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The association between living environment and out-of-hospital cardiac arrest risk in adults: the perspective of daily-life contexts

Archives of Public Health volume 83, Article number: 67 (2025) Cite this article

Abstract

Background

Studying the spatial pattern of out-of-hospital cardiac arrest (OHCA) and its environmental impactors is crucial for both providing timely medical assistance and implementing preventative measures. Existing researches have mainly focused on natural and sociodemographic environments, usually at a macro- or meso-scale, while giving less attention to understanding the association between environment and OHCA risk from the perspective of daily-life contexts.

Methods

In this study, we utilized 1843 eligible OHCA cases from core districts of Beijing in 2020 and employed modified Besag-York-Mollié (BYM2) Bayesian models to investigate the association between living environment (consisting of food environment, physical activity environment, healthcare environment and leisure environment) and adult OHCA risk, as well as its age disparities, at a 1 × 1 km2 cell resolution.

Results

The results show that: (1) Fewer living environment factors are associated with the OHCA risk in the young/middle-aged group compared to the elderly group. (2) Unhealthy food destination like barbecue restaurants in living area is associated with increased OHCA risk in both age groups. (3) Facility inducing sedentary activity like chess rooms and healthcare facilities are associated with increased OHCA risk, but only among the elderly groups. (4) The decreased OHCA risk in the young/middle-aged group is related to public gathering places for socialization and relaxation in living area like coffee shops, while for the elder groups, decreased OHCA risk is associated with more green spaces in the living area.

Conclusions

The findings suggest that living environment may impact adult OHCA risk through shaping daily habits or providing access to health resources, with the underlying mechanism varying across different age groups. Future planning should fully consider and leverage the impact of living environment in order to effectively reduce OHCA risk.

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Text box 1. Contributions to the literature

• The study provides a micro-level perspective for comprehending the environmental risks associated with out-of-hospital cardiac arrest within the daily-life context.

• The living environment emerges as a significant predictor of out-of-hospital cardiac arrest risk by potentially influencing dietary and physical activity habits, access to healthcare facilities, and availability of leisure amenities.

• The results have the potential to optimize the allocation of resuscitation resources by improving risk estimation for out-of-hospital cardiac arrest Additionally, they can inform environmental planning and design guidelines for further pre-event environmental interventions.

Introduction

Out-of-hospital cardiac arrest (OHCA) is one of the leading causes of death around the world, and has a high mortality rate [1, 2]. It requires nearly immediate response to save the patients’ life. Timely bringing emergency resources like automated external defibrillator (AED) to the spot is one of the crucial responding steps [3]. In the meanwhile, it is also important to prevent the disease by paying extra attention to high-risk area.

It is therefore important to understand the spatial pattern of OHCA in order to precisely locate the cases. OHCA cases have long been discovered not to be randomly distributed across space [4,5,6] and are associated with certain geographical factors. Most of the extant researches have examined the impacts brought by natural environment factors and sociodemographic environment factors. On the aspect of natural environment, pollutants like fine particulate matter (PM2.5), carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2) are proved to be associated with the increment of OHCA incidence in different countries with the effect degree varying with age [7,8,9]. Temperature that is too high or too low is also positively related to the occurrence of OHCA [10, 11]. On the aspect of sociodemographic characteristics, factors such as age, race and socioeconomic status of the local population have been proven to be associated with the heterogeneous distribution of OHCA events [12,13,14,15,16]. These researches are usually conducted at a macro- or meso-scale. Only few of them have discussed the relation between environment and OHCA under the context of daily life, detecting the influences brought by ones’ living environment.

Living environment consists of amenities or resources people frequently interact with in their daily life. It might impact people’s health outcome through changing their living habits or offering health resources. Existing evidences have exemplified that sport-supporting facilities in neighborhood can encourage people to adopt more outdoor physical activities [17,18,19], while limited resources of health facilities and fresh food destinations induce unhealthy lifestyle and might cause disease [20,21,22]. Specifically, studies have proved that people from communities with higher walkability tend to have lower cardiovascular disease (CVD) risk [23], and areas with more unhealthy food resources have higher odds of cardiac arrest and myocardial infarction [24, 25]. It is therefore plausible to link living environment with certain characteristics to high-risk area of OHCA. In addition, with most of the OHCA cases happened at home [26, 27], further understanding of relation between living environment and OHCA risk might provide more straightforward spatial clues to locate high-risk area of the disease. Finally, compared to contextual background like gender and age composition, living environment is easier to altered and interfered when it comes to prevention against the disease.

