Ending Pandemics Ini.a.ve. Adam W. Crawley, MPH ISDS Webinar January 22, 2016

Transcription

1 Ending Pandemics Ini.a.ve Adam W. Crawley, MPH ISDS Webinar January 22, 2016

2 Skoll Global Threats Ending Pandemics Our Approach Faster Detec0on and Repor0ng Through Novel Surveillance Systems Verifica0on Through Crowdsourced Epidemic Intelligence Coordina0on Through Regional Disease Surveillance Networks Assessing Progress Through Measurement

3 SGTF Approach for Ending Pandemics Our vision is to end pandemics. We work to decrease the likelihood of a pandemic event occurring by shortening the 0me from outbreak start to detec0on, with a focus on disease hotspots, where viruses with pandemic poten0al are likely to emerge. We aim to achieve this by enabling partners, through the use of innova0ve approaches, to: Quickly capture and share informa0on to detect human & animal outbreaks; Train and link field epidemiologists to verify outbreaks faster; and Support and connect regional disease surveillance systems to encourage collabora0on between na0onal public health systems. Our hypothesis is that individual projects, leveraging new technologies and innova0ve approaches, will spread beyond pilot countries through regional surveillance networks and other convenings. 1. We are measuring global baseline in more detail to determine best impact measure

4 Tradi.onal public health systems require repor.ng outbreaks through mul.ple levels of health authori.es Patient visits healthcare provider Potential outbreak reported Send samples to lab Patient Zero Patient experiences symptoms Frontline healthcare workers Identify unusual events Lag time between outbreak and official detection enables virus to spread Local officials and field epidemiologists Gather data Identify issue and submit report Confirm occurrence of outbreak Outbreak confirmed Labs Test samples Verify cause of outbreak (e.g. virus strain) Ministry of Health National response activated Inform World Health Organization

5 Parallel to the human-centered public health system is a repor.ng mechanism for animal health Patient Zero Local healthcare workers Local health officials Labs Human health Animal health The "One Health" concept integrates the human-animal-environment nexus in disease detection Ministry of Health, WHO Ministry of Agriculture, FAO, OEI Sick animals Local Veterinarian Local food/ agriculture officials Labs

6 Current SGTF work to decrease detec.on.me Patient Zero Detect outbreaks at the patient level by leveraging technology Local healthcare workers 1 2 Detect outbreaks in animal populations by leveraging technology Local health officials Verify cases faster by training and linking field epidemiologists Labs Ministry of Health Ministry of Agriculture 3 Connect national & regional surveillance systems by building networks Sick animals Local Veterinarian Local food/ agriculture officials Labs Note SGTF is currently evaluating opportunities for Point of Care diagnostics for verification.

7 Epidemic curve CASES Rou0ne disease repor0ng TIME 7

8 Epidemic curve CASES Rou0ne disease repor0ng Clinician reports TIME 8

9 Epidemic curve CASES Rou0ne disease repor0ng Clinician reports Sen0nel networks TIME 9

10 Epidemic curve CASES Rou0ne disease repor0ng Clinician reports Sen0nel networks Digital disease detec0on TIME 10

11 Epidemic curve CASES Rou0ne disease repor0ng Clinician reports Sen0nel networks Digital disease detec0on Par0cipatory epidemiology TIME 11

12 Epidemic curve CASES Rou0ne disease repor0ng Clinician reports Sen0nel networks Digital disease detec0on Par0cipatory epidemiology Even earlier: disease detec0on in animals TIME 12

13 Faster Laboratory Repor.ng Using Mobile Fondation Mérieux, in partnership with the Ministry of Health of Senegal and PATH, is building a scalable mobile phone and web platform to transfer epidemiological data from 120 medical laboratories across Senegal to the Ministry of Health (MoH) to shorten the time between detection and response to reduce biothreats and stop pandemics.

