Environmental determinants of health in Europe: a multi-factor approach

Abstract

Introduction:

The One Health approach provides a multidisciplinary framework that recognises the intrinsic interconnections between environmental conditions and population health. However, empirical evidence on how multiple environmental pressures jointly shape health outcomes remains limited, particularly from a European comparative perspective.

Methods:

This study analyses the relationship between a broad set of environmental factors and self-rated health in Europe, adopting a multidimensional approach. A multilevel logistic regression model was estimated, integrating diverse environmental exposures, including climatic conditions, pollution processes, pressure on water resources, components of natural capital, and environmental health hazards. These contextual indicators, interpreted as macro-contextual environmental characteristics rather than precise measures of individual exposure, are combined with individual-level data from Round 11 (2023) of the European Social Survey, covering 21 European countries. In addition, an environmental pressure index is constructed to capture the cumulative nature of contextual exposures and to facilitate territorial comparison.

Results:

Several environmental factors were significantly associated with self-rated health, even after controlling for sociodemographic and economic characteristics. The findings indicate that the accumulation of environmental burdens is unevenly associated with health across Europe, generating a consistent territorial gradient.

Discussion:

These results underscore the role of the environment as a structural determinant of health opportunities and demonstrate that the impact of environmental exposure is shaped by social and institutional contexts. These findings provide a relevant evidence base for public health policymakers to design targeted interventions aimed at mitigating environmental inequalities and improving population well-being in Europe.

1 Introduction

Across Europe, climate change, accelerated urbanisation, and intensifying environmental pressures have emerged as key structural determinants of human health (Frumkin et al., 2008; Gould and Rudolf, 2015; Yenew et al., 2025). Epidemiological research has consistently documented associations between multiple environmental exposures and a wide range of adverse health outcomes (Masood et al., 2014; Weilnhammer et al., 2021; Alam et al., 2024). However, much of the comparative literature has examined these relationships in a fragmented manner, focusing on specific exposures such as air pollution (Anderson et al., 2012; Yan et al., 2025) or on analyses conducted at urban or regional scales (Royé et al., 2019; Klompmaker et al., 2021). Although highly valuable, this approach limits the assessment of the combined effects of multiple environmental pressures within a European comparative framework and constrains our understanding of how the accumulation of environmental burdens at the territorial level contributes to cross-national health inequalities.

Against this background, the One Health approach provides an integrative framework for the analysis of health by recognising the close interdependence between human, environmental, and animal systems (Allen et al., 2017; One Health High-Level Expert Panel et al., 2022). While this perspective offers a comprehensive understanding of the interconnected nature of health determinants, it requires complementary analytical approaches to specify the mechanisms through which environmental conditions affect population health. In this regard, environmental epidemiology provides a valuable framework for examining how environmental exposures translate into measurable health outcomes (March and Susser, 2006; Bain and Awah, 2014; Krieger, 2024). This body of research distinguishes between direct exposure pathways and broader structural processes that jointly shape health. The former operate through physiological responses to environmental exposures such as air pollution, thermal stress, or environmental contamination, which have been consistently associated with increased cardiovascular and respiratory morbidity, premature mortality, and the spread of infectious diseases (Costello et al., 2009; Hoek et al., 2013; Watts et al., 2015; Messina et al., 2019).

However, these associations are not evenly distributed across populations. A substantial body of evidence indicates that vulnerability to environmental risks is socially structured, with socioeconomic position, gender, age, and place of residence influencing both exposure and susceptibility (Marmot, 2005; Krieger, 2011; Li et al., 2021; Braveman, 2023; Krieger, 2024).

Moreover, beyond these direct effects, environmental health research underscores the importance of indirect and institutional pathways through which environmental conditions influence health. These include the deterioration of living conditions, inequalities in access to essential resources and the varying capacity of institutions to prevent or mitigate environmental risks (Berkman et al., 2014; Atwoli et al., 2021; Braveman, 2023; Krieger, 2024). From an ecosocial perspective, such structural conditions become embodied over time, contributing to the production and persistence of health inequalities across social groups (Krieger and Davey Smith, 2004; Krieger, 2011; Krieger, 2024).

Taken together, these perspectives underscore that the relationship between environmental conditions and health cannot be reduced to individual biological exposure alone but must be understood within broader social and institutional contexts. From this perspective, the understanding of human health has moved beyond the classical biomedical paradigm, giving way to complex multidimensional models grounded in the biopsychosocial approach. These models integrate biological factors, social contexts, and ecological conditions, as well as individuals’ subjective experiences (Terris, 1994; Gavividia and Talavera, 2012; Serrano-del-Rosal, 2024).

Within this framework, self-rated health has become a widely used proxy for overall health status by capturing the physical, psychological and social dimensions of wellbeing (Idler, 2003; Schnittker and Bacak, 2014). The literature has consistently shown that self-rated health is closely associated with objective indicators, such as the prevalence of chronic diseases (Wu et al., 2013; Schnittker and Bacak, 2014). Moreover, it robustly predicts morbidity, mortality, and the use of healthcare services, even after accounting for medical diagnoses and sociodemographic characteristics (Idler and Benyamini, 1997; Jylhä, 2009). Likewise, several studies have emphasised its high degree of comparability across countries and cultural contexts, reinforcing its suitability for international analyses of health inequalities (Jylhä et al., 1998).

