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Identification of sex-associated network patterns in Vaccine-Adverse Event Association Network in VAERS.

Zhang Y, Wu P, Luo Y, Tao C - J Biomed Semantics (2015)

Bottom Line: Besides effectiveness in reducing the morbidity and mortality from many infectious diseases, a successful vaccine program also requires a rigorous assessment on their safety.Specifically, we compared network properties of (1) vaccine - adverse event association network based on reports collected over a 23 year period as well as each year; and (2) sex-specific vaccine-adverse event association network.We observed that (1) network diameter and average path length don't change dramatically over a 23-year period, while the average node degree of these networks changes due to the different number of reports during different periods of time; (2) vaccine - adverse event associations derived from different sexes show sex-associated patterns in sex-specific vaccine-AE association networks.

View Article: PubMed Central - PubMed

Affiliation: Division of Biostatistics and Bioinformatics, University of Maryland Greenebaum Cancer Center, Baltimore, USA ; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, USA.

ABSTRACT

Background: Vaccines are one of the most important public health successes in last century. Besides effectiveness in reducing the morbidity and mortality from many infectious diseases, a successful vaccine program also requires a rigorous assessment on their safety. Due to the limitations of adverse event (AE) data from clinical trials and post-approval surveillance systems, novel computational approaches are needed to organize, visualize, and analyze such high-dimensional complex data.

Results: In this paper, we proposed a network-based approach to investigate the vaccine-AE association network from the Vaccine AE Reporting System (VAERS) data. Statistical summary was calculated using the VAERS raw data and represented in the Resource Description Framework (RDF). The RDF graph was leveraged for network analysis. Specifically, we compared network properties of (1) vaccine - adverse event association network based on reports collected over a 23 year period as well as each year; and (2) sex-specific vaccine-adverse event association network. We observed that (1) network diameter and average path length don't change dramatically over a 23-year period, while the average node degree of these networks changes due to the different number of reports during different periods of time; (2) vaccine - adverse event associations derived from different sexes show sex-associated patterns in sex-specific vaccine-AE association networks.

Conclusions: We have developed a network-based approach to investigate the vaccine-AE association network from the VAERS data. To our knowledge, this is the first time that a network-based approach was used to identify sex-specific association patterns in a spontaneous reporting system database. Due to unique limitations of such passive surveillance systems, our proposed network-based approaches have the potential to summarize and analyze the associations in passive surveillance systems by (1) identifying nodes of importance, irrespective of whether they are disproportionally reported; (2) providing guidance on sex-specific recommendations in personalized vaccinology.

No MeSH data available.


Related in: MedlinePlus

Sample RDF graph representation of vaccine AE association
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Fig2: Sample RDF graph representation of vaccine AE association

Mentions: FigureĀ 2 shows the meta-level RDF graph representation of a vaccine symptom association. Each unique association (vaccine-symptom pair) has an unique identifier. The corresponding vaccine, symptom, demographic distribution, and PRR values are also represented in RDF. SPARQL queries can be conducted to retrieve useful information for network analysis which we will introduce in the next section.Fig. 2


Identification of sex-associated network patterns in Vaccine-Adverse Event Association Network in VAERS.

Zhang Y, Wu P, Luo Y, Tao C - J Biomed Semantics (2015)

Sample RDF graph representation of vaccine AE association
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4541751&req=5

Fig2: Sample RDF graph representation of vaccine AE association
Mentions: FigureĀ 2 shows the meta-level RDF graph representation of a vaccine symptom association. Each unique association (vaccine-symptom pair) has an unique identifier. The corresponding vaccine, symptom, demographic distribution, and PRR values are also represented in RDF. SPARQL queries can be conducted to retrieve useful information for network analysis which we will introduce in the next section.Fig. 2

Bottom Line: Besides effectiveness in reducing the morbidity and mortality from many infectious diseases, a successful vaccine program also requires a rigorous assessment on their safety.Specifically, we compared network properties of (1) vaccine - adverse event association network based on reports collected over a 23 year period as well as each year; and (2) sex-specific vaccine-adverse event association network.We observed that (1) network diameter and average path length don't change dramatically over a 23-year period, while the average node degree of these networks changes due to the different number of reports during different periods of time; (2) vaccine - adverse event associations derived from different sexes show sex-associated patterns in sex-specific vaccine-AE association networks.

View Article: PubMed Central - PubMed

Affiliation: Division of Biostatistics and Bioinformatics, University of Maryland Greenebaum Cancer Center, Baltimore, USA ; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, USA.

ABSTRACT

Background: Vaccines are one of the most important public health successes in last century. Besides effectiveness in reducing the morbidity and mortality from many infectious diseases, a successful vaccine program also requires a rigorous assessment on their safety. Due to the limitations of adverse event (AE) data from clinical trials and post-approval surveillance systems, novel computational approaches are needed to organize, visualize, and analyze such high-dimensional complex data.

Results: In this paper, we proposed a network-based approach to investigate the vaccine-AE association network from the Vaccine AE Reporting System (VAERS) data. Statistical summary was calculated using the VAERS raw data and represented in the Resource Description Framework (RDF). The RDF graph was leveraged for network analysis. Specifically, we compared network properties of (1) vaccine - adverse event association network based on reports collected over a 23 year period as well as each year; and (2) sex-specific vaccine-adverse event association network. We observed that (1) network diameter and average path length don't change dramatically over a 23-year period, while the average node degree of these networks changes due to the different number of reports during different periods of time; (2) vaccine - adverse event associations derived from different sexes show sex-associated patterns in sex-specific vaccine-AE association networks.

Conclusions: We have developed a network-based approach to investigate the vaccine-AE association network from the VAERS data. To our knowledge, this is the first time that a network-based approach was used to identify sex-specific association patterns in a spontaneous reporting system database. Due to unique limitations of such passive surveillance systems, our proposed network-based approaches have the potential to summarize and analyze the associations in passive surveillance systems by (1) identifying nodes of importance, irrespective of whether they are disproportionally reported; (2) providing guidance on sex-specific recommendations in personalized vaccinology.

No MeSH data available.


Related in: MedlinePlus