This paper specifically focuses on living environment on a neighborhood level, examining how the environmental factors that exist in and shape one’s daily life are associated with adult OHCA risk. We are also interested in the age disparities, since that people of different age groups tend to have distinct daily activity pattern, leading to varied interactions with their living environment [28].

Methods

Study setting and design

The study used data collected from the capital city of China, Beijing. As one of the biggest cities in the world, Beijing has more than 21 million people and has undergone rapid urbanization and spatial reconstruction, especially in its central urban area. It has been found that the prevalence rate of chronic disease, living habits and health resources vary between urban and rural area in China [29, 30], which might lead to different patterns between living environment and OHCA risk. Therefore, this study focuses on the six core districts of Beijing, which consists of two city center districts (Xicheng and Dongcheng Districts) and four suburban districts (Haidian, Chaoyang, Shijingshan and Fengtai Districts) (Fig. 2). Covering only 8% of the area but accommodating 52% of the population [31], these core districts are highly urbanized and high-density area of Beijing city, with relatively high socioeconomic status and many well-equipped communities compared to peripheral urban areas and rural areas.

OHCA data was obtained from the pre-hospital emergency databases of Beijing Emergency Medical Center for the one-year period of 1 January to 31 December 2020, which included all pre-hospital emergency cases in Beijing throughout the year. Data contained information such as the sex and age of the patient, the type of the places where the emergency happened (e.g., residence, workplace, hotel, school et al.) and geographical coordinates of the event. Data collection followed the guidelines of the Utstein style template [32]. Cases were identified as OHCA in this study if the patient was recorded to have a cardiac arrest out of the hospital. Because the study aims to discuss the relation between living environment and the disease risk, only cases that occurred in residential area within the six core districts were included. Cases without sex, age or coordinate information and with non-natural causes such as drowning, trauma, electric shock, hanging or overdose were also excluded.

Since this study aims to discuss the relationship between people’s living environment and their risk of having OHCA, we divided the study area into 1 × 1 km2 cells, for the distance between two main roads in China is about 700-1200km [33, 34] and a 1 × 1 km2 cell stands for the size of a relatively complete living unit, covering a nearly 500m-radius extent for daily activity. Finally, the whole study area was mapped in 1353 cells, based on which the OHCA counts and environmental variables were calculated. modified Besag-York-Mollié (BYM2) Bayesian models were then utilized to provide estimates of OHCA relative risk and investigate the association between living environment and OHCA risk.

Environmental variables

Previous studies have respectively discussed the influence of food destinations and healthcare facilities on cardiac arrest [24, 35]. Living environment in this study is described in a more comprehensive way, using four dimensions: (1) food environment, which could influence dietary habits; (2) physical activity environment, which could shape physical activity habits; (3) healthcare environment, which reflects the availability of medical resources; and (4) leisure environment, which describes the distribution of public leisure places in the city.

The food environment measures destinations providing dining services and stores selling food, which may trigger disease or protect people from getting sick. Screened through a collinearity test and combined with the local context of Beijing, three types of food destinations were finally selected: barbecue restaurants, dessert shops, and supermarkets. Using the central point of each cell, a 2 × 2 km2 pane is formed, representing the reach of available resources for people living in each 1 × 1 km2 cell. The counts of destinations within reach were used as the density due to the same area size of each pane.

The physical activity environment includes facilities or environmental layouts that influence different types of physical activity which have been previously confirmed to reduce the risk of CVDs. Improvements in the walking and exercising environments can positively influence physical activity [19, 23]. Three types of environmental factors considering various degrees of physical activity were therefore included in the following analysis. Chess rooms and digital entertainment hubs represent places for sedentary activities like card games, board games or digital video games. Road connectivity represents walkability in residential area that might encourage daily walking for leisure or transport. Sports facilities are composed of public parks and stadiums, where people can do exercise, such as walking and jogging, or play various kinds of sports, such as ping-pong, badminton and basketball. The proximity of chess rooms, digital entertainment hubs and sports facilities was computed by referring to the calculation of Walk Score, an index measuring the proximity of a specific household to a range of amenities [36, 37]. The straight-line distance between the closest locations of each category and the central point of each cell was calculated. The numbers were then standardized into a score ranging from 0 to 100, with a distance less than 500 m valued as 100 and distance over 1.6 km valued as 0. A higher score means that the cell has higher proximity to certain types of facilities. Road connectivity was expressed by the count of intersections in each 2 × 2 km2 pane mentioned above.

The healthcare environment includes medical facilities that serve as health promoters. The presence of local medical facilities may allow timely treatment and access to medications as well as encourage regular healthcare visits, and therefore serve as preventive care for heart disease. Hospitals (including community hospitals and general hospitals) and pharmacies were chosen to describe the healthcare environment, with proximity to each calculated as for the physical activity environment.