14 Digital, informal surveillance systems are genera0ng an increasing amount of signals EpiCore will link volunteer epidemiologists to validate early signals of disease outbreaks Field Epidemiology Training Programs in 55 countries will have access to training in digital disease detec0on methods

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16 PARTICIPATORY SURVEILLANCE TRADITIONAL PARTICIPATORY SPEED SCALABILITY SENSITIVITY SPECIFICITY CREDIBILITY

17 PARTICIPATORY SURVEILLANCE Flu Near You Canada United States Influenzanet Belgium Denmark France Italy Netherlands Portugal Spain Sweden United Kingdom Salud Boricua Puerto Rico DoctorMe & PODD Thailand SACIDS Kenya Tanzania FluTracking Australia Guardians of Health Brazil Saude na Copa 2014 World Cup

18 Mass Gathering Par.cipatory Surveillance in Brazil World Cup 2014 & Olympics 2016 Brazil Ministry of Health has championed the World Cup pilot to engage fans to report symptoms of illness SGTF is leading discussions with the Interna.onal Olympics Commi\ee to build on this success for the 2016 games in Rio

19 Mass Gathering Par.cipatory Surveillance in Brazil

20 Cross Border Par.cipatory Surveillance in Africa Complemen.ng the interna.onal disease surveillance strategies with: Par.cipatory engagement of local communi.es. Applica.on of mobile technologies for disease surveillance; repor.ng including event repor.ng & feedback Inter-sectoral collabora.on: Animal Health, Human Health & ICT sectors (EpiHack)

21 Thailand: Opendream & The DoctorMe Applica.on SE Asia is a hotspot for emerging diseases: Avian Influenza, SARS, Dengue Thailand boasts a rapidly emerging technology sector: Mobile phone penetra0on: 131.8% of popula0on Internet access: 35.8% of popula0on In 2011 Opendream, Thai Health Promo0on Founda0on and Folk Doctor Founda0on launch DoctorMe mobile applica0on: 400,000+ downloads 35,000 ac0ve monthly users!

22 The Chiang Mai grant further extends our innova.ons in par.cipatory epidemiology Project Goals Establish human / animal par0cipatory epidemiology approaches in Chiang Mai with local partners Detect both human and animal illnesses in real time, before an epidemic occurs Empower human and animal health officials with more timely data to respond faster and contain outbreaks Scale successes beyond Chiang Mai Expand in Thailand through national health organizations and village governments Scale successes to additional countries using the Mekong Basin Disease Surveillance network and CORDS Engage regional philanthropies and corporate partners to support expansion Focus area: Chiang Mai Province in Thailand

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24 Smolinski et al. (2015) American Journal of Public Health

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26 Flu Season Number of Par.cipants by State Number of Participants lat > long Total number of Par0cipants= 49,814

27 Flu Season Number of Par.cipants by State Adjusted for Popula.on 50 lat Percent of Population > long Total number of Par0cipants= 49,814

28 Gender 0.6 Relative Frequencies population FNY US 0.0 Female Gender Male Chi-Square Test: χ 2 =1946.8, df=1, p< * A larger propor0on of females par0cipated to the study, with respect to the baseline value

29 Distribu.on of Age Groups 0.20 Relative Frequencies population FNY US 0.00 < < Age Group Chi-Square Test: χ 2 = , df=7, p< * All age classes were represented in the sample, however, a significant difference between the repar00on in age of ac0ve par0cipants and U.S. popula0on is observed

30 Health Development Index as SES Proxy (by county) Density of HDI Density US population FNY participants HDI Two-sample Kolmogorov-Smirnov Test: D= p< *The distribu0ons of HDI is significantly different for FNY par0cipants and US popula0on

31 Characteris.cs Assessed Variable Gender Descrip.on Male; Female Age Group 13-29; 30-39; 40-49; 50-59; 60-69; ILI Status at First Survey Household Members Reported ILI as defined by CDC (fever with cough and/ or sore throat) 1 at first survey; did not report ILI at first survey Reported for at least one other household member; did not report for other household members Health Development Index Con0nuous Scale 1-10, 1 indica0ng low SES, 10 indica0ng high SES

32 Summary of Results Variable OR (p-value) ILI ILI Status at 1 st Survey (yes) 0.22 (<0.0001) Household Members (yes) 3.29 (<0.0001) Health Development Index 1.12 (<0.0001) Gender (Females) 0.75 (<0.0001) Age Group (70-80) 1.23 (<0.0001) (60-70) 1.14 (0.0001) (40-50) 0.70 (<0.0001) (30-40) 0.54 (<0.0001) House HDI Gender (13-30) 0.67 (<0.0001) Odds Ratio

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35 Ques.ons? 35