Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm

Xie, Gang, Guo, Yuming, Tong, Shilu and Ma, Lin (2014) Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm. BMC Medical Research Methodology, 14 35: 1-10. doi:10.1186/1471-2288-14-35


Author Xie, Gang
Guo, Yuming
Tong, Shilu
Ma, Lin
Title Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm
Journal name BMC Medical Research Methodology   Check publisher's open access policy
ISSN 1471-2288
Publication date 2014-03-04
Sub-type Article (original research)
DOI 10.1186/1471-2288-14-35
Open Access Status DOI
Volume 14
Issue 35
Start page 1
End page 10
Total pages 10
Place of publication London, United Kingdom
Publisher BioMed Central
Collection year 2015
Language eng
Formatted 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 an epidemic 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 stochastic temporal 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) algorithm is 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 demonstrate the 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 July 1995 Chicago heatwave, the HHT algorithm needed to handle the mode mixing issue. The HHT algorithm estimated 510 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 a real 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.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Public Health Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Thu, 06 Mar 2014, 10:52:44 EST by Yuming Guo on behalf of School of Public Health