The leisure environment was measured by the distribution of leisure places represented by coffee shops, green space (e.g. lawns, groves) and blue space (e.g. lakes, streams). Coffee shops have been found to be associated with high business vitality and high residential quality [38], which might serve as a proxy for a pleasant living environment. Green space in urban areas has been revealed to be associated with improved mental health, and the health benefits of being close to blue space have also been discussed [39,40,41,42]. The density of coffee shops was calculated to describe the distribution, in the same way as the indices for the food environment dimension. The size of the green space and the blue space within each 2 × 2 km2 pane mentioned above were the proxies for the proportion of these two types of open space.

Covariates were selected as control variables to account for potential confounding factors, including the PM2.5 concentration, land surface temperature, relative humidity, housing price and the residential density of each cell. The first three covariates (PM2.5 concentration, land surface temperature, relative humidity) were chosen to describe the natural environment of the area. These variables were represented by their mean values of each cell in the year 2020. Housing price was used to represent the socioeconomic status of each cell. The prices of residential districts across the study area were interpolated using inverse distance weight to form an overall spatial distribution of housing price, then the average value of the raster within each cell was calculated. Since the study focused specifically on OHCA cases occurring in residential areas, the spatial distribution of housing estates should also be a confounder. The residential density was therefore considered, expressed as the count of housing estates within the cell. After conducting the collinearity test on all the variables and removing the ones of which the variance inflator factor is higher than 4, the PM2.5 concentration, relative humidity, and housing price were included in the final models.

All the destination data, the road data and the green and blue space data were from the Baidu Map of 2020. The PM2.5 concentration, land surface temperature and relative humidity were obtained from datasets provided by National Tibetan Plateau / Third Pole Environment Data Center [43,44,45,46,47]. The housing price data was from the Beijing City Lab website [48].

Statistical analysis

Considering the potential geographical pattern of OHCA cases, it is important to include the spatial effect when modelling the disease risk, in order to take into account all crucial information from the data [49]. In this paper, we adopted BYM2 model based on generalized linear Poisson model to provide estimates of disease relative risk and discuss the association between living environment and OHCA risk.

BYM2 model is a modified version of Besag-York-Mollié (BYM) model [50]. BYM model has been widely used for disease risk mapping by adjusting for spatial autocorrelation through spatial smoothing, in order to avoid random noise brought by the variation of population size [51, 52]. In comparison to BYM model, the parameters of BYM2 model offer clearer interpretations. BYM2 model includes both structured and unstructured spatial error term. The structured term captures spatial autocorrelation, which means geographically contiguous areas have similar or dissimilar values, while the unstructured term represents the heterogeneity among different spatial units [50]. We simultaneously constructed models for the entire adult population and for age-specific groups to investigate the age disparities. The models can be represented as follows:

$$\text{log}\left({y}_{i}\right)={\beta }_{0}+{\sum }_{j=1}^{p}{\beta }_{j}{X}_{ij}+{\theta }_{i}+\text{log}({E}_{i})$$
(1)
$${\theta }_{i}=\frac{1}{\sqrt{{\tau }_{i}}}\left({v}_{i}\sqrt{1-\varphi }+{u}_{i}\sqrt{\varphi }\right)$$
(2)
$${E}_{i}=\sum_{k=1}^{K}{Pop}_{ik}\times \frac{{\sum }_{i=1}^{n}{y}_{ik}}{{\sum }_{i=1}^{n}{Pop}_{ik}}$$
(3)

Here, observed disease counts \({y}_{i}\) is characterized by a Poisson distribution. The expected disease cases in the ith spatial unit (i = 1, …, n) \({E}_{i}\) is included to account for the impact of demographical structure. \({Pop}_{ik}\) represents the population counts of the kth demographical group (k = 1, …, K) in the ith spatial unit. For model considering the entire adult population, K = 4 (combinations of 2 sex groups and 2 age groups), while for age-specific group models, K = 2 (2 sex groups). \({\beta }_{0}\) denotes the fixed intercept, \({X}_{ij}\) represents the jth independent variable of spatial unit i, and \({\beta }_{j}\) signifies the coefficients of the jth independent variable. \({\theta }_{i}\) corresponds to the spatial effect of spatial unit i. The spatial structured effect is represented by \({u}_{i}\) and unstructured random effect is denoted by \({v}_{i}\). The mixing parameter \(0 \le \varphi \le 1\) quantifies the proportion of the marginal variance explained by spatial structured effect [49, 53,54,55].

