Examining the interplay between air pollution, vegetation greenness, and stroke prevalence in East Africa: An ecological perspective

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Results showed a positive correlation between PM 2.5 and SO 2 , and a negative association between Enhanced Vegetation Index (EVI) and stroke fatality prevalence. The linear regression model showed that the increase of 1 index in EVI could lead to the reduction of stroke-related deaths by 845.57 ± 295.96 deaths per 100,000 persons. Also, a 1 µg/m 3 increase of PM 2.5 and SO 2 concentrations predicted a corresponding increase of stroke-related death by 3.06 ± 1.25 and 139.28 ± 64.33 deaths per 100,000 persons, respectively. Furthermore, the analysis of the influence of these environmental variables on the prevalence of mortality attributable to stroke by age group showed its rise with age, both in intensity and statistical significance. For instance, a rise of 1 unit in EVI predicted the reduction of the stroke-related death rate by 9.18 ± 6.45 and 2133.93 ± 701.07 deaths per 100,000 persons in the age groups of 20–29 and 70–79 years old, respectively. A rise in 1 µg/m 3 of PM 2.5 and SO 2 is expected to trigger the mortality incident rise from 0.05 ± 0.03 to 7.77 ± 3.01 and 4.28 ± 1.40 to 426.21 ± 152.38 deaths per 100,000 persons in respective age groups of 20–29 and 70–79 years. The exposure to CO and O 3 did not demonstrate a significant effect on the stroke-related death rate in the region for the period of the study. air pollution greenness vegetation index stroke mortality rate East Africa public health Figures Figure 1 Figure 2 Figure 3 Introduction Environmental factors play a crucial role in determining the state of human health. Among the various environmental factors, air pollution and surface vegetation cover have been shown to have significant, though often opposing, impacts on human health. Air pollution remains one of the most pressing environmental health challenges globally (Health Organization and WHO 2016). The most harmful substances include particulate matter (PM 2.5 and PM 10 ), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), carbon monoxide (CO), and tropospheric ozone (O 3 ). Short-term exposure to these pollutants can lead to respiratory symptoms such as coughing, throat irritation, and shortness of breath. More severe consequences include exacerbations of asthma and chronic obstructive pulmonary disease (COPD). Long-term exposure is associated with more serious conditions, like cardiovascular diseases, lung cancer, and even premature death (Pope and Dockery 2006 ). A comprehensive study by the World Health Organization (WHO) estimated that air pollution causes approximately 7 million premature deaths annually, hence, underscoring its global health burden (Organization 2018 ). In contrast to air pollution, exposure to surface greenness has been linked to a range of health benefits. Surface greenness encompasses parks, gardens, street trees, and other forms of vegetation in urban and rural settings. It contributes to physical, mental, and social well-being through various mechanisms. The greenness encourages physical activity by providing appealing spaces for exercise and recreation. It has been shown that regular physical activity is crucial for preventing chronic diseases such as obesity, diabetes, and cardiovascular diseases (I. M. Lee et al. 2012 ). Secondly, exposure to natural environments has been shown to reduce stress, anxiety, and depression. Even the concept of biophilia, which suggests the inherent affinity of humans to nature, supports the idea that natural settings improve mental health (Ulrich 1984 ; Yan et al. 2024 ). Additionally, surface greenness can improve social cohesion by providing communal spaces where people can gather and interact. This sense of community can further enhance mental health and well-being (Maas et al. 2009 ; Picavet et al. 2016 ). Moreover, vegetation can mitigate the urban heat island effect, reduce noise pollution, and improve air quality by absorbing pollutants, thus providing indirect health benefits. While the benefits of surface greenness on health are well understood, the interaction between vegetation and air pollution is complex. On one hand, vegetation can help reduce air pollution levels. Trees and plants can absorb pollutants and filter particulate matter from the air, thus improving air quality (Nowak et al. 2006 ). This suggests that increasing urban greenness could be a strategy to combat the adverse health effects of air pollution. On the other hand, vegetation can also be a source of certain pollutants. For instance, trees and plants emit volatile organic compounds (VOCs) that can contribute to the formation of ground-level ozone (Guenther 1995 ). Several studies assessed the effect of both greenspaces and air pollution in developed countries of Europe and America, but few are similar studies in developing countries, especially in sub-Saharan Africa. This study assesses the combined effects of air pollution and surface greenness on human health in East African countries. Among the various vacuum through which air pollution impacts health, we selected the stroke-related death prevalence in the adult and elderly population of East African countries. Despite recent advances in treatment and prevention, stroke remains the second leading cause of death and the third leading cause of death and serious long-term disability combined (Feigin et al. 2022 ). In the search for novel modifiable risk factors, there is growing interest in the adverse effects of environmental agents, among the most pervasive is ambient air pollution (Kulick et al. 2023b ). Evidence from epidemiological studies has demonstrated a strong association between air pollution and cardiovascular diseases including stroke (K. K. Lee et al. 2018 ). The prevalence of stroke in Africa varies by country and it is influenced by multiple factors, including healthcare infrastructure, lifestyle, and prevalence of risk factors such as hypertension, diabetes, and smoking, however, stroke is a growing public health concern in the region due to increasing rate of these risk factors. Common risk factors for stroke in East Africa include hypertension, diabetes, obesity, tobacco use and physical inactivity (Adeloye 2014 ). There is also a high prevalence of infectious diseases, such as HIV/AIDS which can contribute to an increased risk of stroke (Akinyemi et al. 2021b ; Niohuru 2023 ). Furthermore, similar to global trends, stroke prevalence increases with age in Africa, however, the region may experience a higher proportion of strokes in younger adults compared to high-income countries due to different risk factor profiles and healthcare access. Material and methods Data source We focused on five countries from East Africa including Burundi, Kenya, Rwanda, Uganda and the United Republic of Tanzania (here referred to as Tanzania). The prevalence of stroke-related mortality rate by country, age, sex and year have been derived from the Global Burden of Disease (GBD) using the data visualization tool developed by the Institute for Health Metrics and Evaluation (IHME) (Institute for Health Metrics and Evaluation (IHME) 2020 ). We paid our attention to the death prevalence associated with stroke for the period of 20 years (2001–2020). The regional vegetation cover and greenness were assessed using the Enhanced Vegetation Index (EVI). We acquire EVI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) of the environmental remote sensing satellite Terra (Didan 2015 , 2021 ). The Terra/MODIS Vegetation Indices Monthly (MODIS13A3) Version 6.1 data are provided monthly at 1 km spatial resolution as a gridded Level 3 product in the sinusoidal projection. The MODIS Normalized Difference Vegetation Index (NDVI) and EVI provide continuity for historical applications, while EVI minimizes canopy background variations and maintains sensitivity over dense vegetation conditions (Didan 2015 ). Since the region understudy is characterized by unique terrain and diversified climates that influence vegetation, biodiversity, and human occupations (Camberlin 2018 ; Nicholson 2019 ), we preferred EVI over NDVI in our analysis. EVI data was downloaded for the same period as the prevalence of diseases associated with air pollution (2001–2020). In addition to EVI and the prevalence of stroke-related deaths, we acquired air pollutant data from open-source repositories. We used PM 2.5 from the Atmospheric Analysis Group (ACAG). ACAG uses satellite, simulation, and monitor-based sources to estimate global and regional PM 2.5 surface concentrations, demonstrating a good correlation with ground-monitored PM 2.5 (ACAG 2023 ). We also collected the ensemble area-averaged sulfur dioxide (SO 2 ) surface mass concentration from MERRA-2 reanalysis (product name: M2TMNXAER v5.12.4) (Global Modeling and Assimilation Office (GMAO) 2015a ). The same ensemble from MERRA-2 reanalysis was used for the carbon monoxide (CO) surface concentration (M2TMNXCHM v5.12.4) (Global Modeling and Assimilation Office (GMAO) 2015b ). In addition to SO 2 and CO, we used instantaneous 3-dimensional tropospheric ozone (O 3 ) monthly mean data (M2IMNPASM v5.12.4) which consists of 42 pressure levels (Global Modeling and Assimilation Office (GMAO) 2015c ). For our analysis, we focused on the 500 hPa pressure level because it is the best estimate of the East African region's surface values (Kebacho 2022 ). All these data were downloaded from the NASA Earth Data repository using the GIOVANNI data tool (Berrick et al. 2009 ). Figures 1 and 2 show the spatial distribution of air pollutants used in this study and the enhanced vegetation index, respectively. Data analysis methodology Data analysis was done by averaging the stroke-related death rates by year and age groups for each country. We assessed the change of environmental variables (EVI and air pollutants) with an interval period of five years to demonstrate the change in these variables. The relationship between stroke-related death rate and air pollutants and EVI was first established using Pearson’s correlation analysis, generating both correlation matrix and spatial correlation maps. Furthermore, we used the multilinear regression model to assess the likelihood of stroke-related death incidences by EVI and air pollutants of interest. The graphic analysis was used to disclose the spatial and temporal variability of the variables understudy. Results and discussions Results The summary statistics of the variables used in this study are shown in Table 1 ( mean ± standard deviation). The averages have been done for a period of 20 years (2001–2020). The EVI is highest in Burundi and Rwanda (0.34 ± 0.01) followed by Uganda (0.33 ± 0.01) and Tanzania (0.30 ± 0.01), while the lowest EVI is observed in Kenya (0.22 ± 0.02). Rwanda averaged the highest concentration of PM 2.5 , SO 2 and CO, while O 3 is highest in Tanzania and Kenya when compared with other countries in the study area. The highest death rate from stroke was averaged in Rwanda (779.16 ± 123.31 deaths per 100,000 persons), Burundi came in the second place (776.47 ± 81.69 deaths per 100,000 persons), and the lowest incidence rates were averaged in Kenya (584.52 ± 19.37 deaths per 100,000 persons). Table 1 Summary statistics (mean ± standard deviation) by country of variables used in this study Variables Burundi Kenya Rwanda Tanzania Uganda Stroke (cases) 776.47 ± 81.69 584.52 ± 19.37 779.39 ± 123.31 658.11 ± 26.52 638.07 ± 90.25 EVI (index) 0.34 ± 0.01 0.22 ± 0.02 0.34 ± 0.01 0.30 ± 0.01 0.33 ± 0.01 PM 2.5 (µg/m 3 ) 36.38 ± 3.22 22.05 ± 1.36 37.59 ± 3.32 22.94 ± 4.13 32.11 ± 2.85 SO 2 (µg/m 3 ) 0.79 ± 0.13 0.23 ± 0.01 1.13 ± 0.38 0.18 ± 0.08 0.44 ± 0.10 O 3 (ppb) 41.04 ± 1.44 42.42 ± 1.50 40.84 ± 1.51 42.43 ± 1.36 41.65 ± 1.58 CO (ppb) 132.22 ± 6.01 78.88 ± 2.06 135.34 ± 10.73 94.36 ± 3.92 111.80 ± 5.78 Note . Stroke cases are deaths per 100,000 persons Figure 3 Spatial and temporal distribution of the enhanced vegetation index Table 2 exhibits Pearson’s correlation matrix for the variables used in this study. It was observed that there was a weak correlation between all variables in this study. The stroke-related death prevalence cases per 100,000 persons are negatively correlated with EVI and O 3 , reflecting the general decreasing death prevalence from stroke per increase of these variables. Moreover, the correlation matrix exhibits that, generally, stroke-related death rates increase with the increase of PM 2.5 , and SO 2 concentrations in the region. At the same time, EVI is negatively correlated with O 3 and SO 2 , and positively correlated with the remaining air pollutant concentrations in the region. Table 2 Correlation matrix for the variables used in this study Stroke EVI PM 2.5 SO 2 O 3 CO Stroke 1 EVI -0,351 1 PM 2.5 0,468 0,090 1 SO 2 0,345 -0,055 -0,244 1 O 3 -0,448 -0,290 -0,417 -0,184 1 CO 0,027 0,214 0,131 -0,029 -0,014 1 The spatial correlations between EVI and air pollutants of interest in this study are shown in Fig. 3 . In the great part of the East African territory, concentrations of PM 2.5 and CO are positively correlated with EVI. The highest positive correlation between PM 2.5 and EVI is observed in the southeast of Uganda, Southwest of Kenya and the Eastern part of Rwanda. CO is mostly positively correlated in the northern part of Tanzania, and almost the whole territories of Kenya, Uganda and Rwanda. O 3 and EVI are negatively correlated on almost all pixels of the territory. SO 2 and EVI did not demonstrate a spatial correlation on most of the territory. Further assessment of the effect of air pollution and space greenness on stroke-related mortality rate was done utilizing linear regression models. According to the modelling results (Table 3 ), the increase of the EVI leads to a decrease in death incidences from stroke with each unit increase in EVI leading to a decrease of 845.57 ± 295.96 deaths per 100,000 persons in mortality rate. PM 2.5 and SO 2 project an increase in stroke-related mortality. The increase in SO 2 concentration by 1µg/m 3 is expected to increase the stroke mortality rate by 139.28 ± 64.33 deaths per 100,000 persons, and the increase of PM 2.5 by 1µg/m 3 results in 3.06 ± 1.25 deaths per 100,000 persons. Moreover, O 3 acts in favour of a decreased mortality rate, while CO has not resulted in any significant effect on stroke-related death prevalence in the region. Table 3 Linear regression fit results for the total population (20 years up) Variables Coefficients Standard Error t Stat p-value Lower 95% Upper 95% Intercept 909.78 183.19 4.97 0.00 516.88 1302.68 EVI -845.57 295.96 -2.86 0.01 -1480.35 -210.79 PM 2.5 3.06 1.25 2.44 0.03 0.37 5.75 SO 2 139.28 64.33 2.17 0.05 1.31 277.26 O 3 -4.53 2.64 -1.71 0.11 -10.20 1.14 CO 0.46 0.96 0.48 0.64 -1.60 2.53 Multiple R 0.81 Observations 20 R Square 0.66 F-statistics 5.44 Adjusted R Square 0.54 Significance F 0.006 Standard Error 12.85 Note. The table gives the linear regression results for the combined effect of vegetation greenness and air pollution on the death prevalence from stroke. The relationship between air pollution and health harms is influenced significantly by the age of individuals. Different age groups, such as children, adults, and the elderly exhibit varying levels of vulnerability to air pollution due to differences in physiology, exposure, and underlying health conditions. Furthermore, stroke-related death prevalences across different age groups also vary significantly due to various factors including resilience, prevalence of risk factors, and underlying health conditions. In children and adolescents, there is a very low prevalence of stroke-related deaths. In young adults (20–39 years), strokes are more likely to be caused by congenital conditions, lifestyle and trauma. In adults (40–59 years), stroke-related death increases. The combination of lifestyle factors, hypertension, diabetes, and hyperlipidemia become prevalent, and