Multi-country multi-city study on heterogeneous impacts of urbanicity on heat-related mortality

Multi-country multi-city study on heterogeneous impacts of urbanicity on heat-related mortality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Multi-country multi-city study on heterogeneous impacts of urbanicity on heat-related mortality Whanhee Lee, Jieun Oh, Yoorim Bang, Cinoo Kang, Jieun Min, Dohoon Kwon, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6332861/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Urbanicity might have complicated impacts on heat-related mortality, intertwined with national and regional context; however, the relevant evidence is limited. This multi-country study investigated the complex roles of urbanicity on heat-related mortality, considering spatial heterogeneity across different urban contexts. Through the Multi-City multi-Country (MCC) research collaborative network, the data on mortality and ambient temperature from 397 cities in 12 countries were collected, and we examined the heterogeneous associations between urbanicity characteristics and heat-mortality risk by region. Higher population density was associated with increased heat-mortality risk in North America and Europe, while the inverse association was observed in Latin America; however, the association was not significant in East Asia. Among urban environment factors, increased PM 2.5 concentrations, CO 2 emissions, and urban heat island intensity generally contributed to a higher heat-mortality risk. Whereas greater greenness mitigated the heat-mortality risk, particularly in Europe. Our findings highlight heterogeneous roles of urbanicity on vulnerability to heat risk and could provide scientific evidence for establishing targeted urban health strategies against climate change. Earth and environmental sciences/Environmental social sciences Health sciences/Health care Heat Air temperature Mortality Urban climate Figures Figure 1 Figure 2 Figure 3 Figure 4 Main The association between high ambient temperatures and an increased risk of morbidity or mortality is established in numerous studies 1 – 3 . As climate change drives more frequent and intense extreme heat events, the burden of heat-related health outcomes is expected to rise, making it a critical concern worldwide 2 . However, in general, heat-related risks are not uniformly distributed and can vary due to regional differences, socioeconomic disparities, and environmental conditions 1 . Among these conditions, urbanization might play a key role in shaping heat-related health risk 4 – 6 . The world, especially in newly industrialized countries, has experienced unprecedented urbanization during recent decades, which is a multifaceted phenomenon involving social and environmental challenges 7 , 8 . Despite the positive aspects of urbanization, such as higher income, technological innovation, and better healthcare services and education, a growing body of evidence suggests that the environmentally detrimental impacts of urbanicity on human health might exist 4 . Several studies reported that a higher level of urbanicity (e.g., a higher population density) might be associated with increased heat-related mortality risk, and ‘urban heat island’ phenomenon has been suggested as a major risk factor together with other characteristics of cities, such as dense population, limited accessibility to emergency medical infrastructures due to the high population density, poor ventilation, and less green areas. 6 , 9 Although urbanization as a global phenomenon occurs in most countries, previous studies present mixed results on the association between urbanicity and health impacts from heat, depending on countries or regions. Several studies based on European, North American, and some of East Asian countries showed an increased heat-related risk in more urbanized areas, while the association was less pronounced in China and Latin American countries 4–6,9−14 . Meanwhile, several studies in the United States exhibited different results where one study found that urban counties had a higher heat-related mortality risk than non-urban counties 15 , and other literature reported that less urban areas are more vulnerable to heat than urbanized areas 16 . These previous findings imply that the association between urbanicity and health outcomes may also differ by the development level of each region. For example, urbanization in less industrialized regions often involves positive health factors, such as improved socioeconomic and medical infrastructure, while urban concentrations in highly industrialized regions are likely to experience increased factors that can negatively affect health, such as urban heat island phenomenon 9 , 10 . Therefore, studies focused on a single country or region might have limitations in comprehensively understanding the complex and context-dependent roles of urbanicity on heat-mortality risk, highlighting the need for multi-city and multi-country research. In this study, we utilized data from the Multi-Country Multi-City (MCC) collaborative network 17 , to investigate the effects of urbanicity on heat-related mortality across 397 cities and 12 countries and describe the spatial heterogeneity. We also examined different roles of urban environment indicators by a regional level of urbanicity. Results Spatial distribution of mortality, temperature, and urbanicity We examined a total of 12,718,478 deaths from 397 cities in 12 countries (further details in the supplementary information). The daily mean temperatures during the warm period ranged from 19.2°C in Europe to 24.5°C in East Asia (Table 1 and Fig. 1 ). Urbanicity and related characteristics are described in Supplementary Tables 1 and 2. The level of urbanicity, as indicated by population density, was higher in cities from East Asia and Europe, compared to cities from North America. Table 1 Descriptive statistics of 397 cities by country and region. Region and country Number of cities Period Temperature (°C) Mortality Daily mean 75th − 99th percentiles Total Daily mean per city Latin America 24 23.9 25.0 27.7 1898720 42 Brazil 15 1997–2011 25.5 26.4 28.5 1008574 37 Mexico 9 1998–2014 21.3 22.6 26.4 890146 48 North America 157 22.4 24.8 29.3 5363194 16 Canada 20 1990–2015 17.3 19.9 25.3 907065 14 United States 137 1990–2006 23.2 25.5 29.9 4456129 16 East Asia 70 24.5 27.0 30.5 1471727 23 China 14 2000–2015 25.1 27.4 31.1 363305 11 South Korea 37 2000–2018 24.8 27.3 30.5 451630 20 Japan 19 2011–2015 23.6 26.0 30.2 656792 15 Europe 146 19.2 21.2 26.1 3984837 11 France 18 2000–2014 19.3 21.4 27.2 512911 16 Germany 12 1993–2015 17.4 20 26.6 973952 29 Italy 16 2001–2010 23.6 26 30 197698 13 Spain 41 1990–2014 22.6 24.8 29.1 848235 7 UK 59 1990–2016 15.9 17.6 22.5 1452041 7 Heat-mortality risk by region Figure 2 shows the heat-mortality risk estimated at the 99th vs. 75th temperature percentiles. To examine regional differences, results are presented for Latin America, North America, East Asia, and Europe. While the heat-mortality risk was evident across all regions, Europe experienced the highest risk, followed by East Asia, Latin America, and North America. Impacts of urbanicity on heat-mortality risk The impacts of urbanicity in heat-related mortality are shown in Fig. 3 . We used two measures of population density to evaluate the level of urbanicity: population per area (overall population density), which represents the total number of people in a given area, and population per built-up area (built-up population density), which focuses on the population specifically within developed areas. Results are presented as the percentage change in risk per interquartile range (IQR) increase in urbanicity. The higher overall population density was associated with increased heat-mortality risk, especially in North America and Europe, while this association was not prominent in Latin America and East Asia. Built-up population density showed a similar pattern; however, in Latin America, an evident inverse association was observed, with higher built-up population density decreasing heat-mortality risk. The sensitivity analyses showed that different modelling choices did not substantially alter the main findings (Supplementary Tables 3 and 4). Other characteristics of urbanicity and heat-mortality risk To further explore the complexity of urbanicity, we first selected several related environmental factors and assessed their correlations with urbanicity (Table 2 ). Overall, the normalized difference vegetation index (NDVI) was negatively correlated with urbanicity, while fine particulate matter smaller than 2.5µm (PM 2.5 ) concentrations were positively correlated with urbanicity. The correlations with environmental characteristics were more pronounced in built-up population density than in overall population density. Table 2 Partial correlations between urbanicity and each environmental characteristic, controlling for other characteristics. Urbanicity Region Correlation coefficients (p-values) Urban environment characteristics NDVI 1 PM 2.5 concentration 2 CO 2 emission (urban-related sectors) 3 Urban heat island intensity 4 Overall population density Total -0.07 (0.18) 0.07 (0.16) 0.19 (< 0.001) 0.06 (0.23) Latin America -0.30 (0.18) 0.61 (< 0.001) 0.21 (0.36) -0.20 (0.38) North America 0.25 (0.01) 0.14 (0.10) 0.56 (< 0.001) 0.14 (0.08) East Asia 0.07 (0.55) -0.36 (< 0.001) 0.39 (< 0.001) 0.50 (< 0.001) Europe 0.02 (0.84) 0.14 (0.10) 0.20 (0.02) -0.20 (0.02) Built-up population density Total -0.45 (< 0.001) 0.35 (< 0.001) -0.03 (0.61) 0.00 (0.97) Latin America 0.03 (0.89) -0.10 (0.67) 0.05 (0.83) -0.05 (0.81) North America -0.09 (0.29) -0.29 (< 0.001) 0.28 (< 0.001) -0.10 (0.24) East Asia -0.21 (0.09) 0.43 (< 0.001) -0.01 (0.96) 0.12 (0.34) Europe -0.66 (< 0.001) -0.03 (0.70) 0.09 (0.30) 0.04 (0.62) 1 controlling for PM 2.5, CO 2 (urban-related sectors), and Urban heat island intensity. 