Spatial Patterns and Neighborhood Characteristics of Overall Suicide Clusters in Florida From 2001 to 2010

Geographic Patterns of Youth Suicide in San Diego County

OBJECTIVE

Our objectives were to evaluate for any cluster patterns of youth suicide deaths and characterize the level of child opportunity in the communities where suicide deaths occurred.

METHODS

Decedents <18 years were identified from San Diego County Medical Examiner death reports from 2000 to 2020. We mapped decedents' residential Zone Improvement Plan (ZIP) codes and calculated suicide rates per 10,000 youths. ZIP codes identified in overlapping spatial statistical approaches - the spatial scan statistic and Local Moran with Empirical Bayes (EB) rates - were considered a cluster for the final analysis. We obtained Child Opportunity Index (COI) scores for each ZIP code to determine if there were differences in: 1) ZIP codes with suicide deaths compared to ZIPs with no deaths 2) differences in distribution of suicide death rates across quintiles of COI.

RESULTS

Scan statistic identified 25 ZIP codes within a cluster (RR 2.6, P = 0.00066). Local Moran with EB rates identified two ZIP codes as a high-high cluster (P < 0.05). The location identified as a cluster in both approaches was in Alpine. The median COI for ZIP codes with suicide deaths was higher at 63.5 (IQR 38-83) compared to 47 (IQR 22.5-75.5) for ZIP codes without suicide deaths. There was a significant difference in suicide rates between Very Low and Moderate levels of Overall opportunity (P = .013).

CONCLUSION

We identified a cluster of youth suicides in one of the most populous counties in the country. These findings serve to inform policies and prevention programs that aim to mitigate youth suicide mortality.

An exploratory study on spatiotemporal clustering of suicide in Korean adolescents

BACKGROUND

Adolescent suicides are more likely to form clusters than those of other age groups. However, the definition of a cluster in the space-time dimension has not been established, neither are the factors contributing to it well known. Therefore, this study aimed to identify space-time clusters in adolescent suicides in Korea and to examine the differences between clustered and non-clustered cases using novel statistical methods.

METHODS

From 2016 to 2020, the dates and locations, including specific addresses from which the latitude and longitude of all student suicides (aged 9-18 years) in Korea were obtained through student suicide reports. Sociodemographic characteristics of the adolescents who died by suicide were collected, and the individual characteristics of each student who died by suicide were reported by teachers using the Strengths and Difficulties Questionnaire (SDQ). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) analysis was used to assess the clustering of suicides.

RESULTS

We identified 23 clusters through the data analysis of 652 adolescent suicides using DBSCAN. By comparing the size of each cluster, we identified 63 (9.7%) spatiotemporally clustered suicides among adolescents, and the temporal range of these clusters was 7-59 days. The suicide cluster group had a lower economic status than the non-clustered group. There were no significant differences in other characteristics between the two groups.

CONCLUSION

This study has defined the space-time cluster of suicides using a novel statistical method. Our findings suggest that when an adolescent suicide occurs, close monitoring and intervention for approximately 2 months are needed to prevent subsequent suicides. Future research using DBSCAN needs to involve a larger sample of adolescents from various countries to further corroborate these findings.

The application of spatial analysis to understanding the association between area-level socio-economic factors and suicide: a systematic review

BACKGROUND

Little is known about what impact the use of different spatial methodological approaches may have on understanding the relationship between area-level socio-economic factors and suicide.

METHODS

In this systematic review, we searched PubMed, Embase, CINAHL and PsycInfo for original empirical studies examining the relationship between socio-economic factors and suicide with a spatial lens, published up to January 22, 2022. Data on applied spatial methods, indicators of socio-economic factors, and risk of suicide related to socio-economic factors were extracted. The protocol for this systematic review was registered with PROSPERO (CRD42021251387).

RESULTS

A systematic search yielded 6290 potentially relevant results; 58 studies met the inclusion criteria for review. Of the 58 included studies, more than half of the studies (n = 34; 58.6%) used methods that accounted for spatial effects in analyses of the association between socio-economic factors and suicide or examined spatial autocorrelation, while 24 (41.4%) studies applied univariate and multivariate models without considering spatial effects. Bayesian hierarchical models and spatial regression models were commonly used approaches to correct for spatial effects. The risk of suicide relating to socio-economic factors varied greatly by local areas and between studies using various socio-economic indicators. Areas with higher deprivation, higher unemployment, lower income, and lower education level were more likely to have higher suicide risk. There was no significant difference in results between studies using conventional versus spatial statistic methods.

