Geographic Disparities in Suicide Mortality in South Korea (2012-2023): Identifying Hot/Cold Spots and Associated Socioeconomic Factors

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Suicide is not only an individual concern but also an indicator of the broader community’s mental health, necessitating consideration of regional-level factors. Objective This study aims to analyze the spatiotemporal distribution patterns of suicide mortality in South Korea and to identify regional factors associated with lower suicide rates, thereby providing an evidence base for community-based suicide prevention strategies. Methods Age-standardized suicide mortality rates and regional characteristics were collected for 229 administrative districts (Si/Gun/Gu) from 2012 to 2023. Spatial autocorrelation was assessed using Moran’s I, and spatiotemporal clusters were identified through Emerging Hotspot Analysis. Panel regression analysis was then conducted on the 70 identified cold spot regions. Results Suicide rates exhibited statistically significant spatial autocorrelation across all years. Of the 75 identified regions, 70 were classified as cold spots and 5 as hot spots. An increase in the proportion of the elderly population (β = − 0.548, p < .01) and a higher number of social welfare facilities per 100,000 population (β = − 0.117, p < .05) were significantly associated with a reduction in suicide mortality in the following year. Conclusion Suicide rates in South Korea showed clear spatiotemporal disparities across regions. The proportion of the elderly population and the number of social welfare facilities were identified as significant regional factors influencing suicide mortality. This study highlights the need for region-specific suicide prevention policies. suicide suicide prevention spatial autocorrelation spatial analyses regional disparity Figures Figure 1 Figure 2 Figure 3 Introduction Suicide remains a critical public health concern worldwide, and South Korea has reported the highest suicide rate among OECD countries since 2004 [ 1 ]. As of the most recent data, Korea’s suicide rate stands at 24.1 per 100,000 population—more than double the OECD average of 10.7 and significantly higher than Lithuania, which ranks second at 18.5 [ 2 ]. According to Statistics Korea, the suicide rate increased by 8.5% in 2023 compared to 2022, marking the highest rate observed since 2018 [ 3 ]. Numerous studies have investigated the underlying causes of Korea’s persistently high suicide rate [ 4 , 5 ]. Although efforts such as the enactment of the Suicide Prevention Act and the establishment of the Korea Suicide Prevention Center have been made to address the issue [ 6 ], the suicide rate has shown little improvement and continues to impose a considerable burden on society. Suicide risk varies among individuals, and regional characteristics may contribute to these disparities. For this reason, suicide has been regarded as an indicator of not only an individual’s mental health but also the mental health of the society to which the individual belongs [ 7 , 8 ]. Durkheim (1897) argued that suicide is influenced by social context and occurs as a result of a lack of social integration [ 9 ]. Studies have shown that both individual-level interventions and targeted regional strategies can effectively reduce suicide risk [ 10 ]. In this context, it is necessary to consider not only individual-level exploratory factors but also regional factors when addressing suicide risk. Building on the evidence of regional disparities observed internationally, many countries have reported regional differences in suicide rates [ 11 – 13 ]. Despite these findings, studies investigating the spatial and temporal clustering of suicide in South Korea —given its persistently high suicide rate—have remained relatively scarce compared to research conducted in other countries. In addition, although several studies in South Korea over the past decade have used spatial analysis to examine regional differences in suicide mortality, most have focused on identifying geographic areas with relatively high or low suicide rates compared to other regions [ 14 , 15 ]. As part of efforts to reduce the persistently increasing suicide rate in South Korea, it is necessary to identify regional differences in suicide mortality and investigate their underlying causes. Understanding these differences is crucial for prevention and may inform broader efforts to reduce national suicide rates. Accordingly, this study aims to examine the spatial and regional dependence of high suicide rates using 12 years of regional suicide mortality data from South Korea and to identify high-risk areas through an analysis of the spatiotemporal distribution characteristics of suicide mortality. Based on these findings, the study seeks to propose efficient and effective national- and community-based strategies for reducing suicide rates. Materials and Methods Data Collection and Variables The suicide rate was calculated as the age-standardized mortality rate per 100,000 population using cause-of-death statistics from the Korean Statistical Information Service (KOSIS). These statistics are derived from legally mandated death registrations, in which physicians report the cause of death using International Classification of Diseases, 10th Revision (ICD-10) codes X60–X84 [ 16 ]. Suicide rate data were collected annually at the city ( Si ), county ( Gun ), and district ( Gu ) levels from 2012 to 2023. In addition to the suicide rates, regional characteristic variables were collected for the same period. These variables include demographic factors such as the proportion of elderly population, crude divorce rate, total fertility rate, and population growth rate. Environmental and social factors include the number of medical institution beds per 1,000 population, the number of social welfare facilities per 100,000 population, and the green area ratio. Financial factors include the fiscal independence ratio. Detailed descriptions and definitions of these variables are provided in Supplemental Table 1 . Due to administrative changes, data for Michuhol-gu in Incheon prior to 2018 was not available from Statistics Korea, so only data from 2018 onwards were included. Data for the number of hospital beds per 1,000 population in Gwacheon were not available after 2019, so only data from before 2019 were included in the analysis. All other variables cover the same years and regions as the suicide rate. Spatial data for the 229 Si, Gun , and Gu administrative districts were obtained from the 2023 administrative boundary dataset provided by V-World, a national platform that integrates spatial information from central and local governments [ 17 ]. Spatial Autocorrelation Analysis Spatiotemporal analysis was conducted for 229 regions defined according to Korea’s administrative division system. To further examine spatial autocorrelation in suicide rates across these regions from 2012 to 2023, Moran’s I was calculated. Spatial autocorrelation describes the degree to which similar values occur near one another in geographic space. To analyze this phenomenon, researchers often employ Moran’s I , which identifies patterns of spatial clustering, dispersion, or randomness among observed regional values [ 18 ]. Moran’s I ranges between − 1 and 1. A value approaching 1 suggests a strong positive spatial relationship, meaning similar values are located near each other. Conversely, values nearing − 1 imply a negative spatial relationship, where neighboring areas tend to have dissimilar values. Values close to 0 indicate a random spatial pattern with little to no spatial autocorrelation [ 19 , 20 ]. Emerging Hotspot and Cluster