Rates of increase of antibiotic resistance and ambient temperature in Europe: a cross-national analysis of 28 countries between 2000 and 2016
Bibliographic record
Abstract
Abstract Background Widely recognized as a major public health threat globally, the rapid increase of antibiotic resistance in bacteria could soon render our most effective method to combat infections obsolete. Factors influencing the burden of resistance in human populations remain poorly described, though temperature is known to play an important role in mechanisms at the bacterial level. Methods We performed an ecologic analysis of country level antibiotic resistance prevalence in 3 common bacterial pathogens across 28 countries in Europe, and used multivariable models to evaluate associations with minimum temperature and other predictors over a 17-year period (2000-2016). We quantified the effects of minimum temperature, population density, and antibiotic consumption on the rate of change of antibiotic resistance across geographies. Findings For three common bacterial pathogens and four classes of antibiotics, we found evidence of a long-term effect of ambient minimum temperature on rates of increase of antibiotic resistance across 28 countries in Europe between 2000-2016. Specifically, we show that across all antibiotic classes for the pathogens E. coli and K. pneumoniae , European countries with 10°C warmer ambient temperatures have experienced more rapid increases in antibiotic resistance over the 17-year period, ranging between 0.33%/year (95% CI 0.2, 0.5) and 1.2%/year (0.4, 1.9), even after accounting for recognized drivers of resistance including antibiotic consumption and population density. We found a decreasing relationship for S. aureus and methicillin of −0.4%/year (95% CI −0.7, 0.0), reflecting widespread declines in MRSA across Europe over the study period. Interpretation Ambient temperature may be an important modulator of the rate of change of antibiotic resistance. Our findings suggest that rising temperatures globally may hasten the spread of resistance and complicate efforts to mitigate it. Funding Canadian Institutes of Health Research Fellowship (D.R.M.).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".