medRxiv : the preprint server for health sciences · 2025
Seasonality and Trends in Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis Before and During the COVID-19 Pandemic: A Pharmacovigilance Study.
СтатьяПрепринтНазвание и аннотация: Перевод в очереди
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Mukherjee Eric Milan, Park Dodie, Krantz Matthew S, Stone Cosby A, Martin-Pozo Michelle, Phillips Elizabeth
Аннотация
IMPORTANCE: Seasonal variation in adverse drug reactions has clinical and mechanistic implications for understanding disease mechanisms and risk mitigation strategies. Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis (SJS/TEN) is a life-threatening mucocutaneous reaction with high morbidity and mortality, which may have a seasonal component.
OBJECTIVE: To determine whether the reporting of SJS/TEN to the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) follows a seasonal pattern, incorporating both traditional seasonal analyses and time-series modeling.
DESIGN: Cross-sectional, population-based analysis of FAERS reports from January 2010 to December 2019. Seasonal differences were assessed using Kruskal-Wallis tests and Seasonal-Trend Decomposition using Loess (STL). Seasonal autoregressive integrated moving average (SARIMA) models were used to counterfactually forecast SJS/TEN and comparator conditions during the COVID-19 pandemic, assessing changes in reporting.
SETTING: Population-based analysis of spontaneous adverse event reports submitted to FAERS.
PARTICIPANTS: All deduplicated FAERS reports with complete event dates from 2010 to 2019 were included. SJS/TEN cases were identified using standardized MedDRA terms. Comparator analyses of known seasonal conditions - photosensitivity reactions, influenza, and respiratory syncytial virus (RSV) - served as positive controls.
EXPOSURES: Drug exposures as recorded in FAERS.
MAIN OUTCOMES AND MEASURES: The primary outcome was the monthly and seasonal proportion of unique SJS/TEN reports, normalized using all FAERS reports during a particular interval as the denominator. Seasonality strength was quantified from STL decomposition (range 0-1). SARIMA models were applied to pre-COVID data to counterfactually forecast trends from March 2020 to December 2023. Forecast accuracy was evaluated using mean squared error (MSE), root mean squared error (RMSE), and residual diagnostics.
RESULTS: Among 5,900 SJS/TEN cases reported from 2010-2019, no significant monthly or seasonal variation was detected (p > 0.05), and seasonality strength was low (0.163). Positive controls (influenza, RSV, photosensitivity) showed expected strong seasonality. SARIMA forecasts indicated a mild increase in SJS/TEN reporting during the pandemic, compared to its previous declining trend. Influenza and RSV dropped below predictions during the pandemic, while photosensitivity remained relatively consistent.
CONCLUSIONS AND RELEVANCE: SJS/TEN reporting to FAERS does not exhibit apparent seasonality, in contrast to positive controls. Time-series modeling confirmed these findings and highlighted the relative stability of SJS/TEN reporting during the pandemic compared to respiratory viruses.
OBJECTIVE: To determine whether the reporting of SJS/TEN to the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) follows a seasonal pattern, incorporating both traditional seasonal analyses and time-series modeling.
DESIGN: Cross-sectional, population-based analysis of FAERS reports from January 2010 to December 2019. Seasonal differences were assessed using Kruskal-Wallis tests and Seasonal-Trend Decomposition using Loess (STL). Seasonal autoregressive integrated moving average (SARIMA) models were used to counterfactually forecast SJS/TEN and comparator conditions during the COVID-19 pandemic, assessing changes in reporting.
SETTING: Population-based analysis of spontaneous adverse event reports submitted to FAERS.
PARTICIPANTS: All deduplicated FAERS reports with complete event dates from 2010 to 2019 were included. SJS/TEN cases were identified using standardized MedDRA terms. Comparator analyses of known seasonal conditions - photosensitivity reactions, influenza, and respiratory syncytial virus (RSV) - served as positive controls.
EXPOSURES: Drug exposures as recorded in FAERS.
MAIN OUTCOMES AND MEASURES: The primary outcome was the monthly and seasonal proportion of unique SJS/TEN reports, normalized using all FAERS reports during a particular interval as the denominator. Seasonality strength was quantified from STL decomposition (range 0-1). SARIMA models were applied to pre-COVID data to counterfactually forecast trends from March 2020 to December 2023. Forecast accuracy was evaluated using mean squared error (MSE), root mean squared error (RMSE), and residual diagnostics.
RESULTS: Among 5,900 SJS/TEN cases reported from 2010-2019, no significant monthly or seasonal variation was detected (p > 0.05), and seasonality strength was low (0.163). Positive controls (influenza, RSV, photosensitivity) showed expected strong seasonality. SARIMA forecasts indicated a mild increase in SJS/TEN reporting during the pandemic, compared to its previous declining trend. Influenza and RSV dropped below predictions during the pandemic, while photosensitivity remained relatively consistent.
CONCLUSIONS AND RELEVANCE: SJS/TEN reporting to FAERS does not exhibit apparent seasonality, in contrast to positive controls. Time-series modeling confirmed these findings and highlighted the relative stability of SJS/TEN reporting during the pandemic compared to respiratory viruses.