Cross-sectional associations between air pollution and chronic bronchitis: an ESCAPE meta-analysis across five cohorts

BACKGROUND: This study aimed to assess associations of outdoor air pollution on prevalence of chronic bronchitis symptoms in adults in five cohort studies (Asthma-E3N, ECRHS, NSHD, SALIA, SAPALDIA) participating in the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. METHODS: Annual average particulate matter (PM10, PM2.5, PMabsorbance, PMcoarse), NO2, nitrogen oxides (NOx) and road traffic measures modelled from ESCAPE measurement campaigns 2008-2011 were assigned to home address at most recent assessments (1998-2011). Symptoms examined were chronic bronchitis (cough and phlegm for ≥3 months of the year for ≥2 years), chronic cough (with/without phlegm) and chronic phlegm (with/without cough). Cohort-specific cross-sectional multivariable logistic regression analyses were conducted using common confounder sets (age, sex, smoking, interview season, education), followed by meta-analysis. RESULTS: 15 279 and 10 537 participants respectively were included in the main NO2 and PM analyses at assessments in 1998-2011. Overall, there were no statistically significant associations with any air pollutant or traffic exposure. Sensitivity analyses including in asthmatics only, females only or using back-extrapolated NO2 and PM10 for assessments in 1985-2002 (ECRHS, NSHD, SALIA, SAPALDIA) did not alter conclusions. In never-smokers, all associations were positive, but reached statistical significance only for chronic phlegm with PMcoarse OR 1.31 (1.05 to 1.64) per 5 µg/m(3) increase and PM10 with similar effect size. Sensitivity analyses of older cohorts showed increased risk of chronic cough with PM2.5abs (black carbon) exposures. CONCLUSIONS: Results do not show consistent associations between chronic bronchitis symptoms and current traffic-related air pollution in adult European populations

Simulation of population-based commuter exposure to NO2 using different air pollution models

We simulated commuter routes and long-term exposure to traffic-related air pollution during commute in a representative population sample in Basel (Switzerland), and evaluated three air pollution models with different spatial resolution for estimating commute exposures to nitrogen dioxide (NO2) as a marker of long-term exposure to traffic-related air pollution. Our approach includes spatially and temporally resolved data on actual commuter routes, travel modes and three air pollution models. Annual mean NO2 commuter exposures were similar between models. However, we found more within-city and within-subject variability in annual mean (±SD) NO2 commuter exposure with a high resolution dispersion model (40 ± 7 µg m−3, range: 21–61) than with a dispersion model with a lower resolution (39 ± 5 µg m−3; range: 24–51), and a land use regression model (41 ± 5 µg m−3; range: 24–54). Highest median cumulative exposures were calculated along motorized transport and bicycle routes, and the lowest for walking. For estimating commuter exposure within a city and being interested also in small-scale variability between roads, a model with a high resolution is recommended. For larger scale epidemiological health assessment studies, models with a coarser spatial resolution are likely sufficient, especially when study areas include suburban and rural areas

Associations between extreme temperatures and cardiovascular cause-specific mortality: results from 27 countries

BACKGROUND: Cardiovascular disease is the leading cause of death worldwide. Existing studies on the association between temperatures and cardiovascular deaths have been limited in geographic zones and have generally considered associations with total cardiovascular deaths rather than cause-speci fi c cardiovascular deaths. METHODS: We used uni fi ed data collection protocols within the Multi-Country Multi-City Collaborative Network to assemble a database of daily counts of speci fi c cardiovascular causes of death from 567 cities in 27 countries across 5 continents in overlapping periods ranging from 1979 to 2019. City-speci fi c daily ambient temperatures were obtained from weather stations and climate reanalysis models. To investigate cardiovascular mortality associations with extreme hot and cold temperatures, we fi t case-crossover models in each city and then used a mixed-effects meta-analytic framework to pool individual city estimates. Extreme temperature percentiles were compared with the minimum mortality temperature in each location. Excess deaths were calculated for a range of extreme temperature days. RESULTS: The analyses included deaths from any cardiovascular cause (32 154 935), ischemic heart disease (11 745 880), stroke (9 351 312), heart failure (3 673 723), and arrhythmia (670 859). At extreme temperature percentiles, heat (99th percentile) and cold (1st percentile) were associated with higher risk of dying from any cardiovascular cause, ischemic heart disease, stroke, and heart failure as compared to the minimum mortality temperature, which is the temperature associated with least mortality. Across a range of extreme temperatures, hot days (above 97.5th percentile) and cold days (below 2.5th percentile) accounted for 2.2 (95% empirical CI [eCI], 2.1-2.3) and 9.1 (95% eCI, 8.9-9.2) excess deaths for every 1000 cardiovascular deaths, respectively. Heart failure was associated with the highest excess deaths proportion from extreme hot and cold days with 2.6 (95% eCI, 2.4-2.8) and 12.8 (95% eCI, 12.2-13.1) for every 1000 heart failure deaths, respectively. CONCLUSIONS: Across a large, multinational sample, exposure to extreme hot and cold temperatures was associated with a greater risk of mortality from multiple common cardiovascular conditions. The intersections between extreme temperatures and cardiovascular health need to be thoroughly characterized in the present day-and especially under a changing climate

