Anthropology Program, University of La Verne, La Verne, California, USA.
Am J Phys Anthropol. 2020 Sep;173(1):168-178. doi: 10.1002/ajpa.24081. Epub 2020 May 29.
Described as an indiscriminate killer by many chroniclers, the Black Death descended on London during the 14th century. To best understand the pattern of transmission among demographic groups, models should include multiple demographic and health covariates concurrently, something rarely done when examining Black Death, but implemented in this study to identify which demographic groups had a higher susceptibility. Female predisposition to the Black Death was also explored, focusing on whether social inequality added to vulnerability.
Three attritional cemeteries from the Wellcome Osteological Research Database were compared with the Black Death cemetery, East Smithfield. A Cox proportional hazards regression estimated hazards ratios of dying of the Black Death, using transition analysis ages-at-death as the time variable, and sex and frailty as covariates. Additionally, a binomial logistic regression generated odds ratios for age-at-death, sex, and frailty.
The Cox model produced a significant hazards ratio for individuals deemed frail. Similarly, the logit model calculated significantly increased odds ratios for frail individuals, and decreased odds for individuals aged 65+.
The older individuals were not undergoing growth during times of great stress in London pre-dating the Black Death epidemic, which may explain the decreased odds of contracting the Black Death. Further, although women dealt with social inequality, which partially led to the demographic puzzle of the Medieval "missing" women, women's susceptibility to infection by the Black Death was not increased. The phenomenon of the missing women may be due to a combination of factors, including infant and child mortality and preservation.
许多编年史家将黑死病描述为一种不分青红皂白的杀手,它在 14 世纪袭击了伦敦。为了更好地了解人群中传播的模式,模型应该同时包含多个人口统计学和健康协变量,这在研究黑死病时很少做到,但在本研究中实施,以确定哪些人群更容易受到影响。还探讨了女性对黑死病的易感性,重点关注社会不平等是否会增加脆弱性。
从 Wellcome 骨骼研究数据库中的三个消耗性墓地与东史密斯菲尔德的黑死病墓地进行了比较。使用过渡分析年龄作为时间变量,性别和脆弱性作为协变量,Cox 比例风险回归估计了死于黑死病的风险比。此外,二项逻辑回归生成了年龄、性别和脆弱性的死亡风险比。
Cox 模型对被认为脆弱的个体产生了显著的风险比。同样,logit 模型计算出脆弱个体的感染风险比显著增加,而 65 岁以上个体的感染风险比降低。
在黑死病流行之前,伦敦经历了巨大的压力,年龄较大的个体在这段时间内没有经历生长,这可能解释了他们感染黑死病的几率降低。此外,尽管女性面临社会不平等,这在一定程度上导致了中世纪“失踪”女性的人口学难题,但女性感染黑死病的易感性并没有增加。“失踪”女性的现象可能是多种因素共同作用的结果,包括婴儿和儿童死亡率以及保存。