A quasi-experimental analysis comparing antimicrobial usage on COVID-19 and non-COVID-19 wards
Bibliographic record
Abstract
Objective: To describe antimicrobial usage (AMU) trends before and during the coronavirus disease 2019 (COVID-19) pandemic, between COVID-19 and non-COVID-19 wards, and if there was any association with a COVID-19 order set. Design: Quasi-experimental retrospective interrupted time series analysis of AMU rates with a contemporaneous comparison of COVID-19 versus non-COVID-19 control wards. Analysis using incidence rate ratios (IRR) was conducted using a Poisson regression generalized linear model. Setting: Five COVID-19 and 4 comparable non-COVID-19 wards and 6 intensive care units (ICUs) at 4 hospitals during pandemic waves 1-4. Participants: All inpatients receiving systemic antimicrobials. Intervention: The COVID-19 checkbox antimicrobial order set was implemented in March 2020, to be used only if considered clinically indicated with modification in August 2021. Main Outcomes and Measures: The primary outcome was a change in AMU rates (defined daily dose per 100 patient days per month) comparing pre- versus peri-pandemic periods and COVID-19 versus control non-COVID-19 wards. Secondary outcomes included antifungal usage rate in ICUs and assessing AMUs following implementation and modification of a COVID-19 order set. Results: Significantly greater rates of AMU (IRR[95%CI]) were observed on COVID-19 wards versus non-COVID-19 wards during waves 1-4 for all systemic antimicrobials (1.76[1.71-1.81], 1.10[1.07-1.13], 1.48[1.43-1.53], and 1.06[1.03-1.09]); for azithromycin (11.76[9.80-14.23], 10.96[9.49-12.74], 12.41[10.73-14.45], and 4.88[4.31-5.55]); and for ceftriaxone (2.39[2.16-2.65], 3.64[3.29-4.03], 2.94[2.67-3.23], and 1.62[1.49-1.76]). Conclusions: We observed significantly increased AMU rates of all systemic agents during the first 4 waves of the pandemic and on COVID-19 wards compared with control wards for azithromycin and ceftriaxone. These agents saw a twofold reduction following order-set removal, suggesting that the clinical decision-support tool order set, as utilized, had influenced prescribing behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".