JOURNAL (INTERNATIONAL) Predicting the Number of Suicides in Japan: A Vector Autoregression Time Series Model Using Internet Search Queries

Kazuya Taira (Kyoto univ.), Rikuya Hosokawa (Kyoto univ.), Tomoya Itatani (Kanazawa univ.), Sumio Fujita

JMIR Public Health and Surveillance (JMIR Public Health and Surveillance)

December 03, 2021

Background: The number of suicides in Japan increased during the COVID-19 pandemic. Predicting the number of suicides is critical to take timely preventive measures. Objective: This study aimed to clarify whether the number of suicides can be predicted by suicide-related search queries used before searching for the keyword "suicide." Methods: This study used the infoveillance approach for suicide in Japan by search trends in search engines. The monthly number of suicides by gender, collected and published by the National Police Agency, was used as an outcome variable. The number of searches by gender with queries associated with "suicide" on "Yahoo Search" from January 2016 to December 2020 was used as a predictive variable. The following five phrases highly relevant to suicide were used as search terms before searching for the keyword "suicide," and extracted and used for analyses: "abuse," "work, don’t want to go," "company, want to quit," "divorce," and "no money." The Augmented Dickey–Fuller and Johansen's tests were performed for the original series and to verify the existence of unit roots and cointegration for each variable, respectively. The vector autoregression model was applied to predict the number of suicides. The Breusch–Godfrey Lagrangian multiplier (BG-LM) test, autoregressive conditional heteroskedasticity Lagrangian multiplier (ARCH-LM) test, and Jarque–Bera (JB) test were employed to confirm model convergence. In addition, a Granger causality test was performed for each predictive variable. Results: The queries used in the converged models were "divorce" for men (BG-LM test: p = 0.55; ARCH-LM test: p = 0.63; JB test: p = 0.66) and "no money" for women (BG-LM test: p = 0.17; ARCH-LM test: p = 0.15; JB test: p = 0.10). In the Granger causality test for each variable, "divorce" was significant for both men (F= 3.29, p = 0.041) and women (F = 3.23, p = 0.044). Conclusions: The number of suicides can be predicted by search queries related to the keyword "suicide." Previous studies have reported that financial poverty and divorce are associated with suicide. The results of this study, in which search queries on "no money" and "divorce" predicted suicide, support the findings of previous studies. Further research on the economic poverty of women and those with complex problems is necessary.

Paper : Predicting the Number of Suicides in Japan: A Vector Autoregression Time Series Model Using Internet Search Queries (external link)