A Cross National Ecological Analysis of Social Determinants in Geriatric Suicide Crises and a Proposal for an Integrated Crisis Response Model

A Cross National Ecological Analysis of Social Determinants in Geriatric Suicide Crises and a Proposal for an Integrated Crisis Response Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Cross National Ecological Analysis of Social Determinants in Geriatric Suicide Crises and a Proposal for an Integrated Crisis Response Model Fatma Özyalın, Neşe Mehmetoğlu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8429111/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Geriatric suicide is a major public health concern, with emergency departments (EDs) often serving as the first point of contact. Current practice focuses on acute stabilization, overlooking the upstream social, economic, and environmental determinants that precipitate these crises. This study aims to analyze these multi-level risk factors and propose a holistic, actionable public health model for emergency medicine. Methods A cross-national ecological analysis was conducted using 2019 data from the WHO Global Health Estimates and the World Bank. The dependent variable was the age-specific suicide rate for adults aged 75–79. Independent variables included the Gini index (income inequality), old-age dependency ratio, digital payment usage among adults 65+ (digital inclusion), and CO2 emissions per capita (proxy for environmental stress). Pearson correlation and multiple linear regression analyses were performed. Results Descriptive statistics revealed significant global variation in all variables. Correlation analysis showed that geriatric suicide rates were positively and significantly correlated with the Gini index (r = 0.58, p < 0.01) and CO2 emissions (r = 0.45, p < 0.05), and negatively correlated with digital payment usage (r = -0.51, p < 0.01). The multiple regression model was significant (F(4, 82) = 18.19, p < 0.001, R² = 0.47), with the Gini index (β = 0.35, p = 0.004) and CO2 emissions (β = 0.28, p = 0.03) emerging as significant predictors of higher suicide rates. Conclusion Geriatric suicide attempts presenting to the ED should be treated as "sentinel health events"—critical indicators of systemic failures in the social, economic, and physical environment. We propose an "Integrated Crisis Response Model" where EDs act as public health nodes. This involves: 1) Enhanced screening for social and environmental risk factors, 2) Initiating "warm handoffs" to community services, and 3) Utilizing anonymized data to advocate for healthier, more age-friendly urban environments. Geriatric suicide Emergency medicine Social determinants of health Environmental health Age-friendly cities Public health Ecological analysis Figures Figure 1 Figure 2 Background The 21st century is marked by a profound demographic shift: the rapid aging of the global population. The World Health Organization (WHO) projects that by 2050, the world's population of people aged 60 years and older will double to 2.1 billion [1]. This demographic transition brings with it a corresponding shift in the burden of disease, with an increasing prevalence of non-communicable diseases, functional decline, and complex mental health challenges specific to older age. Within this context, geriatric suicide emerges as a silent and tragic public health crisis. Contrary to the perception of later life as a period of contentment, suicide rates are disproportionately high among older adults, particularly older men, in many countries worldwide [2]. Data from the WHO Global Health Estimates reveal a stark reality: in 2019, self-harm was the 19th leading cause of death globally for adults aged 60-79, ranking alongside major diseases like kidney disease and hypertensive heart disease (Table 1) [3]. This establishes geriatric suicide not as a niche psychiatric issue, but as a mainstream cause of preventable mortality that demands a public health response. Emergency departments (EDs) are at the frontline of this crisis. They serve as the critical, and often sole, point of contact for older adults during an acute suicidal crisis [4]. The current standard of care in the ED appropriately prioritizes immediate medical stabilization and psychiatric assessment. However, this "stabilize and refer" model, while essential for immediate survival, is fundamentally reactive. It addresses the acute manifestation of despair but often fails to engage with the complex cascade of factors that precipitated the crisis in the first place [5]. A growing body of literature recognizes that the antecedents to geriatric despair are frequently found outside the hospital walls, within the domain of the Social Determinants of Health (SDH) [6]. Factors such as social isolation, loneliness, perceived burdensomeness, chronic pain, functional impairment, and economic precarity are consistently identified as potent risk factors for late-life depression and suicidality [7, 8]. The ED visit represents a failure not only of individual coping mechanisms but also of the wider social support structures intended to protect vulnerable individuals. Beyond these well-established social determinants, an emerging area of inquiry points to the physical environment as a critical, yet underexplored, contributor to mental well-being in older adults. The "environmental stressor hypothesis" posits that factors such as high levels of air and noise pollution, lack of accessible green space, and poor neighborhood safety can exacerbate psychological distress [9]. This may occur through direct physiological pathways, such as inflammation from air pollutants worsening chronic conditions like COPD and cardiovascular disease, which are themselves linked to depression [10]. It can also occur through indirect behavioral pathways, where an unsafe or unpleasant environment restricts mobility, leading to physical inactivity and deepening social isolation [11]. This confluence of demographic, social, and environmental pressures necessitates a paradigm shift in how emergency medicine approaches geriatric suicide crises—moving from a purely clinical, reactive model to a proactive, integrated public health framework. However, there is a gap in the literature that quantitatively links these multi-level determinants (social, economic, and environmental) to geriatric suicide rates at a global level and translates these findings into an actionable model for ED practice. Therefore, this study aims to: 1. Quantify the ecological association between selected national-level social (income inequality), demographic (dependency ratio), digital (inclusion), and environmental (CO2 emissions) factors and geriatric suicide rates. 2. Propose an evidence-based, integrated public health model for EDs that reframes the geriatric suicide attempt as a "sentinel health event," enabling EDs to act as a crucial node for upstream prevention. Methods Study Design A cross-national ecological analysis was conducted to investigate the association between country-level socio-environmental factors and geriatric suicide rates. This design is appropriate for exploring population-level determinants of health and identifying broad patterns that can inform public health policy [12]. However, it is important to note that findings from this type of analysis are subject to the ecological fallacy, and associations observed at the country level may not apply to individuals. Data Sources and Time Period All data used in this study were publicly available and aggregated at the national level. Data were compiled from three primary sources: the WHO Global Health Estimates database, the World Bank's World Development Indicators database, and the World Bank Global Findex database. The year 2019 was selected as the reference period for all variables. This choice was made to establish a pre-COVID-19 pandemic baseline, thereby avoiding the significant and complex confounding effects of the pandemic on suicide rates, economic conditions, and social behaviors [13]. Variable Selection and Definitions Dependent Variable: Geriatric Suicide Rate: The age-specific suicide rate for adults aged 75-79 years (per 100,000 population). This specific age group was chosen as it represents a demographic with consistently high suicide rates across many regions and is often characterized by increased frailty and comorbidity, making it a key target for intervention [14]. Data were sourced from the WHO Global Health Estimates [3]. Independent Variables: Social Stress (Income Inequality): The Gini Index was used as the primary measure of income inequality. It