Temperature variability and mortality in rural and urban areas in Zhejiang province, China: An application of a spatiotemporal index

The synergistic effect of high temperature and relative humidity on non-accidental deaths at different urbanization levels

Numerous studies have examined the impact of temperature on mortality, yet research on the combined effect of temperature and humidity on non-accidental deaths remains limited. This study investigates the synergistic impact of high temperature and humidity on non-accidental deaths in China, assessing the influence of urban development and urbanization level. Utilizing the distributed lag nonlinear model (DLNM) of quasi-Poisson regression, we analyzed the relationship between Wet Bulb Globe Temperature (WBGT) and non-accidental deaths in 30 Chinese cities from 2010 to 2016, including Guangzhou during 2012-2016. We stratified temperature and humidity across these cities to evaluate the influence of varying humidity levels on deaths under high temperatures. Then, we graded the duration of heat and humidity in these cities to assess the impact of deaths with different durations. Additionally, the cities were categorized based on gross domestic product (GDP), and a vulnerability index was calculated to examine the impact of urban development and urbanization level on non-accidental deaths. Our findings reveal a pronounced synergistic effect of high temperature and humidity on non-accidental deaths, particularly at elevated humidity levels. The synergies of high temperature and humidity are extremely complex. Moreover, the longer the duration of high temperature and humidity, the higher the risk of non-accidental death. Furthermore, areas with higher urbanization exhibited lower relative risks (RR) associated with the synergistic effects of heat and humidity. Consequently, it is imperative to focus on damp-heat related mortality among vulnerable populations in less developed regions.

Projecting heat-related cardiovascular mortality burden attributable to human-induced climate change in China

BACKGROUND

Cardiovascular disease (CVD) has been found to be particularly vulnerable to climate change and temperature variability. This study aimed to assess the extent to which human-induced climate change contributes to future heat-related CVD burdens.

METHODS

Daily data on CVD mortality and temperature were collected in 161 Chinese communities from 2007 to 2013. The association between heat and CVD mortality was established using a two-stage time-series design. Under the natural forcing, human-induced, and combined scenarios, we then separately projected excess cause-/age-/region-/education-specific mortality from future high temperature in 2010-2100, assuming no adaptation and population changes.

FINDINGS

Under shared socioeconomic pathway with natural forcing scenario (SSP2-4.5-nat), heat-related attributable fraction of CVD deaths decreased slightly from 3.3% [95% empirical confidence interval (eCI): 0.3, 5.8] in the 2010s to 2.8% (95% eCI: 0.1, 5.2) in the 2090s, with relative change of -0.4% (95% eCI: -0.8, 0.0). However, for combined natural and human-induced forcings, this estimate would surge to 8.9% (95% eCI: 1.5, 15.7), 14.4% (95% eCI: 1.5, 25.3), 21.3% (95% eCI: -0.6, 39.4), and 28.7% (95% eCI: -3.3, 48.0) in the 2090s under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. When excluding the natural forcing, the number of human-induced heat-related CVD deaths would increase from approximately eight thousand (accounting for 31% of total heat-related CVD deaths) in the 2010s to 33,052 (68%), 63,283 (80%), 101,091 (87%), and 141,948 (90%) in the 2090s under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. Individuals with stroke, females, the elderly, people living in rural areas, and those with lower education level would exhibit heightened susceptibility to future high temperature. In addition, Southern and Eastern regions of China were expected to experience a faster increase in heat-related attributable fraction of CVD deaths.

INTERPRETATION

Human activities would significantly amplify the future burden of heat-related CVD. Our study findings suggested that active adaptation and mitigation measures towards future warming could yield substantial health benefits for the patients with CVD.

FUNDING

National Natural Science Foundation of China.

Cause-specific mortality and burden attributable to temperature variability in China

BACKGROUND

Few large-scale, nationwide studies have assessed cause-specific mortality risks and burdens associated with temperature variability (TV).

OBJECTIVE

To estimate associations between TV and cause-specific mortality and quantify the mortality burden in China.

METHODS

Data on daily total and cause-specific mortality in 272 Chinese cities between 2013 and 2015 were recorded. TVs were computed as the standard deviations of daily minimum and maximum temperatures over a duration of 2 to 7 days. The time-series quasi-Poisson regression model with adjustment of the cumulative effects of daily mean temperature over the same duration was applied to evaluate the city-specific associations of TV and mortality. Then, we pooled the effect estimates using a random-effects meta-analysis and calculated the mortality burdens.

RESULTS

Overall, TV showed significant and positive associations with total and cause-specific mortality. The TV-mortality associations were generally stronger when using longer durations. A 1 °C increase in TV at 0-7 days (TV0-7) was associated with a 0.79 % [95 % confidence interval (CI): 0.55 %, 0.96 %] increase in total mortality. Mortality fractions attributable to TV0-7 were 4.37 % for total causes, 4.75 % for overall cardiovascular disease, 4.37 % for coronary heart disease, 5.05 % for stroke, 8.28 % for ischaemic stroke, 1.08 % for haemorrhagic stroke, 6.93 % for respiratory disease, and 6.81 % for COPD, respectively. The mortality risk and burden were generally higher in the temperate monsoon zone, females, and elders.

CONCLUSION

This nationwide study indicated that TV was an independent risk factor of mortality, and could result in significant burden for main cardiorespiratory diseases.

A study of temperature variability on admissions and deaths for cardiovascular diseases in Northwestern China

OBJECTIVE

To explore the effect of temperature variability (TV) on admissions and deaths for cardiovascular diseases (CVDs).

METHOD

The admissions data of CVDs were collected in 4 general hospitals in Jinchang City, Gansu Province from 2013 to 2016. The monitoring data of death for CVDs from 2013 to 2017 were collected through the Jinchang City Center for Disease Control and Prevention. Distributed lag nonlinear model (DLNM) was combined to analyze the effects of TV (daily temperature variability (DTV) and hourly temperature variability (HTV)) on the admissions and deaths for CVDs after adjusting confounding effects. Stratified analysis was conducted by age and gender. Then the attribution risk of TV was evaluated.

RESULTS

There was a broadly linear correlation between TV and the admissions and deaths for CVDs, but only the association between TV and outpatient and emergency room (O&ER) visits for CVDs have statistically significant. DTV and HTV have similar lag effect. Every 1 ℃ increase in DTV and HTV was associated with a 3.61% (95% CI: 1.19% ~ 6.08%), 3.03% (95% CI: 0.27% ~ 5.86%) increase in O&ER visits for CVDs, respectively. There were 22.75% and 14.15% O&ER visits for CVDs can attribute to DTV and HTV exposure during 2013-2016. Males and the elderly may be more sensitive to the changes of TV. Greater effect of TV was observed in non-heating season than in heating season.

CONCLUSION

TV was an independent risk factor for the increase of O&ER visits for CVDs, suggesting effective guidance such as strengthening the timely prevention for vulnerable groups before or after exposure, which has important implications for risk management of CVDs.

Ambient temperature variability and blood pressure in a prospective cohort of 50,000 Chinese adults

Blood pressure has been shown to change by outdoor temperature, but whether intra- and inter-day temperature variability (TV) will bring higher effect on BP is not clear. Based on a prospective cohort study, the mixed effect model was selected to estimate the relationship between TV (daily temperature variability (DTV) and hourly temperature variability (HTV)) and BP (systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and mean arterial pressure (MAP)) after adjusting for confounding variables. We found that there was a positive linear correlation between TV and BP. The results of DTV and HTV were basically consistent, but the effect estimates of HTV seemed to be larger. Gender, age, BMI, education level and BP status may modify the relationship between TV and BP. The effect of TV on BP was greater in non-heating season than in heating season. Our work contributes to a further macro mechanism evidence for the TV-CVDs association.

