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Assessing the association between temperature frequency and mortality can provide insights into human adaptation to local ambient temperatures. We collected daily time-series data on mortality and temperature from 757 locations in 47 countries/regions during 1979-2020. We used a two-stage time series design to assess the association between temperature frequency and all-cause mortality. The results were pooled at the national, regional, and global levels. We observed a consistent decrease in the risk of mortality as the normalized frequency of temperature increases across the globe. The average increase in mortality risk comparing the 10th to 100th percentile of normalized frequency was 13.03% (95% CI: 12.17-13.91), with substantial regional differences (from 4.56% in Australia and New Zealand to 33.06% in South Europe). The highest increase in mortality was observed for high-income countries (13.58%, 95% CI: 12.56-14.61), followed by lower-middle-income countries (12.34%, 95% CI: 9.27-15.51). This study observed a declining risk of mortality associated with higher temperature frequency. Our findings suggest that populations can adapt to their local climate with frequent exposure, with the adapting ability varying geographically due to differences in climatic and socioeconomic characteristics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Background: Temperature variability (TV) is associated with increased mortality risks. However, the independent impacts of interday and intraday are still unknown., Methods: We proposed a new method to decompose TV into interday TV and intraday TV through algebra derivation. Intraday TV was defined as the weighted average standard deviation (SD) of minimum temperature and maximum temperature on each day. Interday TV was defined as the weighted SD of daily mean temperatures during the exposure period. We then performed an illustrative analysis using data on daily mortality and temperature in France in 2019-2021., Results: The novel interday and intraday TV indices were good proxies for existing indicators, inlcluding diurnal temperature range (DTR) and temperature change between neighbouring days (TCN). In the illustrative analyses, interday and intraday TVs showed differentiated mortality risks. Mortality burden related to TV was mainly explained by the intraday component, accounting for an attributable fraction (AF) of 1.81% (95% CI: 0.64%, 2.97%) of total mortality, more than twice the AF of interday TV (0.86%, 95% CI: 0.47%, 1.24%)., Conclusions: This study proposed a novel method for identifying and isolating the different components of temperature variability and offered a comprehensive way to investigate their health impacts., (© 2023. The Author(s).)

Background: Although seasonal variations in mortality have been recognized for millennia, the role of temperature remains unclear. We aimed to assess seasonal variation in mortality and to examine the contribution of temperature., Methods: We compiled daily data on all-cause, cardiovascular and respiratory mortality, temperature and indicators on location-specific characteristics from 719 locations in tropical, dry, temperate and continental climate zones. We fitted time-series regression models to estimate the amplitude of seasonal variation in mortality on a daily basis, defined as the peak-to-trough ratio (PTR) of maximum mortality estimates to minimum mortality estimates at day of year. Meta-analysis was used to summarize location-specific estimates for each climate zone. We estimated the PTR with and without temperature adjustment, with the differences representing the seasonal effect attributable to temperature. We also evaluated the effect of location-specific characteristics on the PTR across locations by using meta-regression models., Results: Seasonality estimates and responses to temperature adjustment varied across locations. The unadjusted PTR for all-cause mortality was 1.05 [95% confidence interval (CI): 1.00-1.11] in the tropical zone and 1.23 (95% CI: 1.20-1.25) in the temperate zone; adjusting for temperature reduced the estimates to 1.02 (95% CI: 0.95-1.09) and 1.10 (95% CI: 1.07-1.12), respectively. Furthermore, the unadjusted PTR was positively associated with average mean temperature., Conclusions: This study suggests that seasonality of mortality is importantly driven by temperature, most evidently in temperate/continental climate zones, and that warmer locations show stronger seasonal variations in mortality, which is related to a stronger effect of temperature., (© The Author(s) 2021; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: In 2014, the Morwell brown coal mine, located in the Latrobe Valley of South eastern Australia, caught fire covering nearby areas in plumes of smoke over a 6-week period., Aims: To investigate the association between exposure to mine fire related air pollution and the risk of mortality., Methods: Time series models were used to evaluate the risk of mortality during the first 30 days of the mine fire, when the smoke was most intense, and in the following six months. Associations were also investigated between mine fire related PM 2.5 and mortality., Results: During the 30-day mine fire period, there was an increased risk of death from injury in the most exposed town of Morwell, however no increased risk was observed for all-cause, cardiovascular or respiratory mortality. In the broader Latrobe Valley, males and residents aged 80 and above were at greatest risk of death from injury during the mine fire. In Morwell, during the six months after the mine fire there was an increased risk of all-cause mortality and death from Ischaemic Heart Disease (IHD). Males and residents aged 80 and above in the broader Latrobe Valley, were at increased risk of death from IHD six months after the fire., Conclusions: Coal mine fire exposure was associated with an increase in injury deaths during the mine fire and cardiovascular deaths in the six months after the fire. These findings assist in identifying at risk groups, and improving targeted health advice for future air pollution exposures in the community., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: Ambient temperature may contribute to seasonality of mortality; in particular, a warming climate is likely to influence the seasonality of mortality. However, few studies have investigated seasonality of mortality under a warming climate., Methods: Daily mean temperature, daily counts for all-cause, circulatory, and respiratory mortality, and annual data on prefecture-specific characteristics were collected for 47 prefectures in Japan between 1972 and 2015. A quasi-Poisson regression model was used to assess the seasonal variation of mortality with a focus on its amplitude, which was quantified as the ratio of mortality estimates between the peak and trough days (peak-to-trough ratio (PTR)). We quantified the contribution of temperature to seasonality by comparing PTR before and after temperature adjustment. Associations between annual mean temperature and annual estimates of the temperature-unadjusted PTR were examined using multilevel multivariate meta-regression models controlling for prefecture-specific characteristics., Results: The temperature-unadjusted PTRs for all-cause, circulatory, and respiratory mortality were 1.28 (95% confidence interval (CI): 1.27-1.30), 1.53 (95% CI: 1.50-1.55), and 1.46 (95% CI: 1.44-1.48), respectively; adjusting for temperature reduced these PTRs to 1.08 (95% CI: 1.08-1.10), 1.10 (95% CI: 1.08-1.11), and 1.35 (95% CI: 1.32-1.39), respectively. During the period of rising temperature (1.3 °C on average), decreases in the temperature-unadjusted PTRs were observed for all mortality causes except circulatory mortality. For each 1 °C increase in annual mean temperature, the temperature-unadjusted PTR for all-cause, circulatory, and respiratory mortality decreased by 0.98% (95% CI: 0.54-1.42), 1.39% (95% CI: 0.82-1.97), and 0.13% (95% CI: - 1.24 to 1.48), respectively., Conclusion: Seasonality of mortality is driven partly by temperature, and its amplitude may be decreasing under a warming climate.

