Literature DB >> 23727421

System factors to explain H1N1 state vaccination rates for adults in US emergency response to pandemic.

Carlo Davila-Payan1, Julie Swann2, Pascale M Wortley3.   

Abstract

INTRODUCTION: During the 2009-2010 H1N1 pandemic, vaccine in short supply was allocated to states pro rata by population, yet the vaccination rates of adults differed by state. States also differed in their campaign processes and decisions. Analyzing the campaign provides an opportunity to identify specific approaches that may result in higher vaccine uptake in a future event of this nature.
OBJECTIVE: To determine supply chain and system factors associated with higher state H1N1 vaccination coverage for adults in a system where vaccine was in short supply.
METHODS: Regression analysis of factors predicting state-specific H1N1 vaccination coverage in adults. Independent variables included state campaign information, demographics, preventive or health-seeking behavior, preparedness funding, providers, state characteristics, and H1N1-specific state data.
RESULTS: The best model explained the variation in state-specific adult vaccination coverage with an adjusted R-squared of 0.76. We found that higher H1N1 coverage of adults is associated with program aspects including shorter lead-times (i.e., the number of days between when doses were allocated to a state and were shipped, including the time for states to order the doses) and less vaccine directed to specialist locations. Higher vaccination coverage is also positively associated with the maximum number of ship-to locations, past seasonal influenza vaccination coverage, the percentage of women with a Pap smear, the percentage of the population that is Hispanic, and negatively associated with a long duration of the epidemic peak.
CONCLUSION: Long lead-times may be a function of system structure or of efficiency and may suggest monitoring or redesign of distribution processes. Sending vaccine to sites with broad access could be useful when covering a general population. Existing infrastructure may be reflected in the maximum number of ship-to locations, so strengthening routine influenza vaccination programs may help during emergency vaccinations also. Future research could continue to inform program decisions.
Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Adults; Coverage; Estimates; Factors; Pandemic; State-specific

Mesh:

Substances:

Year:  2013        PMID: 23727421     DOI: 10.1016/j.vaccine.2013.05.069

Source DB:  PubMed          Journal:  Vaccine        ISSN: 0264-410X            Impact factor:   3.641


  5 in total

1.  COVID-19 vaccine distribution planning using a congested queuing system-A real case from Australia.

Authors:  Hamed Jahani; Amir Eshaghi Chaleshtori; Seyed Mohammad Sadegh Khaksar; Abdollah Aghaie; Jiuh-Biing Sheu
Journal:  Transp Res E Logist Transp Rev       Date:  2022-05-30       Impact factor: 10.047

2.  Supply, then demand? Health expenditure, political leanings, cost obstacles to care, and vaccine hesitancy predict state-level COVID-19 vaccination rates.

Authors:  Joshua Teperowski Monrad; Sebastian Quaade; Timothy Powell-Jackson
Journal:  Vaccine       Date:  2022-09-08       Impact factor: 4.169

3.  FRED (a Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations.

Authors:  John J Grefenstette; Shawn T Brown; Roni Rosenfeld; Jay DePasse; Nathan T B Stone; Phillip C Cooley; William D Wheaton; Alona Fyshe; David D Galloway; Anuroop Sriram; Hasan Guclu; Thomas Abraham; Donald S Burke
Journal:  BMC Public Health       Date:  2013-10-08       Impact factor: 3.295

4.  Contextual generalized trust and immunization against the 2009 A(H1N1) pandemic in the American states: A multilevel approach.

Authors:  Björn Rönnerstrand
Journal:  SSM Popul Health       Date:  2016-09-10

5.  System factors to explain 2009 pandemic H1N1 state vaccination rates for children and high-risk adults in US emergency response to pandemic.

Authors:  Carlo Davila-Payan; Julie Swann; Pascale M Wortley
Journal:  Vaccine       Date:  2013-11-25       Impact factor: 3.641

  5 in total

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