Reducing unnecessary urine culturing and antibiotic overprescribing in long-term care: a before-and-after analysis
Bibliographic record
Abstract
Background: Antibiotic use in long-term care homes is highly variable. High rates of antibiotic use are associated with antibiotic resistance and Clostridium difficile infection. We asked 2 questions regarding a program designed to improve diagnosis and management of urinary tract infections in long-term care: whether the program decreased urine culturing and antibiotic prescribing rates and whether specific strategies of the program were more or less likely to be adopted. Methods: The study included 10 long-term care homes in Ontario, Canada, between December 2015 and May 2017. We assessed the implementation of the program’s 9 strategies via semistructured interviews with key informants. Using a before-and-after study design, and on the basis of monthly facility-level records, we measured changes in the rates of urine specimens sent for culture and susceptibility testing, prescriptions for antibiotics commonly used to treat urinary tract infections and total antibiotic prescriptions, using Poisson regression. Results: Participating homes implemented an average of 6.1 of the 9 strategies. Urine culturing decreased from 3.20 to 2.09 per 1000 resident-days from the baseline to the intervention phase (adjusted incidence rate ratio [IRRadjusted] = 0.72, 95% confidence interval [CI] 0.63–0.82), urinary antibiotic prescriptions fell from 1.52 to 0.83 per 1000 resident-days (IRRadjusted = 0.60, 95% CI 0.47–0.74) and total antibiotic prescriptions fell from 3.85 to 2.60 per 1000 resident-days (IRRadjusted = 0.74, 95% CI 0.65–0.83). After adjusting for secular trends, these reductions were not statistically significant. Interpretation: We demonstrated a reduction in urine culturing and antibiotic use following implementation of the Urinary Tract Infection Program. This initial analysis supports a broader implementation of this program, although ongoing evaluation is required to monitor secular trends in urine culturing and antibiotic use.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".