Surveillance of emerging diseases and networks.


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How networks support emerging diseases surveillance.

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Surveillance of emerging diseases and networks.

  1. 1. Surveillance of emerging diseases and networks: “the whole is (still) more than its part” Dr Jean-Jacques BERNATAS, MD (Montpellier), MSc (DEA Paris 6) Public Health Specialist Senior medical advisor, International SOS Jakarta, Indonesia - 17th December 2010 - Universitas Indonesia, Center for Research and Integrated Development Tropical Health and Infectious Diseases
  2. 2. Plan  Introduction  Definition and concepts  Examples  Rapid assessment
  3. 3. Introduction -1  Health surveillance is not a new deal:  14th century: Republic of Venice, Italy. Surveillance of bubonic plague on ships before disembarkation  1878, USA: congress authorized Public Health Services to collect data for detecting “pestilential diseases” and undertake quarantine measures  Worldwide smallpox eradication in the 1970’s succeeded because of an active surveillance of the new cases  1980’s: introduction of computers improved data aggregation and analysis  1990’s to 2000’s web revolution
  4. 4. Introduction -2  SARS epidemic in 2003 demonstrated the efficiency of a global surveillance network: GOARN/WHO (created in 2000), and also our global fragility facing new pathogens  Core of the IHR 2005 and PHEIC  At the national level: US-CDC (USA), InVS (France), …  At regional level: ECDC (Europe), MDBS (Mekong Region), …
  5. 5.  Health-related Events under Surveillance  Detection Decision Notification Action International Health Regulations (IHR) 2005 decision instrument (simplified from annex 2 of IHR). Introduction -3
  6. 6. Definitions and concepts  Surveillance  Emerging disease  Network  Systems theory
  7. 7. Definitions and concepts: Surveillance (1)  Operational concept: “… process that is used to collect, manage, analyze, interpret and report information about the status of specific diseases or their antecedents in a specific population “ (J. W. Buehler in “Modern Epidemiology”, Rothman &al.)  Objectives:  Descriptive epidemiology of health problems TIME-PLACE-PERSONS  Monitoring, planning PH interventions:  Evaluation  Education and policy  (Research?): they nourish each other
  8. 8. Definitions and concepts: Surveillance (2)  Attributes of surveillance (US CDC, 2001):  Timeliness: depends on the objective of the system. TB: quarterly reports; Influenza: weekly reports; Ebola or other hemorrhagic fever: hours.  Sensitivity: ability to detect an event at interest  Predictive value: are reported cases really cases? Does it measure what it aims to measure.  Representativeness: /target population.  Data quality: accuracy, completeness.  Simplicity: time, money wasting; error risk mitigation.  Flexibility: adaptation to needs and circumstances.  Acceptability: willing to participate, motivation, perreniality
  9. 9. Definitions and concepts: Emerging disease  Emergence of a new human pathogen:  Emergence of human pathogenicity in commensal human species (S. Aureus MRSA)  Interspecies transfer from animals to human: A(H1N1)so, A(H5N1), HIV, SARS-CoronaV  Presence of a known human pathogen in new areas (West-Nile in NYC in 1999 then in all US; chikungunya in Indian ocean) or dramatic and sudden extension of pre existing pathogen (DF in South-East Asia, )  Emergence of knowledge: identification of a new pathogen in specific human diseases (HCV in Egypt, HHV8 and Kaposi sarcoma in Africa): 1918 “Spanish flu”
  10. 10. Definitions and concepts: Emerging disease  Human-animal contacts  Virus-to-human adaptation: influenza viruses  Virus-to-vector adaptation: chikungunya (Reunion Island, 2005: Aedes aegyptiAedes albopictus)  Climate change  Movements of population: short-term (travel) vs. long-term (migration) perspectives  Density of population, both human and animal (animal husbandry) Weiss RA, Nature Medicine, 2004
  11. 11. Definitions and concepts: Network (“jaringan”?)  Nodes and vertices (vertex/vertices):  Topology: description of the relations between nodes  Other properties: connectivity, directed vs. non directed, eccentricity, radius, diameter, coloring graph  Why?!
