How externalities impact an evaluation of strategies to prevent antimicrobial resistance in health care organizations
Bibliographic record
Abstract
BACKGROUND: The rates of antimicrobial-resistant organisms (ARO) continue to increase for both hospitalized and community patients. Few resources have been allocated to reduce the spread of resistance on global, national and local levels, in part because the broader economic impact of antimicrobial resistance (i.e. the externality) is not fully considered when determining how much to invest to prevent AROs, including strategies to contain antimicrobial resistance, such as antimicrobial stewardship programs. To determine how best to measure and incorporate the impact of externalities associated with the antimicrobial resistance when making resource allocation decisions aimed to reduce antimicrobial resistance within healthcare facilities, we reviewed the literature to identify publications which 1) described the externalities of antimicrobial resistance, 2) described approaches to quantifying the externalities associated with antimicrobial resistance or 3) described macro-level policy options to consider the impact of externalities. Medline was reviewed to identify published studies up to September 2016. MAIN BODY: is a cost or a benefit associated with one person's activity that impacts others who did not choose to incur that cost or benefit. We did not identify a well-accepted method of accurately quantifying the externality associated with antimicrobial resistance. We did identify three main methods that have gained popularity to try to take into account the externalities of antimicrobial resistance, including regulation, charges or taxes on the use of antimicrobials, and the right to trade permits or licenses for antimicrobial use. To our knowledge, regulating use of antimicrobials is the only strategy currently being used by health care systems to reduce antimicrobial use, and thereby reduce AROs. To justify expenditures on programs that reduce AROs (i.e. to formally incorporate the impact of the negative externality of antimicrobial resistance associated with antimicrobial use), we propose an alternative approach that quantifies the externalities of antimicrobial use, combining the attributable cost of AROs with time-series analyses showing the relationship between antimicrobial utilization and incidence of AROs. CONCLUSION: Based on the findings of this review, we propose a methodology that healthcare organizations can use to incorporate the impact of negative externalities when making resource allocation decisions on strategies to reduce AROs.
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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 | 0.149 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".