The Unequal Global Geographic Distribution of Suicide: Spatial Patterns and Its Relationship with Alcohol

The Unequal Global Geographic Distribution of Suicide: Spatial Patterns and Its Relationship with Alcohol | 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 The Unequal Global Geographic Distribution of Suicide: Spatial Patterns and Its Relationship with Alcohol Oguz Han Aydilek, Selma Metintas, Muhammed Fatih Onsuz, Emrah Atay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8398774/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 Objective To demonstrate inequalities in the distribution of the global suicide rate, a key component of deaths of despair, using spatial autocorrelation, and to evaluate, using spatial analyses, the association of suicide with sociodemographic variables, particularly alcohol consumption. Methods Using suicide data from the 2017–2021 period, five-year average age-standardized suicide rates were calculated, and their spatial autocorrelation was examined. In addition, the relationship between suicide and alcohol consumption was evaluated using Bivariate Moran’s I, while other potential predictors related to suicide were analyzed using spatial regression models. Furthermore, Lorenz curves were used to demonstrate the unequal distribution of suicide rates and the other variables, and Gini coefficients were calculated. Results The spatial autocorrelation of suicide rates was high, and clusters were evident. Regions where suicide rates clustered at high levels were Eastern Europe and South Africa, while low-rate clusters were observed particularly in the Middle East. However, the clusters showed some differences by sex. In spatial regression models, unemployment, economic freedom, and population density were found to be factors affecting suicide. Conclusion The fact that suicide rates show a marked spatial autocorrelation on a global scale may provide an indication for more detailed regional investigations. In the regression models, the different effects of unemployment, economic freedom, population density, and alcohol consumption by sex may highlight the importance of gender-based approaches in suicide prevention policies. Suicide Alcohol Consumption Spatial Analysis Spatial Inequality Figures Figure 1 Figure 2 Figure 3 Highlights • Suicide rates vary markedly by location and gender worldwide and are unevenly distributed. • Eastern Europe and Southern Africa were hotspot regions for suicide rates. • In Europe, places with more alcohol use tended to have higher suicide rates. • Sex-stratified analyses showed a positive association between economic freedom and suicide rates in men, and a negative association in women. 1. Introduction Suicide, deaths from chronic liver disease attributable to excessive alcohol consumption, and drug poisonings due to drug overdose are defined as deaths of despair (DoD). From this perspective, DoD comprises three core components, and suicide arguably stands out as the most important contributor to DoD (Shanahan et al., 2019 ; Seetsa Makateng, 2024 ). Suicide is a complex phenomenon influenced by many factors (Simões et al., 2020 ). It is a behavior carried out deliberately by an individual who is aware of its irreversible and fatal outcome (World Health Organization, 2014 , 2002 ). The World Health Organization (WHO) estimates that in 2021, more than 700,000 people lost their lives due to suicide alone. As an important public health problem, suicide ranks 10th among all causes of death, while it is the 3rd leading cause of death among those aged 15–29 years (WHO, 2025). At the same time, suicide deeply affects not only the individual but also their surroundings. One study emphasized that each suicide affects approximately 60 people in the person’s social environment (Pitman et al., 2014 ). A meta-analysis reported that ninety percent of publications on suicide originate from North America and Europe, whereas low- and middle-income countries are insufficiently represented in this field. (Guzmán et al., 2019 ). Evidence from studies indicates that suicide rates can vary greatly between countries and even between different regions within the same country (Hawton and van Heeringen, 2009 ; Kapusta et al., 2011 ). However, the reasons underlying these regional differences have not been fully clarified. (Helbich et al., 2012 ; Mann et al., 2005 ). For these reasons, evaluating suicide rates on a global scale using spatial analyses may contribute to understanding the relationship between local factors and suicide. This is important because, when developing effective prevention strategies, it is critical to understand how suicide rates behave across different demographic groups and to examine their spatial autocorrelation (Crawford et al., 2010 ; O’Farrell et al., 2016 ). For younger age groups, alcohol use disorder (AUD) emerges as a significant cause of death, aside from suicide (World Health Organization, 2014 ). In the distribution of disease burden caused by alcohol-related injuries, suicide and self-harm rank second, following road traffic injuries (Roth et al., 2018 ). The Global Burden of Disease (GBD) study estimates that approximately 15% of all suicide deaths are alcohol-related, meaning that more than 100,000 people die annually due to alcohol-related self-harm (Chikritzhs and Livingston, 2021 ). Alcohol dependence and alcohol poisoning are considered significant risk factors for suicidal behavior (Hufford, 2001 ; Norström and Rossow, 2016 ; Pompili et al., 2010 ). A systematic review and meta-analysis reported that alcohol consumption increases the risk of suicide, with an odds ratio of 1.64 (Amiri and Behnezhad, 2020 ). In addition to individual-level studies showing that people with AUD have an increased risk of suicidal ideation, self-harm, and completed suicide, there are also studies indicating that heavy drinking at the time of the event increases the risk of suicide, as well as local studies demonstrating population-level associations between alcohol consumption and suicide rates (Borges et al., 2017 ; Norström and Rossow, 2016 ). This association has been explained through mechanisms such as alcohol increasing psychological distress, enhancing aggression, and raising the tendency to act on suicidal thoughts (Cherpitel et al., 2004 ; Hufford, 2001 ). Additionally, cultural factors appear to mediate the role of alcohol in self-harm; for example, there is some evidence that alcohol is more strongly associated with self-harm in cultures where alcohol use is more prevalent among men and where drinking is often aimed at intoxication. Moreover, although the evidence that alcohol contributes to suicide and self-harm is pretty strong, there remains uncertainty about the magnitude of the causal relationship, as alcohol use disorder, intoxication, and self-harm may share some underlying risk factors (Norström and Rossow, 2016 ). The contribution of studies that evaluate global alcohol consumption frequency and suicide rates together, considering cultural differences, may help eliminate this uncertainty. However, studies that assess alcohol use frequency and suicide rates on a global scale using spatial analysis are limited. The primary aim of this study is to identify inequalities in the global distribution of suicide by examining its spatial clustering and quantifying spatial autocorrelation. To assess the association between alcohol consumption and suicide, we employ measures of spatial cross-correlation and then estimate spatial regression models while controlling for selected sociodemographic covariates. In addition, by stratifying the sociodemographic predictors by sex, we investigate whether the direction and magnitude of these associations are similar or different between females and males. 2. Method 2.1.Study Area and Data This ecological study was conducted in 183 out of 194 countries (94%) recognized by the United Nations (UN) for which suicide rate data are available for 2017–2021 (UN, 2025; WHO, 2017). In this study, the data were based on the International Classification of Diseases (ICD-10) codes for suicide, and suicide mortality rates per 100,000 population were used. Since suicide rates did not show a significant change over time in the time series analysis of the study period, the weighted average of the standardized rates was calculated. In addition, due to the different trends observed in male and female suicide rates, age-standardized suicide rates were calculated separately by gender as well as combined. Standardization was performed based on the WHO World Standard Population (Ahmad et al., 2001 ). As a result, the age-standardized 5-year average suicide rate (SSR) per 100,000 population, calculated with corrections, was determined as the dependent variable of the study. In this study, we aimed to evaluate the influence of alcohol consumption, which is a significant cause of death, particularly among young people, on suicide rates (WHO, 2014). To do this, we selected per capita annual alcohol consumption as the primary explanatory variable. This data was sourced from the Our World in Data database (Ritchie and Roser, 2022 ). In addition, in order to demonstrate the net effect of alcohol consumption on suicide in the advanced regression models, we included in our analyses several variables that could act as potential confounders. For this purpose, for the selection of other variables that could be explanatory, the Benjamini-Hochberg correction was applied by ranking the p-values in ascending order, dividing them by the total number of variables, and multiplying them by their respective ranks. The aim was to observe the effect of alcohol consumption on suicide when the other variables were controlled for (Bogdan et al., 2008 ). As a result of this process, the variables selected from the relevant databases were unemployment prevalence rate, economic freedom index, number of psychiatrists, and population density, as their p-values were lower than the calculated threshold (International Labour Organization, 2019 ; The Heritage Foundation, 2019 ; Our World in Data, 2019 ; World Bank Group, 2019). The selected independent variables belonged to the year 2019, which is the midpoint of the study period. The units of the independent variables were as follows: percentage for unemployment, annual per capita consumption in liters for alcohol consumption, number of people per square kilometer for population density, and number per 100,000 people for the number of psychiatrists. The Economic Freedom Index measures how free an economy is on a scale from 0 to 100. It examines factors such as property rights, government integrity, the effectiveness of the courts, the tax burden, government spending, and business freedom and trade. A score of 100 means there is complete economic freedom, with no interference or pressure (The Heritage Foundation, 2019 ). The data were extracted and compiled using SQL Server Management Studio version 19. A digital vector world map was created in QGIS version 3.28, and the dataset was integrated for analysis and visualization (QGIS Project, 2025). 2.2.Statistical Analyses We used several tools for our statistical analyses, including GeoDa (version 1.22.0.21), R (version 4.0.0), and QGIS (version 3.36.1). We conducted basic statistical tests and analyzed spatial patterns using Univariate and Bivariate Local Moran’s I and Local Indicators of Spatial Association (LISA). Our spatial regression models included Ordinary Least Squares (OLS), Spatial Lag Model (SLM), and Spatial Error Model (SEM) with a significance level of p < 0.05. We log-transformed non-normally distributed variables, such as the unemployment rate, alcohol consumption, and economic freedom index. To conduct the spatial analyses, a k-nearest neighbors (kNN) spatial weights matrix was constructed using countries’ centroids (k = 4). A robustness test was performed by repeating the primary analyses using k = 6 and k = 8 and assessing the similarity of the results. Row standardization was applied to stabilize the model and prevent countries with more neighbors from disproportionately influencing the analyses. The purpose of using a kNN-based weights matrix instead of first-order contiguity was to enable calculations on a global map where land borders may be interrupted by seas and oceans, and to include island countries that would otherwise have no neighbors. One of the most common approaches used to calculate the degree of geographical correlation is Moran's I statistic. Moran's I is the slope of the linear fit to the scatter plot and takes values between + 1 and − 1. A value of + 1 indicates strong positive spatial autocorrelation (all the same next to each other), 0 indicates random spatial ordering, and − 1 (all opposite next to each other) indicates strong negative spatial autocorrelation (Tiefelsdorf and Boots, 1995 , 1997 ). Moran's I has a limitation: it does not identify clustering within the study area. Local Indicators of Spatial Association (LISA) add further value beyond Moran’s I by distinguishing spatial interactions at the local scale. In spatial analyses, the display of High-High (hot spots), Low-Low (cold spots), and Low-High or High-Low areas is provided by LISA maps. For example, in a High-High (hot spot) area, the suicide rate is above the mean both in the country itself and in its neighboring countries, and this pattern is statistically significant (Anselin, 1995 ; Anselin et al., 2010 ). The Lorenz curve shows inequality in the distribution of a measured variable. The Gini coefficient (where 0 indicates perfect equality and 1 indicates complete inequality) reduces this inequality to a single number. In this study, inequality in the distribution of suicide rates was demonstrated not only through spatial autocorrelation but also using the Lorenz curve. In addition, Lorenz curves were presented in a single figure to highlight distributional inequalities across all associated variables (Moothathu, 1990 ). Spatial regression models should be implemented in three stages: first, Ordinary Least Squares (OLS), followed by the Spatial Lag Model (SLM) and the Spatial Error Model (SEM). OLS does not take spatial autocorrelation into account. Therefore, if Moran’s I statistic is significant, SLM and SEM are subsequently applied. SLM captures spatial interactions between neighboring units, whereas SEM detects spatial dependence in the error terms. After OLS, the appropriate model is determined based on the Lagrange Multiplier (LM) tests; whichever of the LM Lag or LM Error is significant indicates the model that should be selected. If both LM tests are significant, Robust LM tests are used to determine which model has dominance. If ambiguity remains, the model with the lower Akaike Information Criterion (AIC) is selected (Anselin et al., 2007 , 2006 ; Ward and Gleditsch, 2008 ). In our study, because we used spatial autocorrelation analyses and spatial regression models such as SEM and SLM, missing data were completed using a multiple imputation approach. If the analyses had been conducted with missing data for any country, our spatial analyses would not have been fully feasible, as the spatial weights matrix was based on k-nearest neighbors using country centroids; the gap created by that country on the map and the resulting inability to compute centroid-based distances would have posed a serious problem. In other words, there could have been “holes” in the map. Therefore, for countries for which data could not be obtained from institutional sources, values were estimated based on a “borrowing information” principle from geographically and socioeconomically similar countries. For this purpose, the mice (Multivariate Imputation by Chained Equations) package in R was used. Under a missing at random assumption, 20 imputed datasets were generated for the variables using the predictive mean matching method. Each imputed dataset was used to estimate the spatial error model (SEM) separately, and the regression coefficients and standard errors from the SEM and SLM models were pooled using Rubin’s rules. By contrast, for the LISA maps and Moran’s I values we produced, an approximate “master” dataset was created using the 20 different imputation sets, since generating a separate map for each set would not have been practical. Within this framework, values for countries with missing data were extracted separately from each imputed dataset and averaged, and these averages were then added to the initial dataset constructed with countries for which data were available from institutional sources. The LISA maps presented in Fig. 1 and Fig. 2 , and the univariate and bivariate Moran’s I statistics shown in these figures, were computed using this master dataset. 2.3.Ethics Committee Approval Ethical approval was not necessary for this study, as the data were sourced from publicly available online resources. 