atherosclerosis and heart disease begin to play a more significant role. In older adults (60–79 years) and the elderly (80 + years), stroke-related deaths are highest. Age-related factors such as long-term hypertension, and other risk factors contribute to the increase of stroke-related deaths in these age groups (Benjamin et al. 2019 ; Feigin et al. 2014 ; J. xiao Li et al. 2023 ; World Health Organization 2018 ). To assess the effects of air pollution and EVI on stroke-related death prevalence, we stratified the stroke-related mortality incidences into age groups (from young adults and higher population groups) and fitted the linear regression model. Table 4 gives the results of the linear regression coefficients for each age group and variable. The results show that stroke-related death incidences are negatively associated with EVI and CO for the age group from 20 to 59 years, negatively associated with EVI and O 3 concentrations for the age group from 70 and above, and positively associated with other air pollutants, respectively. This association is statistically significant for PM 2.5 and SO 2 for all age groups, and significant for EVI in older adults and the elderly (60 years and more). Table 4 Linear regression results for age-stratified assessment of air pollution and surface greenness on the stroke-related death prevalence Variable \(\:\varvec{\beta\:}\) (20–29) \(\:\varvec{\beta\:}\) (30–39) \(\:\varvec{\beta\:}\) (40–49) \(\:\varvec{\beta\:}\) (50–59) \(\:\varvec{\beta\:}\) (60–69) \(\:\varvec{\beta\:}\) (70–79) \(\:\varvec{\beta\:}\) (80+) Intercept 0.64 (3.99) 0.47 (12.80) -15.71 (59.83) -41.11 (193.81) 283.27 (364.02) 1492.51*** (433.93) 3416.74*** (481.58) EVI -9.18* (6.45) -26.02 (20.68) -90.63 (96.66) -290.73 (313.13) -1026.35* (588.13) -2133.93** (701.07) -2333.86** (705.98) PM 25 0.05* (0.03) 0.17* (0.09) 0.77* (0.41) 2.38* (1.33) 5.44** (2.49) 7.77** (3.01) 6.03* (3.30) SO 2 4.28*** (1.40) 13.44** (4.89) 60.19*** (21.01) 12.87*** (68.06) 388.67*** (136.47) 426.21** (152.38) 130.53 (169.12) O 3 0.05 (0.06) 0.17 (0.18) 0.98 (0.86) 3.17 (2.80) 0.25 (5.25) -8.10 (6.26) -21.69** (6.95) CO 0.00 (0.02) 0.00 (0.07) -0.05 (0.31) -0.13 (1.02) 0.40 (1.91) 1.45 (2.28) 1.71 (2.53) Multiple R 0.71 0.69 0.67 0.66 0.73 0.83 0.83 R 2 0.50 0.48 0.44 0.43 0.54 0.68 0.68 Adj. R 2 0.32 0.29 0.28 0.23 0.37 0.57 0.57 F statistics 2.79* 2.58* 2.22* 2.15* 3.24** 6.03*** 5.98*** Note . Statistical significance: *significant at 10%, ** significant at 5%, and ***significant at 1%. From the model results it can be noticed that the effects of EVI on stroke-related death incidences increase with age as these incidences also are. For incidence, the model predicted that when there is an increase of one unit in annual mean EVI, may lead to a reduction of stroke-related deaths by 9.18 ± 6.45 deaths per 100,000 persons aged from 20 to 29 years, while this number decreases by 2333.86 ± 705.98 deaths reduction per 100,000 persons aged above 80 years. This tendency is also demonstrated by other air pollutants such as PM 2.5 and SO 2 . Furthermore, O 3 seems (no statistical significance) to increase stroke-related death incidences for the age group from 20 to 69 years, and further, it acts in decreasing those deaths for the age group from 70 and above (statistically significant). This phenomenon is reversed for the CO concentrations, although there is no statistical significance for the CO estimates for all age groups. Discussion Enhanced vegetation index and air pollution This study used twenty years of satellite and model-derived data on the Enhanced Vegetation Index (EVI) and air pollutants of PM 2.5 , SO 2 , O 3 , and CO to assess their influence on human health through stroke-related mortality prevalence in five countries of the East African region. Our correlation analysis found that the mean annual concentrations of PM 2.5 and CO are positively correlated with the EVI, while O 3 and SO 2 showed a negative correlation with EVI. Scientific evidence has proved that changes in air quality alter the surface solar radiation and affect photosynthetic activity and surface greenness (Kashyap et al. 2023 ). Air pollutants have a detrimental effect on vegetation as they act as mechanical barriers to light penetration, blocking stomatal opening and indirect effects by altering the soil properties in addition to direct damage to the vegetation leaves (Kashyap et al. 2023 ; Nowak et al. 2014 ). The correlation analysis showed that EVI is negatively correlated with tropospheric O 3 in the region. The interaction between tropospheric ozone and vegetation is complex. High levels of tropospheric O 3 can damage plant tissues, reduce photosynthesis, and lower EVI values, indicating stressed or damaged vegetation. Healthy vegetation can help absorb and reduce O 3 precursors, thereby mitigating local O 3 formation. However, the changes in vegetation cover and health can alter emissions of biogenic volatile organic compounds, influencing local and regional ozone concentration levels (Fountoukis et al. 2007 ; Xi et al. 2019 ; Zhou et al. 2018 ). Moreover, SO 2 concentration has diverse effects on the vegetation. The effects include leaf injury and chlorosis, reduced photosynthesis activities, growth suppression and altered physiology and metabolism mechanisms (Cotrozzi 2020 ; H. K. Lee et al. 2017 ). All these impacts alter the space's greenness. Furthermore, to investigate the unexpected effect of the positive correlation between EVI and CO and PM 2.5 , we performed a spatial variability analysis of air pollution and EVI in East Africa, in addition to spatial correlation which results are presented in Fig. 3 . Spatial patterns of PM 2.5 (Figure S2) and CO (Figure S5) vary from west to East, making them have higher concentrations in Uganda, Rwanda and Burundi when compared with their concentrations in Tanzania and Kenya (Figure SM1). The same regions with higher concentrations of PM 2.5 and CO are also characterized by higher surface greenness as shown in Figure S1 . Countries such as Burundi, Rwanda and Uganda have higher levels of PM 2.5 and CO concentrations over their territories. The main sources of these pollutants are assumed to be the savannah fires which are dominating in (or around) these countries (Opio et al. 2021 ). The vegetation ecological zones in the regions vary from the tropical evergreen broadleaf forest in the west of Burundi, Rwanda and Uganda to sparse grass/shrubland in the northeastern part of Kenya (Hawinkel et al. 2016 ), making the western part of East Africa greener than the western part. This positive correlation between EVI and CO and PM 2.5 may be influenced by the study design, while most of the studies assessed the relationship between surface greenness and air pollution in urban areas, this study assesses the effect on the extended territory including the rural areas which are marked by more forests and agricultural lands covered by crops most of the time. Recall that the urbanization rate of East Africa is 27.1% (Yatta 2018 ), even though it is increasing, it is still the lowest on the continent (AfDB 2023 ). Air pollution and stroke-related death prevalence Despite significant advances in stroke treatment and prevention, stroke remains among the leading causes of morbidity and mortality worldwide (Pu et al. 2023 ). Traditional cardiovascular risk factors such as hypertension, diabetes, sedentary behavior, and smoking, do not fully account for variations in stroke risk. Researchers have turned their attention to novel modifiable risk factors, including environmental agents, to better understand stroke incidence and outcomes. Ambient air pollution has emerged as a critical factor for cerebrovascular disease (Kulick et al. 2023c ; Sagheer et al. 2024 ). Our analysis has demonstrated that, in East African Countries, PM 2.5 exposure is associated with an increase in stroke-related death rate. Our results are in agreement with other studies on the effect of PM 2.5 on stroke-related incidences (Jiang et al. 2020 ; Kulick et al. 2023a ; Zhang et al. 2018 ). It has been reported that the mechanisms by which PM 2.5 induces stroke include inflammation and oxidative stress, blood pressure coagulation, and direct neurotoxicity (Elsayed et al. 2020 ; Kulick et al. 2023a ; Z. Li et al. 2022 ). Particulate-related air pollution in East African countries is an increasing issue. Specifically, the main sources of particulate matter pollutants in this region include residential fuel use, fossil fuel consumption for energy production, transportation, and waste burning (Gilbert et al. 2021 ; Pollution 2019 ). Furthermore, our analysis demonstrated that SO 2 and O 3 also increase the fate of stroke-related death in the region. This result agrees with the study by Wu et al., ( 2021 ) who found an increase in Years of Life Lost (YLLs) from total stroke, hemorrhagic and ischemic stroke, in China, with the increase of the SO2 concentration. Furthermore, Keller et al., ( 2023 ) reported the association of O3, NO2 and SO2 and PM2.5 with increased stroke mortality rate in Germany. The mechanisms by which SO2 and O3 are similar to those of PM2.5 (Jiang et al. 2020 ; Kulick et al. 2023a ; Zhang et al. 2018 ). The assessment of the tropospheric O 3 on the stroke-related death prevalence, in this study, has shown double tendencies. In the age group from 20 years to 69 years, exposure to ground-level O 3 is associated with an increase in stroke-related death incidences. However, for the age group of 70 years and above, the exposure to O 3 resulted in a negative association with the prevalence of stroke-related death in the region which is also observed for the general population consideration. The negative association of the ozone to stroke-related death prevalence contradicts the known evidence. The tropospheric O 3 has significant impacts on health, including an increased risk of stroke and stroke-related death (J. Wang et al. 2024 ). Both short-term and long-term exposure to ozone can lead to higher mortality rates due to cardiovascular diseases, including stroke (J. Wang et al. 2024 ). The O 3 exposure alters blood pressure, blood coagulation, inflammation, and oxidative stress, among other factors that may influence stroke risk by damaging blood arteries (Dewan and Lakhani 2022). While the influence of O 3 on stroke prevalence is clearer in young and adult populations, the relationship between ozone exposure and stroke in elderly people is complex and not as strongly established as other risk factors. Elderly individuals often have multiple, more dominant risk factors for stroke, such as hypertension, diabetes, and heart disease which may overshadow the impact of O 3 exposure (Minja et al. 2022 ). Furthermore, O 3 tends to cause acute respiratory effects more than chronic cardiovascular effects, and the reduction of outdoor activities by elderly people often leads to lower exposure to O 3 and a lesser observed impact of O 3 on stroke risk. Furthermore, this study has not found a statistically significant effect of CO exposure on the increase in stroke-related death prevalence in East Africa. The controversial outcomes of CO in stroke-related incidences have been observed by other scholars. A study by Kettunen et al., ( 2007 ) found that the exposure of CO during the cold seasons was not associated with stroke increase in Finland, and justified the results by the difference in seasonal CO exposure. Research conducted in various Chinese cities on the connection between environmental pollution and stroke cases has revealed that hospitalization rates for ischemic stroke are positively correlated with exposure to major air pollution gasses, such as CO and sulfur dioxide, while only nitrogen dioxide is linked to hemorrhagic stroke (Liu et al. 2017 ). The number of stroke patients admitted to the emergency room in Hong Kong, China, is inversely connected with the maximum daily concentration of CO in the air. This could be because exposure to CO can prevent strokes (Tian et al. 2015 ). However, numerous studies have associated CO exposure with stroke-related mortality and mortality (Kwak et al. 2021 ; Siracusa et al. 2021 ; Veiraiah 2020 ; Y. Wang et al. 2021 ). This suggested a more supervised study to draw the effect of CO on the stroke-related death rate in East Africa. Enhanced vegetation index and stroke-related death prevalence Multiple linear regression model results shown in Tables 3 & 4 showed that EVI reduces the prevalence of stroke-related deaths in East Africa. although the common risk factors of stroke in East Africa are similar to those found globally, there are some region-specific factors such as hypertension, diabetes mellitus, obesity, smoking, alcohol use, physical inactivity (Akinyemi et al. 2021a ), unhealthy diet, and stress (Bolaji 2024 ). Our findings are consistent with earlier research conducted in several locations, which showed that places with more vegetation cover have better cardiovascular health outcomes, including fewer fatalities from stroke (Adeloye 2014 ; Hawinkel et al. 2016 ; Minja et al. 2022 ). A cohort study found that people who have green spaces within 300 meters of their homes have a 16% lower risk of experiencing an ischemic stroke, which is the most common type of stroke (Avellaneda-Gómez et al. 2022 ). Higher EVI values typically correlate with greater access to parks and recreational areas, promoting physical activity. Frequent physical activity helps control blood pressure, weight, and cardiovascular health in general, offering protection against stroke. Moreover, green space is associated with lower stress levels and improved mental health. It has been found that chronic stress is a risk for hypertension and stroke (Minja et al. 2022 ). In addition, higher EVI in residential areas often indicate a higher socioeconomic level linked with better healthier lifestyles and lower stroke mortality rates. Conversely, areas with low EVI may suffer from greater disadvantages and higher stress levels contributing to higher stroke prevalence and mortality (J. xiao Li et al. 2023 ). Conclusion This study analyzed the effect of air pollution and space greenness on the stroke-related death rate in East African Countries. The correlation analysis revealed a negative association of stroke prevalence deaths with the EVI and the concentrations of ground-level O 3 . The concentrations of PM 2.5 and SO 2 have been found to positively correlate with the stroke-related death rate in the region, while CO did not demonstrate a significant correlation with the fate of the stroke in the region. Furthermore, the linear regression model showed that the EVI favorites the decrease in the stroke-related death prevalence in East Africa, while the exposure to PM 2.5 and SO 2 acts in the increasing direction of the death rate coming from the stroke in the region. The fit of linear regression with age-stratified exposure demonstrated that the effect of environmental factors on the stroke death prevalence increases with the increase of the age of an individual as it does the fatality incidents related to stroke. The findings of our research validate the pressing necessity of mitigating air pollution exposure via emission controls to lower the incidence and fatality of strokes and the promotion of recreational green spaces. However, our study has some limitations. Firstly, because of the adequate monitoring network all over the territory, we used satellite-driven and model reanalysis data at sparse spatial resolution for air pollution. Secondly, we used stroke-related death from the global burden of diseases estimated which could be having misclassification of the country estimate of stroke incidences. Declarations Ethical Responsibilities of Authors All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors. Ethical approval Not applicable Consent to participate Not applicable Consent to publish Not applicable Availability of data and material The dataset regarding stroke mortality rate may be found on the Global Burden of Disease repository (https://ghdx.healthdata.org/gbd-2021). Data on fine particulate matter air pollution (PM 2.5 ) may found on the Atmospheric Composition Analysis Group repository (https://sites.wustl.edu/acag/). Air pollution concentrations of SO 2 , O 3 and CO are freely available at https://giovanni.gsfc.nasa.gov/giovanni/. Other analysis and program codes used in this study are available on request from the corresponding author. Competing interest The authors declare no competing interests. Funding No funding was received for conducting this study. Financial interests None Authors’ contributions Valérien Baharane : Conceptualization, Methodology, Investigation, Software, Writing–Original draft preparation, Visualization, Formal analysis. Andrey Borisovich Shatalov : Supervision, Review & Editing. Emmanuel Igwe : Review & Editing References ACAG. (2023). Home | Atmospheric Composition Analysis Group | Washington University in St. Louis. 