Optimizing Distribution of Pandemic Influenza Antiviral Drugs

Details

• Alternative Title:
  Emerg Infect Dis
• Personal Author:
  Singh, Bismark ; Huang, Hsin-Chan ; Morton, David P. ; Johnson, Gregory P. ; Gutfraind, Alexander ; Galvani, Alison P. ; Clements, Bruce ; Meyers, Lauren A.
• Description:
  We provide a data-driven method for optimizing pharmacy-based distribution of antiviral drugs during an influenza pandemic in terms of overall access for a target population and apply it to the state of Texas, USA. We found that during the 2009 influenza pandemic, the Texas Department of State Health Services achieved an estimated statewide access of 88% (proportion of population willing to travel to the nearest dispensing point). However, access reached only 34.5% of US postal code (ZIP code) areas containing <1,000 underinsured persons. Optimized distribution networks increased expected access to 91% overall and 60% in hard-to-reach regions, and 2 or 3 major pharmacy chains achieved near maximal coverage in well-populated areas. Independent pharmacies were essential for reaching ZIP code areas containing <1,000 underinsured persons. This model was developed during a collaboration between academic researchers and public health officials and is available as a decision support tool for Texas Department of State Health Services at a Web-based interface.
• Subjects:
  Antiviral Agents Decision Support Techniques Geography Humans Influenza, Human Influenza A Virus, H1N1 Subtype Models, Theoretical Pharmacies
• Source:
  Emerg Infect Dis. 21(2):251-258.
• Document Type:
  Journal Article
• Place as Subject:
  Texas
• Volume:
  21
• Issue:
  2
• Collection(s):
  Emerging Infectious Diseases
• Main Document Checksum:
  urn:sha256:7a933a43ace61c71a898c73219a70ad2df2103c104e97f7802d5cf105b23d6e5
• Download URL:
  https://stacks.cdc.gov/view/cdc/27431/cdc_27431_DS1.pdf
• File Type:
  [PDF - 725.90 KB ]