CONCLUSION

An increasing number of studies have applied spatial methods, including Bayesian spatial models and spatial regression models, to explore the relationship between area-level socio-economic factors and suicide. This review of spatial studies provided further evidence that area-level socio-economic factors are generally inversely associated with suicide risk, with or without accounting for spatial autocorrelation.

Neighborhood social context and suicide mortality: A multilevel register-based 5-year follow-up study of 2.7 million individuals

Previous studies have linked neighborhood social characteristics to suicide mortality. However, the effects of the operational definition of neighborhoods and the general importance of neighborhood context on suicide mortality have received little attention, with most studies using various administrative areas as neighborhood delineations. In this study, neighborhoods were delineated by micro-areas generated with an automated redistricting algorithm and divided by physical barriers, such as large roads. The geographic data were linked to register data on the Danish adult population in the age range of 20-59 years in December 2013 (N = 2,672,799 individuals nested into 7943 neighborhoods). This cohort was followed for five years to evaluate the association between suicide mortality and neighborhood socioeconomic deprivation, social fragmentation, and population density. We used the median hazard ratio (MHR) to quantify the general contextual effect (GCE) of neighborhoods on suicide mortality and hazard ratios to quantify the specific contextual effects (SCEs) using multilevel survival models stratified by age group. The results showed a larger GCE and larger SCEs of neighborhoods on suicide mortality for individuals aged 20-39 years compared with those aged 40-59 years. After controlling for individual characteristics, higher suicide mortality was observed for individuals living in the least densely populated neighborhoods and the most socially fragmented neighborhoods for both age groups. We found cross-level interactions between neighborhood population density and gender and ethnicity for those aged 40-59 years, as well as between neighborhood social fragmentation and ethnicity for those aged 20-39 years. The results indicate that beyond individual characteristics, the neighborhood social context may affect the risk of suicide, especially for people aged 20-39 years.

Spatial and space-time clusters of suicides in the contiguous USA (2000-2019)

The present study investigates the spatiotemporal variations in suicide mortality and tests associations between several covariates and suicides for the years 2000-2019 in the contiguous USA. The epidemiological disease surveillance software (SaTScan^TM) was used to identify spatiotemporal variations in suicide mortality rates and to test for significant spatial and space-time clusters with elevated relative suicide risk. The analysis was done with age-adjusted suicide mortality counts data from the Centers for Disease Control (CDC) with (International Classification of Diseases) ICD-10 codes. Specifically, data with codes ICD-10 codes X60-X84.9 and Y87.0, plus ICD-10 113 codes from the CDC, was used. Fourteen significant spatial clusters and five significant space-time clusters of suicide in the contiguous USA were found, including nine significant bivariate spatial clusters of suicide deaths and opioid deaths. Based on these data, there exist significant and non-random suicide mortality clusters after adjusting for multiple covariates or risk factors. The covariates studied provide evidence to develop a better understanding of possible associations in geographical areas where the suicide mortality rates are higher than expected. In addition, there is a significant association between several of the studied risk factors and suicide mortality. While most suicide clusters are also opioid clusters, there exist some clusters with high opioid deaths that are not suicide clusters. These results have the potential to provide a scientific framework that is based on surveillance, allowing health agencies to intervene and reduce elevated rates of suicides in selected counties in the U.S. The study is limited due to the resolution of the data at the county level, and some covariate data was unavailable for the entire period of the study.

Quantitative Methods to Detect Suicide and Self-Harm Clusters: A Systematic Review

Suicide and self-harm clusters exist in various forms, including point, mass, and echo clusters. The early identification of clusters is important to mitigate contagion and allocate timely interventions. A systematic review was conducted to synthesize existing evidence of quantitative analyses of suicide and self-harm clusters. Electronic databases including Medline, Embase, Web of Science, and Scopus were searched from date of inception to December 2020 for studies that statistically analyzed the presence of suicide or self-harm clusters. Extracted data were narratively synthesized due to heterogeneity among the statistical methods applied. Of 7268 identified studies, 79 were eligible for narrative synthesis. Most studies quantitatively verified the presence of suicide and self-harm clusters based on the scale of the data and type of cluster. A Poisson-based scan statistical model was found to be effective in accurately detecting point and echo clusters. Mass clusters are typically detected by a time-series regression model, although limitations exist. Recently, the statistical analysis of suicide and self-harm clusters has progressed due to advances in quantitative methods and geospatial analytical techniques, most notably spatial scanning software. The application of such techniques to real-time surveillance data could effectively detect emerging clusters and provide timely intervention.