Comparison To incorporate both spatial and temporal dimensions of suicide rates, Emerging Hotspot Analysis was conducted using a three-dimensional space-time cube framework. Using a fixed set of 229 Si, Gun, and Gu administrative districts, temporal trends from 2012 to 2023 were evaluated across 2,748 space-time cubes (229 districts × 12 years) (Fig. 1 ). This method extends traditional spatial analysis by accounting for temporal dynamics, allowing the identification of evolving spatial patterns over time. The Getis-Ord Gi* statistic was calculated for each cube to distinguish between clusters with high values (hot spots) and those with low values (cold spots), and each location was subsequently categorized into one of 17 spot types [ 21 ]. A Mixed-Effects Model was used to compare the Least Squares Means (LSMEAN) and Standard Errors (SE) of all variables across the suicide rate cluster types identified in the analysis. Panel Regression Analysis Panel data analysis was applied to estimate longitudinal changes in suicide rates over time and their regional variations across different areas. Based on the spatiotemporal analysis, 75 clusters were identified (70 cold spots and 5 hot spots), and panel regression was conducted on the 70 cold spot regions. Suicide rates and related variables were compiled at the Si/Gun/Gu level across the 12-year period. To account for potential autocorrelation across consecutive time periods, we implemented a lagged-effect approach, using socioeconomic and environmental characteristics measured at year t-1 to estimate their effects on suicide rates in the subsequent year (t). Three estimation methods were applied: pooled OLS, fixed effects, and random effects models. To select the appropriate model, we conducted the Breusch-Pagan LM test and Hausman test. All analyses were performed using R 4.4.2 and ArcGIS Pro 3.3.2. Results To examine the current status of suicide rates in Korea, we analyzed the annual number of suicide deaths and the age-standardized suicide mortality rate per 100,000 population from 2012 to 2023 (Fig. 2 ). The suicide rate remained consistently high over the 12-year period, with rates of 25.1 in 2013, 23.9 in 2014, 22.6 in both 2018 and 2019, and 22.7 in 2023. Spatial autocorrelation analysis revealed statistically significant spatial clustering of suicide rates across all regions and years (Table 1 ), with Moran’s I values ranging from 0.067 to 0.192, consistently indicating positive spatial autocorrelation (p < 0.05). Table 1 Results of Spatial Autocorrelation Analysis of Suicide Rates (Moran's Index) Year Moran's Index Z-score p -value 2012 0.192 6.562 < 0.001 2013 0.120 4.166 < 0.001 2014 0.068 2.425 0.015 2015 0.137 4.741 < 0.001 2016 0.103 3.586 < 0.001 2017 0.067 2.381 < 0.001 2018 0.144 4.958 < 0.001 2019 0.159 5.507 < 0.001 2020 0.115 3.989 < 0.001 2021 0.164 5.630 < 0.001 2022 0.131 4.518 < 0.001 2023 0.148 5.105 < 0.001 Spatiotemporal analysis over the 12-year period identified five types of clusters across 75 regions: 56 Consecutive Cold Spots, 6 New Cold Spots, 6 Oscillating Cold Spots, 2 Sporadic Cold Spots, and 5 Sporadic Hot Spots (Fig. 3 ). Regions classified as “consecutive cold spots”—with statistically significant cold spot status in two consecutive years—were mainly concentrated in the Seoul and Incheon metropolitan areas. In contrast, “new cold spots,” which appeared only in the final year, and “oscillating areas,” where regions alternated between hot and cold spot statuses over the years, were mostly located in the southern regions, including Jeollabuk-do, Jeollanam-do, and Gyeongsangnam-do. Sporadic hot spots were found in the western part of Chungcheongnam-do, notably in cities such as Boryeong and Seosan. When comparing the means of each variable across the five cluster types, the suicide rate was lower in the consecutive cold spot regions (LSMEAN = 21.4, Standard Error [SE] = 0.4), while the fiscal independence ratio was higher than in other regions (LSMEAN = 40.5, SE = 1.6) (Table 2 & Supplemental Table 2) . In sporadic hot spots, the average crude divorce rate was higher (LSMEAN = 2.3, SE = 0.2), and the average number of medical institution beds per 1,000 population was the lowest (LSMEAN = 9.8, SE = 3.7). Table 2 Differences in Regional Characteristics According to Types of Suicide Rate Clusters (Mixed-Effects Model) Variables Consecutive Cold Spot (n = 56) New Cold Spot (n = 6) Oscillating Cold Spot (n = 6) Sporadic Cold Spot (n = 2) Sporadic Hot Spot (n = 5) p -value LSMEAN (SE) LSMEAN (SE) LSMEAN (SE) LSMEAN (SE) LSMEAN (SE) Proportion of Elderly Population 13.4 (0.5) 29.7 (1.4) 26.7 (1.4) 24.3 (2.5) 24.3 (1.6) < 0.001 Crude Divorce Rate 2.0 (0.1) 1.9 (0.2) 1.9 (0.2) 2.1 (0.3) 2.3 (0.2) 0.581 Total Fertility Rate 0.9 (0.2) 1.2 (0.3) 1.3 (0.3) 1.1 (0.2) 1.2 (0.3) < 0.001 Population Growth Rate 0.3 (2.8) -0.3 (1.5) -0.7 (1.6) 0.5 (1.6) 0.1 (1.3) 0.011 Number of medical institution beds per 1,000 population 10.0 (1.1) 11.5 (3.4) 20.4 (3.4) 37.1 (5.9) 9.8 (3.7) < 0.001 Number of Social Welfare Facilities per 100,000 Population 12.2 (7.7) 26.2 (6.4) 24.3 (8.5) 34.0 (11.3) 20.8 (5.8) < 0.001 Fiscal Independence Ratio 40.5 (1.6) 16.4 (4.8) 15.0 (4.8) 22.3 (8.4) 20.6 (5.3) < 0.001 Green Area Ratio 51.9 (2.9) 77.8 (8.9) 78.9 (8.9) 92.2 (15.5) 63.5 (9.8) < 0.001 Age-standardized suicide mortality (per 100,000) 21.4 (0.4) 25.0 (1.2) 24.5 (1.2) 22.8 (2.1) 30.6 (1.3) < 0.001 Values are presented as least squares means ± standard errors. p-values derived from Type 3 Tests of Fixed Effects in mixed-effects models. Significance level set at α = 0.05. Panel regression analysis was conducted using three models: pooled OLS, fixed effects (FE), and random effects (Table 3 ). The Breusch-Pagan LM test (χ² = 37.17, p < .001) and the Hausman test (χ² = 39.8, p < .001) supported the use of the fixed effects model. In the FE model, two regional variables showed significant associations with next-year suicide rates: a higher proportion of elderly population (b = − 0.548, p < .01) and more social welfare facilities per 100,000 people (b = − 0.117, p < .05) were associated with lower suicide rates. Other variables—including hospital bed availability, green area ratio, fiscal independence, crude divorce rate, fertility rate, and population growth—were not statistically significant. Descriptive statistics for all variables included in the panel regression are presented in Supplemental Table 3 . Table 3 Panel Regression Results for Cold Spot Regions: Regional Determinants of Suicide Rates (2012–2023) Variables OLS FE RE b (SE) b (SE) b (SE) Proportion of Elderly Population .05 (.041) − .548** (.165) .007 (.054) Crude Divorce Rate 4.347* (.502) − .226 (.946) 3.558** (.606) Total Fertility Rate 4.436* (.711) 1.198 (1.644) 5.092** (.787) Population Growth Rate − .057 (.076) − .07 (.096) − .042 (.084) Number of medical institution beds per 1,000 population .025 (.018) .012 (.107) .028 (.026) Number of Social Welfare Facilities per 100,000 Population − .014 (.029) − .117 (.036)* − .015 (.036) Fiscal Independence Ratio − .043** (.015) − .011 (.048) − .053 (.021) Green Area Ratio .000 (.009) − .038 (.062) .0004 (.012) R-squared 0.264 0.125 0.182 Note. Standard errors in parentheses. OLS = Ordinary Least Squares; FE = Fixed Effects; RE = Random Effects. * p < .05, ** p < .01 Discussion This study confirmed that there was a statistically significant spatial autocorrelation in suicide rates in Korea each year from 2012 to 2023. Through a spatiotemporal analysis covering a 12-year period and all 229 Si, Gun, and Gu administrative districts, five distinct types of hot spots and cold spots representing characteristic spatial clustering patterns were identified. Cold spots