R&D and market size: who benefits from orphan drug regulation?

Since the early 80s, orphan drug regulations have been introduced to stimulate R&D for rare diseases. We develop a theoretical model to study the heterogeneous impact on optimal R&D decisions of the incentives for diseases with different levels of prevalence. We show the mechanisms through which the type of incentives deployed by orphan drug regulations may stimulate R&D more for orphan diseases with comparatively high prevalence, thus increasing inequality within the class of orphan diseases. Using data from the Food and Drug Administration on the number of orphan designations, our empirical analysis shows that, while R&D has increased over time for all orphan diseases, the increase has been much greater for the less rare. According to our baseline specification, the difference between the predicted number of orphan designations for a disease belonging to the highest and the lowest class of prevalence is 5.6 times larger after 2008 than it was in 1983. Our findings support the idea that the type of incentives in place may be responsible for this increase in inequality within orphan diseases

R&D and market size: who benefits from orphan drug legislation?

Since the early 80s, incentives have been introduced to stimulate R&D for rare diseases. We develop a theoretical model to study the impact of push and pull incentives on the intensive and extensive margin of optimal R&D investments. The model describes the mechanisms by which the type of incentives provided may favor R&D for orphan diseases with comparatively high prevalence. In our empirical analysis, we merge data on orphan drug designations by the Food and Drug Administration with Orphanet data on disease characteristics. In line with the theoretical results, we find evidence supporting the idea that the incentives adopted may have contributed substantially to widening the gap between more and less rare diseases classified as orphan. Our theoretical and empirical findings together suggest that, if providing some therapeutic option to patients with very rare diseases is a priority, a revision of the current system of incentives should be considered

R&D and market size: who benefits from orphan drug legislation?

Since the early 80s, incentives have been introduced to stimulate R&D for rare diseases. We develop a theoretical model to study the impact of push and pull incentives on the intensive and extensive margin of optimal R&D investments. The model describes the mechanisms by which the type of incentives provided may favor R&D for orphan diseases with comparatively high prevalence. In our empirical analysis, we merge data on orphan drug designations by the Food and Drug Administration with Orphanet data on disease characteristics. In line with the theoretical results, we find evidence supporting the idea that the incentives adopted may have contributed substantially to widening the gap between more and less rare diseases classified as orphan. Our theoretical and empirical findings together suggest that, if providing some therapeutic option to patients with very rare diseases is a priority, a revision of the current system of incentives should be considered

R&D and market size: who benefits from orphan drug regulation?

Since the early 80s, orphan drug regulations have been introduced to stimulate R&D for rare diseases. We develop a theoretical model to study the heterogeneous impact on optimal R&D decisions of the incentives for diseases with different levels of prevalence. We show the mechanisms through which the type of incentives deployed by orphan drug regulations may stimulate R&D more for orphan diseases with comparatively high prevalence, thus increasing inequality within the class of orphan diseases. Using data from the Food and Drug Administration on the number of orphan designations, our empirical analysis shows that, while R&D has increased over time for all orphan diseases, the increase has been much greater for the less rare. According to our baseline specification, the difference between the predicted number of orphan designations for a disease belonging to the highest and the lowest class of prevalence is 5.6 times larger after 2008 than it was in 1983. Our findings support the idea that the type of incentives in place may be responsible for this increase in inequality within orphan diseases

Predicting MEG resting-state functional connectivity using microstructural information

Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from ninety healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity and a myelin measure (derived from multi-component relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer timeseries in the delta, theta, alpha and beta frequency bands. Non-negative matrix factorization was performed to identify the components of the functional connectivity. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest-path-length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity

The late complications of totally implantable central venous access ports: The results from an Italian multicenter prospective observation study

a b s t r a c t Purpose: The principal aim of this study is to analyze the incidence of late complications in oncologic patients with totally implanted central venous access ports. Methods: A prospective multicenter observational study was conducted in 26 Italian oncologic outpatient clinics. 1076 cancer patients with Totally Implanted Central Venous Access Ports (TIAP) were observed. 515 devices were observed in patients under treatment and 561 in patients who went to the outpatient clinic only for flushing. Results: Late complications observed in patients under treatment were: 3 pocket infections (0.09/1000 days of port observation), 1 cutaneous infection (0.03/1000 days of port observation), 8 occlusions (0.24/ 1000 days of port observation) and 12 others. In patients using the device only for flushing we observed 4 cases of device related bacteremia (0.04/ 1000 days of port observation), 1 pocket infection (0.01/1000 days of port observation), 1 cutaneous infection (0.01/1000 days of port observation), 3 occlusions (0.03/1000 days of port observation) and 7 other complications. Conclusions: The low incidence of complications suggests that TIAP is safe and reliable for long term intermittent venous access. Our results support the use of TIAP in the oncology patients