To analyse the relationship between the environment and human health, it is necessary to consider the territorial dimension of environmental exposures. Health geography has emphasised that environmental and social conditions are not distributed homogeneously, but are organised and experienced unevenly across space, generating a differentiated territorial pattern of risk and protection for health (Macintyre et al., 2002). In addition, environmental exposures tend to cluster and interact within specific territorial contexts where environmental burdens and social inequalities converge. In this regard, national-level contextual analysis makes it possible to examine how different environmental determinants combine to produce environments that are more or less favourable to population health.

Among the environmental factors most relevant to human health, climatic context has received particular attention in the scientific literature. In particular, thermal stress, defined as the cumulative effect of temperature, humidity, and wind on the human body, is a fundamental determinant of health (Yenew et al., 2025). Prolonged exposure to adverse thermal conditions is associated with alterations in thermoregulatory mechanisms, resulting in increased mortality and morbidity, particularly from cardiovascular and respiratory causes (Wen et al., 2023; Kazi et al., 2024). Heat stress has also been shown to exert negative effects on mental health, including mood disturbance, anxiety, and depression (Beecher et al., 2016; Li et al., 2023; Chen et al., 2024).

The recent increase in drought events has made water availability a key environmental determinant of health in Europe. Over the past few decades, more than half of European regions have experienced extreme or exceptional drought episodes (van Daalen et al., 2022; Salvador et al., 2023), indicating increasing pressure on water resources. This intensification not only reduces water availability but also deteriorates its quality, thereby affecting domestic supply, food production, and the balance of aquatic ecosystems (Stanke et al., 2013; Mishra et al., 2021; Salvador et al., 2023). These processes disproportionately affect territories with lower adaptive capacities or more vulnerable infrastructures, thereby reinforcing territorial health inequalities (Pörtner et al., 2022; Salvador et al., 2023; Yenew et al., 2025).

Environmental pollution is another important pathway affecting human health. Increased agricultural and industrial waste contributes to eutrophication and degradation of aquatic ecosystems, with consequences for water availability and quality (Peñuelas and Sardans, 2022). In the atmospheric sphere, exposure to pollutants and greenhouse gas emissions is strongly associated with increased morbidity and mortality from cardiorespiratory diseases and exacerbation of chronic diseases (Manisalidis et al., 2020; Wilker et al., 2023).

The physical structure of territories and the availability of natural environments exert substantial influence on population health through multiple mechanisms (Barton and Grant, 2012; Krefis et al., 2018; Nieuwenhuijsen, 2021; Tiwari and Mathur, 2024). In particular, the presence of green and blue environments is associated with a lower prevalence of mental disorders, reduced stress levels, and decreased risk of cardiovascular disease (Kodali et al., 2023; Bu et al., 2024; Davis et al., 2025). These benefits can be explained by environmental regulation processes, which contribute to improvements in air quality and reductions in noise exposure (Barton and Grant, 2012; Nieuwenhuijsen, 2021; Ferrante et al., 2025), and psychosocial pathways that enhance psychological wellbeing (Marselle et al., 2021; Davis et al., 2025) and encourage the use of space for recreational activities (Krefis et al., 2018; Wang et al., 2024; Cao et al., 2025). In contrast, intensive urbanization and land artificialisation have been linked to greater exposure to adverse environmental conditions, such as the urban heat island effect (Siebielec et al., 2016), as well as poorer health outcomes, particularly in densely populated areas (Vuono et al., 2025).

Certain direct environmental exposures, together with the risks associated with extreme events, significantly impact physical and mental health. In particular, prolonged exposure to persistent chemicals, such as pesticides, has been linked to neurotoxic, carcinogenic and developmental disorders, especially under conditions of chronic exposure (Dhankhar and Kumar, 2023; Rodrigues et al., 2025). Similarly, the generation and inadequate management of hazardous waste has been associated with significant risks to human health, including toxic, carcinogenic, and developmental effects (Fazzo et al., 2017; Hasan et al., 2020). Noise pollution in urban areas constitutes a significant source of stress and is associated with sleep disorders, mental health problems, and increased cardiovascular risk (Peralta et al., 2025). Extreme climatic events such as wildfires and floods have immediate health impacts, including injuries and respiratory problems, but may also trigger medium- and long-term consequences such as anxiety, depression, and post-traumatic stress disorder (Zhong et al., 2018; Cianconi et al., 2020; Hadiwijoyo et al., 2025).

The environmental factors described operate through interrelated biological, ecological, and social mechanisms, influencing health through direct and indirect effects that accumulate across different territories (Figure 1). Their joint analysis enables a more comprehensive understanding of environmental processes and clarifies how contextual differences between European countries contribute to health inequalities.

FIGURE 1

Building on these considerations, the selection of environmental indicators in this study is guided by the aim of capturing multiple dimensions of environmental exposure that have been identified in the literature as relevant for human health. Specifically, the analysis incorporates indicators reflecting climatic conditions, environmental pollution, pressure on natural resources, the physical configuration of territories, and exposure to environmental risks and extreme events, in order to account for both risk-related and potentially protective environmental factors.

This approach allows for a more comprehensive assessment of how different environmental pressures coexist and interact within national contexts, moving beyond the analysis of isolated exposures. At the same time, it recognises that these indicators represent contextual characteristics rather than precise measures of individual exposure, and therefore capture structural conditions that shape population health at the territorial level. Despite the extensive evidence on individual environmental factors, comparatively fewer studies have examined their cumulative and simultaneous impact within a cross-national European framework. Existing research has often focused on specific exposures or particular geographical scales, which may limit a more integrated understanding of how multiple environmental pressures jointly shape health outcomes across countries.

