SynthesisIndian journal of pharmacology
Chemoprophylaxis against COVID-19 among health-care workers using Ivermectin in low- and middle-income countries: A systematic review and meta-analysis.
Synthesis in Indian journal of pharmacology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled 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.
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
4 citing papers in PubMed, 2 syntheses or guidelines pooled it, 5 citations in OpenAlex.
- The role of ivermectin in the prevention and treatment of SARS-CoV-2 infection: a meta-analysis of randomized controlled trials.BMC infectious diseases · 2026Pooled it
- Repurposed drug studies on the primary prevention of SARS-CoV-2 infection during the pandemic: systematic review and meta-analysis.BMJ open respiratory research · 2023Pooled it
- COVID-19-related stress, anxiety and vaccine hesitancy among healthcare workers in a tertiary hospital in South-Western Nigeria.Scientific reports · 2025Article
- Latent profile analysis of security among patients with COVID-19 infection in mobile cabin hospitals and its relationship with psychological capital.Frontiers in public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease-2019 (COVID-19) is a novel viral infectious disease that the World Health Organization (WHO) has announced to be a pandemic. This meta-analysis was aimed at providing evidence for the use of ivermectin to prevent COVID-19 among hospital workers in low-resource countries. Medical databases including African Journals online, Google Scholar, PubMed, Cochrane library, EMBASE, COVID-19 research database (WHO), Clinicaltrials.gov, and SCOPUS were searched for studies on Ivermectin as a chemoprophylactic drug against COVID-19 among hospital personnel in settings with limited resources. Preprint servers such as bioRxiv and medRxiv as well as the gray literature were also searched. Studies adjudged to be eligible were identified using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses algorithm. Statistical analyses were done using Stata version 14.3. Seven studies were selected for the meta-analysis. The total sample size was 2652. There were two randomized controlled trials and five nonrandomized studies. Some studies dosed Ivermectin daily while some dosed it weekly. However, one of the studies dosed it monthly. The studies reported variable clinical benefits. I2 statistic was 92%, and random effect model was used. The pooled odd ratio was 0.11 (95% confidence interval 0.09-0.13). This implies that 89% of the participants benefited from taking Ivermectin as a form of preexposure chemoprophylaxis. Ivermectin has a significant clinical benefit as a preventive drug against COVID-19 for hospital personnel in settings with limited resources.
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.