Dataset Information

Estimating Direct and Indirect Protective Effect of Influenza Vaccination in the United States.

ABSTRACT: With influenza vaccination rates in the United States recently exceeding 45% of the population, it is important to understand the impact that vaccination is having on influenza transmission. In this study, we used a Bayesian modeling approach, combined with a simple dynamical model of influenza transmission, to estimate this impact. The combined framework synthesized evidence from a range of data sources relating to influenza transmission and vaccination in the United States. We found that, for seasonal epidemics, the number of infections averted ranged from 9.6 million in the 2006-2007 season (95% credible interval (CI): 8.7, 10.9) to 37.2 million (95% CI: 34.1, 39.6) in the 2012-2013 season. Expressed in relative terms, the proportion averted ranged from 20.8% (95% CI: 16.8, 24.3) of potential infections in the 2011-2012 season to 47.5% (95% CI: 43.7, 50.8) in the 2008-2009 season. The percentage averted was only 1.04% (95% CI: 0.15, 3.2) for the 2009 H1N1 pandemic, owing to the late timing of the vaccination program in relation to the pandemic in the Northern hemisphere. In the future, further vaccination coverage, as well as improved influenza vaccines (especially those offering better protection in the elderly), could have an even stronger effect on annual influenza epidemics.

SUBMITTER: Arinaminpathy N

PROVIDER: S-EPMC5860220 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Estimating Direct and Indirect Protective Effect of Influenza Vaccination in the United States.

Arinaminpathy Nimalan N Kim Inkyu Kevin IK Gargiullo Paul P Haber Michael M Foppa Ivo M IM Gambhir Manoj M Bresee Joseph J

American journal of epidemiology 20170701 1

With influenza vaccination rates in the United States recently exceeding 45% of the population, it is important to understand the impact that vaccination is having on influenza transmission. In this study, we used a Bayesian modeling approach, combined with a simple dynamical model of influenza transmission, to estimate this impact. The combined framework synthesized evidence from a range of data sources relating to influenza transmission and vaccination in the United States. We found that, for ...[more]

PMID: 28369163

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Project description:INTRODUCTION:To evaluate the public health benefit of yearly influenza vaccinations, CDC estimates the number of influenza cases and hospitalizations averted by vaccine. Available input data on cases and vaccinations is aggregated by month and the estimation model is intentionally simple, raising concerns about the accuracy of estimates. METHODS:We created a synthetic dataset with daily counts of influenza cases and vaccinations, calculated "true" averted cases using a reference model applied to the daily data, aggregated the data by month to simulate data that would actually be available, and evaluated the month-level data with seven test methods (including the current method). Methods with averted case estimates closest to the reference model were considered most accurate. To examine their performance under varying conditions, we re-evaluated the test methods when synthetic data parameters (timing of vaccination relative to cases, vaccination coverage, infection rate, and vaccine effectiveness) were varied over wide ranges. Finally, we analyzed real (i.e., collected by surveillance) data from 2010 to 2017 comparing the current method used by CDC with the best-performing test methods. RESULTS:In the synthetic dataset (population 1 million persons, vaccination uptake 55%, seasonal infection risk without vaccination 12%, vaccine effectiveness 48%) the reference model estimated 28,768 averted cases. The current method underestimated averted cases by 9%. The two best test methods estimated averted cases with <1% error. These two methods also worked well when synthetic data parameters were varied over wide ranges (?6.2% error). With the real data, these two methods estimated numbers of averted cases that are a median 8% higher than the currently-used method. CONCLUSIONS:We identified two methods for estimating numbers of influenza cases averted by vaccine that are more accurate than the currently-used algorithm. These methods will help us to better assess the benefits of influenza vaccination.

Dataset Information

Estimating Direct and Indirect Protective Effect of Influenza Vaccination in the United States.

Publications

Estimating Direct and Indirect Protective Effect of Influenza Vaccination in the United States.

Similar Datasets

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