To contribute to this line of research, the present study examines the relationship between a broad set of environmental health hazards and self-rated health in Europe. By integrating individual-level data from Round 11 (2023) of the European Social Survey with diverse contextual indicators, this study employs a multilevel logistic regression model. The objective is to assess the relative importance of these factors and explore how the accumulation of environmental pressures is associated with territorial inequalities across 21 European countries. Ultimately, the findings aim to provide empirical evidence that can inform public health and urban policy debates on environmental inequalities in Europe.

2 Methods

2.1 Research design and variables

The study is based on a cross-sectional multilevel design using repeated cross-sectional data, in which individuals (level 1) are nested within country contexts (level 2). It integrates individual perceptions with territorial environmental indicators, drawing on data from the European Social Survey (2023), a nationally representative survey that ensures high standards of comparability across participating countries. This framework allows for the examination of health outcomes as a result of the interaction between personal characteristics and the macro-environmental context.

The dependent variable was self-rated health. In the European Social Survey, it is measured by the question “How is your health in general?”, in which respondents evaluate their health on a five-point Likert scale: “very bad”, “bad”, “fair”, “good” and “very good”. To facilitate the interpretation of the results and simplify the estimation of the model, the dependent variable was dichotomised, grouping the categories “very good” and “good” as indicative of good health (66.94% of the sample), whereas the remaining categories reflect less favourable health (33.06% of the sample). This dichotomization is consistent with long-standing epidemiological practice (Eikemo et al., 2008; Pinillos-Franco and García-Prieto, 2017; Vonneilich et al., 2020), as empirical evidence suggests that binary health indicators maintain high predictive validity and yield results comparable to ordinal specifications (Manor et al., 2000; Martikainen et al., 2005; Bourne, 2009).

At the individual-level, several sociodemographic variables were included in the model to account for systematic differences in health status. This allowed for the estimation of the net effect of environmental factors on self-rated health after accounting for these individual-level characteristics. The model includes gender, age, and income (the latter two treated as continuous variables), as well as educational level (which was recategorized into three levels: primary, secondary, and higher studies). Residential context was also included based on a settlement typology distinguishing between big cities, suburbs or outskirts of a city, towns or small cities, country villages, and farms or homes in the countryside.

Regarding the contextual dimension, several territorial environmental and macro-indicators were included in the analysis, selected based on empirical evidence highlighting environmental factors that are relevant to human health. Their inclusion in the model enables the operationalization of these dimensions at the national level and facilitates cross-national comparisons across Europe.

The bioclimatic conditions and their potential impact on health were operationalized by distinguishing between ambient exposure and the human physiological response. Environmental exposure was measured using the Annual Mean Air Temperature (2023) which serves as a standard physical metric of climatic conditions. In contrast, the physiological strain and human perception of this environment were captured through the Annual Average Universal Thermal Climate Index (UTCI). While the former characterizes the external thermal state, the UTCI integrates multiple meteorological components such as humidity, and radiation to provide an aggregate measure of the actual thermal stress experienced by the human body (Pantavou et al., 2025).

Water resource impacts were assessed by considering both water availability and quality. Water availability was measured using the Water Exploitation Index Plus (WEI+) developed by Eurostat (2022a), which captures the degree of pressure on renewable water resources, with higher values indicating greater exploitation. Water quality was operationalized using Industrial Nitrogen Emissions and Industrial Phosphorus Emissions (European Industry Emission Portal, 2022) and Industrial Phosphorus Emissions (European Industry Emission Portal, 2022). Higher levels of these chemical discharges are associated with accelerated water degradation processes, such as eutrophication, which can indirectly affect human health through the disruption of local ecosystems (Peñuelas and Sardans, 2022).

Air pollution was operationalized using two complementary indicators reflecting distinct mechanisms of environmental exposure. First, Net Greenhouse Gas Emissions (World Bank, 2022) served as an indicator of national contributions to anthropogenic climate change, with potential implications for health (Yenew et al., 2025). Second, Total Atmospheric Pollutant Emissions, were used to capture direct, acute, and chronic exposure to hazardous substances. This composite measure integrates the emissions of fine particulate matter (PM₂.₅), ammonia (NH₃), nitrogen oxides (NOx), sulfur oxides (SOx), and non-methane volatile organic compounds (NMVOC), obtained from the European Environment Agency (2023). These specific pollutants are extensively documented for their adverse effects on the respiratory and cardiovascular systems and represent the primary chemical burden on urban and regional air quality (Manisalidis et al., 2020).

Land use and land cover indicators were used to characterize the physical structure of national territories and the availability of natural spaces. Specifically, the analysis included the percentage of Forest Cover (Eurostat, 2022b), proportion of Artificial Surfaces (Eurostat, 2022a), and extent of Blue Spaces (CORINE Land Cover, 2018), defined as inland and coastal water bodies. While higher proportions of Forest Cover and Blue Spaces are interpreted as environmental factors with a protective effect on health, higher shares of Artificial Surfaces reflect increased artificialisation of land and potential adverse effects on wellbeing.

Additional mechanisms of environmental pressure were captured using specific indicators: Hazardous Waste generation per capita, (Eurostat, 2020), Pesticide Use (World Bank, 2022), and Noise Pollution in urban areas (Eurostat, 2022b). For which higher values are associated with greater exposure to environmental risks. The noise indicator is based on urban environments, where exposure to environmental noise is typically highest due to population density, traffic intensity, and built infrastructure, and where the majority of the population is concentrated. As a country-level measure, it provides a harmonized proxy of overall acoustic pollution that is comparable across countries.