Background: Various retrospective studies have reported on the increase of mortality risk due to higher diurnal temperature range (DTR). This study projects the effect of DTR on future mortality across 445 communities in 20 countries and regions., Methods: DTR-related mortality risk was estimated on the basis of the historical daily time-series of mortality and weather factors from Jan 1, 1985, to Dec 31, 2015, with data for 445 communities across 20 countries and regions, from the Multi-Country Multi-City Collaborative Research Network. We obtained daily projected temperature series associated with four climate change scenarios, using the four representative concentration pathways (RCPs) described by the Intergovernmental Panel on Climate Change, from the lowest to the highest emission scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5). Excess deaths attributable to the DTR during the current (1985-2015) and future (2020-99) periods were projected using daily DTR series under the four scenarios. Future excess deaths were calculated on the basis of assumptions that warmer long-term average temperatures affect or do not affect the DTR-related mortality risk., Findings: The time-series analyses results showed that DTR was associated with excess mortality. Under the unmitigated climate change scenario (RCP 8.5), the future average DTR is projected to increase in most countries and regions (by -0·4 to 1·6°C), particularly in the USA, south-central Europe, Mexico, and South Africa. The excess deaths currently attributable to DTR were estimated to be 0·2-7·4%. Furthermore, the DTR-related mortality risk increased as the long-term average temperature increased; in the linear mixed model with the assumption of an interactive effect with long-term average temperature, we estimated 0·05% additional DTR mortality risk per 1°C increase in average temperature. Based on the interaction with long-term average temperature, the DTR-related excess deaths are projected to increase in all countries or regions by 1·4-10·3% in 2090-99., Interpretation: This study suggests that globally, DTR-related excess mortality might increase under climate change, and this increasing pattern is likely to vary between countries and regions. Considering climatic changes, our findings could contribute to public health interventions aimed at reducing the impact of DTR on human health., Funding: Korea Ministry of Environment., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: To date, few studies have investigated the causal relationship between mortality and long-term exposure to a low level of fine particulate matter (PM2.5) concentrations., Methods and Findings: We studied 242,320 registered deaths in Queensland between January 1, 1998, and December 31, 2013, with satellite-retrieved annual average PM2.5 concentrations to each postcode. A variant of difference-in-differences (DID) approach was used to investigate the association of long-term PM2.5 exposure with total mortality and cause-specific (cardiovascular, respiratory, and non-accidental) mortality. We observed 217,510 non-accidental deaths, 133,661 cardiovascular deaths, and 30,748 respiratory deaths in Queensland during the study period. The annual average PM2.5 concentrations ranged from 1.6 to 9.0 μg/m3, which were well below the current World Health Organization (WHO) annual standard (10 μg/m3). Long-term exposure to PM2.5 was associated with increased total mortality and cause-specific mortality. For each 1 μg/m3 increase in annual PM2.5, we found a 2.02% (95% CI 1.41%-2.63%; p < 0.01) increase in total mortality. Higher effect estimates were observed in Brisbane than those in Queensland for all types of mortality. A major limitation of our study is that the DID design is under the assumption that no predictors other than seasonal temperature exhibit different spatial-temporal variations in relation to PM2.5 exposure. However, if this assumption is violated (e.g., socioeconomic status [SES] and outdoor physical activities), the DID design is still subject to confounding., Conclusions: Long-term exposure to PM2.5 was associated with total, non-accidental, cardiovascular, and respiratory mortality in Queensland, Australia, where PM2.5 levels were measured well below the WHO air quality standard., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: YG is an Academic Editor and a member of editorial board of PLOS Medicine.

Background: Increased atmospheric particulate matter (PM) concentrations are commonly observed during desert dust days in Iran, but there is still no evidence of their effects on human health. We aimed to evaluate the association between daily mortality and exposure to PM 10 and PM 2.5 during dust and non-dust days in Tehran and Ahvaz, two major Middle Eastern cities with different sources, intensity, and frequency of desert dust days., Methods: We identified desert dust days based on exceeding a daily PM 10 concentration threshold of 150 µg/m 3 between 2014 and 2017, checking for low PM 2.5 /PM 10 ratio typical of dust days. We used a time-stratified case-crossover design to estimate the short-term effects of PM 10 and PM 2.5 concentrations on daily mortality during dust and non-dust days. Data was analyzed using conditional Poisson regression models., Results: Higher concentrations of PM and frequency of desert dust days were observed in Ahvaz rather than Tehran. In Ahvaz, the effect of PM 10 at lag 0 was much higher during dust days, an increment of 10 μg/m 3 was associated with 3.28% (95%CI = [2.42, 4.15]) increase of daily mortality, than non-dust days, 1.03% (95%CI = [-0.02, 2.08]), while in Tehran, was slightly higher during non-dust days, 0.72% (95%CI = [0.23, 1.23]), than in dust days, 0.49% (95%CI = [-0.22, 1.20]). No statistically significant associations were observed between PM 2.5 and daily mortality in Ahvaz, while in Teheran the effect of PM 2.5 increased significantly during non-dust days at lag 2, 1.89% (95%CI = [0.83, 1.2.95] and lag 3, 1.88% (95%CI = [0.83, 1.2.95])., Conclusion: The study provides evidence that exposure to PM during Middle East dust days is an important risk factor to human health in arid regions and areas affected by desert dust events., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: There is strong experimental evidence that physiologic stress from high temperatures is greater if humidity is higher. However, heat indices developed to allow for this have not consistently predicted mortality better than dry-bulb temperature., Objectives: We aimed to clarify the potential contribution of humidity an addition to temperature in predicting daily mortality in summer by using a large multicountry dataset., Methods: In 445 cities in 24 countries, we fit a time-series regression model for summer mortality with a distributed lag nonlinear model (DLNM) for temperature (up to lag 3) and supplemented this with a range of terms for relative humidity (RH) and its interaction with temperature. City-specific associations were summarized using meta-analytic techniques., Results: Adding a linear term for RH to the temperature term improved fit slightly, with an increase of 23% in RH (the 99th percentile anomaly) associated with a 1.1% [95% confidence interval (CI): 0.8, 1.3] decrease in mortality. Allowing curvature in the RH term or adding terms for interaction of RH with temperature did not improve the model fit. The humidity-related decreased risk was made up of a positive coefficient at lag 0 outweighed by negative coefficients at lags of 1-3 d. Key results were broadly robust to small model changes and replacing RH with absolute measures of humidity. Replacing temperature with apparent temperature, a metric combining humidity and temperature, reduced goodness of fit slightly., Discussion: The absence of a positive association of humidity with mortality in summer in this large multinational study is counter to expectations from physiologic studies, though consistent with previous epidemiologic studies finding little evidence for improved prediction by heat indices. The result that there was a small negative average association of humidity with mortality should be interpreted cautiously; the lag structure has unclear interpretation and suggests the need for future work to clarify. https://doi.org/10.1289/EHP5430.

Background: The systematic evaluation of the results of time-series studies of air pollution is challenged by differences in model specification and publication bias., Methods: We evaluated the associations of inhalable particulate matter (PM) with an aerodynamic diameter of 10 μm or less (PM 10 ) and fine PM with an aerodynamic diameter of 2.5 μm or less (PM 2.5 ) with daily all-cause, cardiovascular, and respiratory mortality across multiple countries or regions. Daily data on mortality and air pollution were collected from 652 cities in 24 countries or regions. We used overdispersed generalized additive models with random-effects meta-analysis to investigate the associations. Two-pollutant models were fitted to test the robustness of the associations. Concentration-response curves from each city were pooled to allow global estimates to be derived., Results: On average, an increase of 10 μg per cubic meter in the 2-day moving average of PM 10 concentration, which represents the average over the current and previous day, was associated with increases of 0.44% (95% confidence interval [CI], 0.39 to 0.50) in daily all-cause mortality, 0.36% (95% CI, 0.30 to 0.43) in daily cardiovascular mortality, and 0.47% (95% CI, 0.35 to 0.58) in daily respiratory mortality. The corresponding increases in daily mortality for the same change in PM 2.5 concentration were 0.68% (95% CI, 0.59 to 0.77), 0.55% (95% CI, 0.45 to 0.66), and 0.74% (95% CI, 0.53 to 0.95). These associations remained significant after adjustment for gaseous pollutants. Associations were stronger in locations with lower annual mean PM concentrations and higher annual mean temperatures. The pooled concentration-response curves showed a consistent increase in daily mortality with increasing PM concentration, with steeper slopes at lower PM concentrations., Conclusions: Our data show independent associations between short-term exposure to PM 10 and PM 2.5 and daily all-cause, cardiovascular, and respiratory mortality in more than 600 cities across the globe. These data reinforce the evidence of a link between mortality and PM concentration established in regional and local studies. (Funded by the National Natural Science Foundation of China and others.)., (Copyright © 2019 Massachusetts Medical Society.)