Seasonal Temperature Variability and Mortality in the Medicare Population

BACKGROUND

Seasonal temperature variability remains understudied and may be modified by climate change. Most temperature-mortality studies examine short-term exposures using time-series data. These studies are limited by regional adaptation, short-term mortality displacement, and an inability to observe longer-term relationships in temperature and mortality. Seasonal temperature and cohort analyses allow the long-term effects of regional climatic change on mortality to be analyzed.

OBJECTIVES

We aimed to carry out one of the first investigations of seasonal temperature variability and mortality across the contiguous United States. We also investigated factors that modify this association. Using adapted quasi-experimental methods, we hoped to account for unobserved confounding and to investigate regional adaptation and acclimatization at the ZIP code level.

METHODS

We examined the mean and standard deviation (SD) of daily temperature in the warm (April-September) and cold (October-March) season in the Medicare cohort from 2000 to 2016. This cohort comprised 622,427,230 y of person-time in all adults over the age of 65 y from 2000 to 2016. We used daily mean temperature obtained from gridMET to develop yearly seasonal temperature variables for each ZIP code. We used an adapted difference-in-difference approach model with a three-tiered clustering approach and meta-analysis to observe the relationship between temperature variability and mortality within ZIP codes. Effect modification was assessed with stratified analyses by race and population density.

RESULTS

For every 1°C increase in the SD of warm and cold season temperature, the mortality rate increased by 1.54% [95% confidence interval (CI): 0.73%, 2.15%] and 0.69% (95% CI: 0.22%, 1.15%) respectively. We did not see significant effects for seasonal mean temperatures. Participants who were classified by Medicare into an "other" race group had smaller effects than those classified as White for Cold and Cold SD and areas with lower population density had larger effects for Warm SD.

DISCUSSION

Warm and cold season temperature variability were significantly associated with increased mortality rates in U.S. individuals over the age of 65 y, even after controlling for seasonal temperature averages. Warm and cold season mean temperatures showed null effects on mortality. Cold SD had a larger effect size for those who were in the racial subgroup other, whereas Warm SD was more harmful for those living in lower population density areas. This study adds to the growing calls for urgent climate mitigation and environmental health adaptation and resiliency. https://doi.org/10.1289/EHP11588.

Health Risks of Temperature Variability on Hospital Admissions in Cape Town, 2011-2016

Epidemiological studies have provided compelling evidence of associations between temperature variability (TV) and health outcomes. However, such studies are limited in developing countries. This study aimed to investigate the relationship between TV and hospital admissions for cause-specific diseases in South Africa. Hospital admission data for cardiovascular diseases (CVD) and respiratory diseases (RD) were obtained from seven private hospitals in Cape Town from 1 January 2011 to 31 October 2016. Meteorological data were obtained from the South African Weather Service (SAWS). A quasi-Poisson regression model was used to investigate the association between TV and health outcomes after controlling for potential effect modifiers. A positive and statistically significant association between TV and hospital admissions for both diseases was observed, even after controlling for the non-linear and delayed effects of daily mean temperature and relative humidity. TV showed the greatest effect on the entire study group when using short lags, 0-2 days for CVD and 0-1 days for RD hospitalisations. However, the elderly were more sensitive to RD hospitalisation and the 15-64 year age group was more sensitive to CVD hospitalisations. Men were more susceptible to hospitalisation than females. The results indicate that more attention should be paid to the effects of temperature variability and change on human health. Furthermore, different weather and climate metrics, such as TV, should be considered in understanding the climate component of the epidemiology of these (and other diseases), especially in light of climate change, where a wider range and extreme climate events are expected to occur in future.

Effects of local factors on adaptation to heat in Spain (1983-2018)

The European Union is currently immersed in policy development to address the effects of climate change around the world. Key plans and processes for facilitating adaptation to high temperatures and for reducing the adverse effects on health are among the most urgent measures. Therefore, it is necessary to understand those factors that influence adaptation. The aim of this study was to provide knowledge related to the social, climate and economic factors that are related to the evolution of minimum mortality temperatures (MMT) in Spain in the rural and urban contexts, during the 1983-2018 time period. For this purpose, local factors were studied regarding their relationship to levels of adaptation to heat. MMT is an indicator that allows for establishing a relationship to between mortality and temperature, and is a valid indicator to assess the capacity of adaptation to heat of a certain population. MMT is obtained through the maximum daily temperature and daily mortality of the study period. The evolution of MMT values for Spain was established in a previous paper. An ecological, longitudinal and retrospective study was carried out. Generalized linear models (GLM) were performed to identify the variables that appeared to be related to adaptation. The adaptation was calculated as the difference in variation in MMT based on the average increase in maximum daily temperatures. In terms of adaptation to heat, urban populations have adapted more than non-urban populations. Seventy-nine percent (n = 11) of urban provinces have adapted to heat, compared to twenty-one percent (n = 3) of rural provinces that have not adapted. In terms of urban zones, income level and habituation to heat (values over the 95th percentile) were variables shown to be related to adaptation. In contrast, among non-urban provinces, a greater number of housing rehabilitation licenses and a greater number of health professionals were variables associated with higher increases in MMT, and therefore, with adaptation. These results highlight the need to carry out studies that allow for identifying the local factors that are most relevant and influential in population adaptation. More studies carried out at a small scale are needed.

Temperature variability associations with cardiovascular and respiratory emergency department visits in Dhaka, Bangladesh

BACKGROUND

Greenhouse gas emissions are changing the Earth's climate, most directly by modifying temperatures and temperature variability (TV). Residents of low- and middle-income countries (LMICs) are likely more adversely affected, due to lack of air conditioning to compensate. To date, there is no local epidemiological evidence documenting the cardio-respiratory health effects of TV in Dhaka, Bangladesh, one of the most climate change vulnerable cities in the world.

OBJECTIVES

We assessed short-term TV associations with daily cardiovascular disease (CVD) and respiratory emergency department (ED) visits, as well as effect modification by age and season.

METHODS

TV was calculated from the standard deviations of the daily minimum and maximum temperatures over exposure days. Time-series regression modeling was applied to daily ED visits for respiratory and CVD from January 2014 through December 2017. TV effect sizes were estimated after controlling for long-term trends and seasonality, day-of-week, holidays, and daily mean relative humidity and ambient temperature.

RESULTS

A 1 °C increase in TV was associated with a 1.00% (95 %CI: 0.05%, 1.96%) increase in CVD ED visits at lag 0-1 days (TV0-1) and a 2.77% (95 %CI: 0.24%, 5.20%) increase in respiratory ED visits at lag 0-7 days (TV0-7). TV-CVD associations were larger in the monsoon and cold seasons. Respiratory ED visit associations varied by age, with older adults more affected by the TV across all seasons. A 1 °C increase in TV at lag 0-7 days (TV0-7) was associated with a 7.45% (95 %CI: 2.33%, 12.57%) increase in respiratory ED visits among patients above 50 years of age.

CONCLUSION

This study provided novel and important evidence that cardio-pulmonary health in Dhaka is adversely affected year-round by day-to-day increases in TV, especially among older adults. TV is a key factor that should be considered in evaluating the potential human health impacts of climate change induced temperature changes.

Temperature Change between Neighboring Days Contributes to Years of Life Lost per Death from Respiratory Disease: A Multicounty Analysis in Central China

BACKGROUND

Many epidemiological studies have recently assessed respiratory mortality attributable to ambient temperatures. However, the associations between temperature change between neighboring days and years of life lost are insufficiently studied. Therefore, we assessed the attributable risk of temperature change between neighboring days on life loss due to respiratory disease.

