BIO Intelligence Service (France)

BackgroundSharing information derived from individual patient data is restricted by regulatory frameworks due to privacy concerns. Generative artificial intelligence can generate shareable virtual patient populations as proxies for sensitive reference datasets. Explicit demonstration of privacy is demanded.ObjectiveThis study evaluated whether a privacy-by-design technique called “avatars” can generate synthetic datasets replicating all reported information from randomized clinical trials (RCTs).MethodsWe generated 2160 synthetic datasets from two phase 3 RCTs for patients with multiple sclerosis (NCT00213135 and NCT00906399; n=865 and 1516 patients) with different configurations to select one synthetic dataset with optimal privacy and utility for each. Several privacy metrics were computed, including protection against distance-based membership inference attacks. We assessed fidelity by comparing variable distributions and assessed utility by checking that all end points reported in the publications had the same effect directions, were within the reported 95% CIs, and had the same statistical significance.ResultsProtection against membership inference attacks was the hardest privacy metric to optimize, but the technique yielded robust privacy and replication of the primary end points (in 72.5% and 80.8% of the 1080 generated datasets). Utility was uneven across the variables and end points, such that information about some end points could not be captured. With optimized generation configurations, we selected one dataset from each RCT replicating all efficacy end points of the placebo and approved treatment arms while maintaining satisfactory privacy (hidden rate: 85.0% and 93.2%).ConclusionsGenerating synthetic RCT datasets replicating primary and secondary efficacy end points is possible while achieving a satisfactory and explicit level of privacy. To show the potential of this method to unlock health data sharing, we released both placebo arms as open datasets.

Citizen science is increasingly upheld with the potential to underpin all aspects of the environmental policy process. However, to date, contributions of citizen science to environmental decision-making remain sparse and not well understood. Evidence points to a gap between the potential relevance of citizen science for policy and its actual implementation. We lack a comprehensive assessment of the current impacts of citizen science projects on environmental policy, and an identification of the scientific, engagement, and governance characteristics of projects that facilitate successful contributions to policy. This paper addresses that knowledge gap through identifying the characteristics of citizen science projects that support policy. We present an inventory of 503 citizen science projects with environmental policy relevance, and an in-depth analysis of 45 case examples with quantitative assessment of characteristics of the citizen scientist, scientific, socio-economic, and policy dimensions. Our results demonstrate that citizen science can underpin all steps of the environmental policy process, and that a diversity of approaches can be used to achieve this. However, governmental support, scientific excellence, and NGO-leadership facilitate policy linkages. We discuss the main challenges and opportunities identified by project leaders in linking citizen science and policy and present a set of recommendations for promoting the better integration of citizen science in the different phases of the policy cycle. Central among these are clarifying policy needs, facilitating access to citizen science data, and improving their evaluation and recognition by decision-makers. Publisher's Note: A corrigendum relating to this article has been published and can be found at http://doi.org/10.5334/cstp.308 .

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A qualitative, quantitative, and overall quality assessment of life cycle inventory is suggested. The method is composed of five indicators which are set up at three levels of the inventory quality: flows, processes, and the system. The method allows one to assess the reliability of the method generating inventory data (justness of data, completeness of data, representativity of processes, repeatability of system definition) and at the same time to quantify the uncertainty of the resulting data made under the data generation method. LCA practitioners can finally decide the overall inventory quality through the information for the acceptability of the inventory result comparing the objective of quality and the cost necessary to improve the quality. The operation of the method was verified in the application to the production of polyethylene bottles. The proposed method was also found applicable for the validation of data in the ISO’s LCA data documentation format.

<div class="htmlview paragraph">In a project carried out for a major French company in the energy sector, we used the data from the Life Cycle Inventory (LCI) of an industrial transformation process and experimented with three external cost evaluation methods. This paper is a presentation of the results obtained. The first part outlines the three external cost evaluation methods and presents the way we used them. The second part presents the results obtained. The third part discusses the relevance of external costs in a corporate decision making context.</div>