Detecting hidden relations between time series of mortality rates.
In the present report a method is described which may help to decide if a disease is influenced by an environmental factor which fluctuates in time: For each of two naturally arising subgroups of a population (such as males and females) an ARIMA model (autoregressive integrated moving average model) is identified. These models are used as filters to remove the autocorrelation in each series. If the resulting crosscorrelation function between the two filtered series shows a marked peak at time lag 0 this may indicate that such an environmental factor is present. The procedure is demonstrated using yearly data of mortality rates among the elderly.
Helfenstein
U
U
Department of Medicine, University of Zurich, Switzerland.
D000368:Aged; D000369:Aged, 80 and over; D004787:Environmental Pollution / Q000009:adverse effects*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009026:Mortality; D013997:Time Factors