Evidence map›Paper›PMID 40630534›Full record

ArticleResearch square2025

Effectiveness of a clinical decision support algorithm (CDSA) on reducing unnecessary antibiotic prescriptions for upper respiratory tract infections among ambulatory HIV-infected adults in Mozambique: a cluster randomized controlled trial.

Candido Faiela, Troy D Moon, Gustavo Amorim, Mohsin Sidat, Esperança Sevene

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In one paragraph

Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Candido FaielaDepartment of Biological Science, Faculty of Science, Eduardo Mondlane University, Maputo, Mozambique.ORCID 0000-0002-6746-9343
Troy D MoonDepartment of Tropical Medicine and Infectious Diseases, Tulane University Celia Scott Weatherhead School of Public Health and Tropical Medicine, New Orleans, United States of America.ORCID 0000-0002-1637-829X
Gustavo AmorimDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.ORCID 0000-0002-2941-5360
Mohsin SidatDepartment of Community Health, Faculty of Medicine, Eduardo Mondlane University, Maputo, Mozambique.ORCID 0000-0002-8378-2014
Esperança SeveneDepartment of Physiological Science, Faculty of Medicine, Eduardo Mondlane University, Maputo, Mozambique.ORCID 0000-0001-9025-4052

Funding

UEM-Vanderbilt Partnership for Research in Implementation Science Mozambique (PRISM)D43TW009745 · FIC · VANDERBILT UNIVERSITY MEDICAL CENTER · PI D. Troy Moon, Mohsin Sidat · 2015 to 2026
$3.3M
FIC NIH HHS D43 TW009745
6 · The paper itself

Abstract

Background: Antibiotics are widely overprescribed to treat upper respiratory tract infections (URTIs), even though viruses cause most URTIs. We evaluated the effectiveness of a clinical decision support algorithm (CDSA)- based intervention in reducing antibiotic prescriptions among ambulatory HIV-infected adult patients with acute URTI symptoms. Methods: Between June and September 2024, we conducted a multicenter, two-arm parallel, cluster-randomized controlled trial in six primary healthcare facilities in Mozambique. The intervention included applying the CDSA, educating and supervising clinicians, and conducting prescription audits. We used Pearson's chi-square test and relative risk to assess the effectiveness of the intervention in reducing antibiotic prescribing. Results: Three hundred seventy-nine (97.9%) HIV-infected adult patients with URTI symptoms were recruited, 182 (48%) in the intervention arm and 197 (52%) in the control. Most were females (75.5%) and single (57%). Most appeared with common cold and flu-like symptoms. Participants in the intervention arm were less likely to receive an antibiotic prescription (RR 0.41, 95% CI: 0.31 - 0.55) and develop a complication (RR 0.44, 95% CI: 0.16 - 1.20) than those not exposed. The antibiotic prescribing rate was 23.1% for the intervention and 56.3% for the control. The intervention was associated with a significant reduction in antibiotic prescribing by 33.2% (p < 0.001) and a non-significant decrease in incidence of complications by 3.7% (p = 0.096). In both arms, most patients (78%) recovered completely within five days. Amoxicillin (47.8%), azithromycin (21.9%), and phenoxymethylpenicillin (14.1%) were the most prescribed antibiotics. Conclusions: Our CDSA, coupled with education and audits with feedback, effectively reduced antibiotic usage. Furthermore, withholding antibiotics for URTIs did not increase the incidence of complications. The intervention worked in our six sites, but larger studies must be performed with our CDSA across Mozambique to see if these findings also hold up elsewhere. Trial registration: ISRCTN, ISRCTN88272350. Registered 16 May 2024, https://www.isrctn.com/ISRCTN88272350.

Indexed as

AntibioticsClinical decision support algorithmHIVImplementation ScienceMozambiqueUpper respiratory tract infections

Identifiers

PMID40630534
PMCPMC12236923

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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.