were primarily concentrated in metropolitan areas, including Seoul and Incheon. Notably, among the regional factors analyzed, only the proportion of the elderly population and the number of social welfare facilities per 100,000 people showed a statistically significant association with lower suicide rates. The regions identified as "persistent cold spots," where statistically significant cold spots appeared for two consecutive years, were concentrated in metropolitan areas such as Seoul and Incheon. This finding is consistent with previous studies showing that suicide rates tend to be higher in rural areas than in urban areas [ 22 – 24 ]. n contrast, "new cold spots," which appeared only in the final year, and "oscillating cold spots," where cold and hot spots alternated across different years, were distributed in the southern regions, including Jeollabuk-do, Jeollanam-do, and Gyeongsangnam-do. Areas where statistically significant hot spots appeared sporadically over several years were Boryeong and Seosan, located in the western region of Chungcheongnam-do. These areas were also identified as "sporadic hot spots" in a previous study examining suicide mortality rates from 2009 to 2019 [ 25 ]. In cold spot regions, unlike previous findings, this study found that as the proportion of the elderly population increased, suicide rates tended to decrease. Most cold spots were located in urban areas, where infrastructure for the elderly—such as nursing facilities and elderly care services—is well established, potentially reducing issues such as social isolation among older adults and mitigating suicide risk. Moreover, health problems have been identified as the leading cause of suicide among the elderly in Korea [ 6 ], and metropolitan areas tend to have better healthcare infrastructure and accessibility. These findings suggest that suicide rates are not determined solely by the proportion of the elderly population; rather, they are influenced by a combination of factors such as regional infrastructure and social support systems. This study found that a higher number of social welfare facilities per 100,000 population was associated with lower suicide rates. The number of welfare facilities is closely related to the level of public spending on social welfare in a given region, and previous research has confirmed that government social welfare expenditures can influence suicide mortality [ 26 ]. Additionally, studies from six European countries have reported a tendency for lower suicide rates in countries with higher social welfare spending [ 27 ]. Social welfare facilities can function as important psychosocial resources beyond physical infrastructure by fostering interpersonal connections and providing social support networks within communities. Considering that social support has been shown to be effective in suicide prevention [ 28 , 29 ], community-based welfare services may serve as protective factors against suicide. However, since the number of facilities alone does not fully capture the quality or accessibility of services, these findings should be interpreted with caution. Previous studies have reported that the fiscal capacity of local governments is associated with suicide rates [ 30 , 31 ]; however, in this study, fiscal independence did not show a statistically significant association with suicide mortality. Most cold spot regions were located in metropolitan areas such as Seoul, which generally have higher levels of fiscal independence compared to other regions [ 32 ]. This may indirectly support earlier findings suggesting an inverse relationship between local government fiscal capacity and suicide mortality. Nevertheless, socioeconomically vulnerable populations may still exist even in regions with high fiscal independence. High fiscal independence does not necessarily translate into better social services or effective suicide prevention programs for disadvantaged groups. Since budget allocation strategies and policy priorities vary by region, future research should examine not only fiscal independence but also more specific financial indicators, such as the proportion of social welfare spending or budgets related to mental health services. Other variables, including crude divorce rate, total fertility rate, population growth rate, number of hospital beds, and green area ratio, also showed no significant associations with suicide mortality. Although variables like the crude divorce rate have been linked to suicide risk at the individual level [ 33 , 34 ], such associations may be weakened when using aggregated regional-level data. This suggests the possibility of ecological fallacy, indicating the need to distinguish between individual-level risk factors and collective indicators at the regional level when interpreting findings. Most cold spot areas analyzed were located in Seoul, where the per capita park area is known to fall below the minimum standard recommended by the World Health Organization (WHO) [ 35 , 36 ], which may partly explain the divergence from previous research findings. To gain a more nuanced understanding of how environmental or social infrastructure affects suicide outcomes, future studies should consider employing multilevel or mixed-methods approaches that integrate individual and contextual factors. This study has several limitations. First, in addition to the five regional characteristics included in this study, other potential factors that may influence suicide mortality—such as population density, unemployment rate, and urbanization rate—were not considered. This was due to the lack of consistently available, disaggregated data across all Si/Gun/Gu units over the 12-year period from 2012 to 2023. Future research should aim to incorporate a broader range of regional variables. Second, our study adopted a conservative approach by focusing exclusively on cold spot regions with consistently low suicide rates. Out of 75 spatiotemporal clusters identified, 70 were classified as cold spots, allowing for a nuanced examination of factors associated with lower suicide risks. This analytical strategy offers a more stable and generalizable perspective by focusing on cold spots, thus reducing the potential bias introduced by extreme cases or statistical outliers. While the focus on cold spots may limit the scope of comprehensive comparative analysis, this approach provides valuable insights into regional characteristics that contribute to suicide prevention by highlighting protective factors found in areas with consistently low suicide rates. Despite these limitations, this study is significant in that it is the first to identify long-term (12-year) spatial clustering patterns of suicide mortality in South Korea while simultaneously examining regional factors influencing suicide mortality through both spatial and temporal perspectives. Moreover, whereas previous studies have primarily focused on identifying hot spot areas with high suicide rates through spatial analysis, this study is distinctive in that it centered on cold spot areas and examined regional factors associated with lower suicide mortality. By focusing on protective factors rather than risk factors, the study offers novel insights and policy implications for regional strategies aimed at reducing suicide rates. Conclusion This study identified hotspot and cold spot regions of suicide mortality in South Korea through a 12-year spatiotemporal analysis. Among the regional factors analyzed, a higher proportion of elderly population and a greater number of social welfare facilities were significantly associated with lower suicide rates. Understanding the factors that influence the geographic patterns of suicide is essential for