2023 . https://sites.wustl.edu/acag/ . Accessed 9 March 2023 Adeloye, D. (2014, June 26). An estimate of the incidence and prevalence of stroke in Africa: A systematic review and meta-analysis. PLoS ONE . Public Library of Science. https://doi.org/10.1371/journal.pone.0100724 AfDB. (2023). East Africa Regional Economic Outlook 2023: Mid-term growth for East Africa region projected highest on the continent for 2023-4 . https://www.afdb.org/en/news-and-events/press-releases/east-africa-regional-economic-outlook-2023-mid-term-growth-east-africa-region-projected-highest-continent-2023-4-63483 . Accessed 28 June 2024 Akinyemi, R. O., Ovbiagele, B., Adeniji, O. A., Sarfo, F. S., Abd-Allah, F., Adoukonou, T., et al. (2021a). Stroke in Africa: profile, progress, prospects and priorities. Nature Reviews Neurology. https://doi.org/10.1038/s41582-021-00542-4 Akinyemi, R. O., Ovbiagele, B., Adeniji, O. A., Sarfo, F. S., Abd-Allah, F., Adoukonou, T., et al. (2021b, October 1). Stroke in Africa: profile, progress, prospects and priorities. Nature Reviews Neurology . Nature Publishing Group. https://doi.org/10.1038/s41582-021-00542-4 Avellaneda-Gómez, C., Vivanco-Hidalgo, R. M., Olmos, S., Lazcano, U., Valentin, A., Milà, C., et al. (2022). Air pollution and surrounding greenness in relation to ischemic stroke: A population-based cohort study. Environment International , 161 . https://doi.org/10.1016/j.envint.2022.107147 Benjamin, E. J., Muntner, P., Alonso, A., Bittencourt, M. S., Callaway, C. W., Carson, A. P., et al. (2019). Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation, 139 (10), e56–e528. https://doi.org/10.1161/CIR.0000000000000659 Berrick, S. W., Leptoukh, G., Farley, J. D., & Rui, H. (2009). Giovanni: A web service workflow-based data visualization and analysis System. IEEE Transactions on Geoscience and Remote Sensing, 47 (1), 106–113. https://doi.org/10.1109/TGRS.2008.2003183 Bolaji, P. (2024). Risk factors for stroke in the African populace: need for action. South Sudan Medical Journal, 17 (1), 33–36. https://doi.org/10.4314/ssmj.v17i1.7 Camberlin, P. (2018). Climate of Eastern Africa. In Oxford Research Encyclopedia of Climate Science . Oxford University Press. https://doi.org/10.1093/acrefore/9780190228620.013.512 Cotrozzi, L. (2020). Leaf demography and growth analysis to assess the impact of air pollution on plants: A case study on alfalfa exposed to a gradient of sulphur dioxide concentrations. Atmospheric Pollution Research, 11 (1), 186–192. https://doi.org/10.1016/j.apr.2019.10.006 Dewan, S., & Lakhani, A. (2022, December 15). Tropospheric ozone and its natural precursors impacted by climatic changes in emission and dynamics. Frontiers in Environmental Science . Frontiers Media S.A. https://doi.org/10.3389/fenvs.2022.1007942 Didan, K. (2015). LP DAAC - MOD13A2. https://lpdaac.usgs.gov/products/mod13a3v061/ . Accessed 17 June 2024 Didan, K. (2021). MODIS/Terra Vegetation Indices Monthly L3 Global 1km SIN Grid V061. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/https://doi.org/10.5067/MODIS/MOD13A3.061 Elsayed, W. M., Abdel-Gawad, E. H. A., Mesallam, D. I. A., & El-Serafy, T. S. (2020). The relationship between oxidative stress and acute ischemic stroke severity and functional outcome. Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 56 (1), 1–6. https://doi.org/10.1186/s41983-020-00206-y Feigin, V. L., Brainin, M., Norrving, B., Martins, S., Sacco, R. L., Hacke, W., et al. (2022, January 1). World Stroke Organization (WSO): Global Stroke Fact Sheet 2022. International Journal of Stroke . SAGE Publications Inc. https://doi.org/10.1177/17474930211065917 Feigin, V. L., Forouzanfar, M. H., Krishnamurthi, R., Mensah, G. A., Connor, M., Bennett, D. A., et al. (2014). Global and regional burden of stroke during 1990–2010: Findings from the Global Burden of Disease Study 2010. The Lancet, 383 (9913), 245–255. https://doi.org/10.1016/S0140-6736(13)61953-4 Fountoukis, C., Nenes, A., Meskhidze, N., Bahreini, R., Conant, W. C., Jonsson, H., et al. (2007). Aerosol-cloud drop concentration closure for clouds sampled during the International Consortium for Atmospheric Research on Transport and Transformation 2004 campaign. Journal of Geophysical Research Atmospheres, 112 (10), 10–30. https://doi.org/10.1029/2006JD007272 Gilbert, N., Ziqiang, Y., & Hongzhi, M. (2021). Current situation of solid waste management in East African countries and the proposal for sustainable management. African Journal of Environmental Science and Technology, 15 (1), 1–15. https://doi.org/10.5897/ajest2020.2911 Global Modeling and Assimilation Office (GMAO). (2015a). MERRA-2 tavgM_2d_aer_Nx: 2d,Monthly mean,Time-averaged,Single-Level,Assimilation,Aerosol Diagnostics V5.12.4. Goddard Earth Sciences Data and Information Services Center (GES DISC) . Greenbelt, MD, USA. https://doi.org/10.5067/FH9A0MLJPC7N Global Modeling and Assimilation Office (GMAO). (2015b). MERRA-2 tavgM_2d_chm_Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Carbon Monoxide and Ozone Diagnostics V5.12.4, Greenbelt, MD, USA. Goddard Earth Sciences Data and Information Services Center (GES DISC) . https://doi.org/10.5067/WMT31RKEXK8I Global Modeling and Assimilation Office (GMAO). (2015c). MERRA-2 instM_3d_asm_Np: 3d,Monthly mean,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields V5.12.4. Goddard Earth Sciences Data and Information Services Center (GES DISC) . https://doi.org/10.5067/2E096JV59PK7 Guenther, A. (1995). A global model of natural volatile organic compound emissions. Journal of Geophysical Research, 100 (D5), 8873–8892. https://doi.org/10.1029/94JD02950 Hawinkel, P., Thiery, W., Lhermitte, S., Swinnen, E., Verbist, B., Orshoven, J., & Muys, B. (2016). Vegetation response to precipitation variability in East Africa controlled by biogeographical factors. Journal of Geophysical Research: Biogeosciences , 121 . https://doi.org/10.1002/2016JG003436 Health Organization, W., & WHO. (2016). Ambient air pollution: a global assessment of exposure and burden of disease . Clean Air Journal (Vol. 26). Geneva PP - Geneva: World Health Organization. https://doi.org/10.17159/2410-972x/2016/v26n2a4 Institute for Health Metrics and Evaluation (IHME). (2020). Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Results. Seattle, United States. https://vizhub.healthdata.org/gbd-results/ Jiang, Y., Lu, H., Man, Q., Liu, Z., Wang, L., Wang, Y., et al. (2020). Stroke burden and mortality attributable to ambient fine particulate matter pollution in 195 countries and territories and trend analysis from 1990 to 2017. Environmental Research, 184 , 109327. https://doi.org/10.1016/j.envres.2020.109327 Kashyap, R., Kuttippurath, J., & Patel, V. K. (2023). Improved air quality leads to enhanced vegetation growth during the COVID–19 lockdown in India. Applied Geography, 151 , 102869. https://doi.org/10.1016/j.apgeog.2022.102869 Kebacho, L. L. (2022). Large-scale circulations associated with recent interannual variability of the short rains over East Africa. Meteorology and Atmospheric Physics, 134 (1), 1–19. https://doi.org/10.1007/s00703-021-00846-6 Keller, K., Haghi, S. H. R., Hahad, O., Schmidtmann, I., Chowdhury, S., Lelieveld, J., et al. (2023). Air pollution impacts on in-hospital case-fatality rate of ischemic stroke patients. Thrombosis Research, 225 , 116–125. https://doi.org/10.1016/j.thromres.2023.03.006 Kettunen, J., Lanki, T., Tiittanen, P., Aalto, P. P., Koskentalo, T., Kulmala, M., et al. (2007). Associations of fine and ultrafine particulate air pollution with stroke mortality in an area of low air pollution levels. Stroke, 38 (3), 918–922. https://doi.org/10.1161/01.STR.0000257999.49706.3b Kulick, E. R., Kaufman, J. D., & Sack, C. (2023a). Ambient Air Pollution and Stroke: An Updated Review. Stroke. https://doi.org/10.1161/STROKEAHA.122.035498 Kulick, E. R., Kaufman, J. D., & Sack, C. (2023b, March 1). Ambient Air Pollution and Stroke: An Updated Review. Stroke . Lippincott Williams & WilkinsHagerstown, MD. https://doi.org/10.1161/STROKEAHA.122.035498 Kulick, E. R., Kaufman, J. D., & Sack, C. (2023c, March 1). Ambient Air Pollution and Stroke: An Updated Review. Stroke . Wolters Kluwer Health. https://doi.org/10.1161/STROKEAHA.122.035498 Kwak, K., Kim, M., Choi, W. J., Ju, Y. S., & Park, J. T. (2021). Association Between Carbon Monoxide Intoxication and Incidence of Ischemic Stroke: A Retrospective Nested Case-Control Study in South Korea. Journal of Stroke and Cerebrovascular Diseases, 30 (2), 105496. https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105496 Lee, H. K., Khaine, I., Kwak, M. J., Jang, J. H., Lee, T. Y., Lee, J. K., et al. (2017, December 1). The relationship between SO2 exposure and plant physiology: A mini review. Horticulture Environment and Biotechnology . Korean Society for Horticultural Science. https://doi.org/10.1007/s13580-017-0053-0 Lee, I. M., Shiroma, E. J., Lobelo, F., Puska, P., Blair, S. N., Katzmarzyk, P. T., et al. (2012). Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy. The Lancet, 380 (9838), 219–229. https://doi.org/10.1016/S0140-6736(12)61031-9 Lee, K. K., Miller, M. R., & Shah, A. S. V. (2018, January 1). Air pollution and stroke. Journal of Stroke . Korean Stroke Society. https://doi.org/10.5853/jos.2017.02894 Li, J. xiao, Zhong, Q. qiong, Yuan, S. xiang, & Zhu, F. (2023). Trends in deaths and disability-adjusted life-years of stroke attributable to low physical activity worldwide, 1990–2019. BMC Public Health, 23 (1), 1–14. https://doi.org/10.1186/s12889-023-17162-w Li, Z., Bi, R., Sun, S., Chen, S., Chen, J., Hu, B., & Jin, H. (2022). The Role of Oxidative Stress in Acute Ischemic Stroke-Related Thrombosis. Oxidative Medicine and Cellular Longevity . Hindawi Limited. https://doi.org/10.1155/2022/8418820 Liu, H., Tian, Y., Xu, Y., Huang, Z., Huang, C., Hu, Y., & Zhang, J. (2017). Association between ambient air pollution and hospitalization for ischemic and hemorrhagic stroke in China: A multicity case-crossover study. Environmental Pollution, 230 , 234–241. https://doi.org/10.1016/j.envpol.2017.06.057 Maas, J., van Dillen, S. M. E., Verheij, R. A., & Groenewegen, P. P. (2009). Social contacts as a possible mechanism behind the relation between green space and health. Health and Place, 15 (2), 586–595. https://doi.org/10.1016/j.healthplace.2008.09.006 Minja, N. M., Nakagaayi, D., Aliku, T., Zhang, W., Ssinabulya, I., Nabaale, J., et al. (2022, November 10). Cardiovascular diseases in Africa in the twenty-first century: Gaps and priorities going forward. Frontiers in Cardiovascular Medicine . Frontiers Media S.A. https://doi.org/10.3389/fcvm.2022.1008335 Nicholson, S. E. (2019). A Review of Climate Dynamics and Climate Variability in Eastern Africa. In The Limnology, Climatology and Paleoclimatology of the East African Lakes (pp. 25–56). Routledge. https://doi.org/10.1201/9780203748978-2 Niohuru, I. (2023). Disease Burden and Mortality (pp. 35–85). Springer, Cham. https://doi.org/10.1007/978-3-031-19719-2_3 Nowak, D. J., Crane, D. E., & Stevens, J. C. (2006). Air pollution removal by urban trees and shrubs in the United States. Urban Forestry and Urban Greening, 4 (3–4), 115–123. https://doi.org/10.1016/j.ufug.2006.01.007 Nowak, D. J., Hirabayashi, S., Bodine, A., & Greenfield, E. (2014). Tree and forest effects on air quality and human health in the United States. Environmental Pollution, 193 , 119–129. https://doi.org/10.1016/j.envpol.2014.05.028 Opio, R., Mugume, I., & Nakatumba-Nabende, J. (2021). Understanding the trend of NO2, SO2 and CO over East Africa from 2005 to 2020. Atmosphere, 12 (10), 1283. https://doi.org/10.3390/atmos12101283 Organization, W. H. (2018). 9 Out of 10 People Worldwide Breathe Polluted Air, But More Countries Are Taking Action. 9 Out of 10 People Worldwide Breathe Polluted Air, But More Countries Are Taking Action . https://www.who.int/news/item/02-05-2018-9-out-of-10-people-worldwide-breathe-polluted-air-but-more-countries-are-taking-action . Accessed 16 June 2024 Picavet, H. S. J., Milder, I., Kruize, H., de Vries, S., Hermans, T., & Wendel-Vos, W. (2016). Greener living environment healthier people? Exploring green space, physical activity and health in the Doetinchem Cohort Study. Preventive Medicine, 89 , 7–14. https://doi.org/10.1016/j.ypmed.2016.04.021 Pollution, A. (2019). Air Pollution and Health Impacts in Africa, 1–8. https://www.stateofglobalair.org/resources/africa . Accessed 28 June 2024 Pope, C. A., & Dockery, D. W. (2006). Health effects of fine particulate air pollution: Lines that connect. Journal of the Air and Waste Management Association, 56 (6), 709–742. https://doi.org/10.1080/10473289.2006.10464485 Pu, L., Wang, L., Zhang, R., Zhao, T., Jiang, Y., & Han, L. (2023). Projected Global Trends in Ischemic Stroke Incidence, Deaths and Disability-Adjusted Life Years From 2020 to 2030. Stroke, 54 (5), 1330–1339. https://doi.org/10.1161/STROKEAHA.122.040073 Sagheer, U., Al-Kindi, S., Abohashem, S., Phillips, C. T., Rana, J. S., Bhatnagar, A., et al. (2024, February 1). Environmental Pollution and Cardiovascular Disease: Part 1 of 2: Air Pollution. JACC: Advances . Elsevier. https://doi.org/10.1016/j.jacadv.2023.100805 Siracusa, R., Schaufler, A., Calabrese, V., Fuller, P. M., & Otterbein, L. E. (2021, May 1). Carbon Monoxide: from Poison to Clinical Trials. Trends in Pharmacological Sciences . Elsevier Ltd. https://doi.org/10.1016/j.tips.2021.02.003 Tian, L., Qiu, H., Pun, V. C., Ho, K. F., Chan, C. S., & Yu, I. T. S. (2015). Carbon monoxide and stroke: A time series study of ambient air pollution and emergency hospitalizations. International Journal of Cardiology, 201 , 4–9. https://doi.org/10.1016/j.ijcard.2015.07.099 Ulrich, R. S. (1984). View through a window may influence recovery from surgery. Science, 224 (4647), 420–421. https://doi.org/10.1126/science.6143402 Veiraiah, A. (2020, March 1). Carbon monoxide poisoning. Medicine (United Kingdom) . Elsevier Ltd. https://doi.org/10.1016/j.mpmed.2019.12.013 Wang, J., Ju, T., Li, B., Huang, C., Xia, X., Zhang, J., & Li, C. (2024). Characterization of tropospheric ozone pollution, random forest trend prediction and analysis of influencing factors in South-western Europe. Environmental Sciences Europe, 36 (1), 1–20. https://doi.org/10.1186/s12302-024-00863-3 Wang, Y., Li, J., Wang, L., Lin, Y., Zhou, M., Yin, P., & Yao, S. (2021). The impact of carbon monoxide on years of life lost and modified effect by individual- and city-level characteristics: Evidence from a nationwide time-series study in China. Ecotoxicology and Environmental Safety, 210 , 111884. https://doi.org/10.1016/j.ecoenv.2020.111884 World Health Organization. (2018). WHO - The top 10 causes of death. 