Background: The health burden associated with temperature is expected to increase due to a warming climate. Populations living in cities are likely to be particularly at risk, but the role of urban characteristics in modifying the direct effects of temperature on health is still unclear. In this contribution, we used a multi-country dataset to study effect modification of temperature-mortality relationships by a range of city-specific indicators., Methods: We collected ambient temperature and mortality daily time-series data for 340 cities in 22 countries, in periods between 1985 and 2014. Standardized measures of demographic, socio-economic, infrastructural and environmental indicators were derived from the Organisation for Economic Co-operation and Development (OECD) Regional and Metropolitan Database. We used distributed lag non-linear and multivariate meta-regression models to estimate fractions of mortality attributable to heat and cold (AF%) in each city, and to evaluate the effect modification of each indicator across cities., Results: Heat- and cold-related deaths amounted to 0.54% (95% confidence interval: 0.49 to 0.58%) and 6.05% (5.59 to 6.36%) of total deaths, respectively. Several city indicators modify the effect of heat, with a higher mortality impact associated with increases in population density, fine particles (PM2.5), gross domestic product (GDP) and Gini index (a measure of income inequality), whereas higher levels of green spaces were linked with a decreased effect of heat., Conclusions: This represents the largest study to date assessing the effect modification of temperature-mortality relationships. Evidence from this study can inform public-health interventions and urban planning under various climate-change and urban-development scenarios., (© The Author(s) 2019; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Epidemiological studies show that long-term exposure to ambient air pollution reduces life expectancy. Most studies have been in environments with relatively high concentrations such as North America, Europe and Asia. Associations at the lower end of the concentration-response function are not well defined., Objectives: We assessed associations between all-cause mortality and exposure to annual average particulate matter <2.5 μm (PM 2.5 ) and nitrogen dioxide (NO 2 ) in Sydney, Australia, where concentrations are relatively low., Methods: The '45 and Up Study' comprises a prospective longitudinal cohort from the state of New South Wales, Australia with 266,969 participants linked to death registry data. We analyzed data for the participants who resided in Sydney at baseline questionnaire (n = 75,268). Exposures to long-term pollution were estimated using annual averages from a chemical transport model (PM 2.5 ), and a satellite-based land-use regression model (NO 2 ). Socio-demographic information was extracted from the baseline questionnaire. Cox proportional hazard models were applied to estimate associations, while adjusting for covariates., Results: In our cohort mean annual PM 2.5 was 4.5 μg/m 3 and mean NO 2 was 17.8 μg/m 3 . The mortality rate was 4.4% over the 7 years of follow up. Models that adjusted for individual-level and area-level risk factors resulted in a detrimental non statistically significant hazard ratio (HR) of 1.05 (95% CI: 0.98-1.12) per 1 μg/m 3 increase in PM 2.5 , and 1.03 (95% CI: 0.98-1.07) per 5 μg/m 3 increase in NO 2 ., Conclusions: We found evidence that low-level air pollution exposure was associated with increased risk of mortality in this cohort of adults aged 45 years and over, even at the relatively low concentrations seen in Sydney. However, a clear determination of the association with mortality is difficult because the results were sensitive to some covariates. Our findings are supportive of emerging evidence that exposure to low levels of air pollution reduces life expectancy., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Studies worldwide have estimated the number of deaths attributable to long-term exposure to fine airborne particles (PM 2.5 ), but limited information is available on short-term exposure, particularly in China. In addition, most existing studies have assumed that short-term PM 2.5 -mortality associations were linear. For this reason, the use of linear exposure-response functions for calculating disease burden of short-term exposure to PM 2.5 in China may not be appropriate. There is an urgent need for a comprehensive, evidence-based assessment of the disease burden related to short-term PM 2.5 exposure in China. Here, we explored the non-linear association between short-term PM 2.5 exposure and all-cause mortality in 104 counties in China; estimated county-specific mortality burdens attributable to short-term PM 2.5 exposure for all counties in the country and analyzed spatial characteristics of the mortality burden due to short-term PM 2.5 exposure in China. The pooled PM 2.5 -mortality association was non-linear, with a reversed J-shape. We found an approximately linear increased risk of mortality from 0 to 62 μg/m 3 and decreased risk from 62 to 250 μg/m 3 . We estimated a total of 169,862 additional deaths from short-term PM 2.5 exposure throughout China in 2015. Models using linear exposure-response functions for the PM 2.5 -mortality association estimated 32,186 deaths attributable to PM 2.5 exposure, which is 5.3 times lower than estimates from the non-linear effect model. Short-term PM 2.5 exposure contributed greatly to the death burden in China, approximately one seventh of the estimates from the chronic effect. It is essential and crucial to incorporate short-term PM 2.5 -related mortality estimations when considering the disease burden attributable to PM 2.5 in developing countries such as China. Traditional linear effect models likely underestimated the mortality burden due to short-term exposure to PM 2.5 ., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Epidemiological studies increasingly provide evidence about the adverse health effects of temperature variability (TV), which reflects short-term intra- and inter-day temperature change. However, calculation of TV only considers the temporal variability and lacks spatial variability. This study intends to investigate whether the lack of spatial variability in TV calculations has biased the health effect estimates. We collected daily data from the fine-gridded hourly temperatures and more than 2 million all-cause mortality counts in Zhejiang province in China from 2009 to 2015. A spatiotemporal TV index was developed by calculating the standard deviation of the hourly temperatures based on records from multiple sites. This new index could be compared to the two typical temporal TV indices that are calculated based on the hourly temperatures from one-site and area-average records. The three types of TV are compared using a three-stage analytical approach: district-specific time series Poisson regression, meta-analysis, and calculation of attributable mortality fraction. We observe that both spatiotemporal and temporal TVs produce very similar TV-mortality associations, attributable mortality fractions, and model fits at the district level. For example, the mortality increase associated for every increase of 1 °C during 0-7 exposure days is 1.53% (95% CI: 1.31, 1.73) in spatiotemporal TV, whereas it is 1.48% (95% CI: 1.27, 1.68) and 1.45% (95% CI: 1.24, 1.67) in the one-site and area-average temporal TV, respectively. Thus, time series models using temporal TV index are equally good at estimating the associations between TV and mortality as spatiotemporal TV at the district level in population-based epidemiological studies in China. Epidemiological studies using temperature from one site or the averages of multiple sites in TV calculation will not bias the effect estimates of TV. Our study could provide an important guidance method for future TV-related research in China and even in other countries., (Copyright © 2018. Published by Elsevier Inc.)

Background: Temperature-related mortality risks have mostly been studied in urban areas, with limited evidence for urban-rural differences in the temperature impacts on health outcomes., Objectives: We investigated whether temperature-mortality relationships vary between urban and rural counties in China., Methods: We collected daily data on 1 km gridded temperature and mortality in 89 counties of Zhejiang Province, China, for 2009 and 2015. We first performed a two-stage analysis to estimate the temperature effects on mortality in urban and rural counties. Second, we performed meta-regression to investigate the modifying effect of the urbanization level. Stratified analyses were performed by all-cause, nonaccidental (stratified by age and sex), cardiopulmonary, cardiovascular, and respiratory mortality. We also calculated the fraction of mortality and number of deaths attributable to nonoptimum temperatures associated with both cold and heat components. The potential sources of the urban-rural differences were explored using meta-regression with county-level characteristics., Results: Increased mortality risks were associated with low and high temperatures in both rural and urban areas, but rural counties had higher relative risks (RRs), attributable fractions of mortality, and attributable death counts than urban counties. The urban-rural disparity was apparent for cold (first percentile relative to minimum mortality temperature), with an RR of 1.47 [95% confidence interval (CI): 1.32, 1.62] associated with all-cause mortality for urban counties, and 1.98 (95% CI: 1.87, 2.10) for rural counties. Among the potential sources of the urban-rural disparity are age structure, education, GDP, health care services, air conditioners, and occupation types., Conclusions: Rural residents are more sensitive to both cold and hot temperatures than urban residents in Zhejiang Province, China, particularly the elderly. The findings suggest past studies using exposure-response functions derived from urban areas may underestimate the mortality burden for the population as a whole. The public health agencies aimed at controlling temperature-related mortality should develop area-specific strategies, such as to reduce the urban-rural gaps in access to health care and awareness of risk prevention. Future projections on climate health impacts should consider the urban-rural disparity in mortality risks. https://doi.org/10.1289/EHP3556.