2 controlling for NDVI, CO 2 (urban-related sectors), and Urban heat island intensity. 3 controlling for NDVI, PM 2.5, and Urban heat island intensity. 4 controlling for NDVI, PM 2.5, and CO 2 (urban-related sectors). Figure 4 shows the impacts of urban environment factors on heat-related mortality, exhibiting regional heterogeneity. The effects of environmental factors on heat-related mortality were most pronounced in Europe compared to other regions. Specifically, only Europe exhibited a negative relationship between NDVI and heat-related mortality was evident. Furthermore, PM 2.5 concentrations, carbon dioxide (CO 2 ) emission, and the urban heat island intensity (UHI) for nighttime were positively associated with heat-related mortality in Europe, with PM 2.5 concentrations having the largest effect size. In North America, among environmental factors, only CO 2 emissions had a prominent impact, with higher CO 2 emissions associated with an increased risk of heat-related mortality. In East Asia, none of the environmental factors had a substantial impact on heat-related mortality. However, Latin America exhibited an inconsistent pattern from other regions, with higher PM 2.5 concentrations associated with a lower heat-mortality risk. For sensitivity analysis, we conducted meta-regression models separately for each environmental indicator, instead of including all indicators in a single model, and the results were consistent with the main analysis (Supplementary Fig. 1). Discussion This study examined the complex roles of urbanicity on heat-related mortality by region, using the multi-country dataset covering 397 cities in 12 countries. In general, European region presented the highest heat-mortality risk, with urban environment characteristics, including NDVI, PM 2.5 concentrations, CO 2 emissions, and the UHI for nighttime, exerting the most substantial impacts. The association between higher urbanicity levels and increased heat-mortality risk was also observed in the North American region, although their estimated heat-mortality risks were relatively lower than those in Europe. On the other hand, it was difficult to find the association between heat-mortality risk and urbanicity or urban environment characteristics in the East Asian region. Latin America demonstrated a negative association between urbanicity and heat-mortality risk, with lower risks in more urban areas, which contrasts with the trends observed in the European and North American regions. This multi-country study examined regional differences in heat-mortality risk and the impacts of urbanicity and environmental factors on this risk. The highest risk of heat-related mortality was observed in Europe. Health risk due to heat has been a major public health concern in Europe, particularly after the extreme heatwave during the summer of 2003 18 . Europe is known to have lower air conditioning penetration rates, compared to North America and East Asia, and its residential buildings are identified as susceptible to indoor overheating 19 , 20 . Furthermore, we found a strong association between urban environment characteristics (NDVI, PM 2.5 concentrations, CO 2 emissions, and the UHI for nighttime) and heat-mortality risk in Europe. Although the related evidence was limited, we conjecture that these results might be related to the high-density and compact urban forms of many European cities, which can restrict air circulation and trap heat and air pollutants 21 , 22 . On the other hand, in dense environments, green spaces can provide more concentrated cooling and air-purifying benefits, which may be particularly critical in Europe due to low air conditioning prevalence 19 . The association between urbanicity and heat-mortality risk was most pronounced in North America, although the risk was relatively low compared to other regions. This may be explained by the observation that, while most cities in North America had relatively low population densities, some highly developed metropolitan cities, such as New York, Chicago, and Montreal, had particularly high heat-mortality risks. In North America, urban sprawl has been a dominant trend, with surrounding natural landscape developed primarily for housing, resulting in low-density areas, while high-density urban centers concentrate both population and built infrastructure 23 . This urban development pattern may create disparities in heat exposure and related health risks across North American cities. For example, in high-density areas, increased heat accumulation from paved surfaces and buildings, coupled with limited cooling capacity, may contribute to a higher risk of heat-related mortality, compared to low-density areas with more dispersed green spaces 24 , 25 . Meanwhile, in Latin America, in contrast to other regions, higher population density and greater level of urbanization were associated with reduced risk of heat-related mortality. This is supported by the finding of a negative association between PM 2.5 concentrations and the risk of heat-related mortality. This regional heterogeneity may be driven by the diverse urbanization patterns that can result in variations in urban form, infrastructure, and socio-environmental conditions. Furthermore, it might be related to the overall level of economic progress of a country or region, as measured by gross domestic product (GDP) per capita. Among the countries in this study, those in North America and Europe generally exhibited higher levels of GDP per capita, while countries in Latin America showed relatively lower GDP per capita (Supplementary Table 5). Therefore, one plausible hypothesis is that in lower-income regions, urbanization is likely to bring health benefits due to improved environments, whereas in higher-income regions, which have already experienced urbanization and its associated advantages, urban areas may encounter predominantly negative health factors 5 . We found no evident association between urbanicity or urban environment factors and heat-mortality risk in East Asia, in line with the inconsistent findings from existing country-specific studies. Specifically, in Japan, a positive association was observed between population density and heat-mortality risk, while in China, a negative association was found, and South Korea exhibited a U-shaped relationship 4 , 5 , 10 . East Asian countries have experienced urbanization at different times and in varying patterns over the past five decades 26 , and the GDP per capita also varied significantly during the study period (Supplementary Table 5). Although further study is required, these gaps in urbanization and development levels across East Asian countries may contribute to different impacts of urban environments. We should acknowledge several assumptions and limitations of this study. First, the analysis was limited to urban areas in middle- and high-income countries to which the GHS-UCDB could be linked. Therefore, the study areas have all experienced urbanization, to varying degrees, and they do not represent the rural populations. To examine the effect modification of urbanicity in these urban settings, we utilized the interaction term between region and urbanicity in the second stage of our analysis framework, instead of stratifying by rural and urban areas. Further study is required to support the findings in low-income countries. While the study is global, many regions of the world (e.g., Africa) are underrepresented and warrant further research. Second, the data periods vary across countries. Although we restricted the start of the study period to 1990 to mitigate this variability, the estimates in this study should be interpreted as the results for the corresponding period in each country. Third, the city-specific variables used in this study were collected from three other sources (OECD database, GHS-UCDB, and UHI dataset by YCEO) and have different geographic resolutions, which may affect the consistency of data streams across all cities and countries. However, we found that variables obtained from other data sources showed similar country-specific associations with heat-mortality risk (e.g., overall population density vs. built-up population density). Finally, individual-level confounding or effect-modifying variables were not considered in this study. Additional research is warranted on other factors that may vary across cities such as the type of greenspace, air pollution mixture, built environment, etc. Importantly, further investigation is needed on heat-related mortality in relation to different types of urbanization and urbanicity/rurality metrics (e.g., population density, medical care infrastructure, employment, urban greenspace). Regions and countries with many urban cities are grappling with the increased heat-mortality risks and strategies to address these challenges. Urbanicity has a complex influence on heat-mortality risk: urbanicity is related to factors that mitigate heat-mortality risk such as higher income opportunities and access to health care, but urbanicity is also related to factors that could increase heat risks such as poor air quality and higher exposure due to urban heat island effects. Our findings signal the necessity of region- and country-specific heat action plans tailored to the area’s urban development. As a coping strategy for mitigating heat-mortality risks, cities need to develop policies specific to the level of urbanicity and population. Addressing social and environmental challenges related to heat-mortality risk in urban regions can contribute to improving urban population well-being and facilitating sustainable development of cities. Methods Mortality and temperature data We obtained daily time-series data on mortality counts and average air temperatures across 397 cities in 12 countries, which were grouped into four regions: Latin America, North America, East Asia, and Europe, from the MCC Collaborative