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Research Article
Originally Published 22 May 2024
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Extreme Temperatures and Stroke Mortality: Evidence From a Multi-Country Analysis

Barrak Alahmad, MD, PhD https://orcid.org/0000-0002-9523-9537 [email protected], Haitham Khraishah, MD https://orcid.org/0000-0003-0003-5089, Meghana Kamineni, BS https://orcid.org/0000-0002-6698-5151, Dominic Royé, PhD https://orcid.org/0000-0002-5516-6396, Stefania I. Papatheodorou, PhD https://orcid.org/0000-0002-9451-9094, Ana Maria Vicedo-Cabrera, PhD, Yuming Guo, PhD, … Show All … , Eric Lavigne, MD https://orcid.org/0000-0001-6146-9839, Ben Armstrong, PhD https://orcid.org/0000-0003-4407-0409, Francesco Sera, PhD https://orcid.org/0000-0002-8890-6848, Aaron S. Bernstein, MD https://orcid.org/0000-0003-1703-1041, Antonella Zanobetti, PhD https://orcid.org/0000-0001-8170-4736, Eric Garshick, MD https://orcid.org/0000-0001-5931-5638, Joel Schwartz, PhD https://orcid.org/0000-0001-6168-378X, Michelle L. Bell, PhD https://orcid.org/0000-0002-3965-1359, Fahd Al-Mulla, MD, Petros Koutrakis, PhD, and Antonio Gasparrini, PhD https://orcid.org/0000-0002-2271-3568 Multi-Country Multi-City (MCC) NetworkAuthor Info & Affiliations
Stroke
Volume 55, Number 7
https://doi.org/10.1161/STROKEAHA.123.045751
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Abstract

BACKGROUND:

Extreme temperatures contribute significantly to global mortality. While previous studies on temperature and stroke-specific outcomes presented conflicting results, these studies were predominantly limited to single-city or single-country analyses. Their findings are difficult to synthesize due to variations in methodologies and exposure definitions.

METHODS:

Within the Multi-Country Multi-City Network, we built a new mortality database for ischemic and hemorrhagic stroke. Applying a unified analysis protocol, we conducted a multinational case-crossover study on the relationship between extreme temperatures and stroke. In the first stage, we fitted a conditional quasi-Poisson regression for daily mortality counts with distributed lag nonlinear models for temperature exposure separately for each city. In the second stage, the cumulative risk from each city was pooled using mixed-effect meta-analyses, accounting for clustering of cities with similar features. We compared temperature-stroke associations across country-level gross domestic product per capita. We computed excess deaths in each city that are attributable to the 2.5% hottest and coldest of days based on each city’s temperature distribution.

RESULTS:

We collected data for a total of 3 443 969 ischemic strokes and 2 454 267 hemorrhagic stroke deaths from 522 cities in 25 countries. For every 1000 ischemic stroke deaths, we found that extreme cold and hot days contributed 9.1 (95% empirical CI, 8.6–9.4) and 2.2 (95% empirical CI, 1.9–2.4) excess deaths, respectively. For every 1000 hemorrhagic stroke deaths, extreme cold and hot days contributed 11.2 (95% empirical CI, 10.9–11.4) and 0.7 (95% empirical CI, 0.5–0.8) excess deaths, respectively. We found that countries with low gross domestic product per capita were at higher risk of heat-related hemorrhagic stroke mortality than countries with high gross domestic product per capita (P=0.02).

CONCLUSIONS:

Both extreme cold and hot temperatures are associated with an increased risk of dying from ischemic and hemorrhagic strokes. As climate change continues to exacerbate these extreme temperatures, interventional strategies are needed to mitigate impacts on stroke mortality, particularly in low-income countries.

