Heat and health in Antwerp under climate change: Projected impacts and implications for prevention

Abstract

BackgroundExcessive summer heat is a serious environmental health problem in several European cities. Heat-related mortality and morbidity is likely to increase under climate change scenarios without adequate prevention based on locally relevant evidence.MethodsWe modelled the urban climate of Antwerp for the summer season during the period 1986–2015, and projected summer daily temperatures for two periods, one in the near (2026–2045) and one in the far future (2081–2100), under the Representative Concentration Pathway (RCP) 8.5. We then analysed the relationship between temperature and mortality, as well as with hospital admissions for the period 2009–2013, and estimated the projected mortality in the near future and far future periods under changing climate and population, assuming alternatively no acclimatization and acclimatization based on a constant threshold percentile temperature.ResultsDuring the sample period 2009–2013 we observed an increase in daily mortality from a maximum daily temperature of 26 °C, or the 89th percentile of the maximum daily temperature series. The annual average heat-related mortality in this period was 13.4 persons (95% CI: 3.8–23.4). No effect of heat was observed in the case of hospital admissions due to cardiorespiratory causes. Under a no acclimatization scenario, annual average heat-related mortality is multiplied by a factor of 1.7 in the near future (24.1 deaths/year CI 95%: 6.78–41.94) and by a factor of 4.5 in the far future (60.38 deaths/year CI 95%: 17.00–105.11). Under a heat acclimatization scenario, mortality does not increase significantly in the near or in the far future.ConclusionThese results highlight the importance of a long-term perspective in the public health prevention of heat exposure, particularly in the context of a changing climate, and the calibration of existing prevention activities in light of locally relevant evidence.

Public perceptions of the health risks of extreme heat across US states, counties, and neighborhoods

“…Heat-risk perceptions are a key predictor of protective responses (37), and policy to reduce risk should be responsive to risk perceptions, both to bring risk perceptions into line with actual vulnerability (for subpopulations that underestimate their risk), but also to respond to the needs of subpopulations where high risk perceptions correspond with elevated vulnerability. Future efforts designed to increase awareness about the health risks of extreme heat and to understand differences in heat-risk perception and behavior among different populations may become particularly important in places where future exposure is expected to increase the most (5, 60). These results provide insights and a tool for communicators and decision makers to understand the geographic diversity in Americans’ judgments about the health risks of extreme heat and point toward the need for additional analyses that can help identify areas where gaps between actual and perceived risk may exist or may grow in the future.…”

Projections of Temperature-Attributable Deaths in Portuguese Metropolitan Areas: A Time-Series Modelling Approach

“…In this sense, several statistical models in epidemiological studies have been developed to quantify the exposure-response relationship between temperature and mortality [6,7]. Using distributed-lag models to investigate meteorological exposure and lagged relationships is very common in environmental health studies [8][9][10][11][12]. The popularity of the distributed-lag non-linear model (DLNM) derives from its flexible modelling framework that allows joint estimation of exposure-response and lag-response associations, with time-dependent nonlinear effects [13,14].…”

Spatial Variability of Heat-Related Mortality in Barcelona from 1992–2015: A Case Crossover Study Design

“…UrbClim combines information about the urban structure (vegetation, soil sealing, typology, land use and land cover) through a simplified physics approach relevant at urban scales, and generates local climate data at very high spatial resolution of 100 m. The location and time were considered when climate data were extracted from the UrbClim model in order to merge with the georeferenced mortality dataset. The model has been previously validated in several validation campaigns, among which one has focused on the agglomeration of Barcelona [16,21,22]. Using the UrbClim model, daily urban climate data have been composed for all summer periods (1 June-30 September) of the years 1992-2015 ( Figure 1a).…”