developing national suicide prevention policies. Many countries have already integrated spatial data into their suicide monitoring systems to track suicide trends at both regional and national levels [ 37 ]. The findings of this study provide an important evidence base for designing region-specific public health interventions aimed at reducing suicide rates in South Korea. Declarations Funding statement There are no financial conflicts of interest to disclose. Ethics Statement All data used in this study are not derived from human subjects and were obtained from publicly available open sources. This study was approved by the Institutional Review Board of Hanyang University (IRB No. HYUIRB-202504-002). Data availability All data used in this study are publicly available from government sources and can be accessed via KOSIS and affiliated repositories. Declaration of Interest statements None to declare. Author statement Conceptualization, AJ, YJ, HK; Data curation, AJ, YJ; Formal analysis, AJ, YJ; Methodology, AJ, YJ; Supervision, HK; Writing – original draft, AJ, YJ, HK; Writing – review & editing, AJ, YJ, HK. Acknowledgement The authors would like to acknowledge the valuable contributions and thoughtful guidance of Professor Agnus M. Kim from the Department of Preventive Medicine, Hanyang University College of Medicine. 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University","correspondingAuthor":false,"prefix":"","firstName":"Youngshin","middleName":"","lastName":"Ju","suffix":""},{"id":471631104,"identity":"19a13175-620b-4f46-b3b0-256e6c0ba508","order_by":2,"name":"Hyeji Kwon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDACCTB5gIFfAkmQsYEYLZIzSNZicINYLfyzmx+/+FBzR974dvOzxzw1d+wa2A8/YJy5B48ld46ZWc449sxw251j5sY8x54lN/CkGTBueIZbi4FEgpkxD9vhBLMbCWbSQEYyA0MOA+ODA/i0pH8z5vl3OMF4Rvo3aSAjmYH/DSEtOcaPedsOJwAZZtJAhh2DBNCWDXi0SNw5U8Y4s++Z4YwbOWWSc/sOJ7BJPDM4OAOPFv7Z7Zs/fPh2R55/Rvo2iTffDtvz8yc/fNiDRwsQsMHjnYmHgSGxjQEUTfgB8wcYi/EHA4M9AdWjYBSMglEwAgEA9/VY5GvotI0AAAAASUVORK5CYII=","orcid":"","institution":"Hanyang University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Hyeji","middleName":"","lastName":"Kwon","suffix":""}],"badges":[],"createdAt":"2025-06-14 08:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6892660/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6892660/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84776998,"identity":"f7e24f0c-7978-4595-a8cb-03bb87d5c8b4","added_by":"auto","created_at":"2025-06-17 09:07:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150896,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpace-time cube representation of 229 administrative districts (Si, Gun, and Gu) in South Korea from 2012 to 2023(source: https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/learnmorecreatecube.htm).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6892660/v1/239e05f80f758619ae2b7ace.png"},{"id":84774967,"identity":"b6fa3ca3-a832-4737-b567-d2b9b19b6d96","added_by":"auto","created_at":"2025-06-17 08:51:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22385,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnual Number of Suicide Deaths and Age-Standardized Mortality Rates in Korea, 2012–2023\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6892660/v1/60ff0c7e02065d936da73d6f.png"},{"id":84776177,"identity":"8a1d2c13-17ab-4be1-8f69-fa6c6fdd07c4","added_by":"auto","created_at":"2025-06-17 08:59:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121160,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatiotemporal Hot Spot and Cold Spot Analysis of Suicide Rates in Korea, 2012–2023\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6892660/v1/e6dc8d48268606716fa002ac.png"},{"id":89093397,"identity":"efca954e-a475-441a-9e92-ea48d2228456","added_by":"auto","created_at":"2025-08-14 15:08:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1148274,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6892660/v1/45767db7-3131-4ba8-8482-ef27280422ee.pdf"},{"id":84776178,"identity":"e8224f6c-d0b1-402d-92b0-32b64bed2b1c","added_by":"auto","created_at":"2025-06-17 08:59:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25017,"visible":true,"origin":"","legend":"","description":"","filename":"2supplementaltables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6892660/v1/9a47dafe009050f5763c5d2e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geographic Disparities in Suicide Mortality in South Korea (2012-2023): Identifying Hot/Cold Spots and Associated Socioeconomic Factors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSuicide remains a critical public health concern worldwide, and South Korea has reported the highest suicide rate among OECD countries since 2004 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As of the most recent data, Korea\u0026rsquo;s suicide rate stands at 24.1 per 100,000 population\u0026mdash;more than double the OECD average of 10.7 and significantly higher than Lithuania, which ranks second at 18.5 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to Statistics Korea, the suicide rate increased by 8.5% in 2023 compared to 2022, marking the highest rate observed since 2018 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Numerous studies have investigated the underlying causes of Korea\u0026rsquo;s persistently high suicide rate [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although efforts such as the enactment of the Suicide Prevention Act and the establishment of the Korea Suicide Prevention Center have been made to address the issue [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], the suicide rate has shown little improvement and continues to impose a considerable burden on society.\u003c/p\u003e \u003cp\u003eSuicide risk varies among individuals, and regional characteristics may contribute to these disparities. For this reason, suicide has been regarded as an indicator of not only an individual\u0026rsquo;s mental health but also the mental health of the society to which the individual belongs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Durkheim (1897) argued that suicide is influenced by social context and occurs as a result of a lack of social integration [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Studies have shown that both individual-level interventions and targeted regional strategies can effectively reduce suicide risk [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In this context, it is necessary to consider not only individual-level exploratory factors but also regional factors when addressing suicide risk.\u003c/p\u003e \u003cp\u003eBuilding on the evidence of regional disparities observed internationally, many countries have reported regional differences in suicide rates [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Despite these findings, studies investigating the spatial and temporal clustering of suicide in South Korea \u0026mdash;given its persistently high suicide rate\u0026mdash;have remained relatively scarce compared to research conducted in other countries. In addition, although several studies in South Korea over the past decade have used spatial analysis to examine regional differences in suicide mortality, most have focused on identifying geographic areas with relatively high or low suicide rates compared to other regions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs part of efforts to reduce the persistently increasing suicide rate in South Korea, it is necessary to identify regional differences in suicide mortality and investigate their underlying causes. Understanding these differences is crucial for prevention and may inform broader efforts to reduce national suicide rates. Accordingly, this study aims to examine the spatial and regional dependence of high suicide rates using 12 years of regional suicide mortality data from South Korea and to identify high-risk areas through an analysis of the spatiotemporal distribution characteristics of suicide mortality. Based on these findings, the study seeks to propose efficient and effective national- and community-based strategies for reducing suicide rates.