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During the last century, a number of epidemics have swept across the world causing similar mortality peaks in diverse human populations. In particular, the effects of the influenza epidemic of 1918 can be seen in urban and rural human aggregates separated by continents and thousands of miles. This paper examines mortality periodicity, due to diverse population structures, ecology, and exposure to similar pathogens, through the use of time series analyses. Specifically, raw yearly mortality figures for two Italian alpine communities, Acceglio and Bellino, are compared with those of a Mennonite congregation living in Kansas, United States, for the same time periods. Crosscorrelation, auto­correlation, and power spectrum analyses have been applied in order to identify possible mortality periodicity and to compare these cycles across populations. The mortality cycles occur at approximately 10 years in the Mennonite series, and 13 in Acceglio and Bellino. Explanations are proposed for these data and for the significant correlations exhibited by the three time series. The last century of human existence saw a number of major demographic changes on a world-wide basis resulting from a variety of technological breakthroughs and medical developments.For example, as a result of innovations in transportation, there has been a rapid breakdown of reproductive and geographical isolation of small human populations such as the Mennonites. Due in part to this geographical isolation, communities that were exposed to specific pathogens periodically experienced disease epidemics, and mortality patterns were unique to each population. The incidence and duration of these epidemics depended in part on the demographic structure of the population and the unique historical events that introduced the pathogen into the community. The purpose of this paper is to explore the mortality patterns of three human populations living under diverse ecological conditions with exposure to various pandemic diseases. In particular, we examine the periodicity of mortality patterns using power spectral, cross-correlation and autocorrelation analyses, and explore some variables which may contribute to this periodicity.