Artigos

Introduction
The relationship between climate and human health has been widely studied throughout history, but with recent global climate changes, this connection has acquired a new dimension. Heatwaves, changes in rainfall, and drought patterns directly affect the incidence of various diseases and can worsen pre-existing health conditions, becoming critical factors in premature mortality. Studies indicate that extreme temperatures, sudden climate changes, and adverse weather events are associated with increased morbidity and mortality, especially among vulnerable populations (1).
Climate impacts health, notably the cardiovascular and respiratory systems. Excessive heat can increase dehydration and blood viscosity, favoring the occurrence of acute myocardial infarction and stroke. Intense cold, on the other hand, causes peripheral vasoconstriction, elevated blood pressure, and a higher risk of fatal cardiac complications, especially in the elderly and individuals with preexisting conditions (2). Climate change also affects air quality by altering the dispersion of pollutants and allergens and increasing the incidence of respiratory diseases (3). Moreover, it can enhance the distribution of vectors, such as mosquitoes, that facilitate the spread of diseases such as dengue, yellow, and Zika fever(4).
Climatic factors also influence human behavior and social dynamics. Extreme temperatures and adverse weather events can lead to economic losses, affect agricultural production and the availability of drinking water, restrict outdoor activities, and impact mental health by increasing cases of stress, anxiety, and depression. (1).
On the other hand, the presence of green areas in urban environments reduces incidence of direct solar radiation by providing shade and mitigating thermal stress; it also improves air quality, reduces urban noise, and lessens the urban heat island effect, resulting in lower air and surface temperatures (5). Furthermore, these areas offer health benefits, as they can help combat obesity and sedentary behavior, alleviate mental disorders, and support cognitive development (6). In this context, urban afforestation not only provides climatic and environmental benefits, but also positively affects individuals, factors that contribute to the reduction of mortality rates (7).
Local Climate Zones (LCZs) are geographic areas that share similar characteristics in terms of surface cover, urban structures, and microclimatic conditions, exhibiting homogeneous variations in temperature, humidity, and ecosystem (8). The classification of LCZs is based on the density and height of structures in the region and is divided into 17 distinct categories. By synthesizing diverse characteristics related to socioeconomic, demographic, and environmental factors, this type of aggregation has the potential to be used as a factor associated with health outcomes. For instance, studies that investigate the association between health statistics and LCZs are distributed across the globe, including large urban areas such as São Paulo, Nagpur, Los Angeles and London. However most studies designs are distinct between each other, bringing very different perspectives of the general association. As a matter of fact, analyzing the relationship between LCZ and mortality is complex, yet, as many studies showed, LCZ can assist in understanding the distribution of urban heat islands, heat waves, air pollution and socioeconomic vulnerability, which are closely linked to health outcomes. (9–12).
Several previous studies, using different designs, have analyzed the influence of socioeconomic and environmental factors on overall and premature mortality (7). However, these factors are generally studied separately, either due to the high correlation among them or because of specific research objectives. Studying them in isolation overlooks the associations between these factors, which are often connected to how a city develops and to disparities among populations of different income levels and backgrounds.
Moreover, prior research has often analyzed broad scales like states and countries, overlooking intra-urban inequalities affecting mortality in cities (7). It is known that urban development and social class disparities directly impact health and mortality patterns. Our study is innovative in investigating this relationship at the local scale, enabling a more detailed analysis of intra-urban differences. By applying LCZ at the municipal level, it enhances understanding of urban-health interactions and helps public managers address climate change and socio-spatial inequalities (9–12). When providing granular data on the urban territory, this study strengthens the responsibility of local governments in planning and implementing public policies that integrate health, environment, and sustainable urban development. Its application can assist in identifying priority areas for intervention, promoting more equitable, resilient cities adapted to emerging climate demands.
This study analyzed intra-urban mortality differences in Juiz de Fora (MG) and their relationship with the city's land use. The aim was to evaluate the influence of LCZs on overall mortality, considering climatic, socioeconomic factors, and the distribution of urban space.
