| BackgroundClimate change has become an unprecedented challenge for public health worldwide.The Earth’s average temperature increased by 1.09℃ during the 2011-2020 period in comparison to the pre-industrial period.In the context of global warming,the health effects of extreme heat are of increasing public concern.Meanwhile,the cold-related disease burden is also important.It is estimated that 9.43%of global deaths are associated with non-optimal ambient temperatures.The mortality burden associated with low temperatures is higher than high temperatures.Therefore,the health effects of both high and low temperatures need to draw extensive attention.Health events due to extreme temperatures are expected to increase in the future,and studying extreme temperatures and health/mortality has become a hot spot in epidemiological research.Because of the different biological mechanisms on human health,most studies often analyze the adverse effects of high and low temperatures separately during different seasons.Previous studies have shown that the human body may adapt to suboptimal temperature in a certain period.For example,reductions in temperature-related mortality or morbidity.However,most studies explored the long-term adaptation of human body within decades.Limited knowledge is available on the changing nature of temperature-related health risks over shorter time periods such as a few months.Several previous studies have focused on short-term intraseasonal variation in the effects of high temperatures in localized regions such as Brazil.Whether heat-and cold-related mortality risk may vary remain unexplored in Chinese population.Clarifying this issue is essential for assessing the short-term adaptation of population to extreme temperature events and developing health promotion strategies in China.In previous studies,air temperature may be combined with other indicators(e.g.,humidity,wind speed,etc.)to construct multiple composite temperature indicators,but few studies have comprehensively assessed the differences in their health effects.Therefore,it is necessary to select the best temperature indicator by rational analysis when analyzing the effect of temperature.Meanwhile,ambient temperature is susceptible to some surrounding factors.For example,certain greenspace can significantly reduce local temperature in hot summer through shading and other effects,which may have a potential modifying effect on the health effect of suboptimal temperatures and should be evaluated.Objectives1.To describe the epidemiological characteristics of mortality in Shandong Province from 2013 to 2018,and compare the performance of different temperature indicators in estimating temperature-mortality associations at the full-year level.2.To quantify change in temperature-mortality association from early to late hot/cold seasons and to assess vulnerable population.3.To quantify the modification effect of greenspace on temperature-mortality associations in hot and cold seasons.Methods1.Mortality data in Shandong province from 2013 to 2018 were collected from the cause of mortality surveillance system of Shandong Center for Disease Control and Prevention.Daily ambient air temperature data in the same period is calculated from ERA5-land data,and all other temperature indicators are obtained from ERA5-land and ERA5 reanalysis data.There are 12 other temperature indicators:UTCI(Universal thermal climate index),UTCI2(UTCI for indoor environment),UTCI3(UTCI for outdoor shaded space),AT(Apparent temperature),ESI(Environment stress index),HI(Heat index),MRT(Mean radiant temperature),WBT(Wet bulb temperature),WBGT(Wet-bulb globe temperature),WCT(Wind chill temperature),Humidex(Humidity index)and NET(Net effective temperature).NDVI(Normalized Difference Vegetation Index)and EVI(Enhanced Vegetation Index)was calculated from Landsat-8 satellite image data.2.A time-stratified case-crossover design was performed in this study.A two-stage analysis was used in this study.Conditional logistic regression combined with distributed lagged nonlinear model was used in the first stage to fit the association between ambient temperature and mortality at the county-level.Meta-analysis was used in the second stage to pool county-specific data at the province level.3.We calculated fraction of excess deaths attributable to suboptimal temperatures using different temperature indicators,and examined the statistical difference by comparing with the results calculated from the mean air temperature.Hot/cold season was defined as the four consecutive hottest/coldest months of the year,with the first two months setting as the early season and the late two months setting as the late season.Extreme heat/cold was P97.5/P2.5 of the hot/cold season temperature range.Short-term variation was evaluated by comparing the effects of mortality risk for extreme temperatures in early and late hot/cold season.The modification of greenspace on temperature effect in hot and cold seasons was analyzed by the interaction term between NDVI/EVI and temperature functions respectively.In addition,stratified analysis was performed by sex,age,education,and cause of death.Results1.During the study period,there were a total of 3847052 deaths recorded in Shandong Province.Among whom,males,the elderly aged over 75 years,those with junior high school education or less,and patients with circulatory system diseases accounted for more than 50%of the total deaths.The daily number of deaths showed a significant seasonal trend,with the peak occurring in winter each year and a small peak in summer.2.Compared with the mean air temperature,there was no statistical difference in the estimates of attribution risk for most indicators.The attributable fraction of deaths calculated from UTCI3 and WBT were lower than the mean air temperature by 1.41%(95%CI:0.10%,2.69%)and 1.47%(95%CI:0.09%,2.77%),respectively.3.The hot season was from June to September,and the cold season was from November to February.The temperature-mortality association was non-linear in both seasons.The cumulative OR was 3.52(95%CI:3.10,4.00)in the early hot season(June to July)and decreased to 2.85(95%CI:2.48,3.28)in the late hot season(August to September),P=0.028,The OR was 1.61(95%CI:1.38,1.88)in the early cold season(November-December)and increased to 2.81(95%CI:2.27,3.47)in the late cold season(January-February),P=0.001.4.The values of NDVI and EVI was in the range of 0-1.During the hot season,for each 0.1 increase in NDVI and EVI the risk of extreme heat-related mortality decreased by 5.84%(95%CI:-2.27%,13.31%)and 11.22%(95%CI:1.00%,20.39%),respectively.During the cold season,the risk of extreme cold-related mortality did not show significant changes for each 0.1 increase in NDVI/EVI.Conclusion1.There was essentially no difference in mortality burden estimates for different temperature indicators.2.In both hot and cold seasons,the association between temperature and mortality showed a non-linear association.The mortality risk associated with extreme heat was significantly lower in the late hot season than in the early hot season.However,the cold-related mortality risk increased significantly from the early to late cold season.3.The heat-mortality association was significantly modified by greenspace while the modifying effect was insignificant for cold-related mortality risk. |
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