Oral Antibiotic Utilization in Long-Term Care: Are We Following Guidelines or Chasing Resistance?
Bibliographic record
Abstract
Background. The long-term care facility (LTCF) setting may act as a reservoir for antibiotic resistance in the community and hospitals. In British Columbia (BC), the rise in resistance to fluoroquinolones and cotrimoxazole in Enterobacteriaceae has been most pronounced in the elderly. This study represents the first population-based description of antibiotic utilization trends associated with these resistance patterns in LTCFs in BC. Methods. BC PharmaNet is a database that captures all outpatient and LTCF prescriptions in BC. Anonymized, line-listed data were accessed for oral systemic antibiotics covered under Plan B (permanent residents of licensed LTCFs). Utilization rates (Daily Defined Doses (DDD)/1000 LTCF residents/day) between 2007 and 2013 were analyzed, with a focus on drugs used for urinary tract infections (UTI), including: nitrofurantoin, ciprofloxacin, cotrimoxazole, amoxicillin-clavulanate, and cefixime. Results. There were 357,150 LTCF antibiotic prescriptions (1.9% of all PharmaNet) written between 2007 and 2013. 73.3% were for females and 93.0% were for those aged over 65 years. Overall utilization increased 3.6% from 152.1 in 2007 to 157.6 DDD/1000 LTCF residents/day in 2013. 51% of utilization was for the 5 above-listed agents. The table illustrates the change in use for these drugs. *Nitrofurantoin use increased from 35.5 in 2007 to 38.8 DDD/1000 LTCF residents/day in 2012 (9.3%), then decreased as above. Conclusion. While an increase in nitrofurantoin use might be congruent with guidelines, greater use of cefixime and amoxicillin-clavulanate, in the absence of changing trends in respiratory infections, suggest that prescribing may be driven by an increasing prevalence of enterobacteriacae with ESBLs. Stewardship efforts focused on reducing unnecessary cultures and treatment of asymptomatic bacteriuria are urgently needed in BC LTCFs. Disclosures. F. Marra, Merck Canada Inc: Grant Investigator, Research grant
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".