Evidence map›Paper›PMID 29896103›Full record

ArticleFrontiers in pharmacology2018

Characterization of the Differential Adverse Event Rates by Race/Ethnicity Groups for HPV Vaccine by Integrating Data From Different Sources.

Jing Huang, Jingcheng Du, Rui Duan, Xinyuan Zhang, Cui Tao, Yong Chen

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Adverse effect investigation using application software after vaccination against SARS-CoV-2 for healthcare workers.Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy · 2022
    Article
  4. Article
  5. Article
  6. Review
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jing HuangDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, United States.
Jingcheng DuSchool of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States.
Rui DuanDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, United States.
Xinyuan ZhangSchool of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States.
Cui TaoSchool of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States.
Yong ChenDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, United States.

Funding

Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
Biomedical Computing and Informatics Strategies for Infectious Disease ResearchR01AI116794 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H. · 2016 to 2020
$2.9M
Patient Medical History Representation, Extraction, and Inference from EHR DataR01LM011829 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI TAO, CUI · 2014 to 2018
$1.7M
A General Framework to Account for Outcome Reporting Bias in Systematic ReviewsR01LM012607 · NLM · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG · 2017 to 2020
$1.4M
NIAID NIH HHS R01 AI116794NIAID NIH HHS R01 AI130460NLM NIH HHS R01 LM011829NLM NIH HHS R01 LM012607
6 · The paper itself

Abstract

Data from the Vaccine Adverse Event Reporting System (VAERS) contain spontaneously reported adverse events (AEs) from the public. It has been a major data source for detecting AEs and monitoring vaccine safety. As one major limitation of spontaneous surveillance systems, the VAERS reports by themselves sometimes do not provide enough information to answer certain research questions. For example, patient level demographics are very limited in VAERS due to the protection of patient privacy, such that investigation of differential AE rates across race/ethnicity groups cannot be conducted using VAERS data only. For many vaccines, racial and ethnical difference in immune responses has been found in studies based on racially diverse cohorts. It is of great interest to characterize the differential AE rates by race and ethnicity groups for vaccines. In this study, we propose a novel statistical method to integrate VAERS data with data from other resources for vaccine pharmacovigilance research. Specifically, we integrate VAERS data with CDC survey data of vaccine coverage and U.S. census data of race/ethnicity distribution to quantify differential AE rates by race/ethnicity groups for HPV vaccine. We utilize the difference of race/ethnicity distributions across U.S. states to investigate the association between AE reporting rate and race/ethnicity groups at the population level. We identify 9 AEs with significantly different reporting rates between non-Hispanic White females and other race/ethnicity groups.

Indexed as

data integrationHPVrace/ethnicityvaccine adverse event reporting systemvaccine safety

Identifiers

PMID29896103
PMCPMC5987677

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.