Fluctuating temperature modifies heat-mortality association around the globe

Studies have investigated the effects of heat and temperature variability (TV) on mortality. However, few assessed whether TV modifies the heat-mortality association. Data on daily temperature and mortality in the warm season were collected from 717 locations across 36 countries. TV was calculated as the standard deviation of the average of the same and previous days’ minimum and maximum temperatures. We used location-specific quasi-Poisson regression models with an interaction term between the cross-basis term for mean temperature and quartiles of TV to obtain heat-mortality associations under each quartile of TV, and then pooled estimates at the country, regional, and global levels. Results show the increased risk in heat-related mortality with increments in TV, accounting for 0.70% (95% confidence interval [CI]: −0.33 to 1.69), 1.34% (95% CI: −0.14 to 2.73), 1.99% (95% CI: 0.29–3.57), and 2.73% (95% CI: 0.76–4.50) of total deaths for Q1–Q4 (first quartile–fourth quartile) of TV. The modification effects of TV varied geographically. Central Europe had the highest attributable fractions (AFs), corresponding to 7.68% (95% CI: 5.25–9.89) of total deaths for Q4 of TV, while the lowest AFs were observed in North America, with the values for Q4 of 1.74% (95% CI: −0.09 to 3.39). TV had a significant modification effect on the heat-mortality association, causing a higher heat-related mortality burden with increments of TV. Implementing targeted strategies against heat exposure and fluctuant temperatures simultaneously would benefit public health. © 2022 The Author(s)Funding text 1: This study was supported by the Australian Research Council (DP210102076) and the Australian National Health and Medical Research Council (APP2000581). Y.W and B.W. were supported by the China Scholarship Council (nos. 202006010044 and 202006010043); S.L. was supported by an Emerging Leader Fellowship of the Australian National Health and Medical Research Council (no. APP2009866); Y.G. was supported by Career Development Fellowship (no. APP1163693) and Leader Fellowship (no. APP2008813) of the Australian National Health and Medical Research Council; J.K. and A.U. were supported by the Czech Science Foundation (project no. 20–28560S); N.S. was supported by the National Institute of Environmental Health Sciences-funded HERCULES Center (no. P30ES019776); Y.H. was supported by the Environment Research and Technology Development Fund (JPMEERF15S11412) of the Environmental Restoration and Conservation Agency; M.d.S.Z.S.C. and P.H.N.S. were supported by the São Paulo Research Foundation (FAPESP); H.O. and E.I. were supported by the Estonian Ministry of Education and Research (IUT34–17); J.M. was supported by a fellowship of Fundação para a Ciência e a Tecnlogia (SFRH/BPD/115112/2016); A.G. and F.S. were supported by the Medical Research Council UK (grant ID MR/R013349/1), the Natural Environment Research Council UK (grant ID NE/R009384/1), and the EU's Horizon 2020 project, Exhaustion (grant ID 820655); A.S. and F.d.D. were supported by the EU's Horizon 2020 project, Exhaustion (grant