Home Data analyses DOD Health Data Dump: Database Artifact, Smoking Gun — or Something In-Between?

DOD Health Data Dump: Database Artifact, Smoking Gun — or Something In-Between?

DOD Health Data Dump: Database Artifact, Smoking Gun — or Something In-Between?

Robert W. Malone, M.D., M.S.

We may not yet be able to draw definitive conclusions from the trove of data on the health of U.S. military members before and after the rollout of COVID vaccines, but this much is clear: We have a major issue with the overall health of our armed services, and the military either did — or should have — known this.

These are dangerous times, and we are in a 21st-century global information war.

Cannonballs are flying, and there are false-flag operations and concern trollery to the leftright and center of us.

And yet onward we ride. The light brigade.

Recently, and unexpectedly, Drs. Samuel Sigoloff, Peter Chambers and Theresa Long — whistleblowers from within the military — stepped onto the public stage to speak out about COVID vaccines and their potential to cause harm.

They downloaded a massive trove of unclassified data (click here to download the Excel file) on the incidence of various diseases before and after the onset of illegal forced genetic COVID-19 vaccination of our military forces.

Now these are basically raw data from the Defense Medical Epidemiological Database (DMED).URGENT! TAKE ACTION: Tell the FDA Don’t Approve Pfizer’s mRNA Shots for Infants and Children under 5

For the detail-oriented, this is the scrubbed and de-identified (HIPAA-compliant) database derived from the Defense Medical Surveillance System (DMSS), which pulls directly from patient records and other U.S. Department of Defense-related medical record information streams.

These data were pulled with full chain-of-custody documentation based on various CPT codes that are related to known genetic COVID-19 vaccine side effects.

DMSS Data Structure

As raw data, this information needs to be reviewed with care and considered to be both rough and preliminary. For the uninitiated, there are major risks associated with reliance on large, raw (uncorrected) data sets for retrospective (backward in time) data analyses.

The key technical term here is “confounding variables,” but data entry errors (such as multiple entries for the same diagnostic event) or process changes can also introduce huge sources of bias into large data sets like this.

With raw data, it is most useful to consider any data plotting to be sort of a first draft, useful for identifying potential trends or topics that deserve a more detailed analysis.

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But sometimes, when the observed effect size in the raw data is very large or potentially important, alarm bells start ringing even before full analysis is completed.  And that seems to be the case with these data.

Nick Hudson, chairman of the South African PANDATA group (a leader in providing accurate data analysis throughout this pandemic), summarizes the situation like this:

“The DMED record data appears to show a marked increase in 2021 in conditions that have been observed to be side effects of the COVID-19 vaccines.”

For many of these, mechanistic explanations have been established or at least proposed.

It is important to rule out distortions owing to recent changes to the system, such as increased coverage (for example, broader selections of personnel or inclusion of family members), changes in handling of multiple records from single cases, and changes in propensity to report owing to changes in policy, access to the system, participating entities or recent advisories or advertising of the system.

An instructive test would be to check that we do not see a similar rise for conditions that could not plausibly exhibit a significant association with the vaccines, such as broken legs or burns.

This is especially important since the total reports of diseases and injuries have apparently risen by an order of magnitude, which would suggest extremely high prevalence of adverse events among a population that is likely healthier and fitter than the general population.

The data are presented in summary format. Underlying data with dates and depersonalized patient indices, together with vaccination records for the population covered by the database would likely deliver swift and incisive conclusions.

Now for some reason, although this database has apparently been managed for years by the same National Institutes of Health (NIH) subcontractor, and has been included in the Centers for Disease Control and Prevention (CDC) datasets including those reviewed by the CDC’s COVID-19 Vaccine Safety Technical (VaST) Work Group, the geniuses that have been managing it have never identified any issues before the whistleblowers grabbed this download.

This does not inspire confidence, no matter what the final “official” explanation becomes.

VaST review COVID 19

Based on this presentation dated Feb. 4, slides 3 and 13 both indicate Dr. Anthony Fauci and colleagues at the NIH are working with the DOD, and the data from the DMED database was being shared.

This makes it VERY difficult to argue that Fauci did not know this data. It also makes it even harder to believe that, with all these agencies watching the same data, no one thought the historical data was incorrect until the whistleblowers sounded their alert.

