Evidence map›Paper›PMID 38028893›Full record

ArticleAntimicrobial stewardship & healthcare epidemiology : ASHE2023

Antibiotic use in hospitalized patients with COVID-19: a population-based study in Hong Kong.

Joseph Edgar Blais, Weixin Zhang, Yun Lin, Celine Sl Chui, Vincent Chi-Chung Cheng, Benjamin John Cowling, Peng Wu

Open access · goldAbstract read
In one paragraph

Article in Antimicrobial stewardship & healthcare epidemiology : ASHE, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 21% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 5 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 2 countries.

Joseph Edgar BlaisWorld Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administration Region, China.ORCID https://orcid.org/0000-0001-7895-198X
Weixin ZhangWorld Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administration Region, China.
Yun LinWorld Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administration Region, China.
Celine Sl ChuiWorld Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administration Region, China.ORCID https://orcid.org/0000-0003-1513-8726
Vincent Chi-Chung ChengDepartment of Microbiology, Queen Mary Hospital, Hong Kong Special Administration Region, China.ORCID https://orcid.org/0000-0003-1765-7706
Benjamin John CowlingWorld Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administration Region, China.ORCID https://orcid.org/0000-0002-6297-7154
Peng WuWorld Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administration Region, China.ORCID https://orcid.org/0000-0003-1157-9401
Hong Kong Science and Technology Parks Corporation · HKUniversity of Hong Kong · HKChinese University of Hong Kong · HKQueen Mary Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Hong Kong experienced four epidemic waves caused by the ancestral strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2020-2021 and a large Omicron wave in 2022. Few studies have assessed antibacterial prescribing for coronavirus disease 2019 (COVID-19) inpatients throughout the pandemic. Objectives: To describe inpatient antibacterial prescribing and explore factors associated with their prescription. Methods: Electronic health records of patients with COVID-19 admitted to public hospitals in Hong Kong from 21 January 2020 to 30 September 2022 were used to assess the prevalence and rates of inpatient antibacterial drug use (days of therapy/1,000 patient days [DOT/1,000 PD]). We used multivariable logistic regression to investigate potential associations between patients' baseline characteristics and disease severity and prescription of an antibacterial drug during hospital admission. Results: Among 65,810 inpatients with COVID-19, 54.0% were prescribed antibacterial drugs (550.5 DOT/1,000 PD). Compared to waves 1-2 (46.7%; 246.9 DOT/1,000 PD), the prescriptions were lowest during wave 4 (28.0%; 246.9; odds ratio (OR): 0.39, 95% CI: 0.31-0.49) and peaked in early wave 5 (64.6%; 661.2; 0.82, 0.65-1.03). Older age (≥80 years: OR 2.66, 95% CI, 2.49-2.85; 60-79 years: 1.59, 1.51-1.69, compared with 20-59 years), more severe disease (fatal: 3.64, 3.2-4.16; critical: 2.56, 2.14-3.06, compared with severe), and COVID-19 vaccine doses (two doses: 0.74, 0.69-0.78; three doses: 0.69, 0.64-0.74; four doses: 0.52, 0.44-0.62, compared with unvaccinated) were associated with inpatient antibacterial drug use. Conclusions: Antibacterial prescribing changed over time for hospitalized patients with confirmed COVID-19 and was potentially related to patients' demographics, medical conditions, and COVID-19 vaccination status as well as healthcare capacity during epidemic waves.

Identifiers

PMID38028893
PMCPMC10654948
OpenAlexW4388468842

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.