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Corresponding to Ueno and Masuda, Journal of Theoretical Biology, 254, 655-666 (2008), which is located at
http://www.sciencedirect.com/science/article/pii/S0022519308003512

Corresponding to Ueno and Masuda, Journal of Theoretical Biology, 254, 655-666 (2008), which is located at
http://www.sciencedirect.com/science/article/pii/S0022519308003512

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UenoMasuda2008JTB-slide

  1. 1. Controlling nosocomial infection based on structure of hospital social networks Taro Ueno1,2, Naoki Masuda3,4 1 Institute of Molecular Embryology and Genetics, The University of Kumamoto 2 Tokyo Metropolitan Hiroo General Hospital 3 Graduate School of Information Science and Technology, The University of Tokyo 4 JST Presto Ref: J. Theor. Biol., 254, 655-666 (2008).
  2. 2. Objective of this study •Control nosocomial infections •By changing structure of hospital social networks •By vaccinating some individuals beforehand •What is special about that? •Hierarchical network structure •Hospital specific antibiotics-resistant pathogens (e.g. MRSA, VRE). •Severe results in immuno-deficient patients •As initiator or booster of epidemic outbreaks at urban community and world-wide levels
  3. 3. Social networks of a hospital • Data from a community hospital in Tokyo, Japan • Nodes [頂点] • (Hospitalized) Patient (Pt) [入院患者] • Nurse (Ns) [看護師] • (Medical) doctors (Dr) [医師] • Links [枝] • Pt-Ns, Pt-Dr, Ns-Dr, Dr-Dr ← medical record [カルテ] • (Dr-Dr ← same team) • Pt-Pt ← same room [同じ病室] • Ns-Ns ← same ward [同じ病棟]
  4. 4. Def of contact network medical record network medical doctor [医師] nurse [看護師] hospitalized patients [入院患者]
  5. 5. Hierarchical Structure [ネットワークの階層構造] team department [診療科] room [病室] ward [病棟] Dr [医師] Ns [看護師] Pt [入院患者]
  6. 6. Visualization 605 nodes 605$–$42$ persons’$ 3046 links network Dr [医師] 123 Ns [看護師] 94 Pt [入院患者] 388
  7. 7. Visualization 605 nodes 605$–$42$ persons’$ 3046 links network Dr [医師] 123 Ns [看護師] 94 Pt [入院患者] 388
  8. 8. Visualization 605 nodes 605$–$42$ persons’$ 3046 links network Dr [医師] 123 Ns [看護師] 94 Pt [入院患者] 388
  9. 9. Visualization 605 nodes 605$–$42$ persons’$ 3046 links network Dr [医師] 123 Ns [看護師] 94 Pt [入院患者] 388
  10. 10. Visualization 605 nodes 605$–$42$ persons’$ 3046 links network Dr [医師] 123 Ns [看護師] 94 Pt [入院患者] 388 Drs are across-ward transmitters?
  11. 11. Network Properties characteristic path length clustering coefficient hospital network 4.39 0.53 randomized network 3.02 0.029
  12. 12. Network Properties characteristic path length clustering coefficient hospital network 4.39 0.53 randomized network 3.02 0.029 Small-world and hierarchical
  13. 13. Degree Distribution heterogeneous, not really scale-free (too small N)
  14. 14. SIR Model
  15. 15. SIR Model Susceptible S
  16. 16. SIR Model Susceptible S I Infected
  17. 17. SIR Model Susceptible S I Infected
  18. 18. SIR Model Susceptible S
  19. 19. SIR Model Susceptible λ: infection rate S
  20. 20. SIR Model Susceptible Infected λ: infection rate S I
  21. 21. SIR Model Susceptible Infected λ: infection rate μ: recovery rate S I
  22. 22. SIR Model Recovered/ Susceptible Infected λ: infection rate μ: recovery rate Death S I R
  23. 23. SIR Model Recovered/ Susceptible Infected λ: infection rate μ: recovery rate Death S I R dS =-λSI dt Epidemic size dI =λSI-μI = final #R / n dt
  24. 24. Network Intervention Intervention strategies 1 : Reassigning patients to medical doctors [担当入院患者を診療科内で入れ替え] 2 : Dissolving doctors’ teams [医師のチーム 解消] 3 : Introduction of single rooms [病室の個室 化]
  25. 25. Network Intervention Intervention strategies cost 1 : Reassigning patients to medical doctors [担当入院患者を診療科内で入れ替え] 2 : Dissolving doctors’ teams [医師のチーム 解消] 3 : Introduction of single rooms [病室の個室 化]
  26. 26. Network Intervention
  27. 27. Network Intervention Dr t1 t2 w w Ns Ns
  28. 28. Network Intervention Dr t1 t2 w w Ns Ns
  29. 29. Network Intervention Dr t1 t2 w w Ns Ns intervention 1 [患者入れ替え]
  30. 30. Network Intervention Dr t1 t2 Dr Dr w w Ns Ns Pt1 intervention 1 [患者入れ替え]
  31. 31. Network Intervention Dr t1 t2 Dr Dr w w Ns Ns Pt1 intervention 1 [患者入れ替え]
  32. 32. Network Intervention Dr t1 t2 Dr Dr w w Ns Ns Pt1 intervention 1 intervention 2 [患者入れ替え] [チーム解消]
  33. 33. Network Intervention Dr t1 t2 Dr Dr w w Ns Ns Pt1 intervention 1 intervention 2 [患者入れ替え] [チーム解消]
  34. 34. Network Intervention Dr t1 t2 Dr Dr w w Ns Ns Pt1 intervention 1 intervention 2 [患者入れ替え] [チーム解消]
  35. 35. Network Intervention Dr t1 t2 Dr Dr w w Ns Ns Pt1 intervention 1 intervention 2 intervention 3 [患者入れ替え] [チーム解消] [病室個室化]
  36. 36. Network Intervention better Effectiveness: 2 > 1 > 3
  37. 37. vaccination • Constraint: only 20 out of 605 can be vaccinated beforehand (by assuming R state in the SIR dynamics at time 0). • Strategies for selecting vaccinated individuals • Random [無作為] • Degree based [次数] • Betweenness centrality based [媒介中心性] • High-betweenness individuals are mostly Drs. Some Ns also included. • Recalculated betweenness centrality based
  38. 38. better
  39. 39. Conclusions • The observed hospital social network is small-world and hierarchical. • Healthcare workers (particularly Dr) are main transmitters. [医師が感染を媒介する] • Intervention: Restricting interaction between Drs and their visits to different wards is more effective than isolating Pts in single rooms. [チーム解消, 患者入れ替え > 病室個室化] • Vaccination: betweenness-based > degree-based. [媒介中心 性でワクチン接種するとよい] • Mostly doctors, some nurses • But the effectiveness of betweenness-based vaccination may not be general for networks with hierarchical or community structure.

Editor's Notes

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  • 病院内における感染症の特徴として、入院患者は様々な基礎疾患を有しているため、免疫力が低下しており、感染症が重篤化しやすいことがあります。\nまた、病院内での抗生物質の使用により、MRSAやVREといった薬剤耐性菌の出現を引き起こすといった問題点があります。\nさらに最近の報告では、SARSやinfluenzaといった感染症の、地域社会あるいは世界的な感染拡大に病院内での感染拡大が影響を及ぼしている事が示されています。\n
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