METHODS

We obtained daily mortality and weather data and calculated crude rates of years of life lost for 70 counties in Hunan Province, Central China, from 2013 to 2017. A time-series design with distributed lag nonlinear model and multivariate meta-regression was used to pool the relationships between temperature change between neighboring days and rates of years of life lost. Then, we calculated the temperature change between neighboring days related to average life loss per death from respiratory disease.

RESULTS

The total respiratory disease death was 173,252 during the study period. The association between temperature change and years of life lost rates showed a w-shape. The life loss per death attributable to temperature change between neighboring days was 2.29 (95% CI: 0.46-4.11) years, out of which 1.16 (95% CI: 0.31-2.01) years were attributable to moderately high-temperature change between neighboring days, and 0.99 (95% CI: 0.19-1.79) years were attributable to moderately low-temperature change between neighboring days. The temperature change between neighboring days related to life loss per respiratory disease death for females (2.58 years, 95% CI: 0.22-4.93) and the younger group (2.97 years, 95% CI: -1.51-7.44) was higher than that for males (2.21 years, 95% CI: 0.26-4.16) and the elderly group (1.96 years, 95% CI: 0.85-3.08). An average of 1.79 (95% CI: 0.18-3.41) life loss per respiratory disease death was related to non-optimal ambient temperature.

CONCLUSIONS

The results indicated that more attention should be given to temperature change, and more public health policies should be implemented to protect public health.

Projections of future temperature-related cardiovascular mortality under climate change, urbanization and population aging in Beijing, China

Climate change is causing the surface temperature to rise and the extreme weather events to increase in frequency and intensity, which will pose potential threats to the survival and health of residents. Beijing is facing multiple challenges such as coping with climate change, urbanization, and population aging, which puts huge decision-making pressure on decision maker. However, few studies that systematically consider the health effects of climate change, urbanization, and population aging for China. Based on the distributed lag nonlinear model (DLNM) and 13 global climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study obtained the temporal and spatial distribution of surface temperature through statistical downscaling methods, and comprehensively explored the independent and comprehensive effects of urbanization and population aging on the projection of future temperature-related cardiovascular disease (CVD) mortality in the context of climate and population change. The results showed that only improving urbanization can reduce future temperature-related CVD mortality by 1.7-18.3%, and only intensified aging can increase future temperature-related CVD mortality by 48.8-325.9%. Taking into account the improving urbanization and intensified aging, future temperature-related CVD mortality would increase by 44.1-256.6%, and the increase was slightly lower than that of only intensified aging. Therefore, the intensified aging was the biggest disadvantage in tackling climate change, which would obviously magnify the mortality risks of temperature-related CVD in the future. Although the advancement of urbanization would alleviate the adverse effects of the intensified aging population, the mitigation effects would be limited. Even so, Urbanization should be continued to reduce health risks for residents. These findings would contribute to formulate policies related to mitigate climate change and reduce baseline mortality rate (especially the elderly) in international mega-city - Beijing. In addition, relevant departments should improve the medical health care level and optimize the allocation of social resources to better cope with and adapt to climate change.

Global, regional, and national burden of mortality associated with short-term temperature variability from 2000-19: a three-stage modelling study

BACKGROUND

Increased mortality risk is associated with short-term temperature variability. However, to our knowledge, there has been no comprehensive assessment of the temperature variability-related mortality burden worldwide. In this study, using data from the MCC Collaborative Research Network, we first explored the association between temperature variability and mortality across 43 countries or regions. Then, to provide a more comprehensive picture of the global burden of mortality associated with temperature variability, global gridded temperature data with a resolution of 0·5° × 0·5° were used to assess the temperature variability-related mortality burden at the global, regional, and national levels. Furthermore, temporal trends in temperature variability-related mortality burden were also explored from 2000-19.

METHODS

In this modelling study, we applied a three-stage meta-analytical approach to assess the global temperature variability-related mortality burden at a spatial resolution of 0·5° × 0·5° from 2000-19. Temperature variability was calculated as the SD of the average of the same and previous days' minimum and maximum temperatures. We first obtained location-specific temperature variability related-mortality associations based on a daily time series of 750 locations from the Multi-country Multi-city Collaborative Research Network. We subsequently constructed a multivariable meta-regression model with five predictors to estimate grid-specific temperature variability related-mortality associations across the globe. Finally, percentage excess in mortality and excess mortality rate were calculated to quantify the temperature variability-related mortality burden and to further explore its temporal trend over two decades.

FINDINGS

An increasing trend in temperature variability was identified at the global level from 2000 to 2019. Globally, 1 753 392 deaths (95% CI 1 159 901-2 357 718) were associated with temperature variability per year, accounting for 3·4% (2·2-4·6) of all deaths. Most of Asia, Australia, and New Zealand were observed to have a higher percentage excess in mortality than the global mean. Globally, the percentage excess in mortality increased by about 4·6% (3·7-5·3) per decade. The largest increase occurred in Australia and New Zealand (7·3%, 95% CI 4·3-10·4), followed by Europe (4·4%, 2·2-5·6) and Africa (3·3, 1·9-4·6).

INTERPRETATION

Globally, a substantial mortality burden was associated with temperature variability, showing geographical heterogeneity and a slightly increasing temporal trend. Our findings could assist in raising public awareness and improving the understanding of the health impacts of temperature variability.

FUNDING

Australian Research Council, Australian National Health & Medical Research Council.

Does air pollution contribute to urban-rural disparity in male lung cancer diseases in China?

It remains unknown whether exposure to ambient air pollution can be a mediator linking socioeconomic indicator to health outcome. The present study aims to examine the mediation effect of PM2.5 air pollution on the association between urban-rural division and the incidence (mortality) rate of male lung cancer. We performed a nationwide analysis in 353 counties (districts) of China between 2006 and 2015. A structural equation model was developed to determine the mediation effect of exposure to PM2.5. We also tested whether the findings of the mediation effect of exposure to PM2.5 are sensitive to the controls of smoking factors and additional air pollutant, and PM2.5 exposures with different lag structures. According to the results, we found that exposure to PM2.5 significantly mediated the association between urban-rural division and the incidence rate of male lung cancer. Specifically, there were significant associations between urban-rural division, exposure to PM2.5, and the incidence rate of male lung cancer, with PM2.5 exposure accounting for 29.80% of total urban-rural difference in incidence rates of male lung cancer. A similar pattern of results was observed for the mortality rate of male lung cancer. That is, there was a significant mediation effect by PM2.5 on the association of the mortality rate with urban-rural division. The findings of exposure to PM2.5 as a mediator were robust in the three sensitivity analyses. In conclusion, urban-rural difference in exposures to PM2.5 may be a potential factor that contributes to urban-rural disparity in male lung cancer diseases in China. The findings inform that air pollution management and control may be effective measures to alleviate the great difference in male lung cancer diseases between urban and rural areas in China.