ID 820655); V.H. was supported by the Spanish Ministry of Economy, Industry and Competitiveness (grant ID PCIN-2017–046); and A.T. by MCIN/AEI/10.13039/501100011033 (grant CEX2018-000794-S). Statistics South Africa kindly provided the mortality data, but had no other role in the study. Y.G. A.G. M.H. and B. Armstrong set up the collaborative network. Y.G. S.L. and Y.W. designed the study. Y.G. S.L. and A.G. developed the statistical methods. Y.W. B.W. S.L. and Y.G. took the lead in drafting the manuscript and interpreting the results. Y.W. B.W. Y.G. A.G. S.T. A.O. A.U. A.S. A.E. A.M.V.-C. A. Zanobetti, A.A. A. Zeka, A.T. B. Alahmad, B. Armstrong, B.F. C.Í. C. Ameling, C.D.l.C.V. C. Åström, D.H. D.V.D. D.R. E.I. E.L. F.M. F.A. F.D. F.S. G.C.-E. H. Kan, H.O. H. Kim, I.-H.H. J.K. J.M. J.S. K.K. M.H.-D. M.S.R. M.H. M.P. M.d.S.Z.S.C. N.S. P.M. P.G. P.H.N.S. R.A. S.O. T.N.D. V.C. V.H. W.L. X.S. Y.H. M.L.B. and S.L. provided the data and contributed to the interpretation of the results and the submitted version of the manuscript. Y.G. S.L. and Y.W. accessed and verified the data. All of the authors had full access to all of the data in the study and had final responsibility for the decision to submit for publication. The authors declare no competing interests.; Funding text 2: This study was supported by the Australian Research Council ( DP210102076 ) and the Australian National Health and Medical Research Council ( APP2000581 ). Y.W and B.W. were supported by the China Scholarship Council (nos. 202006010044 and 202006010043 ); S.L. was supported by an Emerging Leader Fellowship of the Australian National Health and Medical Research Council (no. APP2009866 ); Y.G. was supported by Career Development Fellowship (no. APP1163693) and Leader Fellowship (no. APP2008813) of the Australian National Health and Medical Research Council ; J.K. and A.U. were supported by the Czech Science Foundation (project no. 20–28560S ); N.S. was supported by the National Institute of Environmental Health Sciences -funded HERCULES Center (no. P30ES019776 ); Y.H. was supported by the Environment Research and Technology Development Fund ( JPMEERF15S11412 ) of the Environmental Restoration and Conservation Agency; M.d.S.Z.S.C. and P.H.N.S. were supported by the São Paulo Research Foundation (FAPESP); H.O. and E.I. were supported by the Estonian Ministry of Education and Research ( IUT34–17 ); J.M. was supported by a fellowship of Fundação para a Ciência e a Tecnlogia ( SFRH/BPD/115112/2016 ); A.G. and F.S. were supported by the Medical Research Council UK (grant ID MR/R013349/1 ), the Natural Environment Research Council UK (grant ID NE/R009384/1 ), and the EU’s Horizon 2020 project, Exhaustion (grant ID 820655 ); A.S. and F.d.D. were supported by the EU’s Horizon 2020 project, Exhaustion (grant ID 820655 ); V.H. was supported by the Spanish Ministry of Economy, Industry and Competitiveness (grant ID PCIN-2017–046 ); and A.T. by MCIN/AEI/10.13039/501100011033 (grant CEX2018-000794-S). Statistics South Africa kindly provided the mortality data, but had no other role in the study

Joint effect of heat and air pollution on mortality in 620 cities of 36 countries

