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Suicide as a contagion: modeling and forecasting emergent outbreaks

自杀作为一种传染病：建模和预测突发疫情

基本信息

批准号：
10088481
负责人：
KATHERINE MARGARET KEYES
金额：
$ 63.48万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-24 至 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10088481
关键词：
Address Adolescent and Young Adult Age Algorithms Area Bayesian Analysis Bayesian Modeling Biological Models Cessation of life Cluster Analysis Communicable Diseases Communities Community based prevention Coupled Data Data Science Data Sources Detection Development Disease Outbreaks Disease model Elderly Environment Epidemic Epidemiologist Epidemiology Event Feeds Firearms Future Geographic Locations Geographic state Geography Goals Health Care Costs Hospitals Hot Spot Individual Intervention Length Life Expectancy Mental disorders Methods Modeling Non-linear Models Opioid Outcome Persons Prevention Process Property Psychiatric epidemiology Public Health Recording of previous events Registries Reporting Risk Risk Factors Role Schools Science Series Signal Transduction Space-Time Clustering Statistical Models Stream Suicide Suicide prevention System Temperature Testing Time Triad Acrylic Resin United States Violence Vital Statistics Weather Work age group base completed suicide contagion data modeling high risk ideation innovation mathematical model models and simulation novel response simulation suicidal suicidal behavior suicidal risk suicide model suicide rate surveillance data transmission process trend vector

项目摘要

Abstract 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 is 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 is 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.

摘要