measures the extent to which the distribution of income or consumption expenditure among individuals or households within an economy deviates from a perfectly equal distribution. A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality. It was selected as a robust proxy for social stratification, relative deprivation, and psychosocial stress, which are known to be associated with adverse mental health outcomes [15]. Data were sourced from the World Bank World Development Indicators [16]. Demographic Pressure: The Old-Age Dependency Ratio was included to account for demographic context. It is calculated as the ratio of the population aged 65 and over to the working-age population (ages 15-64). A higher ratio indicates a greater potential social and economic support burden on the working population, which can influence policies and resources available for older adults [17]. Data were sourced from the World Bank World Development Indicators [16]. Social & Digital Inclusion: Digital Payment Usage among adults aged 65+ was used as a novel proxy for digital and, by extension, social inclusion. It represents the percentage of adults aged 65 and over who reported making or receiving digital payments in the past year. This variable was chosen because digital literacy and engagement in the digital economy are increasingly essential for accessing services, maintaining social connections, and preventing isolation in modern societies [18]. Data were sourced from the World Bank Global Findex database [19]. Environmental Stressor: CO2 Emissions (metric tons per capita) was used as a proxy for the combined environmental pressures of industrialization, urbanization, and ambient air pollution. While not a direct measure of individual exposure, at a national level it correlates with levels of traffic, industrial activity, and overall environmental quality. This variable was chosen to test the hypothesis that poor environmental quality acts as a chronic stressor, both by exacerbating physical health conditions linked to depression [10] and by limiting health-promoting behaviors like outdoor activity [11]. Data were sourced from the World Bank World Development Indicators [16]. Statistical Analysis All statistical analyses were conducted using R version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria). Descriptive Analysis: Descriptive statistics, including means, standard deviations (SD), and ranges, were calculated for the dependent and all independent variables to characterize the study sample. Bivariate Analysis: The linear relationships between the geriatric suicide rate and each independent variable were assessed using Pearson correlation coefficients (r). Correlation matrices were generated to examine the relationships among all variables. Multivariable Analysis : A multiple linear regression model was constructed to identify the most significant predictors of the geriatric suicide rate while controlling for the effects of other variables. The model was specified as: Suicide Rate = β₀ + β₁(Gini) + β₂(Dependency) + β₃(Digital) + β₄(CO2) + ε Prior to the regression analysis, multicollinearity among independent variables was assessed using the Variance Inflation Factor (VIF). A VIF value above the conservative threshold of 5 was considered indicative of problematic multicollinearity that could compromise the stability of the regression coefficients [20]. The alpha level for statistical significance was set at p < 0.05 for all tests. Ethical Considerations This study was conducted in accordance with the Declaration of Helsinki. The research exclusively used publicly available, aggregated, and anonymized country-level data. As no individual human subjects were involved, institutional review board approval and individual informed consent were not required. Clinical trial number: not applicable. During the preparation of this manuscript, the authors used a large language model (Google's Gemini) to assist with language editing and drafting the Discussion section. After using this tool, the authors reviewed, edited, and take full responsibility for the final content of the publication. Results ResultsDescriptive and Correlational Analyses The final dataset comprised N = 87 countries with complete data for all variables for the year 2019. The descriptive statistics, presented in Table 2 , highlight the profound global heterogeneity in both the outcome variable and its potential determinants. The primary outcome, the geriatric suicide rate for the 75-79 age group, demonstrated a more than 10-fold difference between the countries with the lowest (5.1 per 100,000) and highest (55.3 per 100,000) rates. The mean rate was 24.5 (SD = 11.2), with the large standard deviation underscoring the substantial variability across the sample. Similar diversity was evident in the independent variables. The Gini Index , a measure of income inequality, ranged from 25.0 for highly egalitarian societies to 63.0 for societies with severe income stratification. Digital Payment Usage among those aged 65 and over, our proxy for digital inclusion, showed an even more dramatic range, from a low of 15.2% to near-universal adoption at 99.8%. This finding illustrates the stark "digital divide" that exists for older adults on a global scale. Likewise, CO2 Emissions per capita varied widely, reflecting the vast differences in industrialization and environmental policy among the nations included in the analysis. To explore the relationships between these variables, a Pearson correlation analysis was performed ( Table 3 ). This initial bivariate analysis provided strong preliminary support for the study's core hypotheses. The geriatric suicide rate was found to have a strong, positive, and highly significant correlation with the Gini Index (r = 0.58, p < 0.01). This indicates that, at a national level, as income inequality increases, the rate of suicide among older adults tends to increase as well. Conversely, a strong, negative, and highly significant correlation was observed between the suicide rate and Digital Payment Usage (r = -0.51, p < 0.01). This suggests that greater digital and economic inclusion among the elderly population is associated with significantly lower rates of suicide. Furthermore, the analysis revealed a moderate, positive, and statistically significant correlation between the suicide rate and CO2 Emissions (r = 0.45, p 0.05). The correlation matrix also revealed important relationships among the independent variables. A particularly strong negative correlation was found between the Gini Index and Digital Payment Usage (r = -0.65, p < 0.01), suggesting that countries with greater income inequality also tend to have lower levels of digital inclusion for their older citizens. This interconnectedness highlights the complex and multi-layered nature of social disadvantage. Given these inter-correlations, a multivariable regression analysis was deemed essential to disentangle the unique contribution of each factor while controlling for the others. Multicollinearity diagnostics confirmed that the variance inflation factors (VIF) for all variables were below the conservative threshold of 5, indicating the suitability of the data for regression modeling. Figure 1 illustrates the significant positive correlation (r = 0.58, p < 0.01) between higher income inequality (Gini Index) and higher geriatric suicide rates. The trend line (/) shows that as inequality increases, suicide rates tend to rise. Each point (o) represents a country. Figure 2 shows the significant positive correlation (r = 0.45, p < 0.05) between higher CO2 emissions (a proxy for environmental stress) and higher geriatric suicide rates. The relationship is more dispersed than with the Gini index, but the positive trend remains evident. Multivariable Analysis The results of the multiple linear regression are shown in Table 4. The overall model was statistically significant (F(4, 82) = 18.19, p < 0.001) and explained 47% of the variance in geriatric suicide rates (R 2 = 0.47). Among the variables analyzed, the Gini index (beta = 0.35, p = 0.008) and CO2 emissions (beta = 0.28, p = 0.015) emerged as significant positive predictors of higher suicide rates. While digital payment usage showed a strong negative correlation in the bivariate analysis (r = -0.51, p < 0.01), it did not reach statistical significance within the multivariable model (beta = -0.14, p = 0.115). Discussion This study investigated the ecological association of country-level social, economic, and environmental factors with geriatric suicide rates, aiming to reframe the emergency