An updated pharmacovigilance evidence from U.S. tobacco problem reports following the FDA safety communication on e-cigarettes and seizures.

OBJECTIVE

Following the 2019 FDA safety communication on e-cigarette-related seizures, we conducted an updated pharmacovigilance analysis of FDA Tobacco Problem Reports using multimethod disproportionality approaches.

DESIGN

Pharmacovigilance study of spontaneously reported adverse events.

SETTING

FDA Tobacco Problem Reports database, publicly available online.

METHODS

Reports in which the affected individual was not the product user were excluded. Cases were classified as e-cigarette exposure if the product description mentioned "electronic cigarette," while all others were grouped as other tobacco products. Reported health problems were harmonized into standardized AE categories. Disproportionality analyses were conducted using four methods: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and shrinkage-based Observed-to-Expected ratio (O/E). A signal was defined when the lower bound of the 95% confidence or credibility interval exceeded the null.

RESULTS

A total of 1,082 reports were analyzed, of which 891 (82.3%) involved electronic cigarettes and 191 (17.7%) involved other tobacco products. Combustible tobacco products accounted for 99 cases (9.15%) of other tobacco products, while other or unknown tobacco products represented 60 cases (5.55%). Smokeless or other nicotine products were least frequently reported, accounting for 32 cases (2.96%). Reports involving electronic cigarettes were significantly more likely to have been submitted before 2020 compared with reports involving other tobacco products (80.8% vs 50.8%; OR = 4.07, 95% CI 2.93-5.67; p < 0.001). Seizures emerged as the strongest and only consistent safety signal across all disproportionality methods. Respiratory outcomes, including shortness of breath, cough, pneumonia, and respiratory failure, were more frequently reported in e-cigarette cases and showed signals in ROR and PRR analyses, but did not remain significant when assessed with Bayesian approaches.

CONCLUSIONS

Seizures represent a pharmacovigilance signal associated with e-cigarette use. Respiratory events were reported more often among e-cigarette users but did not meet conservative Bayesian thresholds. These findings highlight the need for continued surveillance, studies, and regulatory vigilance regarding the safety of e-cigarettes.