3. Results The SSRs of countries ranged from 0.54 to 70.9 per 100,000 of the total population, with a mean value of 9.38. Suicide rates varied substantially across countries. A strong and positive spatial autocorrelation was found in the distribution of sex-specific suicide rates (SSRs) for females, males, and the total population, with Moran’s I values of 0.537, 0.602, and 0.589, respectively (p < 0.001 for each value). LISA cluster maps and Moran’s I scatter plots illustrating the differences, unequal distribution, and clustering of sex-stratified suicide rates across countries are presented in Fig. 1 . Figure 1 ’s LISA results indicate that High–High (HH) SSR clusters were observed mainly in Eastern Europe, including the Baltic countries, and in countries in southern Africa. This pattern appears largely similar across sexes; however, some countries exhibited sex-specific clustering patterns. Low–Low (LL) SSR clusters are notable in the Middle East, North Africa, and Latin America. Although these low–low clustering patterns also show some variation by sex, the overall picture shows similarities. Outlier clustering patterns were observed quite rarely. The detailed results of the maps in Fig. 1 are provided in Table 1 under the title “Clustering results from the univariate Moran’s I LISA map of the suicide rate. In our analysis, we used the bivariate Moran’s I statistic to look at how suicide rates and per capita alcohol consumption are related across different areas. The results suggest that the two measures tend to cluster rather than being randomly distributed. For women, Moran’s I was 0.202; for men, it was 0.334; and for the total population, it was 0.303. All of these patterns were highly significant, with p-values well below 0.001.The LISA cluster maps and Bivariate Moran’s I scatter plots for suicide rates and per capita alcohol consumption are presented in Fig. 2 . In the bivariate Moran’s I analysis in which suicide rates and alcohol consumption were evaluated together, the LISA map indicates that High–High clusters for males are concentrated in almost all European countries. For females, however, within the High–High clustering pattern, Czechia, Denmark, Finland, and Luxembourg stand out instead of Moldova, Poland, Romania, and Slovakia, which are included in the male map. Low–Low clusters exhibited a broadly similar distribution by sex, despite minor differences, and were concentrated mainly in the Middle East, North Africa, Oceania, and a limited part of Central Asia. Outlier clusters were again observed rarely. In this context, High–Low clusters occur mainly in West Africa, along the Sahel and the Horn of Africa corridor, and in South Asia, whereas Low–High clusters are more focused on Southern and Southeastern Europe and have also been observed in some Northern/Western European countries. The detailed results for Fig. 2 are presented in Table 1 under the title “Results of the bivariate Moran’s I and LISA cluster map assessing suicide rate and per capita alcohol consumption. Table 1 Univariate Moran’s I–LISA clustering results for suicide rates and bivariate Moran’s I–LISA clustering results for suicide rates and per capita alcohol consumption (by gender). Clustering results from the univariate Moran’s I LISA map of the suicide rate Results of the bivariate Moran’s I and LISA cluster map assessing suicide rate and per capita alcohol consumption Male Famele Total Male Famele Total High-High Belarus, Botswana, Estonia, Latvia, Lesotho, Lithuania, Mozambique, Namibia, Poland, Russia, South Africa, Tanzania, Ukraine, Zambia, Zimbabwe, and eSwatini Belarus, Botswana, Congo, Democratic Republic of the Congo, Ethiopia, Guinea, Lesotho, Mozambique, Namibia, North Korea, Norway, Poland, Russia, South Africa, Zambia, Zimbabwe, and eSwatini Belarus, Botswana, Estonia, Latvia, Lesotho, Lithuania, Mozambique, Namibia, Poland, Russia, South Africa, Tanzania, Zambia, Zimbabwe, and eSwatini Austria, Belarus, Belgium, Croatia, Czechia, Estonia, France, Germany, Hungary, Latvia, Lithuania, Moldova, Poland, Romania, Russia, Slovakia, Slovenia, Switzerland, and Ukraine Austria, Belarus, Belgium, Croatia, Czechia, Denmark, Estonia, France, Germany, Hungary, Latvia, Lithuania, Luxembourg, Russia, Slovenia, Switzerland, and Ukraine Austria, Belarus, Belgium, Croatia, Czechia, Estonia, France, Germany, Hungary, Latvia, Lithuania, Moldova, Poland, Russia, Slovakia, Slovenia, Switzerland, and Ukraine p < 0.001 p < 0.001 Low-Low Algeria, Colombia, Greece, Indonesia, Iran, Iraq, Israel, Jordan, Lebanon, Libya, Oman, Papua New Guinea, Saudi Arabia, Syria, Tunisia, Timor-Leste,Türkiye, United Arab Emirates, Yemen Armenia, Azerbaijan, Colombia, Costa Rica, Ecuador, Georgia, Guatemala, Iran, Iraq, Israel, Jordan, Lebanon, Saudi Arabia, Syria, Türkiye, and the United Arab Emirates Armenia, Colombia, Georgia, Greece, Indonesia, Iraq, Iran, Israel, Jordan, Lebanon, Libya, Papua New Guinea, Saudi Arabia, Syria, Timor-Leste, and Türkiye Algeria, Egypt, Israel, Iraq, Iran, Jordan, Libya, Papua New Guinea, Sudan, and Tunisia Algeria, Israel, Iraq, Iran, Jordan, Oman, Papua New Guinea, and Syria Algeria, Egypt, Israel, Iraq, Iran, Jordan, Libya, Papua New Guinea, Sudan, Tunisia, and Turkmenistan p < 0.001 p < 0.001 High-Low Ecuador Yemen Ecuador Chad, Ethiopia, Eritrea, and India Guinea-Bissau, India, Liberia, and Mali Chad, Ethiopia, Eritrea, Mali and India p < 0.001 p < 0.001 Low-High - - - Denmark, Italy, Luxembourg, and Serbia Italy, Moldova, Poland, Romania, Serbia, Slovakia, and the United Kingdom Denmark, Italy, Luxembourg, Romania, and Serbia p < 0.001 p < 0.001 Descriptive statistics for the variables used in the study, along with their correlation coefficients with the suicide rate, are presented in Table 2 . In addition, the Lorenz curves and Gini coefficients indicating the levels of inequality in the global distribution of these variables are presented in Figure 3 . Table 2. Descriptive Statistics and Correlation Coefficients of Suicide Rate and Its Associated Variables Worldwide Observation Count Missing Data Min Max Mean Spearman’s rho p Value Age-Std Suicide Rate(5-Year Avg,per100,000) Female 183 11 0.499 29.202 4.480 Male 183 11 0.475 117.003 15.600 Total 183 11 0.548 70.904 9.820 Population Density (persons per km²) 178 16 2.076 7965.808 82.660 -0.244 0.001 Unemployment Rate (per 100 people) Female 174 20 0.200 36.103 8.650 0.152 0.041 Male 174 20 0.000 27.601 6.601 0.124 0.014 Total 174 20 1.000 26.203 5.106 0.373 0.022 Psychiatrists (per 100,000) 128 66 0.000 47.007 2.003 0.283 0.039 Economic Freedom Index 169 25 38.900 89.420 60.507 -0.181 0.019 Alcohol Consumption (annual per capita, liters) Female 182 12 0.000 7.513 2.210 0.193 0.022 Male 182 12 0.000 27.305 8.643 0.433 0.007 Total 182 12 0.000 16.996 4.776 0.255 < 0.001 The significance of Moran's I in the OLS models indicated the presence of spatial dependence, which warranted the use of spatial models. Among these models, the SEM was favored based on the results of the Lagrange Multiplier (LM) tests and the Akaike Information Criterion (AIC) values. This suggests that unobserved factors that are correlated spatially may influence suicide rates. The results from the SEM indicated a consistently positive and significant effect of unemployment across all models.Economic freedom had a negative effect on female suicides but a positive effect on male suicides. For males, alcohol consumption and population density were also significant, with the model explaining suicide rates moderately to well (Adj. R² = 0.57 for males, 0.39 for females).Model details are reported in Table 3 . Table 3 Evaluation of Suicide Rates Using OLS, SLM, and SEM Models OLS SLM SEM Coef./Val. Sig. Coef. Sig. Coef. Sig. a.Female Constant 0.158 0.0089 0.020 0.7142 0.110 0.0381 Log-likelihood -52.348 -35.465 0.0001 -32.227 0.0001 Adj. R2 0.188 0.342 0.393 AIC 116.697 84.930 76.454 Schwarz criterion 136.305 107.805 96.061 Lag: SuicideFamale (Rho) 0.236 Alcohol consumption 0.079 0.0984 0.054 0.2000 0.029 0.5016 Unemployment rate 0.057 0.3070 0.039 0.4271 0.115 0.0260 Economic Freedom Index -0.172 0.0089 -0.129 0.0007 -0.115 0.0027 Number of psychiatrists per capita -0.006 0.9223 -0.005 0.9234 -0.017 0.7563 Population density 0.013 0.6965 0.045 0.1245 0.048 0.1197 Multicollinearity condition num. 15.037 0.0102 Moran’s I 6.409 0.0000 LM (lag) 33.526 0.0000 Robust LM (lag) 3.935 0.0472 LM (error) 36.756 0.0000 Robust LM (error) 7.165 0.0074 Lagrange multiplier (SARMA) 40.691 0.0000 b.Male Constant 0.214 0.0005 0.098 0.1187 0.195 0.0004 Log-likelihood -54.369 -46.276 0.0001 -31.288 0.0001 Adj. R2 0.506 0.561 0.574 AIC 120.739 106.553 81.335 Schwarz criterion 140.346 129.428 124.867 Lag: SuicideMale (Rho) 0.195 Alcohol consumption 0.198 0.0001 0.180 0.0001 0.145 0.0003 Unemployment rate 0.249 0.0001 0.202 0.0006 0.278 0.0000 Economic Freedom Index 0.239 0.0000 0.211 0.0000 0.205 0.0000 Number of psychiatrists per capita -0.020 0.7455 -0.037 0.5304 -0.0005 0.9933 Population density 0.075 0.0248 0.102 0.0012 0.107 0.0008 Multicollinearity condition num. 7.709 Moran’s I 5.186 0.0000 LM (lag) 15.200 0.0001 Robust LM (lag) 3.153 0.0757 LM (error) 23.788 0.0000 Robust LM (error) 11.742 0.0006 Lagrange multiplier (SARMA) 26.942 0.0000 c.Total Constant 0.194 0.0023 0.070 0.2336 0.168 0.0014 Log-likelihood -46.021 -35.512 0.0002 -29.348 0.0016 Adj. R 2 0.417 0.497 0.557 AIC 104.043 85.024 70.697 Schwarz criterion 123.65 107.899 90.304 Lag: SuicideTot (Rho) 0.244 Alcohol consumption 0.154 0.0002 0.136 0.0171 0.104 0.0080 Unemployment rate 0.178 0.0017 0.142 0.0073 0.208 0.0000 Economic Freedom Index 0.218 0.0001 0.185 0.0000 0.174 0.0001 Number of psychiatrists per capita -0.002 0.9659 -0.022 0.6870 -0.004 0.9323 Population density 0.056 0.0796 0.085 0.0045 0.094 0.0020 Multicollinearity condition num. 7.559 Moran’s I 5.717 0.0001 LM (lag) 19.785 0.0006 Robust LM (lag) 3.425 0.0642 LM (error) 29.090 0.0007 Robust LM (error) 12.729 0.0003 Lagrange multiplier (SARMA) 32.515 0.0007 4. Discussion Consistent with the literature, spatial autocorrelation in suicide rates clearly demonstrates clusters of low and high suicide rates in specific regions, and this finding is an important indication of inequality in the distribution of suicide rates. (Macente and Zandonade, 2012 ; Núñez-González et al., 2018 ). For both sexes, Eastern Europe and Southern Africa were identified as hot spots for suicide rates, while in men, the Baltic countries additionally emerged as hot spots. Cold spots, in contrast, were observed for both sexes, particularly in the Middle East and Latin America, and, among men, additionally in North Africa. One of our most important findings that may serve as evidence that suicide rates can vary by sex is the presence of these differing spatial distributions alongside broadly similar clusters. In addition to the differing distributions in clustering, the marked presence of similar clusters in both sexes is noteworthy and suggests that factors that either inhibit or facilitate suicide are present at the level of the general population in these regions. The emergence of Eastern European countries as hot spots may be related to the stress generated by past socioeconomic transitions and to inadequacies in health and social services. At the same time, higher-than-average levels of alcohol consumption in these countries may have contributed to alcohol use disorders and, subsequently, to adverse psychiatric outcomes such as suicide. The fact that the Baltic countries also share this pattern among men may be related to the absence of health and social services as developed as those in Western Europe, despite these countries being more developed than Eastern European countries, as well as to their Eastern Europe–like conditions, their high levels of alcohol consumption, the greater alcohol use among men compared with women, and unemployment; indeed, in our analyses of the spatial correlation between alcohol consumption and suicide rates, this correlation was found to be higher in men than in women (Brainerd, 2001 ; Razvodosky, 2015; Värnik et al., 1994 ). Moreover, in our SLM model, unemployment did not significantly affect suicide rates among women, but was observed to significantly increase suicide rates among men. In countries located in southern Africa, economic problems and social inequalities, epidemics such as AIDS, the limited availability of health services, and serious difficulties in accessing even these limited services are important reasons for the high suicide rates among both women and men. (Seetsa Makateng, 2024 ). People in Middle Eastern and Latin American countries are known for their strong religious beliefs and spiritual orientation. Although these religious beliefs differ, suicide is prohibited in almost all belief systems, particularly in the monotheistic religions. At the same time, close family, kinship, and friendship ties are known to be more intense in these societies (Hsieh, 2017 ; Stack and Kposowa, 2011 ). This situation may override individualistic ways of life, thereby preventing social isolation and reducing suicide rates in these countries. However, on the other hand, these countries may not maintain records in a transparent and accurate manner to the same extent as more developed countries. Due to having lower levels of health and social services compared with developed countries, and because restrictive and prohibitive policies are more common in these settings, cases that are in fact suicides may not have been recorded or reported as such. In our study, in addition to the fact that the spatial autocorrelation of suicide was markedly high, it was noteworthy that the spatial correlation between suicide rates and alcohol consumption was also pronounced. Among women, hotspots for suicide rates and alcohol consumption were concentrated in Central and Eastern Europe, whereas among men, hotspots were identified not only in Central and Eastern European countries but also in Moldova, Poland, and Romania. This finding, highlighting the co-occurrence of high alcohol consumption and elevated suicide rates, is consistent with the existing literature. Systematic review studies have reported that high alcohol consumption is a risk factor for suicide (Amiri and Behnezhad, 2020 ; Pompili et al., 2010 ). Landberg’s study reported that a one-liter increase in alcohol consumption in Eastern Europe is associated with an approximate 6–8% increase in suicide rates (Landberg, 2008 ). In a study covering 14 European countries, Ramstedt reported a positive and significant relationship between per capita alcohol consumption and gender- and age-specific suicide rates and stated that this relationship was most pronounced in Northern Europe (Ramstedt, 2001 ). Although the relationship between alcohol consumption and suicide is evident in spatial autocorrelation analyses, the explanatory power of alcohol consumption for female suicides lost its significance in spatial regression results. In contrast, alcohol consumption remained a significant factor explaining suicide rates in models for male suicides and the total population. This difference may be attributed to higher levels of alcohol consumption among males compared to females. One study has shown that this difference can be up to 2 times higher (Dawson and Archer, 1992 ). In the advanced regression models we constructed, even after controlling for other variables that could act as confounders, the positive association between alcohol consumption and suicide rates remained statistically significant in men. This can be interpreted as a strong indication that alcohol consumption may be a factor that increases suicide rates, particularly among men. From an epidemiological perspective, although ecological studies are not the most appropriate designs for establishing causality, findings from well-designed ecological studies that are supported by advanced analyses, even if they cannot fully demonstrate causality, may nonetheless be interpreted as providing strong inferential evidence in the pathway toward causal interpretation (Loney and Nagelkerke, 2014 ). A study conducted in Europe reported that unemployment is an important factor in increasing the suicide rate. In that study, the role of unemployment in increasing suicide cases, particularly among men, was highlighted (Breuer, 2015 ). In our study, however, we found that unemployment had an increasing effect on suicide rates not only among men but also among women. Among men, unemployment significantly increased suicide rates in both the OLS and SEM models. Among women, however, the situation was more complex, and the spatial regression models revealed the relationship between female unemployment and suicide rates that appeared to be non-significant in the OLS model. This is because classical models that do not account for spatial patterning treat the data as a single whole and operate on the overall mean (ArcGIS Pro Documentation, 2024). In reality, however, in some countries, suicide rates may remain low despite high female unemployment, due to under- or misrecording or to other social factors such as religious beliefs and societal norms. By contrast, in countries that are similar in terms of governance and lifestyle, suicide rates and unemployment tend to be correlated. Classical models cannot disentangle these patterns and evaluate all countries as a single group. By taking spatial dependence into account, SEM distinguishes between similar countries and, through the structure of the error terms, incorporates potential regional background factors that are not explicitly included in the models, thereby elucidating relationships that initially appear non-significant. In our study, this approach clarified the complexity of the relationship between female unemployment and female suicide. The literature has already reported that unemployment generally increases suicide rates. In a study conducted by Nordt et al. across 63 countries, an 11-year analysis showed that unemployment increased suicide rates by approximately 20% (Nordt et al., 2015 ). One other important finding in our study was that while the Economic Freedom Index score moved in the opposite direction of the female suicide rate, it moved in parallel with the male suicide rate. This situation, which may appear surprising, in fact highlights how important interaction effects are in epidemiology and underscores the necessity of reporting interaction factors separately. This is important in that it shows that suicide rates may display similar patterns in women and men in some respects, yet differ in others. In male suicides, an increase in economic freedom as a result of social roles may also lead to an increase in pressures related to status and achievement. In addition, this freedom may bring individualism with it and thus cause a reduction in social support. This may explain the parallel course of the relationship between male suicide and economic freedom (Durkheim, 1897 ; Graafland, 2023 ; World Health Organization, 2014 ). When considering the situation for women, research has shown that in societies where women’s economic freedoms are restricted and their access to economic opportunities is limited, female suicides increase (Bergen et al., 2021 ; Kasaju et al., 2021 ). Additionally, it has been reported that women are more affected by the factors influencing economic freedom compared to men (Claveria et al., 2024 ). In our study, we observed higher suicide rates in countries with high population density. Wang et al. also reported similar results in a previous study (Wang et al., 2020 ). In a study conducted by Boor, it was reported that suicide rates increased in societies with rising population density, particularly those receiving immigration (Boor, 1981 ). In an ecological study conducted in Belgium by Hooghe and Vanhoutte, in addition to emphasizing the increasing effect of population density on suicide rates, it was also reported that this situation was more pronounced, particularly among men, and that the importance of spatial effects should be taken into account (Hooghe and Vanhoutte, 2011 ). Consequently, one possible explanation for this is that increasing population density reduces access to limited resources and services, which in turn may increase distress in society and subsequently lead to a rise in suicidal behavior. 