Suicide Clusters Among Top 10 High-Risk Occupations: A Study From 2001 to 2016 in Australia

A number of studies have demonstrated elevated risk of suicide in certain occupational groups. We seek to understand a possible new risk factor: suicide contagion, as demonstrated through a suicide cluster analysis. National-level coronial data and census population data were used for the study. We calculated suicide rates to identify "risky" occupations. SaTScan v9.4.1 was used to perform Poisson discrete scan statistic. Suicides occurring in arts and media professionals, construction, manufacturing, and skilled animal and horticultural workers seemed to cluster in time and/or space. Those working in construction settings were at risk of being in both time and space clusters.

Association of Pediatric Suicide With County-Level Poverty in the United States, 2007-2016

IMPORTANCE

Suicide is the second leading cause of death among youths aged 10 to 19 years in the United States, with rates nearly doubling during the past decade. Youths in impoverished communities are at increased risk for negative health outcomes; however, the association between pediatric suicide and poverty is not well understood.

OBJECTIVE

To assess the association between pediatric suicide rates and county-level poverty concentration.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective, cross-sectional study examined suicides among US youths aged 5 to 19 years from January 1, 2007, to December 31, 2016. Suicides were identified using International Statistical Classification of Diseases, Tenth Revision, Clinical Modification codes from the Centers for Disease Control and Prevention's Compressed Mortality File. Data analysis was performed from February 1, 2019, to September 10, 2019.

EXPOSURES

County poverty concentration and the percentage of the population living below the federal poverty level. Counties were divided into 5 poverty concentration categories: 0% to 4.9%, 5.0% to 9.9%, 10.0% to 14.9%, 15.0% to 19.9%, and 20.0% or more of the population living below the federal poverty level.

MAIN OUTCOMES AND MEASURES

The study used a multivariable negative binomial regression model to analyze the association between pediatric suicide rates and county poverty concentration, reporting adjusted incidence rate ratios (aIRRs) with 95% CIs. The study controlled for year, demographic characteristics of the children who died (age, sex, and race/ethnicity), county urbanicity, and county demographic features (age, sex, and racial composition). Subgroup analyses were stratified by method.

RESULTS

From 2007 to 2016, a total of 20 982 youths aged 5 to 19 years died by suicide (17 760 [84.6%] were aged 15-19 years, 15 982 [76.2%] male, and 14 387 [68.6%] white non-Hispanic). The annual suicide rate was 3.35 per 100 000 youths aged 5 to 19 years. In the multivariable model, compared with counties with the lowest poverty concentration (0%-4.9%), counties with poverty concentrations of 10% or greater had higher suicide rates in a stepwise manner (10.0%-14.9%: aIRR, 1.25 [95% CI, 1.06-1.47]; 15.0%-19.9%: aIRR, 1.30 [95% CI, 1.10-1.54]; and 20.0% or more: aIRR, 1.37 [95% CI, 1.15-1.64]). When stratified by method, firearm suicides had the strongest association with county poverty concentration (aIRR, 1.87; 95% CI, 1.41-2.49) in counties with 20% or higher poverty concentration compared with counties with 0% to 4.9% poverty concentration.

CONCLUSIONS AND RELEVANCE

The findings suggest that higher county-level poverty concentration is associated with increased suicide rates among youths aged 5 to 19 years. These findings may guide research into upstream risk factors associated with pediatric suicide to inform suicide prevention efforts.

The geography of suicide in older adults in Hong Kong: An ecological study

OBJECTIVES

The geography of suicide has been widely explored among the general population. However, little is known of the geographic variations in suicides among the older adults and their spatial correlates. This study aims to explore the spatial variations in the elderly suicide rates and their correlates in Hong Kong.

METHODS

Bayesian hierarchical models have been used to estimate smoothed standardized mortality ratios (2006-2015) on suicide in people aged 65 years or older in each geographic unit in Hong Kong. Their associations with the Social Vulnerability Index and the accessibility of eight types of services (ie, recreational services, rehabilitation services, food services, daily necessity services, community services, and transportation services) were further analyzed.

RESULTS

The results suggested that compared with the simple "inner-city high suicide rate and suburban low" pattern in the Western studies and the "central low suicide rate and peripheral high" pattern in the Asian studies, the spatial variations of elderly suicides in Hong Kong exhibit a much more complicated pattern. In Hong Kong, higher elderly suicide clusters were found in both the lower-density areas located in the New Territories and in some inner-city areas. The spatial variations of suicide in the older adults cannot be explained by the Social Vulnerability Index. Instead, service provision such as recreational services, daily necessity resources, and community centers played a more significant role in affecting suicides in the older adults.

