Evidence map›Paper›PMID 36942016›Full record

ArticleAJPM focus2022

Estimating COVID-19 Vaccination and Booster Effectiveness Using Electronic Health Records From an Academic Medical Center in Michigan.

Emily K Roberts, Tian Gu, Abram L Wagner, Bhramar Mukherjee, Lars G Fritsche

Abstract read
In one paragraph

Article in AJPM focus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. A framework for understanding selection bias in real-world healthcare data.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024
    Article
  5. Article
  6. 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

5 authors.

Emily K RobertsDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Tian GuDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Abram L WagnerDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Bhramar MukherjeeDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Lars G FritscheDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Alexey I Nesvizhskii · 1988 to 2026
$178.2M
NCI NIH HHS P30 CA046592
6 · The paper itself

Abstract

Introduction: Observational studies of COVID-19 vaccines' effectiveness can provide crucial information regarding the strength and durability of protection against SARS-CoV-2 infection and whether the protective response varies across different patient subpopulations and in the context of different SARS-CoV-2 variants. Methods: We used a test-negative study design to assess vaccine effectiveness against SARS-CoV-2 infection and severe COVID-19 resulting in hospitalization, intensive care unit admission, or death using electronic health records data of 170,741 adults who had been tested for COVID-19 at the University of Michigan Medical Center between January 1 and December 31, 2021. We estimated vaccine effectiveness by comparing the odds of vaccination between cases and controls during each 2021 calendar quarter and stratified all outcomes by vaccine type, patient demographic and clinical characteristics, and booster status. Results: Unvaccinated individuals had more than double the rate of infections (12.1% vs 4.7%) and >3 times the rate of severe COVID-19 outcomes (1.4% vs 0.4%) than vaccinated individuals. COVID-19 vaccines were 62.1% (95% CI=60.3, 63.8) effective against a new infection, with protection waning in the last 2 quarters of 2021. The vaccine effectiveness against severe disease overall was 73.7% (95% CI=69.6, 77.3) and remained high throughout 2021. Data from the last quarter of 2021 indicated that adding a booster dose augmented effectiveness against infection up to 87.3% (95% CI=85.0, 89.2) and against severe outcomes up to 94.0% (95% CI=89.5, 96.6). Pfizer-BioNTech and Moderna vaccines showed comparable performance when controlling for vaccination timing. Vaccine effectiveness was greater in more socioeconomically affluent areas and among healthcare workers; otherwise, we did not detect any significant modification of vaccine effectiveness by covariates, including gender, race, and SES. Conclusions: COVID-19 vaccines were highly protective against infection and severe COVID-19 resulting in hospitalization, intensive care unit admission, or death. Administration of a booster dose significantly increased vaccine effectiveness against both outcomes. Ongoing surveillance is required to assess the durability of these findings.

Indexed as

COVID-19electronic health recordsHCW, healthcare workerMM, Michigan MedicineNDI, neighborhood disadvantage indexRT-PCR, reverse transcription-polymerase chain reactionSES, socioeconomic statusvaccinesVE, vaccine effectiveness

Identifiers

PMID36942016
PMCPMC9323299

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.