Graphical Abstract

Nonoptimal temperatures, hot and cold, contribute significantly to global mortality and are responsible for ≈5 million deaths each year.1 They could account for 13% of cardiovascular deaths2 and 5.2% of global stroke deaths.3 These figures come from studies that analyzed all-cause, cardiovascular, or stroke deaths. Each stroke subtype presents with its own unique pathology, necessitating specialized attention. Yet, studies on the impact of extreme temperatures on specific stroke subtypes were not as conclusive.
A search on the association between ischemic stroke and ambient temperatures yields conflicting results, with some studies finding no significant association and others noting marginal links with both hot and cold temperatures.4–7 Studies on hemorrhagic stroke suggest a slight protective effect from higher temperatures, but no consistent relationship with cold temperatures was identified.5,6 The existing literature on the relationship between temperature and different stroke subtypes primarily consists of single-city or single-country studies.8 Collectively, these single-city studies are susceptible to publication bias. Further synthesis of the existing evidence is challenged by the range of statistical methods and study designs used. For example, true effects may be obscured when modeling approaches do not consider the complex nonlinear temperature relationships (ie, the U-shape relationship, where both extreme cold and hot temperatures can be detrimental).9
To address these research gaps, we built a multinational, multiregional stroke-specific mortality database and analyzed it with a standardized statistical framework capable of handling complex temperature modeling. In this analysis, we investigate the 2 most common causes of stroke, ischemic and hemorrhagic, and their associations with extreme hot and cold temperatures.

METHODS

Data Collection

The Multi-Country Multi-City (MCC) network, established in 201310 (website: http://mccstudy.lshtm.ac.uk/), constitutes one of the largest environmental health consortia of its kind, with researchers from >49 countries around the globe. We contacted investigators from each country to extract specific stroke deaths from governmental and regional death registries. Data collection in each country followed a unified protocol where investigators were instructed to extract the statistical underlying cause of death only if it was coded using the ninth or tenth revisions of the International Classification of Diseases (ICD-9/ICD-10). The World Health Organization defines the underlying cause of death as “the disease or injury which initiated the train of morbid events leading directly to death.”11 ICD-9 and ICD-10 codes for any cerebrovascular accident were 430 to 438 and I60 to I69, respectively. We narrowed the extraction to include only deaths from hemorrhagic stroke (ICD-9, 430–432; ICD-10, I60–I62) or ischemic stroke (ICD-9, 433–434; ICD-10, I63–I66). Sources of stroke mortality in each country are provided in the Table. The data were for the years 1979 to 2019, although not all cities had data for the entire period. Institutional approvals were obtained by each MCC participant in their respective country. This article follows the Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines. Data were collected within the MCC network under a data sharing agreement and cannot be made publicly available. Researchers can refer to MCC participants, who are listed as coauthors of this article, for information on accessing the data for each country.
Table. Descriptive Statistics of the Official National and Regional Registries for Stroke Mortality and Temperature Data Sets in 25 Countries
CountryStroke data sourceYearsCitiesIschemic strokeHemorrhagic strokeTemp. mean (°C)Temp. SD (°C)
BrazilMinistry of Health1997–20189146 18097 04423.44.2
CanadaCanadian Mortality Database1986–20152692 44856 2426.810.9
Costa RicaInstituto Nacional de Estadística y Censos2000–2017165551622.71.1
CyprusCauses of Death Database, Health Monitoring Unit, Ministry of Health2004–20175395498120.46.2
EcuadorInstituto Nacional de Estadística y Censos2013–201923857480621.05.4
EstoniaEstonian Causes of Death Registry1997–2018823 63753856.28.9
FinlandStatistics Finland1987–2018110 30257915.99.0
GuatemalaInstituto Nacional de Estadística, Unidad de Estadística de Salud2009–20181116291619.41.6
ItalyRegional mortality registry of the Lazio Region2006–201542110831516.37.4
JapanMinistry of Health, labor and Welfare1979–2015471 845 4281 584 97215.28.6
KuwaitNational Center for Health Information, Ministry of Health2000–201614432193727.19.8
MoldovaNational Center for Health Management2001–201012289558110.89.8
PanamaInstituto Nacional de Estadística y Censo, Centro de Información Estadística.2013–2016111821928.11.1
ParaguayMinisterio de Salud Pública y Bienestar Social, Dirección General de Información Estratégica en Salud2004–20191995124823.35.3
PhilippinesPhilippine Statistics Agency2006–2010414 73812 47628.21.4
PortugalStatistics Portugal1990–2018669 74226 81016.35.4
South AfricaStatistics South Africa1997–201351337 55945 65518.05.4
SpainSpain National Institute of Statistics2000–2018651 03325 61517.16.6
SwitzerlandFederal Office of Statistics (Switzerland)1995–2016839 05710 70510.57.5
TaiwanDepartment of Health in Taiwan2008–2016310 54411 07924.15.1
ThailandMinistry of Public Health1999–20085525 99676 67527.62.4
United KingdomOffice of National Statistics1990–201670203 40187 94710.55.3
UruguayMinisterio de Salud Publica2001–2018114 565655818.65.6
United StatesNational Center for Health Statistics1985–2006209534 701373 35014.110.1
VietnamProvincial Department of Health, Ho Chi Minh2010–201315066344428.51.4
Total 1979–20195223 443 9692 454 267  
When available, temperature data for each location were gathered from ground-monitoring stations. In cities that had >1 weather station, we took the average across all stations for each day. In cities without ground-monitoring temperature data, we used downscaled climate reanalysis models for predicting temperature each day.12 Details of the MCC environmental data and sources have been extensively described previously.2,13 City-level climate zones and country-level gross domestic product (GDP) per capita were obtained from the Köppen-Geiger climate classification14 and the World Bank,15 respectively.