Few data are available on the health impacts of heatwaves in China, and in particular, the heatwave definition and vulnerable populations remain to be identified. We collected data on daily maximum temperature and mortality from 31 Chinese capital cities during 2007-2013. A Poisson regression model allowing for over-dispersion was applied to estimate the short-term effects of heatwaves on mortality in hot season (May-September). 15 heatwave definitions combining five heat thresholds (90.0th, 92.5th, 95th, 97.5th and 99th percentiles of daily maximum temperature) and three durations (≥2, ≥3 and ≥4 days) were compared. The pooled effects were then computed using random effect meta-analysis based on the residual maximum likelihood estimation. Effect modification of heatwave-mortality association by individual-level characteristics was tested using a stratified analysis. Potential effect modification by city-level characteristics was examined by meta-regression analysis. Totally, 259 million permanent residents were covered and 4,481,090 non-accidental deaths occurred during the study period. Generally, the magnitude of heatwave impacts increased by intensities and durations of the heatwaves. Heatwave definition using daily maximum temperature ≥ 92.5th percentile with duration ≥3 days produced the best model fit. The pooled relative risks of heatwaves on non-accidental mortality at lag 0, lag 0-2 and lag 0-10 days were 1.06 (95%CI: 1.03-1.09), 1.09 (1.05-1.13) and 1.10 (1.05-1.15), respectively. Compared with non-accidental mortality, higher effect estimates of heatwaves were observed among deaths from ischemic heart diseases, stroke and respiratory diseases, although the differences were not statistically significant. Females, those ≥75 years old and the illiterates were more vulnerable to heatwaves. Cities with higher concentrations of PM 2.5 , higher latitudes, and lower numbers of hospital beds per 10,000 populations had higher mortality risks during heatwaves. These findings may have important implications for developing heat alert systems and early response actions on protecting the vulnerable populations from adverse health effects of heatwave in China., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: Temporal variation of temperature-health associations depends on the combination of two pathways: pure adaptation to increasingly warmer temperatures due to climate change, and other attenuation mechanisms due to non-climate factors such as infrastructural changes and improved health care. Disentangling these pathways is critical for assessing climate change impacts and for planning public health and climate policies. We present evidence on this topic by assessing temporal trends in cold- and heat-attributable mortality risks in a multi-country investigation., Methods: Trends in country-specific attributable mortality fractions (AFs) for cold and heat (defined as below/above minimum mortality temperature, respectively) in 305 locations within 10 countries (1985-2012) were estimated using a two-stage time-series design with time-varying distributed lag non-linear models. To separate the contribution of pure adaptation to increasing temperatures and active changes in susceptibility (non-climate driven mechanisms) to heat and cold, we compared observed yearly-AFs with those predicted in two counterfactual scenarios: trends driven by either (1) changes in exposure-response function (assuming a constant temperature distribution), (2) or changes in temperature distribution (assuming constant exposure-response relationships). This comparison provides insights about the potential mechanisms and pace of adaptation in each population., Results: Heat-related AFs decreased in all countries (ranging from 0.45-1.66% to 0.15-0.93%, in the first and last 5-year periods, respectively) except in Australia, Ireland and UK. Different patterns were found for cold (where AFs ranged from 5.57-15.43% to 2.16-8.91%), showing either decreasing (Brazil, Japan, Spain, Australia and Ireland), increasing (USA), or stable trends (Canada, South Korea and UK). Heat-AF trends were mostly driven by changes in exposure-response associations due to modified susceptibility to temperature, whereas no clear patterns were observed for cold., Conclusions: Our findings suggest a decrease in heat-mortality impacts over the past decades, well beyond those expected from a pure adaptation to changes in temperature due to the observed warming. This indicates that there is scope for the development of public health strategies to mitigate heat-related climate change impacts. In contrast, no clear conclusions were found for cold. Further investigations should focus on identification of factors defining these changes in susceptibility., (Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: In many places, daily mortality has been shown to increase after days with particularly high or low temperatures, but such daily time-series studies cannot identify whether such increases reflect substantial life shortening or short-term displacement of deaths (harvesting)., Objectives: To clarify this issue, we estimated the association between annual mortality and annual summaries of heat and cold in 278 locations from 12 countries., Methods: Indices of annual heat and cold were used as predictors in regressions of annual mortality in each location, allowing for trends over time and clustering of annual count anomalies by country and pooling estimates using meta-regression. We used two indices of annual heat and cold based on preliminary standard daily analyses: a ) mean annual degrees above/below minimum mortality temperature (MMT), and b ) estimated fractions of deaths attributed to heat and cold. The first index was simpler and matched previous related research; the second was added because it allowed the interpretation that coefficients equal to 0 and 1 are consistent with none (0) or all (1) of the deaths attributable in daily analyses being displaced by at least 1 y., Results: On average, regression coefficients of annual mortality on heat and cold mean degrees were 1.7% [95% confidence interval (CI): 0.3, 3.1] and 1.1% (95% CI: 0.6, 1.6) per degree, respectively, and daily attributable fractions were 0.8 (95% CI: 0.2, 1.3) and 1.1 (95% CI: 0.9, 1.4). The proximity of the latter coefficients to 1.0 provides evidence that most deaths found attributable to heat and cold in daily analyses were brought forward by at least 1 y. Estimates were broadly robust to alternative model assumptions., Conclusions: These results provide strong evidence that most deaths associated in daily analyses with heat and cold are displaced by at least 1 y. https://doi.org/10.1289/EHP1756.

Background: Few studies have examined variation in the associations between heat waves and mortality in an international context., Objectives: We aimed to systematically examine the impacts of heat waves on mortality with lag effects internationally., Methods: We collected daily data of temperature and mortality from 400 communities in 18 countries/regions and defined 12 types of heat waves by combining community-specific daily mean temperature ≥90th, 92.5th, 95th, and 97.5th percentiles of temperature with duration ≥2, 3, and 4 d. We used time-series analyses to estimate the community-specific heat wave-mortality relation over lags of 0-10 d. Then, we applied meta-analysis to pool heat wave effects at the country level for cumulative and lag effects for each type of heat wave definition., Results: Heat waves of all definitions had significant cumulative associations with mortality in all countries, but varied by community. The higher the temperature threshold used to define heat waves, the higher heat wave associations on mortality. However, heat wave duration did not modify the impacts. The association between heat waves and mortality appeared acutely and lasted for 3 and 4 d. Heat waves had higher associations with mortality in moderate cold and moderate hot areas than cold and hot areas. There were no added effects of heat waves on mortality in all countries/regions, except for Brazil, Moldova, and Taiwan. Heat waves defined by daily mean and maximum temperatures produced similar heat wave-mortality associations, but not daily minimum temperature., Conclusions: Results indicate that high temperatures create a substantial health burden, and effects of high temperatures over consecutive days are similar to what would be experienced if high temperature days occurred independently. People living in moderate cold and moderate hot areas are more sensitive to heat waves than those living in cold and hot areas. Daily mean and maximum temperatures had similar ability to define heat waves rather than minimum temperature. https://doi.org/10.1289/EHP1026.

