The more vaccines an infant receives at once, the greater the chance the infant will develop an infection, a respiratory illness or developmental delays following their shots, according to a peer-reviewed study published Wednesday in the International Journal of Vaccine Theory, Practice, and Research.
“If safety signals sounded alarms, the results would be deafening,” lead author Karl Jablonowski, Ph.D., senior research scientist at Children’s Health Defense (CHD), told The Defender. “The sheer number of diseases increases exponentially with every added vaccine.”
Jablonowski and CHD’s Chief Scientific Officer Brian Hooker, Ph.D., analyzed 20 years’ worth of data from 1,542,076 vaccine combinations administered to infants under age 1.
The data, collected from July 1, 1991, to May 31, 2011, came from the publicly available Florida Medicaid Database, which contains more than 460 million billing claims from over 10 million people.
The researchers examined the medical diagnoses given to vaccinated infants within 30 days after vaccination. They excluded diagnoses made on the day the babies received the shots, to eliminate any possible preexisting conditions.
The study compared babies who received three “base vaccines” to babies who received those same vaccines plus others in a single pediatrician visit.
The control group consisted of 227,231 cases of infants who in one visit only received the DTP, Haemophilus influenzae type b (Hib) and the inactivated poliovirus vaccine (IPV).
They compared medical outcomes among that group to outcomes for cohorts of infants who also received either the hepatitis B (HepB) vaccine, the pneumococcal vaccine (PCV), or the rotavirus vaccine, or different combinations of two or three of those vaccines administered together.
The researchers found seven cohorts of infants in the database who received different vaccine combinations — ranging, for example, from base vaccines plus HepB to base vaccines plus HepB, PCV and rotavirus — and compared those to the control group.
They used the Fisher’s Exact Test statistical model to compare the frequency of a particular disease following the shots in one cohort with the frequency of the same disease in another cohort.
They also used Bonferroni correction, a powerful statistical tool, to eliminate any random results and implemented a high bar for identifying statistical significance.
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