Methods
Location and Period
The study was conducted in the city of Juiz de Fora, located in the Zona da Mata region of the state of Minas Gerais, Brazil. The city had an estimated population of 540.756 inhabitants in 2022 (13), distributed heterogeneously across central urban regions, periurban areas, and peripheral neighborhoods. The territorial division was made based on Urban Regions (URs), areas derived from census sector aggregates used for administrative purposes by the Juiz de Fora City Hall, allowing for a more granular analysis of local characteristics. Juiz de Fora’s climate in Köppen-Geiger’s classification is considered Temperate Humid Subtropical (Cwa) climate (14). The study period covered the years 2006 to 2014, with 2010 being used as the reference year for demographic and socioeconomic variables.
Data
Mortality data were provided by the Epidemiological Surveillance of the Municipal Health Department of Juiz de Fora, through a request under the Lei de Acesso a Informação (Access to Information Law) (15) and in compliance with the General Data Protection Law (Law No. 13.709/2018) (16). The data were handled without personally identifiable information and under the oversight of health and research authorities. Given proper handling, the data were aggregated by URs. Mortality data from 2006 to 2014 were processed, as the center point of this period includes the year of the 2010 Demographic Census, from which population data by sex and age group were extracted. To ensure comparability between URs, the Standardized Mortality Rate (SMR) was calculated using the world standard population. The SMR values were categorized into quintiles for graphical analysis.
Climatic-environmental information was collected from satellite data, covering the following variables: Daily Mean Temperature (Tmed) and Green Area Index (GAI). The GAI classifies green area coverage into three categories: “No green area,” "Below recommended," and “Recommended,” based on the WHO recommendation of 12 m² of green space per inhabitant (17). This index relates the public green space area per inhabitant by urban region of the city (17). Tmed is the mean air temperature value obtained from two field surveys conducted during 2023. The first survey took place from July 13 to September 16, 2023, and the second extended from October 1 to November 12, 2023. Twenty thermometers were installed throughout the urban area of Juiz de Fora, with the goal of classification of LCZ . The distribution of the thermometers’ sites were based on security of the equipment and the capability of measuring different areas with distinct ways of occupation and soil usage. All equipment was calibrated together with the main meteorological station of the city, located in the Universidade Federal de Jui de Fora and integrated in the system of the Instituto Nacional de Meteorologia (INMET). The data were organized into a spreadsheet, spatialized, and then interpolated using kriging, a spatial regression method (Supplemental Material 1). Data prior to this period at the required granularity for analyzing URs before the period is not available. This variable was stratified into 5 categories. The municipality was classified into LCZ, as described below.
Each LCZ is defined based on factors such as building density, green space presence, and the urban space configuration, and was calculated according to 2018 (8). In this study, the found zones were: (a) The High-Rise Compact Zone is characterized by a high density of vertical buildings, such as tall buildings and skyscrapers, where heat retention by construction materials and low natural ventilation increase temperatures, aggravated by the lack of green areas; (b) The Medium-Building Compact Zone has intermediate-height buildings, allowing slightly better ventilation compared to the Skyscraper Zone, although still facing thermal challenges, mitigated by the greater presence of green areas; (c) The Low-Building Compact Zone features lower-height and lower-density buildings, such as houses and small buildings, along with frequently larger green areas, providing better air circulation and milder temperatures; (d) The Sparsely Built Zone consists of residential areas with large lots and low population density, promoting ventilation and minimizing the heat island effect due to greater vegetation coverage; (e) The Dense Vegetated Area, formed by parks, urban forests, and protected areas, has a significant presence of green spaces that help regulate temperature, increase humidity, and improve air quality; (f) The Pasture Zone includes open areas, typically used for animal farming, with vegetation less dense than forests but still important for local climate regulation. This classification was done for each RU and specifies the main way of human occupation in each of these areas, however it is notable that, as further specified, all of those regions are at some level inhabited, even pasture and dense vegetated areas.