Comparative analysis of daily and hourly temperature variability in association with all-cause and cardiorespiratory mortality in 45 US cities

Temperature variability (TV) has been widely associated with increased mortality risk and burden. Extensive researches have used the standard deviations of several days' daily maximum and minimum temperatures or hourly mean temperatures as daily and hourly TV measures (TV_daily and TV_hourly). However, comparative analysis of daily and hourly TV related to cardiorespiratory mortality is still limited. We collected daily mortality and meteorological data in 45 US metropolises, 1987-2000. A three-stage analysis was adopted to investigate TV-mortality associations using TV_daily and TV_hourly as exposure metrics. We first applied a time-series quasi-Poisson regression to estimate location-specific TV-mortality relationships, which were then pooled using random-effects meta-analysis with maximum likelihood estimation. We additionally calculated attributable fraction (AF) as a reflection of mortality burden associated with TV. Stratified analyses by age were also performed to identify the susceptible group to TV-related risks. There were a total of 15.4 million all-cause deaths, of which 6.1 million were from cardiovascular causes and 1.2 million were from respiratory causes. Per 1 °C increase in TV_daily and TV_hourly was associated with an increase of 0.53% (95% confidence interval: 0.31-0.76%) and 0.52% (0.26-0.79%) in cardiovascular mortality risks, 0.62% (0.26-0.98%) and 0.53% (0.13-0.94%) in respiratory mortality risks. Estimates of cardiovascular AF for TV_daily and TV_hourly were 2.43% (1.42-3.43%) vs. 1.63% (0.82-2.43%), whereas estimates of respiratory AF were 3.07% (1.11-4.99%) vs. 1.89% (0.43-3.34%). Both daily and hourly TV indexes showed approximately linear relationships with different mortality categories and similar lag patterns, but greater fractions were estimated using TV_daily than those using TV_hourly. People over 75 years old were relatively more vulnerable to TV-induced risks of mortality. In conclusion, both TV_daily and TV_hourly significantly increased all-cause and cardiorespiratory mortality risks and burden. Daily and hourly TV metrics exhibited comparable effects of mortality risk, while greater mortality burden was estimated using TV_daily than TV_hourly. Our findings may add significance to TV-mortality research and help to promote optimal health management strategies to better mitigate TV-related health effects.

Effects of different heat exposure patterns (accumulated and transient) and schizophrenia hospitalizations: a time-series analysis on hourly temperature basis

Growing studies have shown that high temperature is a potential risk factor of schizophrenia occurrence. Therefore, elaborate analysis of different temperature exposure patterns, such as cumulative heat exposure within a time period and transient exposure at a particular time point, is of important public health significance. This study aims to utilize hourly temperature data to better capture the effects of cumulative and transient heat exposures on schizophrenia during the warm season in Hefei, China. We included the daily mean temperature and daily schizophrenia hospitalizations into the distributed lag non-linear model (DLNM) to simulate the exposure-response curve and determine the heat threshold (19.4 °C). We calculated and applied a novel indicator-daily excess hourly heat (DEHH)-to examine the effects of cumulative heat exposure over a day on schizophrenia hospitalizations. Temperature measurements at each time point were also incorporated in the DLNM as independent exposure indicators to analyze the impact of transient heat exposure on schizophrenia. Each increment of interquartile range (IQR) in DEHH was associated with elevated risk of schizophrenia hospitalizations from lag 1 (RR = 1.036, 95% confidence interval (CI): 1.016, 1.057) to lag 4 (RR = 1.025, 95% CI: 1.005, 1.046). Men and people over 40 years old were more susceptible to DEHH. Besides, we found a greater risk of heat-related schizophrenia hospitalizations between 0 a.m. and 6 a.m. This study revealed the adverse effects of accumulated and transient heat exposures on schizophrenia hospitalizations. Our findings need to be further tested in other regions with distinct regional features.

Hourly temperature variability and mortality in 31 major Chinese cities: Effect modification by individual characteristics, season and temperature zone

BACKGROUND

In the context of ongoing climate change, temperature variability (TV) has been considered as an important trigger of death. However, evidence of association between mortality and hourly temperature variability (HTV) is scarce at the multi-city level, and the time window of health effects of HTV is lack of investigation. This study aims at quantifying the mortality risk and burden of HTV and exploring subpopulations susceptible to HTV from a large-scale multi-city perspective.

METHODS

Data on daily number of deaths and meteorology were collected for 31 Chinese major cities during 2007-2013. HTV was calculated as the standard deviation of hourly temperature within a few days. The optimal exposure period of HTV was chosen according to multiple scientific criteria. A quasi-Poisson regression combined with distributed lag nonlinear model was used to assess the city-specific HTV-mortality associations. Then, meta-analysis was further applied to pool city-specific effect estimates. Finally, we calculated the fraction of mortality attributable to HTV. Stratification analyses were conducted by individual characteristics (i.e. age, sex, and educational attainment), season, and region.

RESULTS

HTV calculated in a relatively long-time window like 18 d (HTV_0-17) could capture the impact of HTV adequately. Per 1 °C raise of HTV_0-17 associated with 1.38% (95%CI: 0.77, 1.99) increase of non-accidental mortality. During the study period, 5.47% (95%CI: 1.06, 9.64) of non-accidental mortality could be attributed to HTV. The females, the elderly, and individuals with low education level were more susceptible to HTV than their counterparts, respectively. Moreover, a stronger HTV-mortality association was observed in individuals who live in warmer season and temperature zone.

CONCLUSION

HTV is associated with a considerable mortality burden, which may be modified by season, geographic and individual-level factors. Our findings highlight the practical importance of establishing early warning systems and promoting health education to mitigate the impacts of temperature variability.

Non-optimum temperature-related mortality burden in China: Addressing the dual influences of climate change and urban heat islands

Under the dual effects of climate change and urban heat islands (UHI), non-optimum temperature-related mortality burdens are complex and uncertain, and are rarely discussed in China. In this study, by applying city-specific exposure-response functions to multiple temperature and population projections under different climate and urbanization scenarios, we comprehensively assessed the non-optimum temperature-related mortality burdens in China from 2000 to 2050. Our results showed that temperature-related deaths will decrease from 1.19 million in 2010 to 1.08-1.17 million in 2050, with the exception of the most populous scenario. Excess deaths attributable to non-optimal temperatures under representative concentration pathway 8.5 (RCP8.5) were 2.35% greater than those under RCP4.5. This indicates that the surge in heat-related deaths caused by climate change will be offset by the reduction in cold-related deaths. As the climate changes, high-risk areas will be confronted with more severe health challenges, which requires health protection resource relocation strategies. Simultaneously, the net effects of UHIs are beneficial in the historical periods, preventing 3493 (95% CI: 22-6964) deaths in 2000. But UHIs will cause an additional 6951 (95% CI: -17,637-31,539, SSP4-RCP4.5) to 17,041 (95% CI: -10,516-44,598, SSP5-RCP8.5) deaths in 2050. The heavier health burden in RCP8.5 than RCP4.5 indicates that a warmer climate aggravates the negative effects of UHIs. Considering the synergistic behavior of climate change and UHIs, UHI mitigation strategies should not be developed without considering climate change. Moreover, the mortality burden exhibited strong spatial variations, with heavy burdens concentrated in the hotspots including Beijing-Tianjin Metropolitan Region, Yangtze River Delta, Chengdu-Chongqing City Group, Guangzhou, Wuhan, Xi'an, Shandong, and Henan. These hotspots should be priority areas for the allocation of the national medical resources to provide effective public health interventions.

Effect of diurnal temperature change on cardiovascular risks differed under opposite temperature trends

Temperature change between neighboring days (TCN) is an important trigger for cardiovascular diseases, but the modulated effects by seasonal temperature trends have been barely taken into account. A quantified comparison between impacts of positive TCNs (temperature rise) and negative situations (temperature drop) is also needed. We evaluated the associations of TCNs with emergency room (ER) visits for coronary heart disease (CHD) and cerebral infarction (CI) in Beijing, China, from 2008 to 2012. A year was divided into two segments dominated by opposite temperature trends, quasi-Poisson regression with distributed lag nonlinear models estimating TCN-morbidity relations were employed, separately for each period. High morbidities of CHD and CI both occurred in transitional seasons accompanied by large TCNs. Under warming backgrounds, positive TCNs increased CHD risk in patients younger than 65 years, and old people showed limited sensitivity. In the cooling periods, negative TCNs induced CHD risk in females and the elderly; the highest RR showed on lag 6 d. In particular, a same diurnal temperature decrease (e.g., - 2°C) induced greater RR (RR = 1.113, 95% CIs: 1.033-1.198) on old people during warming periods than cooling counterparts (RR = 1.055, 95% CIs: 1.011-1.100). Moreover, positive TCNs elevated CI risk regardless of background temperatures, and males were particularly vulnerable. Seasonal temperature trends modify TCN-cardiovascular morbidity associations significantly, which may provide new insights into the health impact of unstable weathers.

