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Principal Investigator: Katherine M. Keyes
Organization: COLUMBIA UNIVERSITY HEALTH SCIENCES
Fiscal Year: 2024
Award: $639,429
Funding agency: National Institute of Mental Health
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.
Terms: <21+ years old><Address><Adolescent and Young Adult><Adult><Adult Human><Algorithms><Area><Bayesian Analysis><Bayesian Modeling><Bayesian adaptive designs><Bayesian adaptive models><Bayesian belief network><Bayesian belief updating model><Bayesian computation><Bayesian framework><Bayesian hierarchical model><Bayesian inference><Bayesian network analysis><Bayesian network model><Bayesian nonparametric models><Bayesian spatial analysis><Bayesian spatial data model><Bayesian spatial image models><Bayesian spatial models><Bayesian statistical analysis><Bayesian statistical inference><Bayesian statistical models><Bayesian statistics><Bayesian tracking algorithms><Biologic Models><Biological Models><Cell Communication and Signaling><Cell Signaling><Cessation of life><Cluster Analyses><Cluster Analysis><Communicable Diseases><Communities><Community based prevention><Coupled><Data><Data Science><Data Sources><Death><Detection><Development><Disease Outbreaks><Environment><Epidemic><Epidemiologist><Epidemiology><Event><Feeds><Firearms><Future><Geographic Area><Geographic Locations><Geographic Region><Geographical Location><Geography><Goals><Health Care Costs><Health Costs><Healthcare Costs><History><Hospitals><Hot Spot><Individual><Infectious Disease Pathway><Infectious Diseases><Infectious Disorder><Intervention><Intervention Strategies><Intracellular Communication and Signaling><Length><Life Expectancy><Math Models><Mental disorders><Mental health disorders><Methods><Model System><Modeling><Non-linear Models><Nonlinear Models><Opiates><Opioid><Outbreaks><Outcome><Ownership><Persons><Prevention><Probabilistic Models><Probability Models><Process><Property><Psychiatric Disease><Psychiatric Disorder><Psychiatric epidemiology><Public Health><Recording of previous events><Registries><Reporting><Risk><Risk Factors><Role><Schools><Science><Seasons><Series><Signal Transduction><Signal Transduction Systems><Signaling><Space-Time Clustering><Statistical Models><Stream><Suicide><Suicide precaution><Suicide prevention><System><Temperature><Testing><Time><Transmission><US State><United States><Violence><Vital Statistics><Weather><Work><adulthood><age group><age stratification><biological signal transduction><commit suicide><completed suicide><contagion><data modeling><developmental><disease model><disorder model><dynamic system><dynamical system><emergent outbreak><emerging outbreak><epidemiologic><epidemiological><fatal attempt><fatal suicide><geographic site><high risk><histories><ideation><innovate><innovation><innovative><intent to die><interventional strategy><mathematic model><mathematical model><mathematical modeling><mental illness><model of data><model the data><model-based simulation><modeling of the data><models and simulation><new outbreak><novel><novel outbreak><older adult><older adulthood><prevent suicidality><prevent suicide><psychiatric illness><psychological disorder><response><simulation><social role><statistical linear mixed models><statistical linear models><suicidal><suicidal behavior><suicidal risk><suicidality><suicidality prevention><suicide behavior><suicide intervention><suicide model><suicide rate><suicide risk><suicides><surveillance data><transmission process><trend><vector><violent><violent behavior>