Evidence map›Paper›PMID 42078215›Full record

ArticleCureus2026

Early Evaluation of the Food and Drug Administration (FDA) Adverse Event Monitoring System (AEMS): An Analysis of Over 32 Million Pharmacovigilance Reports.

Shaheen E Lakhan

Abstract read
In one paragraph

Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

1 author.

Shaheen E LakhanBioscience, Global Neuroscience Initiative Foundation, Miami, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPost-marketing pharmacovigilance systems are essential for detecting adverse drug reactions not identified during pre-marketing clinical trials. The United States Food and Drug Administration (FDA) introduced the Adverse Event Monitoring System (AEMS) public dashboard on March 11, 2026, a new platform that provides near-real-time access to data from the FDA Adverse Event Reporting System (FAERS). This interface allows clinicians and researchers to query pharmacovigilance data through an interactive visualization platform without requiring advanced data processing. To describe the structure, available features, and reporting patterns of the newly released AEMS dashboard, this study provides an exploratory descriptive analysis of FAERS report characteristics.

methodsA cross-sectional descriptive analysis was conducted using data available through the FDA AEMS public dashboard on the day of its public release. The dashboard modules were explored, and datasets were exported using the built-in data download functionality. Variables examined included total report counts, seriousness of reports, death outcomes, patient sex distribution, reporter type, reporting region, annual reporting trends, and products with the highest total adverse event counts. Descriptive statistics were used to summarize available pharmacovigilance data.

resultsAt the time of analysis, the AEMS dashboard contained 32,815,201 adverse event reports submitted to FAERS. Among these reports, 17,929,119 reports (54.6%) were classified as serious (excluding death), while 2,899,382 reports (8.8%) involved death outcomes. Female patients accounted for 17,292,788 (52.7%) reports, compared with 11,455,663 (34.9%) reports among male patients, while 4,066,750 (12.4%) reports did not specify sex. Annual report volumes increased substantially after 2014, exceeding two million reports annually during recent years, with 2,359,318 reports recorded in 2022 and 2,211,235 reports recorded in 2023.

conclusionsThis study provides one of the earliest evaluations of the newly released FDA AEMS dashboard and demonstrates its capability to support rapid exploratory pharmacovigilance analysis. The use of publicly accessible data improves transparency and reproducibility of pharmacovigilance research. However, inherent limitations of spontaneous reporting databases, including underreporting, reporting bias, incomplete clinical data, and inability to establish causality, remain important considerations when interpreting findings derived from the system.

Indexed as

adverse eventsdrug safetyfaersfdapharmacovigilance

Identifiers

PMID42078215
PMCPMC13133742

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.