Rethinking Suicide Surveillance

Abstract

Google search data have become an increasingly relied-on source of health information with a dual function as both a facilitative and a preventative resource. We tested the power of Google to predict suicide by comparing searches for explicitly suicidal terms with conventional measures of self-reported suicide risk in estimating completed suicides. In addition, we examined the extent to which discrepancies between Google-based and self-report-based estimates of suicide risk and completed suicide were asymmetrical across social-group lines. Relative to traditional self-reported measures of suicide risk, Google search data better estimated completed suicides. Furthermore, misestimation of suicidal risk was asymmetrical for both measures: Greater misestimation was likely in states with higher percentages of racial minorities and lower levels of socioeconomic status. Google search data can inform suicide-prevention efforts at the state level and suicide surveillance aimed at specific demographic groups.