Inequitable by Design: The Patent Culture, Law, and Politics Behind COVID-19 Vaccine Global Access
Bibliographic record
Abstract
COVID-19 vaccine access has been highly inequitable worldwide, with coverage depending largely on a country’s wealth. By the end of 2021, 64.1% of people living in high-income countries had received at least one dose of the vaccine, compared to only 5.4% of those in low-income countries. Similarly, only high- and upper-middle-income countries had received the most effective vaccines. The uneven distribution of these lifesaving vaccines is made complex due to the convergence of several factors, but it suggests that the extraordinary expanding and ossifying market and political power of a few vaccine manufacturers founded on intellectual property and complementary policies is a decisive factor in shaping our healthcare systems and securing equitable access to vaccines. This Article analyzes the power dynamics of vaccine manufacturing and distribution of U.S. pharmaceutical companies in the context of global COVID-19 vaccination. Drawing on the health-justice and law-and-political-economy scholarship of the last decade, this Article demonstrates how a “patent culture” shaped by intellectual property law fundamentally neglects health-equity principles while politicizing healthcare access. These contemporary frameworks suggest that the global COVID-19 vaccine-access problem is the result of avoidable policy choices made by big manufacturers and affluent governments. Despite a long history of inequities in access to healthcare, policy choices—as predicted by Hart’s inverse equity theory—have favored a purposely inequitable-by-design vaccination program driven by the wealth and power of those allowed to control vaccine production and supply globally. Finally, this Article proposes ways to challenge the normalized and institutionalized patent culture that has commodified access to lifesaving medicines beyond national borders. As it examines national and international legal strategies to address the vaccine-access problem, the Article suggests equity-based principles of public value, transparency, and inclusivity to guide healthcare governance and future reformation of the vaccine-access landscape. An interdisciplinary analysis of the first year of the global vaccine rollout provides an account critical to future policies aiming to address the structural conditions needed to attain equitable health outcomes, even after the pandemic.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| 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".