Background The epidemiological evidence on the interaction between heat and ambient air pollution on mortality is still inconsistent. Objectives To investigate the interaction between heat and ambient air pollution on daily mortality in a large dataset of 620 cities from 36 countries. Methods We used daily data on all-cause mortality, air temperature, particulate matter ≤ 10 μm (PM10), PM ≤ 2.5 μm (PM2.5), nitrogen dioxide (NO2), and ozone (O3) from 620 cities in 36 countries in the period 1995–2020. We restricted the analysis to the six consecutive warmest months in each city. City-specific data were analysed with over-dispersed Poisson regression models, followed by a multilevel random-effects meta-analysis. The joint association between air temperature and air pollutants was modelled with product terms between non-linear functions for air temperature and linear functions for air pollutants. Results We analyzed 22,630,598 deaths. An increase in mean temperature from the 75th to the 99th percentile of city-specific distributions was associated with an average 8.9 % (95 % confidence interval: 7.1 %, 10.7 %) mortality increment, ranging between 5.3 % (3.8 %, 6.9 %) and 12.8 % (8.7 %, 17.0 %), when daily PM10 was equal to 10 or 90 μg/m3, respectively. Corresponding estimates when daily O3 concentrations were 40 or 160 μg/m3 were 2.9 % (1.1 %, 4.7 %) and 12.5 % (6.9 %, 18.5 %), respectively. Similarly, a 10 μg/m3 increment in PM10 was associated with a 0.54 % (0.10 %, 0.98 %) and 1.21 % (0.69 %, 1.72 %) increase in mortality when daily air temperature was set to the 1st and 99th city-specific percentiles, respectively. Corresponding mortality estimate for O3 across these temperature percentiles were 0.00 % (-0.44 %, 0.44 %) and 0.53 % (0.38 %, 0.68 %). Similar effect modification results, although slightly weaker, were found for PM2.5 and NO2. Conclusions Suggestive evidence of effect modification between air temperature and air pollutants on mortality during the warm period was found in a global dataset of 620 cities.Massimo Stafoggia, Francesca K. de’ Donato, Masna Rai and Alexandra Schneider were partially supported by the European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655). Jan Kyselý and Aleš Urban were supported by the Czech Science Foundation project (22-24920S). Joana Madureira was supported by the Fundação para a Ciência e a Tecnologia (FCT) (grant SFRH/BPD/115112/2016). Masahiro Hashizume was supported by the Japan Science and Technology Agency (JST) as part of SICORP, Grant Number JPMJSC20E4. Noah Scovronick was supported by the NIEHS-funded HERCULES Center (P30ES019776). South African Data were provided by Statistics South Africa, which did not have any role in conducting the study. Antonio Gasparrini was supported by the Medical Research Council-UK (Grants ID: MR/V034162/1 and MR/R013349/1), the Natural Environment Research Council UK (Grant ID: NE/R009384/1), and the European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655)

Comparison of weather station and climate reanalysis data for modelling temperature-related mortality

Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk. © 2022, The Author(s).The original version of this Article contained an error in Affiliation 25, which was incorrectly given as ‘Faculty of Medicine ArqFuturo INSPER, University of São Paulo, São Paulo, Brazil’. The correct affiliation is listed below. Faculty of Medicine, University of São Paulo, São Paulo, Brazil The original Article has been corrected. © The Author(s) 2022.The study was primarily supported by Grants from the European Commission’s Joint Research Centre Seville (Research Contract ID: JRC/SVQ/2020/MVP/1654), Medical Research Council-UK (Grant ID: MR/R013349/1), Natural Environment Research Council UK (Grant ID: NE/R009384/1), European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655). The following individual Grants also supported this work: J.K and A.U were supported by the Czech Science Foundation, project 20-28560S. A.T was supported by MCIN/AEI/10.13039/501100011033, Grant CEX2018-000794-S. V.H was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant agreement No 101032087. This work was generated using Copernicus Climate Change Service (C3S) information [1985–2019]

Seasonal variation in mortality and the role of temperature: a multi-country multi-city study

Data availability: Data have been collected within the MCC (Multi-Country Multi-City) Collaborative Research Network (https://mccstudy.lshtm.ac.uk) under a data-sharing agreement and cannot be made publicly available. The R code for the analysis is available from the first author.Copyright . 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.This work was primarily supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI [Grant Number 19K19461]. Y.C. was supported by a Senior Research grant [2019R1A2C1086194] from the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT (Information and Communication Technologies). V.H. received support from the Spanish Ministry of Economy, Industry and Competitiveness [Grant ID: PCIN-2017-046]. J.K. and A.U. were supported by the Czech Science Foundation [project 18-22125S]. A.S. acknowledged funding from European Union’s Horizon 2020 research and innovation programme under grant agreement No 820655 (EXHAUSTION). A.G. was supported by the Medical Research Council-UK [Grant ID: MR/R013349/1], the Natural Environment Research Council UK [Grant ID: NE/R009384/1] and the European Union’s Horizon 2020 Project Exhaustion [Grant ID: 820655]. M.H. was supported by the Japan Science and Technology Agency (JST) as part of SICORP [Grant Number JPMJSC20E4]

