Fei Wan

BACKGROUND: Smoking is the leading preventable cause of premature death in the United States. Previous studies of financial incentives for smoking cessation in work settings have not shown that such incentives have significant effects on cessation rates, but these studies have had limited power, and the incentives used may have been insufficient. METHODS: We randomly assigned 878 employees of a multinational company based in the United States to receive information about smoking-cessation programs (442 employees) or to receive information about programs plus financial incentives (436 employees). The financial incentives were $100 for completion of a smoking-cessation program, $250 for cessation of smoking within 6 months after study enrollment, as confirmed by a biochemical test, and $400 for abstinence for an additional 6 months after the initial cessation, as confirmed by a biochemical test. Individual participants were stratified according to work site, heavy or nonheavy smoking, and income. The primary end point was smoking cessation 9 or 12 months after enrollment, depending on whether initial cessation was reported at 3 or 6 months. Secondary end points were smoking cessation within the first 6 months after enrollment and rates of participation in and completion of smoking-cessation programs. RESULTS: The incentive group had significantly higher rates of smoking cessation than did the information-only group 9 or 12 months after enrollment (14.7% vs. 5.0%, P<0.001) and 15 or 18 months after enrollment (9.4% vs. 3.6%, P<0.001). Incentive-group participants also had significantly higher rates of enrollment in a smoking-cessation program (15.4% vs. 5.4%, P<0.001), completion of a smoking-cessation program (10.8% vs. 2.5%, P<0.001), and smoking cessation within the first 6 months after enrollment (20.9% vs. 11.8%, P<0.001). CONCLUSIONS: In this study of employees of one large company, financial incentives for smoking cessation significantly increased the rates of smoking cessation. (ClinicalTrials.gov number, NCT00128375.)

IMPORTANCE: Limited information about the relationship between specific mutations in BRCA1 or BRCA2 (BRCA1/2) and cancer risk exists. OBJECTIVE: To identify mutation-specific cancer risks for carriers of BRCA1/2. DESIGN, SETTING, AND PARTICIPANTS: Observational study of women who were ascertained between 1937 and 2011 (median, 1999) and found to carry disease-associated BRCA1 or BRCA2 mutations. The international sample comprised 19,581 carriers of BRCA1 mutations and 11,900 carriers of BRCA2 mutations from 55 centers in 33 countries on 6 continents. We estimated hazard ratios for breast and ovarian cancer based on mutation type, function, and nucleotide position. We also estimated RHR, the ratio of breast vs ovarian cancer hazard ratios. A value of RHR greater than 1 indicated elevated breast cancer risk; a value of RHR less than 1 indicated elevated ovarian cancer risk. EXPOSURES: Mutations of BRCA1 or BRCA2. MAIN OUTCOMES AND MEASURES: Breast and ovarian cancer risks. RESULTS: Among BRCA1 mutation carriers, 9052 women (46%) were diagnosed with breast cancer, 2317 (12%) with ovarian cancer, 1041 (5%) with breast and ovarian cancer, and 7171 (37%) without cancer. Among BRCA2 mutation carriers, 6180 women (52%) were diagnosed with breast cancer, 682 (6%) with ovarian cancer, 272 (2%) with breast and ovarian cancer, and 4766 (40%) without cancer. In BRCA1, we identified 3 breast cancer cluster regions (BCCRs) located at c.179 to c.505 (BCCR1; RHR = 1.46; 95% CI, 1.22-1.74; P = 2 × 10(-6)), c.4328 to c.4945 (BCCR2; RHR = 1.34; 95% CI, 1.01-1.78; P = .04), and c. 5261 to c.5563 (BCCR2', RHR = 1.38; 95% CI, 1.22-1.55; P = 6 × 10(-9)). We also identified an ovarian cancer cluster region (OCCR) from c.1380 to c.4062 (approximately exon 11) with RHR = 0.62 (95% CI, 0.56-0.70; P = 9 × 10(-17)). In BRCA2, we observed multiple BCCRs spanning c.1 to c.596 (BCCR1; RHR = 1.71; 95% CI, 1.06-2.78; P = .03), c.772 to c.1806 (BCCR1'; RHR = 1.63; 95% CI, 1.10-2.40; P = .01), and c.7394 to c.8904 (BCCR2; RHR = 2.31; 95% CI, 1.69-3.16; P = .00002). We also identified 3 OCCRs: the first (OCCR1) spanned c.3249 to c.5681 that was adjacent to c.5946delT (6174delT; RHR = 0.51; 95% CI, 0.44-0.60; P = 6 × 10(-17)). The second OCCR spanned c.6645 to c.7471 (OCCR2; RHR = 0.57; 95% CI, 0.41-0.80; P = .001). Mutations conferring nonsense-mediated decay were associated with differential breast or ovarian cancer risks and an earlier age of breast cancer diagnosis for both BRCA1 and BRCA2 mutation carriers. CONCLUSIONS AND RELEVANCE: Breast and ovarian cancer risks varied by type and location of BRCA1/2 mutations. With appropriate validation, these data may have implications for risk assessment and cancer prevention decision making for carriers of BRCA1 and BRCA2 mutations.

