Big Data and the Global Public Health Intelligence Network ( GPHIN )

Background: Traditional public health surveillance provides accurate information but is typically not timely. New early warning systems leveraging timely electronic data are emerging, but the public health value of such systems is still largely unknown. Objective: To assess the timeliness and accuracy of pharmacy sales data for both respiratory and gastrointestinal infections and to determine its utility in supporting the surveillance of gastrointestinal illness. Methods: To assess timeliness, a prospective and retrospective analysis of data feeds was used to compare the chronological characteristics of each data stream. To assess accuracy, Ontario antiviral prescriptions were compared to confirmed cases of influenza and cases of influenza-like-illness (ILI) from August 2009 to January 2015 and Nova Scotia sales of respiratory over-the-counter products (OTC) were compared to laboratory reports of respiratory pathogen detections from January 2014 to March 2015. Enteric outbreak data (2011-2014) from Nova Scotia were compared to sales of gastrointestinal products for the same time period. To assess utility, pharmacy sales of gastrointestinal products were monitored across Canada to detect unusual increases and reports were disseminated to the provinces and territories once a week between December 2014 and March 2015 and then a follow-up evaluation survey of stakeholders was conducted. Results: Ontario prescriptions of antivirals between 2009 and 2015 correlated closely with the onset dates and magnitude of confirmed influenza cases. Nova Scotia sales of respiratory OTC products correlated with increases in non-influenza respiratory pathogens in the community. There were no definitive correlations identified between the occurrence of enteric outbreaks and the sales of gastrointestinal OTCs in Nova Scotia. Evaluation of national monitoring showed no significant increases in sales of gastrointestinal products that could be linked to outbreaks that included more than one province or territory. Conclusion: Monitoring of pharmacy-based drug prescriptions and OTC sales can provide a timely and accurate complement to traditional respiratory public health surveillance activities but initial evaluation did not show that tracking gastrointestinal-related OTCs were of value in identifying an enteric disease outbreak in more than one province or territory during the study period.

[1]  L. Budd,et al.  Surveillance networks and spaces of governance: technological openness and international cooperation during the 2009 H1N1 pandemic , 2010 .

[2]  Dylan B. George,et al.  Big Data Opportunities for Global Infectious Disease Surveillance , 2013, PLoS medicine.

[3]  Carol A Gotway Crawford,et al.  A New Source of Data for Public Health Surveillance: Facebook Likes , 2015, Journal of medical Internet research.

[4]  G. Asokan,et al.  Leveraging “big data” to enhance the effectiveness of “one health” in an era of health informatics , 2015, Journal of epidemiology and global health.

[5]  J. Gardy Towards genomic prediction of drug resistance in tuberculosis. , 2015, The Lancet. Infectious diseases.

[6]  Isaac Chun-Hai Fung,et al.  Efficient use of social media during the avian influenza A(H7N9) emergency response. , 2013, Western Pacific surveillance and response journal : WPSAR.

[7]  Sara E. Davies,et al.  The Politics of Surveillance and Response to Disease Outbreaks: The New Frontier for States and Non-state Actors , 2015 .

[8]  Yi Hao,et al.  Chinese social media reaction to the MERS-CoV and avian influenza A(H7N9) outbreaks , 2013, Infectious Diseases of Poverty.

[9]  R. Snow,et al.  Mobile phones and malaria: modeling human and parasite travel. , 2013, Travel medicine and infectious disease.

[10]  K. Denecke,et al.  Social Media and Internet-Based Data in Global Systems for Public Health Surveillance: A Systematic Review , 2014, The Milbank quarterly.

[11]  Dotan A. Haim,et al.  Using Networks to Combine “Big Data” and Traditional Surveillance to Improve Influenza Predictions , 2015, Scientific Reports.

[12]  Jan C. Semenza,et al.  Prototype Early Warning Systems for Vector-Borne Diseases in Europe , 2015, International journal of environmental research and public health.

[13]  Sunmoo Yoon,et al.  What can we learn about the Ebola outbreak from tweets? , 2015, American journal of infection control.

[14]  Eirini Christaki New technologies in predicting, preventing and controlling emerging infectious diseases , 2015, Virulence.

[15]  Phelim Bradley,et al.  Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study , 2015, The Lancet. Infectious diseases.

[16]  Francis X. Diebold,et al.  A Personal Perspective on the Origin(s) and Development of 'Big Data': The Phenomenon, the Term, and the Discipline, Second Version , 2012 .

[17]  David P. Fidler,et al.  Global Public Health Surveillance under New International Health Regulations , 2006, Emerging infectious diseases.

[18]  Luciano Floridi,et al.  The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts , 2015, Science and Engineering Ethics.

[19]  J. Aramini,et al.  Evaluation of a national pharmacy-based syndromic surveillance system. , 2015, Canada communicable disease report = Releve des maladies transmissibles au Canada.

[20]  A Mawudeku,et al.  Big Data and the Global Public Health Intelligence Network (GPHIN). , 2015, Canada communicable disease report = Releve des maladies transmissibles au Canada.

[21]  Femida Gwadry-Sridhar,et al.  Social Media: A Systematic Review to Understand the Evidence and Application in Infodemiology , 2011, eHealth.

[22]  Rumi Chunara,et al.  Public health for the people: participatory infectious disease surveillance in the digital age , 2014, Emerging Themes in Epidemiology.

[23]  R. Merchant,et al.  Integrating social media into emergency-preparedness efforts. , 2011, The New England journal of medicine.

[24]  M. Schatz,et al.  Big Data: Astronomical or Genomical? , 2015, PLoS biology.

[25]  Herman D. Tolentino,et al.  Use of Unstructured Event-Based Reports for Global Infectious Disease Surveillance , 2009, Emerging infectious diseases.

[26]  Patty Kostkova A roadmap to integrated digital public health surveillance: the vision and the challenges , 2013, WWW '13 Companion.

[27]  J. Gardy,et al.  Whole-Genome Sequencing of Measles Virus Genotypes H1 and D8 During Outbreaks of Infection Following the 2010 Olympic Winter Games Reveals Viral Transmission Routes. , 2015, The Journal of infectious diseases.

[28]  T. Bernardo,et al.  Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation , 2013, Journal of medical Internet research.

[29]  Patipat Susumpow,et al.  Participatory disease detection through digital volunteerism: how the doctorme application aims to capture data for faster disease detection in thailand , 2014, WWW.