Use of antimicrobials for animals in New Zealand, and in comparison with other countries
Bibliographic record
Abstract
AIM: To describe the use of antimicrobial drugs for food animals in New Zealand, based on sales data reported to government, changes over time, and in comparison with other countries and human use. METHODS: Data were sourced from official government and industry reports covering 26 European countries, Australia, Canada, New Zealand and the United States of America in 2012, the last year data were available for all countries. The data included antimicrobial sales, and animal and human populations. Antimicrobial use was estimated based on the amount of active ingredient sold, per standardised biomass (population correction unit). RESULTS: The estimated usage of antimicrobials for food animals in New Zealand for 2012 was 9.4 mg active ingredient/kg biomass. Total sales of antimicrobials between 2005–14 increased on average by 2.5% or 1.5 tonnes per year. Over the same time total animal biomass decreased by an estimated 4.3%, with the main decrease being in sheep (25%) and beef cattle (17%), while dairy cattle increased (28%). In the countries examined, the estimated usage of antimicrobials in food producing animals in 2012 varied from 3.8 to 341 mg active ingredient/kg biomass, in Norway and Italy, respectively, with use in New Zealand being the third lowest. Usage of antimicrobials for human health in New Zealand in 2012 was estimated at 121 mg active ingredient/kg biomass, being ranked sixteenth of the countries compared. Use in humans was 12.9 times the use in animals. CONCLUSIONS: New Zealand was the third lowest user of antimicrobials in animal production and used much less than in human medicine. This is the first report of baseline data which may be used by the New Zealand animal health industry to develop, and measure success in, approaches to maximise the life of antimicrobials for animal health and welfare. CLINICAL RELEVANCE: New Zealand veterinarians will soon have to make changes to adopt the World Health Organisation’s global action plan to manage antimicrobial resistance. Having a benchmark of current antimicrobial use will inform priorities and allow measurement of the impact of future programmes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".