Extreme environmental events were captured using two indicators that reflect intense forms of environmental pressure with potential adverse health effects. The proportion of the population exposed to Flood Risk (World Bank, 2022) was included, as it has been consistently associated with significant and persistent increases in mental health issues (Zhong et al., 2018; Nolting et al., 2025). The Burned Area derived from CORINE Land Cover (2018) was used as a proxy for wildfire exposure, given that wildfires constitute a major source of fine particulate matter (PM₂.₅) and gaseous pollutants (Hadiwijoyo et al., 2025). The inclusion of both indicators enables the assessment of not only acute clinical risks but also the deterioration of subjective wellbeing associated with environmental disruption (Siebielec et al., 2016; Grant and Runkle, 2022).

At the same time, Gross Domestic Product (GDP) adjusted for purchasing power parity (PPP) at the country level (World Bank Group) was included as a macroeconomic control variable to account for cross-national differences in economic development that may influence the association between environmental factors and self-rated health. In addition, Median Age at the country level was incorporated as a demographic control variable to account for cross-national differences in population structure, capturing compositional differences across national contexts that may jointly influence both environmental conditions and health outcomes.

Table 1 presents the descriptive statistics for the variables included in the study at both the individual and contextual levels. Prior to estimating the multivariate model, an exploratory correlation analysis of the environmental indicators was conducted (Table 2). This analysis revealed that some variables exhibited correlation coefficients above 0.8, potentially affecting coefficient stability. To reduce multicollinearity and facilitate interpretation, the highly correlated variables were grouped into composite environmental factors. This approach follows established practices in environmental modelling, where grouping correlated indicators allows for a more parsimonious and analytically robust specification.

Specifically, three composite factors were constructed: Thermal Context, derived from the Annual Mean Air Temperature and the Annual Average Universal Thermal Climate Index (UTCI) (Cronbach’s α = 0.96); Water Pollution, measured using Nitrogen and Phosphorus Industrial Emissions (Cronbach’s α = 0.93); and Atmospheric Emissions, combining Net Greenhouse Gas Emissions and Total Atmospheric Pollutant Emissions (Cronbach’s α = 0.97). The consistently high Cronbach’s alpha coefficients indicate a strong internal consistency across all three factors. The final analysis included 12 contextual variables.

2.2 Data sources and study population

To operationalize the variables described in the previous section, data were harmonized from multiple international institutional sources, ensuring the highest standards of data quality and cross-national comparability. Individual-level data, including self-rated health and sociodemographic controls, were obtained from Round 11 (2023) of the European Social Survey (ESS). The initial dataset of 46,162 respondents was refined to a final analytical sample of 28,619 individuals from 21 countries. The sample was restricted to countries with a complete and harmonized information for all contextual indicators and the individual-level variables included in the analysis, ensuring cross-national comparability. Within countries, missing data at the individual level were handled using listwise deletion.

The participating countries are Austria, Belgium, Bulgaria, Croatia, Cyprus, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, the Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, and Sweden.

Contextual indicators correspond to the most recent data available for each variable within the 2018–2024 period, depending on reporting cycles. Priority was given to 2023 data whenever available to ensure temporal alignment with the survey year. A detailed description of all variables, including their definitions, units of measurement, and data sources, is provided in Supplementary Appendix 1. The selection of environmental indicators is guided by the environmental epidemiology literature and aims to capture multiple dimensions of environmental exposure relevant to human health, including climatic conditions, pollution processes, resource availability, and land use. All indicators were selected from harmonised international data sources to ensure cross-national comparability.

Regarding the bioclimatic domain, data for Annual Mean Air Temperature and Annual Average Universal Thermal Climate Index, were retrieved from the Copernicus Climate Change Service (C3S), (2024). Pollution and resource degradation metrics were sourced from three main pillars: the European Environment Agency (2023) provided data on Total Atmospheric Pollutant Emissions. Data on Nitrogen and Phosphorus Industrial Emissions were extracted from the European Industry Emissions Portal (2022), and the Water Exploitation Index Plus was obtained from Eurostat (2022a).

The physical characterization of national territories and natural capital was based on Eurostat databases (2022) for Forest Cover and Artificial Surfaces, while the extent of Blue Spaces and Burned Areas were derived from the CORINE Land Cover inventory provided by the Copernicus Land Monitoring Service (2018). Specific environmental risks and extreme hazards, including Net Greenhouse Gas Emissions, Pesticide Use, and the Flood Risk were harmonized from the World Bank Group (2022).

Finally, to account for cross-national differences in economic development that may influence health outcomes, Gross Domestic Product (GDP) was obtained from the World Bank, 2022 and ensures that the observed associations between environmental factors and health are estimated net of the countries’ overall economic capacity.

2.3 Statistical analysis

The data have a hierarchical structure, with individuals nested within country contexts. The analysis combines individual-level characteristics with contextual environmental and macroeconomic indicators across countries, drawing on cross-sectional survey data from the European Social Survey. A multilevel logistic regression model with a random intercept at the country level was estimated to account for the clustering of observations within countries. This framework enables the simultaneous examination of individual-level and contextual-level factors and is particularly appropriate in comparative research settings where outcomes may vary across higher-level units (Moerbeek et al., 2008). Following the analytical strategies adopted in comparative research (Gelissen, 2007; Franzen and Meyer, 2010; Echavarren, 2016), the model incorporates variables at both the individual and national levels to assess their association with self-rated health. All statistical analyses were performed using StataSE 17 (StataCorp LLC, College Station, TX, United States).