CONCLUSIONS

Strengthening public services, providing more public spaces and activities, and making good use of the community resources might be key and efficient strategies in elderly suicide prevention in Hong Kong. Key points The spatial variations of elderly suicides in Hong Kong show a much more complicated pattern compared with the simple "inner-city high suicide rate and suburban low" pattern in the Western countries and the "central low suicide rate and peripheral high" pattern in some of the Asian countries. In Hong Kong, suicide rates in the city centers were not higher than the average in the city. Clusters of higher suicide rates were mainly found in the New Territories, which is somewhat disconnected from the city and, in some inner-city neighborhoods, with high-density population. The spatial variations of suicide in the older adults in Hong Kong cannot be explained by the neighborhood Social Vulnerability Index as in the existing literature. Neighborhood service provision such as recreational services, daily necessity resources, and community centers played a significant role in affecting suicides in the older adults in Hong Kong.

Spatiotemporal clustering of suicides in the US from 1999 to 2016: a spatial epidemiological approach

PURPOSE

This study aims to describe and characterize the spatial and temporal clustering patterns of suicide in the ten states with the greatest suicide burden in the United States from 1999 to 2016.

METHODS

All suicide deaths from January 1, 1999 to December 31, 2016 in the United States were identified using data from the Wide-ranging Online Data for Epidemiologic Research (WONDER) dataset. The ten states with the highest age-adjusted suicide rates were Montana, Alaska, Wyoming, New Mexico, Nevada, Utah, Idaho, Colorado, Arizona, and Oklahoma. A spatiotemporal scan statistic using a discrete Poisson model was employed to retrospectively detect spatiotemporal suicide clusters.

RESULTS

From 1999 to 2016, a total of 649,843 suicides were recorded in the United States. Nineteen statistically significant spatiotemporal suicide mortality clusters were identified in the states with the greatest suicide rates, and 13.53% of the suicide cases within these states clustered spatiotemporally. The risk ratio of the clusters ranged from 1.45 to 3.64 (p < 0.001). All states had at least one cluster, with three clusters spanning multiple states, and four clusters were found in Arizona. While there was no clear secular trend in the average size of suicide clusters, the number of clusters increased from 1999 to 2016.

CONCLUSIONS

Hot spots for suicidal behavior in the United States warrant public health intervention and continued surveillance. As suicide rates in the US continue to increase annually, public health efforts could be maximized by focusing on regions with substantial clustering.

Spatial Clustering of Suicide and Associated Community Characteristics, Idaho, 2010-2014

Introduction

In 2015, Idaho had the fifth highest suicide rate in the United States. Little is known about the characteristics of areas in Idaho with high suicide rates. To aid suicide prevention efforts in the state, we sought to identify and characterize spatial clusters of suicide.

Methods

We obtained population data from the 2010 US Census and the 2010–2014 American Community Survey, analyzed data on suicides from death certificates, and used a discrete Poisson model in SaTScan to identify spatial clusters of suicide. We used logistic regression to examine associations between suicide clustering and population characteristics.

Results

We found 2 clusters of suicide during 2010–2014 that accounted for 70 (4.7%) of 1,501 suicides in Idaho. Areas within clusters were positively associated with the following population characteristics: median age ≤31.1 years versus >31.1 years (multivariable-adjusted odds ratio [aOR] = 2.4; 95% confidence interval [CI], 1.04–5.6), >53% female vs ≤53% female (aOR = 2.7; 95% CI, 1.3–5.8; P = .01), >1% American Indian/Alaska Native vs ≤1% American Indian/Alaska Native (aOR = 2.9; 95% CI, 1.4–6.3), and >30% never married vs ≤30% never married (aOR = 3.4; 95% CI, 1.5–8.0; P = .004).

Conclusion

Idaho suicide prevention programs should consider using results to target prevention efforts to communities with disproportionately high suicide rates.

Spatial analysis of the relative risk of suicide for Virginia counties incorporating uncertainty of variable estimates

PURPOSE

This research aimed to identify significantly elevated areas of risk for suicide in Virginia adjusting for risk factors and risk factor uncertainty.

METHODS

We fit three Bayesian hierarchical spatial models for relative risk of suicide adjusting for risk factors and considering different random effects. We compared models with and without incorporating parameter estimates' margin of error (MOE) from the American Community Survey and identified counties with significantly elevated risk and highly significantly elevated risk for suicide.

