Résumé
Objectives: To evaluate products reviewed by the Transparency Committee (TC) of the Haute Authorite de Sante (HAS) under the Autorisation d'Acces Precoce (AAP) Early Access Authorization (EAA) pathway and investigate any trends. Method(s): All 97 AAP submissions are publicly available from HAS and were evaluated on or before January 4th, 2023. The TC's opinion was reviewed to obtain the outcome, decision date, therapeutic area, and reasons for rejection. Results were tabulated and descriptive statistics were compiled. Result(s): In total, 79 of the 97 (81%) submissions evaluated were approved for EAA, including renewals of previously granted authorization (6 of 79);18 were rejected. Of the 97 submissions, 35% were indicated for the treatment of solid cancers, 14% for haematological cancers, 10% for ultra-rare diseases, 10% for infectious diseases, 4% for rare diseases, 4% for autoimmune diseases, 4% for skin diseases, and 2% for weight management. Notable approved submissions including those indicated for rare diseases, cancer, autoimmune diseases, and COVID-19, with 93%, 90%, 75%, and 63% of these submissions being granted EAA, respectively. Across the 18 unsuccessful submissions, the main reasons cited for rejection were insufficient efficacy and safety data (78%), lack of innovation compared to existing treatment options (61%), the availability of existing treatment options (56%), and the treatment not being rare enough to qualify for special consideration (28%). Conclusion(s): Since its inception in July 2021, the AAP has proven to be a popular program. As awareness of the program grows and more information becomes available about its benefits and eligibility criteria, it is likely that the number of submissions will continue to increase. However, given the link between submission success and the quality of available data (including a data collection plan), it is recommended manufacturers provide robust evidence to bolster their submissions.Copyright © 2023
Résumé
Graphs that depict numbers of COVID-19 cases often use a linear or logarithmic scale on the y-axis. To examine the effect of scale on how the general public interprets the curves and uses that understanding to infer the urgency of the need for protective actions, we conducted a series of experiments that presented laypeople with the same data plotted on one scale or the other. We found that graphs with a logarithmic, as opposed to a linear, scale resulted in laypeople making less accurate predictions of how fast cases would increase, viewing COVID-19 as less dangerous, and expressing both less support for policy interventions and less intention to take personal actions to combat the disease. Education about the differences between linear and logarithmic graphs reduces but does not eliminate these effects. These results suggest that communications to the general public should mostly use linear graphs. When logarithmic graphs must be used, they should be presented alongside linear graphs of the same data and with guidance on how to interpret the plots. © 2020, Brookings Institution Press. All rights reserved.