| アブストラクト | Personalized and precision vaccination requires consideration of an individual's sex and age. This article proposed systematic methods to study individual differences in adverse reactions following vaccination and chose trivalent influenza vaccine as a use case. Data were extracted from the Vaccine Adverse Event Reporting System from years 1990 to 2014. We first grouped symptoms into the Medical Dictionary for Regulatory Activities System Organ Classes (SOCs). We then applied zero-truncated Poisson regression and logistic regression to identify reporting differences among different individual groups over the SOCs. After that, we further studied detailed symptoms of 4 selected SOCs. In all, 19 of the 26 SOCs and 17 of the 434 symptoms under the 4 selected SOCs show significant reporting differences based on sex and/or age. In addition to detecting previously reported associations among sex, age group, and symptoms, our approach also enabled the detection of new associations. |
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| ジャーナル名 | Biomedical informatics insights |
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| 投稿日 | 2017/5/5 |
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| 投稿者 | Du, Jingcheng; Cai, Yi; Chen, Yong; He, Yongqun; Tao, Cui |
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| 組織名 | School of Biomedical Informatics, The University of Texas, Houston, TX, USA.;Pieces Technology, Dallas, TX, USA.;Department of Biostatistics and Epidemiology, University of Pennsylvania,;Philadelphia, PA, USA.;Medical School, University of Michigan, Ann Arbor, MI, USA. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/28469434/ |
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