Trends in the use of animals and non-animal methods over the last 20 years
Bibliographic record
Abstract
Despite the importance of the animal testing issue, there has been little presentation in the scientific literature of the trends in animal use. This is crucial to resolve, particularly if we are to measure the impact of initiatives to reduce and replace animal experiments that were recently announced in Europe and the USA. For the first time, the number of animals used between 2002 and 2022 are presented for the EU, key animal-using countries in Europe (the UK, France and Germany), and North America (the USA and Canada). Animal testing is on a slow decrease in the EU, 11% in the last 20 years, but animal use in the UK, France and Germany is at similar levels as it was in 2002. Notably there has been a decrease in the production of genetically altered animals in the UK and a decrease in regulatory testing in the EU. Animal use in Canada has been steadily growing, and figures for the USA are still incomplete as laboratory-bred rodents and some other species are not counted. However, globally, the use of non-animal methods in biomedical research is increasing exponentially; this accelerated in the mid-2010s. The UK appears to be the leader in this field. The technological, regulatory, political and economic factors that might explain these trends are discussed. Plain language summaryAnimal testing is an important scientific and ethical issue. Many countries count the numbers of animals they use each year, but it has not been reported recently how the numbers are developing. We need this information if we are to measure the success of initiatives to reduce and replace animal tests that have been recently announced in Europe and the USA. Here, I present the number of animals used in Europe and North America in the last 20 years between 2002 and 2022. There has been little change in the use of animals over this time period. I argue that there have been few regulatory or political drivers over this period that would have influenced change. However, based on the scientific literature, the uptake of non-animal methods is rapidly increasing, which is positive news.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".