Objective: The study aims to determine the factors associated with unsuccessful treatment outcome among patients who have undergone tuberculosis treatment. Methods: An analytic cross-sectional study was employed through secondary data analysis of administrative data collected by the National Tuberculosis Control Program from October 2015 to September 2016. Using multiple logistic regression analysis, factors associated with unsuccessful treatment outcome were determined. Results: Multiple logistic regression analysis revealed that belonging to age groups 25 to 34 (aOR=0.73; 95%CI 0.54-0.99) or 35 to 44 (aOR=0.75; 95%CI 0.56-0.99), being male (aOR=1.30; 95%CI 1.03-1.64), doing crafts and related trades work (aOR=0.66; 95%CI 0.46-0.94), living in either a 4th class city (aOR=0.46; 95%CI 0.26-0.82), 1st class municipality (aOR=0.75; 95%CI 0.57-0.98), 4th class municipality (aOR=0.59; 95%CI 0.38-0.93), having a positive sputum smear result (aOR=1.60; 95%CI 1.29-2.00), having rifampicin-resistant/ multidrug-resistant tuberculosis (aOR=9.32; 95%CI 7.28-11.93), being a treatment after lost to follow-up case (aOR=1.84; 95%CI 1.37-2.47) or a case with previously unknown treatment outcome (aOR=1.42; 95%CI 1.00-2.01) were significant correlates of unsuccessful treatment outcome. Conclusion The study found that age, sex, occupation, residence, sputum smear results, drug resistance, and history of previous treatment were associated with unsuccessful treatment outcome.
Philippines ; Tuberculosis, Pulmonary ; Treatment Outcome ; Regression Analysis

Background: The use of 3D printing in medical education, prosthetics, and preoperative planning requires dimensional accuracy of the models compared to the replicated tissues or organs. Objective. To determine the dimensional accuracy of 3D-printed models replicated from metacarpal bones from cadavers. Methods: Fifty-two models were 3D-printed using fused deposition modeling (FDM), stereolithography (SLA), digital light processing (DLP), and binder jetting method from 13 right first metacarpal bones of cadavers from the College of Medicine, University of the Philippines Manila. Six dimensional parameters of the 3D-printed models and their control bones were measured using 0.01 mm calipers — length, midshaft diameter, base width, base height, head width, and head height. Mean measurements were compared using non-inferiority testing and multidimensional scaling. Results: Mean measurements of the 3D-printed models were slightly larger than their control bones (standard deviation range: 1.219-4.264; standard error range, 0.338-1.183). All models were found to be at least 90% accurate and statistically non-inferior compared to control bones. DLP-printed models were the most accurate (base width, 99.62 %) and most similar to their control bone (–0.05, 90% CI –0.34, 0.24). Through multidimensional scaling, DLP-printed models (coordinate = 0.437) were the most similar to the control bone (coordinate = 0.899). Conclusion The 3D-printed models are dimensionally accurate when compared to bones.
Stereolithography ; Dimensional Measurement Accuracy ; Printing, Three-Dimensional