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Background: The accountability for reasonableness (A4R) framework defines 4 conditions for legitimate healthcare coverage decision processes: Relevance, Publicity, Appeals, and Enforcement. The aim of this study was to reflect on how the diverse features of decision-making processes can be aligned with A4R conditions to guide decision-making towards legitimacy. Rare disease and regenerative therapies (RDRTs) pose special decision-making challenges and offer therefore a useful case study. Methods: Features operationalizing each A4R condition as well as three different approaches to address these features (cost-per-QALY-focused and multicriteria-based) were defined and organized into a matrix. Seven experts explored these features during a panel run under the Chatham House Rule and provided general and RDRT-specific recommendations. Responses were analyzed to identify converging and diverging recommendations. Results: Regarding Relevance, recommendations included supporting deliberation, stakeholder participation and grounding coverage decision criteria in normative and societal objectives. Thirteen of 17 proposed decision criteria were recommended by a majority of panelists. The usefulness of universal cost-effectiveness thresholds to inform allocative efficiency was challenged, particularly in the RDRT context. RDRTs raise specific issues that need to be considered; however, rarity should be viewed in relation to other aspects, such as disease severity and budget impact. Regarding Publicity, panelists recommended transparency about the values underlying a decision and value judgements used in selecting evidence. For Appeals, recommendations included a life-cycle approach with clear provisions for re-evaluations. For Enforcement, external quality reviews of decisions were recommended. Conclusion: Moving coverage decision-making processes towards enhanced legitimacy in general and in the RDRT context involves designing and refining approaches to support participation

In the 12,000 years preceding the Industrial Revolution, human activities led to significant changes in land cover, plant and animal distributions, surface hydrology, and biochemical cycles. Earth system models suggest that this anthropogenic land cover change influenced regional and global climate. However, the representation of past land use in earth system models is currently oversimplified. As a result, there are large uncertainties in the current understanding of the past and current state of the earth system. In order to improve representation of the variety and scale of impacts that past land use had on the earth system, a global effort is underway to aggregate and synthesize archaeological and historical evidence of land use systems. Here we present a simple, hierarchical classification of land use systems designed to be used with archaeological and historical data at a global scale and a schema of codes that identify land use practices common to a range of systems, both implemented in a geospatial database. The classification scheme and database resulted from an extensive process of consultation with researchers worldwide. Our scheme is designed to deliver consistent, empirically robust data for the improvement of land use models, while simultaneously allowing for a comparative, detailed mapping of land use relevant to the needs of historical scholars. To illustrate the benefits of the classification scheme and methods for mapping historical land use, we apply it to Mesopotamia and Arabia at 6 kya (c. 4000 BCE). The scheme will be used to describe land use by the Past Global Changes (PAGES) LandCover6k working group, an international project comprised of archaeologists, historians, geographers, paleoecologists, and modelers. Beyond this, the scheme has a wide utility for creating a common language between research and policy communities, linking archaeologists with climate modelers, biodiversity conservation workers and initiatives.

Agricultural productivity, and its variation in space and time, plays a fundamental role in many theories of human social evolution. However, we often lack systematic information about the productivity of past agricultural systems on a large enough scale in order to be able to test these theories properly. The effect of climate on crop yields has received a great deal of attention resulting in a range of empirical and process-based models, yet the focus has primarily been on current or future conditions. In this paper, we argue for a “bottom-up” approach that estimates productivity, or potential productivity based on information about the agricultural practices and technologies used in past societies. Of key theoretical interest is using this information to estimate the carrying capacity of a given region, independently of estimates of population size. We outline how explicit crop yield models can be combined with high quality historical and archaeological information about past societies, in order to infer the temporal and geographic patterns of change in agricultural productivity and potential. We discuss the kinds of information we need to collect about agricultural techniques and practices in the past, and introduce a new databank initiative we have developed for collating the best available historical and archaeological evidence. A key benefit of our approach lies in making explicit the steps in the process of estimating past productivities and carrying capacities, and in being able to assess the effects of different modelling assumptions. This is undoubtedly an ambitious task, yet promises to provide important insights into fundamental aspects of past societies, and will enable us to test more rigorously key hypotheses about human socio-cultural evolution.