Outcomes of a Provincial Pilot Program to Reduce Unnecessary Urine Culturing and Antibiotic Overuse in Long-term Care
Bibliographic record
Abstract
Abstract Background Antibiotics are frequently prescribed for long-term care residents with asymptomatic bacteriuria, for which there is no indication. In order to help reduce unnecessary urine culturing and concomitant antibiotic use, C. difficile infection, and antibiotic resistance, Public Health Ontario (PHO) developed a multi-component organizational change program. The program focuses on five practice changes, recommends nine implementation strategies that have been linked to barriers, and includes an implementation planning process. Methods A purposive sampling strategy was used to recruit 12 long-term care homes (LTCHs) in the province of Ontario, Canada. LTCHs worked with PHO staff to implement the program over a 4-month period in mid-2016. The outcome evaluation compared rates of urine cultures sent, total antibiotics, and urinary antibiotics (ciprofloxacin, nitrofurantoin, TMP/SMX, and fosfomycin) per 1,000 resident days before and after the implementation phase. A Poisson regression model adjusting for time-trends, seasonality and controlling for autocorrelation, was used. Results Of the 12 LTCHs recruited, as of May 2017, 9 LTCHs provided data, totaling 106 facility-months. During the pre-implementation phase, inter-facility variation in urine culturing rates (mean = 2.4, inter-decile range [IDR] = 4.3), total antibiotic use (median = 3.2, IDR = 5.5), and urinary antibiotic use (median = 1.2, IDR = 2.2), were large (Figure 1). Comparing the post-implementation period to the pre-implementation period, we observed a 31% adjusted decline in urine culturing (incidence rate ration [IRR] = 0.69, 95% CI: 0.51 to 0.94, Figure 2), a 65% adjusted decline in total antibiotic use (IRR = 0.35, 95% CI: 0.13 to 0.92), and a 38% adjusted decline in urinary antibiotic use (IRR = 0.62, 95% CI: 0.23 to 1.68) across the participating facilities. Conclusion While there was variation in baseline urine culturing rates and antibiotic use across LTCHs, preliminary data indicate that these outcomes declined in a relatively short time period following implementation of an organizational change program. Plans to expand the program to the provinces 600 LTCHs could prioritize facilities with high baseline urine culturing rates. Disclosures All authors: No reported disclosures.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".