Increased susceptibility to temperature variation for non-accidental emergency ambulance dispatches in Shenzhen, China

Most studies focused on the temporal trend of mortality risk associated with temperature exposure. The relative role of heat, cold, and temperature variation (TV) on morbidity and its temporal trends are explored insufficiently. This study aims to investigate the temporal trends of emergency ambulance dispatch (EAD) risk and the attributable burden of heat, cold, and hourly temperature variation (HTV). We collected time-series data of daily EAD and ambient temperature in Shenzhen from 2010 to 2017. HTV was calculated as the standard deviation of the hourly temperatures between 2 consecutive days. Quasi-Poisson generalized additive models (GAM) with a time-varying distributed lag nonlinear model (DLNM) were applied to examine temporal trends of the HTV-, heat-, and cold-EAD association. The temporal variation of the attributable fraction (AF%) and attributable number (AN) for different temperature exposures was also calculated. The largest RR was observed in extreme cold [1.30 (95% CI: 1.18, 1.43)] and moderate cold [1.25 (95% CI: 1.17, 1.34)]. Significant increasing trends in HTV-related effects and burden were observed, especially for the extreme HTV effects (P for interaction < 0.05). Decreasing trends were observed in the heat-related effect and burden, though it showed no significance (P for interaction = 0.46). There was no clear change pattern of cold-related effects and burdens. Overall, the three temperature exposure caused 13.7% of EAD, of which 4.1%, 4.3%, and 5.3% were attributed to HTV, heat, and cold, respectively. All the temperature indexes in this study, especially the cold effect, are responsible for the increased risk of EAD. People have become more susceptible to HTV over the recent decade. However, there is no clear evidence to support the temporal change of the population's susceptibility to heat and cold. Thus, in addition to heat and cold, the emergency ambulance service department should pay more attention to HTV under climate change.

Long-term temperature variability and the incidence of cardiovascular diseases: A large, representative cohort study in China

In the context of global climate change, far less is known about the impact of long-term temperature variability (TV), especially in developing countries. The current study aimed to estimate the effect of long-term TV on the incidence of cardiovascular disease (CVD) in China. A total of 23,721 individuals with a mean age of 56.15 years were enrolled at baseline from 2012 to 2016 and followed up during 2017-2019. TV was defined as the standard deviation of daily temperatures during survey years and was categorized into tertiles (lowest≤ 8.78 °C, middle = 8.78-10.07 °C, highest ≥ 10.07 °C). The Cox proportional hazards regression was used to estimate the multivariable-adjusted hazard ratio (HR) between TV and CVD. During the median follow-up of 4.65 years, we ascertained 836 cases of incident CVD. For per 1 °C increase in TV, there was a 6% increase of CVD (HR = 1.06 [95% confidence interval (CI): 1.01-1.11]). A significant positive trend was observed between CVD risk and increasing levels of TV compared to the lowest tertile [HR = 1.34 (95% CI: 1.13-1.59) for the medium tertile, HR = 1.72 (95% CI: 1.35-2.19) for the highest tertile, P_trend < 0.001]. Exposure to high TV would lose 2.11 disease-free years for the population aged 35-65 years and 66 CVD cases (or 7.95% cases) could been attributable to TV higher than 8.11 °C in the current study. The current findings suggested that long-term TV was associated with a higher risk of CVD incidence, it is needed to reduce the TV-related adverse health effect.

The association between ambient temperature variability and myocardial infarction in a New York-State-based case-crossover study: An examination of different variability metrics

BACKGROUND

Short-term temperature variability has been consistently associated with mortality, with limited evidence for cardiovascular outcomes. Previous studies have used multiple metrics to measure temperature variability; however, those metrics do not capture hour-to-hour changes in temperature.

OBJECTIVES

We assessed the correlation between sub-daily temperature-change-over-time metrics and previously-used metrics, and estimated associations with myocardial infarction (MI) hospitalizations.

METHODS

Hour-to-hour change-over-time was measured via three metrics: 24-hr mean absolute hourly first difference, 24-hr maximum absolute hourly first difference, and 24-hr mean hourly first difference. We first assessed the Spearman correlations between these metrics and four previously-used metrics (24-hr standard deviation of hourly temperature, 24-hr diurnal temperature range, 48-hr standard deviation of daily minimal and maximal temperatures, and 48-hr difference of daily mean temperature), using hourly data from the North America Land Data Assimilation System-2 Model. Subsequently, we estimated the association between these metrics and primary MI hospitalization in adult residents of New York State for 2000-2015 using a time-stratified case-crossover design.

RESULTS

The hour-to-hour change-over-time metrics were correlated, but not synonymous, with previously-used metrics. We observed 809,259 MI, 45% of which were among females and the mean (standard deviation) age was 70 (15). An increase from mean to 90th percentile in mean absolute first difference of temperature was associated with a 2.04% (95% Confidence Interval [CI]: 1.30-2.78%) increase in MI rate. An increase from mean to 90th percentile in mean first difference also yielded a positive association (1.86%; 95%CI: 1.09-2.64%). We observed smaller- or similar-in-magnitude positive associations for previously-used metrics.

DISCUSSION

First, short-term hour-to-hour temperature change was positively associated with MI risk. Second, all other variability metrics yielded positive associations with MI, with varying magnitude. In future research on temperature variability, researchers should define their research question, including which aspects of variability they intend to measure, and apply the appropriate metric.

ALTERNATIVE

All metrics of temperature variability, including short-term hour-to-hour temperature changes, were positively associated with MI risk, though the magnitude of effect estimates varied by metric.

The effect of global warming on mortality

There is a significant relationship between ambient temperature and mortality. In healthy individuals with no underlying co-morbid conditions, there is an efficient heat regulation system which enables the body to effectively handle thermal stress. However, in vulnerable groups, especially in elderly over the age of 65 years, infants and individuals with co-morbid cardiovascular and/or respiratory conditions, there is a deficiency in thermoregulation. When temperatures exceed a certain limit, being cold winter spells or heat waves, there is an increase in the number of deaths. In particular, it has been shown that at temperatures above 27 °C, the daily mortality rate increases more rapidly per degree rise compared to when it drops below 27 °C. This is especially of relevance with the current emergency of global warming. Besides the direct effect of temperature rises on human health, global warming will have a negative impact on primary producers and livestock, leading to malnutrition, which will in turn lead to a myriad of health related issues. This is further exacerbated by environmental pollution. Public health measures that countries should follow should include not only health-related information strategies aiming to reduce the exposure to heat for vulnerable individuals and the community, but improved urban planning and reduction in energy consumption, among many others. This will reduce the carbon footprint and help avert global warming, thus reducing mortality.

Air temperature variability and high-sensitivity C reactive protein in a general population of China

BACKGROUND

Along with global climate change, the relationship between temperature variability (TV) and cardiovascular hospitalization and deaths have been well established. However, limited studies were conducted to reveal the underlying mechanism for TV-related cardiovascular diseases.

OBJECTIVES

In the current study, a novel TV calculation, taking account for both interday and intraday TV as well as lag effects, was used to investigate the effect of short-term TV on the level of high-sensitivity C reactive protein (hs-CRP), which is a crucial preclinical predictor for cardiovascular disease (CVD).

