# 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

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

PMC · DOI: 10.21203/rs.3.rs-6972996/v1 · Research Square · 2025-07-02

## TL;DR

A clinical decision support tool helped reduce unnecessary antibiotic use for respiratory infections in HIV patients in Mozambique without increasing complications.

## Contribution

The study demonstrates that a CDSA with clinician education and audits can significantly reduce antibiotic prescriptions for URTIs in HIV-infected adults.

## Key findings

- The intervention reduced antibiotic prescriptions by 33.2% compared to the control group.
- Withholding antibiotics did not increase complication rates among patients with URTIs.
- Amoxicillin, azithromycin, and phenoxymethylpenicillin were the most commonly prescribed antibiotics.

## Abstract

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.

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.

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.

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.

ISRCTN, ISRCTN88272350. Registered 16 May 2024, https://www.isrctn.com/ISRCTN88272350

## Linked entities

- **Chemicals:** Amoxicillin (PubChem CID 33613), Azithromycin (PubChem CID 447043), Phenoxymethylpenicillin (PubChem CID 6869)
- **Diseases:** Upper respiratory tract infections (MONDO:0024355)
- **Species:** Homo sapiens (taxon 9606)

## Full-text entities

- **Diseases:** URTIs (MESH:D012141), flu (MESH:D007251), HIV-infected (MESH:D015658)
- **Chemicals:** Amoxicillin (MESH:D000658), phenoxymethylpenicillin (MESH:D010404), azithromycin (MESH:D017963)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12236923/full.md

## Figures

4 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12236923/full.md

## References

33 references — full list in the complete paper: https://tomesphere.com/paper/PMC12236923/full.md

---
Source: https://tomesphere.com/paper/PMC12236923