As a preliminary step, an empty (null) model was estimated to assess whether self-rated health varied significantly across European countries and to determine whether multilevel modelling was warranted. From this model, the intraclass correlation coefficient (ICC) was derived, indicating the proportion of the total variance in the dependent variable attributable to between-country differences. The literature suggests that ICC values exceeding 5% generally justify the application of multilevel models (Snijders and Bosker, 2012). In the present study, the ICC was 5.4%, indicating sufficient heterogeneity between countries to support the use of this analytical approach.

The model was subsequently extended to include control variables, environmental factors, and indicators to assess their association with the likelihood of reporting good self-rated health. Robust standard errors clustered at the country level were estimated to account for potential heteroskedasticity and within-country dependence. The results are presented as odds ratios (OR), which facilitate the interpretation of the magnitude of the associations and allow comparison of their relative influence on the dependent variable. As the analysis is based on cross-sectional data, the results should be interpreted as associations rather than causal effects.

As a robustness check, additional models were estimated using an ordered logistic specification that preserves the ordinal nature of the dependent variable. The results were substantively consistent in terms of direction, magnitude, and statistical significance of the associations, aligning with evidence suggesting that dichotomization in self-rated health research yields robust effect estimates (Manor et al., 2000; Martikainen et al., 2005; Bourne, 2009). Tests of the proportional odds assumption indicated partial violations, supporting the use of the binary specification as the main model for reasons of interpretability and comparability with previous research.

Based on the indicators that showed statistically significant associations with health in the multilevel logistic regression model, a country-level environmental pressure index was constructed to synthesize the cumulative exposure to these factors and facilitate a comparative analysis of territorial gradients across Europe. The variables were first standardized and then aggregated using a simple arithmetic mean (Cronbach’s α = 0.57). This relatively low internal consistency reflects the multidimensional and formative nature of the index, in which indicators capture distinct but complementary aspects of environmental pressure rather than a single underlying latent construct. The decision to use an unweighted index follows a parsimonious approach and avoids imposing a priori assumptions regarding the relative importance of each environmental factor.

To facilitate cross-country comparisons, the national values of the environmental pressure index and self-rated health were expressed as deviations from the overall European mean. For self-rated health, the reference was defined as the individual-level mean calculated across all respondents in the countries included in the analysis (N = 28,619). For environmental pressure, the reference corresponded to the mean national index values (Figure 2).

FIGURE 2

3 Results

Table 3 presents the results of the multilevel logistic regression model for self-rated health. The target category corresponded to “good health,” combining the responses “very good” and “good,” whereas the remaining categories constituted the reference group.

At the individual level, age, income, gender, and educational level showed statistically significant associations with self-rated health at the 99% confidence level. Odds ratios below 1 indicated a negative association with the likelihood of reporting good health, whereas values above 1 indicated a positive relationship. Both income and educational level were positively associated with the dependent variable. The effect of education is particularly pronounced: the odds ratio for the higher studies category is 2.66, indicating that individuals with higher education are more than twice as likely to report good health compared with those with only primary education. This represents a substantial association within the model, highlighting the relevance of educational inequalities in self-rated health.

In line with the previous literature, age is negatively associated with self-rated health (Verropoulou, 2009; Aguilar-Palacio et al., 2018), while women have lower odds of reporting good health compared with men (Boerma et al., 2016; Roxo et al., 2021). Regarding the residential context, no statistically significant differences were observed across categories.

Among the environmental contextual variables, six showed statistically significant associations with self-rated health, four of them with a confidence level of 99%. GDP also showed a strong and statistically significant association, with higher national income levels linked to higher odds of reporting good health at the individual level.

The Thermal Context factor showed a positive relationship with self-rated health (OR = 1.73), indicating that more favourable thermal conditions are associated with a better perception of health status. In substantive terms, a one-unit increase in this factor is associated with approximately a 70% increase in the odds of reporting good health, indicating a comparatively strong association among environmental variables included in the model.

In contrast, Water Pollution and Pesticide Use exhibited negative and statistically significant associations with the dependent variable (Table 3), suggesting that increased exposure to these environmental hazards is linked with a lower probability of reporting good health. More specifically, higher levels of Water Pollution correspond to a reduction of approximately 25% in the odds of reporting good health (OR = 0.72), whereas the association observed for Pesticide Use is smaller in magnitude (OR = 0.91).

At the territorial level, the availability of Blue Spaces was positively associated with self-rated health (OR = 1.54), suggesting a potentially beneficial relationship between access to aquatic environments and perceived health. Conversely, the extent of Burned Areas was negatively associated with the dependent variable (OR = 0.85), indicating a more moderate reduction in the odds of reporting good health. Finally, higher values of the Water Exploitation Index Plus were associated with lower odds of reporting “very good” or “good” health (OR = 0.80), pointing to a comparatively smaller but statistically significant association.

