A Comparative Analysis of the Temperature‐Mortality Risks Using Different Weather Datasets Across Heterogeneous Regions

Abstract

New gridded climate datasets (GCDs) on spatially resolved modeled weather data have recently been released to explore the impacts of climate change. GCDs have been suggested as potential alternatives to weather station data in epidemiological assessments on health impacts of temperature and climate change. These can be particularly useful for assessment in regions that have remained understudied due to limited or low quality weather station data. However to date, no study has critically evaluated the application of GCDs of variable spatial resolution in temperature-mortality assessments across regions of different orography, climate, and size. Here we explored the performance of populationweighted daily mean temperature data from the global ERA5 reanalysis dataset in the 10 regions in the United Kingdom and the 26 cantons in Switzerland, combined with two local high-resolution GCDs (HadUK-grid UKPOC-9 and MeteoSwiss-grid-product, respectively) and compared these to weather station data and unweighted homologous series. We applied quasi-Poisson time series regression with distributed lag nonlinear models to obtain the GCD- and region-specific temperature-mortality associations and calculated the corresponding cold- and heat-related excess mortality. Although the five exposure datasets yielded different average area-level temperature estimates, these deviations did not result in substantial variations in the temperature-mortality association or impacts. Moreover, local population-weighted GCDs showed better overall performance, suggesting that they could be excellent alternatives to help advance knowledge on climate change impacts in remote regions with large climate and population distribution variability, which has remained largely unexplored in present literature due to the lack of reliable exposure data.Plain Language Summary Thus far, most studies attempting to study the impact of heat and cold on health have used data from weather stations around cities as a proxy for the temperature exposure of a population. Recently, new spatially resolved weather datasets have been released, which provide continuous temperature measurements at local or global scale, and can be particularly useful for supplying data in regions with limited or low quality weather station data. In this study, we aimed to explore the performance of these newly developed exposure datasets compared to weather stations in the United Kingdom and Switzerland, two regions which are heterogeneous in terms of topography and population distribution. We found that despite different temperature observations the datasets yield very similar results. In particular, high-resolution population-weighted temperature datasets showed better performance and thus it can be a good alternative to weather stations, especially in densely populated urban areas with large intracity temperature variability. DE SCHRIJVER ET AL.

Nationwide Analysis of the Heat- and Cold-Related Mortality Trends in Switzerland between 1969 and 2017: The Role of Population Aging

“…We obtained daily mean temperatures on a grid across the full Swiss geography from a gridded climate data set (MeteoSwiss-grid-product) provided by MeteoSwiss ( Federal Office of Meteorology and Climatalogy MeteoSwiss 2020 ). This high-resolution gridded data allowed us to capture the temperature exposures in areas with a sparse monitor network (i.e., rural and mountainous areas) and assign a unique exposure to each of the 2,056 municipalities in Switzerland ( de Schrijver et al. 2021 ; Spangler et al.…”

“…We included the grid cells that intersected the boundaries of the municipality to create the municipality-specific temperature series. To properly account for the heterogeneous distribution of the population due to the irregular orography (Figures S2 and S3), we derived population-weighted daily mean temperature series across the study period for each Swiss municipality, as explained in a previous study ( de Schrijver et al. 2021 ).…”

Effects of Drought on Mortality in Macro Urban Areas of Brazil Between 2000 and 2019

“…We constructed a weekly series between 2000 and 2019 by averaging the days according to the weekly interval defined by the SPEI. Although we used mean temperature data from ERA5‐reanalysis instead of data from weather stations, evidence indicates that the shape of temperature distribution is very close between both sources (de Schrijver et al., 2021; Royé et al., 2020).…”

“…Although we used mean temperature data from ERA5-reanalysis instead of data from weather stations, evidence indicates that the shape of temperature distribution is very close between both sources (de Schrijver et al, 2021;Royé et al, 2020).…”

Non-linear response of temperature-related mortality risk to global warming in England and Wales

“…However, the actual temperature experienced by the population may be underestimated due to urban effects (which is not well represented in global climate models [38,39]). Findings from a recent study suggest minimal impact of population weighting on risk estimates at fixed percentiles of the temperature distribution for most regions of England and Wales (with an exception for London, where lower risk estimate for heat is found for unweighted highresolution gridded climate data) [40], though any differences may be amplified following extrapolation of the risk curve.…”