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Variables\u003c/h2\u003e \u003cp\u003eThe suicide rate was calculated as the age-standardized mortality rate per 100,000 population using cause-of-death statistics from the Korean Statistical Information Service (KOSIS). These statistics are derived from legally mandated death registrations, in which physicians report the cause of death using International Classification of Diseases, 10th Revision (ICD-10) codes X60\u0026ndash;X84 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Suicide rate data were collected annually at the city (\u003cem\u003eSi\u003c/em\u003e), county (\u003cem\u003eGun\u003c/em\u003e), and district (\u003cem\u003eGu\u003c/em\u003e) levels from 2012 to 2023. In addition to the suicide rates, regional characteristic variables were collected for the same period. These variables include demographic factors such as the proportion of elderly population, crude divorce rate, total fertility rate, and population growth rate. Environmental and social factors include the number of medical institution beds per 1,000 population, the number of social welfare facilities per 100,000 population, and the green area ratio. Financial factors include the fiscal independence ratio. Detailed descriptions and definitions of these variables are provided in \u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e. Due to administrative changes, data for Michuhol-gu in Incheon prior to 2018 was not available from Statistics Korea, so only data from 2018 onwards were included. Data for the number of hospital beds per 1,000 population in Gwacheon were not available after 2019, so only data from before 2019 were included in the analysis. All other variables cover the same years and regions as the suicide rate. Spatial data for the 229 \u003cem\u003eSi, Gun\u003c/em\u003e, and \u003cem\u003eGu\u003c/em\u003e administrative districts were obtained from the 2023 administrative boundary dataset provided by V-World, a national platform that integrates spatial information from central and local governments [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpatial Autocorrelation Analysis\u003c/h3\u003e\n\u003cp\u003eSpatiotemporal analysis was conducted for 229 regions defined according to Korea\u0026rsquo;s administrative division system. To further examine spatial autocorrelation in suicide rates across these regions from 2012 to 2023, Moran\u0026rsquo;s I was calculated. Spatial autocorrelation describes the degree to which similar values occur near one another in geographic space. To analyze this phenomenon, researchers often employ Moran\u0026rsquo;s \u003cem\u003eI\u003c/em\u003e, which identifies patterns of spatial clustering, dispersion, or randomness among observed regional values [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moran\u0026rsquo;s \u003cem\u003eI\u003c/em\u003e ranges between \u0026minus;\u0026thinsp;1 and 1. A value approaching 1 suggests a strong positive spatial relationship, meaning similar values are located near each other. Conversely, values nearing \u0026minus;\u0026thinsp;1 imply a negative spatial relationship, where neighboring areas tend to have dissimilar values. Values close to 0 indicate a random spatial pattern with little to no spatial autocorrelation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eEmerging Hotspot and Cluster Comparison\u003c/h3\u003e\n\u003cp\u003eTo incorporate both spatial and temporal dimensions of suicide rates, Emerging Hotspot Analysis was conducted using a three-dimensional space-time cube framework. Using a fixed set of 229 Si, Gun, and Gu administrative districts, temporal trends from 2012 to 2023 were evaluated across 2,748 space-time cubes (229 districts \u0026times; 12 years) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This method extends traditional spatial analysis by accounting for temporal dynamics, allowing the identification of evolving spatial patterns over time. The Getis-Ord Gi* statistic was calculated for each cube to distinguish between clusters with high values (hot spots) and those with low values (cold spots), and each location was subsequently categorized into one of 17 spot types [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A Mixed-Effects Model was used to compare the Least Squares Means (LSMEAN) and Standard Errors (SE) of all variables across the suicide rate cluster types identified in the analysis.\u003c/p\u003e\n\u003ch3\u003ePanel Regression Analysis\u003c/h3\u003e\n\u003cp\u003ePanel data analysis was applied to estimate longitudinal changes in suicide rates over time and their regional variations across different areas. Based on the spatiotemporal analysis, 75 clusters were identified (70 cold spots and 5 hot spots), and panel regression was conducted on the 70 cold spot regions. Suicide rates and related variables were compiled at the Si/Gun/Gu level across the 12-year period. To account for potential autocorrelation across consecutive time periods, we implemented a lagged-effect approach, using socioeconomic and environmental characteristics measured at year t-1 to estimate their effects on suicide rates in the subsequent year (t). Three estimation methods were applied: pooled OLS, fixed effects, and random effects models. To select the appropriate model, we conducted the Breusch-Pagan LM test and Hausman test. All analyses were performed using R 4.4.2 and ArcGIS Pro 3.3.2.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo examine the current status of suicide rates in Korea, we analyzed the annual number of suicide deaths and the age-standardized suicide mortality rate per 100,000 population from 2012 to 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The suicide rate remained consistently high over the 12-year period, with rates of 25.1 in 2013, 23.9 in 2014, 22.6 in both 2018 and 2019, and 22.7 in 2023. Spatial autocorrelation analysis revealed statistically significant spatial clustering of suicide rates across all regions and years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with Moran\u0026rsquo;s \u003cem\u003eI\u003c/em\u003e values ranging from 0.067 to 0.192, consistently indicating positive spatial autocorrelation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eResults of Spatial Autocorrelation Analysis of Suicide Rates (Moran's Index)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" 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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eSpatiotemporal analysis over the 12-year period identified five types of clusters across 75 regions: 56 Consecutive Cold Spots, 6 New Cold Spots, 6 Oscillating Cold Spots, 2 Sporadic Cold Spots, and 5 Sporadic Hot Spots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Regions classified as \u0026ldquo;consecutive cold spots\u0026rdquo;\u0026mdash;with statistically significant cold spot status in two consecutive years\u0026mdash;were mainly concentrated in the Seoul and Incheon metropolitan areas. In contrast, \u0026ldquo;new cold spots,\u0026rdquo; which appeared only in the final year, and \u0026ldquo;oscillating areas,\u0026rdquo; where regions alternated between hot and cold spot statuses over the years, were mostly located in the southern regions, including Jeollabuk-do, Jeollanam-do, and Gyeongsangnam-do. Sporadic hot spots were found in the western part of Chungcheongnam-do, notably in cities such as Boryeong and Seosan.