In addition to standard Poisson distribution, we used zero-inflated Poisson (ZIP) distribution to model the disease counts, aiming to address the potential problem brought by the excess of zeros in the data [54]. Deviance information criterion (DIC) and Watanabe Akaike information criterion (WAIC) were used to assess the goodness of fit for the Bayesian models. The model with lower DIC/WAIC yields a better fit for the data [56].

To ensure the reliability of the findings, an alternative spatial modeling approach, spatial autoregression (SAR) model, was also employed to investigate the relationship between living environment and OHCA risk. SAR model is a generalization of ordinary linear regression model, which incorporates spatial effects by including spatial lag terms [57, 58]. Further elaboration on the formulation and the results of SAR model can be found in the supplementary material provided.

Integrated nested Laplace approximations (INLA) was employed for Bayesian inference. Compared to Markov Chain Monte Carlo, INLA methods provide accurate parameter estimates in less computational time [49]. All the statistical analysis was implanted in R-studio. The population counts and the demographical structure of different spatial unit were obtained from WorldPop datasets (the spatial distribution of population in 2020, China) [59] and the seventh population census (2020) of Beijing [60].

Results

Over the study period, the Beijing emergency medical system had 552,257 calls, of which 3737 were screened as OHCA cases. About 63.0% of the cases were male and 65.6% were 65 years old or above. Most cases (88.9%) occurred in residential area. Of the 3163 residential cases with geographical coordinates, 61.7% were in the six core districts. After excluding cases occurring in non-residential area, out of study area, missing critical information, under the age of 20 (Here we used 20 instead of 18 because the census data is categorized based on a division at the age of 20), and with non-natural causes, 1843 OHCA cases were extracted for further analysis (Fig. 1). These cases are mainly distributed in the middle of the study area (Fig. 2), where there is higher population density.

Fig. 1
figure 1

Process diagram for selecting eligible out-of-hospital cardiac arrest cases in the six core districts of Beijing in 2020

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Fig. 2
figure 2

Spatial distribution of out-of-hospital cardiac arrest (OHCA) cases during 2020 in the six core districts of Beijing

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BYM2 models based on Poisson model and ZIP model were both used to fit the data of the entire adult group and two age-specific groups, with 455 cases aged 20 to 64 and 1388 cases aged over 64. Among the results of these three groups, The DIC and WAIC of ZIP regression models were lower than that of the Poisson models (Table 1). Therefore, the results of ZIP models were selected for further illustration of OHCA relative risk distribution and the association between living environments and OHCA risk.

Table 1 Comparison of Poisson and zero-inflated Poisson (ZIP) regression models for assessing the association between living environment and the risk of out-of-hospital cardiac arrest among different age groups in the six core districts of Beijing in 2020. Deviance information criterion (DIC) and Watanabe Akaike information (WAIC) criterion were used for evaluation, with lower values indicating better fit
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The posterior mean of the OHCA relative risk estimates for different age groups are presented in Fig. 3 (a1-a3). Additionally, We calculated the exceedance probabilities, which represent the likelihood of the estimated value being greater than 2 (Fig. 3 b1-b3), to identify the spatial units with unusually high OHCA risk [53]. From the aspect of the entire adult group, after adjusting for the population size and demographical structure, spatial units in suburban districts exhibit higher OHCA risk compared to the overall regional level. The spatial distribution of OHCA risk demonstrates significant difference between two age groups. In the 20 to 64 age group, spatial units with higher OHCA risk are located in the southern part of study area, particularly in Fengtai District. On the other hand, for the age group over 64 years, the OHCA risk is more spatially dispersed, with some of the high-risk spatial units mainly located in Shijingshan District and Chaoyang District. The high-risk OHCA areas of two age-specific groups were selected out and overlapped on the distribution of living environmental factors to obtain some preliminary observations. As illustrated in Fig.S1, the majority of hotspots for amenities such as barbecue restaurants, chess rooms, hospitals and pharmacies coincide with the high-risk OHCA areas, while factors like coffee shops and green space tend to lie outside these high-risk regions.

