Comparison of weather station and climate reanalysis data for modelling temperature-related mortality

Abstract

BACKGROUND: Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations. Though generally considered representative of the actual ambient conditions and individual’s exposure, their broader application in environmental epidemiology is often constrained by inhomogeneous records and the sparse density of meteorological stations. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. AIM: The aim of the study is to provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. METHODS: We applied the well-established time-series analyses with quasi-Poisson regression (with distributed lag non-linear models and multivariate meta-regression) to model the location specific temperature-mortality associations, across 612 cities within 39 countries over the period 1985–2019, covering a wide range of climates and including low- and middle-income countries. Briefly, we first systematically compared the correlation between daily temperature series derived from ground station observations and ERA5-Land/ERA5 reanalysis, then we evaluated differences in estimated exposure-response functions of temperature-mortality relationships, and finally we compared their performance using fit statistics. RESULTS: Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. CONCLUSIONS: Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk. The consistent spatio-temporal coverage also makes it attractive for quantifying population attributable fractions, an indicator important for health planners and policy makers. KEYWORDS: mortality, temperature, reanalysis, ERA5-Land, distributed lag models.