24 Maggio . https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death . Accessed 29 February 2024 Wu, Z., Li, J., Huang, J., Wang, Y., Cao, R., Yin, P., et al. (2021). Ambient sulfur dioxide and years of life lost from stroke in China: a time-series analysis in 48 cities. Chemosphere, 267 , 128857. https://doi.org/10.1016/j.chemosphere.2020.128857 Xi, W., Du, S., Wang, Y. C., & Zhang, X. (2019). A spatiotemporal cube model for analyzing satellite image time series: Application to land-cover mapping and change detection. Remote Sensing of Environment, 231 , 111212. https://doi.org/10.1016/j.rse.2019.111212 Yan, T., Leng, H., & Yuan, Z. (2024). Construction of the “Full Path” of restorative effects on older adults’ mental health in parks under seasonal differences: taking Changchun as an example. Frontiers in Public Health , 12 . https://doi.org/10.3389/fpubh.2024.1269249 Yatta, F. P. (2018). Urbanization in Africa: Trends, Regional Specificities, and Challenges (pp. 317–339). Springer, Singapore. https://doi.org/10.1007/978-981-10-7799-9_14 Zhang, R., Liu, G., Jiang, Y., Li, G., Pan, Y., Wang, Y., et al. (2018). Acute effects of particulate air pollution on ischemic stroke and hemorrhagic stroke mortality. Frontiers in Neurology, 9 (OCT), 406874. https://doi.org/10.3389/fneur.2018.00827 Zhou, S. S., Tai, A. P. K., Sun, S., Sadiq, M., Heald, C. L., & Geddes, J. A. (2018). Coupling between surface ozone and leaf area index in a chemical transport model: Strength of feedback and implications for ozone air quality and vegetation health. Atmospheric Chemistry and Physics, 18 (19), 14133–14148. https://doi.org/10.5194/acp-18-14133-2018 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4772793","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":342972945,"identity":"736fe1d3-fe3a-4c69-8d51-3912cf6c1640","order_by":0,"name":"Valérien Baharane","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDACCQY2CIO9AUgYWJCihecASIsEKVokEiBcgsDgdvOzBz/32OXxSz6/uuFHgQQDf3t3An4td46ZG/Y8Sy6WnJ1TdrMH6DCJM2c34NUiOSPBTILnAHPihts5aTd4gFoMJHIJaUn/JvnnQH3i/ptn0m7+IUYLv0SOmTTPgcOJGyTYj90myhZ+mTNl0jIHjifOOJPDdlvGQIKHoF/YpNu3Sb45UJ3Y33782c03f2zk+Nt78WtBAjwGYJJY5SDA/oAU1aNgFIyCUTCCAABkd0fU4vPrDAAAAABJRU5ErkJggg==","orcid":"","institution":"Peoples’ Friendship University of Russia named after Patrice Lumumba","correspondingAuthor":true,"prefix":"","firstName":"Valérien","middleName":"","lastName":"Baharane","suffix":""},{"id":342972947,"identity":"6748f61e-2bc9-4813-a58b-1033dfee0f61","order_by":1,"name":"Andrey Borisovich Shatalov","email":"","orcid":"","institution":"Peoples’ Friendship University of Russia named after Patrice Lumumba","correspondingAuthor":false,"prefix":"","firstName":"Andrey","middleName":"Borisovich","lastName":"Shatalov","suffix":""},{"id":342972950,"identity":"ed086bff-66a4-40ab-9cf5-8e4c5f82a9a7","order_by":2,"name":"Emmanuel Igwe","email":"","orcid":"","institution":"Peoples’ Friendship University of Russia named after Patrice Lumumba","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"","lastName":"Igwe","suffix":""}],"badges":[],"createdAt":"2024-07-20 11:02:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4772793/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4772793/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63072318,"identity":"fb8dbdce-26f6-4357-bea5-b4a457efa239","added_by":"auto","created_at":"2024-08-22 20:11:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41946,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of air pollutants averaged for 20 years (2001-2020)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4772793/v1/1f87c59b7cb2aaa5175df197.png"},{"id":63072224,"identity":"151e6877-8151-47c2-93a0-39e6ee7a7245","added_by":"auto","created_at":"2024-08-22 20:11:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":359858,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial and temporal distribution of the enhanced vegetation index\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4772793/v1/6583be31d0542e1fb78518cb.png"},{"id":63072440,"identity":"0774af13-b1fa-4338-855a-b039094358c8","added_by":"auto","created_at":"2024-08-22 20:12:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":319178,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial correlation between EVI and air pollutants\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4772793/v1/1e78c8c890b4c5333e4eb307.png"},{"id":66326906,"identity":"bbdadefa-b351-4d18-9593-46b0af24679a","added_by":"auto","created_at":"2024-10-10 13:02:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1341830,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4772793/v1/5e2fdb49-7e40-436c-862b-6724d2973338.pdf"},{"id":63072428,"identity":"bc83c321-f99e-4962-b076-31653cac2567","added_by":"auto","created_at":"2024-08-22 20:12:03","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":344961,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4772793/v1/684407f8913433e4092653a7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Examining the interplay between air pollution, vegetation greenness, and stroke prevalence in East Africa: An ecological perspective","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEnvironmental factors play a crucial role in determining the state of human health. Among the various environmental factors, air pollution and surface vegetation cover have been shown to have significant, though often opposing, impacts on human health. Air pollution remains one of the most pressing environmental health challenges globally (Health Organization and WHO 2016). The most harmful substances include particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e), nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), carbon monoxide (CO), and tropospheric ozone (O\u003csub\u003e3\u003c/sub\u003e). Short-term exposure to these pollutants can lead to respiratory symptoms such as coughing, throat irritation, and shortness of breath. More severe consequences include exacerbations of asthma and chronic obstructive pulmonary disease (COPD). Long-term exposure is associated with more serious conditions, like cardiovascular diseases, lung cancer, and even premature death (Pope and Dockery \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). A comprehensive study by the World Health Organization (WHO) estimated that air pollution causes approximately 7\u0026nbsp;million premature deaths annually, hence, underscoring its global health burden (Organization \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast to air pollution, exposure to surface greenness has been linked to a range of health benefits. Surface greenness encompasses parks, gardens, street trees, and other forms of vegetation in urban and rural settings. It contributes to physical, mental, and social well-being through various mechanisms. The greenness encourages physical activity by providing appealing spaces for exercise and recreation. It has been shown that regular physical activity is crucial for preventing chronic diseases such as obesity, diabetes, and cardiovascular diseases (I. M. Lee et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Secondly, exposure to natural environments has been shown to reduce stress, anxiety, and depression. Even the concept of biophilia, which suggests the inherent affinity of humans to nature, supports the idea that natural settings improve mental health (Ulrich \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Yan et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, surface greenness can improve social cohesion by providing communal spaces where people can gather and interact. This sense of community can further enhance mental health and well-being (Maas et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Picavet et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, vegetation can mitigate the urban heat island effect, reduce noise pollution, and improve air quality by absorbing pollutants, thus providing indirect health benefits.\u003c/p\u003e \u003cp\u003eWhile the benefits of surface greenness on health are well understood, the interaction between vegetation and air pollution is complex. On one hand, vegetation can help reduce air pollution levels. Trees and plants can absorb pollutants and filter particulate matter from the air, thus improving air quality (Nowak et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This suggests that increasing urban greenness could be a strategy to combat the adverse health effects of air pollution. On the other hand, vegetation can also be a source of certain pollutants. For instance, trees and plants emit volatile organic compounds (VOCs) that can contribute to the formation of ground-level ozone (Guenther \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Several studies assessed the effect of both greenspaces and air pollution in developed countries of Europe and America, but few are similar studies in developing countries, especially in sub-Saharan Africa. This study assesses the combined effects of air pollution and surface greenness on human health in East African countries. Among the various vacuum through which air pollution impacts health, we selected the stroke-related death prevalence in the adult and elderly population of East African countries.\u003c/p\u003e \u003cp\u003eDespite recent advances in treatment and prevention, stroke remains the second leading cause of death and the third leading cause of death and serious long-term disability combined (Feigin et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the search for novel modifiable risk factors, there is growing interest in the adverse effects of environmental agents, among the most pervasive is ambient air pollution (Kulick et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Evidence from epidemiological studies has demonstrated a strong association between air pollution and cardiovascular diseases including stroke (K. K. Lee et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The prevalence of stroke in Africa varies by country and it is influenced by multiple factors, including healthcare infrastructure, lifestyle, and prevalence of risk factors such as hypertension, diabetes, and smoking, however, stroke is a growing public health concern in the region due to increasing rate of these risk factors. Common risk factors for stroke in East Africa include hypertension, diabetes, obesity, tobacco use and physical inactivity (Adeloye \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). There is also a high prevalence of infectious diseases, such as HIV/AIDS which can contribute to an increased risk of stroke (Akinyemi et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Niohuru \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, similar to global trends, stroke prevalence increases with age in Africa, however, the region may experience a higher proportion of strokes in younger adults compared to high-income countries due to different risk factor profiles and healthcare access.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eData source\u003c/p\u003e \u003cp\u003eWe focused on five countries from East Africa including Burundi, Kenya, Rwanda, Uganda and the United Republic of Tanzania (here referred to as Tanzania). The prevalence of stroke-related mortality rate by country, age, sex and year have been derived from the Global Burden of Disease (GBD) using the data visualization tool developed by the Institute for Health Metrics and Evaluation (IHME) (Institute for Health Metrics and Evaluation (IHME) \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We paid our attention to the death prevalence associated with stroke for the period of 20 years (2001\u0026ndash;2020). The regional vegetation cover and greenness were assessed using the Enhanced Vegetation Index (EVI). We acquire EVI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) of the environmental remote sensing satellite Terra (Didan \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Terra/MODIS Vegetation Indices Monthly (MODIS13A3) Version 6.1 data are provided monthly at 1 km spatial resolution as a gridded Level 3 product in the sinusoidal projection. The MODIS Normalized Difference Vegetation Index (NDVI) and EVI provide continuity for historical applications, while EVI minimizes canopy background variations and maintains sensitivity over dense vegetation conditions (Didan \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince the region understudy is characterized by unique terrain and diversified climates that influence vegetation, biodiversity, and human occupations (Camberlin \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nicholson \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), we preferred EVI over NDVI in our analysis. EVI data was downloaded for the same period as the prevalence of diseases associated with air pollution (2001\u0026ndash;2020). In addition to EVI and the prevalence of stroke-related deaths, we acquired air pollutant data from open-source repositories. We used PM\u003csub\u003e2.5\u003c/sub\u003e from the Atmospheric Analysis Group (ACAG). ACAG uses satellite, simulation, and monitor-based sources to estimate global and regional PM\u003csub\u003e2.5\u003c/sub\u003e surface concentrations, demonstrating a good correlation with ground-monitored PM\u003csub\u003e2.5\u003c/sub\u003e (ACAG \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We also collected the ensemble area-averaged sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e) surface mass concentration from MERRA-2 reanalysis (product name: M2TMNXAER v5.12.4) (Global Modeling and Assimilation Office (GMAO) \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e). The same ensemble from MERRA-2 reanalysis was used for the carbon monoxide (CO) surface concentration (M2TMNXCHM v5.12.4) (Global Modeling and Assimilation Office (GMAO) \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e). In addition to SO\u003csub\u003e2\u003c/sub\u003e and CO, we used instantaneous 3-dimensional tropospheric ozone (O\u003csub\u003e3\u003c/sub\u003e) monthly mean data (M2IMNPASM v5.12.4) which consists of 42 pressure levels (Global Modeling and Assimilation Office (GMAO) \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015c\u003c/span\u003e). For our analysis, we focused on the 500 hPa pressure level because it is the best estimate of the East African region's surface values (Kebacho \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). All these data were downloaded from the NASA Earth Data repository using the GIOVANNI data tool (Berrick et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Figures\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and 2 show the spatial distribution of air pollutants used in this study and the enhanced vegetation index, respectively.\u003c/p\u003e \u003cp\u003eData analysis methodology\u003c/p\u003e \u003cp\u003eData analysis was done by averaging the stroke-related death rates by year and age groups for each country. We assessed the change of environmental variables (EVI and air pollutants) with an interval period of five years to demonstrate the change in these variables. The relationship between stroke-related death rate and air pollutants and EVI was first established using Pearson\u0026rsquo;s correlation analysis, generating both correlation matrix and spatial correlation maps. Furthermore, we used the multilinear regression model to assess the likelihood of stroke-related death incidences by EVI and air pollutants of interest. The graphic analysis was used to disclose the spatial and temporal variability of the variables understudy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results and discussions","content":"\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003eThe summary statistics of the variables used in this study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ( mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation). The averages have been done for a period of 20 years (2001\u0026ndash;2020). The EVI is highest in Burundi and Rwanda (0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01) followed by Uganda (0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01) and Tanzania (0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01), while the lowest EVI is observed in Kenya (0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02). Rwanda averaged the highest concentration of PM\u003csub\u003e2.5\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e and CO, while O\u003csub\u003e3\u003c/sub\u003e is highest in Tanzania and Kenya when compared with other countries in the study area. The highest death rate from stroke was averaged in Rwanda (779.16\u0026thinsp;\u0026plusmn;\u0026thinsp;123.31 deaths per 100,000 persons), Burundi came in the second place (776.47\u0026thinsp;\u0026plusmn;\u0026thinsp;81.69 deaths per 100,000 persons), and the lowest incidence rates were averaged in Kenya (584.52\u0026thinsp;\u0026plusmn;\u0026thinsp;19.37 deaths per 100,000 persons).