  12. 12. Definitions and concepts: Network (“jaringan”?) -2  Method for modeling infectious diseases based on contacts patterns. (How a rumor spread all over the world or the “Facebook modeling” …) Fraser, PNAS, 2004 (Christian, CID, 2004) Stochastic models in opposition with deterministic models (famous “R0, S/I/R and differential equations)
  13. 13. Definitions and concepts: System theory  Interacting entities  Primary interactions: positive and negative feedback  Emerging properties and science of complexity (Ilya Prigogine).  Broader framework to conceptualize the interactions in all organized systems, including biological and epidemiological ones  Distributed systems vs. centralized systems
  14. 14. Examples  SISEA/Pasteur  MDBS  TB Christian, CID, 2004
  15. 15. Examples: SISEA/Pasteur -1  Objective: to contribute to the improvement of the detection and handling of epidemic situations in the region, with 3 components:  Strengthen national reference laboratories  Strengthen epidemic detection  Strengthen outbreak response capacities at national and regional levels, in collaboration with WHO  Nodes: healthcare facilities in Vietnam, Laos, China and Cambodia among Pasteur Institutes International Network in South-East Asia; national health authorities  Vertices: monthly reports to national health authorities, and regular workshops.  Findings:  Knowledge of respiratory viruses pattern circulation in SEA,  Emergence of knowledge: meiloidiosis in Cambodia,  Alert and disease control: japanese encephalitis in South Vietnam,  Capacity building and strengthening of national surveillance institution: skills and procedures
  16. 16. 16 Network .... and sub-network International Pasteur Institutes Network – 32 members on 5 continents Examples: SISEA/Pasteur -2 NODES = MODEL
  17. 17. 17 Network of laboratories & hospital-based sentinel sites IPS - pediatric hospital of Nanxiang - Guangxi CDCNIHE - Provincial Hospital of Hai Duong - District hospital of Cam Giang - 19 communes NCLE - Setthathirath Hospital, Vientiane - Mahosot Hospital, Vientiane, - Friendship hospital, Vientiane, - Luanprabang regional hospital IPNT - Provincial Hospital of Binh Dinh - District Hospital of Phu Cat IP HCMC - Ben Tre provincial hospital - Cu Lao Minh district hospitalIP Cambodia - Provincial hospital of Takkeo - Provincial hospital of Kampong Cham Examples: SISEA/Pasteur -3
  18. 18. Vertices: case definition of SARI adopted in Vietnam and used to report the cases: ≤ 05 y.o. > 05 y.o. Cough or breathing difficulty AND One of the following: Tachypnea Chest indrawing General signs of danger Onset of symptoms up to and including 7 days Fever ≥ 38o C (or history of fever) AND Cough OR sore throat OR breathing difficulty AND One of the following: ≥ 30 respirations/min New infiltrate on chest X-ray Inability to speak full sentences Use of accessory respiratory muscles Arterial O2 saturation ≤ 92% on air (no oxygen therapy) Onset of symptoms ≤ 7 days Examples: SISEA/Pasteur -4
  19. 19. Examples: SISEA/Pasteur -5  Findings/outcomes:  Improving surveillance through a better knowledge of some EIDs: ARI&AES. 2 examples:  Improving surveillance in Lao PDR  Improving surveillance in Cambodia:
  20. 20. Improving surveillance in Lao PDR: • Technical assistance in microbiology and epidemiology • ALRI surveillance, complementary approach to the other surveillance system implemented (EWORS, EWARN, ILI): • ILI: 2007 to 2008 : 507 ILI specimens collected → 142 (28 %) +ve for IAV and IBV, 2009 : 533 specimens collected → 139 (26 %) +ve for Influenza IAV and IVB. • ALRI: Jul 08 to Oct 09: 222 specimens collected → 26(11.7%) +ve for IAV and IVB; 24 sputum specimens collected for bacteriology testing: 11(45.8%) +ve: S. pneumonia, H. influenza, S. aureus, P. aeruginosa, K. pneumonia (+ C. albicans) • Contribution to ILI surveillance • Strenghtening virology, bacteriology, epidemiology capacities • Implementing a new sentinel site in Luanprabang Influenza A,B 11% Enterovirus 9% HMPV 1% Para influenza virus 4% Mixed 2% Negative 73% ALRI Virology tested, 2009 N=139 Examples: SISEA/Pasteur -6