5. Strengths and Weaknesses We conducted our study using open data shared by numerous reputable health-related institutions and organizations worldwide, such as the ILO and WHO. We performed our analyses on a global scale without excluding any country. In addition to examining suicide rates through spatial autocorrelation, we also assessed the bivariate spatial autocorrelations between suicide rates and alcohol consumption, which we used as the primary explanatory variable for suicide rates. To reduce the ambiguity arising from classical regression models that ignore spatial dependence and neighborhood effects, we employed the SEM model, which accounts for spatial patterns and variables that might otherwise be overlooked. We also carried out all of these analyses separately for women and men. However, as in every ecological study, ecological bias can be considered one of our weaknesses. Although we obtained our data from reputable institutions, economic and political factors in the countries that supply data to these institutions may result in data quality not being uniform across countries. The possibility that, in some countries, for economic reasons, and in others, for political reasons, data may have been reported lower or higher than they actually are is also an important limitation affecting the accuracy of our results. In addition, although the problems caused by missing data were addressed as far as possible using appropriate statistical methods, estimates cannot fully substitute for real data, and this has been an important limitation of our study. Moreover, the fact that our study design is at the population level and does not take individual effects into account constitutes a serious limitation in terms of the generalizability of the findings. Another limitation is that our study relies only on data from the years 2017–2021. This may have prevented us from capturing changes in health and social conditions over time. Finally, although we identified important hot and cold spot clusters for suicide by sex, detailed results could not be presented because subgroups within these countries were not taken into account. 6. Conclusion and Suggestions When all results are evaluated together, our most important finding is that alcohol consumption is positively correlated with the male suicide rate, with an effect strength close to causality. In both spatial analyses and regression models in which covariates were controlled for, the joint pattern of alcohol consumption and suicide rates was consistently found to be significant among men. The fact that suicide rates and alcohol consumption are correlated in Europe in particular suggests that efforts should be made in these countries to reduce alcohol consumption. Another important finding is that suicide clusters at low or high levels in certain regions are unequally distributed, and that male and female suicides cluster with similar characteristics in some places while exhibiting different patterns in others. Eastern European countries and those in southern Africa stand out as hot spots for both sexes. In these countries, addressing suicide and developing preventive policies that encompass the general population may be considered a fundamental necessity. The fact that economic freedom affects suicide rates differently by sex also highlights the need to develop gender-sensitive policies. As economic freedom increases, gender inequality should be tackled, and social support systems should be strengthened. Migration, a current and significant global issue, is one of the most important drivers of increasing population density. When considered together with our finding that rising population density increases suicide, an increase in suicide cases may emerge in the future in some countries where population density has risen due to migration. Therefore, situations that may lead to increased population density should be evaluated carefully. Since this study was designed ecologically, the results should only be used for hypothesis generation; for causality, further studies in smaller subgroups based on these hypotheses are required. However, the maps we produced for suicide rates and alcohol consumption may help international organizations such as the World Health Organization to decide where to concentrate their public health initiatives aimed at suicide prevention. Declarations Conflict of Interest The authors declare that they have no conflict of interest. Competing Interest The authors have no relevant financial or non-financial interests to disclose. Ethical The data used in the study were obtained from publicly available databases, reports, and similar publications provided by internationally recognized institutions and organizations such as Our World In Data and the World Health Organization. Therefore, ethical committee approval was not deemed necessary. The sources of the obtained data are individually listed and cited in the references section of the study. Funding No funds, grants, or other support was received. Author Contribution OHA: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Visualization, Writing - original draft, Writing - review and editing. SM: Data curation, Formal analysis, Investigation, Methodology, Resources, Supervision, Validation, Writing - original draft, Writing - review and editing. MFO: Methodology, Resources, Supervision, Validation, Writing - original draft, Writing - review and editing. EA: Data curation, Resources, Supervision, Validation, Writing - review and editing Acknowledgement We would like to express our sincere gratitude to Dr. Feyza Akbay, a research assistant in our department, who holds the highest level of certification in English proficiency in the national official foreign language examination and serves as the scientific secretary of the Journal of Public Health of the Eskişehir Turkish World Application and Research Center, for reviewing our manuscript in terms of language clarity and appropriateness as well as overall manuscript criteria, and for making the necessary revisions. Data Availability Suicide rate data used in this study were obtained from the official WHO website. Data on per capita alcohol consumption and the number of psychiatrists per capita were retrieved from the Our World in Data platform; economic freedom indices from The Heritage Foundation website; unemployment rate data from the ILO website; and population density data from the World Bank Group data portal. The full web addresses of the relevant data sources were cited in the Methods section, where appropriate, and listed in the reference list. Upon request, the data used in the analyses can be shared in Excel format. Since all data are publicly available and published by the respective institutions, no additional permission for use was obtained. References ArcGIS Esri (2024). How multiscale geographically weighted regression (MGWR) works. ArcGIS Pro Documentation. Retrieved October 4, 2025, from https://pro.arcgis.com/en/pro-app/3.4/tool-reference/spatial-statistics/how-multiscale-geographically-weighted-regression-mgwr-works.htm Ahmad, O. B., Boschi-Pinto, C., Lopez, A. D., Murray, C. J. L., Lozano, R., & Inoue, M. (2001). Age standardization of rates: A new WHO standard (GPE Discussion Paper Series, No. 31). World Health Organization. Amiri, S., & Behnezhad, S. (2020). Alcohol use and risk of suicide: A systematic review and meta-analysis. Journal of Addictive Diseases , 38 , 200–213. https://doi.org/10.1080/10550887.2020.1736757 Anselin, L. (1995). Local indicators of spatial association (LISA). Geographical Analysis , 27 , 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x Anselin, L., Sridharan, S., & Gholston, S. (2007). Using exploratory spatial data analysis to leverage social indicator databases: The discovery of interesting patterns. Social Indicators Research , 82 , 287–309. https://doi.org/10.1007/s11205-006-9034-x Anselin, L., Syabri, I., & Kho, Y. (2006). GeoDa: An introduction to spatial data analysis. Geographical Analysis , 38 , 5–22. https://doi.org/10.1111/j.0016-7363.2005.00671.x Anselin, L., Syabri, I., & Kho, Y. (2010). GeoDa: An introduction to spatial data analysis. In M. M. Fischer & A. Getis (Eds.), Handbook of applied spatial analysis: Software tools, methods and applications (pp. 73–89). Springer. https://doi.org/10.1007/978-3-642-03647-7_5 van Bergen, D., Bergeijk, O. E. V., & Montesinos, A. H. (2021). Attempted suicide and suicide of young Turkish women in Europe and Turkey: A systematic literature review of characteristics and precipitating factors. PLOS ONE , 16 , e0253274. https://doi.org/10.1371/journal.pone.0253274 Bogdan, M., Ghosh, J. K., & Tokdar, S. T. (2008). A comparison of the Benjamini–Hochberg procedure with some Bayesian rules for multiple testing. In N. Balakrishnan, E. A. P. Rao, & M. J. Silvapulle (Eds.), Beyond parametrics in interdisciplinary research: Festschrift in honor of Professor Pranab K. Sen (pp. 211–230). Institute of Mathematical Statistics. https://doi.org/10.1214/193940307000000158 Boor, M. (1981). Relationship of 1977 state suicide rates to population increases and immigration. Psychological Reports , 49 (3), 856–858. https://doi.org/10.2466/pr0.1981.49.3.856 Borges, G., Bagge, C. L., Cherpitel, C. J., Conner, K. R., Orozco, R., & Rossow, I. (2017). A meta-analysis of acute use of alcohol and the risk of suicide attempt. Psychological Medicine , 47 , 949–957. https://doi.org/10.1017/S0033291716002841 Brainerd, E. (2001). Economic reform and mortality in the former Soviet Union: A study of the suicide epidemic in the 1990s. European Economic Review , 45 (4–6), 1007–1019. Breuer, C. (2015). Unemployment and suicide mortality: Evidence from regional panel data in Europe. Health Economics , 24 , 936–950. https://doi.org/10.1002/hec.3073 Cherpitel, C. J., Borges, G. L. G., & Wilcox, H. C. (2004). Acute alcohol use and suicidal behavior: A review of the literature. Alcoholism: Clinical and Experimental Research, 28(5 Suppl), 18S–28S. https://doi.org/10.1097/01.alc.0000127411.61634.14 Chikritzhs, T., & Livingston, M. (2021). Alcohol and the risk of injury. Nutrients , 13 (8), 2777. https://doi.org/10.3390/nu13082777 Claveria, O., Sorić, M., & Sorić, P. (2024). Analysis of the impact of financial and labour uncertainty on suicide mortality in England. Health & Place , 89 , 103329. https://doi.org/10.1016/j.healthplace.2024.103329 Crawford, M. J., Kuforiji, B., & Ghosh, P. (2010). The impact of social context on socio-demographic risk factors for suicide: A synthesis of data from case–control studies. Journal of Epidemiology & Community Health , 64 , 530–534. https://doi.org/10.1136/jech.2008.084145 Dawson, D. A., & Archer, L. (1992). Gender differences in alcohol consumption: Effects of measurement. British Journal of Addiction , 87 , 119–123. https://doi.org/10.1111/j.1360-0443.1992.tb01909.x Durkheim, É. (1897). Le suicide: Étude de sociologie . Félix Alcan. Graafland, J. (2023). Economic freedom and life satisfaction: A moderated mediation model with individual autonomy and national culture. European Journal of Political Economy , 79 , 102448. https://doi.org/10.1016/j.ejpoleco.2023.102448 Guzmán, E. M., Cha, C. B., Ribeiro, J. D., & Franklin, J. C. (2019). Suicide risk around the world: A meta-analysis of longitudinal studies. Social Psychiatry and Psychiatric Epidemiology , 54 , 1459–1470. https://doi.org/10.1007/s00127-019-01759-x Hawton, K., & van Heeringen, K. (2009). Suicide The Lancet , 373, 1372–1381. https://doi.org/10.1016/S0140-6736(09)60372-X . Helbich, M., Leitner, M., & Kapusta, N. D. (2012). Geospatial examination of lithium in drinking water and suicide mortality. International Journal of Health Geographics , 11 , 19. https://doi.org/10.1186/1476-072X-11-19 Hooghe, M., & Vanhoutte, B. (2011). An ecological study of community-level correlates of suicide mortality rates in the Flemish Region of Belgium, 1996–2005. Suicide and Life-Threatening Behavior , 41 , 453–464. https://doi.org/10.1111/j.1943-278X.2011.00047.x Hsieh, N. (2017). A global perspective on religious participation and suicide. Journal of Health and Social Behavior , 58 , 322–339. https://doi.org/10.1177/0022146517715896 Hufford, M. R. (2001). Alcohol and suicidal behavior. Clinical Psychology Review , 21 , 797–811. https://doi.org/10.1016/S0272-7358(00)00070-2 International Labour Organization (2019). Statistics on unemployment and labour underutilization: SDG indicator 8.5.2 Unemployment rate (%). ILOSTAT database. Retrieved October 4, 2025, from https://ilostat.ilo.org/topics/unemployment-and-labour-underutilization/ Kapusta, N. D., Mossaheb, N., Etzersdorfer, E., Hlavin, G., Thau, K., Willeit, M., Praschak-Rieder, N., Sonneck, G., Leithner-Dziubas, K., & Willeit, M. (2011). Lithium in drinking water and suicide mortality. The British Journal of Psychiatry , 198 , 346–350. https://doi.org/10.1192/bjp.bp.110.091041 Kasaju, S. P., Krumeich, A., & Van der Putten, M. (2021). Suicide and deliberate self-harm among women in Nepal: A scoping review. BMC Women’s Health , 21 , 407. https://doi.org/10.1186/s12905-021-01547-3 Landberg, J. (2008). Alcohol and suicide in Eastern Europe. Drug and Alcohol Review , 27 , 361–373. https://doi.org/10.1080/09595230802093778 Loney, T., & Nagelkerke, N. J. (2014). The individualistic fallacy, ecological studies and instrumental variables: A causal interpretation. Emerging Themes in Epidemiology , 11 , 18. https://doi.org/10.1186/1742-7622-11-18 Macente, L. B., & Zandonade, E. (2012). Spatial distribution of suicide incidence rates in municipalities in the state of Espírito Santo (Brazil), 2003–2007: Spatial analysis to identify risk areas. Brazilian Journal of Psychiatry , 34 , 261–269. https://doi.org/10.1016/j.rbp.2011.11.001 Mann, J. J., Apter, A., Bertolote, J., Beautrais, A., Currier, D., Haas, A., Hegerl, U., Lonnqvist, J., Malone, K., Marusic, A., Mehlum, L., Patton, G., Phillips, M., Rutz, W., Rihmer, Z., Schmidtke, A., Shaffer, D., Silverman, M., Takahashi, Y., & Hendin, H. (2005). Suicide prevention strategies: A systematic review. Journal Of The American Medical Association , 294 , 2064–2074. https://doi.org/10.1001/jama.294.16.2064 Moothathu, T. S. K. (1990). Lorenz curve and Gini index. Calcutta Statistical Association Bulletin , 40 (1–4), 307–324. https://doi.org/10.1177/0008068319900524 Nordt, C., Warnke, I., Seifritz, E., & Kawohl, W. (2015). Modelling suicide and unemployment: A longitudinal analysis covering 63 countries, 2000–11. The Lancet Psychiatry , 2 , 239–245. https://doi.org/10.1016/S2215-0366(14)00118-7 Norström, T., & Rossow, I. (2016). Alcohol consumption as a risk factor for suicidal behavior: A systematic review of associations at the individual and at the population level. Archives of Suicide Research , 20 , 489–506. https://doi.org/10.1080/13811118.2016.1158678 Núñez-González, S., Lara-Vinueza, A. G., Gault, C., & Delgado-Ron, J. A. (2018). Trends and spatial patterns of suicide among adolescents in Ecuador, 1997–2016. Clinical Practice & Epidemiology in Mental Health , 14 , 283–292. https://doi.org/10.2174/1745017901814010283 O’Farrell, I., Corcoran, P., & Perry, I. (2016). The area level association between suicide, deprivation, social fragmentation, and population density in the Republic of Ireland: A national study. Social Psychiatry and Psychiatric Epidemiology , 51 , 839–847. https://doi.org/10.1007/s00127-016-1205-8 Our World in Data (2019). Psychiatrists per 100,000 people. Retrieved October 4, 2025, from https://ourworldindata.org/grapher/psychiatrists-working-in-the-mental-health-sector?tab=table&time=2019 Pitman, A., Osborn, D., King, M., & Erlangsen, A. (2014). Effects of suicide bereavement on mental health and suicide risk. The Lancet Psychiatry , 1 , 86–94. https://doi.org/10.1016/S2215-0366(14)70224-X Pompili, M., Serafini, G., Innamorati, M., Dominici, G., Ferracuti, S., Kotzalidis, G. D., Serra, G., Girardi, P., Janiri, L., Tatarelli, R., Sher, L., & Lester, D. (2010). Suicidal behavior and alcohol abuse. International Journal of Environmental Research and Public Health , 7 , 1392–1431. https://doi.org/10.3390/ijerph7041392 QGIS.org (2025). QGIS User Guide (Version 3.40). Retrieved October 4, 2025, from https://docs.qgis.org/3.40/en/docs/user_manual/index.html Ramstedt, M. (2001). Alcohol and suicide in 14 European countries. Addiction , 96 , S59–S75. https://doi.org/10.1046/j.1360-0443.96.1s1.6.x Razvodovsky, Y. E. (2015). Fatal alcoholic poisonings in Russia and Belarus: A comparative analysis of trends. Alcoholism and Psychiatry Research , 51 , 137–146. Ritchie, H., & Roser, M. (2022). Alcohol consumption. Our World in Data. Retrieved October 4, 2025, from https://ourworldindata.org/alcohol-consumption Roth, G. A., Abate, D., Abate, K. H., & Murray, C. J. L. (2018). Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: A systematic analysis for the Global Burden of Disease Study 2017. The Lancet , 392 , 1736–1788. https://doi.org/10.1016/S0140-6736(18)32203-7 Seetsa Makateng, D. (2024). The philosophical and sociological exposition of suicide in Lesotho. Virajes , 26 , 169–186. https://doi.org/10.17151/rasv.2024.26.2.8 Shanahan, L., Hill, S. N., Gaydosh, L., Steinhoff, A., Costello, E. J., Dodge, K. A., Harris, M., K., & Copeland, W. E. (2019). Does despair really kill? A roadmap for an evidence-based answer. American Journal of Public Health , 109 (6), 854–858. https://doi.org/10.2105/AJPH.2019.305016 Simões, R., Santos, J. C., & Martinho, M. J. (2020). As representações sociais do suicídio em adolescentes: Scoping review. RPESM. https://doi.org/10.19131/rpesm.0273 Stack, S., & Kposowa, A. J. (2011). Religion and suicide acceptability: A cross-national analysis. Journal for the Scientific Study of Religion , 50 , 289–306. https://doi.org/10.1111/j.1468-5906.2011.01568.x The Heritage Foundation (2019). Index of economic freedom: All country scores. Retrieved October 4, 2025, from https://www.heritage.org/index/pages/all-country-scores Tiefelsdorf, M., & Boots, B. (1995). The Exact Distribution of Moran’s I. Environment and Planning. Economy and Space , 27 (6), 985–999. https://doi.org/10.1068/a270985 (Original work published 1995). Tiefelsdorf, M., & Boots, B. (1997). A note on the extremities of local Moran’s I and their impact on global Moran’s I. Geographical Analysis , 29 , 248–257. https://doi.org/10.1111/j.1538-4632.1997.tb00960.x United Nations (2025). Member States. Retrieved October 4, 2025, from https://www.un.org/en/about-us/member-states Värnik, A., Wasserman, D., & Eklund, G. (1994). Suicides in the Baltic countries, 1968–90. Scandinavian Journal of Social Medicine , 22 , 166–169. https://doi.org/10.1177/140349489402200302 Wang, P., Goggins, W. B., Zhang, X., Ren, C., & Lau, K. K. L. (2020). Association of urban built environment and socioeconomic factors with suicide mortality in high-density cities: A case study of Hong Kong. Science of the Total Environment , 739 , 139877. https://doi.org/10.1016/j.scitotenv.2020.139877 Ward, M., & Gleditsch, K. (2008). Spatial regression models. SAGE Publications . https://doi.org/10.4135/9781412985888 World Bank (2019). Population density (people per sq. km of land area) [Data set]. World Bank Open Data. Retrieved October 8, 2025, from https://data.worldbank.org/indicator/EN.POP.DNST?end=2019&start=2019 World Health Organization. (2002). World report on violence and health . World Health Organization. World Health Organization. (2014). Preventing suicide: A global imperative . World Health Organization. World Health Organization (2025). Suicide mortality rate (per 100,000 population) [Indicator]. WHO Data. Retrieved May 12, 2025, from http://data.who.int/indicators/i/F08B4FD/16BBF41 World Health Organization (2025). Suicide worldwide in 2021: Global health estimates. World Health Organization. Retrieved October 27, 2025, from https://www.who.int/publications/i/item/9789240110069 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8398774","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":574743156,"identity":"a5b432e6-aa60-4533-bbd1-5ce0db3b729c","order_by":0,"name":"Oguz Han Aydilek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYDCCA0ikREIBgwE/iAVkEKvFgMFAsgGkxYBYLQxALQZgNh4tfLcPH37N8+eOvDn7GcMbDwxsjI3Pr0788MCAQZ5f7ABWLZLn0tKseXieGe7syTG2SDBIMzO78XYzyIWGM2cnYNVicIbHzJhH4jDjhgM5ZkCVh23MbpzdANKSYHAbnxaDw/Ybzr8BaflvYzzj7OYfBLQYP+ZJOJy44QbYlgNmBvy92/DaInmGLY1xzoHDyRtuPCsG+iXZWOIG7zYgQwKnX/jOMB/+8ObPYdsN55M33vxRYWfY3392M5BhI88vjV0LELBJQGgOaFxIgFVK4FIOAswfIDT7AwjNfwCf6lEwCkbBKBiBAAAexmdn/vIBeAAAAABJRU5ErkJggg==","orcid":"","institution":"Eskişehir Osmangazi University","correspondingAuthor":true,"prefix":"","firstName":"Oguz","middleName":"Han","lastName":"Aydilek","suffix":""},{"id":574743162,"identity":"50549716-02cd-4896-ab81-3b9f1281a3fb","order_by":1,"name":"Selma Metintas","email":"","orcid":"","institution":"Eskişehir Osmangazi University","correspondingAuthor":false,"prefix":"","firstName":"Selma","middleName":"","lastName":"Metintas","suffix":""},{"id":574743164,"identity":"0988be1b-489f-4f6f-956a-86bc2a62f045","order_by":2,"name":"Muhammed Fatih Onsuz","email":"","orcid":"","institution":"Eskişehir Osmangazi University","correspondingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"Fatih","lastName":"Onsuz","suffix":""},{"id":574743166,"identity":"f5328ca1-d949-43fc-8c84-d9701dabc11b","order_by":3,"name":"Emrah Atay","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Emrah","middleName":"","lastName":"Atay","suffix":""}],"badges":[],"createdAt":"2025-12-18 20:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8398774/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8398774/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100595213,"identity":"215c46f9-b923-4b95-bfec-1fbe676b7609","added_by":"auto","created_at":"2026-01-19 13:47:54","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":390226,"visible":true,"origin":"","legend":"","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/79ea826c27d0df515c6d4843.png"},{"id":100595338,"identity":"1bdaf2aa-918e-4d4d-a4bf-46f86f9722f6","added_by":"auto","created_at":"2026-01-19 13:48:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1117085,"visible":true,"origin":"","legend":"","description":"","filename":"TheUnequalGlobalGeographicDistributionofSuicidev1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/78231a39ff52aab6ba9040be.docx"},{"id":100595517,"identity":"570b7ded-b8be-488c-91a2-c7c8da1f19a2","added_by":"auto","created_at":"2026-01-19 13:48:40","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":418412,"visible":true,"origin":"","legend":"","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/1037e4c4b4cfb2fa972e62f1.png"},{"id":100595599,"identity":"c448bf4c-1606-492f-a16b-092e70e768bf","added_by":"auto","created_at":"2026-01-19 13:48:53","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":267290,"visible":true,"origin":"","legend":"","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/48b43808c5042392466bcf42.png"},{"id":100595204,"identity":"9845b52f-5fe8-4df4-aa75-894889ccd431","added_by":"auto","created_at":"2026-01-19 13:47:52","extension":"json","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7480,"visible":true,"origin":"","legend":"","description":"","filename":"df8f0ff6ea9049958143f739ff75ebe9.json","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/f09eeb06ef8814b241b397bd.json"},{"id":100595385,"identity":"ab64819e-25bc-4dd7-b194-d10eb9f09a45","added_by":"auto","created_at":"2026-01-19 13:48:22","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172322,"visible":true,"origin":"","legend":"","description":"","filename":"df8f0ff6ea9049958143f739ff75ebe91enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/739e368426a32c9892f89ca6.xml"},{"id":100594946,"identity":"34aa7954-d7ca-4364-9bc4-3455b6841a31","added_by":"auto","created_at":"2026-01-19 13:46:37","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":390226,"visible":true,"origin":"","legend":"","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/aa0c951d4665308965da1b57.png"},{"id":100595207,"identity":"72699e69-d7db-4555-aca6-4c3fad15aaa7","added_by":"auto","created_at":"2026-01-19 13:47:53","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":418412,"visible":true,"origin":"","legend":"","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/f86784622f8845798fc06536.png"},{"id":100594744,"identity":"1b139b99-0e95-492a-b535-6c6173175bc6","added_by":"auto","created_at":"2026-01-19 13:44:42","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":267290,"visible":true,"origin":"","legend":"","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/cdfac8b1541c6ad4e22b62a7.png"},{"id":100595259,"identity":"d727f2d2-900b-4cc4-996d-036e6b09ff37","added_by":"auto","created_at":"2026-01-19 13:48:04","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":849871,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/b1ed5e12b6ffab69bdd10058.jpeg"},{"id":100594834,"identity":"e815ada6-1f6a-4f44-bab0-1d43cc0e2426","added_by":"auto","created_at":"2026-01-19 13:45:31","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":849331,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/e80c357943961150e936dc24.jpeg"},{"id":100563726,"identity":"382f45f7-b6c7-429a-9a05-f5617a1565c1","added_by":"auto","created_at":"2026-01-19 08:48:21","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":267290,"visible":true,"origin":"","legend":"","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/7c0e46100f446b2d1af63dcc.png"},{"id":100595324,"identity":"408d9fe3-0113-448e-8b54-543fe5f04110","added_by":"auto","created_at":"2026-01-19 13:48:13","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1074,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/07884ef920034142c0946e94.jpeg"},{"id":100595430,"identity":"1cd44683-d60f-4f36-9c28-8eb0164fb33e","added_by":"auto","created_at":"2026-01-19 13:48:27","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":145511,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/63a1f2eb31fd870f3961df4a.png"},{"id":100563741,"identity":"8e2ffe49-ee06-4fa6-9c9f-afda5efb79bd","added_by":"auto","created_at":"2026-01-19 08:48:22","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151960,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/a3b659e3f1e118c855ec07df.png"},{"id":100594909,"identity":"5d74a76b-e7b1-4256-bbea-5b841316ead0","added_by":"auto","created_at":"2026-01-19 13:46:23","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57546,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/cca0b3a54b1b4fac66fc7fe4.png"},{"id":100595405,"identity":"36f5fee7-319a-44ce-9d87-256998b8f535","added_by":"auto","created_at":"2026-01-19 13:48:24","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177317,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/993c09253236c21b840f5ed0.png"},{"id":100563738,"identity":"3fdc0022-3b33-47fb-b96a-35541c2a6b91","added_by":"auto","created_at":"2026-01-19 08:48:21","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174363,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/c68b1e21366bce59a23041ae.png"},{"id":100594745,"identity":"5302a51b-6e19-452f-b27d-017c6082319c","added_by":"auto","created_at":"2026-01-19 13:44:42","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57546,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/07fa508d80cd65be9a867fec.png"},{"id":100594754,"identity":"2bd06d07-54ce-4664-be3c-38486481f394","added_by":"auto","created_at":"2026-01-19 13:44:48","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":935,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/d775c8dce5549e5ed9b9cba4.png"},{"id":100563732,"identity":"3e81335c-a2c0-443f-a347-dba4f5f4f790","added_by":"auto","created_at":"2026-01-19 08:48:21","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":169769,"visible":true,"origin":"","legend":"","description":"","filename":"df8f0ff6ea9049958143f739ff75ebe91structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/26687b4db2f95f37db23e752.xml"},{"id":100563740,"identity":"3d985d64-7239-4665-9d8f-40e074fe68eb","added_by":"auto","created_at":"2026-01-19 08:48:21","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":184516,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/d4f85ccf53915fcd802c25f2.html"},{"id":100563716,"identity":"586ac2a7-65c1-415d-9fdf-e6cb7e590400","added_by":"auto","created_at":"2026-01-19 08:48:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":390226,"visible":true,"origin":"","legend":"\u003cp\u003eMoran's I distribution and LISA cluster maps of suicide rates for males (a), females (b), and the total population (c).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/c774bd8c64d767852f5011d5.png"},{"id":100563720,"identity":"5660264a-5054-41f9-aef6-27d3868a269e","added_by":"auto","created_at":"2026-01-19 08:48:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":418412,"visible":true,"origin":"","legend":"\u003cp\u003eBivariate Moran’s I distribution and LISA cluster maps of suicide rate and per capita annual alcohol consumption for male (a), female (b), and total population (c)\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/d403626323490428c1f27c95.png"},{"id":100595482,"identity":"d4c21269-1225-455f-a85c-3127e7028eaa","added_by":"auto","created_at":"2026-01-19 13:48:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":267290,"visible":true,"origin":"","legend":"\u003cp\u003eInequalities in the Distribution of Suicide Rate and Its Associated Variables Worldwide: Lorenz Curve and Gini Coefficient\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/b17e16806e1c98e38187b618.png"},{"id":108804090,"identity":"47a60274-9581-49e0-8222-772412bdc9fa","added_by":"auto","created_at":"2026-05-08 15:15:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1418498,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8398774/v1/1830f5d0-ca9c-464b-b805-024111a9603f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Unequal Global Geographic Distribution of Suicide: Spatial Patterns and Its Relationship with Alcohol","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Suicide rates vary markedly by location and gender worldwide and are unevenly distributed.\u003c/p\u003e\u003cp\u003e\u0026bull; Eastern Europe and Southern Africa were hotspot regions for suicide rates.\u003c/p\u003e\u003cp\u003e\u0026bull; In Europe, places with more alcohol use tended to have higher suicide rates.\u003c/p\u003e\u003cp\u003e\u0026bull; Sex-stratified analyses showed a positive association between economic freedom and suicide rates in men, and a negative association in women.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eSuicide, deaths from chronic liver disease attributable to excessive alcohol consumption, and drug poisonings due to drug overdose are defined as deaths of despair (DoD). From this perspective, DoD comprises three core components, and suicide arguably stands out as the most important contributor to DoD (Shanahan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Seetsa Makateng, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Suicide is a complex phenomenon influenced by many factors (Sim\u0026otilde;es et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is a behavior carried out deliberately by an individual who is aware of its irreversible and fatal outcome (World Health Organization, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The World Health Organization (WHO) estimates that in 2021, more than 700,000 people lost their lives due to suicide alone. As an important public health problem, suicide ranks 10th among all causes of death, while it is the 3rd leading cause of death among those aged 15\u0026ndash;29 years (WHO, 2025). At the same time, suicide deeply affects not only the individual but also their surroundings. One study emphasized that each suicide affects approximately 60 people in the person\u0026rsquo;s social environment (Pitman et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA meta-analysis reported that ninety percent of publications on suicide originate from North America and Europe, whereas low- and middle-income countries are insufficiently represented in this field. (Guzm\u0026aacute;n et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Evidence from studies indicates that suicide rates can vary greatly between countries and even between different regions within the same country (Hawton and van Heeringen, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kapusta et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, the reasons underlying these regional differences have not been fully clarified. (Helbich et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mann et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). For these reasons, evaluating suicide rates on a global scale using spatial analyses may contribute to understanding the relationship between local factors and suicide. This is important because, when developing effective prevention strategies, it is critical to understand how suicide rates behave across different demographic groups and to examine their spatial autocorrelation (Crawford et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; O\u0026rsquo;Farrell et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor younger age groups, alcohol use disorder (AUD) emerges as a significant cause of death, aside from suicide (World Health Organization, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the distribution of disease burden caused by alcohol-related injuries, suicide and self-harm rank second, following road traffic injuries (Roth et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The Global Burden of Disease (GBD) study estimates that approximately 15% of all suicide deaths are alcohol-related, meaning that more than 100,000 people die annually due to alcohol-related self-harm (Chikritzhs and Livingston, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Alcohol dependence and alcohol poisoning are considered significant risk factors for suicidal behavior (Hufford, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Norstr\u0026ouml;m and Rossow, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pompili et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A systematic review and meta-analysis reported that alcohol consumption increases the risk of suicide, with an odds ratio of 1.64 (Amiri and Behnezhad, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition to individual-level studies showing that people with AUD have an increased risk of suicidal ideation, self-harm, and completed suicide, there are also studies indicating that heavy drinking at the time of the event increases the risk of suicide, as well as local studies demonstrating population-level associations between alcohol consumption and suicide rates (Borges et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Norstr\u0026ouml;m and Rossow, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This association has been explained through mechanisms such as alcohol increasing psychological distress, enhancing aggression, and raising the tendency to act on suicidal thoughts (Cherpitel et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Hufford, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Additionally, cultural factors appear to mediate the role of alcohol in self-harm; for example, there is some evidence that alcohol is more strongly associated with self-harm in cultures where alcohol use is more prevalent among men and where drinking is often aimed at intoxication. Moreover, although the evidence that alcohol contributes to suicide and self-harm is pretty strong, there remains uncertainty about the magnitude of the causal relationship, as alcohol use disorder, intoxication, and self-harm may share some underlying risk factors (Norstr\u0026ouml;m and Rossow, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The contribution of studies that evaluate global alcohol consumption frequency and suicide rates together, considering cultural differences, may help eliminate this uncertainty. However, studies that assess alcohol use frequency and suicide rates on a global scale using spatial analysis are limited.