Research Network database 17 . For each city, daily mortality data were collected for all causes or non-accidental causes (International Classification of Diseases [ICD]-9 codes 0-799 or ICD-10 codes A00-R99). As the periods of available data differed by country, we restricted the study period to begin in 1990. We also used only the four warmest months in each country for the statistical analysis 27 . Information on data collection for each country is described in the supplementary information, and details of the dataset are listed in Table 1 . Urbanicity indicators To represent a direct characteristic of urbanicity, we collected data on population density at the city level using two different measures: overall population density and built-up population density. Overall population density, defined as the total population per square kilometer of area, was obtained from the Organisation for Economic Co-operation and Development (OECD) database, as described in a previous study 6 . Built-up population density, defined as the population per square kilometer of built-up or developed land area, was obtained using the Global Human Settlement Layer Urban Centres Database (GHS-UCDB) developed by the European Union 28 . This database provides several variables for more than 10,000 urban spaces (hereafter referred to as cities), including geography, environment, and socio-economy, with temporal coverage from 1975 to 2015 at 15-year intervals. We linked 397 cities in the MCC database to the GHS-UCDB at the city level, and then extracted data on resident population and built-up area to calculate built-up population density for each city. Urban environment indicators In this study, we focused on environmental factors among the elements of urbanicity, which can play a crucial role in shaping intervention strategies to mitigate health risks in urban areas. We selected four city-level indicators related to urbanicity: (1) NDVI reflecting greenspace, which is known to mitigate heat stress, (2) PM 2.5 concentrations, a major air pollutant liked to various health outcomes (3) CO 2 emissions in the industrial, residential, and transport sectors, representing key sources of greenhouse gases and air pollution, (4) and the UHI for nighttime, a direct measure of urban heat exposure 6 , 9 . Data for NDVI, PM 2.5 concentrations, and CO 2 emissions were extracted from the GHS-UCDB with the same process. We combined CO 2 emissions in the industrial, residential, and transport sectors into a single indicator representing urban-related emissions. The UHI for nighttime was derived from the global UHI dataset developed by the Yale Centre for Earth Observation (YCEO) 29 . The dataset was created using MODIS 8-day TERRA and Aqua Land Surface Temperature data and is available in the Google Earth Engine (GEE) platform. We extracted and averaged all pixels within a 5-km buffer of the centroid of each city. Details of urbanicity and related indicators are described in Supplementary Tables 1 and 2. To derive representative values for different study periods of each country in the MCC database, we obtained annual values by performing linear interpolation for all variables and subsequently, calculated averages within study periods of each country. Statistical analysis The analyses follow a three-stage approach: this study (1) estimated the city-specific heat-mortality associations, (2) pooled the city-specific heat-mortality risk estimates by fitting meta-regression for each region, and (3) evaluated the association between urbanicity or urbanicity-related characteristics and heat-mortality risk with a meta-regression. In the first stage, we estimated the city-specific heat-mortality associations for 397 cities through a quasi-Poisson regression model. We modelled the cross-basis function of temperature and its lag using distributed lag nonlinear models. The exposure-response function was modelled using a quadratic B-spline with two internal knots placed at 50th and 90th percentiles for each city, while the lag-response function was modelled with a natural cubic B-spline with an intercept and two internal knots placed at equally spaced values in the log scale with lag periods to 10 days. To control for long-term trends and seasonality, we included a natural cubic B-spline function with 4 degrees of freedom per year and an indicator for day of the week. We calculated the city-specific relative risk (RR) for mortality by comparing the 99th and the 75th percentiles of temperature in each city. The choices of the aforementioned modelling assumptions and parameters are based on previous studies 2 , 30 . As the second stage, we repeated the intercept-only meta-analysis for each region using the city-specific estimates, to calculate the pooled heat-mortality risk by region. In the third stage, to examine the association between urbanicity and heat-mortality risk across different regions, we performed a multivariate meta-regression analysis. For each urbanicity variable, we fitted a model with an urbanicity variable, indicators for region, and their interaction term, while accounting for country-level variation through a random intercept. Average and range of daily mean temperature and climate zone were included as confounders. From the meta-regression model, we extracted and incorporated the coefficients for the urbanicity variable and its interaction term with region indicators to obtain the region-specific association between urbanicity and heat-related mortality. We tested the residual heterogeneity using the Cochran Q-test and I 2 statistic (Supplementary Table 6). Furthermore, we explored other characteristics that are related to urbanicity, which may account for heat-mortality risk. First, we examined the partial correlation between urbanicity and environment indicators, to determine how each characteristic is related to urbanicity. Next, we fitted a meta-regression model that included all the urban environment indicators simultaneously to control for the influence of other characteristics, including the aforementioned confounders. For sensitivity analysis, we tested several modelling specifications to examine the robustness of our results by varying the knots in the exposure-response function, the number of degrees of freedom per year used to control for long-term trends and seasonality, and the number of lag days. We also conducted meta-regression models separately for each environmental indicator, instead of including all indicators in a single model. Declarations Data availability All the data necessary to support the conclusions of this paper are present in the paper and/or in the Supplementary Information. Data used in this study were collected by collaborators within the MCC Collaborative Research Network under a data sharing agreement and cannot be made publicly available. Code availability The code used to generate the results is available from the authors upon request. Acknowledgments This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Climate Change R&D Project for New Climate Regime, funded by Korea Ministry of Environment (MOE) (grant number:RS-2022-KE002235). This work was also supported by Korea Environment Industry & Technology Institute (KEITI) through Digital Infrastructure Building Project for Monitoring, Surveying and Evaluating the Environmental Health, funded by Korea Ministry of Environment (MOE) (project number: 2021003330004). Author information Consortia MCC Collaborative Research Network Francesco Sera 5 , Michelle L Bell 6,7 , Masahiro Hashizume 8 , Eric Lavigne 10 , Susanne Breitner 11 , Francesca de'Donato 12 , Yoonhee Kim 13 , Chris Fook Sheng Ng 14 , Lina Madaniyazi 14 , Ben Armstrong 15 , Pierre Masselot 15 , Yasushi Honda 16 , Aurelio Tobias 17 , Aleš Urban 18,19 , Ana Maria Vicedo-Cabrera 20,21 , Antonio Gasparrini 15 , Ho Kim 1 , Whanhee Lee 23 , Micheline de Sousa Zanotti Stagliorio Coelho 24 , Paulo Hilario Nascimento Saldiva 24 , Haidong Kan 25 , Mathilde Pascal 26 , Alexandra Schneider 27 , Veronika Huber 28 , Paola Michelozzi 12 , Massimo Stafoggia 12 , Magali Hurtado Diaz 29 , Eunice Elizabeth Félix Arellano 29 , Carmen Íñiguez 30 , Antonella Zanobetti 31 , Joel Schwartz 31 , Yuming Guo 32 , Jouni J. K. Jaakkola 33 . 24 Department of Pathology, Faculty of Medicine, University of São Paulo, São Paulo, Brazil 25 Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China 26 Santé Publique France, Department of Environmental Health, French National Public Health Agency, Saint Maurice, France 27 Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health (GmbH), Neuherberg, Germany 28 Institute for Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, LMU Munich, Munich, Germany 29 Department of Environmental Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico 30 Department of Statistics and Computational Research. Universitat de València, València, Spain 31 Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA 32 Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia 33 Center for Environmental and Respiratory Health Research (CERH), University of Oulu, Oulu, Finland Contributions JO, YB, and WL conceived the study idea and designed the research. JO analysed the data and WL provided advice regarding statistical modelling. JO and YB wrote the manuscript. CK, JM, FS, MLB, MH, EMK, EL, SB, FD, YK, CFSN, LM, BA, PM, YH, AT, AU, AMVC, AG, EH, HK, and WL reviewed and revised the manuscript. FS, MLB, MH, EMK, EL, SB, FD, YK, CFSN, LM, BA, PM, YH, AT, AU, AMVC, AG, and the rest of the MCC Collaborative Research Network provided mortality and temperature data. JO and WL accessed and verified the data. JO and DK curated and processed the data for urbanicity and related characteristics. 