Suicide rates continue to increase in every age group in the United States and in almost every state?in 2016, 44,965 individuals in the US died by suicide. Developing scientifically rigorous surveillance, reporting, and forecasting systems for suicide is essential to craft appropriate public health responses. Here we will bring together geo-located data from Google Extended Trends, National Suicide Prevention Lifeline, Health Cost and Utilization Project, and vital statistics coupled with the National Violent Death Registry to build and validate statistical and mathematical models of queries, calls, attempts, and completions of suicide. Our models aim to address the epidemic by studying it as a communicable process. Our overarching goal is to provide an anticipatory warning system to inform school-based and community-based prevention and treatment capacity.
Our first Aim i s to estimate how suicidal queries, calls, attempts, and completions cluster in space and time. Temporal and spatial autocorrelation of suicidal behavior shares many features with communicable diseases that can be conceptualized in terms of the epidemiological triad?agent (media reporting, person-to-person transmission, lethal vectors), host (history of attempts and psychiatric disorder), and environment (weather, elevation, and temperature)?for which rigorous statistical models have been used to study geographic and temporal risk factors associated with infectious disease, even in the face of incomplete surveillance data. We will estimate the unique and shared autocorrelation of suicide queries, calls, attempts, and completions and test the extent to which autocorrelation varies by developmental stage (i.e., adolescents and young adults versus older adults).
Our second Aim i s to understand the dynamics of suicide risk across developmental stages through simulation of anomalous suicidal outbreaks using mathematical models that represent suicide as a contagion. The models will be coupled with Bayesian inference algorithms to enable simulation, optimization, and estimation of key epidemiological parameters that characterize system dynamics. This effort will bring together 4 data sources (queries, calls, attempts, and completions) to consider the system as a whole, rather than as separate streams of information. We will answer critical questions about the extent to which local and temporal anomalous increases in suicidal outcomes vary across events, as well as the force of contagious transmission, length of time of contagious suicidal crises, and contribution of lethal means. For our third Aim, we will use the model-inference framework to produce granular, local, 6-month predictions of suicide outbreak events. The generated forecasts will help inform when, where, and among whom we can expect suicide outbreaks to develop, and for how long unless prevention efforts are rapidly disseminated. This project brings together experts in mathematical modeling, communicable disease and suicide epidemiology, prevention, and intervention, who will apply state-of-the-art modeling approaches to suicide surveillance and forecasting.
Unprecedented increases in suicide in almost every US state are alarming, and rigorous data science and modeling is needed to develop novel methods for understanding, quantifying, and forecasting suicide outbreaks and transform prevention efforts by identifying high risk areas and groups. Outbreaks of suicide occurring within small geographic areas and after high-profile suicide media coverage suggest that mathematical models capable of simulating suicide as a contagious process are needed to better understand and quantify these dynamics and target prevention efforts that reduce epidemic transmission. The present project leverages four large troves of geospatially-tagged suicide data to estimate spatial and temporal clustering and to develop and validate model systems that simulate anomalous suicide outbreaks, infer critical epidemiological parameters associated with suicide contagion, and forecast future suicide activity in the US, thus providing stakeholders with intervention-ready information. 1