Short-Term Effects of Extreme Heat, Cold, and Air Pollution Episodes on Excess Mortality in Luxembourg

Abstract

This study aims to assess the short-term effects of extreme heat, cold, and air pollution episodes on excess mortality from natural causes in Luxembourg over 1998-2023. Using a high-resolution dataset from downscaled and bias-corrected temperature (ERA5) and air pollutant concentrations (EMEP MSC-W), weekly mortality p-scores were linked to environmental episodes. A distributional regression approach using a logistic distribution was applied to model the influence of environmental risks, capturing both central trends and extreme values of excess mortality. Results indicate that extreme heat, cold, and fine particulate matter (PM_2.5) episodes significantly drive excess mortality. The estimated attributable age-standardized mortality rates are 2.8 deaths per 100,000/year for extreme heat (95% CI: [1.8, 3.8]), 1.1 for extreme cold (95% CI: [0.4, 1.8]), and 6.3 for PM_2.5 episodes (95% CI: [2.3, 10.3]). PM_2.5-related deaths have declined over time due to the reduced frequency of pollution episodes. The odds of extreme excess mortality increase by 1.93 times (95% CI: [1.52, 2.66]) per extreme heat day, 3.49 times (95% CI: [1.77, 7.56]) per extreme cold day, and 1.11 times (95% CI: [1.04, 1.19]) per PM_2.5 episode day. Indicators such as return levels and periods contextualize extreme mortality events, such as the p-scores observed during the 2003 heatwave and COVID-19 pandemic. These findings can guide public health emergency preparedness and underscore the potential of distributional modeling in assessing mortality risks associated with environmental exposures.

Keywords: attributable mortality; distributional regression; environmental risks; excess mortality; extreme events.

Figure 1

Workflow for downscaling and bias-correction of daily mean temperatures on 19 July 2022: (A) raw station data; (B) station data downscaled; (C) raw ERA5 simulations; (D) ERA5 simulations downscaled; (E) bias-corrected ERA5 simulations. Dots in panels (A,C) indicate station locations.

Figure 2

Occurrence of environmental episodes by year and week in Luxembourg from 1998 to 2023.

Figure 3

(A) Time series of weekly age-standardized mortality rates (ASMRs) per 100,000 population for natural-cause deaths in Luxembourg from 1998 to 2023. The black line represents the baseline mortality derived from median regression. (B) Histogram of weekly p-scores overlaid with fitted densities from the normal (yellow) and logistic (blue) distributions.

Figure 4

Marginal effects of (A) extreme heat days, (B) extreme cold days, and (C) PM_2.5 episode days on mean p-scores, based on the μ coefficients of the logistic distribution (with 95% CI). Effects are computed with all other covariates set to zero. Dots represent observed p-scores.

Figure 5

Diagnostics of logistic distribution fit for modeling p-scores. (A) Q-Q plot of normalized quantile residuals; (B) worm plot highlighting deviation from normality.

Figure 6

Yearly time series of the number of natural deaths attributed to environmental episodes in Luxembourg from 1998 to 2023.

Figure 7

The probability of extreme excess mortality occurrence under environmental risks, estimated from logistic distribution (blue) and binomial regression (yellow). Dots indicate if extreme mortality occurred in the sample.

Figure 8

Return level plot of weekly p-scores, comparing estimates from the logistic (blue) and exponential (red) distributions, with 95% confidence intervals.