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Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACxnYGBmYQQ56/+QCQkpAhrKUZqsVwxrEEkBYewtYwQ7UwHMgxAFGEtTA3Mx98XNhml8fYcObzqxs1FjwM7IePbsDvMLZk45ltycXszL3brHOOAR3Gk5Z2A78WHjNp3m3MiY0NZ7cZ57ABtUjwmBHQwv/9N++2+sSGAznPjHP+EaWFh42Zd9thkBbmx7ltRGlhM5bm/Xc8ceOMY2bMuX0SPGyE/GLY3vzwM8+Z6sT5/M2PP+d8q5PjZz98DL+WBgSbTQJM4lMOAvJIbOYPhFSPglEwCkbByAQAlUlGiu08zXYAAAAASUVORK5CYII=","orcid":"","institution":"School of Biomedical Convergence Engineering, College of Information and Biomedical Engineering, Pusan National University","correspondingAuthor":true,"prefix":"","firstName":"Whanhee","middleName":"","lastName":"Lee","suffix":""},{"id":441012909,"identity":"c3abf691-d856-4059-b81d-e65567323d14","order_by":1,"name":"Jieun Oh","email":"","orcid":"https://orcid.org/0000-0002-3934-5829","institution":"Department of Public Health Science, Graduate School of Public Health, Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Jieun","middleName":"","lastName":"Oh","suffix":""},{"id":441012910,"identity":"6d88df08-aa16-46b7-acd0-c2ea1644788d","order_by":2,"name":"Yoorim Bang","email":"","orcid":"","institution":"Institute for Development and Human Security, Ewha Womans University","correspondingAuthor":false,"prefix":"","firstName":"Yoorim","middleName":"","lastName":"Bang","suffix":""},{"id":441012911,"identity":"9365955f-17e2-4683-965f-dc24bf315f61","order_by":3,"name":"Cinoo Kang","email":"","orcid":"","institution":"Department of Public Health Science, Graduate School of Public Health, Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Cinoo","middleName":"","lastName":"Kang","suffix":""},{"id":441012912,"identity":"78fab38a-db09-453e-a1f4-b1a0f6532bf0","order_by":4,"name":"Jieun Min","email":"","orcid":"","institution":"Department of Environmental Medicine, College of Medicine, Ewha Womans University","correspondingAuthor":false,"prefix":"","firstName":"Jieun","middleName":"","lastName":"Min","suffix":""},{"id":441012913,"identity":"ca1abade-667f-450c-94cd-e0332dcb6581","order_by":5,"name":"Dohoon Kwon","email":"","orcid":"","institution":"Department of Public Health Science, Graduate School of Public Health, Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Dohoon","middleName":"","lastName":"Kwon","suffix":""},{"id":441012914,"identity":"b0441e61-91b1-4f62-bae0-3fcdf74a897f","order_by":6,"name":"Francesco Sera","email":"","orcid":"https://orcid.org/0000-0002-8890-6848","institution":"University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Sera","suffix":""},{"id":441012915,"identity":"568631e4-3e62-450d-9bd0-8c355fed1f7b","order_by":7,"name":"Michelle Bell","email":"","orcid":"https://orcid.org/0000-0002-3965-1359","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"","lastName":"Bell","suffix":""},{"id":441012916,"identity":"7fb669ca-3ca7-4f14-b843-07ba6c8f7f74","order_by":8,"name":"Masahiro Hashizume","email":"","orcid":"https://orcid.org/0000-0003-4720-1750","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Masahiro","middleName":"","lastName":"Hashizume","suffix":""},{"id":441012917,"identity":"46be18a0-0770-4658-95ac-45df7a171f15","order_by":9,"name":"Eun Mee Kim","email":"","orcid":"","institution":"Ewha Womans University","correspondingAuthor":false,"prefix":"","firstName":"Eun","middleName":"Mee","lastName":"Kim","suffix":""},{"id":441012918,"identity":"d8495224-cb79-415d-b7d4-e780dd7df6c2","order_by":10,"name":"Eric Lavigne","email":"","orcid":"","institution":"Environmental Health Science and Research Bureau, Health Canada","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Lavigne","suffix":""},{"id":441012919,"identity":"be0fa379-e779-4514-ab9a-330efe860bba","order_by":11,"name":"Susanne Breitner","email":"","orcid":"","institution":"Helmholtz Zentrum München","correspondingAuthor":false,"prefix":"","firstName":"Susanne","middleName":"","lastName":"Breitner","suffix":""},{"id":441012920,"identity":"7cec5c4f-93b8-4fcd-b172-9136df3a5276","order_by":12,"name":"Francesca de'Donato","email":"","orcid":"","institution":"Department of Epidemiology, Lazio Regional Health Service","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"de'Donato","suffix":""},{"id":441012921,"identity":"da7e1e70-bfda-4062-bdec-6701ebaa8ddc","order_by":13,"name":"Yoonhee Kim","email":"","orcid":"https://orcid.org/0000-0003-2517-1087","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Yoonhee","middleName":"","lastName":"Kim","suffix":""},{"id":441012922,"identity":"f71e0090-f30a-483a-8e03-870724e5a974","order_by":14,"name":"Chris Fook Sheng Ng","email":"","orcid":"https://orcid.org/0000-0003-1025-0807","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Chris","middleName":"Fook Sheng","lastName":"Ng","suffix":""},{"id":441012923,"identity":"cded42ce-9a08-40c5-b5d9-1e5b3bc08d29","order_by":15,"name":"Lina Madaniyazi","email":"","orcid":"","institution":"Nagasaki University","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Madaniyazi","suffix":""},{"id":441012924,"identity":"626cae74-3de4-4c56-b602-47497d099df2","order_by":16,"name":"Ben Armstrong","email":"","orcid":"https://orcid.org/0000-0003-4407-0409","institution":"London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Armstrong","suffix":""},{"id":441012925,"identity":"36fa7437-9797-4fb7-830f-09a24ceda0be","order_by":17,"name":"Pierre Masselot","email":"","orcid":"https://orcid.org/0000-0002-7326-1290","institution":"London School of Hygiene and Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Pierre","middleName":"","lastName":"Masselot","suffix":""},{"id":441012926,"identity":"3a84162c-0d78-4fa2-879c-f4ab0bcd913c","order_by":18,"name":"Honda Yasushi","email":"","orcid":"","institution":"National Institute for Environmental Studies","correspondingAuthor":false,"prefix":"","firstName":"Honda","middleName":"","lastName":"Yasushi","suffix":""},{"id":441012927,"identity":"ceb17ae8-8d0e-4b6b-a8be-6ae931056ea8","order_by":19,"name":"Aurelio Tobias","email":"","orcid":"","institution":"Spanish Council for Scientific Research (CSIC)","correspondingAuthor":false,"prefix":"","firstName":"Aurelio","middleName":"","lastName":"Tobias","suffix":""},{"id":441012928,"identity":"6ded3e8e-b4e1-4a99-9cea-73ce8f5591bf","order_by":20,"name":"Aleš Urban","email":"","orcid":"","institution":"Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Aleš","middleName":"","lastName":"Urban","suffix":""},{"id":441012929,"identity":"a7f01d1f-bf4b-4faf-a8a7-c3e4abcd899b","order_by":21,"name":"Ana Vicedo-Cabrera","email":"","orcid":"https://orcid.org/0000-0001-6982-8867","institution":"Institute of Social and Preventive Medicine, University of Bern","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Vicedo-Cabrera","suffix":""},{"id":441012930,"identity":"bb91f6bd-3816-4c4d-9252-ff9e35d59d26","order_by":22,"name":"Antonio Gasparrini","email":"","orcid":"https://orcid.org/0000-0002-2271-3568","institution":"London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Gasparrini","suffix":""},{"id":441012931,"identity":"f0f5cb13-59ac-43b6-b6e6-fd79d42aee3e","order_by":23,"name":"Eunhee Ha","email":"","orcid":"","institution":"Department of Environmental Medicine, College of Medicine, Ewha Womans University","correspondingAuthor":false,"prefix":"","firstName":"Eunhee","middleName":"","lastName":"Ha","suffix":""},{"id":441012932,"identity":"e628df7d-9458-4301-9b08-eafc78113e2f","order_by":24,"name":"Ho Kim","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Ho","middleName":"","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2025-03-29 08:30:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6332861/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6332861/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80371691,"identity":"99950156-29bf-4505-8b19-74f7de0bf14e","added_by":"auto","created_at":"2025-04-11 06:58:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":168599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of average temperature and urbanicity level in 397 cities. \u003c/strong\u003ePoints represent the average temperature during the warm period (A), overall population density (B), and built-up population density (C) for each city during the study period.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6332861/v1/57f80ebd50cfb91a232ad8f4.png"},{"id":80371692,"identity":"d65ecfc4-15df-4619-8650-828523e2b4c4","added_by":"auto","created_at":"2025-04-11 06:58:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe heat-mortality risk by region. \u003c/strong\u003eEstimates were calculated comparing the 99\u003csup\u003eth\u003c/sup\u003e percentile to the 75\u003csup\u003eth\u003c/sup\u003e percentile temperature. The whiskers indicate the 95% empirical confidence intervals.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6332861/v1/ceaa79f0f60386214e138897.png"},{"id":80372993,"identity":"88a31422-a656-4882-a17a-ddbfc10495ae","added_by":"auto","created_at":"2025-04-11 07:14:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between heat-mortality risk and urbanicity by region. \u003c/strong\u003eResults are presented as percentage change in RR (95% CI) per IQR increase in urbanicity. The whiskers indicate the 95% empirical confidence intervals.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6332861/v1/bee44fa36065c1dc821a4d90.png"},{"id":80371700,"identity":"4264569c-252e-4de1-a8c6-54a7ea268f63","added_by":"auto","created_at":"2025-04-11 06:58:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":91147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between heat-mortality risk and urban environment characteristics by region. \u003c/strong\u003eResults are presented as percentage change in RR (95% CI) per IQR increase in each characteristic. The whiskers indicate the 95% empirical confidence intervals.\u003cstrong\u003e \u003c/strong\u003eCO\u003csub\u003e2\u003c/sub\u003e emissions in urban-related sectors include those from the industry, residential, and transport sectors.