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The spread of suicidal behavior among individuals is often described as a contagion; however, rigorous modeling of suicide as a dynamic, contagious process is minimal. Here, we develop and validate a model-inference system depicting suicide ideation and death and use it to quantify the contagion processes in the US associated with two prominent celebrity suicide events: Robin Williams during 2014 and Kate Spade and Anthony Bourdain, which occurred 3 days apart during 2018. We show that both events produced large transient increases of suicide contagion contact rates, i.e., the spread of suicidal thought and behavior, and a period of elevated suicidal ideation in the general population. Our modeling approach provides a framework for quantifying suicidal contagion and better understanding, preventing, and containing its spread.

Background:  The rate of suicide death has been increasing, making understanding risk factors of growing importance. While exposure to explicit suicide-related media, such as description of means in news reports or sensationalized fictional portrayal, is known to increase population suicide rates, it is not known whether prosuicide website forums, which often promote or facilitate information about fatal suicide means, are related to change in suicide deaths overall or by specific means., Objective:  This study aimed to estimate the association of the frequency of Google searches of known prosuicide web forums and content with death by suicide over time in the United States, by age, sex, and means of death., Methods:  National monthly Google search data for names of common prosuicide websites between January 2010 and December 2021 were extracted from Google Health Trends API (application programming interface). Suicide deaths were identified using the CDC (Centers for Disease Control and Prevention) National Vital Statistics System (NVSS), and 3 primary means of death were identified (poisoning, suffocation, and firearm). Distributed lag nonlinear models (DLNMs) were then used to estimate the lagged association between the number of Google searches on suicide mortality, stratified by age, sex, and means, and adjusted for month. Sensitivity analyses, including using autoregressive integrated moving average (ARIMA) modeling approaches, were also conducted., Results:  Months in the United States in which search rates for prosuicide websites increased had more documented deaths by intentional poisoning and suffocation among both adolescents and adults. For example, the risk of poisoning suicide among youth and young adults (age 10-24 years) was 1.79 (95% CI 1.06-3.03) times higher in months with 22 searches per 10 million as compared to 0 searches. The risk of poisoning suicide among adults aged 25-64 was 1.10 (95% CI 1.03-1.16) times higher 1 month after searches reached 9 per 10 million compared with 0 searches. We also observed that increased search rates were associated with fewer youth suicide deaths by firearms with a 3-month time lag for adolescents. These models were robust to sensitivity tests., Conclusions:  Although more analysis is needed, the findings are suggestive of an association between increased prosuicide website access and increased suicide deaths, specifically deaths by poisoning and suffocation. These findings emphasize the need to further investigate sites containing potentially dangerous information and their associations with deaths by suicide, as they may affect vulnerable individuals., (©Nora Clancy Kelsall, Catherine Gimbrone, Mark Olfson, Madelyn S Gould, Jeffrey Shaman, Katherine Keyes. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.07.2024.)

Suicide rates in the United States have increased over the past 15 years, with substantial geographic variation in these increases; yet there have been few attempts to cluster counties by the magnitude of suicide rate changes according to intercept and slope or to identify the economic precursors of increases. We used vital statistics data and growth mixture models to identify clusters of counties by their magnitude of suicide growth from 2008 to 2020 and examined associations with county economic and labor indices. Our models identified 5 clusters, each differentiated by intercept and slope magnitude, with the highest-rate cluster (4% of counties) being observed mainly in sparsely populated areas in the West and Alaska, starting the time series at 25.4 suicides per 100,000 population, and exhibiting the steepest increase in slope (0.69/100,000/year). There was no cluster for which the suicide rate was stable or declining. Counties in the highest-rate cluster were more likely to have agricultural and service economies and less likely to have urban professional economies. Given the increased burden of suicide, with no clusters of counties improving over time, additional policy and prevention efforts are needed, particularly targeted at rural areas in the West., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Competing Interests: The authors report no financial relationships with commercial interests.