In this study, the socioeconomic variable used was the Health Vulnerability Index (HVI), a synthetic indicator combining living conditions and access to healthcare services. It was obtained from the weighted average of the following variables: percentage of households with inadequate or absent water supply, percentage of households with inadequate or absent sewage access, percentage of households with inadequate or absent waste disposal, ratio of residents per household, percentage of illiterate people, percentage of households with a per capita income of up to half the minimum wage, average nominal monthly income of household heads, and percentage of black, brown, and indigenous people (18). Several other variables from the 2010 Census were analyzed but not selected for the study during exploratory analysis due to their high correlation with HVI. These variables include: population, proportion of elderly people, dependency ratio (ratio of economically dependent individuals, such as children and elderly, to the active-age population), household density, poverty proportion, proportion of minorities, and lack of access to water.
Study Design
This is a cross-sectional study that investigates the influence of climatic, environmental, and socioeconomic variables on overall and premature mortality in urban regions and climate zones of Juiz de Fora. The study included an exploratory analysis in which various variables were analyzed and later selected based on theoretical and statistical relevance for the objective analysis.
Exploratory Data Analysis
An exploratory data analysis was conducted, including descriptive statistics to summarize the characteristics of the variables of interest. Additionally, spatial analysis was performed through thematic maps to identify geographical patterns and visually represent the analyzed variables.
Statistical Analysis
The relationship between climatic, socioeconomic variables, and mortality rates was assessed using Spearman correlations and statistical tests. For comparisons between mortality rates and climate zones, the non-parametric Kruskal-Wallis test was used, followed by a post-hoc analysis with the Dunn test (19), to identify which zones led to significant differences in the previous test. P-values below 0.05 were considered statistically significant.
The data were processed and analyzed using the R programming language (version 4.2.3), and all analyses, maps, and graphs were generated using the same software. The code used for the analyses is available on github.com/joao-med/climate-health-jf, together with Supplemental Material.
This study was approved by the local ethics committee (CAAE: 77929923.2.0000.5147). The authors declare no conflicts of interest in the production of this study.
Results
Demographic and Mortality Description
A total of 27,054 deaths were recorded. Regarding sex, 47% of deaths occurred among women (12,660), while 53% were among men (14,392). In terms of race, most deaths occurred among white individuals (66%, or 17,810 deaths), followed by those identified as mixed-race (19%, or 5,036 deaths). Concerning age, the majority of deaths occurred in individuals aged 60 years or older (66%, or 17,891 deaths). As for the LCZ, the highest number of deaths was observed in areas classified as "Compact low-rise" (53%, or 14,397 deaths), followed by "Compact high-rise" (12%, or 3,198 deaths), and "Compact mid-rise" (21%, or 5,567 deaths). The lowest number of deaths occurred in "Sparsely Built" areas (3%, or 780 deaths).
Distribution of Standardized Mortality Rates (SMR)
Regarding the standardized mortality rate by LCZ, values ranged from 4.19 to 6.31 deaths per 1,000 inhabitants. The zone with the lowest mortality rate was "Compact mid-rise" (4.19 deaths/1,000 inhabitants), while the highest rate was observed in the "Dense vegetation" area (6.31 deaths/1,000 inhabitants) (Table 2).
Spatial Distribution
The city of Juiz de Fora was divided into 7 Administrative Regions during the data collection period: Central, East, Northeast, North, South, West, and Southeast. No systematic clustering was found between LCZs and these administrative regions, although some patterns can be observed. Notably, there is a radial urbanization pattern, with higher urbanization in the center and decreasing building density and height toward the periphery. "Compact high-rise" areas are centrally located and are surrounded by "Compact mid-rise" areas, followed by "Compact low-rise" and, finally, "Sparsely Built" or "Dense vegetation" areas. The Western region, however, does not follow this pattern and displays greater LCZ diversity and a more heterogeneous distribution. Around the Paraibuna River, the most important water body in the city, "Compact low-rise" areas are predominantly found (Figure 1).