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6332861/v1/336255eabad84e990b107349.png"},{"id":83144201,"identity":"a3431611-d7b0-422f-8370-2f85b04777d1","added_by":"auto","created_at":"2025-05-20 12:48:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1421843,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6332861/v1/a5b161f5-ad13-49ea-82cf-2aca2762f16d.pdf"},{"id":80373481,"identity":"aafcd14c-24a8-4110-9dbc-aff2b621c72a","added_by":"auto","created_at":"2025-04-11 07:22:32","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":103692,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"Supplementaryinformation250329.docx","url":"https://assets-eu.researchsquare.com/files/rs-6332861/v1/fa6533985c7f493f277f6d8c.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Multi-country multi-city study on heterogeneous impacts of urbanicity on heat-related mortality","fulltext":[{"header":"Main","content":"\u003cp\u003eThe association between high ambient temperatures and an increased risk of morbidity or mortality is established in numerous studies\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. As climate change drives more frequent and intense extreme heat events, the burden of heat-related health outcomes is expected to rise, making it a critical concern worldwide\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, in general, heat-related risks are not uniformly distributed and can vary due to regional differences, socioeconomic disparities, and environmental conditions\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong these conditions, urbanization might play a key role in shaping heat-related health risk\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The world, especially in newly industrialized countries, has experienced unprecedented urbanization during recent decades, which is a multifaceted phenomenon involving social and environmental challenges\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Despite the positive aspects of urbanization, such as higher income, technological innovation, and better healthcare services and education, a growing body of evidence suggests that the environmentally detrimental impacts of urbanicity on human health might exist\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Several studies reported that a higher level of urbanicity (e.g., a higher population density) might be associated with increased heat-related mortality risk, and \u0026lsquo;urban heat island\u0026rsquo; phenomenon has been suggested as a major risk factor together with other characteristics of cities, such as dense population, limited accessibility to emergency medical infrastructures due to the high population density, poor ventilation, and less green areas.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAlthough urbanization as a global phenomenon occurs in most countries, previous studies present mixed results on the association between urbanicity and health impacts from heat, depending on countries or regions. Several studies based on European, North American, and some of East Asian countries showed an increased heat-related risk in more urbanized areas, while the association was less pronounced in China and Latin American countries\u003csup\u003e4\u0026ndash;6,9\u0026minus;14\u003c/sup\u003e. Meanwhile, several studies in the United States exhibited different results where one study found that urban counties had a higher heat-related mortality risk than non-urban counties\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, and other literature reported that less urban areas are more vulnerable to heat than urbanized areas\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. These previous findings imply that the association between urbanicity and health outcomes may also differ by the development level of each region. For example, urbanization in less industrialized regions often involves positive health factors, such as improved socioeconomic and medical infrastructure, while urban concentrations in highly industrialized regions are likely to experience increased factors that can negatively affect health, such as urban heat island phenomenon\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Therefore, studies focused on a single country or region might have limitations in comprehensively understanding the complex and context-dependent roles of urbanicity on heat-mortality risk, highlighting the need for multi-city and multi-country research.\u003c/p\u003e \u003cp\u003eIn this study, we utilized data from the Multi-Country Multi-City (MCC) collaborative network\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, to investigate the effects of urbanicity on heat-related mortality across 397 cities and 12 countries and describe the spatial heterogeneity. We also examined different roles of urban environment indicators by a regional level of urbanicity.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSpatial distribution of mortality, temperature, and urbanicity\u003c/h2\u003e \u003cp\u003eWe examined a total of 12,718,478 deaths from 397 cities in 12 countries (further details in the supplementary information). The daily mean temperatures during the warm period ranged from 19.2\u0026deg;C in Europe to 24.5\u0026deg;C in East Asia (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Urbanicity and related characteristics are described in Supplementary Tables\u0026nbsp;1 and 2. The level of urbanicity, as indicated by population density, was higher in cities from East Asia and Europe, compared to cities from North America.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of 397 cities by country and region.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegion and country\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of cities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e75th \u0026minus;\u0026thinsp;99th percentiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDaily mean per city\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1898720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1997\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1008574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1998\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e890146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5363194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1990\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e907065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1990\u0026ndash;2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4456129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1471727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e363305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e451630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e656792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3984837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e512911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1993\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e973952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e197698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1990\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e848235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1990\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1452041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHeat-mortality risk by region\u003c/h3\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the heat-mortality risk estimated at the 99th vs. 75th temperature percentiles. To examine regional differences, results are presented for Latin America, North America, East Asia, and Europe. While the heat-mortality risk was evident across all regions, Europe experienced the highest risk, followed by East Asia, Latin America, and North America.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eImpacts of urbanicity on heat-mortality risk\u003c/h3\u003e\n\u003cp\u003eThe impacts of urbanicity in heat-related mortality are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. We used two measures of population density to evaluate the level of urbanicity: population per area (overall population density), which represents the total number of people in a given area, and population per built-up area (built-up population density), which focuses on the population specifically within developed areas. Results are presented as the percentage change in risk per interquartile range (IQR) increase in urbanicity. The higher overall population density was associated with increased heat-mortality risk, especially in North America and Europe, while this association was not prominent in Latin America and East Asia. Built-up population density showed a similar pattern; however, in Latin America, an evident inverse association was observed, with higher built-up population density decreasing heat-mortality risk. The sensitivity analyses showed that different modelling choices did not substantially alter the main findings (Supplementary Tables\u0026nbsp;3 and 4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eOther characteristics of urbanicity and heat-mortality risk\u003c/h3\u003e\n\u003cp\u003eTo further explore the complexity of urbanicity, we first selected several related environmental factors and assessed their correlations with urbanicity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Overall, the normalized difference vegetation index (NDVI) was negatively correlated with urbanicity, while fine particulate matter smaller than 2.5\u0026micro;m (PM\u003csub\u003e2.5\u003c/sub\u003e) concentrations were positively correlated with urbanicity. The correlations with environmental characteristics were more pronounced in built-up population density than in overall population density.