Statistical Analysis

The methodology for our statistical analysis largely relies on a protocol we previously outlined in detail.2 We implemented a priori 2-stage statistical analysis to examine stroke-specific deaths using a case-crossover design. By design, the unit of analysis becomes the day and not the individual person, effectively eliminating any possible time-invariant confounding from individual characteristics such as age, gender, smoking, and co-morbidity, among others.16 We first fitted a conditional quasi-Poisson regression for counts of daily mortality in each city. Compared with standard Poisson regression, a quasi-Poisson distribution accounts for overdispersion in the count data. We then fitted a 3-way interaction among year, month, and day of the week.17 The indicator variables from the interaction terms are not estimated; rather, they are eliminated from the likelihood terms by conditioning on the sum of events in each stratum—a computationally efficient alternative to the conditional logistic case-crossover analysis.17 Lagged temperature association with mortality in each city was modeled nonlinearly using distributed lag nonlinear models.18 This technique combines 2 functions to model the risk across predictor and lag spaces. The 2 functions were represented by a quadratic B-spline with 3 internal knots placed at the 10th, 75th, and 90th temperature percentiles of each location19 and a natural splines with 3 internal knots equally spaced in the log scale over a period of 14 lag days. In the second stage, the cumulative risk from each city was pooled using mixed-effect meta-analysis.20 We used meta-predictors (fixed and random effects) to account for potential effect modification and clustering of cities with similar features. As in previous analyses,2 we included 3 fixed-effect predictors: city-level average summer temperature, city-level average winter temperature, and country-level GDP per capita. The model included 2 levels of random effects where cities are nested within country-specific climate zones, allowing cities in the same country and climate zone to borrow information from each other. The country-specific and overall pooled effect estimates were predicted from the meta-regression model. We reported a significance test (P value) for predictors using an extended version of the Wald F test. The best linear unbiased predictions were then extracted for each city. Using the best linear unbiased prediction, we identified the minimum mortality temperature (MMT), which is the temperature of the least number of stroke deaths in each city. We then calculated the cumulative relative risk (RR) across all lag days comparing any given temperature to the MMT. We reported the RR of extreme heat and cold cut points as the 99th and 1st percentiles of each city’s distribution of temperatures, respectively. For a specific day and corresponding temperature, the attributable number of deaths refers to the cumulative impact of temperature on mortality aggregated at the log-RR across lag days up to 14 days. This is an extension to calculate excess deaths in distributed lag nonlinear model frameworks.21 We then reported excess ischemic and hemorrhagic stroke deaths from all nonoptimal temperatures and from a range of extreme hot and cold days by summing the contributions for the coldest and hottest 2.5% of the days based on the temperature distribution within each city. In other words, in any given city, the extreme heat range was defined as all days where the temperature exceeded the 97.5th percentile for that city. Similarly, the extreme cold range was defined as all days where the temperature was below the 2.5th percentile of that city. We reported proportion of excess deaths as the number of additional deaths for every 1000 deaths. The 95% empirical CIs (eCI) around excess deaths estimates were computed from Monte Carlo simulations assuming a multivariate normal distribution of the best linear unbiased prediction of reduced coefficients.
In post hoc analyses, we explored additional meta-variables that could modify the temperature-stroke relationship beyond what is explained by GDP per capita and average summer/winter temperature. This secondary analysis examined variables including: (1) city-level SD of annual temperature, (2) country-level human development index (HDI), (3) city-level average motorized time travel to health care facilities,22 (4) city-level SD of motorized time travel to health care facilities,22 and (5) city-level Global Gridded Relative Deprivation Index.23 These metrics collectively capture additional meteorologic dynamics, socioeconomic factors, health care accessibility, and community-specific health vulnerabilities.