\u003c/p\u003e \u003cp\u003eThe primary aim of this study is to identify inequalities in the global distribution of suicide by examining its spatial clustering and quantifying spatial autocorrelation. To assess the association between alcohol consumption and suicide, we employ measures of spatial cross-correlation and then estimate spatial regression models while controlling for selected sociodemographic covariates. In addition, by stratifying the sociodemographic predictors by sex, we investigate whether the direction and magnitude of these associations are similar or different between females and males.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1.Study Area and Data\u003c/h2\u003e \u003cp\u003eThis ecological study was conducted in 183 out of 194 countries (94%) recognized by the United Nations (UN) for which suicide rate data are available for 2017\u0026ndash;2021 (UN, 2025; WHO, 2017). In this study, the data were based on the International Classification of Diseases (ICD-10) codes for suicide, and suicide mortality rates per 100,000 population were used. Since suicide rates did not show a significant change over time in the time series analysis of the study period, the weighted average of the standardized rates was calculated. In addition, due to the different trends observed in male and female suicide rates, age-standardized suicide rates were calculated separately by gender as well as combined. Standardization was performed based on the WHO World Standard Population (Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). As a result, the age-standardized 5-year average suicide rate (SSR) per 100,000 population, calculated with corrections, was determined as the dependent variable of the study.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to evaluate the influence of alcohol consumption, which is a significant cause of death, particularly among young people, on suicide rates (WHO, 2014). To do this, we selected per capita annual alcohol consumption as the primary explanatory variable. This data was sourced from the Our World in Data database (Ritchie and Roser, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, in order to demonstrate the net effect of alcohol consumption on suicide in the advanced regression models, we included in our analyses several variables that could act as potential confounders. For this purpose, for the selection of other variables that could be explanatory, the Benjamini-Hochberg correction was applied by ranking the p-values in ascending order, dividing them by the total number of variables, and multiplying them by their respective ranks. The aim was to observe the effect of alcohol consumption on suicide when the other variables were controlled for (Bogdan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). As a result of this process, the variables selected from the relevant databases were unemployment prevalence rate, economic freedom index, number of psychiatrists, and population density, as their p-values were lower than the calculated threshold (International Labour Organization, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; The Heritage Foundation, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Our World in Data, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; World Bank Group, 2019). The selected independent variables belonged to the year 2019, which is the midpoint of the study period. The units of the independent variables were as follows: percentage for unemployment, annual per capita consumption in liters for alcohol consumption, number of people per square kilometer for population density, and number per 100,000 people for the number of psychiatrists. The Economic Freedom Index measures how free an economy is on a scale from 0 to 100. It examines factors such as property rights, government integrity, the effectiveness of the courts, the tax burden, government spending, and business freedom and trade. A score of 100 means there is complete economic freedom, with no interference or pressure (The Heritage Foundation, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe data were extracted and compiled using SQL Server Management Studio version 19. A digital vector world map was created in QGIS version 3.28, and the dataset was integrated for analysis and visualization (QGIS Project, 2025).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2.Statistical Analyses\u003c/h2\u003e \u003cp\u003eWe used several tools for our statistical analyses, including GeoDa (version 1.22.0.21), R (version 4.0.0), and QGIS (version 3.36.1). We conducted basic statistical tests and analyzed spatial patterns using Univariate and Bivariate Local Moran\u0026rsquo;s I and Local Indicators of Spatial Association (LISA). Our spatial regression models included Ordinary Least Squares (OLS), Spatial Lag Model (SLM), and Spatial Error Model (SEM) with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. We log-transformed non-normally distributed variables, such as the unemployment rate, alcohol consumption, and economic freedom index.\u003c/p\u003e \u003cp\u003eTo conduct the spatial analyses, a k-nearest neighbors (kNN) spatial weights matrix was constructed using countries\u0026rsquo; centroids (k\u0026thinsp;=\u0026thinsp;4). A robustness test was performed by repeating the primary analyses using k\u0026thinsp;=\u0026thinsp;6 and k\u0026thinsp;=\u0026thinsp;8 and assessing the similarity of the results. Row standardization was applied to stabilize the model and prevent countries with more neighbors from disproportionately influencing the analyses. The purpose of using a kNN-based weights matrix instead of first-order contiguity was to enable calculations on a global map where land borders may be interrupted by seas and oceans, and to include island countries that would otherwise have no neighbors. One of the most common approaches used to calculate the degree of geographical correlation is Moran's I statistic. Moran's I is the slope of the linear fit to the scatter plot and takes values between +\u0026thinsp;1 and \u0026minus;\u0026thinsp;1. A value of +\u0026thinsp;1 indicates strong positive spatial autocorrelation (all the same next to each other), 0 indicates random spatial ordering, and \u0026minus;\u0026thinsp;1 (all opposite next to each other) indicates strong negative spatial autocorrelation (Tiefelsdorf and Boots, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1995\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoran's I has a limitation: it does not identify clustering within the study area. Local Indicators of Spatial Association (LISA) add further value beyond Moran\u0026rsquo;s I by distinguishing spatial interactions at the local scale. In spatial analyses, the display of High-High (hot spots), Low-Low (cold spots), and Low-High or High-Low areas is provided by LISA maps. For example, in a High-High (hot spot) area, the suicide rate is above the mean both in the country itself and in its neighboring countries, and this pattern is statistically significant (Anselin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Anselin et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Lorenz curve shows inequality in the distribution of a measured variable. The Gini coefficient (where 0 indicates perfect equality and 1 indicates complete inequality) reduces this inequality to a single number. In this study, inequality in the distribution of suicide rates was demonstrated not only through spatial autocorrelation but also using the Lorenz curve. In addition, Lorenz curves were presented in a single figure to highlight distributional inequalities across all associated variables (Moothathu, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpatial regression models should be implemented in three stages: first, Ordinary Least Squares (OLS), followed by the Spatial Lag Model (SLM) and the Spatial Error Model (SEM). OLS does not take spatial autocorrelation into account. Therefore, if Moran\u0026rsquo;s I statistic is significant, SLM and SEM are subsequently applied. SLM captures spatial interactions between neighboring units, whereas SEM detects spatial dependence in the error terms. After OLS, the appropriate model is determined based on the Lagrange Multiplier (LM) tests; whichever of the LM Lag or LM Error is significant indicates the model that should be selected. If both LM tests are significant, Robust LM tests are used to determine which model has dominance. If ambiguity remains, the model with the lower Akaike Information Criterion (AIC) is selected (Anselin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ward and Gleditsch, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study, because we used spatial autocorrelation analyses and spatial regression models such as SEM and SLM, missing data were completed using a multiple imputation approach. If the analyses had been conducted with missing data for any country, our spatial analyses would not have been fully feasible, as the spatial weights matrix was based on k-nearest neighbors using country centroids; the gap created by that country on the map and the resulting inability to compute centroid-based distances would have posed a serious problem. In other words, there could have been \u0026ldquo;holes\u0026rdquo; in the map. Therefore, for countries for which data could not be obtained from institutional sources, values were estimated based on a \u0026ldquo;borrowing information\u0026rdquo; principle from geographically and socioeconomically similar countries. For this purpose, the mice (Multivariate Imputation by Chained Equations) package in R was used. Under a missing at random assumption, 20 imputed datasets were generated for the variables using the predictive mean matching method. Each imputed dataset was used to estimate the spatial error model (SEM) separately, and the regression coefficients and standard errors from the SEM and SLM models were pooled using Rubin\u0026rsquo;s rules.\u003c/p\u003e \u003cp\u003eBy contrast, for the LISA maps and Moran\u0026rsquo;s I values we produced, an approximate \u0026ldquo;master\u0026rdquo; dataset was created using the 20 different imputation sets, since generating a separate map for each set would not have been practical. Within this framework, values for countries with missing data were extracted separately from each imputed dataset and averaged, and these averages were then added to the initial dataset constructed with countries for which data were available from institutional sources. The LISA maps presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and the univariate and bivariate Moran\u0026rsquo;s I statistics shown in these figures, were computed using this master dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3.Ethics Committee Approval\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003ewas not necessary for this study, as the data were sourced from publicly available online resources.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe SSRs of countries ranged from 0.54 to 70.9 per 100,000 of the total population, with a mean value of 9.38. Suicide rates varied substantially across countries. A strong and positive spatial autocorrelation was found in the distribution of sex-specific suicide rates (SSRs) for females, males, and the total population, with Moran\u0026rsquo;s I values of 0.537, 0.602, and 0.589, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for each value). LISA cluster maps and Moran\u0026rsquo;s I scatter plots illustrating the differences, unequal distribution, and clustering of sex-stratified suicide rates across countries are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026rsquo;s LISA results indicate that High\u0026ndash;High (HH) SSR clusters were observed mainly in Eastern Europe, including the Baltic countries, and in countries in southern Africa. This pattern appears largely similar across sexes; however, some countries exhibited sex-specific clustering patterns. Low\u0026ndash;Low (LL) SSR clusters are notable in the Middle East, North Africa, and Latin America. Although these low\u0026ndash;low clustering patterns also show some variation by sex, the overall picture shows similarities. Outlier clustering patterns were observed quite rarely. The detailed results of the maps in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e under the title \u0026ldquo;Clustering results from the univariate Moran\u0026rsquo;s I LISA map of the suicide rate.\u003c/p\u003e \u003cp\u003eIn our analysis, we used the bivariate Moran\u0026rsquo;s I statistic to look at how suicide rates and per capita alcohol consumption are related across different areas. The results suggest that the two measures tend to cluster rather than being randomly distributed. For women, Moran\u0026rsquo;s I was 0.202; for men, it was 0.334; and for the total population, it was 0.303. All of these patterns were highly significant, with p-values well below 0.001.The LISA cluster maps and Bivariate Moran\u0026rsquo;s I scatter plots for suicide rates and per capita alcohol consumption are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the bivariate Moran\u0026rsquo;s I analysis in which suicide rates and alcohol consumption were evaluated together, the LISA map indicates that High\u0026ndash;High clusters for males are concentrated in almost all European countries. For females, however, within the High\u0026ndash;High clustering pattern, Czechia, Denmark, Finland, and Luxembourg stand out instead of Moldova, Poland, Romania, and Slovakia, which are included in the male map. Low\u0026ndash;Low clusters exhibited a broadly similar distribution by sex, despite minor differences, and were concentrated mainly in the Middle East, North Africa, Oceania, and a limited part of Central Asia. Outlier clusters were again observed rarely. In this context, High\u0026ndash;Low clusters occur mainly in West Africa, along the Sahel and the Horn of Africa corridor, and in South Asia, whereas Low\u0026ndash;High clusters are more focused on Southern and Southeastern Europe and have also been observed in some Northern/Western European countries. The detailed results for Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e under the title \u0026ldquo;Results of the bivariate Moran\u0026rsquo;s I and LISA cluster map assessing suicide rate and per capita alcohol consumption.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Moran\u0026rsquo;s I\u0026ndash;LISA clustering results for suicide rates and bivariate Moran\u0026rsquo;s I\u0026ndash;LISA clustering results for suicide rates and per capita alcohol consumption (by gender).