Strategic Priority: Data

Data serve as the backbone of a public health approach to suicide prevention. Surveillance data are used by public health practitioners to define the problem of suicide; including its scope, magnitude, and trends over time. Data also direct us to the factors increasing or decreasing suicide risk; show where additional prevention resources are needed most; and help us understand the effectiveness of prevention policies, programs, and practices. However, gaps in data exist, and new data sources and innovative methods remain untapped. For example, data from traditional surveillance systems often lack timeliness, but the growth of syndromic surveillance and near real-time data offer the potential to track trends and identify spikes in suicide and suicide attempts. Additionally, geographic information systems software also provides the ability to geolocate suicide hotspots.

New data sources and innovative data methods can also provide opportunities for rapid preventive action in states and communities. Emerging data science tools, methods, and techniques also hold great promise for advancing our understanding of suicide and suicidal behavior and informing prevention efforts. For example, machine learning models using multiple near real-time data sources have shown potential for estimating weekly suicide trends with high accuracy and timeliness. Improvements in the data and tools to use them, coupled with dissemination to states and communities to drive action, can improve our efforts to stem increases in suicide and reverse current trends.