RESULTS

We collected data on a total of 3 443 969 ischemic stroke and 2 454 267 hemorrhagic stroke deaths from 522 cities in 25 countries (Table). Mortality data covered overlapping time periods that ranged from January 1, 1979 (Japan) to December 31, 2019 (Paraguay and Ecuador).
There were similar features in exposure-response curves for ischemic and hemorrhagic strokes (Figure 1). Both types of strokes exhibited a U-shaped relationship with a higher risk of death at both high and low temperatures. The pooled RR of death from extreme cold (at the first percentile versus MMT) was higher for hemorrhagic stroke (1.49 [95% CI, 1.40–1.58]) than ischemic stroke (1.34 [95% CI, 1.27–1.41]). On the contrary, the pooled RR of death from extreme heat (at the 99th percentile versus MMT) was greater for ischemic stroke (1.13 [95% CI, 1.06–1.20]) than for hemorrhagic stroke (1.02 [95% CI, 1.00–1.05]). The MMT for ischemic stroke (81st percentile) was substantially higher compared with hemorrhagic stroke (97th percentile), which left a high risk of death for only very hot days. The actual temperatures of the MMT for each country are provided in Table S1.
Figure 1. Pooled exposure-response relationships between overall temperature percentiles and relative risk (RR) of ischemic and hemorrhagic stroke mortality from 522 cities in 25 countries.
Out of every 1000 ischemic stroke deaths, the coldest 2.5% of days contributed to 9.1 excess deaths (95% eCI, 8.6–9.4), while the hottest 2.5% of days accounted for 2.2 excess deaths (95% eCI, 1.9–2.4). For hemorrhagic stroke, the coldest 2.5% of days resulted in a larger contribution of 11.2 excess deaths per 1000 hemorrhagic stroke deaths (95% eCI, 10.9–11.4). In contrast, the hottest 2.5% of days resulted in 0.7 excess deaths per 1000 hemorrhagic stroke deaths (95% eCI, 0.5–0.8), translating to roughly 7 excess deaths for every 10 000 hemorrhagic stroke deaths. Country-by-country estimates of excess ischemic and hemorrhagic stroke deaths are shown in Figure 2 and Table S2. Excess deaths from all nonoptimal hot and cold temperatures (not just the coldest and hottest 2.5% of days) are provided in Table S3. Effect sizes of RRs of death in each country are provided in Table S4.
Figure 2. Proportion of excess deaths for every 1000 hemorrhagic and ischemic stroke deaths. The results are shown for (A) extremely cold days (lower than the 2.5th percentile of temperature) and (B) extremely hot days (higher than the 97.5th percentile of temperature).
Our analysis of meta-predictors is presented in Figure 3. We found that country-level GDP per capita modifies the relationship between temperature and hemorrhagic stroke mortality (P=0.02). In contrast, we found no significant effect of the modification of GDP per capita on the temperature-ischemic stroke mortality relationship (P=0.26). Specifically, countries with lower GDP per capita demonstrated a higher risk of heat-related hemorrhagic stroke deaths compared with countries with higher GDP per capita (RR, 1.08 [95% CI, 1.02–1.15] versus 1.00 [95% CI, 0.99–1.01]). Conversely, the heat-related ischemic stroke mortality risk did not significantly differ between countries of high (RR, 1.11 [95% CI, 1.05–1.17]) and low (RR, 1.17 [95% CI, 1.05–1.29]) GDP per capita. Cold temperatures were associated with a high risk of both ischemic and hemorrhagic stroke mortality in countries with a lower GDP per capita. Pooled RRs of stroke mortality from heat and cold stratified by GDP per capita are provided in Table S5. Furthermore, cities that experience extremely cold winters and hot summers have a high risk of hemorrhagic stroke mortality. The impact of summer and winter temperatures on ischemic stroke mortality produced mixed results that were hard to interpret.
Figure 3. Pooled exposure-response relationships between overall temperatures (in °C) and relative risk (RR) of ischemic and hemorrhagic stroke mortality after stratifying by the 25th and 75th percentile of country-level gross domestic product (GDP) per capita (75th, higher GDP countries; 25th, lower GDP countries), city-level mean summer temperature (75th, warmer summers cities; 25th, cooler summers cities), and city-level mean winter temperature (75th, warmer winters cities; 25th, cooler winters cities).
Post hoc analyses of additional meta-predictors showed no significant (P>0.10) effect measure modification by city-level SD of annual temperature, country-level human development index, city-level average motorized time travel to health care facilities, city-level SD of motorized time travel to health care facilities, or deprivation index (results not shown).