\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\u003eSummary statistics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) by country of variables used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurundi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRwanda\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTanzania\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke (cases)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e776.47\u0026thinsp;\u0026plusmn;\u0026thinsp;81.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e584.52\u0026thinsp;\u0026plusmn;\u0026thinsp;19.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e779.39\u0026thinsp;\u0026plusmn;\u0026thinsp;123.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e658.11\u0026thinsp;\u0026plusmn;\u0026thinsp;26.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e638.07\u0026thinsp;\u0026plusmn;\u0026thinsp;90.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVI (index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e36.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e37.59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e22.94\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e32.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e (ppb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e41.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e42.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e40.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e42.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e41.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO (ppb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e132.22\u0026thinsp;\u0026plusmn;\u0026thinsp;6.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e78.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e135.34\u0026thinsp;\u0026plusmn;\u0026thinsp;10.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e94.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e111.80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e. Stroke cases are deaths per 100,000 persons\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e Spatial and temporal distribution of the enhanced vegetation index\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e exhibits Pearson\u0026rsquo;s correlation matrix for the variables used in this study. It was observed that there was a weak correlation between all variables in this study. The stroke-related death prevalence cases per 100,000 persons are negatively correlated with EVI and O\u003csub\u003e3\u003c/sub\u003e, reflecting the general decreasing death prevalence from stroke per increase of these variables. Moreover, the correlation matrix exhibits that, generally, stroke-related death rates increase with the increase of PM\u003csub\u003e2.5\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e concentrations in the region. At the same time, EVI is negatively correlated with O\u003csub\u003e3\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e, and positively correlated with the remaining air pollutant concentrations in the region.\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\u003eCorrelation matrix for the variables used in this study\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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStroke\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEVI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCO\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\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\u003eThe spatial correlations between EVI and air pollutants of interest in this study are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the great part of the East African territory, concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and CO are positively correlated with EVI. The highest positive correlation between PM\u003csub\u003e2.5\u003c/sub\u003e and EVI is observed in the southeast of Uganda, Southwest of Kenya and the Eastern part of Rwanda. CO is mostly positively correlated in the northern part of Tanzania, and almost the whole territories of Kenya, Uganda and Rwanda. O\u003csub\u003e3\u003c/sub\u003e and EVI are negatively correlated on almost all pixels of the territory. SO\u003csub\u003e2\u003c/sub\u003e and EVI did not demonstrate a spatial correlation on most of the territory.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther assessment of the effect of air pollution and space greenness on stroke-related mortality rate was done utilizing linear regression models. According to the modelling results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the increase of the EVI leads to a decrease in death incidences from stroke with each unit increase in EVI leading to a decrease of 845.57\u0026thinsp;\u0026plusmn;\u0026thinsp;295.96 deaths per 100,000 persons in mortality rate. PM\u003csub\u003e2.5\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e project an increase in stroke-related mortality. The increase in SO\u003csub\u003e2\u003c/sub\u003e concentration by 1\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e is expected to increase the stroke mortality rate by 139.28\u0026thinsp;\u0026plusmn;\u0026thinsp;64.33 deaths per 100,000 persons, and the increase of PM\u003csub\u003e2.5\u003c/sub\u003e by 1\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e results in 3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25 deaths per 100,000 persons. Moreover, O\u003csub\u003e3\u003c/sub\u003e acts in favour of a decreased mortality rate, while CO has not resulted in any significant effect on stroke-related death prevalence in the region.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression fit results for the total population (20 years up)\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\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCoefficients\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eStandard Error\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et Stat\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eLower 95%\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eUpper 95%\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e909.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e516.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1302.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-845.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e295.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1480.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-210.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e277.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultiple R\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eObservations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR Square\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eF-statistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdjusted R Square\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSignificance F\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard Error\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote.\u003c/b\u003e The table gives the linear regression results for the combined effect of vegetation greenness and air pollution on the death prevalence from stroke.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe relationship between air pollution and health harms is influenced significantly by the age of individuals. Different age groups, such as children, adults, and the elderly exhibit varying levels of vulnerability to air pollution due to differences in physiology, exposure, and underlying health conditions. Furthermore, stroke-related death prevalences across different age groups also vary significantly due to various factors including resilience, prevalence of risk factors, and underlying health conditions. In children and adolescents, there is a very low prevalence of stroke-related deaths. In young adults (20\u0026ndash;39 years), strokes are more likely to be caused by congenital conditions, lifestyle and trauma. In adults (40\u0026ndash;59 years), stroke-related death increases. The combination of lifestyle factors, hypertension, diabetes, and hyperlipidemia become prevalent, and atherosclerosis and heart disease begin to play a more significant role. In older adults (60\u0026ndash;79 years) and the elderly (80\u0026thinsp;+\u0026thinsp;years), stroke-related deaths are highest. Age-related factors such as long-term hypertension, and other risk factors contribute to the increase of stroke-related deaths in these age groups (Benjamin et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Feigin et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; J. xiao Li et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; World Health Organization \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo assess the effects of air pollution and EVI on stroke-related death prevalence, we stratified the stroke-related mortality incidences into age groups (from young adults and higher population groups) and fitted the linear regression model. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e gives the results of the linear regression coefficients for each age group and variable. The results show that stroke-related death incidences are negatively associated with EVI and CO for the age group from 20 to 59 years, negatively associated with EVI and O\u003csub\u003e3\u003c/sub\u003e concentrations for the age group from 70 and above, and positively associated with other air pollutants, respectively. This association is statistically significant for PM\u003csub\u003e2.5\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e for all age groups, and significant for EVI in older adults and the elderly (60 years and more).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression results for age-stratified assessment of air pollution and surface greenness on the stroke-related death prevalence\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e(20\u0026ndash;29)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(30\u0026ndash;39)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(40\u0026ndash;49)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(50\u0026ndash;59)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(60\u0026ndash;69)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(70\u0026ndash;79)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(80+)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003cp\u003e(3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003cp\u003e(12.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-15.71\u003c/p\u003e \u003cp\u003e(59.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-41.11\u003c/p\u003e \u003cp\u003e(193.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e283.27\u003c/p\u003e \u003cp\u003e(364.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1492.51***\u003c/p\u003e \u003cp\u003e(433.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3416.74***\u003c/p\u003e \u003cp\u003e(481.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.18*\u003c/p\u003e \u003cp\u003e(6.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-26.02\u003c/p\u003e \u003cp\u003e(20.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-90.63\u003c/p\u003e \u003cp\u003e(96.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-290.73\u003c/p\u003e \u003cp\u003e(313.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1026.35*\u003c/p\u003e \u003cp\u003e(588.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2133.93**\u003c/p\u003e \u003cp\u003e(701.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2333.86**\u003c/p\u003e \u003cp\u003e(705.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e25\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05*\u003c/p\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17*\u003c/p\u003e \u003cp\u003e(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77*\u003c/p\u003e \u003cp\u003e(0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.38*\u003c/p\u003e \u003cp\u003e(1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.44**\u003c/p\u003e \u003cp\u003e(2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.77**\u003c/p\u003e \u003cp\u003e(3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.03*\u003c/p\u003e \u003cp\u003e(3.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.28***\u003c/p\u003e \u003cp\u003e(1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.44**\u003c/p\u003e \u003cp\u003e(4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.19***\u003c/p\u003e \u003cp\u003e(21.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.87***\u003c/p\u003e \u003cp\u003e(68.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e388.67***\u003c/p\u003e \u003cp\u003e(136.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e426.21**\u003c/p\u003e \u003cp\u003e(152.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e130.53\u003c/p\u003e \u003cp\u003e(169.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003cp\u003e(0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003cp\u003e(2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003cp\u003e(5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-8.10\u003c/p\u003e \u003cp\u003e(6.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-21.69**\u003c/p\u003e \u003cp\u003e(6.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003cp\u003e(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003cp\u003e(0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003cp\u003e(1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003cp\u003e(1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003cp\u003e(2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003cp\u003e(2.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.79*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.58*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.15*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.24**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.03***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.98***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e. Statistical significance: *significant at 10%, ** significant at 5%, and ***significant at 1%.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom the model results it can be noticed that the effects of EVI on stroke-related death incidences increase with age as these incidences also are. For incidence, the model predicted that when there is an increase of one unit in annual mean EVI, may lead to a reduction of stroke-related deaths by 9.18\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45 deaths per 100,000 persons aged from 20 to 29 years, while this number decreases by 2333.86\u0026thinsp;\u0026plusmn;\u0026thinsp;705.98 deaths reduction per 100,000 persons aged above 80 years. This tendency is also demonstrated by other air pollutants such as PM\u003csub\u003e2.5\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e. Furthermore, O\u003csub\u003e3\u003c/sub\u003e seems (no statistical significance) to increase stroke-related death incidences for the age group from 20 to 69 years, and further, it acts in decreasing those deaths for the age group from 70 and above (statistically significant). This phenomenon is reversed for the CO concentrations, although there is no statistical significance for the CO estimates for all age groups.