COVID vaccine study suppressed by CDC director gets published

A COVID vaccine effectiveness study that was blocked by acting CDC Director Jay Bhattacharya, MD, PhD, has been published. The study was originally supposed to appear in March 2026 in the CDC’s Morbidity and Mortality Weekly Report (MMWR).

Bhattacharya questioned the study design, which compared vaccination status in patients who tested positive for COVID (cases) and those who tested negative (controls). Despite his concerns, it appeared to be a political decision rather than a scientific one. It seems like the current leadership of the CDC does not want anything published that supports the safety and effectiveness of the COVID vaccine. This is one of the reasons that the public’s views of the CDC have become very negative.

Nevertheless, a respected peer-reviewed medical journal concluded that the science was sufficiently compelling to warrant publication of the COVID vaccine effectiveness study. This post will review the key aspects of the study.

Photo by Maksim Goncharenok on Pexels.com

The blocked COVID vaccine effectiveness study was published on 1 June 2026 in JAMA Network Open by Ruth Link-Gelles, PhD, MPH, CDC National Center for Immunization and Respiratory Diseases, and colleagues. The case-control study used the Virtual SARS-CoV-2, Influenza, and Other Respiratory Viruses Network (VISION). The researchers included data from 85,725 emergency department/urgent care visits and 26,073 hospitalizations at 253 emergency departments/urgent care centers and 179 hospitals in seven states from September through December 2025. All patients had COVID-like illness and were tested for SARS-CoV-2 in the 10 days before or 72 hours after an ED/urgent care visit or hospital admission.

The estimated vaccine effectiveness (VE) during the first four months of the 2025-2026 COVID season was 50% against COVID-related emergency department (ED) and urgent care visits and 55% against COVID-related hospitalizations among adults.

Among adults 65 and older, the estimated VE was 48% against ED/urgent care visits and 53% against hospitalizations.

This study found that 2025-2026 COVID-19 vaccines were associated with additional protection against medically attended COVID-19 beyond individuals’ existing immunity, suggesting that adults can reduce their likelihood of severe COVID-19–associated outcomes by obtaining a 2025-2026 COVID-19 vaccination.

In other words, the COVID vaccine was very effective in preventing COVID-related emergency department/urgent care visits and hospitalizations. That’s why we get the vaccine.

As I mentioned above, Bhattacharya objected to the study’s test-negative design, which is commonly used in determining vaccine effectiveness.

The test-negative design (TND) is an observational study method used to estimate vaccine effectiveness in real-world settings. It is generally used for respiratory viruses like influenza and COVID-19.

Recruitment : Individuals presenting at clinics or hospitals with symptoms consistent with the target disease are tested (e.g., by PCR) for the pathogen.

: Individuals presenting at clinics or hospitals with symptoms consistent with the target disease are tested (e.g., by PCR) for the pathogen. Case group : Those who test positive are classified as cases.

: Those who test positive are classified as cases. Control group : Those who test negative are classified as controls.

: Those who test negative are classified as controls. Comparison: Vaccine effectiveness is estimated by comparing the vaccination rates between cases and controls, often using an odds ratio:

TND is used because it is efficient for large-scale surveillance, can assess effectiveness against rare outcomes and disease variants, and uses existing healthcare data, reducing cost and time. The CDC uses TND to estimate flu vaccine effectiveness every year.

Selection bias: Only symptomatic or tested individuals are included, which may not be representative of the general population. Multiple testing sources: If testing occurs for reasons unrelated to symptoms (e.g., contact tracing), it can bias results. Confounding: Healthcare-seeking behavior can influence both exposure and testing. Statistical concerns: Bhattacharya has called the method “logistically ridiculous” or “crap” due to these biases.

The study from Link-Gelles and colleagues attempts to avoid some of the deficiencies of the test design by using a large and diverse base of COVID patients. The researchers stated that “the test-negative design has been used extensively to evaluate effectiveness of respiratory virus vaccines and provides a convenient and efficient method to rapidly evaluate effectiveness of new vaccine products in real-world settings (e.g., through EHR [electronic health] records).”

In an accompanying editorial in JAMA Network Open, titled “Why the Test-Negative Design is Used for Routine Vaccine Monitoring,” Natalie Dean, PhD, Emory Rollins School of Public Health, Atlanta, wrote:

The TND [test-negative design] avoids the need to establish a fully enumerated cohort of vaccinated and unvaccinated populations followed in time…unlike case-control studies that sample healthy community controls, the TND can be implemented efficiently because it leverages controls who are already seeking care at the same sites.

In other words, the test-negative design works because patients who test negative for SARS-CoV-2 can approximate the source population in which COVID cases arise. That allows researchers to estimate VE without the burden of establishing a full population denominator.

Researchers do not ignore the limitations of TND that I listed above. Dean wrote in response to those limitations that:

“It is standard for TND studies to be accompanied by extensive sensitivity analyses, assessing the potential for bias, testing for residual confounding, and checking the robustness of the interpreted findings. The continued refinement of these methods reflects the field’s commitment to improving routine vaccine monitoring.”

Again, it appears that Bhattacharya had a politically based grudge against the study that showed the effectiveness of the COVID vaccine. He focused on one aspect of the study, the test-negative design, and ignored how the researchers addressed those issues with their statistical methods.

Despite Bhattacharya’s stand against the paper, it got published anyway. That’s a win for science.

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