論文誌 (国際) Prediction of Age-Adjusted Mortality From Stroke in Japanese Prefectures by Search Engine Query: An Ecological Study
Kazuya Taira (Kyoto univ.), Sumio Fujita
JMIR Formative Research (JMIR Formative Research)
Background: Stroke is a major cause of death and nursing care in Japan, and regional disparities are large. Objective: The purpose of this study was to clarify the association between stroke-related information retrieval behavior and age-adjusted mortality in each prefecture in Japan. Methods: Age-adjusted mortality from stroke and aging rates were obtained from publicly available Japanese government statistics. A total of 9476 abstracts of Japanese articles related to symptoms and signs of stroke were identified in Ichushi-Web, a Japanese online database of biomedical articles, and 100 highly frequent words (Stroke 100) were extracted. Using data from 2014 to 2019, a random forest analysis was carried out using the age-adjusted mortality from stroke of 47 prefectures as the outcome variable and the standardized retrieval numbers of the Stroke 100 words in the log data of Yahoo! JAPAN Search as predictive variables. Regression analysis was performed using a generalized linear mixed model (GLMM) with the number of standardized searches for Stroke 100 words with high importance scores in the random forest model as the predictive variable. In the regression analysis with GLMM, the aging rate and data year were used as control variables, and the random slope of data year and random intercept were calculated by prefecture. Conclusions: Stroke-related search behavior was associated with age-adjusted mortality from stroke in each prefecture in Japan. Query terms that were strongly associated with age-adjusted mortality rates of stroke suggested the possibility that individual characteristics such as sex and age have an impact on stroke-associated mortality and that it is important to receive medical care early after stroke onset. Further studies on the criteria and timing of alerting are needed by monitoring information-seeking behavior to identify queries that are strongly associated with stroke mortality.