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Limited alignment of publicly competitive disease funding with disease burden in Japan.

Shuhei NomuraDaisuke YoneokaShiori TanakaRyoko MakuuchiHaruka SakamotoAya IshizukaHaruyo NakamuraAnna KubotaKenji Shibuya
Published in: PloS one (2020)
While caution is necessary as this study was not able to consider public in-house funding and the methodological uncertainties could not be ruled out, the analysis may provide a snapshot of the limited alignment between publicly competitive disease-specific funding and the disease burden in the country. The results call for greater management over the allocation of scarce resources on health R&D. DALYs will serve as a crucial, but not the only, consideration in aligning Japan's research priorities with the public health needs. In addition, the algorithms for natural language processing used in this study require continued efforts to improve accuracy.
Keyphrases
  • public health
  • healthcare
  • mental health
  • machine learning
  • emergency department
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  • risk factors
  • quality improvement
  • health information