Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm

Gang Xie, Yuming Guo, Shilu Tong, Lin Ma

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    7 Citations (Scopus)
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    Abstract

    Background: Heatwaves could cause the population excess death numbers to be ranged from tens to thousands within a couple of weeks in a local area. An excess mortality due to a special event (e.g., a heatwave or anepidemic outbreak) is estimated by subtracting the mortality figure under ‘normal’ conditions from the historical daily mortality records. The calculation of the excess mortality is a scientific challenge because of the stochastictemporal pattern of the daily mortality data which is characterised by (a) the long-term changing mean levels (i.e.,non-stationarity); (b) the non-linear temperature-mortality association. The Hilbert-Huang Transform (HHT) algorithmis a novel method originally developed for analysing the non-linear and non-stationary time series data in the field of signal processing, however, it has not been applied in public health research. This paper aimed to demonstratethe applicability and strength of the HHT algorithm in analysing health data.
    Methods: Special R functions were developed to implement the HHT algorithm to decompose the daily mortality time series into trend and non-trend components in terms of the underlying physical mechanism. The excess mortality is calculated directly from the resulting non-trend component series.
    Results: The Brisbane (Queensland, Australia) and the Chicago (United States) daily mortality time series data were utilized for calculating the excess mortality associated with heatwaves. The HHT algorithm estimated 62 excess deaths related to the February 2004 Brisbane heatwave. To calculate the excess mortality associated with the July1995 Chicago heatwave, the HHT algorithm needed to handle the mode mixing issue. The HHT algorithm estimated510 excess deaths for the 1995 Chicago heatwave event. To exemplify potential applications, the HHT decomposition results were used as the input data for a subsequent regression analysis, using the Brisbane data, to investigate the association between excess mortality and different risk factors.
    Conclusions: The HHT algorithm is a novel and powerful analytical tool in time series data analysis. It has areal potential to have a wide range of applications in public health research because of its ability to decompose a nonlinear and non-stationary time series into trend and non-trend components consistently and efficiently.
    Original languageEnglish
    Pages (from-to)1-10
    Number of pages10
    JournalBMC Medical Research Methodology
    Volume14
    Issue number35
    DOIs
    Publication statusPublished - 04 Mar 2014

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