Background: Heat and heat waves have been linked to the increased risk of deaths, hospital admissions, and emergency visits., Objectives: This study presents a spatiotemporal analysis of heat and heat wave effects on elderly mortality (≥65years) in Texas., Methods: We compiled a six-year daily weather and mortality counts dataset from 254 counties in Texas during 2006-2011. Heat index (HI), a combination of temperature and relative humidity, was used as the exposure metric in this study. Associations between daily all-cause elderly mortality and daily maximum HI and heat waves (≥2days of county-specific daily maximum HI>95th percentiles) were examined using a quasi-Poisson regression. A Markov random field smoother was included in the model to account for spatial autocorrelations and spatial heterogeneity. The model also controlled for long-term trend and seasonality in mortality, and the effects of day of the week., Discussion: On average, the lag effect of heat on elderly mortality risk lasted up to 10days, and the cumulative heat effects started to increase rapidly when daily maximum HI exceeded the 90th percentile in Texas. Elderly living in Northwest Texas and parts of West Texas were at greater risk of elderly mortality attributable to heat waves, and the highest relative risk for elderly mortality occurred in El Paso County (4.70, 95% Confidence Interval=4.33, 5.10)., Conclusions: Our study indicates strong geographical variations of heat wave effects on elderly mortality risk in Texas., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Few studies have examined the variation in mortality risk associated with heat during the summer. Here, we apply flexible statistical models to investigate the issue by using a large multicountry data set. We collected daily time-series data of temperature and mortality from 305 locations in 9 countries, in the period 1985-2012. We first estimated the heat-mortality relationship in each location with time-varying distributed lag non-linear models, using a bivariate spline to model the exposure-lag-response over lag 0-10. Estimates were then pooled by country through multivariate meta-analysis. Results provide strong evidence of a reduction in risk over the season. Relative risks for the 99th percentile versus the minimum mortality temperature were in the range of 1.15-2.03 in early summer. In late summer, the excess was substantially reduced or abated, with relative risks in the range of 0.97-1.41 and indications of wider comfort ranges and higher minimum mortality temperatures. The attenuation is mainly due to shorter lag periods in late summer. In conclusion, this multicountry analysis suggests a reduction of heat-related mortality risk over the summer, which can be attributed to several factors, such as true acclimatization, adaptive behaviors, or harvesting effects. These findings may have implications on public health policies and climate change health impact projections., (© The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

We estimated net annual temperature-related mortality in Brisbane, Sydney and Melbourne in Australia using 62 global climate model projections under three IPPC SRES CO2 emission scenarios (A2, A1B and B1). In all cities, all scenarios resulted in increases in summer temperature-related deaths for future decades, and decreases in winter temperature-related deaths. However, Brisbane and Sydney will increase the net annual temperature-related deaths in the future, while a slight decrease will happen in Melbourne. Additionally, temperature-related mortality will largely increase beyond the summer (including January, February, March, November and December) in Brisbane and Sydney, while temperature-related mortality will largely decrease beyond the winter in Melbourne. In conclusion, temperature increases for Australia are expected to result in a decreased burden of cold-related mortality and an increased burden of heat-related mortality, but the balance of these differences varied by city. In particular, the seasonal patterns in temperature-related deaths will be shifted., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

The deteriorating air quality in Chinese cities is attracting growing public concern. We conducted analyses to quantify the associations between daily changes in ambient air pollution and mortality in Nanjing, China. Daily mortality, air pollution, and meteorological data from 1 January 2009 to 31 December 2013 were collected. Over-dispersed Poisson regression models were used to evaluate the risk of daily non-accidental mortality and years of life lost (YLL) from exposure to respirable particulate matter (PM10) and gaseous pollutants (NO2, SO2). Stratified analysis was conducted to indentify the modifying effect of individual-level factors on the association between air pollutants and mortality. We found that interquartile range (IQR) increases in the two-day average of PM10, NO2 and SO2 were significantly associated with 1.6% [95% confidence interval (CI):0.7%-2.6%], 2.9% (95% CI: 1.7%-4.2%) and 2.4% (95% CI: 1.2%-3.6%) higher rates of non-accidental mortality; and related to YLL increases of 20.5 (95% CI: 6.3-34.8), 34.9 (95% CI: 16.9-52.9) and 30.3 (95% CI: 12.2-48.4) years, respectively; Associations between air pollution and mortality were more pronounced in the warm season than in the cool season. We conclude that the risks of mortality and YLL were elevated corresponding to an increase in current ambient concentrations of the air pollutants, and season may modify the effects of outdoor air pollution in Nanjing., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Background: Recent investigations have reported a decline in the heat-related mortality risk during the last decades. However, these studies are frequently based on modeling approaches that do not fully characterize the complex temperature-mortality relationship, and are limited to single cities or countries., Objectives: We assessed the temporal variation in heat-mortality associations in a multi-country data set using flexible modelling techniques., Methods: We collected data for 272 locations in Australia, Canada, Japan, South Korea, Spain, the United Kingdom, and the United States, with a total 20,203,690 deaths occurring in summer months between 1985 and 2012. The analysis was based on two-stage time-series models. The temporal variation in heat-mortality relationships was estimated in each location with time-varying distributed lag nonlinear models, expressed through an interaction between the transformed temperature variables and time. The estimates were pooled by country through multivariate meta-analysis., Results: Mortality risk due to heat appeared to decrease over time in several countries, with relative risks associated to high temperatures significantly lower in 2006 compared with 1993 in the United States, Japan, and Spain, and a nonsignificant decrease in Canada. Temporal changes are difficult to assess in Australia and South Korea due to low statistical power, and we found little evidence of variation in the United Kingdom. In the United States, the risk seems to be completely abated in 2006 for summer temperatures below their 99th percentile, but some significant excess persists for higher temperatures in all the countries., Conclusions: We estimated a statistically significant decrease in the relative risk for heat-related mortality in 2006 compared with 1993 in the majority of countries included in the analysis., Citation: Gasparrini A, Guo Y, Hashizume M, Kinney PL, Petkova EP, Lavigne E, Zanobetti A, Schwartz JD, Tobias A, Leone M, Tong S, Honda Y, Kim H, Armstrong BG. 2015. Temporal variation in heat-mortality associations: a multicountry study. Environ Health Perspect 123:1200-1207; http://dx.doi.org/10.1289/ehp.1409070.

China is suffering from severe air pollution from fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM2.5)], especially East China. But its future trends and potential health impacts remain unclear. The study objectives were to project future trends of PM2.5 and its short-term effect on mortality in East China by 2030. First, daily changes in PM2.5 concentrations between 2005 and 2030 were projected under the "current legislation" scenario (CLE) and the "maximum technically feasible reduction" scenario (MFR). Then, they were linked to six population projections, two mortality rate projections, and PM2.5-mortality associations to estimate the changes in PM2.5-related mortality in East China between 2005 and 2030. Under the CLE scenario, the annual mean PM2.5 concentration was projected to decrease by 0.62 μg/m(3) in East China, which could cause up to 124,000 additional deaths, when considering the population growth. Under the MFR scenario, the annual mean PM2.5 concentration was projected to decrease by 20.41 μg/m(3) in East China. At least 230,000 deaths could be avoided by such a large reduction in PM2.5 concentration under MFR scenario, even after accounting for the population growth. Therefore, our results suggest that reducing PM2.5 concentration substantially in East China would benefit the public health. Otherwise, it may still remain as a great health risk in the future, especially when the population keeps growing.