The standardized mortality rate does not follow a defined spatial pattern throughout the city, with no clear clusters of high or low rates. All administrative regions have at least one spatial unit (RU) within the highest SMR category. Even the Central region, which has one RU with a high SMR, also has the highest concentration of URs with the lowest SMR category within its boundaries and adjacent areas. Higher SMR values are mainly concentrated in areas surrounding the city center (Figure 2C).
Regarding the HVI, a centrifugal distribution pattern is observed, meaning vulnerability increases as one moves away from the center toward the periphery. Areas with “low” vulnerability are concentrated in the Central administrative region and smaller areas in adjacent regions. Around the Central region and the Paraibuna River, a predominance of “medium” vulnerability is noted. “High” vulnerability is primarily concentrated in the peripheral areas of the North, Northeast, East, Southeast, South, and West regions (Figure 2B).
Most of the territory is classified as having “No green space” or “Below recommended” levels of green coverage. “No green space” areas are distributed across all administrative zones of Juiz de Fora, with a higher prevalence in the North and Northeast regions. Areas with “Below recommended” green coverage are mainly located in the Central region and high-population-density peripheries, such as the eastern part of the North zone and the southern part of the Southeast zone. Meanwhile, areas with “Recommended” green coverage are primarily concentrated in the West region and smaller portions of the Central, South, and Northeast regions (Figure 2A).
Average temperature displays a centripetal distribution pattern. The highest average temperature categories are predominant in the Central region and adjacent areas, such as the East, Southeast, and South regions of Juiz de Fora. These areas mainly coincide with LCZs classified as “Compact high-rise” and “Compact low-rise.” Higher temperatures are also observed around the Paraibuna River. In contrast, the lowest average temperatures are concentrated in the Western and Southern parts of the municipality (Figure 2D), overlapping primarily with “Compact low-rise” LCZs.
Inferential Analysis
A weak negative correlation was found between the HVI and average temperature (-0.31), and a moderate positive correlation between the HVI and the outcome (0.54). No strong correlations were observed between the standardized mortality rate and the variables analyzed (Table 3).
To assess differences between LCZ groups, the Kruskal-Wallis test was performed. The test revealed statistically significant differences between groups (H = 18.59, p = 0.002). Subsequently, Dunn’s post hoc test was conducted to identify which group pairs showed significant differences. Results indicated that the “Dense vegetation” area had a higher mortality rate than the “Compact midrise” area (p = 0.003), while the “Compact low-rise” area had a lower mortality rate than the "Compact midrise" area (p = 0.001). No other significant differences (p > 0.05) were found between groups.

Discussion
This study demonstrated that, even in a medium-sized city, it is possible to identify differences in standardized mortality rates across distinct Local Climate Zones. This finding reinforces the potential of LCZs as an urban typology capable of integrally capturing environmental, socioeconomic, and land-use conditions. It was observed that areas classified as having "medium" compactness showed lower standardized mortality rates compared to those in "low" compactness zones and areas with "dense vegetation." These medium-compact zones are predominantly located in the central region of the municipality, which is characterized by better socioeconomic indicators and more consolidated urban infrastructure. This pattern suggests that the interaction between physical characteristics of the built environment and social inequalities may significantly influence intra-urban mortality profiles.

The urban development of Juiz de Fora originated from the construction of the Paraibuna Road — currently Avenue Barão do Rio Branco — which marked the beginning of the city’s urbanization process. This avenue, which crosses the Paraibuna River perpendicularly, served as the structural axis for the formation of the city center, the most planned and consolidated area in terms of urban infrastructure (20). From this avenue, a grid plan was implemented to organize the central space and define the municipality’s initial growth vectors.