Fig. 3
figure 3

Spatial distribution of out-of-hospital cardiac arrest risk based on modified Besag-York-Mollié Bayesian models among different age groups in the six core districts of Beijing in 2020. a1-a3: Posterior mean of relative risk estimates of out-of-hospital cardiac arrest among different age groups. b1-b3: Exceedance probabilities (representing the likelihood of relative risk estimate being greater than 2) among different age groups

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Table 2 presents the posterior mean, posterior standard deviation (SD) and posterior 95% credibility interval (CI) for the coefficient of independent variables. The results were exponentiated and reported on the natural scale. Variables with a posterior 95% CI for the coefficient greater than 1 indicate a statistically significant positive correlation with OHCA risk, while accounting for the influence of natural and sociodemographic factors. For the entire adult group, higher density of barbecue restaurants, higher proximity to chess rooms, hospitals and pharmacies are associated with increased OHCA risk. Conversely, higher density of coffee shops and greater proportion of green space in the spatial unit is associated with decreased OHCA risk. Based on the results of the age-specific groups models, only the density of barbecue restaurants is significantly related to OHCA risk in both age groups. An increase of one barbecue restaurants in the spatial unit is associated with about 2.0% increment in OHCA risk for the age group of 20 to 64 and about 1.4% increment for the age group over 64. For age group of 20 to 64, fewer living environment factors are significantly related to OHCA risk. Apart from the density of barbecue restaurants, only the density of dessert shops and coffee shops show a significant relation with OHCA risk, of which the direction is negative. The results of the age group over 64 are similar to those for the entire adult group, with the exception that the density of coffee shops does not show a significant association with the OHCA risk in the population aged over 64. Density of supermarket, proximity to digital entertainment hubs, road connectivity, proximity to sports facilities, and proportion of blue space do not show significant relationship with OHCA risk in any of the population group.

Table 2 Association between living environment (consisting of food environment, physical activity environment, healthcare environment and leisure environment) and out-of-hospital cardiac arrest risk among three age groups in the six core districts of Beijing in 2020 based on modified Besag-York-Mollié Bayesian models. The exponentiated results of posterior mean, posterior standard deviation (SD) and posterior 95% credibility interval (CI) for the coefficient of each independent variable are reported
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The results based on SAR model were used as reference to access the reliability of the findings. A high degree of consistency between SAR and BYM2 model was observed (Table S2, Table 3). The majority of variables exhibit the same significance and directional outcomes in both types of models, with a few exceptions: (1) For the entire adult group, the results of SAR model indicate that the density of coffee shops and proportion of green space do not show a significant relationship with OHCA risk, whereas the BYM2 model has detected significantly negative association. (2) In the age group of 20 to 64, the density of dessert shops does not exhibit a significant relationship with OHCA risk in the SAR model results, while it shows negative association in the BYM2 model. Furthermore, the road connectivity, which does not demonstrate a significant effect in the BYM2 model, shows a positive and significant correlation with OHCA risk in the SAR model analysis. Overall, the associations in age-specific groups identified by BYM2 models are stable and reliable, with the exception of the density of dessert shops. There is high consistency between the BYM2 and SAR model results for the age group over 64, demonstrating the highest stability among three age groups.

Table 3 Comparison of results between modified Besag-York-Mollié Bayesian (BYM2) models and spatial autoregression models analyzing the association between living environment (consisting of food environment, physical activity environment, healthcare environment and leisure environment) and out-of-hospital cardiac arrest risk among three age groups in the six core districts of Beijing in 2020. Model 1, 2, and 3 represent the findings for adults groups, age group of 20 to 64, and age group over 64, respectively
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Discussion

The study mapped the spatial distribution of OHCA risk in six core districts of Beijing adjusted for background demographic characteristics, indicating that the spatial pattern of OHCA risk still remains after controlling the distribution of population density as well as the sex and age composition.

The relationship between four dimensions of living environment and OHCA risk in different age groups was then examined. The associations remained significant even after adjusting for demographical composition of the cases, and the population density, natural environment and socioeconomic status of the spatial unit, indicating the independent influence of living environment on OHCA risk in highly urbanized region. Furthermore, the associations varied across different age groups, suggesting that individuals of different ages interact with their living environment in unique ways, leading to diverse health outcome.

We observed that the factors of the living environment associated with OHCA risk in young/middle-aged adults are relatively fewer and less consistent compared to the elderly group. This finding may reflect the higher dependence of elderly individuals on their residential area. Young/middle-aged adults have larger activity space, which allow them to interact with a broader range of environmental factors beyond their immediate residential surroundings. In contrast, elderly individuals tend to have shorter travel distances and are more confined to their living area, resulting in a closer relationship between living environment and their health outcome [28, 61]. Considering that elderly population constitute a significant portion of OHCA cases, the influence of living environment should therefore be given more attention.

Among all the living environment factors examined, only the density of barbecue restaurants demonstrates a significant association with both age groups. In China, barbecue restaurants usually serve roasted meat and vegetables that are high in fat and sodium, which customers often consume with beer. The presence of barbecue restaurants therefore exposes people to high-calorie food and alcohol, potentially contributing to the development of unhealthy dietary habits. Previous studies have indicated that the accessibility of food resources has an influence on dietary habits, and is associated with body size [21, 62]. Our study further proves that unhealthy food resources in neighborhood may increase the risk of OHCA, and this relation is significant across adults of all ages.