CONTEXT: Prostate-specific antigen screening has led to an increase in the diagnosis and treatment of localized prostate cancer. However, the role of active treatment of low- and intermediate-risk disease in elderly men is controversial. OBJECTIVE: To estimate the association between treatment (with radiation therapy or radical prostatectomy) compared with observation and overall survival in men with low- and intermediate-risk prostate cancer. DESIGN AND SETTING: Observational US cohort from Surveillance, Epidemiology, and End Results Medicare data. PATIENTS: At total of 44,630 men aged 65 to 80 years who were diagnosed between 1991 and 1999 with organ-confined, well- or moderately differentiated prostate cancer and who had survived more than a year past diagnosis. Patients were followed up until death or study end (December 31, 2002). Patients were classified as having received treatment (n=32,022) if they had claims for radical prostatectomy or radiation therapy during the first 6 months after diagnosis. They were classified as having received observation (n=12,608) if they did not have claims for radical prostatectomy, radiation, or hormonal therapy. Patients who received only hormonal therapy were excluded. MAIN OUTCOME MEASURE: Overall survival. RESULTS: At the end of the 12-year study period, 4663 men (37%) in the observational group and 7639 men (23.8%) in the treatment group had died. The treatment group had longer 5- and 10-year survival than the observation group. After using propensity scores to adjust for potential confounders (tumor characteristics, demographics, and comorbidities), there was a statistically significant survival advantage associated with treatment (hazard ratio, 0.69; 95% confidence interval, 0.66-0.72). A benefit associated with treatment was seen in all subgroups examined, including older men (aged 75-80 years at diagnosis), black men, and men with low-risk disease. CONCLUSIONS: This study suggests a survival advantage is associated with active treatment for low- and intermediate-risk prostate cancer in elderly men aged 65 to 80 years. Because observational data cannot completely adjust for potential selection bias and confounding, these results must be validated in randomized controlled trials of alternative management strategies in elderly men with localized prostate cancer.

IMPORTANCE: As health information technology grows, secondary uses of personal health information offer promise in advancing research, public health, and health care. Public perceptions about sharing personal health data are important for establishing and evaluating ethical and regulatory structures to oversee the use of these data. OBJECTIVE: To measure patient preferences about sharing their electronic health information for secondary purposes (other than their own health care). DESIGN, SETTING, AND PARTICIPANTS: In this conjoint analysis study, we surveyed 3336 adults (568 Hispanic, 500 non-Hispanic African American, and 2268 non-Hispanic white); participants were randomized to 6 of 18 scenarios describing secondary uses of electronic health information, constructed with 3 attributes: uses (research, quality improvement, or commercial marketing), users (university hospitals, commercial enterprises, or public health departments), and data sensitivity (whether it included genetic information about their own cancer risk). This design enabled participants to reveal their preferences for secondary uses of their personal health information. MAIN OUTCOMES AND MEASURES: Participants responded to each conjoint scenario by rating their willingness to share their electronic personal health information on a 1 to 10 scale (1 represents low willingness; 10, high willingness). Conjoint analysis yields importance weights reflecting the contribution of a dimension (use, user, or sensitivity) to willingness to share personal health information. RESULTS: The use of data was a more important factor in the conjoint analysis (importance weight, 64.3%) than the user (importance weight, 32.6%) and data sensitivity (importance weight, 3.1%). In unadjusted linear regression models, marketing uses (β = -1.55), quality improvement uses (β = -0.51), drug company users (β = -0.80), and public health department users (β = -0.52) were associated with less willingness to share health information than research uses and university hospital users (all P < .001). Hispanics and African Americans differentiated less than whites between uses. CONCLUSIONS AND RELEVANCE: Participants cared most about the specific purpose for using their health information, although differences were smaller among racial and ethnic minorities. The user of the information was of secondary importance, and the sensitivity was not a significant factor. These preferences should be considered in policies governing secondary uses of health information.