Estimating mouse and rat use in American laboratories by extrapolation from Animal Welfare Act-regulated species
Abstract Alone among Western nations, the United States has a two-tier system for welfare protections for vertebrate animals in research. Because its Animal Welfare Act (AWA) excludes laboratory rats and mice (RM), government veterinarians do not inspect RM laboratories and RM numbers are only parti...
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| Formato: | article |
| Lenguaje: | EN |
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Nature Portfolio
2021
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| Acceso en línea: | https://doaj.org/article/979f339689d44433b16a622eddce8478 |
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| Sumario: | Abstract Alone among Western nations, the United States has a two-tier system for welfare protections for vertebrate animals in research. Because its Animal Welfare Act (AWA) excludes laboratory rats and mice (RM), government veterinarians do not inspect RM laboratories and RM numbers are only partially reported to government agencies1. Without transparent statistics, it is impossible to track efforts to reduce or replace these sentient animals’ use or to project government resources needed if AWA coverage were expanded to include them. I obtained annual RM usage data from 16 large American institutions and compared RM numbers to institutions’ legally-required reports of their AWA-covered mammals. RM comprised approximately 99.3% of mammals at these representative institutions. Extrapolating from 780,070 AWA-covered mammals in 2017–18, I estimate that 111.5 million rats and mice were used per year in this period. If the same proportion of RM undergo painful procedures as are publicly reported for AWA-covered animals, then some 44.5 million mice and rats underwent potentially painful experiments. These data inform the questions of whether the AWA needs an update to cover RM, or whether the NIH should increase transparency of funded animal research. These figures can benchmark progress in reducing animal numbers in general and more specifically, in painful experiments. This estimate is higher than any others available, reflecting the challenges of obtaining statistics without consistent and transparent institutional reports. |
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