Background: Different locations and study periods were used in the assessment of the relationships between heatwaves and mortality. However, little is known about the comparability and consistency of the previous effect estimates in the literature. This study assessed the heatwave-mortality relationship using different study periods in the three largest Australian cities (Brisbane, Melbourne and Sydney)., Methods: Daily data on climatic variables and mortality for the three cities were obtained from relevant government agencies between 1988 and 2011. A consistent definition of heatwaves was used for these cities. Poisson generalised additive model was fitted to assess the impact of heatwaves on mortality., Results: Non-accidental and circulatory mortality significantly increased during heatwaves across the three cities even with different heatwave definitions and study periods. Using the summer data resulted in the largest increase in effect estimates compared to those using the warm season or the whole year data., Conclusion: The findings may have implications for developing standard approaches to evaluating the heatwave-mortality relationship and advancing heat health warning systems. It also provides an impetus to methodological advance for assessing climate change-related health consequences.

Background: Although studies have provided estimates of premature deaths attributable to either heat or cold in selected countries, none has so far offered a systematic assessment across the whole temperature range in populations exposed to different climates. We aimed to quantify the total mortality burden attributable to non-optimum ambient temperature, and the relative contributions from heat and cold and from moderate and extreme temperatures., Methods: We collected data for 384 locations in Australia, Brazil, Canada, China, Italy, Japan, South Korea, Spain, Sweden, Taiwan, Thailand, UK, and USA. We fitted a standard time-series Poisson model for each location, controlling for trends and day of the week. We estimated temperature-mortality associations with a distributed lag non-linear model with 21 days of lag, and then pooled them in a multivariate metaregression that included country indicators and temperature average and range. We calculated attributable deaths for heat and cold, defined as temperatures above and below the optimum temperature, which corresponded to the point of minimum mortality, and for moderate and extreme temperatures, defined using cutoffs at the 2·5th and 97·5th temperature percentiles., Findings: We analysed 74,225,200 deaths in various periods between 1985 and 2012. In total, 7·71% (95% empirical CI 7·43-7·91) of mortality was attributable to non-optimum temperature in the selected countries within the study period, with substantial differences between countries, ranging from 3·37% (3·06 to 3·63) in Thailand to 11·00% (9·29 to 12·47) in China. The temperature percentile of minimum mortality varied from roughly the 60th percentile in tropical areas to about the 80-90th percentile in temperate regions. More temperature-attributable deaths were caused by cold (7·29%, 7·02-7·49) than by heat (0·42%, 0·39-0·44). Extreme cold and hot temperatures were responsible for 0·86% (0·84-0·87) of total mortality., Interpretation: Most of the temperature-related mortality burden was attributable to the contribution of cold. The effect of days of extreme temperature was substantially less than that attributable to milder but non-optimum weather. This evidence has important implications for the planning of public-health interventions to minimise the health consequences of adverse temperatures, and for predictions of future effect in climate-change scenarios., Funding: UK Medical Research Council., (Copyright © 2015 Gasparrini et al. Open Access article distributed under the terms of CC BY. Published by Elsevier Ltd.. All rights reserved.)

It remains uncertain whether air pollution modifies the magnitude and time course of the temperature mortality association. We applied a distributed lag non-linear model (DLNM) combined with non-linear interaction terms to assess the modifying effects of particulate matter with an aerodynamic diameter of 10 mm or less (PM(10)) on the association between mean temperature and mortality in Guangzhou, China.We found that both cold and hot effects increased with the quartiles of PM(10). The elderly were more vulnerable to cold and hot effects. Men suffered more from cold-related mortality than women, with the gender difference enlarging with the quartiles of PM(10). We identified statistically significant interaction effects between PM(10) and mean temperature on mortality (except for respiratory mortality). Cold and hot effects basically appeared acutely on highly polluted days, while effects were delayed on lowly polluted days. The findings indicate the importance of reducing PM(10) emission on extremely temperature days.

Background: Studies have examined the effects of temperature on mortality in a single city, country, or region. However, less evidence is available on the variation in the associations between temperature and mortality in multiple countries, analyzed simultaneously., Methods: We obtained daily data on temperature and mortality in 306 communities from 12 countries/regions (Australia, Brazil, Thailand, China, Taiwan, Korea, Japan, Italy, Spain, United Kingdom, United States, and Canada). Two-stage analyses were used to assess the nonlinear and delayed relation between temperature and mortality. In the first stage, a Poisson regression allowing overdispersion with distributed lag nonlinear model was used to estimate the community-specific temperature-mortality relation. In the second stage, a multivariate meta-analysis was used to pool the nonlinear and delayed effects of ambient temperature at the national level, in each country., Results: The temperatures associated with the lowest mortality were around the 75th percentile of temperature in all the countries/regions, ranging from 66th (Taiwan) to 80th (UK) percentiles. The estimated effects of cold and hot temperatures on mortality varied by community and country. Meta-analysis results show that both cold and hot temperatures increased the risk of mortality in all the countries/regions. Cold effects were delayed and lasted for many days, whereas heat effects appeared quickly and did not last long., Conclusions: People have some ability to adapt to their local climate type, but both cold and hot temperatures are still associated with increased risk of mortality. Public health strategies to alleviate the impact of ambient temperatures are important, in particular in the context of climate change.

Background: Heatwaves could cause the population excess death numbers to be ranged from tens to thousands within a couple of weeks in a local area. An excess mortality due to a special event (e.g., a heatwave or an epidemic outbreak) is estimated by subtracting the mortality figure under 'normal' conditions from the historical daily mortality records. The calculation of the excess mortality is a scientific challenge because of the stochastic temporal pattern of the daily mortality data which is characterised by (a) the long-term changing mean levels (i.e., non-stationarity); (b) the non-linear temperature-mortality association. The Hilbert-Huang Transform (HHT) algorithm is a novel method originally developed for analysing the non-linear and non-stationary time series data in the field of signal processing, however, it has not been applied in public health research. This paper aimed to demonstrate the applicability and strength of the HHT algorithm in analysing health data., Methods: Special R functions were developed to implement the HHT algorithm to decompose the daily mortality time series into trend and non-trend components in terms of the underlying physical mechanism. The excess mortality is calculated directly from the resulting non-trend component series., Results: The Brisbane (Queensland, Australia) and the Chicago (United States) daily mortality time series data were utilized for calculating the excess mortality associated with heatwaves. The HHT algorithm estimated 62 excess deaths related to the February 2004 Brisbane heatwave. To calculate the excess mortality associated with the July 1995 Chicago heatwave, the HHT algorithm needed to handle the mode mixing issue. The HHT algorithm estimated 510 excess deaths for the 1995 Chicago heatwave event. To exemplify potential applications, the HHT decomposition results were used as the input data for a subsequent regression analysis, using the Brisbane data, to investigate the association between excess mortality and different risk factors., Conclusions: The HHT algorithm is a novel and powerful analytical tool in time series data analysis. It has a real potential to have a wide range of applications in public health research because of its ability to decompose a nonlinear and non-stationary time series into trend and non-trend components consistently and efficiently.