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eClustering results from the univariate Moran\u0026rsquo;s I LISA map of the suicide rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eResults of the bivariate Moran\u0026rsquo;s I and LISA cluster map assessing suicide rate and per capita alcohol consumption\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFamele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFamele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh-High\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelarus, Botswana, Estonia, Latvia, Lesotho, Lithuania, Mozambique, Namibia, Poland, Russia, South Africa, Tanzania, Ukraine, Zambia, Zimbabwe, and eSwatini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBelarus, Botswana, Congo, Democratic Republic of the Congo, Ethiopia, Guinea, Lesotho, Mozambique, Namibia, North Korea, Norway, Poland, Russia, South Africa, Zambia, Zimbabwe, and eSwatini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelarus, Botswana, Estonia, Latvia, Lesotho, Lithuania, Mozambique, Namibia, Poland, Russia, South Africa, Tanzania, Zambia, Zimbabwe, and eSwatini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAustria, Belarus, Belgium, Croatia, Czechia, Estonia, France, Germany, Hungary, Latvia, Lithuania, Moldova, Poland, Romania, Russia, Slovakia, Slovenia, Switzerland, and Ukraine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAustria, Belarus, Belgium, Croatia, Czechia, Denmark, Estonia, France, Germany, Hungary, Latvia, Lithuania, Luxembourg, Russia, Slovenia, Switzerland, and Ukraine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAustria, Belarus, Belgium, Croatia, Czechia, Estonia, France, Germany, Hungary, Latvia, Lithuania, Moldova, Poland, Russia, Slovakia, Slovenia, Switzerland, and Ukraine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow-Low\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlgeria, Colombia, Greece, Indonesia, Iran, Iraq, Israel, Jordan, Lebanon, Libya, Oman, Papua New Guinea, Saudi Arabia, Syria, Tunisia, Timor-Leste,T\u0026uuml;rkiye, United Arab Emirates, Yemen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArmenia, Azerbaijan, Colombia, Costa Rica, Ecuador, Georgia, Guatemala, Iran, Iraq, Israel, Jordan, Lebanon, Saudi Arabia, Syria, T\u0026uuml;rkiye, and the United Arab Emirates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArmenia, Colombia, Georgia, Greece, Indonesia, Iraq, Iran, Israel, Jordan, Lebanon, Libya, Papua New Guinea, Saudi Arabia, Syria, Timor-Leste, and T\u0026uuml;rkiye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAlgeria, Egypt, Israel, Iraq, Iran, Jordan, Libya, Papua New Guinea, Sudan, and Tunisia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAlgeria, Israel, Iraq, Iran, Jordan, Oman, Papua New Guinea, and Syria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAlgeria, Egypt, Israel, Iraq, Iran, Jordan, Libya, Papua New Guinea, Sudan, Tunisia, and Turkmenistan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh-Low\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEcuador\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYemen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEcuador\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChad, Ethiopia, Eritrea, and India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGuinea-Bissau, India, Liberia, and Mali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChad, Ethiopia, Eritrea, Mali and India\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow-High\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenmark, Italy, Luxembourg, and Serbia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eItaly, Moldova, Poland, Romania, Serbia, Slovakia, and the United Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDenmark, Italy, Luxembourg, Romania, and Serbia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDescriptive statistics for the variables used in the study, along with their correlation coefficients with the suicide rate, are presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In addition, the Lorenz curves and Gini coefficients indicating the levels of inequality in the global distribution of these variables are presented in Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Descriptive Statistics and Correlation Coefficients of Suicide Rate and Its Associated Variables Worldwide\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eObservation Count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMissing Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSpearman\u0026rsquo;s rho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge-Std Suicide Rate(5-Year Avg,per100,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e117.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePopulation Density (persons per km\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7965.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUnemployment Rate (per 100 people)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePsychiatrists (per 100,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEconomic Freedom Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAlcohol Consumption (annual per capita, liters)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe significance of Moran's I in the OLS models indicated the presence of spatial dependence, which warranted the use of spatial models. Among these models, the SEM was favored based on the results of the Lagrange Multiplier (LM) tests and the Akaike Information Criterion (AIC) values. This suggests that unobserved factors that are correlated spatially may influence suicide rates. The results from the SEM indicated a consistently positive and significant effect of unemployment across all models.Economic freedom had a negative effect on female suicides but a positive effect on male suicides. For males, alcohol consumption and population density were also significant, with the model explaining suicide rates moderately to well (Adj. R\u0026sup2; = 0.57 for males, 0.39 for females).Model details are reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation of Suicide Rates Using OLS, SLM, and SEM Models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSLM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef./Val.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ea.Female\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-52.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-35.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-32.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchwarz criterion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLag: SuicideFamale (Rho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0260\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Freedom Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0089\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of psychiatrists per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulticollinearity condition num.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoran\u0026rsquo;s I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM (lag)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobust LM (lag)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM (error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobust LM (error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLagrange multiplier (SARMA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eb.Male\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-54.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-46.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-31.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchwarz criterion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLag: SuicideMale (Rho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Freedom Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of psychiatrists per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0248\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulticollinearity condition num.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoran\u0026rsquo;s I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM (lag)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobust LM (lag)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM (error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobust LM (error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLagrange multiplier (SARMA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ec.Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-46.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-35.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-29.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchwarz criterion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLag: SuicideTot (Rho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0171\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0080\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0073\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Freedom Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of psychiatrists per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulticollinearity condition num.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoran\u0026rsquo;s I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM (lag)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobust LM (lag)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM (error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobust LM (error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLagrange multiplier (SARMA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eConsistent with the literature, spatial autocorrelation in suicide rates clearly demonstrates clusters of low and high suicide rates in specific regions, and this finding is an important indication of inequality in the distribution of suicide rates. (Macente and Zandonade, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; N\u0026uacute;\u0026ntilde;ez-Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For both sexes, Eastern Europe and Southern Africa were identified as hot spots for suicide rates, while in men, the Baltic countries additionally emerged as hot spots. Cold spots, in contrast, were observed for both sexes, particularly in the Middle East and Latin America, and, among men, additionally in North Africa.\u003c/p\u003e \u003cp\u003eOne of our most important findings that may serve as evidence that suicide rates can vary by sex is the presence of these differing spatial distributions alongside broadly similar clusters. In addition to the differing distributions in clustering, the marked presence of similar clusters in both sexes is noteworthy and suggests that factors that either inhibit or facilitate suicide are present at the level of the general population in these regions. The emergence of Eastern European countries as hot spots may be related to the stress generated by past socioeconomic transitions and to inadequacies in health and social services. At the same time, higher-than-average levels of alcohol consumption in these countries may have contributed to alcohol use disorders and, subsequently, to adverse psychiatric outcomes such as suicide. The fact that the Baltic countries also share this pattern among men may be related to the absence of health and social services as developed as those in Western Europe, despite these countries being more developed than Eastern European countries, as well as to their Eastern Europe\u0026ndash;like conditions, their high levels of alcohol consumption, the greater alcohol use among men compared with women, and unemployment; indeed, in our analyses of the spatial correlation between alcohol consumption and suicide rates, this correlation was found to be higher in men than in women (Brainerd, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Razvodosky, 2015; V\u0026auml;rnik et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Moreover, in our SLM model, unemployment did not significantly affect suicide rates among women, but was observed to significantly increase suicide rates among men. In countries located in southern Africa, economic problems and social inequalities, epidemics such as AIDS, the limited availability of health services, and serious difficulties in accessing even these limited services are important reasons for the high suicide rates among both women and men. (Seetsa Makateng, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). People in Middle Eastern and Latin American countries are known for their strong religious beliefs and spiritual orientation. Although these religious beliefs differ, suicide is prohibited in almost all belief systems, particularly in the monotheistic religions. At the same time, close family, kinship, and friendship ties are known to be more intense in these societies (Hsieh, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Stack and Kposowa, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This situation may override individualistic ways of life, thereby preventing social isolation and reducing suicide rates in these countries. However, on the other hand, these countries may not maintain records in a transparent and accurate manner to the same extent as more developed countries. Due to having lower levels of health and social services compared with developed countries, and because restrictive and prohibitive policies are more common in these settings, cases that are in fact suicides may not have been recorded or reported as such.\u003c/p\u003e \u003cp\u003eIn our study, in addition to the fact that the spatial autocorrelation of suicide was markedly high, it was noteworthy that the spatial correlation between suicide rates and alcohol consumption was also pronounced. Among women, hotspots for suicide rates and alcohol consumption were concentrated in Central and Eastern Europe, whereas among men, hotspots were identified not only in Central and Eastern European countries but also in Moldova, Poland, and Romania. This finding, highlighting the co-occurrence of high alcohol consumption and elevated suicide rates, is consistent with the existing literature. Systematic review studies have reported that high alcohol consumption is a risk factor for suicide (Amiri and Behnezhad, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pompili et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Landberg\u0026rsquo;s study reported that a one-liter increase in alcohol consumption in Eastern Europe is associated with an approximate 6\u0026ndash;8% increase in suicide rates (Landberg, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In a study covering 14 European countries, Ramstedt reported a positive and significant relationship between per capita alcohol consumption and gender- and age-specific suicide rates and stated that this relationship was most pronounced in Northern Europe (Ramstedt, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Although the relationship between alcohol consumption and suicide is evident in spatial autocorrelation analyses, the explanatory power of alcohol consumption for female suicides lost its significance in spatial regression results. In contrast, alcohol consumption remained a significant factor explaining suicide rates in models for male suicides and the total population. This difference may be attributed to higher levels of alcohol consumption among males compared to females. One study has shown that this difference can be up to 2 times higher (Dawson and Archer, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the advanced regression models we constructed, even after controlling for other variables that could act as confounders, the positive association between alcohol consumption and suicide rates remained statistically significant in men. This can be interpreted as a strong indication that alcohol consumption may be a factor that increases suicide rates, particularly among men. From an epidemiological perspective, although ecological studies are not the most appropriate designs for establishing causality, findings from well-designed ecological studies that are supported by advanced analyses, even if they cannot fully demonstrate causality, may nonetheless be interpreted as providing strong inferential evidence in the pathway toward causal interpretation (Loney and Nagelkerke, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA study conducted in Europe reported that unemployment is an important factor in increasing the suicide rate. In that study, the role of unemployment in increasing suicide cases, particularly among men, was highlighted (Breuer, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In our study, however, we found that unemployment had an increasing effect on suicide rates not only among men but also among women. Among men, unemployment significantly increased suicide rates in both the OLS and SEM models. Among women, however, the situation was more complex, and the spatial regression models revealed the relationship between female unemployment and suicide rates that appeared to be non-significant in the OLS model. This is because classical models that do not account for spatial patterning treat the data as a single whole and operate on the overall mean (ArcGIS Pro Documentation, 2024). In reality, however, in some countries, suicide rates may remain low despite high female unemployment, due to under- or misrecording or to other social factors such as religious beliefs and societal norms. By contrast, in countries that are similar in terms of governance and lifestyle, suicide rates and unemployment tend to be correlated. Classical models cannot disentangle these patterns and evaluate all countries as a single group. By taking spatial dependence into account, SEM distinguishes between similar countries and, through the structure of the error terms, incorporates potential regional background factors that are not explicitly included in the models, thereby elucidating relationships that initially appear non-significant. In our study, this approach clarified the complexity of the relationship between female unemployment and female suicide. The literature has already reported that unemployment generally increases suicide rates. In a study conducted by Nordt et al. across 63 countries, an 11-year analysis showed that unemployment increased suicide rates by approximately 20% (Nordt et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne other important finding in our study was that while the Economic Freedom Index score moved in the opposite direction of the female suicide rate, it moved in parallel with the male suicide rate. This situation, which may appear surprising, in fact highlights how important interaction effects are in epidemiology and underscores the necessity of reporting interaction factors separately. This is important in that it shows that suicide rates may display similar patterns in women and men in some respects, yet differ in others. In male suicides, an increase in economic freedom as a result of social roles may also lead to an increase in pressures related to status and achievement. In addition, this freedom may bring individualism with it and thus cause a reduction in social support. This may explain the parallel course of the relationship between male suicide and economic freedom (Durkheim, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1897\u003c/span\u003e; Graafland, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; World Health Organization, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). When considering the situation for women, research has shown that in societies where women\u0026rsquo;s economic freedoms are restricted and their access to economic opportunities is limited, female suicides increase (Bergen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kasaju et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, it has been reported that women are more affected by the factors influencing economic freedom compared to men (Claveria et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study, we observed higher suicide rates in countries with high population density. Wang et al. also reported similar results in a previous study (Wang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In a study conducted by Boor, it was reported that suicide rates increased in societies with rising population density, particularly those receiving immigration (Boor, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). In an ecological study conducted in Belgium by Hooghe and Vanhoutte, in addition to emphasizing the increasing effect of population density on suicide rates, it was also reported that this situation was more pronounced, particularly among men, and that the importance of spatial effects should be taken into account (Hooghe and Vanhoutte, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Consequently, one possible explanation for this is that increasing population density reduces access to limited resources and services, which in turn may increase distress in society and subsequently lead to a rise in suicidal behavior.