Over the years, urban expansion occurred centrifugally, following the opening of new roads and responding to immediate demands. This growth was marked by weak urban regulation and strong influence from political interests, the construction sector, and real estate speculation (20). As a result, an unequal urban fabric was formed, where areas with better socioeconomic and urban conditions coexist with peripheral regions lacking infrastructure, public services, and leisure opportunities.
For instance, the northern zone of the city houses part of the industrial sector but also includes neighborhoods with lower levels of urban and social development. This pattern of settlement likely resulted in an uneven territorial distribution, significantly affecting population access to urban resources and directly influencing social determinants of health such as income, physical environment, social inclusion, and quality of life. The spatial analysis in this study revealed a moderate association between the territorial configuration of Juiz de Fora’s LCZs and the distribution of the HVI. The city center, where the HVI is lowest, coincides with highly urbanized LCZs (such as “Compact High-Rise” and “Compact Mid-Rise”), which are associated with better infrastructure and access to health services. In contrast, zones with the highest HVI are concentrated in the periphery, in areas with low-rise or scattered buildings, typically regions with lower levels of urban planning and reduced access to public services. This correlation occurs because urban spatial characteristics, such as building type, presence of green spaces, and population density, are closely linked to the socioeconomic conditions of residents, including access to health services, income, and sanitation (21).
The overlap of social vulnerability and environmental exposure in the outskirts of Juiz de Fora constitutes a scenario of environmental injustice (22). These more remote areas exhibit high mortality rates, especially in the southern and southeastern regions of the municipality. It is assumed that these regions face structural challenges, including limited healthcare access and greater socioeconomic vulnerability. Literature indicates that the effects of climate change and rising temperatures disproportionately impact populations already facing structural and socioeconomic limitations, making these communities more prone to illness and premature death (21). The results reinforce that LCZs can serve as a useful tool for identifying and understanding health inequalities, particularly when integrated with indicators such as the HVI.
The relationship between LCZ and the GAI is key to understanding how the environment impacts quality of life in urban areas. Urban vegetation benefits public health by improving air quality, reducing heat stress, and enhancing biodiversity (23). In addition, access to green spaces is associated with various social and psychological benefits, including increased physical activity, reduced anxiety, and improved mental well-being (24). Thus, adequate green areas can serve as an important factor in mitigating social and health inequalities, especially in densely populated urban regions (25).
However, research indicates that zones with high building density often lack sufficient vegetation cover (26). The absence of greenery in urban environments not only limits natural cooling capacity but can also adversely affect city-dwellers’ health and well-being (25).
When analyzing the distribution of green areas concerning LCZs, it becomes evident that regions with high construction density tend to have less vegetation, reinforcing the negative health impacts of the urban environment (26). Nonetheless, the relationship between building height and vegetation cover should be interpreted with caution. It is important to recognize that the presence of vegetation is not determined solely by building height but also by factors such as urban planning policies, landscape design, and local cultural attitudes towards urban space (27).
Regarding mean temperature during the study period, the highest values were concentrated in the central region of the city and the surrounding areas. This is mainly a direct consequence of intense urbanization, with high building density, reduced vegetation cover, and soil impermeability. These characteristics diminish the environment’s capacity for heat dissipation and promote thermal accumulation, especially in central areas where economic activity, traffic, and vertical buildings are concentrated. This pattern is consistent with the spatial configuration of an urban heat island, a phenomenon well documented in the scientific literature (28).
The LCZ configuration in Juiz de Fora appears to follow a pattern similar to that observed in other medium-sized Brazilian cities (29), in which central areas exhibit warmer climate characteristics, while peripheral regions, with more vegetation and lower population density, maintain lower temperatures.
Regardless of temperature distribution, a weak correlation of -0.10 was found between mean temperature and standardized mortality rate. This result aligns with findings in the literature, which suggest that health risks associated with temperature are more evident under extreme heat conditions or with prolonged exposure, particularly in vulnerable populations (30–32).
