIONS Many patients prefer to undergo surgery locally even when travel to a regional center would result in lower operative mortality risk. Therefore, policy makers should consider patient preferences when assessing the expected value of regionalizing major surgery.

Estimating treatment benefits for the elderly: the effect of competing risks.

The current practice of encouraging patients to participate in treatment decisions requires that clinicians be facile in communicating the risks and benefits of therapy. Sharing numeric data can foster the process. However, because the format in which data are presented influences their interpretation [1-3], clinicians need to consider which format best describes the outcomes their patients face. Consider the tension between relative and absolute risk reduction. The interpretation of even a large relative risk reduction is highly dependent on the baseline risk for the specific disease. A 50% reduction in mortality with early intervention, for example, appears different when the risk for death from disease is changed from 2 per 1000 to 200 per 1000. When the mortality risk is low, the absolute survival benefit is small0.1% (2/1000 to 1/1000); when the risk is high, the absolute benefit is great10% (200/1000 to 100/1000). In the former scenario, patients might reasonably choose to forego a noxious intervention. In the latter, however, patients might be more likely to accept the morbidity of treatment. Because this distinction between relative and absolute risk reduction is concealed when benefit is expressed in only relative terms, many have argued that relative risk reductions should be anchored by baseline risk so that the absolute benefit of treatment is clear [2, 4, 5] However, an absolute measure of disease risk (or risk reduction from therapy) is not the ultimate outcome of interest to patients. Overall risk is more important. The difference is the risks patients face from other conditionsthat is, competing risks. When competing risks are great, they matter. The importance of even a 10% absolute survival benefit from treatment is markedly diminished for a patient who is at greater risk for death from other causes, regardless of the proposed therapy. Such great competing risks are most prevalent among the elderly. Although physicians intuitively understand the relevance of competing risks, they may be less sure about how to quantify the effect. We provide a framework to help physicians gauge the effect of competing risks in their elderly patients. Methods Overview To quantify the effect of competing risks, we used age-specific mortality data from U.S. vital statistics and the declining exponential approximation for life expectancy (DEALE) to model age-specific expectations for persons faced with a particular disease-related mortality. We sought to determine, for example, how a new disease with a 5-year mortality rate of 25% would affect the life expectancy of an average 70-year-old man. We then considered two refinements: the first, to better adjust for the individual patient (using self-reported health status), and the second, to describe more thoroughly the outcome (by including disabling events). Modeling the Effect of a New Disease on Life Expectancy Life expectancy and mortality are fundamentally related to probability estimates. In the general population, life expectancy decreases with increasing age, and annual mortality increases. Gompertz was the first to describe this complex mathematic relation using an exponential function that now bears his name. As life expectancy decreases, mortality rates become almost constant over time. When this occurs, the relation between survival and mortality rates can be approximated with a much simpler mathematic relation: a declining exponential function (the DEALE). This approximation was first validated and popularized by Beck and colleagues [6, 7] and is particularly suited to calculating the effect that a new risk has in older patients. The fundamental assumption behind this technique is that life expectancy equals the inverse of the annual mortality rate: Equation 1 Because mortality rates are essentially constant probability estimates when assessed over relatively short time horizons, patient-specific mortality rates can be expressed as the sum of the disease-independent mortality rate (also known as age-specific mortality rate) and a disease-related mortality rate (also known as case-fatality or excess mortality rate): Equation 2 Note that when disease-related mortality is zero (that is, when the patient does not have the disease or when the disease has no effect on survival), the patient-specific mortality rate (and thus life expectancy) is determined solely by the patients age. Calculation of the life expectancy estimates used in Figure 1 and Figure 2 is relatively simple. Because Figure 1 is the central portion of our paper, we now describe it in detail. Normal life expectancy (the top curves) was determined from the most recent data (1991) from the National Center for Health Statistics, U.S. Department of Health and Human Services [8]. On the basis of remaining life expectancy and the DEALE [6, 7], we calculated the