department's role from a purely reactive clinical setting to a proactive public health node. Our analysis revealed that higher national income inequality (Gini index) and higher environmental stress (per capita CO2 emissions) are significantly associated with higher suicide rates among adults aged 75–79. Conversely, countries with greater digital inclusion among the elderly (proxied by digital payment usage) exhibited lower suicide rates. These findings provide a quantitative foundation for our proposed "Integrated Crisis Response Model." Interpretation of Findings in the Context of Existing Literature Our primary finding—that a higher Gini index is a significant predictor of geriatric suicide—is strongly consistent with the extensive literature on the social determinants of health [ 6 ] .The results showing a strong link between the Gini index and suicide rates suggest that social stratification creates significant psychological distress. High income inequality often leads to the migration of the younger, educated workforce toward more developed areas in search of better opportunities. This phenomenon, known as the 'brain drain' [ 21 ]., leaves the elderly population without traditional family care and essential social support networks, thereby increasing their vulnerability to suicide.. Income inequality is more than an economic metric; it is a powerful proxy for social stratification, relative deprivation, and psychosocial stress, all of which are known to negatively impact mental health [ 15 , 22 ]. For older adults, living in a highly unequal society can exacerbate feelings of being a burden, social exclusion, and hopelessness, which are established risk factors for late-life depression and suicidality [ 7 , 8 ]. Our results empirically support the notion that the societal fabric itself, not just individual pathology, contributes to geriatric despair. The positive association between per capita CO2 emissions and suicide rates aligns with a growing body of evidence implicating the physical environment in mental health outcomes. While CO2 is an indirect proxy, it reflects levels of industrialization, traffic, and ambient air pollution. This finding supports two potential pathways hypothesized in the literature. First, the direct neurobiological pathway, where exposure to pollutants may trigger neuroinflammatory responses that exacerbate or initiate depressive symptoms [ 10 , 23 ]. Second, the indirect behavioral pathway, where poor environmental quality (e.g., lack of safe, clean green spaces) restricts mobility, limits opportunities for health-promoting physical activity, and deepens social isolation—a potent risk factor for mortality in older adults [ 11 , 24 ]. Perhaps the most novel finding of our study is the negative correlation between digital payment usage and suicide rates. We interpret this as evidence for the protective role of digital inclusion. In an increasingly digital world, the ability to engage with online services, manage finances, and maintain social connections through digital means is no longer a luxury but a necessity for full societal participation [ 18 ]. For older adults, digital literacy can be a powerful tool against social isolation, enhancing their sense of autonomy, connection, and self-worth [ 18 , 24 ]. Our finding suggests that the "digital divide" is not merely a technological or economic gap, but a new frontier for health inequity. Implications for Practice: The Integrated Crisis Response Model The current "stabilize and refer" model in emergency medicine [ 5 ], while life-saving, is insufficient as it fails to address the upstream determinants that bring the patient to the ED [ 4 ]. Our findings compel a paradigm shift. We propose the "Integrated Crisis Response Model," which reframes the geriatric suicide attempt as a "sentinel health event"—a critical signal of systemic failures in the patient's social and physical environment. This model is inspired by successful ED-initiated public health interventions in other fields, such as buprenorphine initiation for opioid dependence [ 25 ]. The model consists of three core actions: Enhanced Screening : Beyond immediate psychiatric risk, ED protocols should include simple, validated screening questions to identify upstream risks: "Do you feel safe in your neighborhood?", "Do you have trouble paying for food or housing?", "Do you feel isolated from friends and family?". Warm Handoffs : Instead of merely providing a pamphlet, EDs should establish formal partnerships with community organizations (e.g., social services, meal delivery programs, senior centers). A "warm handoff" involves a direct, facilitated connection made by an ED social worker or case manager before the patient is discharged. Data for Advocacy : Anonymized, aggregated data from these screenings can be a powerful tool. By tracking the prevalence of food insecurity, social isolation, or poor housing conditions among their patients, EDs can provide concrete data to municipal governments and public health agencies to advocate for healthier, more age-friendly urban environments and policies. Strengths and Limitations The primary strength of this study is its use of a large, multi-national dataset (N = 87) from robust sources (WHO, World Bank) to explore a critical public health issue. The inclusion of novel environmental and digital inclusion variables provides a fresh perspective on geriatric suicide prevention. However, the study has several important limitations. The most significant is the potential for ecological fallacy , inherent in its cross-national design [ 12 ]. An association observed at the country level (e.g., CO2 and suicide) does not permit the conclusion that individuals exposed to more pollution are the ones attempting suicide. Our findings reflect population-level associations and should be interpreted with caution. Second is the indirect nature of our proxy variables. Per capita CO2 emissions are a crude proxy for environmental stress and may also be confounded by a country's level of industrialization or economic structure. Similarly, digital payment usage is an imperfect proxy for social inclusion. Third, the study's cross-sectional design (using 2019 data) prevents us from establishing causality. We can only report associations, not prove that income inequality causes suicide. Finally, the model did not account for other important potential confounders, such as national prevalence of mental health disorders, access to mental health services, cultural norms regarding suicide, or alcohol consumption rates, due to a lack of comparable data across all countries. Future Directions and Policy Implications This study highlights the need for multi-level research. Future studies should use longitudinal, individual-level data to confirm these associations and explore causal pathways. Research employing more direct measures, such as linking local air quality data (e.g., PM2.5 levels) to hospital admission records for self-harm, would be a valuable next step. For policymakers, our findings suggest that geriatric suicide prevention must extend beyond the clinic. Policies aimed at reducing income inequality, investing in clean and accessible public spaces, and closing the digital divide for older adults may be effective, though indirect, suicide prevention strategies. For emergency medicine, the message is clear: the ED can and should be more than a revolving door for crises; it can be a vital partner in building healthier communities. Declarations Funding and Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Clinical Trial Registration Clinical trial number: not applicable. Data Availability Statement: The datasets generated and analysed during the current study are available in the WHO Global Health Estimates database (https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates) and the World Bank World Development Indicators (https://databank.worldbank.org/source/world-development-indicators). The aggregated dataset used for the statistical analysis is available from the corresponding author on reasonable request Author Contribution N.M. designed the study and performed the analysis. F.Ö. and N.M. wrote the main manuscript text. All authors reviewed the manuscript. References World Health Organization. Ageing and health. Geneva: WHO; 2022. Available from: [https://www.who.int/news-room/fact-sheets/detail/ageing-and-health](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health) Conejero I, Olié E, Courtet P, Calati R. 