An interesting observation is the occurrence of high mean temperatures along the banks of the Paraibuna River, particularly in densely populated areas of the northern region. This contradicts the common assumption that proximity to water bodies results in milder temperatures (33). However, this thermal behavior may be explained by three main factors: the lack of surrounding vegetation and green areas; high urban density coupled with intense vehicle traffic, contributing to air pollution and local temperature rise; and the location of these areas in urban valleys — geographic formations that tend to trap heat due to poor ventilation and thermal inversion (34).
The results of this study strongly indicate that urban population health is deeply influenced by how space is occupied and structured. The integrated analysis of LCZs, mean temperature, HVI, standardized mortality rate, and GAI shows that environmental and social determinants of health operate in tandem and are structurally, rather than randomly, distributed. Central areas, despite being exposed to higher temperatures due to intense urbanization and the formation of urban heat islands, show lower social vulnerability and better health indicators, reflecting the presence of consolidated infrastructure and greater access to health services.
In contrast, peripheral regions, characterized by higher vulnerability and poor infrastructure, present the worst health indicators and a superimposition of risks that reinforces historical inequalities. Social vulnerability increases exposure to adverse urban climate effects and reduces the population’s adaptive capacity (35).
This study has certain limitations that should be considered when interpreting the findings. While LCZ classification is a useful approach to reflect land use and urban form patterns, it does not directly represent the socioeconomic conditions of the analyzed areas. For instance, a zone classified as “mid-rise buildings” could refer to both working-class neighborhoods and high-end condominiums, indicating the heterogeneity that may exist within a single LCZ category. Additionally, as a cross-sectional population-based study, it is not possible to establish causal relationships between the analyzed variables. Nevertheless, the findings offer important hypotheses and reflections on the city’s historical urbanization patterns and their potential links to social and health inequalities. These results highlight the need for urban and environmental interventions aimed at promoting greater territorial equity and health benefits for the population.
This study presents relevant contributions. The adopted approach enabled a comprehensive, yet concise, analysis of the relationship between spatial occupation, urban environment, socioeconomic factors, and health — a topic of growing urgency in the context of the climate crisis (23).
The finding that LCZ classification may be related to certain epidemiological patterns suggests its potential as a decision-support tool in urban planning (36). Its application could assist public officials in identifying priority areas for intervention, promoting more equitable and sustainable urban development.
Nonetheless, it is essential to understand that the goal is not to reorganize urban space with an exclusive focus on modifying environmental variables or reclassifying climate zones. On the contrary, the findings underscore that improving urban conditions must occur alongside policies that invest in health, education, infrastructure, and income redistribution. Any attempt to requalify urban spaces without such integrated actions risks triggering gentrification processes, displacing vulnerable populations, and deepening socio-spatial inequalities (37).
Conclusion
This study analyzed intra-urban differences in mortality in Juiz de Fora (MG) and their relationship with the city’s geographic-historical occupation. It concludes that the relationship between urban space occupation, environment, and health is complex but can be partially understood through the analysis of LCZs. LCZs emerge as a valuable tool for synthesizing environmental, socioeconomic, and demographic characteristics, enabling the examination of standardized mortality rates at the intra-urban scale. This approach supports the development of urban planning strategies and public policies that are more responsive to territorial inequalities.
To promote health and well-being in cities, it is essential to invest not only in improving living conditions but also in enhancing the physical environment, particularly in the most vulnerable areas. The creation and preservation of green spaces should be a central guideline, given their importance in mitigating heat island effects and fostering quality of life. Recognizing territory as a social determinant of health is crucial for advancing toward more inclusive and equitable cities, where access to opportunities, environmental protection, and health promotion are fairly distributed.
Funding
This study was financially supported by a Bolsa de Iniciação Científica da Universidade Federal de Juiz de Fora and by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) through project no. 404734/2021-9.
Data Availability Statement
The dataset for this article is available in the SciELO Data repository on the Ciência & Saúde Coletiva Dataverse at the link: https://doi.org/10.48331/SCIELODATA.LGRWBG
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