Objectives: To better understand the burden of air pollution on deaths, we examined the effects of air pollutants on years of life lost (YLL) in Beijing, China., Design: Retrospective regression analysis using daily time series., Setting: 8 urban districts in Beijing, China., Participants: 80 515 deaths (48 802 male, 31 713 female) recorded by the Beijing death classification system during 2004-08., Main Outcome Measures: Associations between daily YLL and ambient air pollutants (particulate matter with aerodynamic diameter <2.5 µm (PM2.5), PM10, SO2, and NO2), after adjusting for long term trends, seasonality, day of the week, and weather conditions. We also examined mortality risk related to air pollutants., Results: Mean concentrations of daily PM2.5, PM10, SO2 and NO2 were 105.1 μg/m(3), 144.6 μg/m(3), 48.6 μg/m(3), and 64.2 μg/m(3), respectively. All air pollutants had significant effects on years of life lost when we used single pollutant models. An interquartile range (IQR) increase in PM2.5, PM10, SO2, and NO2 was related to YLL increases of 15.8, 15.8, 16.2, and 15.1 years, respectively. The effects of air pollutants on YLL appeared acutely and lasted for two days (lag 0-1); these effects associated with an IQR increase in PM2.5 were greater in women than men (11.1 (95% confidence interval 4.7 to 17.5) v 4.7 (-2.9 to 12.3) YLL) and in people aged up to 65 years than those older than 65 years (12.0 (2.9 to 21) v 3.8 (-0.9 to 8.6) YLL). The mortality risk associated with an IQR increase in PM2.5 was greater for people older than 65 years (2.5% (95% confidence interval 0.6% to 4.5%) increase of mortality) than those aged up to 65 years (0.7% (-0.8% to 2.2%))., Conclusions: YLL provides a complementary measure for examining the effect of air pollutants on mortality. Increased YLL are associated with increased air pollution. This study highlights the need to reduce air pollution in Beijing, China, to protect the health of the population.

Diurnal temperature range (DTR) is an important meteorological indicator associated with global climate change, but little is known about the effects of DTR on mortality. We examined the effects of DTR on cause-/age-/education-specific mortality in Guangzhou, a subtropical city in China during 2003-2010. A quasi-Poisson regression model combined with distributed lag non-linear model was used to examine the effects of DTR, after controlling for daily mean temperature, air pollutants, season and day of the week. A 1 °C increase in DTR at lag 0-4 days was associated with a 0.47% (95% confidence interval: 0.01%-0.93%) increase in non-accidental mortality. Stroke mortality was most sensitive to DTR. Female, the elderly and those with low education were more susceptible to DTR than male, the youth and those with high education, respectively. Our findings suggest that vulnerable subpopulations should pay more attention to protect themselves from unstable daily weather., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Most studies examining the temperature-mortality association in a city used temperatures from one site or the average from a network of sites. This may cause measurement error as temperature varies across a city due to effects such as urban heat islands. We examined whether spatiotemporal models using spatially resolved temperatures produced different associations between temperature and mortality compared with time series models that used non-spatial temperatures. We obtained daily mortality data in 163 areas across Brisbane city, Australia from 2000 to 2004. We used ordinary kriging to interpolate spatial temperature variation across the city based on 19 monitoring sites. We used a spatiotemporal model to examine the impact of spatially resolved temperatures on mortality. Also, we used a time series model to examine non-spatial temperatures using a single site and the average temperature from three sites. We used squared Pearson scaled residuals to compare model fit. We found that kriged temperatures were consistent with observed temperatures. Spatiotemporal models using kriged temperature data yielded slightly better model fit than time series models using a single site or the average of three sites' data. Despite this better fit, spatiotemporal and time series models produced similar associations between temperature and mortality. In conclusion, time series models using non-spatial temperatures were equally good at estimating the city-wide association between temperature and mortality as spatiotemporal models., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Few epidemiological studies have reported the spatial characteristics of the association between particulate matter <10μm in aerodynamic diameter (PM(10)) and mortality in China. This study explored the spatial characteristics of the association between ambient PM(10) and mortality in the urban area of Beijing, China. We collected daily data on air pollution, weather conditions and mortality in the eight urban districts of Beijing from Jan. 1st 2008 to Dec. 31st 2009. A Poisson Generalized Additive Model (GAM) was used to examine the district-specific effects of PM(10) on cause-specific mortality. A Poisson Generalized Additive Mixed Model (GAMM) was used to examine the urban-wide association between PM(10) and cause-specific mortality while controlling for the random effects of districts, compared with GAM which did not control for the random effects of districts. The inter-quartile ranges (IQRs) of annual PM(10) ranged from 83.5 μg/m(3) (Chaoyang district) to 96.0 μg/m(3) (Shijingshan district). A 96.0 μg/m(3) increase of PM(10) was associated with a 7.52% (95%CI: 1.78%-13.56%) increase of cardiovascular mortality in Shijingshan district while an 87.0 μg/m(3) increase of PM(10) was associated with a 7.68% (95%CI: 0.08%-15.86%) increase of respiratory deaths in Dongcheng district. The urban-wide effects derived from GAMM showed that an 88.0 μg/m(3) increase of PM(10) was associated with an increase of 1.30% (95%CI: 0.45%-2.16%), 2.60% (95%CI: 0.14%-5.11%) in non-accidental and respiratory mortality, illustrating, higher results than those from the GAM. In conclusion, there is spatial variation in ambient PM(10) concentration as well as in the effects of PM(10) on cause-specific mortality in the urban area of Beijing. Additionally, GAMM model may be more effective in estimating the spatial association between urban-wide PM(10) and cause-specific mortality., (Copyright © 2012 Elsevier B.V. All rights reserved.)

The impact of climate change on the health of vulnerable groups such as the elderly has been of increasing concern. However, to date there has been no meta-analysis of current literature relating to the effects of temperature fluctuations upon mortality amongst the elderly. We synthesised risk estimates of the overall impact of daily mean temperature on elderly mortality across different continents. A comprehensive literature search was conducted using MEDLINE and PubMed to identify papers published up to December 2010. Selection criteria including suitable temperature indicators, endpoints, study-designs and identification of threshold were used. A two-stage Bayesian hierarchical model was performed to summarise the percent increase in mortality with a 1°C temperature increase (or decrease) with 95% confidence intervals in hot (or cold) days, with lagged effects also measured. Fifteen studies met the eligibility criteria and almost 13 million elderly deaths were included in this meta-analysis. In total, there was a 2-5% increase for a 1°C increment during hot temperature intervals, and a 1-2 % increase in all-cause mortality for a 1°C decrease during cold temperature intervals. Lags of up to 9 days in exposure to cold temperature intervals were substantially associated with all-cause mortality, but no substantial lagged effects were observed for hot intervals. Thus, both hot and cold temperatures substantially increased mortality among the elderly, but the magnitude of heat-related effects seemed to be larger than that of cold effects within a global context.

Background: Heat-related impacts may have greater public health implications as climate change continues. It is important to appropriately characterize the relationship between heatwave and health outcomes. However, it is unclear whether a case-crossover design can be effectively used to assess the event- or episode-related health effects. This study examined the association between exposure to heatwaves and mortality and emergency hospital admissions (EHAs) from non-external causes in Brisbane, Australia, using both case-crossover and time series analyses approaches., Methods: Poisson generalised additive model (GAM) and time-stratified case-crossover analyses were used to assess the short-term impact of heatwaves on mortality and EHAs. Heatwaves exhibited a significant impact on mortality and EHAs after adjusting for air pollution, day of the week, and season., Results: For time-stratified case-crossover analysis, odds ratios of mortality and EHAs during heatwaves were 1.62 (95% confidence interval (CI): 1.36-1.94) and 1.22 (95% CI: 1.14-1.30) at lag 1, respectively. Time series GAM models gave similar results. Relative risks of mortality and EHAs ranged from 1.72 (95% CI: 1.40-2.11) to 1.81 (95% CI: 1.56-2.10) and from 1.14 (95% CI: 1.06-1.23) to 1.28 (95% CI: 1.21-1.36) at lag 1, respectively. The risk estimates gradually attenuated after the lag of one day for both case-crossover and time series analyses., Conclusions: The risk estimates from both case-crossover and time series models were consistent and comparable. This finding may have implications for future research on the assessment of event- or episode-related (e.g., heatwave) health effects.