DISCUSSION

This study comes from what we think is the largest multinational investigation into stroke-specific mortality risk and extreme temperatures. Previous large-scale environmental health investigations used a catch-all outcome such as any stroke mortality, which does not capture distinct pathologies for different types of strokes. We analyzed >3.4 million ischemic stroke deaths and 2.4 million hemorrhagic stroke deaths across 522 cities in 25 countries using the same statistical analysis protocol (ie, applied universally to all cities and countries). We found that both extreme hot and cold temperatures contribute to an increased mortality risk from both ischemic and hemorrhagic strokes. However, the estimated impact of these temperature extremes differed between stroke types. Notably, extreme cold temperatures presented a more pronounced association with the risk of death from hemorrhagic stroke compared with ischemic stroke. In contrast, extreme heat was associated with substantially increased deaths from ischemic stroke, while the estimated risk of death from hemorrhagic stroke was lower. Analysis stratified by GDP per capita showed that low-income countries may bear a higher burden of heat-related hemorrhagic stroke mortality.
Previous estimates of the relationship between ischemic stroke and ambient temperatures have shown conflicting results. In a systematic review and meta-analysis (8 studies, 290 154 patients) conducted by Wang et al,4 no significant relationship was seen between extreme temperatures and ischemic stroke admissions. Similarly, after adjusting for potential confounders, Zorrilla-Vaca et al6 did not find a significant association between the incidence of ischemic stroke and low temperatures. However, Lian et al,5 in another systematic review (20 studies, 2 070 923 events), found that both hot and cold temperatures were marginally associated with an increased risk of ischemic stroke. With regards to hemorrhagic stroke, a meta-analysis by Lian et al5 found that higher temperatures were associated with modest protective effects (−1.9% [95% CI, −2.8 to −0.9%], for every 1 °C increase). No association was found between hemorrhagic stroke and cold ambient temperatures. Zorrilla-Vaca et al6 and Wang et al4 found no association between ambient temperatures and hemorrhagic stroke in their respective meta-analyses. In this multi-country study, we had higher statistical power to disentangle different risks and investigate effect modifiers on the most common causes of stroke with minimal risk of publication bias.
Despite advancements in stroke prevention and treatment, stroke was the second-leading cause of death in 2019, accounting for 11.6% of total deaths worldwide.24 Stroke incidence and mortality burden disproportionately affect low- and middle-income countries, where patients are typically younger. The Global Burden of Disease’s investigation into the global burden of stroke noted that the age-standardized mortality rate for stroke was 3.6× higher in low-income countries compared with high-income countries.24 In this study, we found evidence for effect measure modification in countries with low GDP per capita, where extreme hot temperatures were associated with increased risk of hemorrhagic stroke mortality compared with countries with high GDP per capita, but the risk of ischemic stroke mortality was not associated with increased risk. Colder temperatures in low GDP per capita countries were associated with an increased risk of both ischemic and hemorrhagic stroke deaths compared with estimated effects in high GDP per capita countries; however, the evidence was suggestive and not conclusive. We tested whether motorized time travel to a health care facility could partially explain this difference, but we found no evidence of effect measure modification by this variable. We hypothesize that countries with high GDP per capita may have an increased ability to mitigate exposure to extreme temperatures (eg, indoor cooling and heating) and a decreased rate of outdoor work. Additionally, low-income countries might have limited resources to detect, treat, and manage risk factors for hemorrhagic stroke. This includes potential shortcomings in providing timely surgical intervention for specific types of hemorrhagic strokes, rendering these populations more vulnerable to the adverse effects of extreme temperatures.
