Evidence map›Paper›PMID 37480943›Full record

ArticleOphthalmology2023

Risk of Noninfectious Uveitis after Coronavirus Disease 2019 Vaccination in a United States Claims Database.

Anika Kumar, D Claire Miller, Yuwei Sun, Benjamin F Arnold, Nisha R Acharya

Open access · greenAbstract read
In one paragraph

Article in Ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
6.1field-weighted citation impact, top 3% of its field
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

10 citing papers in PubMed, 22 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

5 authors at 1 institution in 1 country.

Anika KumarF.I. Proctor Foundation, University of California, San Francisco, San Francisco, California.
D Claire MillerF.I. Proctor Foundation, University of California, San Francisco, San Francisco, California.
Yuwei SunF.I. Proctor Foundation, University of California, San Francisco, San Francisco, California.
Benjamin F ArnoldF.I. Proctor Foundation, University of California, San Francisco, San Francisco, California; Department of Ophthalmology, University of California, San Francisco, San Francisco, California.
Nisha R AcharyaF.I. Proctor Foundation, University of California, San Francisco, San Francisco, California; Department of Ophthalmology, University of California, San Francisco, San Francisco, California; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California; Institute for Global Health Sciences, University of California, San Francisco, San Francisco, California. Electronic address: nisha.acharya@ucsf.edu.
University of California, San Francisco · US

Funding

The Impact of the Herpes Zoster Vaccine on Herpes Zoster OphthalmicusR01EY028739 · NEI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NISHA ACHARYA · 2018 to 2026
$4.5M
NEI NIH HHS R01 EY028739
6 · The paper itself

Abstract

purposeTo assess noninfectious uveitis (NIU) risk after coronavirus disease 2019 (COVID-19) vaccination in patients without a history of uveitis.

designA retrospective matched cohort study and self-controlled case series (SCCS) analysis using a longitudinal data asset with claims data from the OptumLabs Data Warehouse from December 11, 2020, through November 30, 2021.

participantsThe matched cohort analysis included patients continuously enrolled for 730 days before December 11, 2020, who received a COVID-19 vaccination during the study period. This COVID-19-vaccinated group was matched to a COVID-19-unvaccinated historical cohort enrolled in 2018 and 2019. The SCCS design included individuals from the vaccinated cohort who experienced an NIU event during the study period. Enrollees with a history of uveitis were excluded.

methodsHazard ratios (HRs) were calculated using Cox proportional hazards models in the matched cohort design. Incidence rate ratios (IRRs) comparing NIU incidence in exposed risk periods after vaccination and unexposed control periods within individuals were calculated using conditional Poisson regression models in the SCCS design. Models were adjusted for age, recent receipt of non-COVID-19 vaccinations, corticosteroid or immunosuppressive use, and smoking history. Subgroup analyses were conducted by vaccination type and age group.

main outcome measuresRates of NIU identified with International Classification of Diseases, Tenth Revision, codes.

resultsThe matched cohort analysis included 4 611 378 patients, with 2 305 689 per cohort. The adjusted HR comparing NIU incidence in the COVID-19-vaccinated and unvaccinated cohort was 0.91 (95% confidence interval [CI], 0.75-1.10; P = 0.33). The SCCS analysis included 686 patients. The IRR comparing NIU risk after vaccination with risk during control intervals was 1.05 (95% CI, 0.89-1.23; P = 0.57). An increased risk was found in the subgroup aged 5 to 44 years (IRR, 1.40; 95% CI, 1.04-1.87; P = 0.024).

conclusionsThe matched cohort and SCCS analyses did not detect increased NIU risk after COVID-19 vaccination overall in individuals without history of uveitis, providing reassurance about the vaccine's safety. The finding of increased risk in the youngest subgroup suggests heightened immune responses in younger individuals, warranting further investigation. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Indexed as

COVID-19UveitisCohort StudiesCOVID-19 VaccinesHumansRetrospective StudiesUnited StatesVaccinationCOVID-19 VaccinesCOVID-19COVID-19 vaccinationIncidenceNoninfectious uveitis

Identifiers

PMID37480943
PMCPMC11009513
OpenAlexW4384927198

What OpenQuestion holds

Textmetadata
LicenceTDM
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.