Despite this, as the data entered the public sphere with the “second opinion” public Senate hearing convened by Sen. Ron Johnson (R-Wis.), the DOD saw fit to communicate with Politifact  rather than the Senator, providing the following statement:

“But Peter Graves, spokesperson for the Defense Health Agency’s Armed Forces Surveillance Division, told PolitiFact by email that ‘in response to concerns mentioned in news reports’ the division reviewed data in the DMED ‘and found that the data was incorrect for the years 2016-2020.’”

Officials compared numbers in the DMED with source data in the DMSS and found the total number of medical diagnoses from those years “represented only a small fraction of actual medical diagnoses.”

The 2021 numbers, however, were up to date, giving the “appearance of significant increased occurrence of all medical diagnoses in 2021 because of the underreported data for 2016-2020,” Graves said.

The DMED system has been taken offline to “identify and correct the root-cause of the data corruption,” Graves said.

As noted above, among the many curious aspects of this statement is that the CDC VaST has apparently been monitoring these data for years, and never identified this “data corruption” as an issue.

So, what do the original data show (prior to the Defense Health Agency’s Armed Forces Surveillance Division correction of the “data corruption”)?

In reviewing these data, what we see are baseline data from 2016 to 2019 (pre SARS-CoV-2/COVID-19), 2020 (the first year of SARS-CoV-2/COVID-19 when no vaccines were available), and 2021 (the year that vaccines were available and mandated for the U.S. military).

As noted above, there are many potential confounding variables, but whatever the cause, if these data are not due to longstanding and previously undiscovered “data corruption,” then we have a major issue with the overall health of our armed services.

And if they are due to previously undiscovered “data corruption,” why wasn’t someone running around with their pants on fire trying to figure out what is going on here long before the whistleblowers brought this to national attention?URGENT! TAKE ACTION: Tell the FDA Don’t Approve Pfizer’s mRNA Shots for Infants and Children under 5

Below are summarized 2021 (+ vaccine) numbers % change relative to 2020 (- vaccine)

  • Total Number of Diseases & Injuries Reported By Year (Ambulatory) up 988% in “uncorrected” data, down 3% in “corrected” data (this is basically a control for the data set).
  • Total Number of Diseases & Injuries Reported By Year (Hospitalization) up 37%.
  • Total Number of Diseases of the Nervous System By Year up 968%.
  • Total Number of Malignant Neuroendocrine Tumor Reports By Year up 276%.
  • Total Number of Acute Myocardial Infarct Reports By Year up 343%.
  • Total Number of Acute Myocarditis Reports By Year up 184%.
  • Total Number of Acute Pericarditis Reports By Year up 70%.
  • Total Number of Pulmonary Embolism Reports By Year up 260%.
  • Total Number of Congenital Malformations Reports By Year up 87%.
  • Total Number of Nontraumatic Subarachnoid Hemorrage Reports By Year up 227%.
  • Total Number of Anxiety Reports By Year up 2,361%.
  • Total Number of Suicide Reports By Year up 227%.
  • Total Number of Neoplasms for All Cancers By Year up 218%.
  • Total Number of Malignant Neoplasms for Digestive Organs By Year up 477%.
  • Total Number of Neoplasms for Breast Cancer By Year up 469%.
  • Total Number of Neoplasms for Testicular Cancer By Year up 298%.
  • Total Number of Female Infertility Reports By Year up 419%.
  • Total Number of Dysmenorrhea Reports By Year up 221.5%.
  • Total Number of Ovarian Dysfunction Reports By Year up 299%.
  • Total Number of Spontaneous Abortion Reports By Year DOWN by 10%.
  • Total Number of Male Infertility Reports By Year up 320%.
  • Total Number of Guillian-Bare Syndrome Reports By Year up 520%.
  • Total Number of Acute Transverse Myelitis Reports By Year up 494%.
  • Total Number of Seizure Reports By Year up 298%.
  • Total Number of Narcolepsy & Cataplexy Reports By Year up 352%.
  • Total Number of Rhabdomyolysis By Year up 672%.
  • Total Number of Multiple Sclerosis Reports By Year up 614%.
  • Total Number of Migraine Reports By Year up 352%.
  • Total Number of Blood Disorder Reports By Year up 204%.
  • Total Number of Hypertension (High Blood Pressure) Reports By Year up 2,130%.
  • Total Number of Cerebral Infarct Reports By Year up 294%.

Originally published by Robert W. Malone. M.D., M.S. on Substack.