Extreme temperatures can cause several physiological changes that can lead to ischemic and hemorrhagic strokes. Extreme cold results in a decreased core body temperature, which can result in increased sympathetic activity, vasoconstriction, increased skeletal muscle tone to conserve heat, and high blood pressure.25,26 Hypertension increases the risk of both ischemic and hemorrhagic strokes.27 Hypothermia is associated with an increased risk of bleeding. Perioperative evidence shows that even mild hypothermia (<1 °C) increases blood loss by ≈16% (4%–26%) and increases the need for blood transfusion.28 Cholesterol crystallization from cold exposure may also increase in atherosclerotic plaques, leading to a risk of plaque rupture, which increases the risk of ischemic stroke.29 On the contrary, extreme heat can result in an elevated body temperature, leading to volume depletion, sympathetic activation, and increased oxygen consumption, eventually leading to tachycardia.30 In individuals with preexisting cardiovascular disease, the hypermetabolic state can lead to ischemia or plaque rupture.31 Hyperthermia causes dehydration, which leads to an abnormally high concentration of red blood cells.32 This can lead to a hypercoagulable state, which can increase the risk for thrombosis and, as a result, ischemic stroke.30 High temperatures can also affect cellular endothelial and protein function, especially the chaperone family of heat shock proteins, leading to systemic inflammation, which contributes to the progression of both ischemic and hemorrhagic stroke.33,34
Even as age-standardized rates of stroke mortality decreased, the absolute number of stroke-related deaths increased by 43% between 1990 and 2019.24 Our findings indicate that out of every 1000 ischemic stroke deaths, 11 can be attributed to the most extreme hot and cold days. A similar estimate was found for hemorrhagic stroke mortality. Though our study may not generate direct evidence for clinical practice, we encourage professional stroke societies and the scientific community to devote further research and attention to emerging environmental risk factors such as extreme temperatures and to identify gaps for future intervention studies. Given the escalating impact of climate change on extreme temperatures, we foresee a widening disparity in stroke mortality between high- and low-income countries, as the latter are likely to bear a larger share of climate effects without effective mitigation strategies.
This study has several limitations. First, the pooled results should not be interpreted as global estimates. There are many countries and regions in the world that were not included in our analysis. In particular, we anticipate that the effects of temperature on stroke mortality may be heightened in regions with high average temperatures and low economic resources. Our data are underrepresented in low-income countries, especially in South Asia, Africa, and the Middle East, and largely reflects urban settings. Second, our data were limited to only fatal ischemic and hemorrhagic strokes. Studying the incidence of nonfatal strokes (ie, hospitalization) will provide further understanding of the actual burden of temperature and stroke on disability and health care systems. Third, hemorrhagic stroke mortality was a composite outcome for 3 categories: nontraumatic subarachnoid hemorrhage (ICD-10, I60), nontraumatic intracerebral hemorrhage (ICD-10, I61), and other and unspecified nontraumatic intracranial hemorrhage (ICD-10, I62). We were not able to collect these subtypes of hemorrhagic stroke mortality. Additionally, although they cannot be confounders, individual-level characteristics such as age, sex, and education were not examined as effect measure modifiers in our study. Our initial data collection protocol was primarily centered on broader, aggregate counts, and as a result, these detailed individual-level demographics were not collected. Finally, our temperature exposure data were based on monitoring stations and climate reanalysis data and not on the actual perceived microenvironment temperature (eg, inside homes). Differences in activity patterns, housing conditions, and occupational exposure (eg, outdoor workers) impact the relationship between personal exposures and the ambient temperatures analyzed in this study.

