Impact of a Comprehensive Antimicrobial Stewardship Program on Institutional Burden of Antimicrobial Resistance: A 14-Year Controlled Interrupted Time-series Study
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance (AMR) constitutes an international public health threat widely believed to result from excessive antimicrobial use (AMU). Numerous authorities have recommended antimicrobial stewardship programs (ASPs) to curb the selection of AMR, but there is a lack of data confirming this benefit. METHODS: A controlled interrupted time-series study spanning 14 years was performed to assess impact of a comprehensive hospital-based ASP that included pharmacist-led audit and feedback on institutional AMR. Patient-level microbiologic and AMU data were obtained from October 2002 to September 2016. Poisson regression models were used to identify changes in the incidence and trend of hospital-acquired (HA) antibiotic-resistant organisms (AROs) and multidrug-resistant organisms (MDROs). Changes in community-acquired (CA)-ARO, CA-MDRO, and inpatient AMU were assessed as controls and process outcomes. RESULTS: Statistically significant shifts in AMU, HA-ARO, and HA-MDRO trends coinciding with ASP implementation were observed, corresponding with a 9% reduction in HA-ARO burden (incidence rate ratio [IRR], 0.91 [95% confidence interval {CI}, .83-.99]; P = .03) and a 13% reduction in HA-MDRO burden (IRR, 0.87 [95% CI, .73-1.04]; P = .13) in the intervention period. In contrast, CA-ARO and CA-MDRO incidence continued to rise, with 40% (IRR, 1.40 [95% CI, 1.28-1.54]; P < .0001) and 68% (IRR, 1.68 [95% CI, 1.57-1.82]; P < .0001) increases in burden found, respectively. CONCLUSIONS: Implementation of a comprehensive ASP resulting in reduced AMU was associated with a significant reduction in institutional AMR, even though community AMR increased during the same period. These results confirm that ASPs play an important role in the fight against AMR.
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 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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".