Evidence map›Paper›PMID 39885661›Full record

ArticleHuman vaccines & immunotherapeutics2025

Effectiveness of inactivated influenza vaccine against laboratory-confirmed influenza in elderly Chinese patients with diabetes: A test-negative design case-control study.

Zhengyan Chen, Tianchi Yang

Abstract read
In one paragraph

Article in Human vaccines & immunotherapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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

2 authors.

Zhengyan ChenMedical Record Statistics Office, The First Affiliated Hospital of Ningbo University, Ningbo, China.
Tianchi YangImmunization Program Department, Ningbo Municipal Center for Disease Control and Prevention, Ningbo, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vaccination coverage for influenza among diabetic populations remains suboptimal. Various factors contribute to this low vaccination rate, with a prominent issue being skepticism among potential vaccine recipients regarding vaccine effectiveness. We conducted a retrospective test-negative case-control study among diabetic patients aged 60 years and older in Ningbo, Zhejiang Province, China, spanning for four influenza seasons from 2018-19 to 2021-22. A total of 2,204 elderly patients were tested for influenza virus nucleic acid or antigen during the study period. After applying exclusion criteria, 199 influenza-positive cases and 199 matched influenza-negative controls were included in the final analysis. Vaccine effectiveness (VE) was estimated using an unconditional multiple logistic regression model, adjusted for age, gender, household registration and underlying health status. VE was calculated as (1-odds ratio(OR)) × 100%. Overall, the adjusted VE against laboratory-confirmed influenza from inactivated influenza vaccines over the study period was 59.4% (95%CI, 36.3% to 74.4%). VE varied across seasons, with the highest observed in the 2020-21 season and the lowest in the 2019-20 season. Importantly, repeated vaccination did not diminish vaccine effectiveness (OR = 1.2; 95% CI: 0.6 to 2.5). These findings provide a robust basis for recommending annual influenza vaccination among all elderly individuals with diabetes, absent any known contraindications.

Indexed as

Diabetes MellitusInfluenza, HumanInfluenza VaccinesVaccine EfficacyAgedAged, 80 and overCase-Control StudiesChinaEast Asian PeopleFemaleHumansMaleMiddle AgedRetrospective StudiesSeasonsVaccinationInfluenza VaccinesVaccines, Inactivatedcase-control studyDiabetes mellituselderly peopleinfluenza vaccinationvaccine effectiveness

Identifiers

PMID39885661
PMCPMC11792806

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Registered trials

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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.