Bibliographic record
Abstract
Global Barriers to Access to InsulinCo-authorstM. Ewen1, D. Beran2, M. Lepeska1, R. Laing3.1Health Action International, Amsterdam, The Netherlands.2. University of Geneva, Geneva, Switzerland3Boston University School of Public Health, Boston, USA.BackgroundInsulin for Type 1 diabetes is a rare example of an absolute need for a given medicine. Although global disease burden data is lacking it is estimated that 490,100 children aged 0-14 have Type 1 diabetes. Type 1 diabetes may represent 5% of the total diabetes burden (estimated at 347 million) or 17.4 million people. Use of insulin in the management of Type 2 diabetes is required for better control and its use is dependent on a variety of health system factors. Barriers to access to insulin in many contexts leads to high morbidity and mortality. AimThe aim of this study is to identify how global factors impact access to insulin at a country level.MethodA variety of data sources were analyzed and combined in order to develop a better overview of the global insulin market. To identify insulin manufacturers a literature review of the insulin market using a variety of databases and Google searches was carried out. For product registration, available websites for the medicine regulatory authorities and ministries of health for each country were searched. A systematic literature review was carried out looking at use of insulin in Type 2 diabetes. The UN Commodity Trade Statistics database was used to extract insulin related trade data. Using the term u201cinsulinu201d, the United States Food and Drug Administrationu2019s Orange Book and the Canadian Online Drug Product Database Online Query and its Patent Register were searched for patents on insulin.ResultsForty independent insulin manufacturers were identified in 17 countries. A total of 55 countries had 1,985 insulin products registered. Of these 58% were human insulin, 38% were analogues, 3% were animal insulin, and 1% was unknown. Novo Nordisk had the highest number of registered products (719) followed by Eli Lilly (470) and Sanofi (361). Together these companies represent 88% of total product registrations globally. Only two countries did not list any insulin on their National Essential Medicines List. In looking at insulin use in people with Type 2 diabetes, the literature showed a range from 2.4% in Taiwan to 23.5% in the US. No patents were found for human insulin. For analogue insulins, 61 patents were found in the US Food and Drug Administrationu2019s Orange Book and 8 patents were listed in the Canadian Online Drug Product Database. For insulin exports between 2003 and 2013, 10 countries made up 98% to 99% of the global value of retail insulin exports. Over this period, Denmark, France, and Germany collectively exported between 85% to 96% of global retail insulin by value. Fifty percent of global imports were to the US, UK, Germany and Japan. Around 60 countries (mostly low- and middle income countries) imported insulin from only one country for making then vulnerable to disruptions in supply. DiscussionThis data provides a unique addition to the knowledge about the global insulin market from different angles. It confirms from the dominance of Eli Lilly, Novo Nordisk and Sanofi with regards to the global insulin market. Although other insulin manufacturers have been identified, their size and market penetration seem to be low. Intellectual Property is not an issue for insulin itself. These global barriers result in high prices of insulin. In the public sector for individuals were US$7.64 for human insulin and US$45.03 for analogues. In the private sector analogues were higher priced (US$39.35) than human insulins (US$16.65). This translates to mean affordability in the public sector was 2.5 daysu2019 wages for human insulins, and 7.5 dayu2019s wages for analogues. In the private sector, it was 3.5 and 9.5 daysu2019 wages for human and analogues insulins, respectively. For other medicines, generic competition has been able to lower prices and improve access, however this has not happened for insulin despite no patents on human insulin. The reasons for this may be linked to a variety of factors, such as the marketing strength of these 3 multi-national companies, push for analogue versus human insulin, the complexity of manufacturing a biological product, lack of government funding for purchasing insulin and lack of civil society mobilisation around the issue of access to insulin. In order to address these global barriers global mechanisms are needed as has been seen for HIV/AIDS and other communicable diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".