State capture through indemnification demands? Effects on equity in the global distribution of COVID-19 vaccines
Bibliographic record
Abstract
BACKGROUND: State capture by the pharmaceutical industry is a form of corruption whereby pharmaceutical companies shift laws or policies about their products away from the best interest of the public and toward their private benefit. State capture often limits equitable access to pharmaceutical products by inflating drug prices and increasing barriers to entry into the pharmaceutical industry. During the COVID-19 pandemic, the high demand and low supply of COVID-19 vaccines has put governments that manage vaccine procurement at risk of capture by COVID-19 vaccine manufacturers, both through bilateral deals and the COVID-19 Vaccine Global Access (COVAX) Facility; this threatens equity in the global distribution of these products. The purpose of this study is to determine whether COVID-19 vaccine manufacturers have been engaging in state capture and, if so, to examine the implications of state capture on equitable access to COVID-19 vaccines. METHODS: A targeted rapid literature search was conducted on state capture by the pharmaceutical industry. Results were limited to journal articles, books, and grey literature published between 2000 and 2021 in or translated to English. A literature search was also conducted for information about state capture during the COVID-19 pandemic. Results were limited to media articles published between March 2020 and July 2021 in or translated to English. All articles were qualitatively analyzed using thematic analysis. RESULTS: COVID-19 vaccine manufacturers have demanded financial indemnification from national governments who procure their vaccines. While most high-income countries are legislatively capable of indemnifying vaccine manufacturers, many low- and middle-income countries (LMICs) are not. A number of LMICs have thus changed their legislations to permit for manufacturers' indemnification demands. Amending legislation in this way is state capture and has led to delays in LMICs and vaccine manufacturers signing procurement contracts. This has critically stalled access to vaccines in LMICs and created disparities in access to vaccines between high-income countries and LMICs. CONCLUSIONS: COVID-19 vaccine manufacturers' indemnification demands constitute state capture in many LMICs though not in high-income countries; this has enhanced global COVID-19 vaccine inequities. Results underscore the need to find alternatives to financial indemnification that do not hinder critical efforts to end the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".