Trends and socioeconomic, demographic, and environmental factors associated with antimicrobial resistance: a longitudinal analysis in 39 hospitals in Chile 2008–2017
Bibliographic record
Abstract
Background: Antimicrobial resistance (AMR) is among the most critical global health threats of the 21st century. AMR is primarily driven by the use and misuse of antibiotics but can be affected by socioeconomic and environmental factors. Reliable and comparable estimates of AMR over time are essential to making public health decisions, defining research priorities, and evaluating interventions. However, estimates for developing regions are scant. We describe the evolution of AMR for critical priority antibiotic-bacterium pairs in Chile and examine their association with hospital and community-level characteristics using multivariate rate-adjusted regressions. Methods: Drawing on multiple data sources, we assembled a longitudinal national dataset to analyse AMR levels for critical priority antibiotic-bacterium combinations in 39 private and public hospitals (2008-2017) throughout the country and characterize the population at the municipality level. We first described trends of AMR in Chile. Second, we used multivariate regressions to examine the association of AMR with hospital characteristics and community-level socioeconomic, demographic, and environmental factors. Last, we estimated the expected distribution of AMR by region in Chile. Findings: . Higher hospital complexity, a proxy for antibiotic use, and poorer local community infrastructure were significantly associated with greater AMR. Interpretation: Consistent with research in other countries in the region, our results show a worrisome increase in clinically relevant AMR in Chile and suggest that hospital complexity and living conditions in the community may affect the emergence and spread of AMR. Our results highlight the importance of understanding AMR in hospitals and their interaction with the community and the environment to curtail this ongoing public health crisis. Funding: This research was supported by the Agencia Nacional de Investigación y Desarrollo (ANID), Fondo Nacional de Desarrollo Científico y Tecnológico FONDECYT, The Canadian Institute for Advanced Research (CIFAR), and Centro UC de Políticas Públicas, Pontificia Universidad Católica de Chile.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".