  21. 21. Improving surveillance in Cambodia – 1 (Vong S and al.): Implementation of the ALRI surveillance activities in April,2007: on Nov 2009, 3177 patients enrolled. In depth clinical classification and data validation (Pr. Mayaud and al., Paris). 9,2 6,7 0 19,1 32 28,8 0 15,5 45 3,6 41,3 17,2 0 5 10 15 20 25 30 35 40 45 50 viro bact B K viro bact B K viro bact B K viro bact B K Percentage% Extra-respiratory pathologies Pneumonia Pleural infections Other respiratory infections Viro and bacterio : # positive results / # samples tested (%) BK : # positive / # cases (%) Examples: SISEA/Pasteur -7
  22. 22. Examples: SISEA/Pasteur -8  Findings/outcomes:  Capacity strenghtening  Equipment, consumables  HR  Network integration and partnerships
  23. 23. 23 International Short Course in Biostatistics - REDI centre - Singapore, November 9-13, 2009. Capacity strengthening and epidemiology 24 trainees/12 from SISEA and 12 from Indonesia Daily and final evaluation; very good input from SISEA trainees Very good perception by the trainees New ties with professionals coming from Indonesia, and Singapore High quality of the collaboration with REDI and NUS, who are demanding for other collaboration in training Examples: SISEA/Pasteur -9
  24. 24. Examples: MDBS (Mekong Basin Disease Surveillance) -1  Objective:  “to strengthen national and Mekong sub-regional capabilities in disease surveillance and response to outbreaks of priority diseases, in order that they can be effectively controlled.”  Nodes: healthcare facilities involved in cross-boarder activities  Vertices: weekly reports, training sessions, workshops  Findings: improved skills and commitment regarding infectious diseases in this area;
  25. 25. Examples: MDBS (Mekong Basin Disease Surveillance) -2 NODES
  26. 26. Examples: MDBS (Mekong Basin Disease Surveillance) -3 VERTICES
  27. 27. Examples: MDBS (Mekong Basin Disease Surveillance) -4 FINDINGS
  28. 28. Examples: TB -1  Objective: integrative part of DOTS  Definition: DOTS strategy= Directly Observed Treatment Short- course strategy  sustained political commitment  uninterrupted supply of quality-assured drugs  access to quality-assured sputum microscopy  standardized short-course chemotherapy including direct observation of the treatment at least during the intensive phase  Recording and reporting system standardized information system enabling outcome assessment  objective of the information system:  Activities evaluation  Burden disease: reported smear+ cases, prevalence of smear + pulmonary cases (prevalence survey), tuberculin survey  And also: HIV co-infections, and drug resistance monitoring (laboratories network)
  29. 29.  Nodes:  National level: local/regional TB centers; TB laboratories  International level= NTP  Vertices:  Standardized quarterly reports: case report and treatment outcomes  Laboratory quality control activities and surveillance of sensitivity  Anti TB drugs management &supply  Is a centralized system: Examples: TB -2
  30. 30.  Findings:  Monitoring NTP at the global, regional and national level.  burden of disease estimation  advocacy for appropriate funding and policy  (Re-)emerging diseases: MDR- and XDR-TB  help to adapt and monitor the response  Triggers operational research: DOTS evaluation and implementation according to specific context and constrains Examples: TB -3
  31. 31. Rapid assessment  SWOT:  Strengths  Weaknesses  Opportunities  Threats
  32. 32. Rapid assessment: Strengths  Distributed systems: “filter-effect”, improving the sensitivity and specificity:  A weak signal will be tested through other centres/nodes:  if confirmed, then amplification= sensitivity ( true +ves)  +ve feedback  If not confirmed, then attenuation= specificity ( false –ves)  -ve feedback  Multiple identical weak signals will sum in a strong signal (noise reduction)  Needs a dense network covering the area at interest  Feed the curiosity (scientific) and develop the exchanges: techniques, procedures, quality control,  Extend the size sample to give more consistency to the findings
  33. 33. Rapid assessment: Weaknesses 1- Quality of the system: ex. Lack of completeness:
  34. 34. Rapid assessment: Weaknesses 2- Appropriate use of data for a comprehensive and coordinated response in due-time:  Cross boarder actions not easy to set up: political and cultural concerns  Continuity/long-term= sustainability  Same data may be interpreted differently by the partners, and may trigger different responses.