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePartial correlations between urbanicity and each environmental characteristic, controlling for other characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUrbanicity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eCorrelation coefficients (p-values)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eUrban environment characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDVI\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e concentration\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e emission\u003c/p\u003e \u003cp\u003e(urban-related sectors)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eUrban heat island intensity\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.07 (0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07 (0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.06 (0.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.30 (0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.20 (0.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.14 (0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.36 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.50 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02 (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.20 (0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.45 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03 (0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.00 (0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.10 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.05 (0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.09 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.29 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.10 (0.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.21 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.01 (0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.12 (0.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.66 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.03 (0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09 (0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.04 (0.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003econtrolling for PM\u003csub\u003e2.5,\u003c/sub\u003e CO\u003csub\u003e2\u003c/sub\u003e (urban-related sectors), and Urban heat island intensity.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003econtrolling for NDVI, CO\u003csub\u003e2\u003c/sub\u003e (urban-related sectors), and Urban heat island intensity.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003econtrolling for NDVI, PM\u003csub\u003e2.5,\u003c/sub\u003e and Urban heat island intensity.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003econtrolling for NDVI, PM\u003csub\u003e2.5,\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e (urban-related sectors).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the impacts of urban environment factors on heat-related mortality, exhibiting regional heterogeneity. The effects of environmental factors on heat-related mortality were most pronounced in Europe compared to other regions. Specifically, only Europe exhibited a negative relationship between NDVI and heat-related mortality was evident. Furthermore, PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) emission, and the urban heat island intensity (UHI) for nighttime were positively associated with heat-related mortality in Europe, with PM\u003csub\u003e2.5\u003c/sub\u003e concentrations having the largest effect size.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn North America, among environmental factors, only CO\u003csub\u003e2\u003c/sub\u003e emissions had a prominent impact, with higher CO\u003csub\u003e2\u003c/sub\u003e emissions associated with an increased risk of heat-related mortality. In East Asia, none of the environmental factors had a substantial impact on heat-related mortality. However, Latin America exhibited an inconsistent pattern from other regions, with higher PM\u003csub\u003e2.5\u003c/sub\u003e concentrations associated with a lower heat-mortality risk.\u003c/p\u003e \u003cp\u003eFor sensitivity analysis, we conducted meta-regression models separately for each environmental indicator, instead of including all indicators in a single model, and the results were consistent with the main analysis (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the complex roles of urbanicity on heat-related mortality by region, using the multi-country dataset covering 397 cities in 12 countries. In general, European region presented the highest heat-mortality risk, with urban environment characteristics, including NDVI, PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, CO\u003csub\u003e2\u003c/sub\u003e emissions, and the UHI for nighttime, exerting the most substantial impacts. The association between higher urbanicity levels and increased heat-mortality risk was also observed in the North American region, although their estimated heat-mortality risks were relatively lower than those in Europe. On the other hand, it was difficult to find the association between heat-mortality risk and urbanicity or urban environment characteristics in the East Asian region. Latin America demonstrated a negative association between urbanicity and heat-mortality risk, with lower risks in more urban areas, which contrasts with the trends observed in the European and North American regions.\u003c/p\u003e \u003cp\u003eThis multi-country study examined regional differences in heat-mortality risk and the impacts of urbanicity and environmental factors on this risk. The highest risk of heat-related mortality was observed in Europe. Health risk due to heat has been a major public health concern in Europe, particularly after the extreme heatwave during the summer of 2003\u003csup\u003e18\u003c/sup\u003e. Europe is known to have lower air conditioning penetration rates, compared to North America and East Asia, and its residential buildings are identified as susceptible to indoor overheating\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Furthermore, we found a strong association between urban environment characteristics (NDVI, PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, CO\u003csub\u003e2\u003c/sub\u003e emissions, and the UHI for nighttime) and heat-mortality risk in Europe. Although the related evidence was limited, we conjecture that these results might be related to the high-density and compact urban forms of many European cities, which can restrict air circulation and trap heat and air pollutants\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. On the other hand, in dense environments, green spaces can provide more concentrated cooling and air-purifying benefits, which may be particularly critical in Europe due to low air conditioning prevalence\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe association between urbanicity and heat-mortality risk was most pronounced in North America, although the risk was relatively low compared to other regions. This may be explained by the observation that, while most cities in North America had relatively low population densities, some highly developed metropolitan cities, such as New York, Chicago, and Montreal, had particularly high heat-mortality risks. In North America, urban sprawl has been a dominant trend, with surrounding natural landscape developed primarily for housing, resulting in low-density areas, while high-density urban centers concentrate both population and built infrastructure\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This urban development pattern may create disparities in heat exposure and related health risks across North American cities. For example, in high-density areas, increased heat accumulation from paved surfaces and buildings, coupled with limited cooling capacity, may contribute to a higher risk of heat-related mortality, compared to low-density areas with more dispersed green spaces\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMeanwhile, in Latin America, in contrast to other regions, higher population density and greater level of urbanization were associated with reduced risk of heat-related mortality. This is supported by the finding of a negative association between PM\u003csub\u003e2.5\u003c/sub\u003e concentrations and the risk of heat-related mortality. This regional heterogeneity may be driven by the diverse urbanization patterns that can result in variations in urban form, infrastructure, and socio-environmental conditions. Furthermore, it might be related to the overall level of economic progress of a country or region, as measured by gross domestic product (GDP) per capita. Among the countries in this study, those in North America and Europe generally exhibited higher levels of GDP per capita, while countries in Latin America showed relatively lower GDP per capita (Supplementary Table\u0026nbsp;5). Therefore, one plausible hypothesis is that in lower-income regions, urbanization is likely to bring health benefits due to improved environments, whereas in higher-income regions, which have already experienced urbanization and its associated advantages, urban areas may encounter predominantly negative health factors\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe found no evident association between urbanicity or urban environment factors and heat-mortality risk in East Asia, in line with the inconsistent findings from existing country-specific studies. Specifically, in Japan, a positive association was observed between population density and heat-mortality risk, while in China, a negative association was found, and South Korea exhibited a U-shaped relationship\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. East Asian countries have experienced urbanization at different times and in varying patterns over the past five decades\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and the GDP per capita also varied significantly during the study period (Supplementary Table\u0026nbsp;5). Although further study is required, these gaps in urbanization and development levels across East Asian countries may contribute to different impacts of urban environments.