States, global power and access to medicines: a comparative case study of China, India and the United States, 2000–2019
Bibliographic record
Abstract
BACKGROUND: What constitutes state`s global power to shape access to medicines? How was it distributed between states and how did this change from 2000 to 2019? In this comparative case study, we explored the powers of China, India and the United States, and discuss whether our findings from the pre-pandemic era were reflected in the global COVID-19 response related to pharmaceuticals. We used an analytical framework from the international relations literature on structural power, and assessed the following power structures after adapting them to the context of access to medicines: finance, production, financial protection, knowledge, trade and official development assistance. RESULTS: We found that from 2000 to 2019 there had been a power-shift towards China and India in terms of finance and production of pharmaceuticals, and that in particular China had increased its powers regarding knowledge and financial protection and reimbursement. The United States remained powerful in terms of finance and knowledge. The data on trade and official development assistance indicate an increasingly powerful China also within these structures. During the COVID-19 pandemic, we found that the patterns from previous decades were continued in terms of cutting-edge innovation coming out of the United States. Trade restrictions from the United States and India contrasted our findings as well as the limited effective aid from the United States. Building on our findings on structural powers, we argue that both structural power and political decisions shaped access to medical technologies during the COVID-19 pandemic. We also examined the roles and positions of the three states regarding developments in global health governance on the COVAX mechanism, the TRIPS Agreement waiver and the pandemic accord in this context. CONCLUSION: From 2000-2019, China and India increased their structural powers to shape global access to medical technologies. The recent COVID-19 pandemic demonstrated that both structural power and political decisions shaped global access to COVID-19 technologies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".