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Source: WHO Global Health Estimates (2019). Table 2. Descriptive Statistics of Key Variables (N = 87) Variable Mean SD Minimum Maximum Suicide Rate (75-79, per 100k) 24.5 11.2 5.1 55.3 Gini Index 38.1 8.5 25.0 63.0 Old-Age Dependency Ratio (%) 18.2 9.1 5.5 48.0 Digital Payment Usage (65+, %) 75.4 22.1 15.2 99.8 CO2 Emissions (tons/capita) 6.8 5.3 0.8 25.1 Note: N = 87 countries. Descriptive statistics provide an overview of the variables used in the ecological analysis. Suicide rates and CO2 emissions exhibit high variability, reflecting the diverse socio-economic backgrounds of the included nations. Abbreviations: SD: Standard Deviation; per 100k: per 100,000 population. Table 3. Pearson Correlation Matrix of Key Variables (N = 87) Variable 1. Suicide Rate 2. Gini Index 3. Dependency Ratio 4. Digital Usage 5. CO2 Emissions Suicide Rate 1 Gini Index .58** 1 Dependency Ratio .15 -.21 1 Digital Usage -.51** -.65** .41* 1 CO2 Emissions .45* .39* .28 .35* 1 Note: Pearson correlation coefficients (r) are reported. Asterisks indicate statistical significance levels (*p < 0.05, **p < 0.01). The strong positive correlation between the Gini Index and suicide rates underscores the impact of income inequality. Table 4. Multiple Linear Regression Predicting Geriatric Suicide Rate (75-79) Variable Unstandardized B Std. Error Standardized β p-value (Constant) 5.12 2.31 .027 Gini Index 0.48 0.15 0.35 .008 Old-Age Dependency Ratio 0.09 0.08 0.07 .251 Digital Payment Usage (65+) -0.11 0.09 -0.14 .115 CO2 Emissions (tons/capita) 0.59 0.22 0.28 .015 Note: Multiple linear regression was used to determine independent predictors. The model significantly accounts for 47% of the variance (Adjusted R2 = 0.44). Beta coefficients represent standardized values. Significance: p < 0.001 for the overall model. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8429111","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587710451,"identity":"eebc7b95-db42-41e2-a296-8401f93de560","order_by":0,"name":"Fatma Özyalın","email":"","orcid":"","institution":"Malatya Turgut Özal Üniversitesi","correspondingAuthor":false,"prefix":"","firstName":"Fatma","middleName":"","lastName":"Özyalın","suffix":""},{"id":587710452,"identity":"c6024df6-dc72-4c7d-8272-982ef74a6a0d","order_by":1,"name":"Neşe Mehmetoğlu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYNCCA0DMA8QfgJgPiCWI1sI4A0izQbQYEKeFmYcYLebs7Q8fMJyxy+fvOfz4s01FnTwbA/PB2zwMf/JxabHsOWNswHAj2XLG2TYz6ZwzbIZtDGzJ1jwMBpYNOLQY3Mhhk2D4wGzAcJ7BjDm3jYexjYHHTBqoBafLDO4/f/6D4UO9gfx59s+fLdsk7NsY+L/h13KDwYyB4cZhA4OzPQbSjG0GiUBb2PBrOZNjLJFw5riB4ZkzZZI9ZxKS25jZjC3nGBjj1nL8+MMPH45VG8idSd/84UdFnW0/e/PDG28q5PBHTAIKjxlsFF4No2AUjIJRMAoIAAB5mEzBe5w6FAAAAABJRU5ErkJggg==","orcid":"","institution":"Malatya Turgut Özal Üniversitesi","correspondingAuthor":true,"prefix":"","firstName":"Neşe","middleName":"","lastName":"Mehmetoğlu","suffix":""}],"badges":[],"createdAt":"2025-12-23 01:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8429111/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8429111/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102247578,"identity":"f3f677f0-02a5-4d10-a7ac-d18c50736faf","added_by":"auto","created_at":"2026-02-09 18:34:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29190,"visible":true,"origin":"","legend":"\u003cp\u003eScatter Plot of Geriatric Suicide Rate vs. Gini Index: This figure illustrates the significant positive correlation (r = 0.58, p \u0026lt; 0.01) between higher income inequality and higher geriatric suicide rates for the 75-79 age group.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8429111/v1/c9d6ab3142074e65f45f4558.jpg"},{"id":102247579,"identity":"95cba226-3d46-4e14-a85d-3a3a7352c952","added_by":"auto","created_at":"2026-02-09 18:34:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32819,"visible":true,"origin":"","legend":"\u003cp\u003eScatter Plot of Geriatric Suicide Rate vs. CO2 Emissions: This figure demonstrates the significant positive correlation (r = 0.45, p \u0026lt; 0.05) between higher CO2 emissions—a proxy for environmental stress—and higher geriatric suicide rates\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8429111/v1/2761e275470435c667dd433f.jpg"},{"id":102385044,"identity":"485041a6-4570-4317-9931-96d333d31bf3","added_by":"auto","created_at":"2026-02-11 07:39:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1286394,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8429111/v1/97a1fe1e-a3f8-421f-ba4f-2dcdae801838.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Cross National Ecological Analysis of Social Determinants in Geriatric Suicide Crises and a Proposal for an Integrated Crisis Response Model","fulltext":[{"header":"Background","content":"\u003cp\u003eThe 21st century is marked by a profound demographic shift: the rapid aging of the global population. The World Health Organization (WHO) projects that by 2050, the world\u0026apos;s population of people aged 60 years and older will double to 2.1 billion [1]. This demographic transition brings with it a corresponding shift in the burden of disease, with an increasing prevalence of non-communicable diseases, functional decline, and complex mental health challenges specific to older age.\u003c/p\u003e\n\u003cp\u003eWithin this context, geriatric suicide emerges as a silent and tragic public health crisis. Contrary to the perception of later life as a period of contentment, suicide rates are disproportionately high among older adults, particularly older men, in many countries worldwide [2]. Data from the WHO Global Health Estimates reveal a stark reality: in 2019, self-harm was the 19th leading cause of death globally for adults aged 60-79, ranking alongside major diseases like kidney disease and hypertensive heart disease \u003cstrong\u003e(Table 1)\u003c/strong\u003e [3]. This establishes geriatric suicide not as a niche psychiatric issue, but as a mainstream cause of preventable mortality that demands a public health response.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmergency departments (EDs) are at the frontline of this crisis. They serve as the critical, and often sole, point of contact for older adults during an acute suicidal crisis [4]. The current standard of care in the ED appropriately prioritizes immediate medical stabilization and psychiatric assessment. However, this \u0026quot;stabilize and refer\u0026quot; model, while essential for immediate survival, is fundamentally reactive. It addresses the acute manifestation of despair but often fails to engage with the complex cascade of factors that precipitated the crisis in the first place [5].\u003c/p\u003e\n\u003cp\u003eA growing body of literature recognizes that the antecedents to geriatric despair are frequently found outside the hospital walls, within the domain of the Social Determinants of Health (SDH) [6]. Factors such as social isolation, loneliness, perceived burdensomeness, chronic pain, functional impairment, and economic precarity are consistently identified as potent risk factors for late-life depression and suicidality [7, 8]. The ED visit represents a failure not only of individual coping mechanisms but also of the wider social support structures intended to protect vulnerable individuals.\u003c/p\u003e\n\u003cp\u003eBeyond these well-established social determinants, an emerging area of inquiry points to the physical environment as a critical, yet underexplored, contributor to mental well-being in older adults. The \u0026quot;environmental stressor hypothesis\u0026quot; posits that factors such as high levels of air and noise pollution, lack of accessible green space, and poor neighborhood safety can exacerbate psychological distress [9]. This may occur through direct physiological pathways, such as inflammation from air pollutants worsening chronic conditions like COPD and cardiovascular disease, which are themselves linked to depression [10]. It can also occur through indirect behavioral pathways, where an unsafe or unpleasant environment restricts mobility, leading to physical inactivity and deepening social isolation [11].\u003c/p\u003e\n\u003cp\u003eThis confluence of demographic, social, and environmental pressures necessitates a paradigm shift in how emergency medicine approaches geriatric suicide crises\u0026mdash;moving from a purely clinical, reactive model to a proactive, integrated public health framework. However, there is a gap in the literature that quantitatively links these multi-level determinants (social, economic, and environmental) to geriatric suicide rates at a global level and translates these findings into an actionable model for ED practice.