age-specific mortality rate for each age cohort from 65 to 85 years of age. Combining the age-specific mortality with the hypothetical disease-related mortality allowed us to calculate the other four curves. The disease-related annual mortality rate can be calculated from 5-year disease-specific survival using the following equation: Equation 3 Figure 1. The effect of selected disease-related mortality rates on the remaining life expectancy of women (left) and men (right) at the time of diagnosis. Figure 2. The effect of age on the distribution of health states in the future. Thus, if the disease-related 5-year mortality rate is 25% (and the 5-year survival rate is 75%), then the disease-related annual mortality rate is 0.06. Equation 4 A 70-year-old man, for example, has a life expectancy of 12.2 years or an annual age-specific mortality rate of 0.08. Equation 5 Given the foregoing disease, the mans all-cause annual mortality rate is 0.14 (= 0.06 + 0.08), and his life expectancy is 7.2 years. Equation 6 Thus, the sum of the age-specific and disease-related mortality rates gives the patient-specific mortality rate, the inverse of which is life expectancy. Normal life expectancy serves as our proxy for disease-independent data. The mortality reflected in this measure is, of course, itself the result of several diseases in the elderlyprimarily cardiovascular disease and cancer. The method we describe produces a valid approximation whenever the disease in question is not a major contributor to the age-specific mortality rate. For example, if the disease in question was all cardiovascular disease or all cancer, then much of the age-specific mortality rate would already account for the mortality from the disease. Completely successful therapy for such a broad category of disease would move a patient well above his or her normal life expectancy by removing the common causes of death. Thus, the method we describe should be applied only when the physician is considering more discrete diagnoses (for example, aortic aneurysm or breast cancer), which make a relatively small contribution to overall mortality. To provide some quantitative data on how great a contributor to all-cause mortality a given disease can be without affecting our method, we did a sensitivity analysis that removed the contribution of a particular disease from normal life expectancy and accordingly revised the estimate of perfect treatment on life expectancy. For example, for a disease that accounts for 40% of all-cause mortality (such as all cardiovascular diagnoses), revised treatment benefit (in years) was three times the benefit estimated by our method. For a disease that accounts for 30% of all-cause mortality (for example, all cancers considered together), the revised benefit was twice as high as the benefit estimated by our method. However, for a disease that constitutes less than 10% of all-cause mortality (this is the case for any individual cancer), the revised benefit is small (for example, less than 20% higher than that estimated by our method). Adjustments for Health Status The adjustments for health status shown in Table 1 are based on data from the East Boston Senior Health Project. All participants were asked the following question: Compared with others your age, would you rate your overall health as excellent, good, fair, or poor? Analyzing the 1437 men and 2332 women separately, we used 5-year follow-up data to calculate, for each health status self-rating, the proportion of patients who died. The ratio of this health status-specific survival to overall survival served as our health status weight. A more precise analysis for men and women, using five age cohorts (ages 65 to 69 years, 70 to 74 years, 75 to 79 years, 80 to 84 years, and 85 years and older) produced essentially the same weights. Table 1. Estimated Physiologic Age of Elderly Patients Adjusted for Their Self-Reported Health Status* Overall, men who described themselves as in excellent health had a lower mortality rate than average (health status weight, 0.52). Men who reported themselves as in good, fair, and poor health had health status weights of 0.89, 1.26, and 1.88, respectively. The analysis for women showed health status weights of 0.64, 0.88, 1.08, and 1.82 for self-reported health status of excellent, good, fair, and poor, respectively. To approximate a physiologic age to reflect health status, we applied the health status weights to four chronologic ages: 65, 70, 75, and 80 years. Using the age-specific annual mortality from U.S. Vital Statistics data [8] and the health status weight, we calculated a health status-adjusted mortality rate as the following: Equation 7 We then returned to the Vital Statistics data to determine the age at which an average person would have this annual mortality rate. These data do not provide annual mortality rates for persons older than 85 years, forcing us to report 85 years and older for the highest mortality rates. The process was done separately for men and women. Future Disabling Events The expectation of future disabling events (Figure 3) is based on cros

The importance of patient preference in the decision to screen for prostate cancer

