Antibiotic use in long-term care facilities
Bibliographic record
Abstract
OBJECTIVES: Evaluation and optimization of antibiotic use (antibiotic stewardship) is being increasingly promoted as a means to reduce antibiotic resistance, adverse events, treatment complications and costs within institutions. Our goal was to examine the prevalence of antibiotic use among long-term care facility residents and the extent of variability across these institutions. METHODS: We conducted a population-based, point-prevalence study of antibiotic use among elderly individuals (n = 37,371) residing in long-term care facilities (n = 363 institutions) in Ontario between April and June 2009, using linked healthcare databases from Canada's largest province. Facilities were grouped into quintiles according to their mean antibiotic dispensing rates and variation was compared across quintiles. RESULTS: There were 2190 (5.9%) long-term care residents receiving antibiotic prescriptions on the study date. The three most prevalent antibiotics were agents most commonly used for the treatment of urinary tract infections, including nitrofurantoin (365, 15.4%), trimethoprim/sulfamethoxazole (338, 14.3%) and ciprofloxacin (304, 12.8%). The majority of treatment courses were at least 10 days in duration (1482, 62.6%), and many exceeded 90 days (495, 20.9%), suggesting chronic prophylaxis. There was substantial variability in antibiotic use across facilities, with a 5-fold variation from the highest-use quintile (10.8%) to the lowest-use quintile (2.2%). This variation persisted after adjustment for multiple facility-level and resident-level factors, including demographic characteristics, healthcare utilization statistics, co-morbidity prevalence, functional status and device dependence. CONCLUSIONS: Antibiotic use is common among long-term care residents, variable across institutions, and may benefit from focused antimicrobial stewardship interventions to standardize treatment indications and duration.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".