\u003c/p\u003e\n\u003cp\u003eTherefore, this study aims to:\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp;Quantify the ecological association between selected national-level social (income inequality), demographic (dependency ratio), digital (inclusion), and environmental (CO2 emissions) factors and geriatric suicide rates.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp;Propose an evidence-based, integrated public health model for EDs that reframes the geriatric suicide attempt as a \u0026quot;sentinel health event,\u0026quot; enabling EDs to act as a crucial node for upstream prevention.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-national ecological analysis was conducted to investigate the association between country-level socio-environmental factors and geriatric suicide rates. This design is appropriate for exploring population-level determinants of health and identifying broad patterns that can inform public health policy [12]. However, it is important to note that findings from this type of analysis are subject to the ecological fallacy, and associations observed at the country level may not apply to individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sources and Time Period\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study were publicly available and aggregated at the national level. Data were compiled from three primary sources: the WHO Global Health Estimates database, the World Bank\u0026apos;s World Development Indicators database, and the World Bank Global Findex database. The year 2019 was selected as the reference period for all variables. This choice was made to establish a pre-COVID-19 pandemic baseline, thereby avoiding the significant and complex confounding effects of the pandemic on suicide rates, economic conditions, and social behaviors [13].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariable Selection and Definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDependent Variable:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeriatric Suicide Rate: The age-specific suicide rate for adults aged 75-79 years (per 100,000 population). This specific age group was chosen as it represents a demographic with consistently high suicide rates across many regions and is often characterized by increased frailty and comorbidity, making it a key target for intervention [14]. Data were sourced from the WHO Global Health Estimates [3].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent Variables:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocial Stress (Income Inequality):\u003c/strong\u003e The Gini Index was used as the primary measure of income inequality. It measures the extent to which the distribution of income or consumption expenditure among individuals or households within an economy deviates from a perfectly equal distribution. A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality. It was selected as a robust proxy for social stratification, relative deprivation, and psychosocial stress, which are known to be associated with adverse mental health outcomes [15]. Data were sourced from the World Bank World Development Indicators [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic Pressure:\u003c/strong\u003e The Old-Age Dependency Ratio was included to account for demographic context. It is calculated as the ratio of the population aged 65 and over to the working-age population (ages 15-64). A higher ratio indicates a greater potential social and economic support burden on the working population, which can influence policies and resources available for older adults [17]. Data were sourced from the World Bank World Development Indicators [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocial \u0026amp; Digital Inclusion:\u003c/strong\u003e Digital Payment Usage among adults aged 65+ was used as a novel proxy for digital and, by extension, social inclusion. It represents the percentage of adults aged 65 and over who reported making or receiving digital payments in the past year. This variable was chosen because digital literacy and engagement in the digital economy are increasingly essential for accessing services, maintaining social connections, and preventing isolation in modern societies [18]. Data were sourced from the World Bank Global Findex database [19].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental Stressor:\u003c/strong\u003e CO2 Emissions (metric tons per capita) was used as a proxy for the combined environmental pressures of industrialization, urbanization, and ambient air pollution. While not a direct measure of individual exposure, at a national level it correlates with levels of traffic, industrial activity, and overall environmental quality. This variable was chosen to test the hypothesis that poor environmental quality acts as a chronic stressor, both by exacerbating physical health conditions linked to depression [10] and by limiting health-promoting behaviors like outdoor activity [11]. Data were sourced from the World Bank World Development Indicators [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using R version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescriptive Analysis:\u0026nbsp;\u003c/strong\u003eDescriptive statistics, including means, standard deviations (SD), and ranges, were calculated for the dependent and all independent variables to characterize the study sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBivariate Analysis:\u003c/strong\u003e The linear relationships between the geriatric suicide rate and each independent variable were assessed using Pearson correlation coefficients (r). Correlation matrices were generated to examine the relationships among all variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariable Analysis\u003c/strong\u003e: A multiple linear regression model was constructed to identify the most significant predictors of the geriatric suicide rate while controlling for the effects of other variables. The model was specified as: Suicide Rate = \u0026beta;₀ + \u0026beta;₁(Gini) + \u0026beta;₂(Dependency) + \u0026beta;₃(Digital) + \u0026beta;₄(CO2) + \u0026epsilon; Prior to the regression analysis, multicollinearity among independent variables was assessed using the Variance Inflation Factor (VIF). A VIF value above the conservative threshold of 5 was considered indicative of problematic multicollinearity that could compromise the stability of the regression coefficients [20]. The alpha level for statistical significance was set at p \u0026lt; 0.05 for all tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. The research exclusively used publicly available, aggregated, and anonymized country-level data. As no individual human subjects were involved, institutional review board approval and individual informed consent were not required. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this manuscript, the authors used a large language model (Google\u0026apos;s Gemini) to assist with language editing and drafting the Discussion section. After using this tool, the authors reviewed, edited, and take full responsibility for the final content of the publication.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eResultsDescriptive and Correlational Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final dataset comprised \u003cstrong\u003eN = 87\u003c/strong\u003e countries with complete data for all variables for the year 2019. The descriptive statistics, presented in \u003cstrong\u003eTable 2\u003c/strong\u003e, highlight the profound global heterogeneity in both the outcome variable and its potential determinants. The primary outcome, the geriatric suicide rate for the 75-79 age group, demonstrated a more than 10-fold difference between the countries with the lowest (5.1 per 100,000) and highest (55.3 per 100,000) rates. The mean rate was 24.5 (SD = 11.2), with the large standard deviation underscoring the substantial variability across the sample.\u003c/p\u003e\n\u003cp\u003eSimilar diversity was evident in the independent variables. The \u003cstrong\u003eGini Index\u003c/strong\u003e, a measure of income inequality, ranged from 25.0 for highly egalitarian societies to 63.0 for societies with severe income stratification. \u003cstrong\u003eDigital Payment Usage\u003c/strong\u003e among those aged 65 and over, our proxy for digital inclusion, showed an even more dramatic range, from a low of 15.2% to near-universal adoption at 99.8%. This finding illustrates the stark \"digital divide\" that exists for older adults on a global scale. Likewise, \u003cstrong\u003eCO2 Emissions\u003c/strong\u003e per capita varied widely, reflecting the vast differences in industrialization and environmental policy among the nations included in the analysis.