RESULTS

Among the 11,623 Chinese population (46.0% male; mean age 49.8 years), the average hs-CRP was 1.4 mg/ L (standard deviation 1.6 mg/L). Statistical significance between TV and hs-CRP was observed for different TV exposure days (TV01-TV07) in adjusted model, with highest effect for TV06. Specifically, per 1 °C increase in TV06 led to 2.241% (95%CI: 1.552%-2.935%) increase in hs-CRP. Female, obesity and elderly population were more susceptible to TV. The largest mediator for the association of TV and hs-CRP was lipoprotein(a), accounting for 8.68%, followed by smoking status (4.78%), alcohol use (3.95%) and systolic BP (3.20%).

CONCLUSION

Short-term TV will significantly increase the level of hs-CRP, suggesting hs-CRP to be the potential biologic mechanisms underlying the cardiovascular effects of TV. And more attention should be paid to unstable weather in the global climate change context. Further developing efficient public health policies on climate change may benefit for global heath.

Impacts of urbanization on the temperature-cardiovascular mortality relationship in Beijing, China

The effect of temperature on cardiovascular disease (CVD) mortality has been extensively studied. However, it remains largely unknown over whether there is any difference between urban and suburban areas within the same city and how urbanization modifies the relationship between temperature and CVD mortality. In order to examine whether the association between temperature and CVD mortality existed difference in urban and suburban areas, and how urbanization modified this association, we used a distributed lag nonlinear model and a generalized additive model to investigate temperature-related CVD mortality in urban and suburban areas in Beijing, China, from 2006 to 2011. The age, gender, and educational attainment of the population were stratified to explore the modifying effect. We observed that the impacts of heat and cold temperature on CVD mortality were higher in suburban areas than in urban areas. In addition, the elderly and illiterate subjects in suburban areas were more vulnerable to both heat and cold than their counterparts in urban areas. Moreover, higher urbanization levels were significantly associated with districts having lower the excess risks for temperature- CVD mortality. Our findings provide evidence that populations in suburban Beijing have higher risk of temperature-related CVD mortality than those in urban areas. Therefore, greater attention should be paid to vulnerable groups in suburban areas to reduce temperature-related health burden.

Socioeconomic inequality in vulnerability to all-cause and cause-specific hospitalisation associated with temperature variability: a time-series study in 1814 Brazilian cities

BACKGROUND

Exposure to temperature variability has been associated with increased risk of mortality and morbidity. We aimed to evaluate whether the association between short-term temperature variability and hospitalisation was affected by local socioeconomic level in Brazil.

METHODS

In this time-series study, we collected city-level socioeconomic data, and daily hospitalisation and weather data from 1814 Brazilian cities between Jan 1, 2000, and Dec 31, 2015. All-cause and cause-specific hospitalisation data was from the Hospital Information System of the Unified Health System in Brazil. City-specific daily minimum and maximum temperatures came from a 0·25° × 0·25° Brazilian meteorological dataset. We represented city-specific socioeconomic level using literacy rate, urbanisation rate, average monthly household income per capita (using the 2000 and 2010 Brazilian census), and GDP per capita (using statistics from the Brazilian Institute of Geography and Statistics for 2000-15), and cities were categorised according to the 2015 World Bank standard. We used quasi-Poisson regression to do time-series analyses and obtain city-specific associations between temperature variability and hospitalisation. We pooled city-specific estimates according to different socioeconomic quartiles or levels using random-effect meta-analyses. Meta-regressions adjusting for demographic and climatic characteristics were used to evaluate the modification effect of city-level socioeconomic indicators on the association between temperature variability and hospitalisation.

FINDINGS

We included a total of 147 959 243 hospitalisations (59·0% female) during the study period. Overall, we estimated that the hospitalisation risk due to every 1°C increase in the temperature variability in the current and previous day (TV_0-1) increased by 0·52% (95% CI 0·50-0·55). For lower-middle-income cities, this risk was 0·63% (95% CI 0·58-0·69), for upper-middle-income cities it was 0·50% (0·47-0·53), and for high-income cities it was 0·39% (0·33-0·46). The socioeconomic inequality in vulnerability to TV_0-1 was especially evident for people aged 0-19 years (effect estimate 1·21% [1·11-1·31] for lower-middle income vs 0·52% [0·41-0·63] for high income) and people aged 60 years or older (0·60% [0·50-0·70] vs 0·43% [0·31-0·56]), and for hospitalisation due to infectious diseases (1·62% [1·46-1·78] vs 0·56% [0·30-0·82]), respiratory diseases (1·32% [1·20-1·44] vs 0·55% [0·37-0·74]), and endocrine diseases (1·21% [0·99-1·43] vs 0·32% [0·02-0·62]).

INTERPRETATION

People living in less developed cities in Brazil were more vulnerable to hospitalisation related to temperature variability. This disparity could exacerbate existing health and socioeconomic inequalities in Brazil, and it suggests that more attention should be paid to less developed areas to mitigate the adverse health effects of short-term temperature fluctuations.

FUNDING

None.

Short-term effect of ambient temperature change on the risk of tuberculosis admissions: Assessments of two exposure metrics

BACKGROUND

Although the effects of seasonal variations and ambient temperature on the incidence of tuberculosis (TB) have been well documented, it is still unknown whether ambient temperature change is an independent risk factor for TB. The aim of this study was to assess the association between ambient temperature change and the risk of TB admissions.

METHOD

A distributed lag non-linear model (DLNM) combined with Poisson generalized linear regression model was performed to assess the association between ambient temperature change and the risk of TB admissions from 2014 to 2018 in Hefei, China. Two temperature change metrics including temperature change between neighboring days (TCN) and diurnal temperature range (DTR) were used to assess the effects of temperature change exposure. Subgroup analyses were performed by gender, age and season. Besides, the attributable risk was calculated to evaluated the public health significance.

RESULTS

The overall exposure-response curves suggested that there were statistically significant associations between two temperature change metrics and the risk of TB admissions. The maximum lag-specific relative risk (RR) of TB admissions was 1.088 (95%CI: 1.012-1.171, lag 4 day) for exposing to large temperature drop (TCN= -4 °C) in winter. Besides, the overall cumulative risk of TB admissions increased continuously and peaked at a lag of 7 days (RR=1.350, 95%CI: 1.120-1.628). Subgroup analysis suggested that exposure to large temperature drop had an adverse effect on TB admissions among males, females and adults. Similarly, large level of DTR exposure (DTR=15 °C) in spring also increased the risk of TB admissions on lag 0 day (RR=1.039, 95%CI: 1.016-1.063), and the cumulative RRs peaked at a lag of 1 days (RR=1.029, 95%CI: 1.012-1.047). We also found that females and elderly people were more vulnerable to the large level of DTR exposure. Additionally, the assessment of attributable risk suggested that taking target measures for the upcoming large temperature drop (b-AF = 4.17%, 95% eCI: 1.24%, 7.22%, b-AN = 1195) may achieve great public health benefits for TB prevention.

CONCLUSION

This study suggests that ambient temperature change is associated with the risk of TB admissions. Besides, TCN may be a better predictor for the TB prevention and public health.

Temperature Variability and Hospital Admissions for Chronic Obstructive Pulmonary Disease: Analysis of Attributable Disease Burden and Vulnerable Subpopulation

Purpose

Chronic obstructive pulmonary disease (COPD) is a major cause of chronic diseases causing considerable social and economic burden globally. Despite substantial evidence on temperature-COPD association, few studies have investigated the acute effect of temperature variability (TV), a potential trigger of exacerbation of COPD disease, and it remains unknown what fraction of the disease burden of COPD is attributable to TV.

Patients and Methods

Based on 71,070 COPD hospitalizations during 2013–2015 in Guangzhou, China, we conducted a time-series analysis using quasi-Poisson regression to assess the association between TV and hospital admission for COPD after adjusting for daily mean temperature. Short-term TV was captured by the standard deviation of hourly or daily temperatures across various exposure days. We also provided the fraction (total number) of COPD attributable to TV. Stratified analyses by admission route, sex, age, occupation, marital status and season were performed to identify vulnerable subpopulations.