\u003c/p\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eEnhanced vegetation index and air pollution\u003c/p\u003e \u003cp\u003eThis study used twenty years of satellite and model-derived data on the Enhanced Vegetation Index (EVI) and air pollutants of PM\u003csub\u003e2.5\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, and CO to assess their influence on human health through stroke-related mortality prevalence in five countries of the East African region. Our correlation analysis found that the mean annual concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and CO are positively correlated with the EVI, while O\u003csub\u003e3\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e showed a negative correlation with EVI. Scientific evidence has proved that changes in air quality alter the surface solar radiation and affect photosynthetic activity and surface greenness (Kashyap et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Air pollutants have a detrimental effect on vegetation as they act as mechanical barriers to light penetration, blocking stomatal opening and indirect effects by altering the soil properties in addition to direct damage to the vegetation leaves (Kashyap et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nowak et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The correlation analysis showed that EVI is negatively correlated with tropospheric O\u003csub\u003e3\u003c/sub\u003e in the region. The interaction between tropospheric ozone and vegetation is complex. High levels of tropospheric O\u003csub\u003e3\u003c/sub\u003e can damage plant tissues, reduce photosynthesis, and lower EVI values, indicating stressed or damaged vegetation. Healthy vegetation can help absorb and reduce O\u003csub\u003e3\u003c/sub\u003e precursors, thereby mitigating local O\u003csub\u003e3\u003c/sub\u003e formation. However, the changes in vegetation cover and health can alter emissions of biogenic volatile organic compounds, influencing local and regional ozone concentration levels (Fountoukis et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Xi et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, SO\u003csub\u003e2\u003c/sub\u003e concentration has diverse effects on the vegetation. The effects include leaf injury and chlorosis, reduced photosynthesis activities, growth suppression and altered physiology and metabolism mechanisms (Cotrozzi \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; H. K. Lee et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). All these impacts alter the space's greenness.\u003c/p\u003e \u003cp\u003eFurthermore, to investigate the unexpected effect of the positive correlation between EVI and CO and PM\u003csub\u003e2.5\u003c/sub\u003e, we performed a spatial variability analysis of air pollution and EVI in East Africa, in addition to spatial correlation which results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Spatial patterns of PM\u003csub\u003e2.5\u003c/sub\u003e (Figure S2) and CO (Figure S5) vary from west to East, making them have higher concentrations in Uganda, Rwanda and Burundi when compared with their concentrations in Tanzania and Kenya (Figure SM1). The same regions with higher concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and CO are also characterized by higher surface greenness as shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Countries such as Burundi, Rwanda and Uganda have higher levels of PM\u003csub\u003e2.5\u003c/sub\u003e and CO concentrations over their territories. The main sources of these pollutants are assumed to be the savannah fires which are dominating in (or around) these countries (Opio et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The vegetation ecological zones in the regions vary from the tropical evergreen broadleaf forest in the west of Burundi, Rwanda and Uganda to sparse grass/shrubland in the northeastern part of Kenya (Hawinkel et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), making the western part of East Africa greener than the western part. This positive correlation between EVI and CO and PM\u003csub\u003e2.5\u003c/sub\u003e may be influenced by the study design, while most of the studies assessed the relationship between surface greenness and air pollution in urban areas, this study assesses the effect on the extended territory including the rural areas which are marked by more forests and agricultural lands covered by crops most of the time. Recall that the urbanization rate of East Africa is 27.1% (Yatta \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), even though it is increasing, it is still the lowest on the continent (AfDB \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAir pollution and stroke-related death prevalence\u003c/p\u003e \u003cp\u003eDespite significant advances in stroke treatment and prevention, stroke remains among the leading causes of morbidity and mortality worldwide (Pu et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Traditional cardiovascular risk factors such as hypertension, diabetes, sedentary behavior, and smoking, do not fully account for variations in stroke risk. Researchers have turned their attention to novel modifiable risk factors, including environmental agents, to better understand stroke incidence and outcomes. Ambient air pollution has emerged as a critical factor for cerebrovascular disease (Kulick et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023c\u003c/span\u003e; Sagheer et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our analysis has demonstrated that, in East African Countries, PM\u003csub\u003e2.5\u003c/sub\u003e exposure is associated with an increase in stroke-related death rate. Our results are in agreement with other studies on the effect of PM\u003csub\u003e2.5\u003c/sub\u003e on stroke-related incidences (Jiang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kulick et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It has been reported that the mechanisms by which PM\u003csub\u003e2.5\u003c/sub\u003e induces stroke include inflammation and oxidative stress, blood pressure coagulation, and direct neurotoxicity (Elsayed et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kulick et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Z. Li et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Particulate-related air pollution in East African countries is an increasing issue. Specifically, the main sources of particulate matter pollutants in this region include residential fuel use, fossil fuel consumption for energy production, transportation, and waste burning (Gilbert et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pollution \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, our analysis demonstrated that SO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e also increase the fate of stroke-related death in the region. This result agrees with the study by Wu et al., (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) who found an increase in Years of Life Lost (YLLs) from total stroke, hemorrhagic and ischemic stroke, in China, with the increase of the SO2 concentration. Furthermore, Keller et al., (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported the association of O3, NO2 and SO2 and PM2.5 with increased stroke mortality rate in Germany. The mechanisms by which SO2 and O3 are similar to those of PM2.5 (Jiang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kulick et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe assessment of the tropospheric O\u003csub\u003e3\u003c/sub\u003e on the stroke-related death prevalence, in this study, has shown double tendencies. In the age group from 20 years to 69 years, exposure to ground-level O\u003csub\u003e3\u003c/sub\u003e is associated with an increase in stroke-related death incidences. However, for the age group of 70 years and above, the exposure to O\u003csub\u003e3\u003c/sub\u003e resulted in a negative association with the prevalence of stroke-related death in the region which is also observed for the general population consideration. The negative association of the ozone to stroke-related death prevalence contradicts the known evidence. The tropospheric O\u003csub\u003e3\u003c/sub\u003e has significant impacts on health, including an increased risk of stroke and stroke-related death (J. Wang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Both short-term and long-term exposure to ozone can lead to higher mortality rates due to cardiovascular diseases, including stroke (J. Wang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The O\u003csub\u003e3\u003c/sub\u003e exposure alters blood pressure, blood coagulation, inflammation, and oxidative stress, among other factors that may influence stroke risk by damaging blood arteries (Dewan and Lakhani 2022). While the influence of O\u003csub\u003e3\u003c/sub\u003e on stroke prevalence is clearer in young and adult populations, the relationship between ozone exposure and stroke in elderly people is complex and not as strongly established as other risk factors. Elderly individuals often have multiple, more dominant risk factors for stroke, such as hypertension, diabetes, and heart disease which may overshadow the impact of O\u003csub\u003e3\u003c/sub\u003e exposure (Minja et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, O\u003csub\u003e3\u003c/sub\u003e tends to cause acute respiratory effects more than chronic cardiovascular effects, and the reduction of outdoor activities by elderly people often leads to lower exposure to O\u003csub\u003e3\u003c/sub\u003e and a lesser observed impact of O\u003csub\u003e3\u003c/sub\u003e on stroke risk.\u003c/p\u003e \u003cp\u003eFurthermore, this study has not found a statistically significant effect of CO exposure on the increase in stroke-related death prevalence in East Africa. The controversial outcomes of CO in stroke-related incidences have been observed by other scholars. A study by Kettunen et al., (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) found that the exposure of CO during the cold seasons was not associated with stroke increase in Finland, and justified the results by the difference in seasonal CO exposure. Research conducted in various Chinese cities on the connection between environmental pollution and stroke cases has revealed that hospitalization rates for ischemic stroke are positively correlated with exposure to major air pollution gasses, such as CO and sulfur dioxide, while only nitrogen dioxide is linked to hemorrhagic stroke (Liu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The number of stroke patients admitted to the emergency room in Hong Kong, China, is inversely connected with the maximum daily concentration of CO in the air. This could be because exposure to CO can prevent strokes (Tian et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, numerous studies have associated CO exposure with stroke-related mortality and mortality (Kwak et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Siracusa et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Veiraiah \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Y. Wang et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This suggested a more supervised study to draw the effect of CO on the stroke-related death rate in East Africa.\u003c/p\u003e \u003cp\u003eEnhanced vegetation index and stroke-related death prevalence\u003c/p\u003e \u003cp\u003eMultiple linear regression model results shown in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed that EVI reduces the prevalence of stroke-related deaths in East Africa. although the common risk factors of stroke in East Africa are similar to those found globally, there are some region-specific factors such as hypertension, diabetes mellitus, obesity, smoking, alcohol use, physical inactivity (Akinyemi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e), unhealthy diet, and stress (Bolaji \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our findings are consistent with earlier research conducted in several locations, which showed that places with more vegetation cover have better cardiovascular health outcomes, including fewer fatalities from stroke (Adeloye \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hawinkel et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Minja et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A cohort study found that people who have green spaces within 300 meters of their homes have a 16% lower risk of experiencing an ischemic stroke, which is the most common type of stroke (Avellaneda-G\u0026oacute;mez et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Higher EVI values typically correlate with greater access to parks and recreational areas, promoting physical activity. Frequent physical activity helps control blood pressure, weight, and cardiovascular health in general, offering protection against stroke. Moreover, green space is associated with lower stress levels and improved mental health. It has been found that chronic stress is a risk for hypertension and stroke (Minja et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, higher EVI in residential areas often indicate a higher socioeconomic level linked with better healthier lifestyles and lower stroke mortality rates. Conversely, areas with low EVI may suffer from greater disadvantages and higher stress levels contributing to higher stroke prevalence and mortality (J. xiao Li et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study analyzed the effect of air pollution and space greenness on the stroke-related death rate in East African Countries. The correlation analysis revealed a negative association of stroke prevalence deaths with the EVI and the concentrations of ground-level O\u003csub\u003e3\u003c/sub\u003e. The concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e have been found to positively correlate with the stroke-related death rate in the region, while CO did not demonstrate a significant correlation with the fate of the stroke in the region. Furthermore, the linear regression model showed that the EVI favorites the decrease in the stroke-related death prevalence in East Africa, while the exposure to PM\u003csub\u003e2.5\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e acts in the increasing direction of the death rate coming from the stroke in the region. The fit of linear regression with age-stratified exposure demonstrated that the effect of environmental factors on the stroke death prevalence increases with the increase of the age of an individual as it does the fatality incidents related to stroke. The findings of our research validate the pressing necessity of mitigating air pollution exposure via emission controls to lower the incidence and fatality of strokes and the promotion of recreational green spaces. However, our study has some limitations. Firstly, because of the adequate monitoring network all over the territory, we used satellite-driven and model reanalysis data at sparse spatial resolution for air pollution. Secondly, we used stroke-related death from the global burden of diseases estimated which could be having misclassification of the country estimate of stroke incidences.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Responsibilities of Authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read, understood, and have complied as applicable with the statement on \u0026quot;Ethical responsibilities of Authors\u0026quot; as found in the Instructions for Authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset regarding stroke mortality rate may be found on the Global Burden of Disease repository (https://ghdx.healthdata.org/gbd-2021). Data on fine particulate matter air pollution (PM\u003csub\u003e2.5\u003c/sub\u003e) may found on the Atmospheric Composition Analysis Group repository (https://sites.wustl.edu/acag/). Air pollution concentrations of SO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e and CO are freely available at https://giovanni.gsfc.nasa.gov/giovanni/. Other analysis and program codes used in this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eVal\u0026eacute;rien Baharane\u003c/em\u003e\u003c/strong\u003e: Conceptualization, Methodology, Investigation, Software, Writing\u0026ndash;Original draft preparation, Visualization, Formal analysis. \u003cstrong\u003e\u003cem\u003eAndrey Borisovich Shatalov\u003c/em\u003e\u003c/strong\u003e: Supervision, Review \u0026amp; Editing.