\u003c/p\u003e \u003cp\u003eWhen comparing the means of each variable across the five cluster types, the suicide rate was lower in the consecutive cold spot regions (LSMEAN\u0026thinsp;=\u0026thinsp;21.4, Standard Error [SE]\u0026thinsp;=\u0026thinsp;0.4), while the fiscal independence ratio was higher than in other regions (LSMEAN\u0026thinsp;=\u0026thinsp;40.5, SE\u0026thinsp;=\u0026thinsp;1.6) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003e\u0026amp; Supplemental Table\u0026nbsp;2)\u003c/b\u003e. In sporadic hot spots, the average crude divorce rate was higher (LSMEAN\u0026thinsp;=\u0026thinsp;2.3, SE\u0026thinsp;=\u0026thinsp;0.2), and the average number of medical institution beds per 1,000 population was the lowest (LSMEAN\u0026thinsp;=\u0026thinsp;9.8, SE\u0026thinsp;=\u0026thinsp;3.7).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in Regional Characteristics According to Types of Suicide Rate Clusters (Mixed-Effects Model)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsecutive Cold Spot (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNew Cold Spot (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOscillating Cold Spot (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSporadic Cold Spot (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSporadic Hot Spot (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSMEAN (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLSMEAN (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLSMEAN (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLSMEAN (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLSMEAN (SE)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of Elderly Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.4 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.7 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.7 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.3 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.3 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude Divorce Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.9 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.1 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.3 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Fertility Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation Growth Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.7 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of medical institution beds per 1,000 population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.0 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.5 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.4 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.1 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.8 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Social Welfare Facilities per 100,000 Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.2 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.2 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.3 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.0 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.8 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiscal Independence Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.5 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.4 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.0 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.3 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.6 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen Area Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.9 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.8 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.9 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.2 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.5 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge-standardized suicide mortality (per 100,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.4 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.0 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.5 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.8 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.6 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eValues are presented as least squares means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard errors.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ep-values derived from Type 3 Tests of Fixed Effects in mixed-effects models. Significance level set at α\u0026thinsp;=\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePanel regression analysis was conducted using three models: pooled OLS, fixed effects (FE), and random effects (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Breusch-Pagan LM test (χ\u0026sup2; = 37.17, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and the Hausman test (χ\u0026sup2; = 39.8, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) supported the use of the fixed effects model. In the FE model, two regional variables showed significant associations with next-year suicide rates: a higher proportion of elderly population (b = \u0026minus;\u0026thinsp;0.548, p\u0026thinsp;\u0026lt;\u0026thinsp;.01) and more social welfare facilities per 100,000 people (b = \u0026minus;\u0026thinsp;0.117, p\u0026thinsp;\u0026lt;\u0026thinsp;.05) were associated with lower suicide rates. Other variables\u0026mdash;including hospital bed availability, green area ratio, fiscal independence, crude divorce rate, fertility rate, and population growth\u0026mdash;were not statistically significant. Descriptive statistics for all variables included in the panel regression are presented in \u003cb\u003eSupplemental Table\u0026nbsp;3\u003c/b\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\u003ePanel Regression Results for Cold Spot Regions: Regional Determinants of Suicide Rates (2012\u0026ndash;2023)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eb (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eb (SE)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of Elderly Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.05 (.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.548** (.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.007 (.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude Divorce Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.347* (.502)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.226 (.946)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.558** (.606)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Fertility Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.436* (.711)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.198 (1.644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.092** (.787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation Growth Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.057 (.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.07 (.096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.042 (.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of medical institution beds per 1,000 population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.025 (.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.012 (.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.028 (.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Social Welfare Facilities per 100,000 Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.014 (.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.117 (.036)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.015 (.