Mortality Baselines and Excess Mortality Events in Belgian and Greek Cities Using Singular Spectrum Analysis
- 1Institute of Health and Society (IRSS), University of Louvain, Brussels, Belgium
Abstract
BackgroundExcess mortality estimates depend on an expected baseline, an unobservable quantity typically derived from models with prescribed functional forms and short reference periods that exclude exceptional events. Singular Spectrum Analysis (SSA) offers a nonparametric alternative but requires subjective component selection and is sensitive to outliers. We combined SSA with automated component selection and outlier interpolation to estimate long-term baselines and compile a catalog of excess mortality events (EMEs).
MethodsWe analyzed weekly all-cause mortality (2015-2024) for six metropolitan areas with contrasting climatic, demographic, and epidemiological contexts: Brussels, Antwerp, and Liege (Belgium), and Athens, Thessaloniki, and Larisa (Greece). SSA was applied to standardized log-mortality rates; components were selected using Monte Carlo SSA, and COVID-19 periods were interpolated iteratively. EMEs were defined as weeks exceeding a quasi-Poisson two-z-score threshold, then screened for coincidence with heat waves, cold waves, COVID-19 periods, and influenza epidemics.
ResultsBaselines were consistent within countries: annual cycles in Belgium, with an additional significant semiannual cycle in Greece reflecting summer mortality. We cataloged 170 EMEs spanning 456 weeks and 14,210 excess deaths. Pandemic weeks accounted for 77% of excess mortality, and 96 weeks coincided with temperature extremes; 56 EMEs, representing 3.7% of excess mortality, remained unmatched.
ConclusionsSSA yields reproducible, low-assumption baselines that capture country-specific seasonality. Because flexible baselines can absorb recurrent seasonal mortality, component selection is a substantive choice that should be reported explicitly, together with its sensitivity.
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BibTeX
@misc{delforge2026mortality,
title = {{Mortality Baselines and Excess Mortality Events in Belgian and Greek Cities Using Singular Spectrum Analysis}},
author = {D. Delforge and P. Charalampous and N. Bustos Sierra and E. De Clercq and E. Katragkou and G. Konstantinoudis and P. Kostoulas and A. Lambrou and B. Masquelier and D. Paraskevis and W. Thiery and M. Tonnelier and N. Speybroeck},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.02.26363997},
url = {https://www.medrxiv.org/content/10.64898/2026.10.02.26363997v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Mortality Baselines and Excess Mortality Events in Belgian and Greek Cities Using Singular Spectrum Analysis
AU - D. Delforge
AU - P. Charalampous
AU - N. Bustos Sierra
AU - E. De Clercq
AU - E. Katragkou
AU - G. Konstantinoudis
AU - P. Kostoulas
AU - A. Lambrou
AU - B. Masquelier
AU - D. Paraskevis
AU - W. Thiery
AU - M. Tonnelier
AU - N. Speybroeck
PY - 2026
DA - 2026/10/02
PB - medRxiv
DO - 10.64898/2026.10.02.26363997
UR - https://www.medrxiv.org/content/10.64898/2026.10.02.26363997v1
AB - BackgroundExcess mortality estimates depend on an expected baseline, an unobservable quantity typically derived from models with prescribed functional forms and short reference periods that exclude exceptional events. Singular Spectrum Analysis (SSA) offers a nonparametric alternative but requires subjective component selection and is sensitive to outliers. We combined SSA with automated component selection and outlier interpolation to estimate long-term baselines and compile a catalog of excess mortality events (EMEs). MethodsWe analyzed weekly all-cause mortality (2015-2024) for six metropolitan areas with contrasting climatic, demographic, and epidemiological contexts: Brussels, Antwerp, and Liege (Belgium), and Athens, Thessaloniki, and Larisa (Greece). SSA was applied to standardized log-mortality rates; components were selected using Monte Carlo SSA, and COVID-19 periods were interpolated iteratively. EMEs were defined as weeks exceeding a quasi-Poisson two-z-score threshold, then screened for coincidence with heat waves, cold waves, COVID-19 periods, and influenza epidemics. ResultsBaselines were consistent within countries: annual cycles in Belgium, with an additional significant semiannual cycle in Greece reflecting summer mortality. We cataloged 170 EMEs spanning 456 weeks and 14,210 excess deaths. Pandemic weeks accounted for 77% of excess mortality, and 96 weeks coincided with temperature extremes; 56 EMEs, representing 3.7% of excess mortality, remained unmatched. ConclusionsSSA yields reproducible, low-assumption baselines that capture country-specific seasonality. Because flexible baselines can absorb recurrent seasonal mortality, component selection is a substantive choice that should be reported explicitly, together with its sensitivity.
ER -