AbstractOBJECTIVE: Routine screening for prostate cancer is controversial because of frequent false-positive results, the potential for slow, non-life-threatening growth of untreated cancer, the uncertainty regarding whether treatment can extend life, and the potential for treatment complications. This study examines how information about prostate-specific antigen (PSA) testing and the uncertain benefits of treating prostate cancer affects patients’ desire for PSA testing. DESIGN: An educational videotape designed to inform men about the uncertainty surrounding PSA screening and the treatment of early-stage prostate cancer was presented to two groups of male patients 50 years of age or older. SETTING: Dartmouth-Hitchcock Medical Center. PATIENTS/PARTICIPANTS: For study 1, men seeking a free prostate cancer screening were preassigned to view the educational videotape (N=184) or another videotape (N=188). For study 2, men scheduled to visit a general internal medicine clinic viewed either the educational videotape (N=103) or no videotape (N=93). MEASUREMENTS AND MAIN RESULTS: The men’s information and preferences about prostate cancer screening and treatment and actual choice of PSA test at the next test opportunity were measured. Men who viewed the educational videotape were: better informed about PSA tests, prostate cancer, and its treatment; preferred no active treatment if cancer were found; and preferred not to be screened (all significant atp≤.002 in both studies). Men viewing the educational video were less likely to have a PSA test (p=.041, study 2). This tendency was not significant at the free-PSA clinic (p=.079). CONCLUSIONS: Preference regarding cancer screening and treatment is greatly affected by information about medical uncertainties. Because informed patient choices vary, PSA screening decisions should incorporate individual preferences.

Cost-effectiveness of preoperative autologons donation in coronary artery bypass grafting

Concern about the safety of the allogeneic blood supply has made preoperative autologous blood donation (PAD) routine before major noncardiac operations. However, the costs and benefits of PAD in elective coronary artery bypass grafting (CABG) are not well established. We used decision analysis to (1) calculate the cost-effectiveness of PAD in CABG, expressed as cost per year of life saved, and (2) compare the health benefits of reducing allogeneic transfusions with the potential risks of autologous blood donation by patients with coronary artery disease. A prospective study of 18 institutions provided data on transfusion practice and blood product costs in CABG. On average, PAD in CABG costs

Procedure-related miscarriages and Down syndrome-affected births: implications for prenatal testing based on women's preferences.

508,000 to

Beyond race or ethnicity and socioeconomic status : Predictors of prenatal testing for down syndrome

909,000 per quality-adjusted year of life saved, depending on the number of units donated. Preoperative autologous blood donation is more cost-effective (as low as

ASSESSMENT OF PATIENT PREFERENCES AMONG MEN WITH PROSTATE CANCER

518,000 per year of life saved) when targeted to younger patients undergoing CABG at centers with high transfusion rates. The cost-effectiveness of PAD is strongly dependent on estimates of posttransfusion hepatitis incidence, but less so on plausible estimates of the current risk of human immunodeficiency virus transmission. Although the actual risk of PAD is uncertain, even a small fatality risk (> 1 per 101,000 donations) associated with blood donation by patients awaiting CABG negates all life expectancy benefits of PAD. At current costs, PAD by patients awaiting CABG is not cost-effective, producing small health benefits at high societal cost. For the individual patient, the risk of donating blood before CABG may well outweigh the benefits associated with fewer allogeneic transfusions.