Deaths by suicide, as well as suicidal ideations, plans and attempts, have been increasing in the US for the past two decades. Deployment of effective interventions would require timely, geographically well-resolved estimates of suicide activity. In this study, we evaluated the feasibility of a two-step process for predicting suicide mortality: a) generation of hindcasts, mortality estimates for past months for which observational data would not have been available if forecasts were generated in real-time; and b) generation of forecasts with observational data augmented with hindcasts. Calls to crisis hotline services and online queries to the Google search engine for suicide-related terms were used as proxy data sources to generate hindcasts. The primary hindcast model (auto) is an Autoregressive Integrated Moving average model (ARIMA), trained on suicide mortality rates alone. Three regression models augment hindcast estimates from auto with call rates (calls), GHT search rates (ght) and both datasets together (calls_ght). The 4 forecast models used are ARIMA models trained with corresponding hindcast estimates. All models were evaluated against a baseline random walk with drift model. Rolling monthly 6-month ahead forecasts for all 50 states between 2012 and 2020 were generated. Quantile score (QS) was used to assess the quality of the forecast distributions. Median QS for auto was better than baseline (0.114 vs. 0.21. Median QS of augmented models were lower than auto, but not significantly different from each other (Wilcoxon signed-rank test, p > .05). Forecasts from augmented models were also better calibrated. Together, these results provide evidence that proxy data can address delays in release of suicide mortality data and improve forecast quality. An operational forecast system of state-level suicide risk may be feasible with sustained engagement between modelers and public health departments to appraise data sources and methods as well as to continuously evaluate forecast accuracy., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: KMK has been compensated for expert witness work in litigation; JS and Columbia University declare partial ownership of SK Analytics; JS was a consultant for BNI. All other authors declare no competing interests., (Copyright: © 2023 Kandula et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Suicide is one of the leading causes of death in the USA and population risk prediction models can inform decisions on the type, location, and timing of public health interventions. We aimed to develop a prediction model to estimate county-level suicide risk in the USA using population characteristics., Methods: We obtained data on all deaths by suicide reported to the National Vital Statistics System between Jan 1, 2005, and Dec 31, 2019, and age, sex, race, and county of residence of the decedents were extracted to calculate baseline risk. We also obtained county-level annual measures of socioeconomic predictors of suicide risk (unemployment, weekly wage, poverty prevalence, median household income, and population density) and state-level prevalence of major depressive disorder and firearm ownership from US public sources. We applied conditional autoregressive models, which account for spatiotemporal autocorrelation in response and predictors, to estimate county-level suicide risk., Findings: Estimates derived from conditional autoregressive models were more accurate than from models not adjusted for spatiotemporal autocorrelation. Inclusion of suicide risk and protective covariates further reduced errors. Suicide risk was estimated to increase with each SD increase in firearm ownership (2·8% [95% credible interval (CrI) 1·8 to 3·9]), prevalence of major depressive episode (1·0% [0·4 to 1·5]), and unemployment rate (2·8% [1·9 to 3·8]). Conversely, risk was estimated to decrease by 4·3% (-5·1 to -3·2) for each SD increase in median household income and by 4·3% (-5·8 to -2·5) for each SD increase in population density. An increase in the heterogeneity in county-specific suicide risk was also observed during the study period., Interpretation: Area-level characteristics and the conditional autoregressive models can estimate population-level suicide risk. Availability of near real-time situational data are necessary for the translation of these models into a surveillance setting. Monitoring changes in population-level risk of suicide could help public health agencies select and deploy targeted interventions quickly., Funding: US National Institute of Mental Health., Competing Interests: Declaration of interests KMK and CR have been compensated for expert witness work on behalf of multi-district litigation plaintiffs in opioid litigation. JS declares partial ownership of SK Analytics and was a consultant for Business Networking International. GM-A was supported by a Fundación ASISA fellowship. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objective: Deaths by suicide correlate both spatially and temporally, leading to suicide clusters. This study aimed to estimate racial patterns in suicide clusters since 2000., Method: Data from the US National Vital Statistics System included all International Classification of Diseases, Tenth Revision (ICD-10)-coded suicide cases from 2000-2019 among American Indian/Alaska Native (AI/AN), Asian/Pacific Islander (A/PI), Black, or White youth and young adults, aged 5-34 years. We estimated age, period, and cohort (APC) trends and identified spatiotemporal clusters using the SaTScan space-time statistic, which identified lower- and higher-than-expected suicide rates (cold and hot clusters) in a prespecified area (150 km) and time interval (15 months). We also calculated the average proportion of deaths by suicide contained in clusters, to quantify the relative importance of spatiotemporal patterning as a driver of overall suicide rates., Results: From 2010-2019, suicide rates increased from between 37% among AI/AN (95% CI = 1.22, 1.55) to 81% among A/PI (95% CI = 1.65, 2.01) groups. Suicide clusters accounted for 0.8%-10.3% of all suicide deaths, across racial groups. Since 2000, the likelihood of detecting cluster increased over time, with considerable differences in the number of clusters in each racial group (4 among AI/AN to 72 among White youth). Among Black youth and young adults, 27 total clusters were identified. Hot clusters were concentrated in southeastern and mid-Atlantic counties., Conclusion: Suicide rates and clusters in youth and young adults have increased in the past 2 decades, requiring attention from policy makers, clinicians, and caretakers. Racially distinct patterns highlight opportunities to tailor individual- and population-level prevention efforts to prevent suicide deaths in emerging high-risk groups., (Copyright © 2022 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.)