Seasonality of mortality under climate change: a multicountry projection study

Data sharing: All data used in our study were obtained from the MCC Collaborative Research Network under a data-sharing agreement and cannot be made publicly available. Researchers can refer to collaborators of the Network, who are listed as coauthors of this Article (primary contact: Antonio Gasparrini, [email protected]), for information on accessing the data for each country. The R code is available on request, and a reproducible example is publicly available on the personal GitHub website of the first author (https://github.com/LinaMadaniyazi).For more on the MCC see https://mccstudy.lshtm.ac.uk/Supplementary Material is available online at: https://www.sciencedirect.com/science/article/pii/S2542519623002693#sec1 .Background: Climate change can directly impact temperature-related excess deaths and might subsequently change the seasonal variation in mortality. In this study, we aimed to provide a systematic and comprehensive assessment of potential future changes in the seasonal variation, or seasonality, of mortality across different climate zones. Methods: In this modelling study, we collected daily time series of mean temperature and mortality (all causes or non-external causes only) via the Multi-Country Multi-City Collaborative (MCC) Research Network. These data were collected during overlapping periods, spanning from Jan 1, 1969 to Dec 31, 2020. We projected daily mortality from Jan 1, 2000 to Dec 31, 2099, under four climate change scenarios corresponding to increasing emissions (Shared Socioeconomic Pathways [SSP] scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We compared the seasonality in projected mortality between decades by its shape, timings (the day-of-year) of minimum (trough) and maximum (peak) mortality, and sizes (peak-to-trough ratio and attributable fraction). Attributable fraction was used to measure the burden of seasonality of mortality. The results were summarised by climate zones. Findings: The MCC dataset included 126 809 537 deaths from 707 locations within 43 countries or areas. After excluding the only two polar locations (both high-altitude locations in Peru) from climatic zone assessments, we analysed 126 766 164 deaths in 705 locations aggregated in four climate zones (tropical, arid, temperate, and continental). From the 2000s to the 2090s, our projections showed an increase in mortality during the warm seasons and a decrease in mortality during the cold seasons, albeit with mortality remaining high during the cold seasons, under all four SSP scenarios in the arid, temperate, and continental zones. The magnitude of this changing pattern was more pronounced under the high-emission scenarios (SSP3-7.0 and SSP5-8.5), substantially altering the shape of seasonality of mortality and, under the highest emission scenario (SSP5-8.5), shifting the mortality peak from cold seasons to warm seasons in arid, temperate, and continental zones, and increasing the size of seasonality in all zones except the arid zone by the end of the century. In the 2090s compared with the 2000s, the change in peak-to-trough ratio (relative scale) ranged from 0·96 to 1·11, and the change in attributable fraction ranged from 0·002% to 0·06% under the SSP5-8.5 (highest emission) scenario. Interpretation: A warming climate can substantially change the seasonality of mortality in the future. Our projections suggest that health-care systems should consider preparing for a potentially increased demand during warm seasons and sustained high demand during cold seasons, particularly in regions characterised by arid, temperate, and continental climates.This study was primarily supported by the Environment Research and Technology Development Fund (grant number JPMEERF20231007) of the Environmental Restoration and Conservation Agency, provided by the Ministry of the Environment of Japan. MH was supported by the Japan Science and Technology Agency as part of the Strategic International Collaborative Research Program (grant number JPMJSC20E4). AG was supported by the UK Medical Research Council (grant number MR/V034162/1) and the EU's Horizon 2020 research project Exhaustion (grant number 820655). AU and JK were supported by the Czech Science Foundation (project 22–24920S). JJKJ was supported by the Academy of Finland (grant number 310372; Global Health Risks Related to Atmospheric Composition and Weather Consortium). FS was supported by the Italian Ministry of University and Research, Department of Excellence project 2023–2027, Rethinking Data Science—Department of Statistics, Computer Science and Applications—University of Florence

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