Goldman Gary S, Miller Neil Z
Epidemiological Research, Independent Computer Scientist, Bogue Chitto, USA.
Medical Research, Institute of Medical and Scientific Inquiry, Santa Fe, USA.
Cureus. 2023 Feb 2;15(2):e34566. doi: 10.7759/cureus.34566. eCollection 2023 Feb.
Introduction In 2011, we published a study that found a counterintuitive, positive correlation, = 0.70 ( < .0001), demonstrating that among the most highly developed nations ( = 30), those that require more vaccines for their infants tend to have higher infant mortality rates (IMRs). Critics of the paper recently claimed that this finding is due to "inappropriate data exclusion," i.e., the failure to analyze the "full dataset" of all 185 nations. Objective In the present study, we examine various claims postulated by these critics and the validity of their scientific methods, and we perform several investigations to assess the reliability of our original findings. Methods The critics select 185 nations and use linear regression to report a correlation between the number of vaccine doses and IMRs. They also perform multiple linear regression analyses of the Human Development Index (HDI) vs. IMR with additional predictors and investigate IMR vs. percentage vaccination rates for eight different vaccines. We perform odds ratio, sensitivity, and replication analyses. Results The critics' reanalysis combines 185 developed and Third World nations that have varying rates of vaccination and socioeconomic disparities. Despite the presence of inherent confounding variables, a small, statistically significant positive correlation of = 0.16 ( < .03) is reported that corroborates the positive trend in our study. Multiple linear regression analyses report high correlations between IMR and HDI, but the number of vaccine doses as an additional predictor is not statistically significant. This finding is a likely consequence of known misclassification errors in HDI. Linear regression of IMR as a function of percentage vaccination rates reports statistically significant inverse correlations for 7 of 8 vaccines. However, several anomalies in the scatter plots of the data suggest that the chosen linear model is problematic. Our odds ratio analysis conducted on the original dataset controlled for several variables. None of these variables lowered the correlation below 0.62, thus robustly confirming our findings. Our sensitivity analysis reported statistically significant positive correlations between the number of vaccine doses and IMR when we expanded our original analysis from the top 30 to the 46 nations with the best IMRs. Additionally, a replication of our original study using updated 2019 data corroborated the trend we found in our first paper ( = 0.45, .002). Conclusions A positive correlation between the number of vaccine doses and IMRs is detectable in the most highly developed nations but attenuated in the background noise of nations with heterogeneous socioeconomic variables that contribute to high rates of infant mortality, such as malnutrition, poverty, and substandard health care.
引言 2011年,我们发表了一项研究,发现了一种与直觉相反的正相关关系,r = 0.70(P <.0001),表明在最发达的国家(n = 30)中,那些要求为婴儿接种更多疫苗的国家往往有更高的婴儿死亡率(IMR)。该论文的批评者最近声称,这一发现是由于“不适当的数据排除”,即未能分析所有185个国家的“完整数据集”。
目的 在本研究中,我们检验了这些批评者提出的各种说法及其科学方法的有效性,并进行了多项调查以评估我们原始发现的可靠性。
方法 批评者选择了185个国家,并使用线性回归报告疫苗接种剂量数量与婴儿死亡率之间的相关性。他们还对人类发展指数(HDI)与婴儿死亡率进行了多元线性回归分析,并增加了预测变量,并调查了8种不同疫苗的婴儿死亡率与疫苗接种率百分比之间的关系。我们进行了优势比、敏感性和重复分析。
结果 批评者的重新分析结合了185个发达国家和第三世界国家,这些国家的疫苗接种率和社会经济差距各不相同。尽管存在内在的混杂变量,但报告了一个小的、具有统计学意义的正相关关系r = 0.16(P <.03),证实了我们研究中的积极趋势。多元线性回归分析报告了婴儿死亡率与人类发展指数之间的高度相关性,但作为额外预测变量的疫苗接种剂量数量没有统计学意义。这一发现可能是人类发展指数中已知错误分类误差的结果。将婴儿死亡率作为疫苗接种率百分比的函数进行线性回归,报告了8种疫苗中的7种具有统计学意义的负相关关系。然而,数据散点图中的几个异常表明所选择的线性模型存在问题。我们对原始数据集进行的优势比分析控制了几个变量。这些变量均未使相关性降至0.62以下,从而有力地证实了我们的发现。我们的敏感性分析报告,当我们将原始分析从排名前30的国家扩展到婴儿死亡率最佳的46个国家时,疫苗接种剂量数量与婴儿死亡率之间存在统计学意义的正相关关系。此外,使用更新的2019年数据对我们的原始研究进行重复验证,证实了我们在第一篇论文中发现的趋势(r = 0.45,P <.002)。
结论 在最发达的国家中,可以检测到疫苗接种剂量数量与婴儿死亡率之间的正相关关系,但在社会经济变量异质性较高、导致婴儿死亡率较高(如营养不良、贫困和医疗保健不合格)的国家的背景噪声中,这种相关性会减弱。