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Part I was dedicated to diagnostic services and its impact on health: not solely on the health of patients but also on their social support structure. Access to diagnostic is an essential step forward in order to benet from appropriate health care and treatment.
Accordingly, improving access to diagnostic services simultaneously impacts disease market R&D attractiveness by increasing market size. Part II introduces the second key actor in a patient’s diagnostic and therapeutic \odyssey »: pharmaceutical rms whose R&D investment decisions ultimately impact treatment opportunities and the health of patients with rare diseases. Incentives aiming to increase protability of rare disease markets have been introduced to encourage pharmaceutical innovation. Chapter 3 investigates the impact of the Orphan Drug legislation implemented in 2000 in Europe, while Chapter 4 analyzes the distribution of R&D among rare diseases.

Introductory Section

This introductory section to Chapters 3 and 4 presents the challenges encountered and the solutions found in setting up an original database used in both chapters, which are devoted to innovation in rare disease areas.

Challenges in Setting up an Original Dataset

This section provides a detailed description of the original dataset constructed for Chapters 3 and 4, which focus on R&D investment allocated to rare diseases. It comprises yearly disease-level data on rare disease biomedical research. While data on clinical trial activities for rare diseases were readily accessible, academic publications were not registered in the existing databases. Still, pharmaceutical advances are conditioned upon the constitution of knowledge on the diagnostic of diseases, their etiology, and natural history.
This prerequisite can be proxied by the stock of academic publications on rare diseases. Biomedical innovation is also a valuable source of information for R&D on rare diseases, which compliments pharmaceutical innovation.
MEDLINE is the largest database of academic references on life sciences and biomedical topics. This database is maintained by the United States NLM at the NIH. To search content on the MEDLINE database, one can use PubMed, which is a free search engine.
Simple searches on PubMed can be carried out by entering keywords into PubMed’s search window presented in Figure II.0.1.
When computing the number of academic publications per rare disease, we cannot simply use the names of rare diseases as keywords for a number of reasons. First, because there are between 5,000 and 8,000 distinct rare diseases, a separate manual research was not an option here. Second, rare diseases have complicated names and a large number of synonyms. For example, the disease \Glycogen storage disease due to glucose-6-phosphatase deciency type Ib » has 16 synonyms. Moreover, even if most scientic publications are available in English, an exhaustive search would require covering several other languages and publications in a large number of countries. Finally, we cannot assume that a publication that mentions a rare disease’s name qualies as biomedical research on that rare disease. We thus used Orphanet codes for rare diseases to count their occurrence in biomedical literature.
Orphanet is the reference portal for information on rare diseases and orphan drugs in Europe. It was established in 1997 by the French Ministry of Health and the French National Institute for Health and Medical Research (INSERM). The database and website is maintained by the European Commission. Orphanet attributes a unique identier to each disease and transmits information on expert services in its 37 partner countries worldwide with the input of national partner teams. The database includes information on orphan drugs, expert centers, research projects, diagnostic tests, registries, bio-banks, and patient organizations (Rodwell and Ayme, 2015).
MedGen is also a free search engine to access specialized information on genetic disorders.
One can search MedGen using Orphanet codes to nd information on genetic disorders.
When searching PubMed using MedGen UID, the URL of the result webpage for \Alexander Disease » is: from_uid=78724. We apply the same methodology as described above to replace % in by all the MedGen UID collected in the Excel output. The diculty here is that PubMed displays only 20 results per page. To webscrape all the citation contents corresponding to one MedGen UID, we had to scroll through all of the pages one after the other and retrieve their textual content. The Selenium library and Chrome Driver in Python were used to automate the sending of these requests. Figure II.0.5 displays the Excel output, along with the citation content. The 20 dierent citations are displayed in columns C through V (20 columns in total), except for the nal page that may have contained less results.
Using this output, we then had to retrieve all the dates inside the citation. We explored the citation content to nd regularities in date reporting and obtained a list of publication dates (see Figure II.0.6). Finally, using Stata, we counted the yearly number of publications at the rare disease level.
We faced various complications during the data collection. For example when one citation contained multiple numbers, we had to identify the correct publication date. There were also sudden changes in date reporting methods from Pubmed. Python codes to reproduce the webscraping is available in the nal Appendix.
We searched MEDLINE in July 2017 for all dates from its inception to present day using the MEDGEN unique identier of the 8,755 diseases classied as rare diseases. We then generated the number of scientic publications for each rare disease (See Figure II.0.7 for nal results in Stata).

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European Initiatives to Foster R&D in Rare Disease Areas: The Orphan Drug Legislation after 18 Years

With 36 million people aected by a rare disease in the EU with few treatment options as highlighted in the General Introduction, the allocation of pharmaceutical R&D resources in rare diseases is crucial as R&D investments determine treatment and care opportunities for patients with rare diseases.
Patients with rare diseases are likely to be in poor health related to unaccountable genetic inheritance as well as multiple obstacles in accessing appropriate tests and treatments for their conditions (Schieppati et al., 2008).

Table of contents :

1 Social Determinants of Time to Diagnosis
1 Introduction
2 Literature Review
3 Data and Methods
4 Results
5 Discussion and Conclusion
6 Appendix
2 Children’s health shock externalities on mothers’ health
1 Introduction
2 Background Literature
3 Data and Descriptive Statistics
4 Empirical Strategy
5 Results
6 Discussion and Conclusion
7 Appendix
Introductory Section
3 European Initiatives to Foster R&D on Rare Diseases
1 Introduction
2 Context
3 Data and Empirical Strategy
4 Results
5 Discussion
6 Appendix
4 Allocation of R&D resources for Rare Diseases
1 Introduction
2 A Conceptual Framework
3 Data
4 Methods
5 Results
6 Discussion and Conclusion
7 Appendix
5 Orphan Drugs and Longevity in the US, Revisited (1999-2015) 201
1 Introduction
2 Data and Empirical Strategy
3 Results
4 Discussion and Conclusion
5 Appendix
6 Cost-eectiveness threshold for health care technologies
1 Introduction
2 L’utilisation du seuil d’acceptabilite des technologies de sante
3 Le paradigme ecience-equite
4 Conclusion


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