Special Issue "Pharmacoepidemiology in Vaccine: Generating the Real-World Data to Promote Vaccine Safety and Uptake"
Deadline for manuscript submissions: closed (30 May 2021) | Viewed by 40111
Interests: pharmacoepidemiology; pharmacovigilance; adverse event following immunization; signal detection
We are currently living in a post-vaccine era where historically known communicable and life-threatening infectious diseases are in under control by an effective immunization strategy. In 2018, approximately 86% of the world’s children received vaccines that would protect them against diphtheria, tetanus, pertussis, polio, and measles, which consequently prevented 2 to 3 million deaths. With the vaccines being recognized as an efficacious, efficient and cost-effective disease prevention strategy, many countries implement nationwide immunization program or campaign to achieve near perfect vaccine coverage rates. Recently, however, vaccine safety came under scrutiny, which is inevitable given the number of individuals receiving vaccines around the world. Given the rarity of adverse events following immunization (AEFIs), they often go undetected due to limited sample size or only affecting a subpopulation with limited representation in the clinical trials. In efforts to address this issue, researchers are utilizing post-marketing surveillance data to generate real-world data to promote vaccine safety. Meanwhile, it is also imperative to grasp a growing anti-vaccine movement that leads to vaccine hesitancy. To confront this resistance to vaccines, a more precise understanding of the factors, both at individual- and community-levels, that affect vaccine hesitancy would be essential for well-being of the global public health.
This special issue titled as “Pharmacoepidemiology in vaccine: Generating the real-world data to promote vaccine safety and uptake” will introduce the readers of the Journal a pharmacoepidemiologic approach to address vaccine safety and hesitancy. Specifically, this issue encompasses the following areas of research:
(1) identifying unsuspected AEFIs by applying novel statistical methods in post-marketing surveillance data;
(2) conducting observational study for casual inference on the safety of vaccine(s);
(3) describing interventions or strategies to combat vaccine hesitancy.
Manuscripts that address any of these research areas are welcome for submission. Manuscripts will follow standard Journal peer-review procedures, and those accepted for publication will appear in this special issue.
Prof. Ju-Young Shin
Dr. Ju Hwan Kim
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Vaccines is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- adverse event following immunization
- signal detection