Besides the density of barbecue restaurants, the proximity to chess rooms and healthcare facilities are also related to increased OHCA risk, but only among elderly individuals. In China, chess rooms serve as daily activity facilities for people living around, especially for aging individuals. Residents who spend excessive time playing card or board game may engage in less outdoor physical activity [63]. Moreover, sedentary behavior and physical inactivity have been found to be associated with negative cardiovascular health effects [64]. Therefore, the presence of chess rooms in living area might increase the disease risk by encouraging elderly people to maintain a sedentary position for extended periods and potentially reducing their physical activity levels. In the meanwhile, proximity to sports facilities, which could potentially promote physical activities, does not exhibit significant associations with OHCA risk across any of the age groups. This observation could be attributed to the notion that while moderate physical activities are beneficial for cardiovascular health, engaging in vigorous sports activities may have adverse effects. Researches have indicated that prolonged and intense exercise can trigger temporary inflammation [65], and there is an elevated risk of sudden cardiac death during or immediately following physical exertion [66]. In terms of the healthcare environment, the presence of medical facilities in the living area can play a role in preventing disease by promoting regular visits to healthcare providers and providing timely medical services or preventive care for at-risk individuals [35, 67,68,69]. The availability of healthcare resources has been proved to be inversely associated with overall mortality and mortality of CVD [70,71,72]. However, the association between health facilities and the occurrence of CVD and cardiac arrest was proved to be positive or not statistically significant in previous studies [35, 67, 73]. In line with these studies, our study found a significant positive association between the proximity to pharmacies and hospitals and the increased risk of OHCA among elderly individuals. One plausible explanation is self-selection, where elderly individuals at a higher risk of OHCA may opt to reside in close proximity to healthcare facilities. Self-selection has been identified as a potential bias contributing to the unclear direction of impact between living environments and individual behavior (e.g., physical activity) due to the use of cross-sectional study designs [74]. The relationship between healthcare facilities and health outcomes within neighborhoods may also be influenced by self-selection. A longitudinal study conducted among older Canadians has demonstrated that poor self-assessed health can serve as a predictor of residential mobility, as poor health status can prompt individuals to enhance their access to support and health-related services [75]. Therefore, the presence of healthcare facilities should be viewed more as an indicator of the clustering of high-need populations, rather than environmental factor protecting against OHCA.

Environmental factors that are related to decreased OHCA risk are different among two age groups. In the case of young/middle-aged adults, the OHCA risk is negatively associated with the density of coffee shops in their living area, while the decrease of OHCA risk in elderly adults is related to the distribution of green space of their living environment. Coffee shops have been referred to as “third place”, which is a place outside of home and the workplace where people can go for socialization and leisure, particularly in areas where coffee culture is prevalent [76]. Researches on the concept of third place have underscored its role in enhancing social interactions and fostering a sense of community belonging [77]. A study conducted on students from University of California has specifically highlighted that cafés and coffee shops serve as preferred and pleasant place to rest like urban park, and can offer psychological benefits by promoting emotional relaxation [78]. With China’s rapid economic growth in recent years, coffee shops have begun to thrive in big cities like Beijing and Shanghai, especially in areas undergoing rapid gentrification [79]. Coffee shops are used as indicators of urban vitality with its central role in fostering interpersonal interactions in urban life, typically targeting white-collar workers and young people [80]. The negative association between coffee shops and OHCA risk therefore suggests that residential areas with public places that facilitate social connections and leisure activities can be beneficial for the cardiovascular health of young/middle-aged individuals. On the other hand, the positive impact of green space has long been discussed. Green space has been found to promote health by reducing environmental harm, relieving tensions and facilitating physical activity and social interaction [81]. Empirical studies have also demonstrated the positive impact of green space on health outcome. For example, green space has been proved to be associated with improved mental health, wellbeing, and reduced use of anxiolytic [39, 41, 82]. Specifically, increased level of neighborhood greenness has been found to be related to decreased cardiovascular mortality and lower risk of hypertension, coronary heart disease and stroke [23, 83]. Our results show that green space may also has positive impact on reducing the risk of acute disease like OHCA, but only among the elderly population. This effect could be attributed to the higher frequency of green space utilization in neighborhoods by elderly individuals as well as the fact that these natural spaces provide pleasant living environments within urban areas. A Swedish study on urban green space has found out that compared to younger adults, green space is of greater importance to older residents by providing opportunities for engaging in nature-related activities and offering aesthetic values [84]. Additionally, there is synthesized evidence illustrating the preferences of older adults for nature-based recreation and landscape characteristics [85]. In line with these research findings, our study has revealed the age differences in health benefits derived from green space.