\u003c/p\u003e"},{"header":"5. Strengths and Weaknesses","content":"\u003cp\u003eWe conducted our study using open data shared by numerous reputable health-related institutions and organizations worldwide, such as the ILO and WHO. We performed our analyses on a global scale without excluding any country. In addition to examining suicide rates through spatial autocorrelation, we also assessed the bivariate spatial autocorrelations between suicide rates and alcohol consumption, which we used as the primary explanatory variable for suicide rates. To reduce the ambiguity arising from classical regression models that ignore spatial dependence and neighborhood effects, we employed the SEM model, which accounts for spatial patterns and variables that might otherwise be overlooked. We also carried out all of these analyses separately for women and men.\u003c/p\u003e \u003cp\u003eHowever, as in every ecological study, ecological bias can be considered one of our weaknesses. Although we obtained our data from reputable institutions, economic and political factors in the countries that supply data to these institutions may result in data quality not being uniform across countries. The possibility that, in some countries, for economic reasons, and in others, for political reasons, data may have been reported lower or higher than they actually are is also an important limitation affecting the accuracy of our results. In addition, although the problems caused by missing data were addressed as far as possible using appropriate statistical methods, estimates cannot fully substitute for real data, and this has been an important limitation of our study. Moreover, the fact that our study design is at the population level and does not take individual effects into account constitutes a serious limitation in terms of the generalizability of the findings. Another limitation is that our study relies only on data from the years 2017\u0026ndash;2021. This may have prevented us from capturing changes in health and social conditions over time. Finally, although we identified important hot and cold spot clusters for suicide by sex, detailed results could not be presented because subgroups within these countries were not taken into account.\u003c/p\u003e"},{"header":"6. Conclusion and Suggestions","content":"\u003cp\u003eWhen all results are evaluated together, our most important finding is that alcohol consumption is positively correlated with the male suicide rate, with an effect strength close to causality. In both spatial analyses and regression models in which covariates were controlled for, the joint pattern of alcohol consumption and suicide rates was consistently found to be significant among men. The fact that suicide rates and alcohol consumption are correlated in Europe in particular suggests that efforts should be made in these countries to reduce alcohol consumption.\u003c/p\u003e \u003cp\u003eAnother important finding is that suicide clusters at low or high levels in certain regions are unequally distributed, and that male and female suicides cluster with similar characteristics in some places while exhibiting different patterns in others. Eastern European countries and those in southern Africa stand out as hot spots for both sexes. In these countries, addressing suicide and developing preventive policies that encompass the general population may be considered a fundamental necessity. The fact that economic freedom affects suicide rates differently by sex also highlights the need to develop gender-sensitive policies. As economic freedom increases, gender inequality should be tackled, and social support systems should be strengthened.\u003c/p\u003e \u003cp\u003eMigration, a current and significant global issue, is one of the most important drivers of increasing population density. When considered together with our finding that rising population density increases suicide, an increase in suicide cases may emerge in the future in some countries where population density has risen due to migration. Therefore, situations that may lead to increased population density should be evaluated carefully.\u003c/p\u003e \u003cp\u003eSince this study was designed ecologically, the results should only be used for hypothesis generation; for causality, further studies in smaller subgroups based on these hypotheses are required. However, the maps we produced for suicide rates and alcohol consumption may help international organizations such as the World Health Organization to decide where to concentrate their public health initiatives aimed at suicide prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eCompeting Interest\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eEthical\u003c/h2\u003e\n\u003cp\u003eThe data used in the study were obtained from publicly available databases, reports, and similar publications provided by internationally recognized institutions and organizations such as Our World In Data and the World Health Organization. Therefore, ethical committee approval was not deemed necessary. The sources of the obtained data are individually listed and cited in the references section of the study.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo funds, grants, or other support was received.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eOHA: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Visualization, Writing - original draft, Writing - review and editing. SM: Data curation, Formal analysis, Investigation, Methodology, Resources, Supervision, Validation, Writing - original draft, Writing - review and editing. MFO: Methodology, Resources, Supervision, Validation, Writing - original draft, Writing - review and editing. EA: Data curation, Resources, Supervision, Validation, Writing - review and editing\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to Dr. Feyza Akbay, a research assistant in our department, who holds the highest level of certification in English proficiency in the national official foreign language examination and serves as the scientific secretary of the Journal of Public Health of the Eskişehir Turkish World Application and Research Center, for reviewing our manuscript in terms of language clarity and appropriateness as well as overall manuscript criteria, and for making the necessary revisions.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eSuicide rate data used in this study were obtained from the official WHO website. Data on per capita alcohol consumption and the number of psychiatrists per capita were retrieved from the Our World in Data platform; economic freedom indices from The Heritage Foundation website; unemployment rate data from the ILO website; and population density data from the World Bank Group data portal. The full web addresses of the relevant data sources were cited in the Methods section, where appropriate, and listed in the reference list. Upon request, the data used in the analyses can be shared in Excel format. Since all data are publicly available and published by the respective institutions, no additional permission for use was obtained.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArcGIS Esri (2024). How multiscale geographically weighted regression (MGWR) works. ArcGIS Pro Documentation. Retrieved October 4, 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pro.arcgis.com/en/pro-app/3.4/tool-reference/spatial-statistics/how-multiscale-geographically-weighted-regression-mgwr-works.htm\u003c/span\u003e\u003cspan address=\"https://pro.arcgis.com/en/pro-app/3.4/tool-reference/spatial-statistics/how-multiscale-geographically-weighted-regression-mgwr-works.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad, O. B., Boschi-Pinto, C., Lopez, A. D., Murray, C. J. L., Lozano, R., \u0026amp; Inoue, M. (2001). Age standardization of rates: A new WHO standard (GPE Discussion Paper Series, No. 31). World Health Organization.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmiri, S., \u0026amp; Behnezhad, S. (2020). Alcohol use and risk of suicide: A systematic review and meta-analysis. \u003cem\u003eJournal of Addictive Diseases\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e, 200\u0026ndash;213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10550887.2020.1736757\u003c/span\u003e\u003cspan address=\"10.1080/10550887.2020.1736757\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnselin, L. (1995). Local indicators of spatial association (LISA). \u003cem\u003eGeographical Analysis\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e, 93\u0026ndash;115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1538-4632.1995.tb00338.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1538-4632.1995.tb00338.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnselin, L., Sridharan, S., \u0026amp; Gholston, S. (2007). Using exploratory spatial data analysis to leverage social indicator databases: The discovery of interesting patterns. \u003cem\u003eSocial Indicators Research\u003c/em\u003e, \u003cem\u003e82\u003c/em\u003e, 287\u0026ndash;309. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11205-006-9034-x\u003c/span\u003e\u003cspan address=\"10.1007/s11205-006-9034-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnselin, L., Syabri, I., \u0026amp; Kho, Y. (2006). GeoDa: An introduction to spatial data analysis. \u003cem\u003eGeographical Analysis\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e, 5\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.0016-7363.2005.00671.x\u003c/span\u003e\u003cspan address=\"10.1111/j.0016-7363.2005.00671.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnselin, L., Syabri, I., \u0026amp; Kho, Y. (2010). GeoDa: An introduction to spatial data analysis. In M. M. Fischer \u0026amp; A. Getis (Eds.), Handbook of applied spatial analysis: Software tools, methods and applications (pp. 73\u0026ndash;89). Springer. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-642-03647-7_5\u003c/span\u003e\u003cspan address=\"10.1007/978-3-642-03647-7_5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Bergen, D., Bergeijk, O. E. V., \u0026amp; Montesinos, A. H. (2021). Attempted suicide and suicide of young Turkish women in Europe and Turkey: A systematic literature review of characteristics and precipitating factors. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, e0253274. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0253274\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0253274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBogdan, M., Ghosh, J. K., \u0026amp; Tokdar, S. T. (2008). A comparison of the Benjamini\u0026ndash;Hochberg procedure with some Bayesian rules for multiple testing. In N. Balakrishnan, E. A. P. Rao, \u0026amp; M. J. Silvapulle (Eds.), Beyond parametrics in interdisciplinary research: Festschrift in honor of Professor Pranab K. Sen (pp. 211\u0026ndash;230). Institute of Mathematical Statistics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1214/193940307000000158\u003c/span\u003e\u003cspan address=\"10.1214/193940307000000158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoor, M. (1981). Relationship of 1977 state suicide rates to population increases and immigration. \u003cem\u003ePsychological Reports\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(3), 856\u0026ndash;858. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2466/pr0.1981.49.3.856\u003c/span\u003e\u003cspan address=\"10.2466/pr0.1981.49.3.856\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorges, G., Bagge, C. L., Cherpitel, C. J., Conner, K. R., Orozco, R., \u0026amp; Rossow, I. (2017). A meta-analysis of acute use of alcohol and the risk of suicide attempt. \u003cem\u003ePsychological Medicine\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e, 949\u0026ndash;957. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0033291716002841\u003c/span\u003e\u003cspan address=\"10.1017/S0033291716002841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrainerd, E. (2001). Economic reform and mortality in the former Soviet Union: A study of the suicide epidemic in the 1990s. \u003cem\u003eEuropean Economic Review\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(4\u0026ndash;6), 1007\u0026ndash;1019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBreuer, C. (2015). Unemployment and suicide mortality: Evidence from regional panel data in Europe. \u003cem\u003eHealth Economics\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e, 936\u0026ndash;950. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hec.3073\u003c/span\u003e\u003cspan address=\"10.1002/hec.3073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCherpitel, C. J., Borges, G. L. G., \u0026amp; Wilcox, H. C. (2004). Acute alcohol use and suicidal behavior: A review of the literature. Alcoholism: Clinical and Experimental Research, 28(5 Suppl), 18S\u0026ndash;28S. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/01.alc.0000127411.61634.14\u003c/span\u003e\u003cspan address=\"10.1097/01.alc.0000127411.61634.14\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChikritzhs, T., \u0026amp; Livingston, M. (2021). Alcohol and the risk of injury. \u003cem\u003eNutrients\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(8), 2777. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu13082777\u003c/span\u003e\u003cspan address=\"10.3390/nu13082777\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClaveria, O., Sorić, M., \u0026amp; Sorić, P. (2024). Analysis of the impact of financial and labour uncertainty on suicide mortality in England. \u003cem\u003eHealth \u0026amp; Place\u003c/em\u003e, \u003cem\u003e89\u003c/em\u003e, 103329. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.healthplace.2024.103329\u003c/span\u003e\u003cspan address=\"10.1016/j.healthplace.2024.103329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrawford, M. J., Kuforiji, B., \u0026amp; Ghosh, P. (2010). The impact of social context on socio-demographic risk factors for suicide: A synthesis of data from case\u0026ndash;control studies. \u003cem\u003eJournal of Epidemiology \u0026amp; Community Health\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e, 530\u0026ndash;534. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jech.2008.084145\u003c/span\u003e\u003cspan address=\"10.1136/jech.2008.084145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDawson, D. A., \u0026amp; Archer, L. (1992). Gender differences in alcohol consumption: Effects of measurement. \u003cem\u003eBritish Journal of Addiction\u003c/em\u003e, \u003cem\u003e87\u003c/em\u003e, 119\u0026ndash;123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1360-0443.1992.tb01909.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1360-0443.1992.tb01909.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDurkheim, \u0026Eacute;. (1897). \u003cem\u003eLe suicide: \u0026Eacute;tude de sociologie\u003c/em\u003e. F\u0026eacute;lix Alcan.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraafland, J. (2023). Economic freedom and life satisfaction: A moderated mediation model with individual autonomy and national culture. \u003cem\u003eEuropean Journal of Political Economy\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e, 102448. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejpoleco.2023.102448\u003c/span\u003e\u003cspan address=\"10.1016/j.ejpoleco.2023.102448\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuzm\u0026aacute;n, E. M., Cha, C. B., Ribeiro, J. D., \u0026amp; Franklin, J. C. (2019). Suicide risk around the world: A meta-analysis of longitudinal studies. \u003cem\u003eSocial Psychiatry and Psychiatric Epidemiology\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e, 1459\u0026ndash;1470. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00127-019-01759-x\u003c/span\u003e\u003cspan address=\"10.1007/s00127-019-01759-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHawton, K., \u0026amp; van Heeringen, K. (2009). \u003cem\u003eSuicide The Lancet\u003c/em\u003e, 373, 1372\u0026ndash;1381. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(09)60372-X\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(09)60372-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelbich, M., Leitner, M., \u0026amp; Kapusta, N. D. (2012). Geospatial examination of lithium in drinking water and suicide mortality. \u003cem\u003eInternational Journal of Health Geographics\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1476-072X-11-19\u003c/span\u003e\u003cspan address=\"10.1186/1476-072X-11-19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHooghe, M., \u0026amp; Vanhoutte, B. (2011). An ecological study of community-level correlates of suicide mortality rates in the Flemish Region of Belgium, 1996\u0026ndash;2005. \u003cem\u003eSuicide and Life-Threatening Behavior\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e, 453\u0026ndash;464. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1943-278X.2011.00047.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1943-278X.2011.00047.