METHODS: A cross-sectional analytical study was carried out. Self-administered questionnaires were given to 400 patients who registered at an outpatient clinic in April 2018. Convenience sampling was applied.
RESULTS: Monthly income, education level, occupation, and knowledge level are significantly associated with attitude of the respondents toward organ and tissue donation. Occupation influenced attitude toward organ donation. Knowledge of organ donation and brain death both significantly affected attitude toward organ donation.
CONCLUSION: The greater the knowledge of organ donation and brain death, the more positive impression or attitude toward organ donation. Education level and income are the main predictors that influence attitude toward organ donation. Hence, it is important for public health units to promote and deliver public education on organ donation, change public misconceptions, and work parallel with hospitals to increase organ donation rates in Sabah.
METHODS: Using data from a multicenter study of 1,126 Asian patients with ovarian cancer (including 147 BRCA PV carriers), we developed models incorporating cancer history, clinicopathologic features, and reproductive factors to estimate the likelihood of carrying PVs. We assessed discrimination, calibration, and accuracy and compared genetic testing costs with a universal testing strategy.
RESULTS: Our final model demonstrated good calibration and strong discriminatory power, with an area under the curve of 0.80 (95% confidence interval, 0.74-0.87). Factors included in the model were age at diagnosis, ethnicity, personal and family cancer history, and clinicopathologic features. The model had 77% accuracy at the optimal threshold for testing compared with 13% accuracy with universal testing, reducing genetic testing costs from ∼USD4,371 to ∼USD1,974 per identified carrier. Notably, while maintaining 100% sensitivity, the model reduced testing by 15%, yielding potential savings of ∼USD68,182 per 800 patients each year.
CONCLUSIONS: Targeted testing using a prediction model potentially offers a more efficient and scalable alternative when universal testing is not feasible, optimizing impact in resource-limited settings.
IMPACT: This work supports more affordable and equitable BRCA testing pathways for Asian patients with ovarian cancer.