Citation

Lee JY. J. Affect. Disord. 2019; 262: 155-164.

Affiliation

Department of Psychiatry, University of Maryland Medical Center, Baltimore, MD, United States; University of Maryland/Sheppard Pratt Psychiatry Residency Program, Baltimore, MD, USA. Electronic address: jooyoung.lee@som.umaryland.edu.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.jad.2019.11.014

PMID

31733460

Abstract

BACKGROUND: Various associations between monthly Google search volumes (MGSVs) and monthly suicide rates (MSRs) have been reported. However, these studies often analyzed a limited number of search terms using suboptimal statistical methods. While controlling for spurious associations, this study examined a wide array of suicide-related search terms to elucidate if their MGSVs correlated with future MSRs.

METHODS: MGSVs of 111 candidate suicide-related terms were calculated by averaging 10 time-series data per term obtained from Google Trends. Box-Jenkins transfer function modeling was applied to time-series data of MGSV and MSR among the total, male, and female populations of the United States between 2004 and 2017. Cross-correlation coefficients between MGSVs and MSRs were calculated at lags -3, -2, and -1. Sensitivity analysis identified cross-correlations whose direction and significance (p<0.05) persisted in two other time spans: 126 and 84 months.

RESULTS: Eighty-nine terms were analyzed. MGSVs of 31 terms significantly correlated with MSRs in the total, male, or female population. In the sensitivity analysis, three terms stably exhibited significant positive correlation: "generalized anxiety disorder" (total; lag -3), "anxiety disorder" (total and male; lag -3), and "laid off" (total, male, and female; lag -2). The term sleep problem (total and female; lag -1) consistently showed significant negative correlations. LIMITATIONS: Sex- or age-specific search-volume data, lags of less than a month, and potential confounding factors of MGSV and MSR were not explored.

CONCLUSIONS: trends in MGSV of four terms tend to precede changes in MSR. These terms may enable more accurate forecasting of future suicides.

Copyright © 2019 Elsevier B.V. All rights reserved.

Language: en

Keywords

Google Trends; Suicide; Suicide prevention; Suicide risk; Time-series analysis