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsieh, N. (2017). A global perspective on religious participation and suicide. \u003cem\u003eJournal of Health and Social Behavior\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e, 322\u0026ndash;339. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0022146517715896\u003c/span\u003e\u003cspan address=\"10.1177/0022146517715896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHufford, M. R. (2001). Alcohol and suicidal behavior. \u003cem\u003eClinical Psychology Review\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e, 797\u0026ndash;811. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0272-7358(00)00070-2\u003c/span\u003e\u003cspan address=\"10.1016/S0272-7358(00)00070-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Labour Organization (2019). Statistics on unemployment and labour underutilization: SDG indicator 8.5.2 Unemployment rate (%). ILOSTAT database. Retrieved October 4, 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ilostat.ilo.org/topics/unemployment-and-labour-underutilization/\u003c/span\u003e\u003cspan address=\"https://ilostat.ilo.org/topics/unemployment-and-labour-underutilization/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKapusta, N. D., Mossaheb, N., Etzersdorfer, E., Hlavin, G., Thau, K., Willeit, M., Praschak-Rieder, N., Sonneck, G., Leithner-Dziubas, K., \u0026amp; Willeit, M. (2011). Lithium in drinking water and suicide mortality. \u003cem\u003eThe British Journal of Psychiatry\u003c/em\u003e, \u003cem\u003e198\u003c/em\u003e, 346\u0026ndash;350. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1192/bjp.bp.110.091041\u003c/span\u003e\u003cspan address=\"10.1192/bjp.bp.110.091041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKasaju, S. P., Krumeich, A., \u0026amp; Van der Putten, M. (2021). Suicide and deliberate self-harm among women in Nepal: A scoping review. \u003cem\u003eBMC Women\u0026rsquo;s Health\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e, 407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12905-021-01547-3\u003c/span\u003e\u003cspan address=\"10.1186/s12905-021-01547-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandberg, J. (2008). Alcohol and suicide in Eastern Europe. \u003cem\u003eDrug and Alcohol Review\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e, 361\u0026ndash;373. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09595230802093778\u003c/span\u003e\u003cspan address=\"10.1080/09595230802093778\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoney, T., \u0026amp; Nagelkerke, N. J. (2014). The individualistic fallacy, ecological studies and instrumental variables: A causal interpretation. \u003cem\u003eEmerging Themes in Epidemiology\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1742-7622-11-18\u003c/span\u003e\u003cspan address=\"10.1186/1742-7622-11-18\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacente, L. B., \u0026amp; Zandonade, E. (2012). Spatial distribution of suicide incidence rates in municipalities in the state of Esp\u0026iacute;rito Santo (Brazil), 2003\u0026ndash;2007: Spatial analysis to identify risk areas. \u003cem\u003eBrazilian Journal of Psychiatry\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e, 261\u0026ndash;269. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rbp.2011.11.001\u003c/span\u003e\u003cspan address=\"10.1016/j.rbp.2011.11.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMann, J. J., Apter, A., Bertolote, J., Beautrais, A., Currier, D., Haas, A., Hegerl, U., Lonnqvist, J., Malone, K., Marusic, A., Mehlum, L., Patton, G., Phillips, M., Rutz, W., Rihmer, Z., Schmidtke, A., Shaffer, D., Silverman, M., Takahashi, Y., \u0026amp; Hendin, H. (2005). Suicide prevention strategies: A systematic review. \u003cem\u003eJournal Of The American Medical Association\u003c/em\u003e, \u003cem\u003e294\u003c/em\u003e, 2064\u0026ndash;2074. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.294.16.2064\u003c/span\u003e\u003cspan address=\"10.1001/jama.294.16.2064\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoothathu, T. S. K. (1990). Lorenz curve and Gini index. \u003cem\u003eCalcutta Statistical Association Bulletin\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(1\u0026ndash;4), 307\u0026ndash;324. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0008068319900524\u003c/span\u003e\u003cspan address=\"10.1177/0008068319900524\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNordt, C., Warnke, I., Seifritz, E., \u0026amp; Kawohl, W. (2015). Modelling suicide and unemployment: A longitudinal analysis covering 63 countries, 2000\u0026ndash;11. \u003cem\u003eThe Lancet Psychiatry\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, 239\u0026ndash;245. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2215-0366(14)00118-7\u003c/span\u003e\u003cspan address=\"10.1016/S2215-0366(14)00118-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorstr\u0026ouml;m, T., \u0026amp; Rossow, I. (2016). Alcohol consumption as a risk factor for suicidal behavior: A systematic review of associations at the individual and at the population level. \u003cem\u003eArchives of Suicide Research\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e, 489\u0026ndash;506. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13811118.2016.1158678\u003c/span\u003e\u003cspan address=\"10.1080/13811118.2016.1158678\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN\u0026uacute;\u0026ntilde;ez-Gonz\u0026aacute;lez, S., Lara-Vinueza, A. G., Gault, C., \u0026amp; Delgado-Ron, J. A. (2018). Trends and spatial patterns of suicide among adolescents in Ecuador, 1997\u0026ndash;2016. \u003cem\u003eClinical Practice \u0026amp; Epidemiology in Mental Health\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 283\u0026ndash;292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/1745017901814010283\u003c/span\u003e\u003cspan address=\"10.2174/1745017901814010283\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Farrell, I., Corcoran, P., \u0026amp; Perry, I. (2016). The area level association between suicide, deprivation, social fragmentation, and population density in the Republic of Ireland: A national study. \u003cem\u003eSocial Psychiatry and Psychiatric Epidemiology\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e, 839\u0026ndash;847. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00127-016-1205-8\u003c/span\u003e\u003cspan address=\"10.1007/s00127-016-1205-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOur World in Data (2019). Psychiatrists per 100,000 people. Retrieved October 4, 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ourworldindata.org/grapher/psychiatrists-working-in-the-mental-health-sector?tab=table\u0026amp;time=2019\u003c/span\u003e\u003cspan address=\"https://ourworldindata.org/grapher/psychiatrists-working-in-the-mental-health-sector?tab=table\u0026amp;time=2019\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePitman, A., Osborn, D., King, M., \u0026amp; Erlangsen, A. (2014). Effects of suicide bereavement on mental health and suicide risk. \u003cem\u003eThe Lancet Psychiatry\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e, 86\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2215-0366(14)70224-X\u003c/span\u003e\u003cspan address=\"10.1016/S2215-0366(14)70224-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePompili, M., Serafini, G., Innamorati, M., Dominici, G., Ferracuti, S., Kotzalidis, G. D., Serra, G., Girardi, P., Janiri, L., Tatarelli, R., Sher, L., \u0026amp; Lester, D. (2010). Suicidal behavior and alcohol abuse. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 1392\u0026ndash;1431. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph7041392\u003c/span\u003e\u003cspan address=\"10.3390/ijerph7041392\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQGIS.org (2025). QGIS User Guide (Version 3.40). Retrieved October 4, 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://docs.qgis.org/3.40/en/docs/user_manual/index.html\u003c/span\u003e\u003cspan address=\"https://docs.qgis.org/3.40/en/docs/user_manual/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamstedt, M. (2001). Alcohol and suicide in 14 European countries. \u003cem\u003eAddiction\u003c/em\u003e, \u003cem\u003e96\u003c/em\u003e, S59\u0026ndash;S75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1360-0443.96.1s1.6.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1360-0443.96.1s1.6.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRazvodovsky, Y. E. (2015). Fatal alcoholic poisonings in Russia and Belarus: A comparative analysis of trends. \u003cem\u003eAlcoholism and Psychiatry Research\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e, 137\u0026ndash;146.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie, H., \u0026amp; Roser, M. (2022). Alcohol consumption. Our World in Data. Retrieved October 4, 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ourworldindata.org/alcohol-consumption\u003c/span\u003e\u003cspan address=\"https://ourworldindata.org/alcohol-consumption\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoth, G. A., Abate, D., Abate, K. H., \u0026amp; Murray, C. J. L. (2018). Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980\u0026ndash;2017: A systematic analysis for the Global Burden of Disease Study 2017. \u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e392\u003c/em\u003e, 1736\u0026ndash;1788. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(18)32203-7\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(18)32203-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeetsa Makateng, D. (2024). The philosophical and sociological exposition of suicide in Lesotho. \u003cem\u003eVirajes\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e, 169\u0026ndash;186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17151/rasv.2024.26.2.8\u003c/span\u003e\u003cspan address=\"10.17151/rasv.2024.26.2.8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShanahan, L., Hill, S. N., Gaydosh, L., Steinhoff, A., Costello, E. J., Dodge, K. A., Harris, M., K., \u0026amp; Copeland, W. E. (2019). Does despair really kill? A roadmap for an evidence-based answer. \u003cem\u003eAmerican Journal of Public Health\u003c/em\u003e, \u003cem\u003e109\u003c/em\u003e(6), 854\u0026ndash;858. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2105/AJPH.2019.305016\u003c/span\u003e\u003cspan address=\"10.2105/AJPH.2019.305016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSim\u0026otilde;es, R., Santos, J. C., \u0026amp; Martinho, M. J. (2020). As representa\u0026ccedil;\u0026otilde;es sociais do suic\u0026iacute;dio em adolescentes: Scoping review. RPESM. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.19131/rpesm.0273\u003c/span\u003e\u003cspan address=\"10.19131/rpesm.0273\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStack, S., \u0026amp; Kposowa, A. J. (2011). Religion and suicide acceptability: A cross-national analysis. \u003cem\u003eJournal for the Scientific Study of Religion\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e, 289\u0026ndash;306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1468-5906.2011.01568.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1468-5906.2011.01568.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe Heritage Foundation (2019). Index of economic freedom: All country scores. Retrieved October 4, 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.heritage.org/index/pages/all-country-scores\u003c/span\u003e\u003cspan address=\"https://www.heritage.org/index/pages/all-country-scores\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiefelsdorf, M., \u0026amp; Boots, B. (1995). The Exact Distribution of Moran\u0026rsquo;s I. Environment and Planning. \u003cem\u003eEconomy and Space\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(6), 985\u0026ndash;999. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1068/a270985\u003c/span\u003e\u003cspan address=\"10.1068/a270985\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(Original work published 1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiefelsdorf, M., \u0026amp; Boots, B. (1997). A note on the extremities of local Moran\u0026rsquo;s I and their impact on global Moran\u0026rsquo;s I. \u003cem\u003eGeographical Analysis\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e, 248\u0026ndash;257. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1538-4632.1997.tb00960.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1538-4632.1997.tb00960.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations (2025). Member States. Retrieved October 4, 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.un.org/en/about-us/member-states\u003c/span\u003e\u003cspan address=\"https://www.un.org/en/about-us/member-states\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026auml;rnik, A., Wasserman, D., \u0026amp; Eklund, G. (1994). Suicides in the Baltic countries, 1968\u0026ndash;90. \u003cem\u003eScandinavian Journal of Social Medicine\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e, 166\u0026ndash;169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/140349489402200302\u003c/span\u003e\u003cspan address=\"10.1177/140349489402200302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, P., Goggins, W. B., Zhang, X., Ren, C., \u0026amp; Lau, K. K. L. (2020). Association of urban built environment and socioeconomic factors with suicide mortality in high-density cities: A case study of Hong Kong. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e739\u003c/em\u003e, 139877. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2020.139877\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2020.139877\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWard, M., \u0026amp; Gleditsch, K. (2008). Spatial regression models. \u003cem\u003eSAGE Publications\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4135/9781412985888\u003c/span\u003e\u003cspan address=\"10.4135/9781412985888\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Bank (2019). Population density (people per sq. km of land area) [Data set]. World Bank Open Data. Retrieved October 8, 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldbank.org/indicator/EN.POP.DNST?end=2019\u0026amp;start=2019\u003c/span\u003e\u003cspan address=\"https://data.worldbank.org/indicator/EN.POP.DNST?end=2019\u0026amp;start=2019\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. (2002). \u003cem\u003eWorld report on violence and health\u003c/em\u003e. World Health Organization.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. (2014). \u003cem\u003ePreventing suicide: A global imperative\u003c/em\u003e. World Health Organization.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2025). Suicide mortality rate (per 100,000 population) [Indicator]. WHO Data. Retrieved May 12, 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://data.who.int/indicators/i/F08B4FD/16BBF41\u003c/span\u003e\u003cspan address=\"http://data.who.int/indicators/i/F08B4FD/16BBF41\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2025). Suicide worldwide in 2021: Global health estimates. World Health Organization. Retrieved October 27, 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789240110069\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789240110069\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Suicide, Alcohol Consumption, Spatial Analysis, Spatial Inequality","lastPublishedDoi":"10.21203/rs.3.rs-8398774/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8398774/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo demonstrate inequalities in the distribution of the global suicide rate, a key component of deaths of despair, using spatial autocorrelation, and to evaluate, using spatial analyses, the association of suicide with sociodemographic variables, particularly alcohol consumption.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing suicide data from the 2017\u0026ndash;2021 period, five-year average age-standardized suicide rates were calculated, and their spatial autocorrelation was examined. In addition, the relationship between suicide and alcohol consumption was evaluated using Bivariate Moran\u0026rsquo;s I, while other potential predictors related to suicide were analyzed using spatial regression models. Furthermore, Lorenz curves were used to demonstrate the unequal distribution of suicide rates and the other variables, and Gini coefficients were calculated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe spatial autocorrelation of suicide rates was high, and clusters were evident. Regions where suicide rates clustered at high levels were Eastern Europe and South Africa, while low-rate clusters were observed particularly in the Middle East. However, the clusters showed some differences by sex. In spatial regression models, unemployment, economic freedom, and population density were found to be factors affecting suicide.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe fact that suicide rates show a marked spatial autocorrelation on a global scale may provide an indication for more detailed regional investigations. In the regression models, the different effects of unemployment, economic freedom, population density, and alcohol consumption by sex may highlight the importance of gender-based approaches in suicide prevention policies.\u003c/p\u003e","manuscriptTitle":"The Unequal Global Geographic Distribution of Suicide: Spatial Patterns and Its Relationship with Alcohol","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 08:48:16","doi":"10.21203/rs.3.rs-8398774/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":"c79777a0-f97f-4d3a-aaab-264a90726815","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-05T06:41:09+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T06:56:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 08:48:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8398774","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8398774","identity":"rs-8398774","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}