1828. Interrupted Time Series Analysis of a Population-Level Academic Detailing Intervention on UTIs in British Columbia’s Nursing Homes
Bibliographic record
Abstract
In 2016, an academic detailing (AD) intervention took place in 115 nursing homes in British Columbia. AD meetings, attended by physicians, nurses, and nursing home staff, were organized to reduce unnecessary antibiotic treatment of urinary tract infections (UTI), and in particular, asymptomatic bacteriuria. Meta-analysis of AD indicates general effectiveness for creating small prescribing changes; however, there are no large-scale evaluations of AD for nursing home antimicrobial stewardship (AMS). UTI-linked prescriptions for nursing home residents were extracted from Pharmanet, an administrative database of prescriptions dispensed in community pharmacies. Changes in the days of supplied (DOS) prescriptions were assessed with an ecologic interrupted time series analysis. Eighty-two local health areas (LHAs) were included with 50 intervention LHAs (61%). The study period was June 2015 to March 2017 and the intervention began on July 2016. Multilevel segmented regression was used for statistical analysis. During the study period, 9,822 residents received 23,141 UTI-linked prescriptions. Intervention and control had an overall average of 101 and 15 DOS, respectively. Both intervention and control had a decreasing pre-intervention trend (average of −1.4 and −0.2 DOS per month, respectively). While the expected post-intervention rate for the intervention group was −1.1 [−1.8, −0.3] DOS per month, the observed trend was −2.8 [−2.8, −0.7] DOS per month; 169.9% lower than expected [−59.7%, 663.7%]. The control’s average post-intervention trend was unchanged, −0.1 [−0.6, 0.2] DOS per month. For the intervention group, there were 4,714 [–1,921, 6,113] fewer days of UTI prescriptions in the intervention period. In this pragmatic ecologic evaluation, AD was associated with reductions in UTI-coded antibiotic prescribing. The lack of large-scale AMS studies in nursing homes has hindered AMS implementation in this setting. Thus, these preliminary results address a key gap in the AMS literature. Further evaluation of this intervention with a multiple baseline design is warranted. 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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".