It is important to note that the aforementioned findings are based on a highly urbanized context as data exclusively originated from the six core districts of Beijing. In contrast, peripheral urban and rural areas might exhibit diverse distributions of the living environmental factors and variations in how residents interact with these factors, potentially resulting in distinct relationships between the living environment and OHCA risk. For example, in less urbanized areas, healthcare services are often more sparsely distributed and of lower quality due to budget constraints [30, 86], which cannot fully cover the need of rural residents. In such contexts, proximity to hospitals might represent increased opportunities for prompt medical intervention, which could lead to lower disease incidence. A research conducted in China has revealed that limited access to healthcare is associated with higher odds of activities of daily living disability and cognitive impairment among elderly adults residing in rural areas, while no significant associations were found among older adults in urban settings [87].

In summary, our findings demonstrate the significance of the living environment in relation to OHCA risk in highly urbanized regions, highlighting that environmental factors might influence OHCA risk by shaping people’s daily habits or providing access to health resources in these regions. Individuals of different age groups adopt varied patterns of daily activity, leading to distinct modes of space utilization. Therefore, the association between living environment and OHCA risk exhibits age disparities. Building upon these results, further study can improve predictive models for OHCA distribution pattern by considering the complex influence of living environment. More detailed studies and accurate predictions can assist in developing planning guidelines to prioritize areas with the greatest need for post-event treatment, while also fostering healthier living environment to support pre-event environmental interventions in the future. For example, the deployment of AEDs and public training in cardiopulmonary resuscitation could be customized to align with the spatial pattern of OHCA occurrences. A study conducted in the city of Milan has introduced a framework for implementation of AEDs. This framework integrates an optimization algorithm with a geographic risk function that estimates the probability of OHCA incidents based on demographic, socio-economic and land-use characteristics. The research has demonstrated the effectiveness of this comprehensive approach compared to a purely statistical method that relies solely on retrospective OHCA data [88]. In addition to the geographical factors emphasized in the study, our findings have underscored the important role of living environment. Therefore, in the process of estimating OHCA risk to optimize resource allocation, the distribution of facilities related to increased OHCA risk – such as unhealthy food destinations, chess rooms, and healthcare facilities – should also be considered to enhance efficacy. In the meanwhile, the approach of using environmental designs to prompt heath behaviors or reduce disease risk proactively has been long advocated and supported by solid evidence, which can be realized through the implementation of design guidelines or regulations [89, 90]. For instance, the Active Design Guidelines established in New York city aim to offer evidence-based strategies for reducing physical inactivity through designs and construction of the built environment, with its effectiveness having been assessed [91]. In the context of OHCA, since the complicated mechanisms between environment and the disease remain partly understood, it is important to first amass substantial evidence. Drawing from empirical instances, preliminary ideas can be prepared for further sophisticated and measurable environmental indices. Based on this research, we suggest that urban planning could mitigate OHCA risk by providing more natural green spaces for elderly population and creating more public social gathering places for young/middle-aged individuals.

Strengths and limitations

The major strengths of this study are: (1) We adopted a daily-life perspective to examine the impact of environmental risk on OHCA, providing evidence on a micro scale that sheds light on how people’s everyday living environment might influence the occurrence of acute disease like OHCA. (2) We conducted a comprehensive analysis of four dimensions of the living environment, ensuring a thorough exploration of the potential environmental influences on OHCA risk. The natural and socio-demographic characteristics of the spatial units were controlled to ensure the results are valid. (3) We investigate the association between living environment and OHCA risk among different age groups, further exploring how the variations in daily interaction with living environment across different age groups can contribute to different associations with disease risk.

However, the study has several limitations: (1) The influence of living environment on OHCA might be a result of long-term cumulative effects. The environment data we used was only cross-sectional data in 2020 and we were not able to get actual living period of the patients. Longitudinal studies with patients living for a long time in the area where they had the OHCA are needed to reveal the real causal effect of living environment on OHCA risk. (2) Individual-level covariates, such as comorbidities, smoking status and medication use, which could potentially impact OHCA, were not available in the emergency call database and therefore were not included in the aggregated data. This might introduce results susceptible to ecological fallacy when extrapolated to explain associations at the individual level. More detailed information regarding individual characteristics should be collected in future studies. (3) In our study, recognition of OHCA patients depends on the Medical Priority Dispatch System. While it is a widely used computer-based emergency medical dispatch system in the world, the actual distribution of OHCA cases may be biased due to the lack of confirmed data from first responders on the spot. (4) The OHCA cases analyzed in this study occurred in 2020. Although the decision to make an OHCA emergency call might not be put off by lockdown, and the influences of living environment are more of a long-term process which started long before the pandemics, the possible impact from COVID-19 itself on OHCA was not considered.