During the COVID-19 pandemic, US populations have experienced elevated rates of financial and psychological distress that could lead to increases in suicide rates. Rapid ongoing mental health monitoring is critical for early intervention, especially in regions most affected by the pandemic, yet traditional surveillance data are available only after long lags. Novel information on real-time population isolation and concerns stemming from the pandemic's social and economic impacts, via cellular mobility tracking and online search data, are potentially important interim surveillance resources. Using these measures, we employed transfer function model time-series analyses to estimate associations between daily mobility indicators (proportion of cellular devices completely at home and time spent at home) and Google Health Trends search volumes for terms pertaining to economic stress, mental health, and suicide during 2020 and 2021 both nationally and in New York City. During the first pandemic wave in early-spring 2020, over 50% of devices remained completely at home and searches for economic stressors exceeded 60,000 per 10 million. We found large concurrent associations across analyses between declining mobility and increasing searches for economic stressor terms (national proportion of devices at home: cross-correlation coefficient (CC) = 0.6 (p-value <0.001)). Nationally, we also found strong associations between declining mobility and increasing mental health and suicide-related searches (time at home: mood/anxiety CC = 0.53 (<0.001), social stressor CC = 0.51 (<0.001), suicide seeking CC = 0.37 (0.006)). Our findings suggest that pandemic-related isolation coincided with acute economic distress and may be a risk factor for poor mental health and suicidal behavior. These emergent relationships warrant ongoing attention and causal assessment given the potential for long-term psychological impact and suicide death. As US populations continue to face stress, Google search data can be used to identify possible warning signs from real-time changes in distributions of population thought patterns., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Jeffrey Shaman and Columbia University declare partial ownership of SK Analytics. Sasikiran Kandula was a consultant for SK Analytics. Jeffrey Shaman was a consultant for BNI. Katherine Keyes has been compensated as an expert witness in litigation. No other disclosures were reported. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction: In the U.S., state-level household firearm ownership is strongly associated with firearm suicide mortality rates. Whether the recent increases in firearm suicide are explained by state-level household firearm ownership rates and trends remains unknown., Methods: Mortality data from the U.S. National Vital Statistics System and an estimate of state-level household firearm ownership rate were used to conduct hierarchical age-period-cohort (random-effects) modeling of firearm suicide mortality between 2001 and 2016. Models were adjusted for individual-level race and sex and for state-level poverty rate, unemployment rate, median household income in U.S. dollars, population density, and elevation., Results: Between 2001 and 2016, the crude national firearm suicide mortality rate increased from 6.8 to 8.0 per 100,000, and household firearm ownership rate remained relatively stable, at around 40%. Both variables were markedly heterogeneous and correlated at the state level. Age-period-cohort models revealed period effects (affecting people across ages) and cohort effects (affecting specific birth cohorts) underlying the recent increases in firearm suicide. Individuals born after 2000 had higher firearm suicide rates than most cohorts born before. A 2001-2006 decreasing period effect was followed, after 2009, by an increasing period effect that peaked in 2015. State-level household firearm ownership rates and trends did not explain cohort effects and only minimally explained period effects., Conclusions: State-level firearm ownership rates largely explain the state-level differences in firearm suicide but only marginally explain recent increases in firearm suicide. Although firearms in the home increase firearm suicide risk, the recent national rise in firearm suicide might be the result of broader, more distal causes of suicide risk., (Copyright © 2021 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

The role of sex, race, and suicide method on recent increases in suicide mortality in the United States remains unclear. Estimating the age, period, and cohort effects underlying suicide mortality trends can provide important insights for the causal hypothesis generating process. We generated updated age-period-cohort effect estimates of recent suicide mortality rates in the US, examining the putative roles of sex, race, and method for suicide, using data from all death certificates in the US between 1999 and 2018. After designating deaths as attributable to suicide according to ICD-10 underlying cause of death codes X60-X84, Y87.0, and U03, we (i) used hexagonal grids to describe rates of suicide by age, period, and cohort visually and (ii) modeled sex-, race-, and suicide method-specific age, period, and cohort effects. We found that, while suicide mortality increased in the US between 1999 and 2018 across age, sex, race, and suicide method, there was substantial heterogeneity in age and cohort effects by method, sex, and race, with a first peak of suicide risk in youth, a second peak in older ages-specific to male firearm suicide, and increased rates among younger cohorts of non-White individuals. Our findings should prompt discussion regarding age-specific clinical firearm safety interventions, drivers of minoritized populations' adverse early-life experiences, and racial differences in access to and quality of mental healthcare., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

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.