In contrast, no statistically significant associations were observed between self-rated health and several contextual indicators included in the model. Specifically, Atmospheric Emissions, Artificial Surfaces, Forest Cover, Flood Risk, Hazardous Waste, and Noise Pollution did not reach conventional levels of statistical significance (p > 0.05). These findings suggest that, after accounting for the full set of individual- and contextual-level variables in the multilevel model, the independent association of these environmental dimensions with the likelihood of reporting good health is not clearly detectable within the present model specification. In addition, the estimated coefficients are generally small in magnitude and accompanied by relatively wide confidence intervals, indicating limited precision. This pattern may reflect territorial heterogeneity, potential nonlinear relationships, or underlying mechanisms not captured within the cross-sectional framework.

4 Discussion

Multilevel analysis indicated that certain environmental elements or processes were significantly associated with self-rated health, even after controlling for individual and macroeconomic factors, consistent with the view of the environment as a structural determinant of health. These results are consistent with Krieger’s (2011), Krieger’s (2024) Eco-social theory, which states that environmental exposures are embodied across the life course and interact with pre-existing social inequalities. In this sense, self-rated health captures not only current health states but also the cumulative effects of environmental exposures. These results are consistent with estimates from the World Health Organization, which suggest that a substantial proportion of the global burden of disease is attributable to modifiable environmental risk factors (Prüss-Ustün et al., 2016; Landrigan et al., 2018). In this sense, the environment can be understood as a cross-cutting determinant that interacts with pre-existing conditions and may exacerbate health inequalities (Watts et al., 2015).

A particularly salient finding is that the Thermal Context emerged as the environmental factor with the largest observed association with self-rated health in the model (Table 3). This pattern suggests that more favourable thermal conditions tend to correspond to better self-rated health. It should be noted that the use of average indicators primarily captures the influence of overall thermal comfort on health rather than exposure to extreme temperature events.

In Europe, a substantial proportion of climate-related morbidity stems from prolonged exposure to moderately suboptimal thermal conditions rather than exclusively from extreme events (Gasparrini et al., 2015; Alahmad et al., 2025). In particular, chronic exposure to cold has been linked to physiological responses such as changes in plasma viscosity, blood pressure, and immune function (Crawford et al., 2003; Liddell and Morris, 2010). These processes may also involve hemoconcentration and increased blood viscosity, which can promote thrombogenesis and contribute to a higher risk of acute cerebrovascular and cardiovascular events, especially among vulnerable populations (Keatinge et al., 1984; Analitis et al., 2008). Furthermore, such thermal conditions may influence the seasonality of respiratory infections and other inflammatory processes that shape the overall health status (Walkowiak et al., 2024).

Simultaneously, daily thermal experience, captured in the model through the Thermal Context factor, operates not only through physiological responses but also through indirect and socially mediated pathways. In particular, institutional and built-environment characteristics, including housing energy efficiency, the capacity to maintain adequate indoor temperatures, the availability of green spaces, and the intensity of the urban heat island effect, may influence how thermal conditions are experienced (Wilkinson et al., 2007; Liddell and Morris, 2010; Gasparrini et al., 2015; Nazish et al., 2024). These factors may contribute to the thermal habitability of everyday spaces and help explain why exposure to thermal stress is unevenly distributed, tending to be concentrated in more socially disadvantaged contexts.

In this regard, epidemiological research has consistently shown that the health impacts of thermal stress are socially patterned, with socioeconomic conditions, particularly income, playing a central role in shaping vulnerability to temperature-related risks (O’Neill et al., 2003; Hajat and Kosatky, 2010; Benmarhnia et al., 2015). In contexts of energy poverty, limited capacity to adapt housing conditions or access cooling and heating resources may constrain the ability of vulnerable populations to cope with thermal stress, thereby reinforcing existing health inequalities.

Beyond thermal conditions, the model highlights the relevance of environmental pressures linked to the degradation of aquatic ecosystems. Specifically, Water Pollution, Pesticide Use, and the Water Exploitation Index Plus showed consistent negative associations with self-rated health. These findings are consistent with previous research suggesting that persistent exposure to chemical pollutants and sustained pressure on water resources have been associated with risks to physical health and with broader impacts on wellbeing, particularly through reduced access to safe drinking water and the degradation of environmental quality (de Vries, 2021; Salvador et al., 2023; Rodrigues et al., 2025). In addition, these relationships may be mediated by institutional and infrastructural factors, such as water governance, public investment in sanitation systems, and the capacity to ensure equitable access to clean water. From this perspective, water degradation may contribute to a sense of environmental insecurity, understood as a condition of physical and psychological vulnerability arising from constrained access to essential resources and perceived risks associated with living in contaminated environments (Yenew et al., 2025).

The negative association observed for Burned Areas is consistent with research suggesting that wildfires have been linked not only to direct impacts on respiratory health but also to indirect and persistent effects on mental health and subjective wellbeing (Grant and Runkle, 2022). These include a higher prevalence of post-traumatic stress disorder, depression, and anxiety, as documented in previous research, which may persist for years after the event (Grant and Runkle, 2022; Yenew et al., 2025). According to these authors, such patterns are related to prolonged processes of environmental degradation, including the loss of ecosystem services, landscape alteration, and disruption of links with the environment. These processes may contribute to sustained feelings of insecurity and uncertainty, as well as forms of environmental grief associated with the loss of biodiversity and ecosystems (Yenew et al., 2025), which can in turn influence self-rated health at the population level.