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiscal Independence Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.043** (.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.011 (.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.053 (.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen Area Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.000 (.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.038 (.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.0004 (.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote. Standard errors in parentheses. OLS\u0026thinsp;=\u0026thinsp;Ordinary Least Squares; FE\u0026thinsp;=\u0026thinsp;Fixed Effects; RE\u0026thinsp;=\u0026thinsp;Random Effects.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study confirmed that there was a statistically significant spatial autocorrelation in suicide rates in Korea each year from 2012 to 2023. Through a spatiotemporal analysis covering a 12-year period and all 229 Si, Gun, and Gu administrative districts, five distinct types of hot spots and cold spots representing characteristic spatial clustering patterns were identified. Cold spots were primarily concentrated in metropolitan areas, including Seoul and Incheon. Notably, among the regional factors analyzed, only the proportion of the elderly population and the number of social welfare facilities per 100,000 people showed a statistically significant association with lower suicide rates.\u003c/p\u003e \u003cp\u003eThe regions identified as \"persistent cold spots,\" where statistically significant cold spots appeared for two consecutive years, were concentrated in metropolitan areas such as Seoul and Incheon. This finding is consistent with previous studies showing that suicide rates tend to be higher in rural areas than in urban areas [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. n contrast, \"new cold spots,\" which appeared only in the final year, and \"oscillating cold spots,\" where cold and hot spots alternated across different years, were distributed in the southern regions, including Jeollabuk-do, Jeollanam-do, and Gyeongsangnam-do. Areas where statistically significant hot spots appeared sporadically over several years were Boryeong and Seosan, located in the western region of Chungcheongnam-do. These areas were also identified as \"sporadic hot spots\" in a previous study examining suicide mortality rates from 2009 to 2019 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn cold spot regions, unlike previous findings, this study found that as the proportion of the elderly population increased, suicide rates tended to decrease. Most cold spots were located in urban areas, where infrastructure for the elderly\u0026mdash;such as nursing facilities and elderly care services\u0026mdash;is well established, potentially reducing issues such as social isolation among older adults and mitigating suicide risk. Moreover, health problems have been identified as the leading cause of suicide among the elderly in Korea [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and metropolitan areas tend to have better healthcare infrastructure and accessibility. These findings suggest that suicide rates are not determined solely by the proportion of the elderly population; rather, they are influenced by a combination of factors such as regional infrastructure and social support systems.\u003c/p\u003e \u003cp\u003eThis study found that a higher number of social welfare facilities per 100,000 population was associated with lower suicide rates. The number of welfare facilities is closely related to the level of public spending on social welfare in a given region, and previous research has confirmed that government social welfare expenditures can influence suicide mortality [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, studies from six European countries have reported a tendency for lower suicide rates in countries with higher social welfare spending [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Social welfare facilities can function as important psychosocial resources beyond physical infrastructure by fostering interpersonal connections and providing social support networks within communities. Considering that social support has been shown to be effective in suicide prevention [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], community-based welfare services may serve as protective factors against suicide. However, since the number of facilities alone does not fully capture the quality or accessibility of services, these findings should be interpreted with caution.\u003c/p\u003e \u003cp\u003ePrevious studies have reported that the fiscal capacity of local governments is associated with suicide rates [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; however, in this study, fiscal independence did not show a statistically significant association with suicide mortality. Most cold spot regions were located in metropolitan areas such as Seoul, which generally have higher levels of fiscal independence compared to other regions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This may indirectly support earlier findings suggesting an inverse relationship between local government fiscal capacity and suicide mortality. Nevertheless, socioeconomically vulnerable populations may still exist even in regions with high fiscal independence. High fiscal independence does not necessarily translate into better social services or effective suicide prevention programs for disadvantaged groups. Since budget allocation strategies and policy priorities vary by region, future research should examine not only fiscal independence but also more specific financial indicators, such as the proportion of social welfare spending or budgets related to mental health services.\u003c/p\u003e \u003cp\u003eOther variables, including crude divorce rate, total fertility rate, population growth rate, number of hospital beds, and green area ratio, also showed no significant associations with suicide mortality. Although variables like the crude divorce rate have been linked to suicide risk at the individual level [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], such associations may be weakened when using aggregated regional-level data. This suggests the possibility of ecological fallacy, indicating the need to distinguish between individual-level risk factors and collective indicators at the regional level when interpreting findings. Most cold spot areas analyzed were located in Seoul, where the per capita park area is known to fall below the minimum standard recommended by the World Health Organization (WHO) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which may partly explain the divergence from previous research findings. To gain a more nuanced understanding of how environmental or social infrastructure affects suicide outcomes, future studies should consider employing multilevel or mixed-methods approaches that integrate individual and contextual factors.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, in addition to the five regional characteristics included in this study, other potential factors that may influence suicide mortality\u0026mdash;such as population density, unemployment rate, and urbanization rate\u0026mdash;were not considered. This was due to the lack of consistently available, disaggregated data across all Si/Gun/Gu units over the 12-year period from 2012 to 2023. Future research should aim to incorporate a broader range of regional variables. Second, our study adopted a conservative approach by focusing exclusively on cold spot regions with consistently low suicide rates. Out of 75 spatiotemporal clusters identified, 70 were classified as cold spots, allowing for a nuanced examination of factors associated with lower suicide risks. This analytical strategy offers a more stable and generalizable perspective by focusing on cold spots, thus reducing the potential bias introduced by extreme cases or statistical outliers. While the focus on cold spots may limit the scope of comprehensive comparative analysis, this approach provides valuable insights into regional characteristics that contribute to suicide prevention by highlighting protective factors found in areas with consistently low suicide rates.