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

Objective To determine how pregnant women of varying ages, races, ethnicities, and socioeconomic backgrounds value procedure-related miscarriage and Down–syndrome-affected birth. Methods We studied cross-sectionally 534 sociodemographically diverse pregnant women who sought care at obstetric clinics and practices throughout the San Francisco Bay area. Preferences for procedure-related miscarriage and the birth of an infant affected by Down syndrome were assessed using the time trade-off and standard gamble metrics. Because current guidelines assume that procedure-related miscarriage and Down syndrome–affected birth are valued equally, we calculated the difference in preference scores for those two outcomes. We also collected detailed information on demographics, attitudes, and beliefs. Results On average, procedure-related miscarriage was preferable to Down syndrome–affected birth, as evidenced by positive differences in preference scores for them (time trade-off difference: mean = 0.09, median = 0.06; standard gamble difference: mean = 0.11, median = 0.02; P < .001 for both, one-sample sign test). There was substantial subject-to-subject variation in preferences that correlated strongly with attitudes about miscarriage, Down syndrome, and diagnostic testing. Conclusion Pregnant women tend to find the prospect of a Down syndrome–affected birth more burdensome than a procedure-related miscarriage, calling into question the equal risk threshold for prenatal diagnosis. Individual preferences for those outcomes varied profoundly. Current guidelines do not appropriately consider individual preferences in lower-risk women, and the process for developing prenatal testing guidelines should be reconsidered to better reflect individual values.

Use of contrast for image enhancement during stress echocardiography is cost-effective and reduces additional diagnostic testing.

OBJECTIVE: To identify predictors of prenatal genetic testing decisions and explore whether racial or ethnic and socioeconomic differences are explained by knowledge, attitudes, and preferences. METHODS: This was a prospective cohort study of 827 English-, Spanish-, or Chinese-speaking pregnant women presenting for care by 20 weeks of gestation at 1 of 23 San Francisco Bay–area obstetrics clinics and practices. Our primary outcome measure for women aged less than 35 years was any prenatal genetic testing use compared with none, and for women aged 35 years or older, prenatal testing strategy (no testing, screening test first, straight to invasive diagnostic testing). Baseline questionnaires were completed before any prenatal test use; test use was assessed after 30 gestational weeks. RESULTS: Among women aged less than 35 years, no racial or ethnic differences in test use emerged. Multivariable analyses yielded three testing predictors: prenatal care site (P = .024), inclination to terminate pregnancy of a Down-syndrome–affected fetus (odds ratio 2.94, P = .002) and belief that modern medicine interferes too much in pregnancy (odds ratio .85, P = .036). Among women aged 35 years or older, observed racial or ethnic and socioeconomic differences in testing strategy were mediated by faith and fatalism, value of testing information, and perceived miscarriage risk. Multivariable predictors of testing strategy included these 3 mediators (P = .035, P < .001, P = .037, respectively) and health care system distrust (P = .045). A total of 29.5% of screen-positive women declined amniocentesis; 6.6% of women screening negative underwent amniocentesis. CONCLUSION: Racial or ethnic and socioeconomic differences in prenatal testing strategy are mediated by risk perception and attitudes. Screening is not the best choice for many women. Optimal prenatal testing counseling requires clarification of risks and consideration of key attitudes and preferences regarding the possible sequence of events after testing decisions. LEVEL OF EVIDENCE: II-2

Representation and Analysis of Medical Decision Problems with Influence Diagrams

PURPOSE We developed a self-administered paper based instrument to assess patient preferences quantified as utilities for common outcomes associated with the management of prostate cancer. MATERIALS AND METHODS A total of 50 patients was invited to test a self-administered paper based instrument designed to assess preferences for health outcomes associated with the management of localized prostate cancer. The 50 patients were selected from a group of 625 randomly identified men with prostate cancer who responded to a survey instrument designed to assess health related quality of life. The 50 patients selected for this pilot project were chosen because of the wide range of responses to the quality of life survey. Patient utilities were assessed for the 5 health states of overall quality of life, problems related to prostate cancer, and problems related to urinary, bowel and sexual dysfunction. RESULTS Patients were able to complete the assigned tasks. The self-administered instrument had high test-retest reliability. In addition results obtained from this instrument showed a correlation with results obtained from assessments using other instruments, including an analog scale, a computer based system known as U-Titer, a quality of life survey and the Health Utility Index:3. CONCLUSIONS A self-administered paper based instrument can be used to assess patient utilities for health states associated with prostate cancer management. Results from the instrument tested appear to be reliable and valid, and are comparable to those obtained from other assessment techniques. A self-administered paper based instrument has distinct advantages when conducting large survey studies because it can be incorporated at relatively low cost.