Estimating averted illnesses from influenza vaccination for children and pregnant women - El Salvador, Panama, and Peru, 2011-2018

BACKGROUND

Estimating the burden of disease averted by vaccination can assist policymakers to implement, adjust, and communicate the value of vaccination programs. Demonstrating the use of a newly available modeling tool, we estimated the burden of influenza illnesses averted by seasonal influenza vaccination in El Salvador, Panama, and Peru during 2011-2017 among two influenza vaccine target populations: children aged 6-23 months and pregnant women.

METHODS

We derived model inputs, including incidence, vaccine coverage, vaccine effectiveness, and multipliers from publicly available country-level influenza surveillance data and cohort studies. We also estimated changes in illnesses averted when countries' vaccine coverage was achieved using four different vaccine deployment strategies.

RESULTS

Among children aged 6-23 months, influenza vaccination averted an estimated cumulative 2,161 hospitalizations, 81,907 medically-attended illnesses, and 126,987 overall illnesses during the study period, with a prevented fraction ranging from 0.3 % to 12.5 %. Among pregnant women, influenza vaccination averted an estimated cumulative 173 hospitalizations, 6,122 medically attended illnesses, and 16,412 overall illnesses, with a prevented fraction ranging from 0.2 % to 10.9 %. Compared to an influenza vaccine campaign with equal vaccine distribution during March-June, scenarios in which total cumulative coverage was achieved in March and April consistently resulted in the greatest increase in averted illness (23 %-3,129 % increase among young children and 22 %-3,260 % increase among pregnant women).

DISCUSSION

Influenza vaccination campaigns in El Salvador, Panama, and Peru conducted between 2011 and 2018 prevented hundreds to thousands of influenza-associated hospitalizations and illnesses in young children and pregnant women. Existing vaccination programs could prevent additional illnesses, using the same number of vaccines, by achieving the highest possible coverage within the first two months of an influenza vaccine campaign.

An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States

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.

Breed-to-wean farm factors associated with influenza A virus infection in piglets at weaning

Breed-to-wean pig farms play an important role in spreading influenza A virus (IAV) because suckling piglets maintain, diversify and transmit IAV at weaning to other farms. Understanding the nature and extent of which farm factors drive IAV infection in piglets is a prerequisite to reduce the burden of influenza in swine. We evaluated the association between IAV infection in piglets at weaning and farm factors including farm features, herd management practices and gilt- and piglet-specific management procedures performed at the farm. Voluntarily enrolled breed-to-wean farms (n = 83) agreed to share IAV diagnostic testing and farm data from July 2011 through March 2017 including data obtained via the administration of a survey. There were 23% IAV RT-PCR positive samples of the 12,814 samples submitted for IAV testing within 2989 diagnostic submissions with 30% positive submissions. Among all the factors evaluated (n = 24), and considering the season-adjusted multivariable analysis, only sow IAV vaccination and gilt IAV status at entry significantly reduced (p-value<0.05) IAV infections in piglets at weaning. Results from this study indicate that veterinarians and producers could manage these identified factors to reduce the burden of influenza in piglets prior to wean and perhaps, reduce the spread of IAV to other farms and people.