2022 Fiscal Year Annual Research Report
コロナ後における健康環境改善のための社会的脆弱性の検出
| Project/Area Number |
22F21725
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| Research Institution | The University of Tokyo |
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| Host Researcher |
貞広 幸雄 東京大学, 大学院情報学環・学際情報学府, 教授 (10240722)
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| Foreign Research Fellow |
WANG SIQIN 東京大学, 大学院情報学環・学際情報学府, 外国人特別研究員
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| Project Period (FY) |
2022-04-22 – 2023-03-31
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| Keywords | vulnerability / health care |
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| Outline of Annual Research Achievements |
I delineated the vulnerable neighbourhoods with low healthcare access and further evaluated the disparity in healthcare access and built environment of areas at different levels of vulnerability. The outcome datasets and findings provide nuanced and timely evidence to government and health authorities to have a holistic and latest understanding of social vulnerability to COVID-19 and healthcare access at a fine-grained level.
I founds that low healthcare access areas appear in the peri-urban space between the 23-special-ward region and the Tama region, in the south of South Tama and the majority of West Tama where is less covered by public transit. Compared to the adult group, the elderly group experiences significant inequity of healthcare access particularly in the peri-urban areas where driving is the dominant transport mode to access healthcare facilities.
I found that Twitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022 (correlation coefficient=0.533; P<.001); this correlation coefficient is higher at 0.652 when we advanced the Twitter-identified suicide risks 1 month earlier to compare with the actual suicide records. These 2 indicators were also spatially correlated by city with a correlation coefficient of 0.699 (P<.001) for the 10-year period. Among the 267 cities with the top quintile of suicide risks identified from both tweets and actual suicide records, 73.5% (n=196) of cities overlapped.
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| Research Progress Status |
令和4年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和4年度が最終年度であるため、記入しない。
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[Journal Article] Public surveillance of social media for suicide using advanced deep learning models in Japan: time series study from 2012 to 20222023
Author(s)
Wang, S., Ning, H., Huang, X., Xiao, Y., Zhang, M., Yang, E. F., ... & Zeng, Y.
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Journal Title
Journal of medical internet research
Volume: 25
Pages: e47225
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