RESULTS

Incorporating MOEs and using a mixing parameter between unstructured and spatially structured random effects achieved the best model fit. Fifty-two counties had significantly elevated risk and 18 had highly significantly elevated risk of suicide. Models without MOEs underestimated relative risk and over-identified counties with elevated risk.

CONCLUSIONS

Accounting for uncertainty in parameter estimates achieved better model fit. Efficient allocation of resources for suicide prevention can be attained by targeting clusters of counties with elevated risk.

County-Level Trends in Suicide Rates in the U.S., 2005-2015

INTRODUCTION

Understanding the geographic patterns of suicide can help inform targeted prevention efforts. Although state-level variation in age-adjusted suicide rates has been well documented, trends at the county-level have been largely unexplored. This study uses small area estimation to produce stable county-level estimates of suicide rates to examine geographic, temporal, and urban-rural patterns in suicide from 2005 to 2015.

METHODS

Using National Vital Statistics Underlying Cause of Death Files (2005-2015), hierarchical Bayesian models were used to estimate suicide rates for 3,140 counties. Model-based suicide rate estimates were mapped to explore geographic and temporal patterns and examine urban-rural differences. Analyses were conducted in 2016-2017.

RESULTS

Posterior predicted mean county-level suicide rates increased by >10% from 2005 to 2015 for 99% of counties in the U.S., with 87% of counties showing increases of >20%. Counties with the highest model-based suicide rates were consistently located across the western and northwestern U.S., with the exception of southern California and parts of Washington. Compared with more urban counties, more rural counties had the highest estimated suicide rates from 2005 to 2015, and also the largest increases over time.

CONCLUSIONS

Mapping county-level suicide rates provides greater granularity in describing geographic patterns of suicide and contributes to a better understanding of changes in suicide rates over time. Findings may inform more targeted prevention efforts as well as future research on community-level risk and protective factors related to suicide mortality.

What calls for service tell us about suicide: A 7-year spatio-temporal analysis of neighborhood correlates of suicide-related calls

Previous research has shown that neighborhood-level variables such as social deprivation, social fragmentation or rurality are related to suicide risk, but most of these studies have been conducted in the U.S. or northern European countries. The aim of this study was to analyze the spatio-temporal distribution of suicide in a southern European city (Valencia, Spain), and determine whether this distribution was related to a set of neighborhood-level characteristics. We used suicide-related calls for service as an indicator of suicide cases (n = 6,537), and analyzed the relationship of the outcome variable with several neighborhood-level variables: economic status, education level, population density, residential instability, one-person households, immigrant concentration, and population aging. A Bayesian autoregressive model was used to study the spatio-temporal distribution at the census block group level for a 7-year period (2010–2016). Results showed that neighborhoods with lower levels of education and population density, and higher levels of residential instability, one-person households, and an aging population had higher levels of suicide-related calls for service. Immigrant concentration and economic status did not make a relevant contribution to the model. These results could help to develop better-targeted community-level suicide prevention strategies.

Mapping suicide mortality in Ohio: A spatial epidemiological analysis of suicide clusters and area level correlates

Previous studies have investigated spatial patterning and associations of area characteristics with suicide rates in Western and Asian countries, but few have been conducted in the United States. This ecological study aims to identify high-risk clusters of suicide in Ohio and assess area level correlates of these clusters. We estimated spatially smoothed standardized mortality ratios (SMR) using Bayesian conditional autoregressive models (CAR) for the period 2004 to 2013. Spatial and spatio-temporal scan statistics were used to detect high-risk clusters of suicide at the census tract level (N=2952). Logistic regression models were used to examine the association between area level correlates and suicide clusters. Nine statistically significant (p<0.05) high-risk spatial clusters and two space-time clusters were identified. We also identified several significant spatial clusters by method of suicide. The risk of suicide was up to 2.1 times higher in high-risk clusters than in areas outside of the clusters (relative risks ranged from 1.22 to 2.14 (p<0.01)). In the multivariate model, factors strongly associated with area suicide rates were socio-economic deprivation and lower provider densities. Efforts to reduce poverty and improve access to health and mental health medical services on the community level represent potentially important suicide prevention strategies.

Spatio-Temporal Analysis of Suicide-Related Emergency Calls

Considerable effort has been devoted to incorporate temporal trends in disease mapping. In this line, this work describes the importance of including the effect of the seasonality in a particular setting related with suicides. In particular, the number of suicide-related emergency calls is modeled by means of an autoregressive approach to spatio-temporal disease mapping that allows for incorporating the possible interaction between both temporal and spatial effects. Results show the importance of including seasonality effect, as there are differences between the number of suicide-related emergency calls between the four seasons of each year.