Identifying the need for good practices in Health Technology Assessment : summary of the ISPOR HTA Council Working Group Report on Good Practices in HTA

The systematic use of evidence to inform healthcare decisions, particularly health technology assessment (HTA), has gained increased recognition. HTA has become a standard policy tool for informing decision makers who must manage the entry and use of pharmaceuticals, medical devices, and other technologies (including complex interventions) within health systems, for example, through reimbursement and pricing. Despite increasing attention to HTA activities, there has been no attempt to comprehensively synthesize good practices or emerging good practices to support populationbased decision-making in recent years. After the identification of some good practices through the release of the ISPOR Guidelines Index in 2013, the ISPOR HTA Council identified a need to more thoroughly review existing guidance. The purpose of this effort was to create a basis for capacity building, education, and improved consistency in approaches to HTA-informed decision-making. Our findings suggest that although many good practices have been developed in areas of assessment and some other key aspects of defining HTA processes, there are also many areas where good practices are lacking. This includes good practices in defining the organizational aspects of HTA, the use of deliberative processes, and measuring the impact of HTA. The extent to which these good practices are used and applied by HTA bodies is beyond the scope of this report, but may be of interest to future researchers

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost