A New Method to Estimate Heat Exposure Days and Its Impacts in China

Understanding the spatiotemporal trends of temperature in the context of global warming is significant for public health. Although many studies have examined changes in temperature and the impacts on human health over the past few decades in many regions, they have often been carried out in data-rich regions and have rarely considered acclimatization explicitly. The most frequent temperature (MFT) indicator provides us with the ability to solve this problem. MFT is defined as the longest period of temperature throughout the year to which a human is exposed and therefore acclimates. In this study, we propose a new method to estimate the number of heat exposure days from the perspective of temperature distribution and MFT, based on the daily mean temperature readings of 2142 weather stations in eight major climate zones in China over the past 20 years. This method can be used to calculate the number of heat exposure days in terms of heat-related mortality risk without the need for mortality data. We estimated the distribution and changes of annual mean temperature (AMT), minimum mortality temperature (MMT), and the number of heat exposure days in different climate zones in China. The AMT, MMT, and number of heat exposure days vary considerably across China. They all tend to decrease gradually from low to high latitudes. Heat exposure days are closely related to the risk of heat-related mortality. In addition, we utilized multiple linear regression (MLR) to analyze the association between the risk of heat-related mortality and the city and its climatic characteristics. Results showed that the number of heat exposure days, GDP per capita, urban population ratio, proportion of elderly population, and climate zone were found to modify the estimate on heat effect, with an R2 of 0.71. These findings will be helpful for the creation of public policies protecting against high-temperature-induced mortalities.

Evolution of the threshold temperature definition of a heat wave vs. evolution of the minimum mortality temperature: a case study in Spain during the 1983–2018 period

Background An area of current study concerns analysis of the possible adaptation of the population to heat, based on the temporal evolution of the minimum mortality temperature (MMT). It is important to know how is the evolution of the threshold temperatures (Tthreshold) due to these temperatures provide the basis for the activation of public health prevention plans against high temperatures. The objective of this study was to analyze the temporal evolution of threshold temperatures (Tthreshold) produced in different Spanish regions during the 1983–2018 period and to compare this evolution with the evolution of MMT. The dependent variable used was the raw rate of daily mortality due to natural causes ICD X: (A00-R99) for the considered period. The independent variable was maximum daily temperature (Tmax) during the summer months registered in the reference observatory of each region. Threshold values were determined using dispersion diagrams (annual) of the prewhitened series of mortality temperatures and Tmax. Later, linear fit models were carried out between the different values of Tthreshold throughout the study period, which permitted detecting the annual rate of change in Tthreshold. Results The results obtained show that, on average, Tthreshold has increased at a rate of 0.57 ºC/decade in Spain, while Tmax temperatures in the summer have increased at a rate of 0.41 ºC/decade, suggesting adaptation to heat. This rate of evolution presents important geographic heterogeneity. Also, the rate of evolution of Tthreshold was similar to what was detected for MMT. Conclusions The temporal evolution of the series of both temperature measures can be used as indicators of population adaptation to heat. The temporal evolution of Tthreshold has important geographic variation, probably related to sociodemographic and economic factors, that should be studied at the local level.

COVID-19 pandemic increased the magnitude of mortality risks associated with cold temperature in Italy: A nationwide time-stratified case-crossover study

Backgrounds: The coronavirus disease 2019 (COVID-19) pandemic and some containment measures have changed many people lives and behaviours. Whether the pandemic could change the association between cold temperature and mortality remains unknown. Objectives: We aimed to assess whether the association between cold temperature and all-cause mortality in the pandemic period has changed compared to non-COVID-19 period (2015-2019) in Italy. Methods: We collected daily all-cause mortality data and meteorological data for 107 Italian provinces from 1, January 2015 to 31, May 2020. A time-stratified case-crossover design with the distributed lag non-linear model was used to examine the association between cold temperature and all-cause mortality during the first three months (from March to May in 2020) of the COVID-19 outbreak and the same months in 2015-2019. Results: The relative risk (RR) of all-cause mortality at extreme cold temperature (2.5th percentile of temperature at 3 °C) in comparison with the minimum mortality temperature (24 °C) was 4.75 [95% confidence interval (CI): 3.90-5.79] in the pandemic period, which is more than triple higher than RR [1.41 (95%CI: 1.33-1.50)] in the same months during 2015-2019. The shift in cold-mortality association was particularly significant for people aged 65-74 years [RR (95%CI): 5.98 (3.78-9.46) in 2020 versus 1.29 (1.10-1.51) in 2015-2019], 75-84 years [5.25 (3.79-7.26) versus 1.40 (1.25-1.56)], and ≥ 85 years [5.03 (3.90-6.51) versus 1.52 (1.39-1.66)], but not significant for those aged 0-64 years [1.95 (1.17-3.24) versus 1.24 (1.05-1.48)]. Conclusion: The findings suggest that the COVID-19 pandemic enhanced the risk of cold temperature on mortality in Italy, particularly among the elderly people. Further studies are warranted to understand the exact mechanism when detailed data are available.

Heat ascribed mortality in southeast England

<p>Global warming induced climate change is bringing periods of extremely hot summer days called heatwaves across the world. Its frequency, intensity and magnitude have escalated multifold in recent decades and have been predicted to keep intensifying. Many past studies have only focused on cities for heatwave risk assessment overlooking the risks in suburban and rural areas. The aim of this work is to form a scientific framework for preparing and managing the human-health impacts of heatwaves in more pastoral regions. We associated  the extreme temperature with mortality to evaluate its risk using recent data on daily-deaths and maximum temperature from nine counties of southeast England for the period of 1981-2014. The reproduced methodology will also be applied to OPERANDUM project’s test regions called open-air laboratories across Europe. The relationship between temperature and daily-deaths has been examined using  a poisson regression model combined with a distributed-lag nonlinear model (DLNM). We computed the absolute excess (numbers) and relative excess (fraction) deaths owed to temperature or relative risk (RR) of mortality by comparing the extremely hot temperature (99<sup>th</sup> percentile) with the minimum mortality temperature (MMT). Total heat ascribed mortality is given by the sum of the contributions from all the days of the time-series, and its ratio with the total number of deaths. Significant and non-linear associations between temperature and daily-deaths were noticed. The overall cumulative RR at the extremely hot vs. MMT was 1.292 (95% CI: 1.251–1.333). The results of this study can help in location-centric heat management action plans to certain areas at most risk.</p><p>Acknowledgements: This work is supported by the European Union's Horizon 2020 research and innovation programme; funded by and carried out within the framework of OPERANDUM project (Grant no. 776848).</p><p><strong>Key words</strong>: Heatwaves, climate change, mortality, DLNM, risk.</p>

Probability Risk of Heat- and Cold-Related Mortality to Temperature, Gender, and Age Using GAM Regression Analysis

We have examined the heat and cold-related mortality risk subject to cold and heat extremes by using a generalized additive model (GAM) regression technique to quantify the effect of the stimulus of mortality in the presence of covariate data for 2007–2014 in Nicosia, Cyprus. The use of the GAM technique with multiple linear regression allowed for the continuous covariates of temperature and diurnal temperature range (DTR) to be modeled as smooth functions and the lag period was considered to relate mortality to lagged values of temperature. Our findings indicate that the previous three days’ temperatures were strongly predictive of mortality. The mortality risk decreased as the minimum temperature (Tmin) increased from the coldest days to a certain threshold temperature about 20–21°C (different for each age group and gender), above which the mortality risk increased as Tmin increased. The investigated fixed factors analysis showed an insignificant association of gender-mortality, whereas the age-mortality association showed that the population over 80 was more vulnerable to temperature variations. It was recommended that the minimum mortality temperature is calculated using the minimum daily temperatures because it has a stronger correlation to the probability for risk of mortality. It is still undetermined as to what degree a change in existing climatic conditions will increase the environmental stress to humans as the population is acclimatized to different climates with different threshold temperatures and minimum mortality temperatures.