Institut für Medizinische Informatik, Biometrie und Epidemiologie, Universitätsklinikum Essen, Essen, Germany.
School of Public Health, Department of Epidemiology, Boston University, Boston, USA.
Eur J Epidemiol. 2022 May;37(5):437-445. doi: 10.1007/s10654-022-00871-8. Epub 2022 Apr 29.
We aimed to review Semmelweis's complete work on puerperal sepsis mortality in maternity wards in relation to exposure to cadavers and chlorine handwashing and other factors from the perspective of modern epidemiological methods. We reviewed Semmelweis' complete work and data as published by von Györy 1905 according to current standards. We paid particular attention to Semmelweis's definition of mortality in and of itself, to concepts of modern epidemiology that were already recognizable in Semmelweis's work, and to bias sources. We did several quantitative bias analyses to address selection bias and information bias from outcome measurement error. Semmelweis addressed biases that have become known to modern epidemiology, such as confounding, selection bias and bias from outcome misclassification. Our bias analysis shows that differential loss to follow-up is an unlikely explanation for his results. Bias due to outcome misclassification would only be relevant if misclassification differed between time periods. Confounding by health status was likely but could not be quantitatively addressed. Semmelweis was aware that cause-specific mortality is a function of incidence and prognosis. He reasoned in potential outcome terms to estimate the reduced number of deaths from an intervention. He advanced a hypothesis of clinic overcrowding as a risk factor for puerperal sepsis mortality that turns out to be wrong. Semmelweis' data provide a great pool for illustrating the logic of scientific discovery by use of the numerical method. The explanatory power of his work was strong and Semmelweis was able to refute several previous causal explanations.
我们旨在运用现代流行病学方法,从接触尸体、氯洗手和其他因素的角度,全面评估塞梅尔维斯关于产房产后脓毒症死亡率的研究。我们按照当前标准,全面回顾了塞梅尔维斯发表于 von Györy 1905 年的著作和数据。我们特别关注塞梅尔维斯对死亡率本身的定义、在他的著作中已经可以识别的现代流行病学概念,以及偏倚来源。我们进行了多次定量偏倚分析,以解决因结果测量误差导致的选择偏倚和信息偏倚。塞梅尔维斯解决了现代流行病学已经熟知的偏倚问题,如混杂、选择偏倚和因结果分类错误导致的偏倚。我们的偏倚分析表明,随访丢失的差异不太可能是他的结果的解释。只有在不同时间段内分类错误存在差异的情况下,因结果分类错误导致的偏倚才是相关的。健康状况混杂是可能的,但无法进行定量处理。塞梅尔维斯意识到特定原因死亡率是发病率和预后的函数。他从潜在结果的角度推断干预后死亡人数减少的情况。他提出了一个假设,认为诊所过于拥挤是产后脓毒症死亡率的一个危险因素,但事实证明这是错误的。塞梅尔维斯的数据为通过数值方法说明科学发现的逻辑提供了很好的范例。他的工作具有很强的解释力,塞梅尔维斯能够反驳几个先前的因果解释。