CONCLUSIONS

The public health burden from extreme hot and cold temperatures is substantial. Extreme temperatures increase the risk of hemorrhagic and ischemic stroke deaths. As climate change is driving more extreme temperatures and weather events, urgent attention to clinical care, adaptation, and mitigation is needed to minimize the risk of death from stroke, especially in low-income countries.

ARTICLE INFORMATION

Supplemental Material

Tables S1–S5

APPENDIX

Multi-Country Multi-City (MCC) Network Group author list:
Souzana Achilleos (School of Health Sciences, Cyprus University of Technology, Limassol, Cyprus; Department of Primary Care and Population Health, University of Nicosia Medical School, Cyprus), Fiorella Acquaotta (Department of Earth Sciences, University of Torino, Turin, Italy), Shih-Chun Pan (National Institute of Environmental Health Science, National Health Research Institutes, Zhunan, Taiwan), Micheline Sousa Zanotti Stagliorio Coelho (Institute of Advanced Studies, University of São Paulo, Brazil), Valentina Colistro (Department of Quantitative Methods, School of Medicine, University of the Republic, Montevideo, Uruguay), Tran Ngoc Dang (Department of Environmental Health, Faculty of Public Health, University of Medicine and Pharmacy at Ho Chi Minh City, Vietnam), Do Van Dung (Department of Environmental Health, Faculty of Public Health, University of Medicine and Pharmacy at Ho Chi Minh City, Vietnam), Francesca K. De’ Donato (Department of Epidemiology, Lazio Regional Health Service, Rome, Italy), Alireza Entezari (Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran), Yue-Liang Leon Guo (Environmental and Occupational Medicine and Institute of Environmental and Occupational Health Sciences, National Taiwan University and National Taiwan University Hospital, Taipei; National Institute of Environmental Health Science, National Health Research Institutes, Zhunan, Taiwan), Masahiro Hashizume (Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Japan), Yasushi Honda (Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Japan), Ene Indermitte (Institute of Family Medicine and Public Health, University of Tartu, Estonia), Carmen Íñiguez (Department of Statistics and Computational Research, Universitat de València, Spain; CIBER de Epidemiología y Salud Pública, Madrid, Spain), Jouni J.K. Jaakkola (Center for Environmental and Respiratory Health Research and Medical Research Center Oulu (MRC Oulu), University of Oulu, Finland), Ho Kim (Graduate School of Public Health, Seoul National University, South Korea), Whanhee Lee (School of the Environment, Yale University, New Haven, CT; School of Biomedical Engineering, College of Information and Biomedical Engineering, Pusan National University, Yangsan, South Korea), Shanshan Li (Department of Epidemiology and Preventive Medicine and Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia), Joana Madureira (Department of Enviromental Health, Instituto Nacional de Saúde Dr Ricardo Jorge, Porto, Portugal; EPIUnit, Instituto de Saude Publica, Universidade do Porto, Portugal; Laboratory for Integrative and Translational Research in Population Health, Porto, Portugal), Fatemeh Mayvaneh (Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran), Hans Orru (Institute of Family Medicine and Public Health, University of Tartu, Estonia), Ala Overcenco (Laboratory of Management in Science and Public Health, National Agency for Public Health of the Ministry of Health, Chisinau, Moldova), Martina S. Ragettli (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Switzerland), Niilo R.I. Ryti (Center for Environmental and Respiratory Health Research, Medical Research Center Oulu (MRC Oulu), and Biocenter Oulu, University of Oulu, Finland), Paulo Hilario Nascimento Saldiva (Department of Pathology, Faculty of Medicine, University of São Paulo, Brazil), Noah Scovronick (Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA), Xerxes Seposo (School of Tropical Medicine and Global Health, Nagasaki University, Japan), Susana Pereira Silva (Department of Epidemiology, Instituto Nacional de Saúde Dr Ricardo Jorge, Lisboa, Portugal), Massimo Stafoggia (Department of Epidemiology, Lazio Regional Health Service, Rome, Italy), Aurelio Tobias (Institute of Environmental Assessment and Water Research, Spanish Council for Scientific Research, Barcelona, Spain; School of Tropical Medicine and Global Health, Nagasaki University, Japan).

Footnote

Nonstandard Abbreviations and Acronyms

eCI
empirical confidence intervals
GDP
gross domestic product
ICD
International Classification of Disease
MCC
Multi-Country Multi-City
MMT
minimum mortality temperature
RR
relative risk

Supplemental Material

File (str_stroke-2023-045751_supp1.pdf)
File (str_stroke-2023-045751_supp2.pdf)

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Published In

Go to Stroke
Stroke
Volume 55 • Number 7 • July 2024
Pages: 1847 - 1856
PubMed: 38776169

Copyright

© 2024 American Heart Association, Inc.

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History

Received: 8 November 2023
Revision received: 27 March 2024
Accepted: 9 April 2024
Published online: 22 May 2024
Published in print: July 2024

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Keywords

  1. climate change
  2. extreme cold
  3. hemorrhagic stroke
  4. ischemic stroke
  5. temperature

