Singh Bismark, Huang Hsin-Chan, Morton David P, Johnson Gregory P, Gutfraind Alexander, Galvani Alison P, Clements Bruce, Meyers Lauren A
Emerg Infect Dis. 2015 Feb;21(2):251-8. doi: 10.3201/eid2102.141024.
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.
我们提供了一种数据驱动的方法，用于在流感大流行期间，从目标人群的总体可及性角度优化基于药房的抗病毒药物配送，并将其应用于美国得克萨斯州。我们发现，在2009年流感大流行期间，得克萨斯州州卫生服务部在全州范围内实现了约88%的可及性（愿意前往最近配药点的人口比例）。然而，在包含不到1000名未充分参保人员的美国邮政编码（ZIP代码）区域，可及性仅达到34.5%。优化后的配送网络使总体预期可及性提高到91%，在难以到达的地区提高到60%，并且两到三家主要连锁药房在人口密集地区实现了近乎最大覆盖。独立药房对于覆盖包含不到1000名未充分参保人员的邮政编码区域至关重要。该模型是学术研究人员与公共卫生官员合作开发的，可通过基于网络的界面作为得克萨斯州州卫生服务部的决策支持工具使用。
