A Recovery-Oriented Intervention for People With Psychosis: A Pilot Randomized Controlled Trial.

OBJECTIVE: This pilot randomized controlled trial evaluated the effectiveness of critical time intervention-task shifting (CTI-TS) for people with psychosis in Santiago, Chile, and Rio de Janeiro. CTI-TS is a 9-month intervention involving peer support workers and is designed to maintain treatment effects up to 18 months. METHODS: A total of 110 people with psychosis were recruited when they enrolled in community mental health clinics (Santiago, N=60; Rio de Janeiro, N=50). Participants within each city were randomly assigned to either CTI-TS or usual care for 9 months. Primary outcomes were quality of life, measured with the World Health Organization Quality of Life Assessment-Brief Version (WHOQOL-BREF), and unmet needs, measured with the Camberwell Assessment of Need (CAN), at 18-month follow-up. Results were analyzed according to intention-to-treat guidelines. Generalized estimating equations, with observations clustered within cities, and multiple imputation for missing data were used. RESULTS: At 18 months, both groups showed improved primary outcomes. In both unadjusted and fully adjusted analyses, no significant differences between CTI-TS and usual care (WHOQOL-BREF question on quality of life and CAN mean number of unmet needs) were found. CONCLUSIONS: Three factors might explain the lack of difference between CTI-TS and usual care: first-contact enrollment precluded rapport prior to randomization, a minority of patients were uncomfortable with peers being on the treatment team, and primary outcome measures may not have been sensitive enough to capture the effects of a recovery-oriented intervention. The results have implications for the design of transitional services for people with psychosis, especially in Latin America.

Practical problems with clinical guidelines for breast cancer prevention based on remaining lifetime risk.

BACKGROUND: Clinical guidelines for breast cancer chemoprevention and MRI screening involve estimates of remaining lifetime risk (RLR); in the United States, women with an RLR of 20% or higher meet "high-risk" criteria for MRI screening. METHODS: We prospectively followed 1764 women without breast cancer to compare the RLRs and 10-year risks assigned by the risk models International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and to compare both sets of model-assigned 10-year risks to subsequent incidence of breast cancer in the cohort. We used chi-square statistics to assess calibration and the area under the receiver operating characteristic curve (AUC) to assess discrimination. All statistical tests are two-sided. RESULTS: The models classified different proportions of women as high-risk (IBIS = 59.3% vs BOADICEA = 20.1%) using the RLR threshold of 20%. The difference was smaller (IBIS = 52.9% vs BOADICEA = 43.2%) using a 10-year risk threshold of 3.34%. IBIS risks (mean = 4.9%) were better calibrated to observed breast cancer incidence (5.2%, 95% confidence interval (CI) = 4.2% to 6.4%) than were those of BOADICEA (mean = 3.7%) overall and within quartiles of model risk (P = .20 by IBIS and P = .07 by BOADICEA). Both models gave similar discrimination, with AUCs of 0.67 (95% CI = 0.61 to 0.73) using IBIS and 0.68 (95% CI = 0.62 to 0.74) using BOADICEA. Model sensitivities at thresholds for a 20% false-positive rate were also similar, with 41.8% using IBIS and 38.0% using BOADICEA. CONCLUSION: RLR-based guidelines for high-risk women are limited by discordance between commonly used risk models. Guidelines based on short-term risks would be more useful, as models are generally developed and validated under a short fixed time horizon (≤10 years).

Validation of family cancer history data in high-risk families: the influence of cancer site, ethnicity, kinship degree, and multiple family reporters.

Information on family cancer history (FCH) is often collected for first-degree relatives, but more extensive FCH information is critical for greater accuracy in risk assessment. Using self-reported diagnosis of cancer as the gold standard, we examined differences in the sensitivity and specificity of relative-reported FCH by cancer site, race/ethnicity, language preference, and kinship degree (1,524 individuals from 557 families; average number of relatives per family = 2.7). We evaluated the impact of FCH data collected in 2007-2013 from multiple relatives by comparing mean values and proportions for the number of relatives with any cancer, breast cancer, or ovarian cancer as reported by a single relative and by multiple relatives in the same family. The sensitivity of FCH was lower in Hispanics, Spanish-speaking persons, and third-degree relatives (e.g., for all cancers, sensitivities were 80.7%, 87.4%, and 91.0% for third-, second-, and first-degree relatives, respectively). FCH reported by multiple relatives included a higher number of relatives with cancer than the number reported by a single relative (e.g., mean increase of 1.2 relatives with any cancer), with more relatives diagnosed with any cancer, breast cancer, and ovarian cancer in 52%, 36% and 12% of families, respectively. Collection of FCH data from multiple relatives may provide a more comprehensive picture of FCH and may potentially improve risk assessment and preventive care.

Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance.

INTRODUCTION: Clinicians use different breast cancer risk models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used models that include nongenetic factors - BCRAT, also called Gail model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick model, generally used for patients with above-average risk. METHODS: We evaluated the performance of the BCRAT and IBIS models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine model performance across subgroups of women. For model calibration, we divided the cohort into quartiles of model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs). RESULTS: The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort's 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL X4(2) = 7.2, P value 0.13) than BCRAT (HL X4(2) = 22.0, P value <0.001). The IBIS model also showed better discrimination (AUC = 69.5%, CI = 63.8% to 75.2%) than did the BCRAT model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). CONCLUSIONS: Models developed using extended family history and genetic data, such as the IBIS model, also perform well in women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Extending such models to include additional nongenetic information may improve performance in women across the breast cancer risk continuum.