Results

We found a linear relationship between TV and COPD hospitalization, with a 1°C increase in hourly TV and daily TV associated with 4.3% (95%CI: 2.2–6.4) and 4.0% (2.3–5.8) increases in COPD, respectively. The greater relative risks of TV identified males, people aged 0–64 years, blue collar, and divorced/widowed people as vulnerable population. There were 12.0% (8500 cases) of COPD hospitalization attributable to hourly TV during the study period. Daily TV produced similar estimates of relative effects (relative risk) but grater estimates of absolute effects (attributable fraction) than hourly TV.

Conclusion

We concluded that TV was an independent risk factor of COPD morbidity, especially among the susceptible subgroups. These findings would be helpful to guide the development of targeted public intervention.

The effect of diurnal temperature range on blood pressure among 46,609 people in Northwestern China

BACKGROUND

A large number of studies have found a positive association between diurnal temperature range (DTR) and cardiovascular diseases (CVDs) incidence and mortality. Few studies regarding the effects of DTR on blood pressure (BP) are available.

OBJECTIVE

To investigate the effects of DTR on BP in Jinchang, northwestern China.

METHODS

Based on a prospective cohort research, a total of 46,609 baseline survey data were collected from 2011 to 2015. The meteorological observation data and environmental monitoring data were collected in the same period. The generalized additive model (GAM) was used to estimate the relationship between DTR and BP after adjusting for confounding variables.

RESULTS

Our study found that there was a positive linear correlation between DTR and systolic blood pressure (SBP) and plus pressure (PP), and a negative linear correlation between DTR and diastolic blood pressure (DBP). With a 1 °C increase of DTR, SBP and PP increased 0.058 mmHg (95%CI: 0.018-0.097) and 0.114 mmHg (95%CI: 0.059-0.168) respectively, and DBP decreased 0.039 mmHg (95%CI:-0.065 ~ -0.014). There was a significant interaction between season and DTR on SBP and PP. DTR had the greatest impact on SBP and PP in hot season. The association between DTR and BP varied significantly by education level.

CONCLUSION

There was a significant association between DTR and BP in Jinchang, an area with large temperature change at high altitudes in northwestern China. These results provide new evidence that DTR is an independent risk factor for BP changes among general population. Therefore, effective control and management of BP in the face of temperature changes can help prevent CVDs.

Fine-scale mapping of an evidence-based heat health risk index for high-density cities: Hong Kong as a case study

The most recent extreme heat recorded in Europe re-alerts the world to the threat of heat stress. Future extreme heat events are reported to be more frequent, long-lasting, and intense. The intense exposure to hot temperatures can cause an excess of heat-related deaths, leading to an increasing risk of heat-related health. In reducing Heat Health Risk (HHR), the use of fine-scale evidence-based mapping of heat-related health risk index (HHRI) and its underlying contributors is essential for policy-making and site-specific action plans. However, its use is still considered to be at an early stage, especially in high-density cities like Hong Kong. This study conducted a spatially explicit assessment of HHR in Hong Kong and constructed a HHRI based on indicators categorized through Principle Component Analysis (PCA) into four meaningful components representing social/language, social isolation, socioeconomic, and urbanization/environmental risks. The applicability of the index was validated against heat-related mortality data at the community level. The community-level maps of HHRI and its subcomponents revealed that portions of Kowloon Peninsula had always suffered exceptionally high HHR ten years ago and after, but the hot spots and problematic communities experienced displacement and the dominant underlying factors of their HHR also varied. Results also showed that HHRI correlated fairly well with the heat-related deaths ratio (R² = 0.60) at the community level for most of Hong Kong (62.33% of all communities that contain 81.69% of total population). Our analysis results helped generate an evidence-based index to assess HHR in high-density cities like Hong Kong and provided fine-scale maps of the index and its subcomponents, with the aim of benefiting site-specific policy making and optimizing the existing action plans.

Examining the diurnal temperature range enigma: why is human health related to the daily change in temperature?

An increasing number of epidemiological studies are finding statistical evidence that diurnal temperature range (DTR) is positively correlated to human morbidity and mortality despite the lack of clear clinical understanding. We examine a 14-year daily time series of emergency department (ED) admissions to the University of Virginia Medical Center in Charlottesville, Virginia, relative to long-term climate records from the Charlottesville/Albemarle County Airport weather station and the Spatial Synoptic Classification. DTR has a consistent strong positive correlation (r ~ 0.5) with maximum temperature in all months but only a weak, negative correlation (r ~- 0.1) with minimum temperature except in late summer (r ~- 0.4). Warm season DTR is highest on dry air mass days with low dew point temperatures. Cool season DTR is unrelated to morning temperature. Using a distributed lag non-linear model with an emphasis on DTR and its seasonal variation, after stratifying the models by season, we find that ED visits are linked to extreme cold events (cold days and nights) and high DTR in the cold season. In the warm season, ED visits are also linked to high DTR, but these are cool, dry, and pleasant days. The existing confusion regarding interpretation of DTR impacts on health might be rectified through a more careful analysis of the underlying physical factors that drive variations in DTR over the course of a year.

Mortality risk and burden associated with temperature variability in China, United Kingdom and United States: Comparative analysis of daily and hourly exposure metrics

BACKGROUND

Temperature variability (TV) is closely associated with climate change, but there is no unified TV definition worldwide. Two novel composite TV indexes were developed recently by calculating the standard deviations of several days' daily maximum and minimum temperatures (TV_daily), or hourly mean temperatures (TV_hourly).

OBJECTIVES

This study aimed to compare the mortality risks and burden associated with TV_daily and TV_hourly using large time-series datasets collected from multiple locations in China, United Kingdom and United States.

METHODS

We collected daily mortality and hourly temperature data through 1987 to 2012 from 63 locations in China (8 communities, 2006-2012), United Kingdom (10 regions, 1990-2012), and USA (45 cities, 1987-2000). TV-mortality associations were investigated using a three-stage analytic approach separately for China, UK, and USA. First, we applied a time-series regression for each location to derive location-specific TV-mortality curves. A second-stage meta-analysis was then performed to pool these estimated associations for each country. Finally, we calculated mortality fraction attributable to TV based on above-described location-specific and pooled estimates.

RESULTS

Our dataset totally consisted of 23, 089, 328 all-cause death cases, including 93, 750 from China, 7,573,716 from UK and 15, 421, 862 from USA, respectively. In despite of a relatively wide uncertainty in China, approximately linear relationships were consistently identified for TV_daily and TV_hourly. In the three countries, generally similar lag patterns of TV effects were consistently observed for TV_daily and TV_hourly. A 1 °C rise in TV_daily and TV_hourly at lag 0-7 days was associated with mortality increases of 0.93% (95% confidence interval [CI]: 0.12, 1.74) and 0.97% (0.18, 1.77) in China, 0.33% (0.15, 0.51) and 0.41% (0.21, 0.60) in UK, and 0.55% (0.41, 0.70) and 0.51% (0.35, 0.66) in USA, respectively. Larger attributable fractions were estimated using TV_daily than those using TV_hourly, with estimates at 0-10 days of 3.69% (0.51, 6.75) vs. 2.59% (0.10, 5.01) in China, 1.14% (0.54, 1.74) vs. 0.98% (0.55, 1.42) in UK, and 2.57% (1.97, 3.16) vs. 1.67% (1.15, 2.18) in USA, respectively. Our meta-regression analyses indicated higher vulnerability to TV-induced mortality risks in warmer locations.

