ArticleInfection control and hospital epidemiology2020
Effect of antibiotic time-outs on modification of antibiotic prescriptions in nursing homes.
Article in Infection control and hospital epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Implementation of antimicrobial stewardship interventions on prescribing rates for urinary tract infections in residential aged care facilities: a systematic review and meta-analysis.Systematic reviews · 2026Pooled it
- Barriers to the implementation of antimicrobial stewardship programmes in long-term care facilities: a scoping review.The Journal of antimicrobial chemotherapy · 2024Article
- Antibiotic postprescribing modification opportunities among nursing home residents treated for urinary tract infection.Infection control and hospital epidemiology · 2023Article
- Decision Tools and Studies to Improve the Diagnosis of Urinary Tract Infection in Nursing Home Residents: A Narrative Review.Drugs & aging · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAntibiotic overuse and misuse is a common problem in nursing homes. Antibiotic time-out (ATO) interventions have led to improvements in antibiotic uses in hospitals, but their impact in nursing homes remain understudied.
objectiveTo evaluate the impact of a stewardship intervention, promoting use of ATOs on the frequency and types of antibiotic change events (ACEs) in nursing homes.
designControlled before-and-after intervention study.
settingNursing homes in Wisconsin and Pennsylvania.
methodData on antibiotic prescriptions in 11 nursing homes were collected for 25 months. We categorized ACEs as (1) early discontinuation, (2) class modification, or (3) administration modification. Class modification ACEs were further classified based on whether the change narrowed, expanded, or had no effect on bacterial spectrum coverage. Analyses were performed using a difference-in-difference (DiD) approach.
resultOf 2,647 antibiotic events initiated in study nursing homes, 376 (14.2%) were associated with an ACE. The overall proportion of ACEs did not significantly differ between intervention and control nursing homes. Early discontinuation ACEs increased in intervention nursing homes (DiD, 2.5%; P = .01), primarily affecting residents initiated on broad-spectrum antibiotics (DiD, 2.9%; P < .01). Class modification ACEs decreased in intervention nursing homes but remained unchanged in control nursing homes.
conclusionThe impact of an ATO intervention in study nursing homes was mixed with increases in early discontinuation ACEs offset by reductions in class modification ACEs. More research on the potential value of ATO interventions in nursing homes is warranted.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.