OBJECTIVE: Since states' public health systems differ as to pandemic preparedness, this study explored whether such heterogeneity among states could affect the nation's overall influenza rate. DESIGN: The Centers for Disease Control and Prevention produced a uniform set of scores on a 100-point scale from its 2008 national evaluation of state preparedness to distribute materiel from the Strategic National Stockpile (SNS). This study used these SNS scores to represent each state's relative preparedness to distribute influenza vaccine in a timely manner and assumed that "optimal" vaccine distribution would reach at least 35% of the state's population within 4 weeks. The scores were used to determine the timing of vaccine distribution for each state: each 10-point decrement of score below 90 added an additional delay increment to the distribution time. SETTING AND PARTICIPANTS: A large-scale agent-based computational model simulated an influenza pandemic in the US population. In this synthetic population each individual or agent had an assigned household, age, workplace or school destination, daily commute, and domestic intercity air travel patterns. MAIN OUTCOME MEASURES: Simulations compared influenza case rates both nationally and at the state level under 3 scenarios: no vaccine distribution (baseline), optimal vaccine distribution in all states, and vaccine distribution time modified according to state-specific SNS score. RESULTS: Between optimal and SNS-modified scenarios, attack rates rose not only in low-scoring states but also in high-scoring states, demonstrating an interstate spread of infections. Influenza rates were sensitive to variation of the SNS-modified scenario (delay increments of 1 day versus 5 days), but the interstate effect remained. CONCLUSIONS: The effectiveness of a response activity such as vaccine distribution could benefit from national standards and preparedness funding allocated in part to minimize interstate disparities.
OBJECTIVE: Since states' public health systems differ as to pandemic preparedness, this study explored whether such heterogeneity among states could affect the nation's overall influenza rate. DESIGN: The Centers for Disease Control and Prevention produced a uniform set of scores on a 100-point scale from its 2008 national evaluation of state preparedness to distribute materiel from the Strategic National Stockpile (SNS). This study used these SNS scores to represent each state's relative preparedness to distribute influenza vaccine in a timely manner and assumed that "optimal" vaccine distribution would reach at least 35% of the state's population within 4 weeks. The scores were used to determine the timing of vaccine distribution for each state: each 10-point decrement of score below 90 added an additional delay increment to the distribution time. SETTING AND PARTICIPANTS: A large-scale agent-based computational model simulated an influenza pandemic in the US population. In this synthetic population each individual or agent had an assigned household, age, workplace or school destination, daily commute, and domestic intercity air travel patterns. MAIN OUTCOME MEASURES: Simulations compared influenza case rates both nationally and at the state level under 3 scenarios: no vaccine distribution (baseline), optimal vaccine distribution in all states, and vaccine distribution time modified according to state-specific SNS score. RESULTS: Between optimal and SNS-modified scenarios, attack rates rose not only in low-scoring states but also in high-scoring states, demonstrating an interstate spread of infections. Influenza rates were sensitive to variation of the SNS-modified scenario (delay increments of 1 day versus 5 days), but the interstate effect remained. CONCLUSIONS: The effectiveness of a response activity such as vaccine distribution could benefit from national standards and preparedness funding allocated in part to minimize interstate disparities.
Authors: Ira M Longini; Azhar Nizam; Shufu Xu; Kumnuan Ungchusak; Wanna Hanshaoworakul; Derek A T Cummings; M Elizabeth Halloran Journal: Science Date: 2005-08-03 Impact factor: 47.728
Authors: Leslie M Beitsch; Samata Kodolikar; Tim Stephens; Daniel Shodell; Art Clawson; Nir Menachemi; Robert G Brooks Journal: Public Health Rep Date: 2006 Nov-Dec Impact factor: 2.792
Authors: M Elizabeth Halloran; Neil M Ferguson; Stephen Eubank; Ira M Longini; Derek A T Cummings; Bryan Lewis; Shufu Xu; Christophe Fraser; Anil Vullikanti; Timothy C Germann; Diane Wagener; Richard Beckman; Kai Kadau; Chris Barrett; Catherine A Macken; Donald S Burke; Philip Cooley Journal: Proc Natl Acad Sci U S A Date: 2008-03-10 Impact factor: 11.205
Authors: Neil M Ferguson; Derek A T Cummings; Christophe Fraser; James C Cajka; Philip C Cooley; Donald S Burke Journal: Nature Date: 2006-04-26 Impact factor: 49.962