Latest Vaccine/Autism “Safety” Study Based on Flawed Data

The Canary Party calls on reporters to take a second look at the CDC’s biased science.

(Cambridge, MA) “The CDC continued its propaganda campaign on behalf of its bloated vaccine schedule last week,” said Mark Blaxill, Chairman of the Canary Party.  “Despite significant scientific evidence showing connections between vaccines and autism, and deep problems with CDC’s vaccine safety science, few reporters dug deeper into the quality of this new study (DeStefano et al. 2013).”  As recently noted in a peer-reviewed publication, Drs Catherine DeSoto and Robert Hitlan[1] documented major methodological flaws in the 2010 CDC study (Price et al.) said to disprove any link between a mercury preservative in vaccines and autism.   This second paper regarding antigens, by CDC staffer Frank DeStefano and colleagues, uses the exact same flawed data set, again to deny the link between vaccines and autism.

“How deeply flawed was DeStefano’s analysis?” continues Blaxill, “Simply put, the study design could not have been more biased. The number, type and timing of vaccines that US children receive are a function of birth year: recommendations for DTP, Rotavirus and Varicella all changed during the years of the study, depending on the year the child was born.  But the CDC data set used a data sample that matched cases and controls by birth year and then only analyzed the differences within their patched groupings (called “strata” in statistics). Matching on birth year meant nearly all variation associated with how many vaccines were recommended was removed from the study as a starting point.  If they had said ‘we are controlling for the vaccine schedule the child followed’ in an analysis of how safe the vaccine schedule was, this would have seemed absurd. But that is exactly what they did.”

 “The model they were trying to test in their first study was whether exposure to Thimerosal via vaccination was associated with any increased risk of autism.  To do this, they needed to compare persons with different levels of exposure.  They could not do so because they matched on birth-year, which itself defines exposure level.  This ensures that cases were only compared to controls with the same exposure,” said lead author Dr. Catherine DeSoto.  “Children who received high and low exposure were not compared with their methods. It is like testing if smoking causes lung cancer but only comparing persons with and without cancer who smoked exactly the same amount -- and then statistically testing if they smoked different amounts.  The design flaw is called overmatching, and it makes the results of both studies invalid.”