We examined the variation in association between high temperatures and elderly mortality (age ≥ 75 years) from year to year in 83 US cities between 1987 and 2000. We used a Poisson regression model and decomposed the mortality risk for high temperatures into: a "main effect" due to high temperatures using lagged non-linear function, and an "added effect" due to consecutive high temperature days. We pooled yearly effects across both regional and national levels. The high temperature effects (both main and added effects) on elderly mortality varied greatly from year to year. In every city there was at least one year where higher temperatures were associated with lower mortality. Years with relatively high heat-related mortality were often followed by years with relatively low mortality. These year to year changes have important consequences for heat-warning systems and for predictions of heat-related mortality due to climate change.

Background: Although interest in assessing the impacts of temperature on mortality has increased, few studies have used a case-crossover design to examine nonlinear and distributed lag effects of temperature on mortality. Additionally, little evidence is available on the temperature-mortality relationship in China or on what temperature measure is the best predictor of mortality., Objectives: Our objectives were to use a distributed lag nonlinear model (DLNM) as a part of case-crossover design to examine the nonlinear and distributed lag effects of temperature on mortality in Tianjin, China and to explore which temperature measure is the best predictor of mortality., Methods: We applied the DLNM to a case-crossover design to assess the nonlinear and delayed effects of temperatures (maximum, mean, and minimum) on deaths (nonaccidental, cardiopulmonary, cardiovascular, and respiratory)., Results: A U-shaped relationship was found consistently between temperature and mortality. Cold effects (i.e., significantly increased mortality associated with low temperatures) were delayed by 3 days and persisted for 10 days. Hot effects (i.e., significantly increased mortality associated with high temperatures) were acute and lasted for 3 days and were followed by mortality displacement for nonaccidental, cardiopulmonary, and cardiovascular deaths. Mean temperature was a better predictor of mortality (based on model fit) than maximum or minimum temperature., Conclusions: In Tianjin, extreme cold and hot temperatures increased the risk of mortality. The effects of cold last longer than the effects of heat. Combining the DLNM and the case-crossover design allows the case-crossover design to flexibly estimate the nonlinear and delayed effects of temperature (or air pollution) while controlling for season.

Background: The relationship between temperature and mortality has been explored for decades and many temperature indicators have been applied separately. However, few data are available to show how the effects of different temperature indicators on different mortality categories, particularly in a typical subtropical climate., Objective: To assess the associations between various temperature indicators and different mortality categories in Brisbane, Australia during 1996-2004., Methods: We applied two methods to assess the threshold and temperature indicator for each age and death groups: mean temperature and the threshold assessed from all cause mortality was used for all mortality categories; the specific temperature indicator and the threshold for each mortality category were identified separately according to the minimisation of AIC. We conducted polynomial distributed lag non-linear model to identify effect estimates in mortality with one degree of temperature increase (or decrease) above (or below) the threshold on current days and lagged effects using both methods., Results: Akaike's Information Criterion was minimized when mean temperature was used for all non-external deaths and deaths from 75 to 84 years; when minimum temperature was used for deaths from 0 to 64 years, 65-74 years, ≥ 85 years, and from the respiratory diseases; when maximum temperature was used for deaths from cardiovascular diseases. The effect estimates using certain temperature indicators were similar as mean temperature both for current day and lag effects., Conclusion: Different age groups and death categories were sensitive to different temperature indicators. However, the effect estimates from certain temperature indicators did not significantly differ from those of mean temperature., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Although interests in assessing the relationship between temperature and mortality have arisen due to climate change, relatively few data are available on lag structure of temperature-mortality relationship, particularly in the Southern Hemisphere. This study identified the lag effects of mean temperature on mortality among age groups and death categories using polynomial distributed lag models in Brisbane, Australia, a subtropical city, 1996-2004. For a 1 °C increase above the threshold, the highest percent increase in mortality on the current day occurred among people over 85 years (7.2% (95% CI: 4.3%, 10.2%)). The effect estimates among cardiovascular deaths were higher than those among all-cause mortality. For a 1 °C decrease below the threshold, the percent increases in mortality at 21 lag days were 3.9% (95% CI: 1.9%, 6.0%) and 3.4% (95% CI: 0.9%, 6.0%) for people aged over 85 years and with cardiovascular diseases, respectively. These findings may have implications for developing intervention strategies to reduce and prevent temperature-related mortality., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Background: Previous studies have found high temperatures increase the risk of mortality in summer. However, little is known about whether a sharp decrease or increase in temperature between neighbouring days has any effect on mortality., Method: Poisson regression models were used to estimate the association between temperature change and mortality in summer in Brisbane, Australia during 1996-2004 and Los Angeles, United States during 1987-2000. The temperature change was calculated as the current day's mean temperature minus the previous day's mean., Results: In Brisbane, a drop of more than 3 °C in temperature between days was associated with relative risks (RRs) of 1.157 (95% confidence interval (CI): 1.024, 1.307) for total non-external mortality (NEM), 1.186 (95%CI: 1.002, 1.405) for NEM in females, and 1.442 (95%CI: 1.099, 1.892) for people aged 65-74 years. An increase of more than 3 °C was associated with RRs of 1.353 (95%CI: 1.033, 1.772) for cardiovascular mortality and 1.667 (95%CI: 1.146, 2.425) for people aged <65 years. In Los Angeles, only a drop of more than 3 °C was significantly associated with RRs of 1.133 (95%CI: 1.053, 1.219) for total NEM, 1.252 (95%CI: 1.131, 1.386) for cardiovascular mortality, and 1.254 (95%CI: 1.135, 1.385) for people aged ≥ 75 years. In both cities, there were joint effects of temperature change and mean temperature on NEM., Conclusion: A significant change in temperature of more than 3 °C, whether positive or negative, has an adverse impact on mortality even after controlling for the current temperature.

Older adults are generally amongst the most vulnerable to heat and cold. While temperature-related health impacts are projected to increase with global warming, the influence of population aging on these trends remains unclear. Here we show that at 1.5 °C, 2 °C, and 3 °C of global warming, heat-related mortality in 800 locations across 50 countries/areas will increase by 0.5%, 1.0%, and 2.5%, respectively; among which 1 in 5 to 1 in 4 heat-related deaths can be attributed to population aging. Despite a projected decrease in cold-related mortality due to progressive warming alone, population aging will mostly counteract this trend, leading to a net increase in cold-related mortality by 0.1%–0.4% at 1.5–3 °C global warming. Our findings indicate that population aging constitutes a crucial driver for future heat- and cold-related deaths, with increasing mortality burden for both heat and cold due to the aging population. This study reveals that population aging intensifies heat- and cold-related deaths, more so than climate change, in 50 countries. At 1.53 °C global warming, aging contributes to rising heat-related deaths, offsetting declines in cold related death. [ABSTRACT FROM AUTHOR]

Heat-related mortality has been identified as one of the key climate extremes posing a risk to human health. Current research focuses largely on how heat mortality increases with mean global temperature rise, but it is unclear how much climate change will increase the frequency and severity of extreme summer seasons with high impact on human health. In this probabilistic analysis, we combined empirical heat-mortality relationships for 748 locations from 47 countries with climate model large ensemble data to identify probable past and future highly impactful summer seasons. Across most locations, heat mortality counts of a 1-in-100 year season in the climate of 2000 would be expected once every ten to twenty years in the climate of 2020. These return periods are projected to further shorten under warming levels of 1.5 °C and 2 °C, where heat-mortality extremes of the past climate will eventually become commonplace if no adaptation occurs. Our findings highlight the urgent need for strong mitigation and adaptation to reduce impacts on human lives. The risk of heat-mortality is increasing sharply. The authors report that heat-mortality levels of a 1-in-100-year summer in the climate of 2000 can be expected once every ten to twenty years in the current climate and at least once in five years with 2 °C of global warming. [ABSTRACT FROM AUTHOR]

During editing, the article title, 'The Role of Humidity in Associations of High Temperature with Mortality: A Multicountry, Multicity Study,' was inadvertently changed to 'The Role of Humidity in Associations [...]