Abstract

Budget Impact Analyses require a set of essential information on health technology innovation, including expected rates of adoption. There is an absence of studies investigating trends, magnitude of budgetary effects and determinants of diffusion rates for health technology innovations worldwide during the last decades. The present study proposes a pilot assessment on main determinants influencing diffusion rates of pharmaceutical innovations within the Brazilian Unified National Health System (SUS). Data from the Brazilian Health Informatics Department (DATASUS) was gathered to establish the main determinants of diffusion rates of health technology innovations in Brazil, specifically referring to pharmaceutical innovations incorporated in the Brazilian Program for Specialized Pharmaceutical Services (CEAF) at SUS. Information was retrieved on DATASUS relating to patients who had used one of the medicines incorporated into CEAF at least three years prior to the beginning of the study (2015) for treatment of each health condition available. Thus, data from patients adopting 10 different medicines were analyzed in the study. Results from the zero-one inflated beta model showed a higher influence on diffusion rates of pharmaceutical innovations due to: number of pharmaceutical competitors for treatment of the same disease available at CEAF (negative); medicine used in combination with other medication (positive); and innovative medicine within the SUS (positive). Further research on diffusion rates of health technology innovations is required, including wider scope of diseases and medications, potential confusion factors and other variables that may influence rates of adoption in different health systems.

Each year, evidence-based clinical guidelines gain more space in the health professionals’ practice and in services organization. Due to the scarcity of scientific publications focused on diseases of poverty, the development of well-founded clinical guidelines becomes more and more important. In view of that, this paper aims to evaluate the quality of Brazilian guidelines for those diseases. The AGREE II method was used to evaluate 16 guidelines for poverty-related diseases (PRD) and 16 guidelines for global diseases whose treatment require high-cost technologies (HCD), with the ultimate aim of comparing the results. It was found that, in general, the guideline development quality standard is higher for the HCD guidelines than for the PRD guidelines, with emphasis on the "rigour of development" (48% and 7%) and "editorial independence" (43% and 1%) domains, respectively, which had the greatest discrepancies. The HCD guidelines showed results close to or above international averages, whereas the PRD guidelines showed lower results in the 6 domains evaluated. It can be concluded that clinical protocol development priorities need some redirecting in order to qualify the guidelines that define the healthcare organization and the care of vulnerable populations.

and support vector machine prediction models. The remaining data was equally divided into a validation dataset for parameter tuning and a test dataset for evaluation. Results: The Random Forest model achieved an area-under-ROC of 0.88 with precision= 0.82 and recall= 0.37. The SVM model achieved an area-under-ROC of 0.72 with precision= 0.56 and recall= 0.44. In particular, we found Tramadol, a pain killer, might be effective to treat the obesity. ConClusions: The discussions on drug usage are dramatically expanding on social media sites with some drugrepositioning mentions not sufficiently evaluated by researchers. Machine-learning methods are capable of capturing the drug-repositioning contents. Integrating social media data with other data sources, such as biological, chemical, literature and EHR would boost the identification of drug-repositioning opportunities in a real-time and systematic fashion.