Elsevier

Journal of Theoretical Biology

Volume 300, 7 May 2012, Pages 161-172
Journal of Theoretical Biology

Economic analysis of the use of facemasks during pandemic (H1N1) 2009

https://doi.org/10.1016/j.jtbi.2012.01.032Get rights and content

Abstract

A large-scale pandemic could cause severe health, social, and economic impacts. The recent 2009 H1N1 pandemic confirmed the need for mitigation strategies that are cost-effective and easy to implement. Typically, in the early stages of a pandemic, as seen with pandemic (H1N1) 2009, vaccines and antivirals may be limited or non-existent, resulting in the need for non-pharmaceutical strategies to reduce the spread of disease and the economic impact. We construct and analyze a mathematical model for a population comprised of three different age groups and assume that some individuals wear facemasks. We then quantify the impact facemasks could have had on the spread of pandemic (H1N1) 2009 and examine their cost effectiveness. Our analyses show that an unmitigated pandemic could result in losses of nearly $832 billion in the United States during the length of the pandemic. Based on present value of future earnings, hospital costs, and lost income estimates due to illness, this study estimates that the use of facemasks by 10%, 25%, and 50% of the population could reduce economic losses by $478 billion, $570 billion, and $573 billion, respectively. The results show that facemasks can significantly reduce the number of influenza cases as well as the economic losses due to a pandemic.

Highlights

► We model an influenza epidemic where three age groups wear facemasks. ► We analyze the cost effectiveness of the use of facemasks during an epidemic. ► Facemasks can reduce the number of influenza cases as well as economic losses. ► Our analyses show facemasks could reduce economic losses by $570 billion.

Introduction

On June 11, 2009, the World Health Organization (WHO) declared the outbreak of novel influenza A (H1N1) (referred to as pandemic (H1N1) 2009 per WHO nomenclature) a pandemic. The emergence of an unexpected or novel strain of influenza poses problems in combating the spread of infection. Vaccines are typically the first line of defense against influenza viruses (Germann et al., 2006), however, in the case of novel viruses vaccines may not be readily available. In addition to vaccines, public health campaigns encouraging good hygiene have been used to reduce the spread of influenza.

During the pandemic (H1N1) 2009 outbreak several non-pharmaceutical mitigation strategies were used including school closures, social distancing, and facemasks (Condon and Sinha, 2009). Influenza spreads through person-to-person contact via airborne particles as well as by direct and indirect (e.g., via fomites) contacts. Several studies have shown that facemasks can be an effective mitigation strategy. A recent study on facemasks and hand hygiene showed a 10–50% transmission reduction for influenza-like illnesses (Aiello et al., 2010). Other studies have also shown that facemasks cannot only act as a barrier (Del Valle et al., 2010) but they can redirect and decelerate exhaled air flows to prevent them from entering the breathing zones of others (Tang and Settles, 2009). Several laboratory studies on mask effectiveness have shown that N95 respirators are 21.5% effective in protecting against the inhalation of nanoparticles, while surgical masks were only 2.4% effective (an Lee et al., 2008). However, a study by Loeb et al. (2009) found that surgical masks and N95 respirators offered about the same percentage of protection for nurses in hospitals. Although several studies have shown that both surgical masks and N95 provide similar protection against influenza, a recent editorial by Killingley (2011) discusses two studies and argues that the results are still inconclusive and that more research is needed. For our model we will focus on N95 respirators since we are interested in analyzing optimal interventions, however, our analyses may be applicable to surgical masks based on Loeb et al. (2009) results.

Using a mathematical model, Tracht et al. (2010) analyzed the effectiveness of facemasks in reducing the spread of pandemic (H1N1) 2009. They compared the impact that surgical and N95 masks could have on reducing the spread of influenza. Their results showed that facemasks can be an effective intervention strategy for mitigating an airborne disease. We expand upon that model by dividing the population into three age groups and quantifying the impact of facemasks (also referred to as N95 respirators) have on the spread of the disease as well as their cost effectiveness.

Section snippets

Methods

Following the approaches developed in Del Valle et al. (2005) and Tracht et al. (2010), the population is divided into two subgroups: a mask-wearing group (subscript M) and a non-mask wearing group. People alternate between mask and non-mask groups based on the number of individuals infected with pandemic (H1N1) 2009. We also separate the population into three different age group classifications: children between ages 0–17 (superscript 1), adults between ages 18–64 (superscript 2), and seniors

Effective reproduction number, Reff

The effective reproduction number, Reff, is the average number of secondary cases produced by a typical infectious individual during the infectious period (Hethcote, 2000, van den Driessche and Watmough, 2002). The success of mitigation strategies is measured by their ability to reduce the spread of disease. In an epidemic model the magnitude of the effective reproduction number, Reff, determines whether an epidemic occurs and its severity (Del Valle et al., 2005). When Reff>1, the disease will

Estimation of parameter values

While the use of facemasks and our model can be applicable to other viral respiratory infections, we use pandemic (H1N1) 2009 parameter values. The epidemiology of pandemic (H1N1) 2009 has been estimated by several researchers since the outbreak in May 2009 (Tuite et al., 2010, Tang et al., 2010, Yang et al., 2009, Pourbohloul et al., 2009, Center for Infectious Disease Research and Policy, 2010; Center for Disease Control and Prevention, 2009b, Centers for Disease Control and Prevention, 2010;

Results

We use this model to analyze three different scenarios, using different values for Runcavg: 1.25, 1.3, and 1.35. We also analyze three variations in mask effectiveness and evaluate each case with 10%, 25%, and 50% of susceptible and exposed individuals wearing facemasks. When 10%, 25%, and 50% of susceptible and exposed individuals are wearing masks, the fraction of infectious individuals wearing masks is 30%, 40%, and 50%, respectively. All simulations assume that there are 1800 infectious

Sensitivity analysis

The results presented above used assumptions based on the best available information, however, in order to better understand the model and its sensitivity to certain parameters, we analyzed different parameter values and scenarios. This sensitivity analysis examines the effects of age-specific compliance rates, which age groups wear masks, limiting the number of available masks, and limiting the amount of money spent on masks.

Age-specific compliance: Higher compliance rates from the adult group

Economic analysis

An influenza pandemic has the potential to have a tremendous impact on the economy; several loss estimates have been predicted (Ewers and Dauelsberg, 2007). The Congressional Budget Office estimated a 4.25% reduction in Gross Domestic Product (GDP) as the result of a severe pandemic similar to the 1918 Spanish Influenza pandemic, and a 1% drop in GDP for a more mild pandemic (Arnold et al., 2006). While there are many mitigation strategies that can be used to reduce the impact of a pandemic,

Discussion

The standard pharmaceutical mitigation strategies used during an influenza outbreak are vaccines and antivirals. In the case of a novel virus these strategies may not be readily available and can be very costly, thus, there is a need for non-pharmaceutical interventions to reduce disease spread. In the absence of vaccines, non-pharmaceutical interventions, such as hand washing and facemasks, become the first line of defense. We used a mathematical model with three different age groups to

Acknowledgments

We would like to thank Lori R. Daeulsberg her helpful comments and suggestions.

This research has been supported at Los Alamos National Laboratory under the Department of Energy contract DE-AC52-06NA25396 and a grant from NIH/NIGMS in the Models of Infectious Disease Agent Study (MIDAS) program (U01-GM097658-01).

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