It is equally important to recognise that the environment is not only a source of risk but may also play a protective role in health (Cao et al., 2025). In this regard, the model results indicate that the presence of Blue Spaces is positively associated with self-rated health, suggesting that aquatic environments may be understood as an environmental resource associated with physical and emotional wellbeing. Previous research has shown that proximity to bodies of water may contribute to stress reduction, improved mental health, and the promotion of active lifestyles, dimensions closely linked to overall health perception (Völker and Kistemann, 2011; Wang et al., 2024; Cao et al., 2025). From an eco-social perspective, water-based environments can be interpreted as elements of the environment with predominantly beneficial or buffering roles, whose influence on health may derive from sustained contact with these environments and their capacity to mitigate certain adverse environmental exposures (Georgiou et al., 2021).

In contrast, other environmental indicators included in the analysis, such as Atmospheric Emissions, Noise Pollution, Artificial Surfaces, and Forest Cover, do not exhibit statistically significant associations with self-rated health in the present model specification. This pattern does not necessarily imply that these factors are irrelevant for health, but may instead reflect the fact that self-rated health captures broad and subjective dimensions of wellbeing, which do not fully encompass all the effects of environmental conditions on health. Some environmental exposures are more closely associated with acute morbidity or mortality outcomes rather than with subjective health assessments (Yan et al., 2025), while others may operate through indirect or mediated pathways shaped by policies, infrastructure, and adaptive capacities (Liddell and Morris, 2010; Krefis et al., 2018; Zhong et al., 2018; Ma et al., 2023; Walkowiak et al., 2024).

Furthermore, the nature of the dependent variable should be taken into account. Self-rated health is a subjective and multidimensional indicator that reflects not only physical health status but also broader dimensions such as quality of life, expectations, and individual perceptions. As such, certain environmental exposures, even when they have well-documented effects on objective health outcomes, may not be fully reflected in individuals’ overall health assessments. In this sense, respondents may assign greater weight to other factors, such as socioeconomic conditions or access to resources, when evaluating their health.

Taken together, these findings highlight the importance of considering the specificity of the health indicator employed and are consistent with the idea that self-rated health tends to capture chronic and everyday exposures that extend beyond conventional clinical diagnoses, including the influence of social and institutional mediating factors (Fayers and Sprangers, 2002; Cislaghi and Cislaghi, 2019).

Health disparities are also structured along sociodemographic lines, as consistently documented in previous studies (Wu et al., 2013; Schnittker and Bacak, 2014). Age exhibits a negative association with self-rated health, which may reflect cumulative processes of biological aging and the progressive deterioration of perceived health over the life course. In contrast, income and educational attainment display a strong positive relationship, underscoring the role of economic and cultural capital in shaping unequal health trajectories. These resources are typically linked to improved material living conditions and tend to enhance individuals’ capacity to interpret, anticipate, and manage health-related risks, thereby contributing to cumulative advantages over time.

Similarly, women are less likely to report good health than men, a recurring pattern in European studies (Biedma-Velázquez et al., 2019; Padrón-Armas et al., 2025). This result has been attributed to a combination of factors, including a greater burden of social roles (García-Rodríguez et al., 2022), higher exposure to psychosocial stress (Biedma-Velázquez et al., 2024), and gender differences in the legitimacy of pain (García-Rodríguez et al., 2023).

Finally, residential context exhibits relatively small differences, with statistically significant differences observed solely among individuals living in farms or homes in the countryside. This finding indicates that, once its independent contribution is taken into account, place of residence plays a relatively minor role in shaping self-rated health.

In line with this integrated approach, the construction of an environmental pressure index enables the results of the multilevel model to be interpreted within a comparative national framework, capturing the contextual dimension of environmental exposure that is significantly associated with health. Figure 2 presents this information through a two-panel design: a choropleth map (Figure 2A) displays the spatial distribution of environmental pressure across countries, while a scatterplot (Figure 2B) illustrates its relationship with average self-rated health, both expressed as deviations from the European mean.

As shown in the scatterplot (Figure 2B), a general tendency towards a negative gradient can be observed, whereby higher levels of environmental pressure tend to correspond to poorer self-rated health outcomes, consistent with the associations identified in the multilevel model. However, the dispersion observed around this gradient indicates that the relationship is neither strictly linear nor deterministic, as countries with similar levels of environmental pressure may display different health outcomes.

The distribution of countries observed in the scatterplot (Figure 2B) suggests broader North–South and Centre–Periphery gradients across Europe. While these patterns are consistent with the spatial distribution shown in the choropleth map (Figure 2A), they cannot be explained solely by climatic factors but also reflect wider social and institutional differences. Northern and central countries, such as Sweden and the Netherlands, appear towards the upper-left quadrant of the scatterplot, reflecting contexts characterised by lower environmental pressure and better perceived health outcomes. In contrast, countries such as Portugal, as well as several peripheral countries in Eastern Europe, including Lithuania and Latvia, are positioned towards areas of higher environmental pressure and comparatively poorer health outcomes.

At the same time, countries such as Austria and Italy display above-average health outcomes despite moderate levels of environmental pressure. This pattern highlights the existence of heterogeneous national configurations and points to the complex interplay between environmental exposure and the social and institutional capacity to buffer environmental pressures.

This apparent mismatch highlights the multidimensional nature of health as conceptualised within the biopsychosocial framework. As illustrated by the dispersion of countries in the scatterplot (Figure 2B), similar levels of environmental pressure do not necessarily translate into comparable health outcomes. From this perspective, social, institutional, and cultural factors, alongside differences in welfare regimes, environmental policies, and the capacity and effectiveness of health systems, may shape the extent to which environmental pressures are reflected in health outcomes.