Conclusions

The study explored the association between living environment and OHCA risk among different age groups of adults in the six core districts of Beijing, adjusted for population size, natural environment and socio-demographic characteristics. In conclusion, we found that the OHCA risk of young/middle-aged adults and elderly adults in highly urbanized areas are related to their living environment in different extent. First, there are fewer environmental factors significantly associated with OHCA risk in young/middle-aged individuals. Second, unhealthy food resources like barbecue restaurants are associated with increased OHCA risk in both age groups. Third, facilities encouraging sedentary activity (e.g., chess rooms) are related to increased OHCA risk of elder individuals. Meanwhile, the healthcare amenities (e.g., pharmacies and hospitals) are positively associated with OHCA risk of elder individuals, which may be the result of self-selection. Fourth, for young/middle-aged individuals, more public gathering places for socialization and relaxation in living area, such as coffee shops, are associated with decreased OHCA risk, while for elderly individuals, more green spaces in neighborhood are correlated with lower risk of OHCA. These findings contribute to existing research by discussing the topic in a Chinese highly-urbanized area context and comprehensively investigating living environmental impacts on OHCA risk and its age disparities. The results might help improve prediction model of OHCA risk in further studies and optimize planning guidelines in practice.

Data availability

As the pre-hospital emergency database used in the research contains individual information, it cannot be published. Please direct any additional queries to the corresponding author.

Abbreviations

AED:

Automated external defibrillator

BYM:

Besag-York-Mollié

BYM2:

Modified Besag-York-Mollié

CI:

Credibility interval

CVD:

Cardiovascular disease

DIC:

Deviance information criterion

INLA:

Integrated nested Laplace approximations

OHCA:

Out-of-hospital cardiac arrest

SAR:

Spatial autoregression

SD:

Standard deviation

WAIC:

Watanabe Akaike information Criterion

ZIP:

Zero-inflated Poisson

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Acknowledgements

Not applicable.

Funding

This research was supported under the National Natural Science Foundation of China (42271234; 72174003), the Basic and Applied Basic Research Foundation of Guangdong Province (2023A1515110511), and the special fund of the National Clinical Key Specialty Construction Program, P. R. China (2022) 301-2305.

Author information

Authors and Affiliations

  1. School of Geography and Planning, Sun Yat-Sen University, Guangzhou, China

    Yitong Liao & Suhong Zhou

  2. Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou, China

    Yitong Liao & Suhong Zhou

  3. Beijing Emergency Medical Center, Beijing, China

    Hui Chen

  4. School of Architecture and Urban Planning, Guangzhou University, Guangzhou, China

    Shuli Zhou

  5. Department of Emergency Medicine, Peking University Third Hospital, Beijing, China

    Yuanwei Fu, Qiangrong Zhai & Qingbian Ma

  6. Key Laboratory of Molecular Cardiovascular Sciences, Ministry of Education, Beijing, China

    Yuanwei Fu, Qiangrong Zhai & Qingbian Ma

  7. Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China

    Yan Xiong

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  1. Yitong Liao
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  2. Hui Chen
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  3. Shuli Zhou
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  4. Yuanwei Fu
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  6. Qingbian Ma
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  8. Suhong Zhou
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Contributions

The concept was drafted by SZ1. SZ2 and YL assisted in the paper conceptualization. YL and SZ1 contributed to the methodology. Formal analysis and investigation were conducted by YL, HC and SZ1. YL and YF wrote the original draft. All authors participated in the review and editing of the manuscript. SZ2, SZ1, QM acquired funding for the study, while HC, YX, QM, YF and QZ provided resources. The project was supervised by SZ2 and QM. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Qingbian Ma or Suhong Zhou.

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This study was approved by the Ethics Committee of Peking University Third Hospital (approval number: 2021–225-02). We confirm that all methods were performed in accordance with the Declaration of Helsinki. The requirement of written informed consent was waived by the Ethics Committee of Peking University Third Hospital given the nature of a retrospective study. The patient information was anonymized prior to analysis.

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Liao, Y., Chen, H., Zhou, S. et al. The association between living environment and out-of-hospital cardiac arrest risk in adults: the perspective of daily-life contexts. Arch Public Health 83, 67 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13690-025-01556-w

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13690-025-01556-w

Keywords

Archives of Public Health

ISSN: 2049-3258