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study is significant in that it is the first to identify long-term (12-year) spatial clustering patterns of suicide mortality in South Korea while simultaneously examining regional factors influencing suicide mortality through both spatial and temporal perspectives. Moreover, whereas previous studies have primarily focused on identifying hot spot areas with high suicide rates through spatial analysis, this study is distinctive in that it centered on cold spot areas and examined regional factors associated with lower suicide mortality. By focusing on protective factors rather than risk factors, the study offers novel insights and policy implications for regional strategies aimed at reducing suicide rates.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified hotspot and cold spot regions of suicide mortality in South Korea through a 12-year spatiotemporal analysis. Among the regional factors analyzed, a higher proportion of elderly population and a greater number of social welfare facilities were significantly associated with lower suicide rates. Understanding the factors that influence the geographic patterns of suicide is essential for developing national suicide prevention policies. Many countries have already integrated spatial data into their suicide monitoring systems to track suicide trends at both regional and national levels [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The findings of this study provide an important evidence base for designing region-specific public health interventions aimed at reducing suicide rates in South Korea.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no financial conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are not derived from human subjects and were obtained from publicly available open sources. This study was approved by the Institutional Review Board of Hanyang University (IRB No. HYUIRB-202504-002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are publicly available from government sources and can be accessed via KOSIS and affiliated repositories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, AJ, YJ, HK; Data curation, AJ, YJ; Formal analysis, AJ, YJ; Methodology, AJ, YJ; Supervision, HK; Writing – original draft, AJ, YJ, HK; Writing – review \u0026amp; editing, AJ, YJ, HK.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the valuable contributions and thoughtful guidance of Professor Agnus M. Kim from the Department of Preventive Medicine, Hanyang University College of Medicine.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKDCA, Prevention Agency (KDCA) National Injury Information Portal (2025) Korea Disease Control and - Self-harm and Suicide [Internet] Accessed: Feb. 24, 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kdca.go.kr/injury/biz/injury/damgInfo/siSucdeMain.do;jsessionid=3B35B1A01983E3E0D9664A53C0203CA8\u003c/span\u003e\u003cspan address=\"https://www.kdca.go.kr/injury/biz/injury/damgInfo/siSucdeMain.do;jsessionid=3B35B1A01983E3E0D9664A53C0203CA8\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMOHW (2025) Ministry of Health and Welfare, Korea Foundation for Suicide Prevention, White Paper on Suicide Prevention [Internet] Accessed: Feb. 24, 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kfsp.or.kr/home/kor/board.do?menuPos=82\u0026amp;act=detail\u0026amp;idx=4818\u0026amp;searchValue1=0\u0026amp;searchKeyword=\u0026amp;pageIndex=1#none\u003c/span\u003e\u003cspan address=\"https://www.kfsp.or.kr/home/kor/board.do?menuPos=82\u0026amp;act=detail\u0026amp;idx=4818\u0026amp;searchValue1=0\u0026amp;searchKeyword=\u0026amp;pageIndex=1#none\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMOHW (2025) Ministry of Health and Welfare, 2023 Suicide Mortality Statistics Report. 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Land 11(4):474\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamchand R, Colpe L, Claassen C, Brinton S, Carr C, McKeon R, Schoenbaum M (2021) Prioritizing improved data and surveillance for suicide in the United States in response to COVID-19, vol 111. American Public Health Association\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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, suicide prevention, spatial autocorrelation, spatial analyses, regional disparity","lastPublishedDoi":"10.21203/rs.3.rs-6892660/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6892660/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSouth Korea has recorded the highest suicide rate among OECD countries since 2004, and suicide remains a critical public health issue. Suicide is not only an individual concern but also an indicator of the broader community\u0026rsquo;s mental health, necessitating consideration of regional-level factors.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to analyze the spatiotemporal distribution patterns of suicide mortality in South Korea and to identify regional factors associated with lower suicide rates, thereby providing an evidence base for community-based suicide prevention strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAge-standardized suicide mortality rates and regional characteristics were collected for 229 administrative districts (Si/Gun/Gu) from 2012 to 2023. Spatial autocorrelation was assessed using Moran\u0026rsquo;s I, and spatiotemporal clusters were identified through Emerging Hotspot Analysis. Panel regression analysis was then conducted on the 70 identified cold spot regions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSuicide rates exhibited statistically significant spatial autocorrelation across all years. Of the 75 identified regions, 70 were classified as cold spots and 5 as hot spots. An increase in the proportion of the elderly population (β = \u0026minus;\u0026thinsp;0.548, p\u0026thinsp;\u0026lt;\u0026thinsp;.01) and a higher number of social welfare facilities per 100,000 population (β = \u0026minus;\u0026thinsp;0.117, p\u0026thinsp;\u0026lt;\u0026thinsp;.05) were significantly associated with a reduction in suicide mortality in the following year.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSuicide rates in South Korea showed clear spatiotemporal disparities across regions. The proportion of the elderly population and the number of social welfare facilities were identified as significant regional factors influencing suicide mortality. This study highlights the need for region-specific suicide prevention policies.\u003c/p\u003e","manuscriptTitle":"Geographic Disparities in Suicide Mortality in South Korea (2012-2023): Identifying Hot/Cold Spots and Associated Socioeconomic Factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 08:51:37","doi":"10.21203/rs.3.rs-6892660/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":"a5d9c69e-77e2-45ce-85e0-b9eb278e503e","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-14T15:08:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 08:51:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6892660","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6892660","identity":"rs-6892660","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}