  35. 35. Rapid assessment: Opportunities  IT development: from ancient paper register (TB register) era to web-based reporting system (TB, MDBS)  Political and economical development, international cooperation, necessary in our global village: countries have mutual advantages to cooperate, both developed and developing countries  “Public health emergency of international concern” (PHEIC, IHR 2005): increasing global threats (SARS, AI, SI, bioterrorism) and global awareness improved fund raising  Progress in knowledge (molecular biology) forces us to imagine new possibilities and increases awareness on the extraordinary adaptability of the human pathogens to our weapons: objective tends to cooperate rather than to eradicate
  36. 36. Rapid assessment: Threats  Multiplication of networks not consistently interconnected: conflictual information, adverse effect in term of PH action  Political and/or economical consequences: is it possible that neighboring adverse countries share fully sensitive information (Ex. North Korea/South Korea, Myanmar/Thailand, China, Japan, …)  limitation of the global world?  Sharing biological material: whom do the strains collected belong to?
  37. 37. Conclusions & prospects -1  The whole is more than its part:  TB control NP success is to some extend due to a strong and simple interconnected information system  Quick response rely on a dense and fluid network: SRAS y = x2 - x 0 500 1000 1500 2000 2500 3000 3500 4000 0 10 20 30 40 50 60 70 number of vertices numberofcontacts(fullyconnected)  Adding vertices to a network:  Multiplies the number of interactions  Increases sensitivity and sensibility
  38. 38. 38 ≠ AND ≠ Timeliness, accuracy and adaptability to correct quickly what Science bet before A(H1N1)sw-o regarding a possible pandemic: Conclusions & prospects -2
  39. 39. References  Modern Infectious Epidemiology, 2nd Ed. Johan Giesecke. 2002, Arnold  Modern Epidemiology, 3rd Ed. KJ Rothman, S Greensland, TL Lash. 2008, Lipicott Williams & Wlkins  IHR 2005, WHO  Management of Tuberculosis: A Guide to the Essentials of Good Clinical Practice , N. Aït-Khaled, E. Alarcón, R. Armengol et al. 6th Ed. International Union Against Tuberculosis and Lung Disease (The Union), 2010.  MDBS project, df  Public Health Surveillance: A Historical Review with a Focus on HIV/AIDS. Michael A. Stoto. RAND Health, 2003. See
  40. 40. Acknowledgements  Universitas Indonesia, Center for Research and Integrated Development Tropical Health and Infectious Diseases  Pasteur Institute and International Pasteur Institutes Network:  Institut Pasteur du Cambodge: Dr Sirenda Vong, Dr Sowath, Dr Laurence Borand, Sophie Goyet, Dr Philippe Buchy, Dr Bertrand Guillard. Pr Jean-Louis Sarthou,  NIHE: Pr Nguyen Tran Hien, Dr Nguyen Thi Thuong, Dr. Nguyen Van Duong  Institut Pasteur Nha Trang: Pr Bui Trong Chien, Dr. Vien Quang Mai, Dr.Trinh Thi Xuan Mai  Institut Pasteur Ho Chi Minh Ville: Pr Tran Ngoc Huu, Dr.Kien Quoc, Dr. Huong Vu Thi Hu Que  Institut Pasteur de Shanghai: Dr Wei Wang, Dr Peijun Ren, Dr Jin Zhang, Dr Changgui Dong, Dr Yize Li, Dr Peng Lu, Dr Vincent Deubel, M. I. Robin  NCLE: Dr Phengta Vongprachanh, Dr Hansila Phoupaseuth, Dr. Somvay Ongkhammy, Dr Matthida, Dr Darouny Phonekeo, Dr. Noikaseumsy Sithivong, Dr Thongchanh Sissouk, M. Phayvan, Dr Anne-Charlotte Sentilhes  Unité de Coordination : Mme Silvia Ostberg, Dr Roberto Bruzzone (HKU-Pasteur Institute  Institut Pasteur Paris: Dr Isabelle Catala, Dr Marc Jouan, Dr Arnaud Fontanet, Kathrin Victoir  REDI centre: Dr Rodney HOFF, Dr. Za Reed, Dr Philippe Cavallier, Mrs. Quake Ai Li  NUS: Pr. CHIA, Dr. Elizabeth Alderman Jahncke  And International SOS/AEA company