\u003c/p\u003e\n\u003cp\u003eTo explore the relationships between these variables, a Pearson correlation analysis was performed (\u003cstrong\u003eTable 3\u003c/strong\u003e). This initial bivariate analysis provided strong preliminary support for the study's core hypotheses. The geriatric suicide rate was found to have a strong, positive, and highly significant correlation with the \u003cstrong\u003eGini Index\u003c/strong\u003e (r = 0.58, p \u0026lt; 0.01). This indicates that, at a national level, as income inequality increases, the rate of suicide among older adults tends to increase as well.\u003c/p\u003e\n\u003cp\u003eConversely, a strong, negative, and highly significant correlation was observed between the suicide rate and \u003cstrong\u003eDigital Payment Usage\u003c/strong\u003e (r = -0.51, p \u0026lt; 0.01). This suggests that greater digital and economic inclusion among the elderly population is associated with significantly lower rates of suicide. Furthermore, the analysis revealed a moderate, positive, and statistically significant correlation between the suicide rate and \u003cstrong\u003eCO2 Emissions\u003c/strong\u003e (r = 0.45, p \u0026lt; 0.05), lending initial support to the \"environmental stressor\" hypothesis. The Old-Age Dependency Ratio was not significantly correlated with the suicide rate (r = 0.15, p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eThe correlation matrix also revealed important relationships among the independent variables. A particularly strong negative correlation was found between the \u003cstrong\u003eGini Index and Digital Payment Usage\u003c/strong\u003e (r = -0.65, p \u0026lt; 0.01), suggesting that countries with greater income inequality also tend to have lower levels of digital inclusion for their older citizens. This interconnectedness highlights the complex and multi-layered nature of social disadvantage. Given these inter-correlations, a multivariable regression analysis was deemed essential to disentangle the unique contribution of each factor while controlling for the others. Multicollinearity diagnostics confirmed that the variance inflation factors (VIF) for all variables were below the conservative threshold of 5, indicating the suitability of the data for regression modeling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e illustrates the significant positive correlation (r = 0.58, p \u0026lt; 0.01) between higher income inequality (Gini Index) and higher geriatric suicide rates. The trend line (/) shows that as inequality increases, suicide rates tend to rise. Each point (o) represents a country.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e shows the significant positive correlation (r = 0.45, p \u0026lt; 0.05) between higher CO2 emissions (a proxy for environmental stress) and higher geriatric suicide rates. The relationship is more dispersed than with the Gini index, but the positive trend remains evident.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariable Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the multiple linear regression are shown in Table 4. The overall model was statistically significant (F(4, 82) = 18.19, p \u0026lt; 0.001) and explained 47% of the variance in geriatric suicide rates (R\u003csup\u003e2\u003c/sup\u003e = 0.47). Among the variables analyzed, the Gini index (beta = 0.35, p = 0.008) and CO2 emissions (beta = 0.28, p = 0.015) emerged as significant positive predictors of higher suicide rates. While digital payment usage showed a strong negative correlation in the bivariate analysis (r = -0.51, p \u0026lt; 0.01), it did not reach statistical significance within the multivariable model (beta = -0.14, p = 0.115).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the ecological association of country-level social, economic, and environmental factors with geriatric suicide rates, aiming to reframe the emergency department's role from a purely reactive clinical setting to a proactive public health node. Our analysis revealed that higher national income inequality (Gini index) and higher environmental stress (per capita CO2 emissions) are significantly associated with higher suicide rates among adults aged 75\u0026ndash;79. Conversely, countries with greater digital inclusion among the elderly (proxied by digital payment usage) exhibited lower suicide rates. These findings provide a quantitative foundation for our proposed \"Integrated Crisis Response Model.\"\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation of Findings in the Context of Existing Literature\u003c/h2\u003e \u003cp\u003eOur primary finding\u0026mdash;that a higher Gini index is a significant predictor of geriatric suicide\u0026mdash;is strongly consistent with the extensive literature on the social determinants of health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] .The results showing a strong link between the Gini index and suicide rates suggest that social stratification creates significant psychological distress. High income inequality often leads to the migration of the younger, educated workforce toward more developed areas in search of better opportunities. This phenomenon, known as the 'brain drain' [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]., leaves the elderly population without traditional family care and essential social support networks, thereby increasing their vulnerability to suicide.. Income inequality is more than an economic metric; it is a powerful proxy for social stratification, relative deprivation, and psychosocial stress, all of which are known to negatively impact mental health [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For older adults, living in a highly unequal society can exacerbate feelings of being a burden, social exclusion, and hopelessness, which are established risk factors for late-life depression and suicidality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Our results empirically support the notion that the societal fabric itself, not just individual pathology, contributes to geriatric despair.\u003c/p\u003e \u003cp\u003eThe positive association between per capita CO2 emissions and suicide rates aligns with a growing body of evidence implicating the physical environment in mental health outcomes. While CO2 is an indirect proxy, it reflects levels of industrialization, traffic, and ambient air pollution. This finding supports two potential pathways hypothesized in the literature. First, the direct neurobiological pathway, where exposure to pollutants may trigger neuroinflammatory responses that exacerbate or initiate depressive symptoms [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Second, the indirect behavioral pathway, where poor environmental quality (e.g., lack of safe, clean green spaces) restricts mobility, limits opportunities for health-promoting physical activity, and deepens social isolation\u0026mdash;a potent risk factor for mortality in older adults [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePerhaps the most novel finding of our study is the negative correlation between digital payment usage and suicide rates. We interpret this as evidence for the protective role of digital inclusion. In an increasingly digital world, the ability to engage with online services, manage finances, and maintain social connections through digital means is no longer a luxury but a necessity for full societal participation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For older adults, digital literacy can be a powerful tool against social isolation, enhancing their sense of autonomy, connection, and self-worth [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our finding suggests that the \"digital divide\" is not merely a technological or economic gap, but a new frontier for health inequity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Practice: The Integrated Crisis Response Model\u003c/h2\u003e \u003cp\u003eThe current \"stabilize and refer\" model in emergency medicine [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], while life-saving, is insufficient as it fails to address the upstream determinants that bring the patient to the ED [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Our findings compel a paradigm shift. We propose the \u003cb\u003e\"Integrated Crisis Response Model,\"\u003c/b\u003e which reframes the geriatric suicide attempt as a \"sentinel health event\"\u0026mdash;a critical signal of systemic failures in the patient's social and physical environment. This model is inspired by successful ED-initiated public health interventions in other fields, such as buprenorphine initiation for opioid dependence [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The model consists of three core actions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEnhanced Screening\u003c/b\u003e: Beyond immediate psychiatric risk, ED protocols should include simple, validated screening questions to identify upstream risks: \"Do you feel safe in your neighborhood?