\u003c/p\u003e \u003cp\u003eWe should acknowledge several assumptions and limitations of this study. First, the analysis was limited to urban areas in middle- and high-income countries to which the GHS-UCDB could be linked. Therefore, the study areas have all experienced urbanization, to varying degrees, and they do not represent the rural populations. To examine the effect modification of urbanicity in these urban settings, we utilized the interaction term between region and urbanicity in the second stage of our analysis framework, instead of stratifying by rural and urban areas. Further study is required to support the findings in low-income countries. While the study is global, many regions of the world (e.g., Africa) are underrepresented and warrant further research. Second, the data periods vary across countries. Although we restricted the start of the study period to 1990 to mitigate this variability, the estimates in this study should be interpreted as the results for the corresponding period in each country. Third, the city-specific variables used in this study were collected from three other sources (OECD database, GHS-UCDB, and UHI dataset by YCEO) and have different geographic resolutions, which may affect the consistency of data streams across all cities and countries. However, we found that variables obtained from other data sources showed similar country-specific associations with heat-mortality risk (e.g., overall population density vs. built-up population density). Finally, individual-level confounding or effect-modifying variables were not considered in this study. Additional research is warranted on other factors that may vary across cities such as the type of greenspace, air pollution mixture, built environment, etc. Importantly, further investigation is needed on heat-related mortality in relation to different types of urbanization and urbanicity/rurality metrics (e.g., population density, medical care infrastructure, employment, urban greenspace).\u003c/p\u003e \u003cp\u003eRegions and countries with many urban cities are grappling with the increased heat-mortality risks and strategies to address these challenges. Urbanicity has a complex influence on heat-mortality risk: urbanicity is related to factors that mitigate heat-mortality risk such as higher income opportunities and access to health care, but urbanicity is also related to factors that could increase heat risks such as poor air quality and higher exposure due to urban heat island effects. Our findings signal the necessity of region- and country-specific heat action plans tailored to the area’s urban development. As a coping strategy for mitigating heat-mortality risks, cities need to develop policies specific to the level of urbanicity and population. Addressing social and environmental challenges related to heat-mortality risk in urban regions can contribute to improving urban population well-being and facilitating sustainable development of cities.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Methods","content":"\u003ch2\u003eMortality and temperature data\u003c/h2\u003e\u003cp\u003eWe obtained daily time-series data on mortality counts and average air temperatures across 397 cities in 12 countries, which were grouped into four regions: Latin America, North America, East Asia, and Europe, from the MCC Collaborative Research Network database\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. For each city, daily mortality data were collected for all causes or non-accidental causes (International Classification of Diseases [ICD]-9 codes 0-799 or ICD-10 codes A00-R99). As the periods of available data differed by country, we restricted the study period to begin in 1990. We also used only the four warmest months in each country for the statistical analysis\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Information on data collection for each country is described in the supplementary information, and details of the dataset are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003ch3\u003eUrbanicity indicators\u003c/h3\u003e\u003cp\u003eTo represent a direct characteristic of urbanicity, we collected data on population density at the city level using two different measures: overall population density and built-up population density. Overall population density, defined as the total population per square kilometer of area, was obtained from the Organisation for Economic Co-operation and Development (OECD) database, as described in a previous study\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Built-up population density, defined as the population per square kilometer of built-up or developed land area, was obtained using the Global Human Settlement Layer Urban Centres Database (GHS-UCDB) developed by the European Union\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. This database provides several variables for more than 10,000 urban spaces (hereafter referred to as cities), including geography, environment, and socio-economy, with temporal coverage from 1975 to 2015 at 15-year intervals. We linked 397 cities in the MCC database to the GHS-UCDB at the city level, and then extracted data on resident population and built-up area to calculate built-up population density for each city.\u003c/p\u003e\u003ch2\u003eUrban environment indicators\u003c/h2\u003e\u003cp\u003eIn this study, we focused on environmental factors among the elements of urbanicity, which can play a crucial role in shaping intervention strategies to mitigate health risks in urban areas. We selected four city-level indicators related to urbanicity: (1) NDVI reflecting greenspace, which is known to mitigate heat stress, (2) PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, a major air pollutant liked to various health outcomes (3) CO\u003csub\u003e2\u003c/sub\u003e emissions in the industrial, residential, and transport sectors, representing key sources of greenhouse gases and air pollution, (4) and the UHI for nighttime, a direct measure of urban heat exposure\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eData for NDVI, PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, and CO\u003csub\u003e2\u003c/sub\u003e emissions were extracted from the GHS-UCDB with the same process. We combined CO\u003csub\u003e2\u003c/sub\u003e emissions in the industrial, residential, and transport sectors into a single indicator representing urban-related emissions. The UHI for nighttime was derived from the global UHI dataset developed by the Yale Centre for Earth Observation (YCEO)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The dataset was created using MODIS 8-day TERRA and Aqua Land Surface Temperature data and is available in the Google Earth Engine (GEE) platform. We extracted and averaged all pixels within a 5-km buffer of the centroid of each city.\u003c/p\u003e\u003cp\u003eDetails of urbanicity and related indicators are described in Supplementary Tables\u0026nbsp;1 and 2. To derive representative values for different study periods of each country in the MCC database, we obtained annual values by performing linear interpolation for all variables and subsequently, calculated averages within study periods of each country.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe analyses follow a three-stage approach: this study (1) estimated the city-specific heat-mortality associations, (2) pooled the city-specific heat-mortality risk estimates by fitting meta-regression for each region, and (3) evaluated the association between urbanicity or urbanicity-related characteristics and heat-mortality risk with a meta-regression.\u003c/p\u003e\u003cp\u003eIn the first stage, we estimated the city-specific heat-mortality associations for 397 cities through a quasi-Poisson regression model. We modelled the cross-basis function of temperature and its lag using distributed lag nonlinear models. The exposure-response function was modelled using a quadratic B-spline with two internal knots placed at 50th and 90th percentiles for each city, while the lag-response function was modelled with a natural cubic B-spline with an intercept and two internal knots placed at equally spaced values in the log scale with lag periods to 10 days. To control for long-term trends and seasonality, we included a natural cubic B-spline function with 4 degrees of freedom per year and an indicator for day of the week. We calculated the city-specific relative risk (RR) for mortality by comparing the 99th and the 75th percentiles of temperature in each city. The choices of the aforementioned modelling assumptions and parameters are based on previous studies\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. As the second stage, we repeated the intercept-only meta-analysis for each region using the city-specific estimates, to calculate the pooled heat-mortality risk by region.\u003c/p\u003e\u003cp\u003eIn the third stage, to examine the association between urbanicity and heat-mortality risk across different regions, we performed a multivariate meta-regression analysis. For each urbanicity variable, we fitted a model with an urbanicity variable, indicators for region, and their interaction term, while accounting for country-level variation through a random intercept. Average and range of daily mean temperature and climate zone were included as confounders. From the meta-regression model, we extracted and incorporated the coefficients for the urbanicity variable and its interaction term with region indicators to obtain the region-specific association between urbanicity and heat-related mortality. We tested the residual heterogeneity using the Cochran Q-test and I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e statistic (Supplementary Table\u0026nbsp;6).