ArticlePLoS neglected tropical diseases2022
Population-based assessment of factors influencing antibiotic prescribing for adults with dengue infection in Taiwan.
Article in PLoS neglected tropical diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed, 4 citations in OpenAlex.
- Global burden of dengue on healthcare systems: a meta-analysis of healthcare resource utilisation.EClinicalMedicine · 2026Article
- Economic Burden of Inappropriate Antibiotic Prescribing Attributable to Dengue in Singapore.PharmacoEconomics - open · 2026Article
- Antibiotic Use in Pediatric Dengue Cases in Taiwan: A National Administrative Database Analysis.The American journal of tropical medicine and hygiene · 2025Article
- Management of adult sepsis in resource-limited settings: global expert consensus statements using a Delphi method.Intensive care medicine · 2025Review
- Prescribing antibiotics for children with dengue infection in Taiwan: who are at risk and who are high prescribers?International journal for quality in health care : journal of the International Society for Quality in Health Care · 2024Article
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 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAntibiotic treatment for dengue is likely considerable and potentially avoidable but has not been well characterized. This study aimed to assess antibiotic prescribing for confirmed dengue cases in outpatient and inpatient settings and to identify associated patient, physician and contextual factors.
methods57,301 adult dengue cases reported in Taiwan between 2008-2015 were analyzed. We assessed both outpatient and inpatient claims data of dengue patients from a week before to a week after their dengue infections were confirmed under Taiwan's National Health Insurance program. A multivariable logistic regression with generalized estimating equations was used to estimate the probability of antibiotic prescribing in dengue patients.
resultsOverall, 24.6% of dengue patients were prescribed an antibiotic during the 14 day-assessment period. Antibiotics were prescribed in 6.1% and 30.1% of outpatient visits and inpatient admissions, respectively. Antibiotic prescriptions were reduced by ~50% in epidemic years. Among inpatients, advanced age, females, and major comorbidities were risk factors for receipt of an antibiotic; antibiotics were used in 26.0% of inpatients after dengue was diagnosed. Significant differences in antibiotic prescribing practices were observed among physicians in outpatient settings but not in inpatient settings.
conclusionsIn addition to patient and physician demographic characteristics, contextual factors such as care setting and during epidemics significantly influenced prescription of antibiotics. Characterization of prescribing patterns should help direct programs to curb antibiotic prescribing.
Indexed as
Identifiers
What OpenQuestion holds
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.