\", \"Do you have trouble paying for food or housing?\", \"Do you feel isolated from friends and family?\".\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWarm Handoffs\u003c/b\u003e: Instead of merely providing a pamphlet, EDs should establish formal partnerships with community organizations (e.g., social services, meal delivery programs, senior centers). A \"warm handoff\" involves a direct, facilitated connection made by an ED social worker or case manager before the patient is discharged.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eData for Advocacy\u003c/b\u003e: Anonymized, aggregated data from these screenings can be a powerful tool. By tracking the prevalence of food insecurity, social isolation, or poor housing conditions among their patients, EDs can provide concrete data to municipal governments and public health agencies to advocate for healthier, more age-friendly urban environments and policies.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThe primary strength of this study is its use of a large, multi-national dataset (N\u0026thinsp;=\u0026thinsp;87) from robust sources (WHO, World Bank) to explore a critical public health issue. The inclusion of novel environmental and digital inclusion variables provides a fresh perspective on geriatric suicide prevention.\u003c/p\u003e \u003cp\u003eHowever, the study has several important limitations. The most significant is the potential for \u003cb\u003eecological fallacy\u003c/b\u003e, inherent in its cross-national design [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. An association observed at the country level (e.g., CO2 and suicide) does not permit the conclusion that individuals exposed to more pollution are the ones attempting suicide. Our findings reflect population-level associations and should be interpreted with caution.\u003c/p\u003e \u003cp\u003eSecond is the indirect nature of our proxy variables. Per capita CO2 emissions are a crude proxy for environmental stress and may also be confounded by a country's level of industrialization or economic structure. Similarly, digital payment usage is an imperfect proxy for social inclusion.\u003c/p\u003e \u003cp\u003eThird, the study's cross-sectional design (using 2019 data) prevents us from establishing causality. We can only report associations, not prove that income inequality \u003cem\u003ecauses\u003c/em\u003e suicide. Finally, the model did not account for other important potential confounders, such as national prevalence of mental health disorders, access to mental health services, cultural norms regarding suicide, or alcohol consumption rates, due to a lack of comparable data across all countries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFuture Directions and Policy Implications\u003c/h2\u003e \u003cp\u003eThis study highlights the need for multi-level research. Future studies should use longitudinal, individual-level data to confirm these associations and explore causal pathways. Research employing more direct measures, such as linking local air quality data (e.g., PM2.5 levels) to hospital admission records for self-harm, would be a valuable next step.\u003c/p\u003e \u003cp\u003eFor policymakers, our findings suggest that geriatric suicide prevention must extend beyond the clinic. Policies aimed at reducing income inequality, investing in clean and accessible public spaces, and closing the digital divide for older adults may be effective, though indirect, suicide prevention strategies. For emergency medicine, the message is clear: the ED can and should be more than a revolving door for crises; it can be a vital partner in building healthier communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding and Declarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration Clinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe datasets generated and analysed during the current study are available in the WHO Global Health Estimates database (https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates) and the World Bank World Development Indicators (https://databank.worldbank.org/source/world-development-indicators). The aggregated dataset used for the statistical analysis is available from the corresponding author on reasonable request\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.M. designed the study and performed the analysis. F.\u0026Ouml;. and N.M. wrote the main manuscript text. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Ageing and health. Geneva: WHO; 2022. Available from: [https://www.who.int/news-room/fact-sheets/detail/ageing-and-health](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health)\u003c/li\u003e\n\u003cli\u003eConejero I, Oli\u0026eacute; E, Courtet P, Calati R. Suicide in older adults: current perspectives. *Clin Interv Aging*. 2018;13:691-699.[https://doi.org/10.2147/CIA.S130670](https://doi.org/10.2147/CIA.S130670)\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva: WHO; 2020. Available from: [https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death](https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death)\u003c/li\u003e\n\u003cli\u003eTadros A, Bisanzo M, Glick R. The emergency department as a gateway for the suicidal patient. *Emergency Medicine Practice*. 2013;15(3):1-21. Available from: [https://www.ebmedicine.net/topics/psychiatric/suicidal-patient](https://www.ebmedicine.net/topics/psychiatric/suicidal-patient)\u003c/li\u003e\n\u003cli\u003eBetz ME, Boudreaux ED. Managing suicidal patients in the emergency department. *Ann Emerg Med*. 2016;67(2):276-282. 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Ecologic studies in epidemiology: concepts, principles, and methods. \u003cem\u003eAnnu Rev Public Health\u003c/em\u003e. 1995;16:61-81. https://doi.org/10.1146/annurev.pu.16.050195.000425\u003c/li\u003e\n\u003cli\u003ePirkis J, John A, Shin S, DelPozo-Banos M, Arya V, Analuisa-Aguilar P, et al. Suicide trends in the early months of the COVID-19 pandemic: an interrupted time-series analysis of preliminary data from 21 countries. \u003cem\u003eLancet Psychiatry\u003c/em\u003e. 2021;8(7):579-588. https://doi.org/10.1016/S2215-0366(21)00091-2\u003c/li\u003e\n\u003cli\u003eShah A, Bhat R, Zarate-Escudero S, DeLeo D, Erlangsen A. Suicide rates in five-year age-bands after the age of 60 years: a comparison of 40 countries. \u003cem\u003eMed Sci Law\u003c/em\u003e. 2016;56(1):17-22. https://doi.org/10.1177/0025802414562168\u003c/li\u003e\n\u003cli\u003eWilkinson R, Pickett K. Income inequality and health: a causal review. \u003cem\u003eSoc Sci Med\u003c/em\u003e. 2006;62(7):1768-1784. https://doi.org/10.1016/j.socscimed.2005.08.035\u003c/li\u003e\n\u003cli\u003eThe World Bank. World Development Indicators. Washington, D.C.: The World Bank Group; 2023. Available from: https://databank.worldbank.org/source/world-development-indicators\u003c/li\u003e\n\u003cli\u003eLee R, Mason A. Some macroeconomic aspects of global population aging. \u003cem\u003eDemography\u003c/em\u003e. 2017;54(4):1547-1571. https://doi.org/10.1007/s13524-017-0591-z\u003c/li\u003e\n\u003cli\u003eSeifert A. The importance of digital literacy for the well-being of older adults. \u003cem\u003eGerontology\u003c/em\u003e. 2020;66(5):427-434. https://doi.org/10.1159/000505223\u003c/li\u003e\n\u003cli\u003eThe World Bank. The Global Findex Database 2021. Washington, D.C.: The World Bank Group; 2022. Available from: https://www.worldbank.org/en/publication/globalfindex\u003c/li\u003e\n\u003cli\u003eHair JF, Black WC, Babin BJ, Anderson RE. \u003cem\u003eMultivariate Data Analysis\u003c/em\u003e. 8th ed. Cengage Learning; 2018.\u003c/li\u003e\n\u003cli\u003eStark O. \u003cem\u003eThe new economics of the brain drain\u003c/em\u003e. World Economics. 2005;6(2):137.