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Surveillance of gastroenteritis using drug sales data in France

Young Researcher Forum, Brussels, 13 th November 2013. Surveillance of gastroenteritis using drug sales data in France Mathilde Pivette, PharmD, MPH mathilde.pivette@ehesp.fr Pr Avner Bar-Hen Dr Pascal Crépey Dr Judith Mueller. Context. Drug sales Non-specific surveillance data

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Surveillance of gastroenteritis using drug sales data in France

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  1. Young Researcher Forum, Brussels, 13th November 2013 • Surveillance of gastroenteritis using drug sales data in France • Mathilde Pivette, PharmD, MPHmathilde.pivette@ehesp.fr • Pr Avner Bar-HenDr Pascal CrépeyDr Judith Mueller EHESP

  2. Context • Drug sales • Non-specific surveillance data • Outbreak detection • Infectious disease surveillance • Gastroenteritis • High frequency disease • ~ 3 millions GP consultations • 50 000 hospitalizations < 5 years old EHESP

  3. Objective • To assess the value of drug sales data as an early epidemic detection tool for gastroenteritis in France • By assessing correlation with reference data • By determining if drug data could provide an early signal of seasonal outbreak • By assessing prospective outbreak detection EHESP

  4. Data • Stratified sample of pharmacies • 1647 in 2009 to 4627 pharmacies in 2013 (20%) • Number of boxes sold of all products • Prescribed/ Non-prescribed • Data obtained at D+1 • Geographic location of the pharmacies (region) EHESP

  5. Indicator drug selection • Intestinal antiinfectives antidiarrhoeals (A07A) • Intestinal adsorbents antidiarrhoeals (A07B) • Antidiarrheal microorganisms (A07F) • Other antidiarrheals (A07X) • Motility inhibitors (A07H) • Antiemetics and antinauseants (A04A9) • Oral rehydration solutions • Dietetic products for diarrhea and vomiting Selection of 8 groups (256 products) • Reference data • Sentinel network of 1300 GP throughout France (www.sentiweb.fr) • Acute diarrhea cases reported each week EHESP

  6. Sales of drugs for gastroenteritis and number of reported cases (Sentinel network), 2009-2012, France Results Cross-correlation EHESP

  7. Epidemics detection Detection Method : Serfling method • Epidemic periods • Upper limit of the CI : threshold • Periodic baseline level • Evaluation : • Detection window : Start of epidemic from Sentinel network +/- 4 weeks • Evaluation criteria: • Sensitivity • False alert rate • Timeliness Selection of model parameters that optimize the 3 criteria EHESP

  8. The selected detection model for non-prescribeddrugs allows the detection of seasonal outbreaks 2.25 weeks earlier • Detection performance of the selected model (IC 95%, cut-off 30%) • Sensitivity : 100% • False alert rate : 0% • Mean timeliness: -2.25 weeks (min -3; median -2.5, max -1) Detection week (Drugs) EHESP

  9. The selected detection model for prescribeddrugs allows the detection of seasonal outbreaks 0.2 weeks earlier • Detection performance of the selected model (IC 99%, cut-off 30%) • Sensitivity : 100% • False alert rate : 0% • Mean timeliness: -0.2 weeks (min -2; median 0, max +1) • Drug sales EHESP

  10. Detection of epidemic 3 weeks earlier than sentinel network in 2012-2013 Training period • Prospective detection during 2012-2013 • Non-prescribed Drug sales • Detection week (Drugs) • Threshold EHESP

  11. Next step : regional analyses • Example of the 2012/2013 seasonal epidemic. • First epidemic week from drug sales • First epidemic week from Sentinel network • Detection from non-prescribed drugs 3 weeks earlier than detection from reference data, with a beginning at the east of France. EHESP

  12. Discussion • Confirmation of the potential of drug sales analysis for gastroenteritis surveillance • Prescribed drugs: high correlation with reported cases / No benefit for early detection • Adequacy between the 2 sources • Non prescribed drugs :Detection on average 2,25 weeks earlier (daily analysis: 16.7 days earlier, detection after 7 epidemics days) • Purchase of drugs during the early phase of illness • Reflects patient behaviors EHESP

  13. Advantages • Limits • Selection of indicator drugs : specificity • Use of medications vary by demographic factors • Population source not precisely known : incidence ? • Relevant tool to determine dynamics and detect outbreaks • Reporting lag of one day • rapid assessment of Public Health situation • prospective analyses • Automatically collection of data EHESP

  14. Conclusion • Useful and valid tool for real-time monitoring of GI • Earlier indicator of gastroenteritis outbreak • Other infectious diseases EHESP

  15. Thank you • QUESTIONS ? EHESP

  16. EHESP

  17. ANNEXES • Epidemics detection • Detection Method (Serfling method) : • Periodic regression models • Key parameters : • highest pruning percentile (varying from 15% to 40%) • prediction interval (varying from 90%,95%,99%) • Number of consecutive weeks to detect an epidemic

  18. The selected detection model for non-prescribeddrugs allows the detection of seasonal outbreaks 2.25 weeks earlier • Detection performance of the selected model (IC 95%, cut-off 30%) • Sensitivity : 100% • False alert rate : 0% • Mean timeliness: -2.25 weeks (min -3; median -2.5, max -1) EHESP

  19. The selected detection model for prescribeddrugs allows the detection of seasonal outbreaks 0.6 weeks earlier • Detection performance of the selected model (IC 99%, cut-off 30%) • Sensitivity : 100% • False alert rate : 0% • Mean timeliness: -0.6 weeks (min -2; median 0, max +1) EHESP

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