Bailey’s Reanalysis Fails to Debunk, and Inadvertently Supports, Miller-Goldman’s Positive Correlation between Number of Vaccine Doses and Infant Mortality Rates
Authors: Goldman Gary S. , Miller Neil Z. 
Published: June 2022

Background&mdash;In 2011, Miller and Goldman published a study in Human and Experimental Toxicology that found a counterintuitive, positive correlation, r = 0.70 (r2 = 0.49, p &lt; .0001), demonstrating that as nations require more vaccine doses for their infants, infant mortality rates (IMRs) tend to increase (worsen). The dataset (n = 30) included the United States, a nation that required the most vaccines for their infants, and all nations with better IMRs than the United States. Dr. E. Bailey, a professor at BYU, and her students, recently read the Miller-Goldman study and found it "troublesome that this manuscript is in the top 5% of all research outputs" and falsely claimed that its findings were due to "inappropriate data exclusion," i.e., failure to analyze the "full dataset" of all 185 nations. The "Bailey reanalysis," titled Infant vaccination does not predict increased infant mortality rate: correcting past misinformation, was posted to the medRxiv preprint server on September 10, 2021 (version 1) and October 5, 2021 (version 3) and Europe PMC preprint server on September 10, 2021. Objective&mdash;This present study examines the various claims postulated by the Bailey reanalysis and assesses the robustness of their methodology, analyses, and reported results and conclusions. Methods&mdash;Data discussed in this paper are based on the previously mentioned study by Miller and Goldman and the Bailey reanalysis. Results&mdash;Linear regression analysis of IMR and the number of vaccine doses for each country yield a statistically significant positive correlation of r = 0.70 (p &lt; .0001) for the top nations (n = 30) chosen by Miller-Goldman and r = 0.16 (p &lt; .04) for the "entire dataset" chosen by Bailey et al (n = 185). Bailey also conducted linear regression analyses (for the year 2019) of IMRs as a function of vaccination rates for each of eight different vaccines and reported statistically significant inverse correlations for 7 of 8 vaccines over the entire range of vaccination rates. However, Miller and Goldman reanalyzed the Bailey analyses for nations with vaccination rates below 60% and found no statistically significant correlation for six vaccines (DPT, Hib, hepatitis B, polio, rotavirus, and measles) and statistically significant positive correlations for tuberculosis (r = 0.8, p &lt; .005) and pneumococcal (r = 0.6 p &lt; .023) vaccines. Conclusions&mdash;Bailey&rsquo;s reanalysis corroborates a statistically significant positive correlation originally reported by Miller and Goldman. However, Bailey&rsquo;s reported correlation (r = +0.16, p &lt; .04) is small, likely due to poor methodology (failing to account for covariates, i.e., disparities among numerous socioeconomic factors that add uncertainty to their conclusion). The r-value reported by the Bailey reanalysis demonstrates an effect size that is about one-fourth (0.16/0.70) that reported by Miller-Goldman&mdash;underscoring how critically important it is for Bailey's reanalysis to eliminate confounding variables. Moreover, Bailey&rsquo;s linear regression analyses of IMR as a function of vaccination rates for each of eight different vaccines demonstrate that some countries with low vaccination rates have low IMRs, while other countries with high vaccination rates have high IMRs. Rather than supporting a strong inverse correlation, the Bailey reanalysis demonstrates high vaccination rates are neither necessary nor sufficient to cause low IMR.