\u003c/p\u003e\u003cp\u003eFurthermore, we explored other characteristics that are related to urbanicity, which may account for heat-mortality risk. First, we examined the partial correlation between urbanicity and environment indicators, to determine how each characteristic is related to urbanicity. Next, we fitted a meta-regression model that included all the urban environment indicators simultaneously to control for the influence of other characteristics, including the aforementioned confounders.\u003c/p\u003e\u003cp\u003eFor sensitivity analysis, we tested several modelling specifications to examine the robustness of our results by varying the knots in the exposure-response function, the number of degrees of freedom per year used to control for long-term trends and seasonality, and the number of lag days. We also conducted meta-regression models separately for each environmental indicator, instead of including all indicators in a single model.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data necessary to support the conclusions of this paper are present in the paper and/or in the Supplementary Information. Data used in this study were collected by collaborators within the MCC Collaborative Research Network under a data sharing agreement and cannot be made publicly available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe code used to generate the results is available from the authors upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Korea Environment Industry \u0026amp; Technology Institute (KEITI) through Climate Change R\u0026amp;D Project for New Climate Regime, funded by Korea Ministry of Environment (MOE) (grant number:RS-2022-KE002235). This work was also supported by Korea Environment Industry \u0026amp; Technology Institute (KEITI) through Digital Infrastructure Building Project for Monitoring, Surveying and Evaluating the Environmental Health, funded by Korea Ministry of Environment (MOE) (project number: 2021003330004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsortia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMCC Collaborative Research Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrancesco Sera\u003csup\u003e5\u003c/sup\u003e, Michelle L Bell\u003csup\u003e6,7\u003c/sup\u003e, Masahiro Hashizume\u003csup\u003e8\u003c/sup\u003e, Eric Lavigne\u003csup\u003e10\u003c/sup\u003e, Susanne Breitner\u003csup\u003e11\u003c/sup\u003e, Francesca de'Donato\u003csup\u003e12\u003c/sup\u003e, Yoonhee Kim\u003csup\u003e13\u003c/sup\u003e, Chris Fook Sheng Ng\u003csup\u003e14\u003c/sup\u003e, Lina Madaniyazi\u003csup\u003e14\u003c/sup\u003e, Ben Armstrong\u003csup\u003e15\u003c/sup\u003e, Pierre Masselot\u003csup\u003e15\u003c/sup\u003e, Yasushi Honda\u003csup\u003e16\u003c/sup\u003e, Aurelio Tobias\u003csup\u003e17\u003c/sup\u003e, Aleš Urban\u003csup\u003e18,19\u003c/sup\u003e, Ana Maria Vicedo-Cabrera\u003csup\u003e20,21\u003c/sup\u003e, Antonio Gasparrini\u003csup\u003e15\u003c/sup\u003e, Ho Kim\u003csup\u003e1\u003c/sup\u003e, Whanhee Lee\u003csup\u003e23\u003c/sup\u003e\u003csub\u003e,\u003c/sub\u003e Micheline de Sousa Zanotti Stagliorio Coelho\u003csup\u003e24\u003c/sup\u003e, Paulo Hilario Nascimento Saldiva\u003csup\u003e24\u003c/sup\u003e, Haidong Kan\u003csup\u003e25\u003c/sup\u003e, Mathilde Pascal\u003csup\u003e26\u003c/sup\u003e, Alexandra Schneider\u003csup\u003e27\u003c/sup\u003e, Veronika Huber\u003csup\u003e28\u003c/sup\u003e, Paola Michelozzi\u003csup\u003e12\u003c/sup\u003e, Massimo Stafoggia\u003csup\u003e12\u003c/sup\u003e, Magali Hurtado Diaz\u003csup\u003e29\u003c/sup\u003e, Eunice Elizabeth Félix Arellano\u003csup\u003e29\u003c/sup\u003e, Carmen Íñiguez\u003csup\u003e30\u003c/sup\u003e, Antonella Zanobetti\u003csup\u003e31\u003c/sup\u003e, Joel Schwartz\u003csup\u003e31\u003c/sup\u003e, Yuming Guo\u003csup\u003e32\u003c/sup\u003e, Jouni J. K. Jaakkola\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e24\u003c/sup\u003eDepartment of Pathology, Faculty of Medicine, University of São Paulo, São Paulo, Brazil\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e25\u003c/sup\u003eDepartment of Environmental Health, School of Public Health, Fudan University, Shanghai, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e26\u003c/sup\u003eSanté Publique France, Department of Environmental Health, French National Public Health Agency, Saint Maurice, France\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e27\u003c/sup\u003eInstitute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health (GmbH), Neuherberg, Germany\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e28\u003c/sup\u003eInstitute for Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, LMU Munich, Munich, Germany\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e29\u003c/sup\u003eDepartment of Environmental Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e30\u003c/sup\u003eDepartment of Statistics and Computational Research. Universitat de València, València, Spain\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e31\u003c/sup\u003eDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e32\u003c/sup\u003eDepartment of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e33\u003c/sup\u003eCenter for Environmental and Respiratory Health Research (CERH), University of Oulu, Oulu, Finland\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJO, YB, and WL conceived the study idea and designed the research. JO analysed the data and WL provided advice regarding statistical modelling. JO and YB wrote the manuscript. CK, JM, FS, MLB, MH, EMK, EL, SB, FD, YK, CFSN, LM, BA, PM, YH, AT, AU, AMVC, AG, EH, HK, and WL reviewed and revised the manuscript. FS, MLB, MH, EMK, EL, SB, FD, YK, CFSN, LM, BA, PM, YH, AT, AU, AMVC, AG, and the rest of the MCC Collaborative Research Network provided mortality and temperature data. JO and WL accessed and verified the data. JO and DK curated and processed the data for urbanicity and related characteristics. All authors contributed to the interpretation of the results and the submitted version of the manuscript. EH, HK, and WL supported and counselled all processes of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Whanhee Lee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGasparrini, A.\u003cem\u003e et al.\u003c/em\u003e Changes in susceptibility to heat during the summer: a multicountry analysis. \u003cem\u003eAmerican journal of epidemiology\u003c/em\u003e \u003cstrong\u003e183\u003c/strong\u003e, 1027-1036 (2016). \u003c/li\u003e\n\u003cli\u003eGasparrini, A.\u003cem\u003e et al.\u003c/em\u003e Projections of temperature-related excess mortality under climate change scenarios. \u003cem\u003eThe Lancet Planetary Health\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, e360-e367 (2017). \u003c/li\u003e\n\u003cli\u003eGuo, Y.\u003cem\u003e et al.\u003c/em\u003e Heat wave and mortality: a multicountry, multicommunity study. \u003cem\u003eEnvironmental health perspectives\u003c/em\u003e \u003cstrong\u003e125\u003c/strong\u003e, 087006 (2017). \u003c/li\u003e\n\u003cli\u003eLee, W.\u003cem\u003e et al.\u003c/em\u003e Effects of urbanization on vulnerability to heat-related mortality in urban and rural areas in South Korea: a nationwide district-level time-series study. \u003cem\u003eInternational Journal of Epidemiology\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 111-121 (2022). \u003c/li\u003e\n\u003cli\u003eLee, W.\u003cem\u003e et al.\u003c/em\u003e Heat-mortality risk and the population concentration of metropolitan areas in Japan: a nationwide time-series study. \u003cem\u003eInternational Journal of Epidemiology\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 602-612 (2021). \u003c/li\u003e\n\u003cli\u003eSera, F.\u003cem\u003e et al.\u003c/em\u003e How urban characteristics affect vulnerability to heat and cold: a multi-country analysis. \u003cem\u003eInternational journal of epidemiology\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 1101-1112 (2019). \u003c/li\u003e\n\u003cli\u003eTian, Y., Tsendbazar, N.-E., van Leeuwen, E., Fensholt, R. \u0026amp; Herold, M. 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This multi-country study investigated the complex roles of urbanicity on heat-related mortality, considering spatial heterogeneity across different urban contexts. Through the Multi-City multi-Country (MCC) research collaborative network, the data on mortality and ambient temperature from 397 cities in 12 countries were collected, and we examined the heterogeneous associations between urbanicity characteristics and heat-mortality risk by region. Higher population density was associated with increased heat-mortality risk in North America and Europe, while the inverse association was observed in Latin America; however, the association was not significant in East Asia. Among urban environment factors, increased PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, CO\u003csub\u003e2\u003c/sub\u003e emissions, and urban heat island intensity generally contributed to a higher heat-mortality risk. Whereas greater greenness mitigated the heat-mortality risk, particularly in Europe. Our findings highlight heterogeneous roles of urbanicity on vulnerability to heat risk and could provide scientific evidence for establishing targeted urban health strategies against climate change.\u003c/p\u003e","manuscriptTitle":"Multi-country multi-city study on heterogeneous impacts of urbanicity on heat-related mortality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-11 06:58:28","doi":"10.21203/rs.3.rs-6332861/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6d31805d-0d04-40ba-92a4-71e161f43dd9","owner":[],"postedDate":"April 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46954922,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":46954923,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-05-20T12:40:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-11 06:58:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6332861","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6332861","identity":"rs-6332861","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}