\u003cstrong\u003e\u0026nbsp;\u003cem\u003eEmmanuel Igwe\u003c/em\u003e:\u0026nbsp;\u003c/strong\u003eReview \u0026amp; Editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eACAG. (2023). Home | Atmospheric Composition Analysis Group | Washington University in St. Louis. \u003cem\u003e2023\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sites.wustl.edu/acag/\u003c/span\u003e\u003cspan address=\"https://sites.wustl.edu/acag/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 9 March 2023\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdeloye, D. (2014, June 26). An estimate of the incidence and prevalence of stroke in Africa: A systematic review and meta-analysis. \u003cem\u003ePLoS ONE\u003c/em\u003e. Public Library of Science. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0100724\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0100724\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfDB. (2023). \u003cem\u003eEast Africa Regional Economic Outlook 2023: Mid-term growth for East Africa region projected highest on the continent for 2023-4\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.afdb.org/en/news-and-events/press-releases/east-africa-regional-economic-outlook-2023-mid-term-growth-east-africa-region-projected-highest-continent-2023-4-63483\u003c/span\u003e\u003cspan address=\"https://www.afdb.org/en/news-and-events/press-releases/east-africa-regional-economic-outlook-2023-mid-term-growth-east-africa-region-projected-highest-continent-2023-4-63483\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 28 June 2024\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkinyemi, R. O., Ovbiagele, B., Adeniji, O. A., Sarfo, F. S., Abd-Allah, F., Adoukonou, T., et al. (2021a). Stroke in Africa: profile, progress, prospects and priorities. Nature Reviews Neurology. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41582-021-00542-4\u003c/span\u003e\u003cspan address=\"10.1038/s41582-021-00542-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkinyemi, R. O., Ovbiagele, B., Adeniji, O. A., Sarfo, F. S., Abd-Allah, F., Adoukonou, T., et al. (2021b, October 1). Stroke in Africa: profile, progress, prospects and priorities. \u003cem\u003eNature Reviews Neurology\u003c/em\u003e. Nature Publishing Group. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41582-021-00542-4\u003c/span\u003e\u003cspan address=\"10.1038/s41582-021-00542-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvellaneda-G\u0026oacute;mez, C., Vivanco-Hidalgo, R. M., Olmos, S., Lazcano, U., Valentin, A., Mil\u0026agrave;, C., et al. (2022). Air pollution and surrounding greenness in relation to ischemic stroke: A population-based cohort study. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e161\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envint.2022.107147\u003c/span\u003e\u003cspan address=\"10.1016/j.envint.2022.107147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenjamin, E. J., Muntner, P., Alonso, A., Bittencourt, M. S., Callaway, C. W., Carson, A. P., et al. (2019). Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation, \u003cem\u003e139\u003c/em\u003e(10), e56\u0026ndash;e528. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIR.0000000000000659\u003c/span\u003e\u003cspan address=\"10.1161/CIR.0000000000000659\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerrick, S. W., Leptoukh, G., Farley, J. D., \u0026amp; Rui, H. (2009). Giovanni: A web service workflow-based data visualization and analysis System. IEEE Transactions on Geoscience and Remote Sensing, \u003cem\u003e47\u003c/em\u003e(1), 106\u0026ndash;113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/TGRS.2008.2003183\u003c/span\u003e\u003cspan address=\"10.1109/TGRS.2008.2003183\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolaji, P. (2024). Risk factors for stroke in the African populace: need for action. South Sudan Medical Journal, \u003cem\u003e17\u003c/em\u003e(1), 33\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4314/ssmj.v17i1.7\u003c/span\u003e\u003cspan address=\"10.4314/ssmj.v17i1.7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamberlin, P. (2018). Climate of Eastern Africa. In \u003cem\u003eOxford Research Encyclopedia of Climate Science\u003c/em\u003e. Oxford University Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/acrefore/9780190228620.013.512\u003c/span\u003e\u003cspan address=\"10.1093/acrefore/9780190228620.013.512\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCotrozzi, L. (2020). Leaf demography and growth analysis to assess the impact of air pollution on plants: A case study on alfalfa exposed to a gradient of sulphur dioxide concentrations. Atmospheric Pollution Research, \u003cem\u003e11\u003c/em\u003e(1), 186\u0026ndash;192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.apr.2019.10.006\u003c/span\u003e\u003cspan address=\"10.1016/j.apr.2019.10.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDewan, S., \u0026amp; Lakhani, A. (2022, December 15). Tropospheric ozone and its natural precursors impacted by climatic changes in emission and dynamics. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e. Frontiers Media S.A. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fenvs.2022.1007942\u003c/span\u003e\u003cspan address=\"10.3389/fenvs.2022.1007942\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDidan, K. (2015). LP DAAC - MOD13A2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lpdaac.usgs.gov/products/mod13a3v061/\u003c/span\u003e\u003cspan address=\"https://lpdaac.usgs.gov/products/mod13a3v061/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 17 June 2024\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDidan, K. (2021). MODIS/Terra Vegetation Indices Monthly L3 Global 1km SIN Grid V061. NASA EOSDIS Land Processes Distributed Active Archive Center. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.5067/MODIS/MOD13A3.061\u003c/span\u003e\u003cspan address=\"10.5067/MODIS/MOD13A3.061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElsayed, W. M., Abdel-Gawad, E. H. A., Mesallam, D. I. A., \u0026amp; El-Serafy, T. S. (2020). The relationship between oxidative stress and acute ischemic stroke severity and functional outcome. Egyptian Journal of Neurology, Psychiatry and Neurosurgery, \u003cem\u003e56\u003c/em\u003e(1), 1\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s41983-020-00206-y\u003c/span\u003e\u003cspan address=\"10.1186/s41983-020-00206-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeigin, V. L., Brainin, M., Norrving, B., Martins, S., Sacco, R. L., Hacke, W., et al. (2022, January 1). World Stroke Organization (WSO): Global Stroke Fact Sheet 2022. \u003cem\u003eInternational Journal of Stroke\u003c/em\u003e. SAGE Publications Inc. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/17474930211065917\u003c/span\u003e\u003cspan address=\"10.1177/17474930211065917\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeigin, V. L., Forouzanfar, M. H., Krishnamurthi, R., Mensah, G. A., Connor, M., Bennett, D. A., et al. (2014). Global and regional burden of stroke during 1990\u0026ndash;2010: Findings from the Global Burden of Disease Study 2010. The Lancet, \u003cem\u003e383\u003c/em\u003e(9913), 245\u0026ndash;255. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(13)61953-4\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(13)61953-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFountoukis, C., Nenes, A., Meskhidze, N., Bahreini, R., Conant, W. C., Jonsson, H., et al. (2007). Aerosol-cloud drop concentration closure for clouds sampled during the International Consortium for Atmospheric Research on Transport and Transformation 2004 campaign. Journal of Geophysical Research Atmospheres, \u003cem\u003e112\u003c/em\u003e(10), 10\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2006JD007272\u003c/span\u003e\u003cspan address=\"10.1029/2006JD007272\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilbert, N., Ziqiang, Y., \u0026amp; Hongzhi, M. (2021). Current situation of solid waste management in East African countries and the proposal for sustainable management. African Journal of Environmental Science and Technology, \u003cem\u003e15\u003c/em\u003e(1), 1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5897/ajest2020.2911\u003c/span\u003e\u003cspan address=\"10.5897/ajest2020.2911\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal Modeling and Assimilation Office (GMAO). (2015a). MERRA-2 tavgM_2d_aer_Nx: 2d,Monthly mean,Time-averaged,Single-Level,Assimilation,Aerosol Diagnostics V5.12.4. \u003cem\u003eGoddard Earth Sciences Data and Information Services Center (GES DISC)\u003c/em\u003e. Greenbelt, MD, USA. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5067/FH9A0MLJPC7N\u003c/span\u003e\u003cspan address=\"10.5067/FH9A0MLJPC7N\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal Modeling and Assimilation Office (GMAO). (2015b). MERRA-2 tavgM_2d_chm_Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Carbon Monoxide and Ozone Diagnostics V5.12.4, Greenbelt, MD, USA. \u003cem\u003eGoddard Earth Sciences Data and Information Services Center (GES DISC)\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5067/WMT31RKEXK8I\u003c/span\u003e\u003cspan address=\"10.5067/WMT31RKEXK8I\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal Modeling and Assimilation Office (GMAO). (2015c). MERRA-2 instM_3d_asm_Np: 3d,Monthly mean,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields V5.12.4. \u003cem\u003eGoddard Earth Sciences Data and Information Services Center (GES DISC)\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5067/2E096JV59PK7\u003c/span\u003e\u003cspan address=\"10.5067/2E096JV59PK7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuenther, A. (1995). A global model of natural volatile organic compound emissions. Journal of Geophysical Research, \u003cem\u003e100\u003c/em\u003e(D5), 8873\u0026ndash;8892. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/94JD02950\u003c/span\u003e\u003cspan address=\"10.1029/94JD02950\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHawinkel, P., Thiery, W., Lhermitte, S., Swinnen, E., Verbist, B., Orshoven, J., \u0026amp; Muys, B. (2016). Vegetation response to precipitation variability in East Africa controlled by biogeographical factors. \u003cem\u003eJournal of Geophysical Research: Biogeosciences\u003c/em\u003e, \u003cem\u003e121\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/2016JG003436\u003c/span\u003e\u003cspan address=\"10.1002/2016JG003436\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealth Organization, W., \u0026amp; WHO. (2016). \u003cem\u003eAmbient air pollution: a global assessment of exposure and burden of disease\u003c/em\u003e. \u003cem\u003eClean Air Journal\u003c/em\u003e (Vol. 26). Geneva PP - Geneva: World Health Organization. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17159/2410-972x/2016/v26n2a4\u003c/span\u003e\u003cspan address=\"10.17159/2410-972x/2016/v26n2a4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstitute for Health Metrics and Evaluation (IHME). (2020). \u003cem\u003eGlobal Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Results.\u003c/em\u003e Seattle, United States. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, Y., Lu, H., Man, Q., Liu, Z., Wang, L., Wang, Y., et al. (2020). Stroke burden and mortality attributable to ambient fine particulate matter pollution in 195 countries and territories and trend analysis from 1990 to 2017. Environmental Research, \u003cem\u003e184\u003c/em\u003e, 109327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envres.2020.109327\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2020.109327\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKashyap, R., Kuttippurath, J., \u0026amp; Patel, V. K. (2023). Improved air quality leads to enhanced vegetation growth during the COVID\u0026ndash;19 lockdown in India. Applied Geography, \u003cem\u003e151\u003c/em\u003e, 102869. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.apgeog.2022.102869\u003c/span\u003e\u003cspan address=\"10.1016/j.apgeog.2022.102869\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKebacho, L. L. (2022). Large-scale circulations associated with recent interannual variability of the short rains over East Africa. Meteorology and Atmospheric Physics, \u003cem\u003e134\u003c/em\u003e(1), 1\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00703-021-00846-6\u003c/span\u003e\u003cspan address=\"10.1007/s00703-021-00846-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeller, K., Haghi, S. H. R., Hahad, O., Schmidtmann, I., Chowdhury, S., Lelieveld, J., et al. (2023). Air pollution impacts on in-hospital case-fatality rate of ischemic stroke patients. Thrombosis Research, \u003cem\u003e225\u003c/em\u003e, 116\u0026ndash;125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.thromres.2023.03.006\u003c/span\u003e\u003cspan address=\"10.1016/j.thromres.2023.03.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKettunen, J., Lanki, T., Tiittanen, P., Aalto, P. P., Koskentalo, T., Kulmala, M., et al. (2007). Associations of fine and ultrafine particulate air pollution with stroke mortality in an area of low air pollution levels. Stroke, \u003cem\u003e38\u003c/em\u003e(3), 918\u0026ndash;922. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/01.STR.0000257999.49706.3b\u003c/span\u003e\u003cspan address=\"10.1161/01.STR.0000257999.49706.3b\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulick, E. R., Kaufman, J. D., \u0026amp; Sack, C. (2023a). Ambient Air Pollution and Stroke: An Updated Review. Stroke. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/STROKEAHA.122.035498\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.122.035498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulick, E. R., Kaufman, J. D., \u0026amp; Sack, C. (2023b, March 1). Ambient Air Pollution and Stroke: An Updated Review. \u003cem\u003eStroke\u003c/em\u003e. Lippincott Williams \u0026amp; WilkinsHagerstown, MD. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/STROKEAHA.122.035498\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.122.035498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulick, E. R., Kaufman, J. D., \u0026amp; Sack, C. (2023c, March 1). Ambient Air Pollution and Stroke: An Updated Review. \u003cem\u003eStroke\u003c/em\u003e. Wolters Kluwer Health. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/STROKEAHA.122.035498\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.122.035498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwak, K., Kim, M., Choi, W. J., Ju, Y. S., \u0026amp; Park, J. T. (2021). Association Between Carbon Monoxide Intoxication and Incidence of Ischemic Stroke: A Retrospective Nested Case-Control Study in South Korea. Journal of Stroke and Cerebrovascular Diseases, \u003cem\u003e30\u003c/em\u003e(2), 105496. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105496\u003c/span\u003e\u003cspan address=\"10.1016/j.jstrokecerebrovasdis.2020.105496\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, H. K., Khaine, I., Kwak, M. J., Jang, J. H., Lee, T. Y., Lee, J. K., et al. (2017, December 1). The relationship between SO2 exposure and plant physiology: A mini review. \u003cem\u003eHorticulture Environment and Biotechnology\u003c/em\u003e. Korean Society for Horticultural Science. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13580-017-0053-0\u003c/span\u003e\u003cspan address=\"10.1007/s13580-017-0053-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, I. M., Shiroma, E. J., Lobelo, F., Puska, P., Blair, S. N., Katzmarzyk, P. T., et al. (2012). Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy. The Lancet, \u003cem\u003e380\u003c/em\u003e(9838), 219\u0026ndash;229. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(12)61031-9\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(12)61031-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, K. K., Miller, M. R., \u0026amp; Shah, A. S. V. (2018, January 1). Air pollution and stroke. \u003cem\u003eJournal of Stroke\u003c/em\u003e. Korean Stroke Society. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5853/jos.2017.02894\u003c/span\u003e\u003cspan address=\"10.5853/jos.2017.02894\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, J. xiao, Zhong, Q. qiong, Yuan, S. xiang, \u0026amp; Zhu, F. (2023). Trends in deaths and disability-adjusted life-years of stroke attributable to low physical activity worldwide, 1990\u0026ndash;2019. BMC Public Health, \u003cem\u003e23\u003c/em\u003e(1), 1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-023-17162-w\u003c/span\u003e\u003cspan address=\"10.1186/s12889-023-17162-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Z., Bi, R., Sun, S., Chen, S., Chen, J., Hu, B., \u0026amp; Jin, H. (2022). The Role of Oxidative Stress in Acute Ischemic Stroke-Related Thrombosis. \u003cem\u003eOxidative Medicine and Cellular Longevity\u003c/em\u003e. Hindawi Limited. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2022/8418820\u003c/span\u003e\u003cspan address=\"10.1155/2022/8418820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, H., Tian, Y., Xu, Y., Huang, Z., Huang, C., Hu, Y., \u0026amp; Zhang, J. (2017). Association between ambient air pollution and hospitalization for ischemic and hemorrhagic stroke in China: A multicity case-crossover study. Environmental Pollution, \u003cem\u003e230\u003c/em\u003e, 234\u0026ndash;241. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2017.06.057\u003c/span\u003e\u003cspan address=\"10.1016/j.envpol.2017.06.057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaas, J., van Dillen, S. M. E., Verheij, R. A., \u0026amp; Groenewegen, P. P. (2009). Social contacts as a possible mechanism behind the relation between green space and health. Health and Place, \u003cem\u003e15\u003c/em\u003e(2), 586\u0026ndash;595. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.healthplace.2008.09.006\u003c/span\u003e\u003cspan address=\"10.1016/j.healthplace.2008.09.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinja, N. M., Nakagaayi, D., Aliku, T., Zhang, W., Ssinabulya, I., Nabaale, J., et al. (2022, November 10). Cardiovascular diseases in Africa in the twenty-first century: Gaps and priorities going forward. \u003cem\u003eFrontiers in Cardiovascular Medicine\u003c/em\u003e. Frontiers Media S.A. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcvm.2022.1008335\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2022.1008335\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNicholson, S. E. (2019). A Review of Climate Dynamics and Climate Variability in Eastern Africa. In \u003cem\u003eThe Limnology, Climatology and Paleoclimatology of the East African Lakes\u003c/em\u003e (pp. 25\u0026ndash;56). Routledge. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1201/9780203748978-2\u003c/span\u003e\u003cspan address=\"10.1201/9780203748978-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiohuru, I. (2023). Disease Burden and Mortality (pp. 35\u0026ndash;85). Springer, Cham. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-031-19719-2_3\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-19719-2_3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNowak, D. J., Crane, D. E., \u0026amp; Stevens, J. C. (2006). Air pollution removal by urban trees and shrubs in the United States. Urban Forestry and Urban Greening, \u003cem\u003e4\u003c/em\u003e(3\u0026ndash;4), 115\u0026ndash;123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ufug.2006.01.007\u003c/span\u003e\u003cspan address=\"10.1016/j.ufug.2006.01.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNowak, D. J., Hirabayashi, S., Bodine, A., \u0026amp; Greenfield, E. (2014). Tree and forest effects on air quality and human health in the United States. Environmental Pollution, \u003cem\u003e193\u003c/em\u003e, 119\u0026ndash;129. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2014.05.028\u003c/span\u003e\u003cspan address=\"10.1016/j.envpol.2014.05.028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOpio, R., Mugume, I., \u0026amp; Nakatumba-Nabende, J. (2021). Understanding the trend of NO2, SO2 and CO over East Africa from 2005 to 2020. Atmosphere, \u003cem\u003e12\u003c/em\u003e(10), 1283. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/atmos12101283\u003c/span\u003e\u003cspan address=\"10.3390/atmos12101283\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization, W. H. (2018). 9 Out of 10 People Worldwide Breathe Polluted Air, But More Countries Are Taking Action. \u003cem\u003e9 Out of 10 People Worldwide Breathe Polluted Air, But More Countries Are Taking Action\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news/item/02-05-2018-9-out-of-10-people-worldwide-breathe-polluted-air-but-more-countries-are-taking-action\u003c/span\u003e\u003cspan address=\"https://www.who.int/news/item/02-05-2018-9-out-of-10-people-worldwide-breathe-polluted-air-but-more-countries-are-taking-action\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 16 June 2024\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePicavet, H. S. J., Milder, I., Kruize, H., de Vries, S., Hermans, T., \u0026amp; Wendel-Vos, W. (2016). Greener living environment healthier people? Exploring green space, physical activity and health in the Doetinchem Cohort Study. Preventive Medicine, \u003cem\u003e89\u003c/em\u003e, 7\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ypmed.2016.04.021\u003c/span\u003e\u003cspan address=\"10.1016/j.ypmed.2016.04.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePollution, A. (2019). Air Pollution and Health Impacts in Africa, 1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.stateofglobalair.org/resources/africa\u003c/span\u003e\u003cspan address=\"https://www.stateofglobalair.org/resources/africa\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 28 June 2024\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePope, C. A., \u0026amp; Dockery, D. W. (2006). Health effects of fine particulate air pollution: Lines that connect. Journal of the Air and Waste Management Association, \u003cem\u003e56\u003c/em\u003e(6), 709\u0026ndash;742. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10473289.2006.10464485\u003c/span\u003e\u003cspan address=\"10.1080/10473289.2006.10464485\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePu, L., Wang, L., Zhang, R., Zhao, T., Jiang, Y., \u0026amp; Han, L. (2023). Projected Global Trends in Ischemic Stroke Incidence, Deaths and Disability-Adjusted Life Years From 2020 to 2030. Stroke, \u003cem\u003e54\u003c/em\u003e(5), 1330\u0026ndash;1339. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/STROKEAHA.122.040073\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.122.040073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSagheer, U., Al-Kindi, S., Abohashem, S., Phillips, C. T., Rana, J. S., Bhatnagar, A., et al. (2024, February 1). Environmental Pollution and Cardiovascular Disease: Part 1 of 2: Air Pollution. \u003cem\u003eJACC: Advances\u003c/em\u003e. Elsevier. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacadv.2023.100805\u003c/span\u003e\u003cspan address=\"10.1016/j.jacadv.2023.100805\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiracusa, R., Schaufler, A., Calabrese, V., Fuller, P. M., \u0026amp; Otterbein, L. E. (2021, May 1). Carbon Monoxide: from Poison to Clinical Trials. \u003cem\u003eTrends in Pharmacological Sciences\u003c/em\u003e. Elsevier Ltd. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tips.2021.02.003\u003c/span\u003e\u003cspan address=\"10.1016/j.tips.2021.02.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian, L., Qiu, H., Pun, V. C., Ho, K. F., Chan, C. S., \u0026amp; Yu, I. T. S. (2015). Carbon monoxide and stroke: A time series study of ambient air pollution and emergency hospitalizations. International Journal of Cardiology, \u003cem\u003e201\u003c/em\u003e, 4\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijcard.2015.07.099\u003c/span\u003e\u003cspan address=\"10.1016/j.ijcard.2015.07.099\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlrich, R. S. (1984). View through a window may influence recovery from surgery. Science, \u003cem\u003e224\u003c/em\u003e(4647), 420\u0026ndash;421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.6143402\u003c/span\u003e\u003cspan address=\"10.1126/science.6143402\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeiraiah, A. (2020, March 1). Carbon monoxide poisoning. \u003cem\u003eMedicine (United Kingdom)\u003c/em\u003e. Elsevier Ltd. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.mpmed.2019.12.013\u003c/span\u003e\u003cspan address=\"10.1016/j.mpmed.2019.12.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, J., Ju, T., Li, B., Huang, C., Xia, X., Zhang, J., \u0026amp; Li, C. (2024). Characterization of tropospheric ozone pollution, random forest trend prediction and analysis of influencing factors in South-western Europe. Environmental Sciences Europe, \u003cem\u003e36\u003c/em\u003e(1), 1\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12302-024-00863-3\u003c/span\u003e\u003cspan address=\"10.1186/s12302-024-00863-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y., Li, J., Wang, L., Lin, Y., Zhou, M., Yin, P., \u0026amp; Yao, S. (2021). The impact of carbon monoxide on years of life lost and modified effect by individual- and city-level characteristics: Evidence from a nationwide time-series study in China. Ecotoxicology and Environmental Safety, \u003cem\u003e210\u003c/em\u003e, 111884. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoenv.2020.111884\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoenv.2020.111884\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. (2018). WHO - The top 10 causes of death. \u003cem\u003e24 Maggio\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 29 February 2024\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, Z., Li, J., Huang, J., Wang, Y., Cao, R., Yin, P., et al. (2021). Ambient sulfur dioxide and years of life lost from stroke in China: a time-series analysis in 48 cities. Chemosphere, \u003cem\u003e267\u003c/em\u003e, 128857. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chemosphere.2020.128857\u003c/span\u003e\u003cspan address=\"10.1016/j.chemosphere.2020.128857\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXi, W., Du, S., Wang, Y. C., \u0026amp; Zhang, X. (2019). A spatiotemporal cube model for analyzing satellite image time series: Application to land-cover mapping and change detection. Remote Sensing of Environment, \u003cem\u003e231\u003c/em\u003e, 111212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2019.111212\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2019.111212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan, T., Leng, H., \u0026amp; Yuan, Z. (2024). Construction of the \u0026ldquo;Full Path\u0026rdquo; of restorative effects on older adults\u0026rsquo; mental health in parks under seasonal differences: taking Changchun as an example. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2024.1269249\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2024.1269249\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYatta, F. P. (2018). Urbanization in Africa: Trends, Regional Specificities, and Challenges (pp. 317\u0026ndash;339). Springer, Singapore. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-981-10-7799-9_14\u003c/span\u003e\u003cspan address=\"10.1007/978-981-10-7799-9_14\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, R., Liu, G., Jiang, Y., Li, G., Pan, Y., Wang, Y., et al. (2018). Acute effects of particulate air pollution on ischemic stroke and hemorrhagic stroke mortality. Frontiers in Neurology, \u003cem\u003e9\u003c/em\u003e(OCT), 406874. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fneur.2018.00827\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2018.00827\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, S. S., Tai, A. P. K., Sun, S., Sadiq, M., Heald, C. L., \u0026amp; Geddes, J. A. (2018). Coupling between surface ozone and leaf area index in a chemical transport model: Strength of feedback and implications for ozone air quality and vegetation health. Atmospheric Chemistry and Physics, \u003cem\u003e18\u003c/em\u003e(19), 14133\u0026ndash;14148. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/acp-18-14133-2018\u003c/span\u003e\u003cspan address=\"10.5194/acp-18-14133-2018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"air pollution, greenness, vegetation index, stroke mortality rate, East Africa, public health","lastPublishedDoi":"10.21203/rs.3.rs-4772793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4772793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the impact of PM\u003csub\u003e2.5\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, CO, and surface greenness on stroke-related mortality rates in East Africa. Results showed a positive correlation between PM\u003csub\u003e2.5\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e, and a negative association between Enhanced Vegetation Index (EVI) and stroke fatality prevalence. The linear regression model showed that the increase of 1 index in EVI could lead to the reduction of stroke-related deaths by 845.57\u0026thinsp;\u0026plusmn;\u0026thinsp;295.96 deaths per 100,000 persons. Also, a 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase of PM\u003csub\u003e2.5\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e concentrations predicted a corresponding increase of stroke-related death by 3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25 and 139.28\u0026thinsp;\u0026plusmn;\u0026thinsp;64.33 deaths per 100,000 persons, respectively. Furthermore, the analysis of the influence of these environmental variables on the prevalence of mortality attributable to stroke by age group showed its rise with age, both in intensity and statistical significance. For instance, a rise of 1 unit in EVI predicted the reduction of the stroke-related death rate by 9.18\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45 and 2133.93\u0026thinsp;\u0026plusmn;\u0026thinsp;701.07 deaths per 100,000 persons in the age groups of 20\u0026ndash;29 and 70\u0026ndash;79 years old, respectively. A rise in 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e of PM\u003csub\u003e2.5\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e is expected to trigger the mortality incident rise from 0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 to 7.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01 and 4.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40 to 426.21\u0026thinsp;\u0026plusmn;\u0026thinsp;152.38 deaths per 100,000 persons in respective age groups of 20\u0026ndash;29 and 70\u0026ndash;79 years. The exposure to CO and O\u003csub\u003e3\u003c/sub\u003e did not demonstrate a significant effect on the stroke-related death rate in the region for the period of the study.\u003c/p\u003e","manuscriptTitle":"Examining the interplay between air pollution, vegetation greenness, and stroke prevalence in East Africa: An ecological perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-22 19:39:04","doi":"10.21203/rs.3.rs-4772793/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":"afdb074a-ade1-4c2f-b67a-a0f1e5b18fb2","owner":[],"postedDate":"August 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-10T12:53:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-22 19:39:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4772793","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4772793","identity":"rs-4772793","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}