Subjects

  1. Cerebrovascular Disease/Stroke
  2. Epidemiology
  3. Risk Factors

Authors

Affiliations

Barrak Alahmad, MD, PhD https://orcid.org/0000-0002-9523-9537 [email protected]
Environmental Health Department (B.A., A.S.B., A.Z., J.S., P.K.), Harvard T.H. Chan School of Public Health, Boston, MA.
Dasman Diabetes Institute, Kuwait City, Kuwait (B.A., F.A.-M.).
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Haitham Khraishah, MD https://orcid.org/0000-0003-0003-5089
Cardiology Division, University of Maryland Medical Center, University of Maryland, Baltimore (H.K.).
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Meghana Kamineni, BS https://orcid.org/0000-0002-6698-5151
Harvard Medical School, Boston, MA (M.K.).
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Dominic Royé, PhD https://orcid.org/0000-0002-5516-6396
Climate Research Foundation, Madrid, Spain (D.R.).
CIBER de Epidemiología y Salud Pública, Madrid, Spain (D.R.).
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Stefania I. Papatheodorou, PhD https://orcid.org/0000-0002-9451-9094
Department of Epidemiology (S.I.P.), Harvard T.H. Chan School of Public Health, Boston, MA.
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Ana Maria Vicedo-Cabrera, PhD
Institute of Social and Preventive Medicine (A.M.V.-C.), University of Bern, Switzerland.
Oeschger Center for Climate Change Research (A.M.V.-C.), University of Bern, Switzerland.
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Yuming Guo, PhD
Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia (Y.G.).
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Eric Lavigne, MD https://orcid.org/0000-0001-6146-9839
School of Epidemiology & Public Health, Faculty of Medicine, University of Ottawa, Canada (E.L.).
Environmental Health Science and Research Bureau, Health Canada, Ottawa (E.L.).
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Ben Armstrong, PhD https://orcid.org/0000-0003-4407-0409
Department of Public Health Environments and Society (B.A.), London School of Hygiene & Tropical Medicine, United Kingdom.
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Francesco Sera, PhD https://orcid.org/0000-0002-8890-6848
Department of Statistics, Computer Science and Applications G. Parenti, University of Florence, Italy (F.S.).
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Aaron S. Bernstein, MD https://orcid.org/0000-0003-1703-1041
Environmental Health Department (B.A., A.S.B., A.Z., J.S., P.K.), Harvard T.H. Chan School of Public Health, Boston, MA.
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Antonella Zanobetti, PhD https://orcid.org/0000-0001-8170-4736
Environmental Health Department (B.A., A.S.B., A.Z., J.S., P.K.), Harvard T.H. Chan School of Public Health, Boston, MA.
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Eric Garshick, MD https://orcid.org/0000-0001-5931-5638
Pulmonary, Allergy, Sleep and Critical Care Medicine Section, Department of Medicine, Veterans Affairs Boston Healthcare System, West Roxbury, MA (E.G.).
Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA (E.G.).
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Joel Schwartz, PhD https://orcid.org/0000-0001-6168-378X
Environmental Health Department (B.A., A.S.B., A.Z., J.S., P.K.), Harvard T.H. Chan School of Public Health, Boston, MA.
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Michelle L. Bell, PhD https://orcid.org/0000-0002-3965-1359
School of the Environment, Yale University, New Haven, CT (M.L.B.).
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Fahd Al-Mulla, MD
Dasman Diabetes Institute, Kuwait City, Kuwait (B.A., F.A.-M.).
View all articles by this author
Petros Koutrakis, PhD
Environmental Health Department (B.A., A.S.B., A.Z., J.S., P.K.), Harvard T.H. Chan School of Public Health, Boston, MA.
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Antonio Gasparrini, PhD https://orcid.org/0000-0002-2271-3568
Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society (A.G.), London School of Hygiene & Tropical Medicine, United Kingdom.
View all articles by this author
Multi-Country Multi-City (MCC) Network
Barcelona, Spain
School of Tropical Medicine and Global Health, Nagasaki University, Japan

Notes

For Sources of Funding and Disclosures, see page 1855.
*
A list of all Multi-Country Multi-City (MCC) Network group authors is given in the Appendix.
Supplemental Material is available at Supplemental Material.
Correspondence to: Barrak Alahmad, MD, PhD, Harvard T.H. Chan School of Public Health, 401 Park Dr, 4th floor, Room 412G, Boston, MA 02115. Email [email protected]

Disclosures

Disclosures Dr Garshick receives grant support from the US Department of Veterans Affairs and is an author for UpToDate. Dr Zanobetti receives grant support from the National Institutes of Health. Dr Bell receives grant support from US Environmental Protection Agency, National Institutes of Health, High Tide Foundation, Health Effects Institute, Yale Women Faculty Forum, Environmental Defense Fund, Wellcome Trust Foundation, Yale Climate Change and Health Center, Robert Wood Johnson Foundation, Hutchinson Postdoctoral Fellowship, and Institute of Physics. The other authors report no conflicts.

Sources of Funding

This study was supported by the Kuwait Foundation for the Advancement of Science (Grant ID: CB21-63BO-01), the Medical Research Council-UK (Grant ID: MR/V034162/1), the European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655), the Swiss National Science Foundation (Grant ID: TMSGI3_211626), and the National Institutes of Health (Grant ID: R01ES034038).

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Extreme Temperatures and Stroke Mortality: Evidence From a Multi-Country Analysis
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