CONCLUSIONS

Our study added multi-country evidence for increased mortality risk associated with short-term exposure to large temperature variability. Daily and hourly TV exposure metrics produced generally comparable risk effects, but the attributable mortality burden tended to be higher using TV_daily instead of TV_hourly.

The association between temperature variability and cause-specific mortality: Evidence from 47 Japanese prefectures during 1972-2015

BACKGROUND

In the context of climate change, extreme temperature events are known to be associated with increased mortality risk. However, data about the mortality risk related to temperature variability (TV) accounting for both intra- and inter-day variations in temperature are limited.

OBJECTIVES

The present study aims to quantify the associations between TV and cause-specific mortality in Japan, evaluate whether the effects of TV are modified by prefecture-level characteristics and examine the temporal trend in mortality risk of TV.

METHODS

Data on daily all-cause and 11 cause-specific mortality and meteorological variables in 47 Japanese prefectures from 1972 to 2015 were collected. TV was defined as the standard deviation of daily minimum and maximum temperatures during exposure days. A quasi-Poisson regression model combined with a distributed lag non-linear model was firstly applied to assess the prefecture-specific mortality effects of TV, adjusting for potential confounders. The pooled effects of TV at the national level were then obtained via a meta-analysis through the restricted maximum-likelihood estimation. Potential effect modification by prefecture characteristics was firstly examined using a meta-regression analysis, and the joint modification of season and humidity was then evaluated after including product terms in two-stage analyses. Finally, the temporal trend in TV effects was evaluated by a random-effect meta regression model after obtaining the prefecture-year-specific effects.

RESULTS

TV had significant adverse effects on all-cause and cause-specific mortality. The effects of TV were more detrimental to those with asthma and senility. In general, the estimates of mortality risk increased with longer exposure days. A 1 °C increase in TV at 0-7 days of exposure was associated with a 0.9% (95% confidence intervals: 0.82%-0.98%) increase in all-cause mortality. All-cause mortality risk of TV showed a decreasing trend during our study period. TV effects were larger in densely populated prefectures and on warm and humid days.

CONCLUSIONS

TV-related death is a significant issue in Japan that requires effective interventions.

Impact of summer heat on mortality and years of life lost: Application of a novel indicator of daily excess hourly heat

BACKGROUND

Previous studies have widely assessed heat-mortality relationships across global regions, while the epidemiological evidence regarding the heat effect on years of life lost (YLL) is relatively sparse. Current investigations using daily mean data cannot take hourly temperature variation into consideration and may underestimate heat effects. We developed a novel indicator, daily excess hourly heat (DEHH), to precisely evaluate the potential heat effects on mortality and YLL.

METHODS

Hourly data on temperature and daily information, including concentrations of air pollutants, relative humidity, and records of all registered deaths were obtained in Wuhan, China during the warm seasons (May-September) of 2009-2012. DEHH, developed in this study, is defined as daily total hourly temperatures that exceed a specific heat threshold. By performing time series regression analyses, we assessed the changes in daily mortality and YLL per interquartile range (IQR) increase in DEHH across different lag days.

RESULTS

The heat threshold evaluated by the Akaike Information Criterion for DEHH calculation is 30 °C (92th percentile of whole-year mean temperature distribution). Daily average DEHH was 13.9 °C, with an IQR of 19.9 °C. Linear exposure-response curves were found between DEHH and two health outcomes. Generally, heat effects lasted for 2-3 days and DEHH at lag 0-1 was most strongly associated with increased mortality and YLL. The effects were especially remarkable for stroke and ischemic heart disease mortality. Most intense effect on YLL was found in non-accidental deaths (20.11, 95% confidence interval: 8.90-31.33) at lag 0-1. More DEHH-related mortality and YLL from cardiovascular deaths were observed among males. People aged 0-74 years and males suffered more from YLL burden due to high temperatures.

CONCLUSIONS

Our study demonstrated that DEHH may be an alternative indicator to precisely measure heat effects on daily mortality and YLL. Further DEHH-based evidence from large scale investigations is needed so as to better understand heat-associated health burden and improve public response to extremely high temperatures.

Socio-geographic disparity in cardiorespiratory mortality burden attributable to ambient temperature in the United States

Compared with relative risk, attributable fraction (AF) is more informative when assessing the mortality burden due to some environmental exposures (e.g., ambient temperature). Up to date, however, available AF-based evidence linking temperature with mortality has been very sparse regionally and nationally, even for the leading mortality types such as cardiorespiratory deaths. This study aimed to quantify national and regional burden of cardiorespiratory mortality (CRM) attributable to ambient temperature in the USA, and to explore potential socioeconomic and demographic sources of spatial heterogeneity between communities. Daily CRM and weather data during 1987-2000 for 106 urban communities across the mainland of USA were acquired from the publicly available National Morbidity, Mortality and Air Pollution Study (NMMAPS). We did the data analysis using a three-stage analytic approach. We first applied quasi-Poisson regression incorporated with distributed lag nonlinear model to estimate community-specific temperature-CRM associations, then pooled these associations at the regional and national level through a multivariate meta-analysis, and finally estimated the temperature-AF of CRM and performed subgroup analyses stratified by community-level characteristics. Both low and high temperatures increased short-term CRM risk, while temperature-CRM associations varied by regions. Nationally, the fraction of cardiorespiratory deaths caused by the total non-optimum, low, and high temperatures was 7.58% (95% empirical confidence interval, 6.68-8.31%), 7.15% (6.31-7.85%), and 0.43% (0.37-0.46%), respectively. Greater temperature-AF was identified in two northern regions (i.e., Industrial Midwest and North East) and communities with lower temperature and longitude, higher latitude, and moderate humidity. Additionally, higher vulnerability appeared in locations with higher urbanization level, more aging population, less White race, and lower socioeconomic status. Ambient temperature may be responsible for a large fraction of cardiorespiratory deaths. Also, temperature-AF of CRM varied considerably by geographical and climatological factors, as well as community-level disparity in socioeconomic status.

Spatiotemporal and demographic variation in the association between temperature variability and hospitalizations in Brazil during 2000-2015: A nationwide time-series study

BACKGROUND

Extreme temperature events are known to be adversely associated with a range of health outcomes, but little is known about the effect of less extreme, but more frequent fluctuation in temperature. We examined the spatiotemporal and demographic variation in the effect of temperature variability (TV) on nationwide hospitalizations in Brazil during 2000-2015.

METHODS

Data on daily hospitalizations and weather variables were collected from 1814 cities, comprising 78.4% of Brazilian population. TV was defined as the standard deviation of daily minimum and maximum temperatures during exposure days. City-specific TV effect was estimated using a quasi-Poisson regression model, and then pooled at the national and regional level using meta-analysis. Stratified analyses were performed by sex, 10 age-groups, and 11 cause categories. Meta-regression was applied to city-year-specific estimates to examine the temporal change.

RESULTS

The estimate of TV effect peaked on 0-1 days' exposure, contributing to 3.5% [95% confidence interval (CI): 3.1-3.8%] of hospitalizations nationwide, equalling 221 (95%CI: 200-242) cases per 100,000 population annually. The effect estimate varied across 11 cause categories, which was strongest for respiratory admissions. Males, particular those 10-49 year old were more affected than females but there was no sex difference for the attributable hospitalization rate. The attributable rate for the under-fives was twice as high as for the elderly, and five times higher than in adults. The majority of the most affected cities were located in the central west and the inland of northeast. The risk of hospitalization related to TV showed a significant increase over the 16-year period at the national level.

CONCLUSIONS

In Brazil, the effect of TV on hospitalization is acute, and varies by spatial, sex, age, and cause category. Given there is no evidence regarding TV adaptation, hospitalization burden associated with TV is likely to further increase and warrants consideration when developing future public health policies in the context of climate change.