\u003c/li\u003e\n\u003cli\u003ePickett KE, Wilkinson RG. Inequality: an underacknowledged source of mental illness and distress. \u003cem\u003eBr J Psychiatry\u003c/em\u003e. 2015;207(6):476-478. https://doi.org/10.1192/bjp.bp.115.162295\u003c/li\u003e\n\u003cli\u003eFonken Y, Vert C, Gaskill P, Latzman R, O\u0026apos;Dell T, Fonken L. The neuroinflammatory response to air pollution: A review of the literature and potential mechanisms. \u003cem\u003eNeurosci Biobehav Rev\u003c/em\u003e. 2020;114:142-155. https://doi.org/10.1016/j.neubiorev.2020.04.015\u003c/li\u003e\n\u003cli\u003eHolt-Lunstad J, Smith TB, Baker M, Harris T, Stephenson D. Loneliness and social isolation as risk factors for mortality: a meta-analytic review. \u003cem\u003ePerspect Psychol Sci\u003c/em\u003e. 2015;10(2):227-237. https://doi.org/10.1177/1745691614568352\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Onofrio G, O\u0026apos;Connor PG, Pantalon MV, Chawarski MC, Busch SH, Owens PH, et al. Emergency department-initiated buprenorphine/naloxone treatment for opioid dependence: a randomized clinical trial. \u003cem\u003eJAMA\u003c/em\u003e. 2015;313(16):1636-1644. https://doi.org/10.1001/jama.2015.3474\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Global Rank of Self-harm as a Cause of Death for Adults Aged 60-79 (2019)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCause of Death\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIschemic heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e...\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e...\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-harm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKidney diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource: WHO Global Health Estimates, 2019\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Table 1 illustrates the relative position of self-harm among the leading causes of mortality for the 60-79 age group globally. Being ranked 19th indicates that suicide is a significant but often overlooked public health priority compared to chronic physical diseases. \u003cstrong\u003eSource:\u003c/strong\u003e WHO Global Health Estimates (2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Descriptive Statistics of Key Variables (N = 87)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"607\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSuicide Rate (75-79, per 100k)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGini Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOld-Age Dependency Ratio (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigital Payment Usage (65+, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCO2 Emissions (tons/capita)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e N = 87 countries. Descriptive statistics provide an overview of the variables used in the ecological analysis. Suicide rates and CO2 emissions exhibit high variability, reflecting the diverse socio-economic backgrounds of the included nations. \u003cstrong\u003eAbbreviations:\u003c/strong\u003e SD: Standard Deviation; per 100k: per 100,000 population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Pearson Correlation Matrix of Key Variables (N = 87)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1. Suicide Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2. Gini Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3. Dependency Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4. Digital Usage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5. CO2 Emissions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Suicide Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Gini Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.58**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependency Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigital Usage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-.51**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-.65**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.41*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCO2 Emissions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.45*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.39*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.35*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Pearson correlation coefficients (r) are reported. Asterisks indicate statistical significance levels (*p \u0026lt; 0.05, **p \u0026lt; 0.01). The strong positive correlation between the Gini Index and suicide rates underscores the impact of income inequality.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Multiple Linear Regression Predicting Geriatric Suicide Rate (75-79)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized \u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(Constant)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGini Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOld-Age Dependency Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigital Payment Usage (65+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCO2 Emissions (tons/capita)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Multiple linear regression was used to determine independent predictors. The model significantly accounts for 47% of the variance (Adjusted R2 = 0.44). Beta coefficients represent standardized values. Significance: p \u0026lt; 0.001 for the overall model.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Geriatric suicide, Emergency medicine, Social determinants of health, Environmental health, Age-friendly cities, Public health, Ecological analysis","lastPublishedDoi":"10.21203/rs.3.rs-8429111/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8429111/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGeriatric suicide is a major public health concern, with emergency departments (EDs) often serving as the first point of contact. Current practice focuses on acute stabilization, overlooking the upstream social, economic, and environmental determinants that precipitate these crises. This study aims to analyze these multi-level risk factors and propose a holistic, actionable public health model for emergency medicine.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-national ecological analysis was conducted using 2019 data from the WHO Global Health Estimates and the World Bank. The dependent variable was the age-specific suicide rate for adults aged 75\u0026ndash;79. Independent variables included the Gini index (income inequality), old-age dependency ratio, digital payment usage among adults 65+ (digital inclusion), and CO2 emissions per capita (proxy for environmental stress). Pearson correlation and multiple linear regression analyses were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDescriptive statistics revealed significant global variation in all variables. Correlation analysis showed that geriatric suicide rates were positively and significantly correlated with the Gini index (r\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and CO2 emissions (r\u0026thinsp;=\u0026thinsp;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and negatively correlated with digital payment usage (r = -0.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The multiple regression model was significant (F(4, 82)\u0026thinsp;=\u0026thinsp;18.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u0026sup2; = 0.47), with the Gini index (β\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;=\u0026thinsp;0.004) and CO2 emissions (β\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;=\u0026thinsp;0.03) emerging as significant predictors of higher suicide rates.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eGeriatric suicide attempts presenting to the ED should be treated as \"sentinel health events\"\u0026mdash;critical indicators of systemic failures in the social, economic, and physical environment. We propose an \"Integrated Crisis Response Model\" where EDs act as public health nodes. This involves: 1) Enhanced screening for social and environmental risk factors, 2) Initiating \"warm handoffs\" to community services, and 3) Utilizing anonymized data to advocate for healthier, more age-friendly urban environments.\u003c/p\u003e","manuscriptTitle":"A Cross National Ecological Analysis of Social Determinants in Geriatric Suicide Crises and a Proposal for an Integrated Crisis Response Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 18:34:13","doi":"10.21203/rs.3.rs-8429111/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9114093d-d66d-4705-96b9-f621b6198dc4","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T07:38:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 18:34:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8429111","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8429111","identity":"rs-8429111","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}