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Detecting hidden relations between time series of mortality rates


Helfenstein, Ulrich (1990). Detecting hidden relations between time series of mortality rates. Methods of information in medicine, 29(1):57-60.

Abstract

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.

Abstract

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.

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13 citations in Web of Science®
17 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:January 1990
Deposited On:05 Jul 2016 13:41
Last Modified:08 Dec 2017 19:50
Publisher:Schattauer
ISSN:0026-1270
PubMed ID:2308527

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