Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model

• Project/Area Number: 25560363
• Research Category: Grant-in-Aid for Challenging Exploratory Research
• Allocation Type: Multi-year Fund
• Research Field: Applied health science
• Research Institution: Kanagawa Cancer Center Research Institute (2015-2016); Yamagata University (2013-2014)
• Principal Investigator: Narimatsu Hiroto 地方独立行政法人神奈川県立病院機構神奈川県立がんセンター(臨床研究所), その他部局等, 部長 (50524419)
• Co-Investigator(Kenkyū-buntansha): 中田 善規 帝京大学, 公私立大学の部局等, 教授 (60287018)
• Research Collaborator: NAKAMURA Sho 山形大学, 医学部, 病院助教 (00740656)
• Project Period (FY): 2013-04-01 – 2017-03-31
• Project Status: Completed (Fiscal Year 2016)
• Budget Amount: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000); Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000); Fiscal Year 2014: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000); Fiscal Year 2013: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
• Keywords: 個別化医療 / ゲノム / 肥満 / 経営工学 / 効率性 / データ包絡法 / 高血圧 / 高脂血症 / 個別化予防医療 / 予防医療 / ゲノム解析 / 公衆衛生学

Outline of Final Research Achievements

Data envelopment analysis (DEA) is a method of operations research. DEA might be used to evaluate individuals’ susceptibility to obesity, which could help establish effective risk models for the onset of obesity. Using the coefficients for the participants’ single nucleotide polymorphisms, we then calculated their genetic predisposition score (GPS). To evaluate the effects of genetic factors and efficiency score calculated by DEA on body mass index (BMI), we used multiple linear regression analysis. Our results indicated that efficiency score has more impact on GPS. The feasibility of applying DEA to predict obesity was presented.

Research Products

• [Journal Article] データ包絡法による個別化医療への挑戦 (2017)
  • Author(s): 成松宏人
  • Journal Title: BIO CLINICA Volume: 32 Pages: 79-83
• [Journal Article] Gene-environment interactions in obesity: implication for future applications in preventive medicine. (2016)
  • Author(s): Nakamura S, Narimatsu H, Sato H, Sho R, Otani K, Kawasaki R, Karasawa S, Daimon M, Yamashita H, Kubota I, Ueno Y, Kato T, Yoshioka T, Fukao A, Kayama T.
  • Journal Title: J Hum Genet. Volume: 61 Issue: 4 Pages: 317-322
  • DOI: 10.1038/jhg.2015.148
  • NAID: 40020802847
  • Peer Reviewed
• [Journal Article] Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model of obesity. (2015)
  • Author(s): Narimatsu H, Nakata Y, Nakamura S, Sato H, Sho R, Otani K, Kawasaki R, Kubota I, Ueno Y, Kato T, Yamashita H, Fukao A, Kayama T.
  • Journal Title: PLoS One. Volume: 10 Issue: 5 Pages: e0126443-e0126443
  • DOI: 10.1371/journal.pone.0126443
  • Peer Reviewed / Open Access / Acknowledgement Compliant
• [Journal Article] Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model of obesity (2015)
  • Author(s): Hiroto Narimatsu, Yoshinori Nakata, Sho Nakamura, Hidenori Sato, Ri Sho, Katsumi Otani, Ryo Kawasaki, Isao Kubota, Yoshiyuki Ueno, Takeo Kato, Hidetoshi Yamashita, Akira Fukao, Takamasa Kayama
  • Journal Title: Plos One Volume: in press
  • Peer Reviewed / Open Access / Acknowledgement Compliant