According to Health Geography, the environment should be understood as a component of the structural framework that shapes opportunities for health in everyday life (Macintyre et al., 2002). These structures encompass the conditions that facilitate or constrain the maintenance of good health, depending on the capacity of territories to buffer or amplify environmental pressures through public policies, welfare systems, the built environment, and material living conditions. In this sense, the patterns observed in Figure 2 suggest that environmental pressures operate within broader territorial contexts that condition their impact on population health.

Overall, the environmental pressure index illustrates territorial gradients across Europe and provides an integrated framework for examining how the accumulation of environmental pressures is unevenly related to differences in self-rated health. The findings are consistent with viewing the environment as a structural component that shapes opportunities for health, whose influence is mediated by the social and institutional contexts in which environmental exposures occur. Taken together, these results point to the importance of approaches that integrate multiple environmental exposures and explicitly consider their interaction with social context in order to better understand health inequalities at the European scale.

5 Conclusions

The evidence presented suggests that the environment constitutes a relevant structural determinant of health in Europe, whose influence persists after accounting for individual sociodemographic characteristics and national macroeconomic conditions. The analysis indicates that different forms of environmental pressure, alongside the availability of protective environmental resources, are associated with health outcomes. These findings highlight the importance of explicitly incorporating environment into the study of health inequalities, considering both harmful exposures and protective environmental assets that shape population wellbeing.

Through a multilevel approach and an integrated perspective on environmental exposures, the analysis shows that self-rated health captures cumulative and everyday interactions between individuals and their environments. Within this framework, the environmental pressure index provides a synthetic measure of these exposures and facilitates a comparative assessment of territorial gradients in health across Europe.

This study therefore reinforces the need to understand health as a multidimensional phenomenon shaped by the interaction between environmental conditions and the social and institutional frameworks within which they occur. From this perspective, the environment can be regarded as a key structural component shaping health opportunities across Europe, whose influence is mediated by the social and institutional characteristics of territories.

6 Limitations and policy implications

This section outlines the main limitations of the study, together with their policy implications and directions for future research.

The cross-sectional nature of the study implies that environmental exposures and self-rated health are analysed within a specific temporal context. This design prevents the examination of processes of accumulation, adaptation, or change over time, as well as the assessment of potential lagged effects of environmental exposures. Consequently, the findings should be interpreted as population-level associations rather than as evidence of causal relationships.

The national level of aggregation constitutes another relevant limitation. It is recognized that subnational environmental heterogeneity can generate measurement error when country-level indicators are used to approximate individual exposure. This approach reflects the lack of comparable, high-resolution geocoded data available in a homogeneous and harmonized manner across all 21 countries and environmental dimensions considered. Accordingly, environmental indicators are interpreted as contextual characteristics that capture structural conditions at the country level, rather than precise measures of individual exposure. In this sense, the results reflect country-level associations and should not be interpreted as evidence of individual-level relationships.

While the use of aggregated indicators may obscure regional variation, the primary objective of this analysis is to identify macro-contextual patterns and cross-national differences rather than to estimate individual-level exposure. Therefore, the results reported in this study likely represent conservative estimates of contextual effects, which reinforces the robustness of the statistically significant associations identified.

In addition, the possibility of omitted variable bias cannot be fully ruled out. Although the models include key sociodemographic and macroeconomic controls, other factors such as institutional quality, public policies, or lifestyle-related variables may also influence both environmental conditions and health outcomes. As a result, the associations observed should be interpreted with caution, acknowledging the potential influence of unobserved confounders.

Finally, an additional limitation relates to the availability and temporal consistency of contextual data. At the European level, agencies provide a consolidated set of indicators, but their updating is not uniform. Although data from 2023 were prioritised to align with the European Social Survey, certain variables required drawing on the most recent data available within a 2018–2024 window. While this temporal heterogeneity may introduce a degree of imprecision, it is unlikely to substantially affect the identification of broader population-level patterns. Moreover, the analytical sample was restricted to countries with complete information for all contextual indicators, which may limit the generalisability of the findings.

Despite these limitations, the findings provide relevant insights into the relationship between environmental pressures and health. From a policy perspective, the results underscore the need to systematically integrate the environmental dimension into public health strategies, moving beyond sectoral approaches that address risks in isolation. The comparative evidence suggests that environmental pressure does not automatically translate into poorer health outcomes, highlighting the role of social and institutional factors in mediating this relationship.

In this regard, public policies emerge as a key mechanism capable of mitigating the negative effects of environmental pressures on population health. Differences in the design and integration of environmental policies, in territorial and urban planning, as well as in the capacity and effectiveness of health systems, help explain why countries with similar levels of environmental degradation have different health outcomes. This evidence reinforces the understanding of health as a multidimensional phenomenon closely linked to the social conditions within which it is produced.

From this perspective, institutional interventions should prioritise improvements in structural environmental conditions, including thermal habitability, sustainable water resource management, and the reduction of pressures associated with ecosystem degradation. Furthermore, the results highlight the potential of protective environmental resources as key elements in health promotion, reinforcing the importance of incorporating environmental equity criteria into territorial and urban planning.

Building on these findings, future research should further examine the relationship between environment and health using longitudinal designs that allow for the analysis of cumulative exposure processes and potential lagged effects across the life course. In addition, incorporating more detailed indicators of environmental policies, built-environment characteristics, and institutional frameworks would enable a more explicit examination of the mechanisms through which social context moderates the relationship.

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