Innovations in pandemic financing have been developed in response to the significant burden that a pandemic can place on a country’s financial resources. One such innovation is the World Bank’s Pandemic Emergency Financing Facility (PEF) (Katz and Seifman 2016).3 A type of disaster risk pool, the PEF provides poorly resourced countries with an infusion of funds to help with the costs of response in the early stages of an epidemic or pandemic. The maximum total coverage over a three-year period is US$500 million. Notably, the US$500 million coverage is much lower than the estimated US$3.8 billion cost of the multinational response to the 2014 West Africa Ebola epidemic (USAID and CDC 2016). Because the PEF is designed to trigger early in an outbreak, the anticipated funding is less than would be required for a full-fledged response once a widespread pandemic is under way.
Risk transfer mechanisms such as insurance offer an injection of financial resources to help insured parties rapidly scale up disease response activities. As such, the utility of risk transfer mechanisms depends, in large part, on the absorptive capacity of the insured party. A country must have the ability to use insurance payouts effectively to access additional human resources (clinicians, community health workers), personal protective equipment and other medical equipment consumables, and vaccines and therapeutics, from either domestic or international resources.
Adequacy of Evidence on Pandemics in LMICs
Much of the available data regarding pandemics (including the morbidity and mortality impacts of historical pandemics) and the effectiveness of different preparedness efforts and interventions come from HICs and upper-middle-income countries. Understanding of the prevalence of risk drivers, especially regarding spark risk, has improved markedly in both high- and low-income contexts. However, gaps in surveillance and reporting infrastructure in LMICs mean that, during a pandemic, many cases may never be detected or reported to the appropriate authorities (Katz and others 2012). Particularly in LICs, empirical data on outbreak occurrences may be biased downward systematically.
Additionally, the means to disseminate collected data rapidly may not exist. For example, data may be kept in paper archives, so resource-intensive digitization may be required to analyze and report data to a wider audience. Data dissemination challenges are further compounded by a publication bias that results in over-representation of HICs in the scientific literature (Jones and others 2008).
SUMMARY OF PANDEMIC INTERVENTION COSTS AND COST-EFFECTIVENESS
Few data are available regarding costs and cost-effectiveness of pandemic preparedness and response measures, and they focus almost exclusively on HICs. The available data suggest that the greatest cost-related benefits in pandemic preparedness and response are realized from early recognition and mitigation of disease—that is, catching and stopping sparks before they spread. Costs can be reduced if action is taken before an outbreak becomes a pandemic. Similarly, once a pandemic has begun, preventing illness generally is more cost-effective than treating illness, especially because hospitalizations typically have the highest direct cost per person. High costs also may occur as a result of interventions (such as quarantines and school closures) that lead to economic disruption. These interventions may be more cost-effective during a severe pandemic.
Program and Health System Costs
No systematic time-series data exist on global spending on pandemic preparedness, and arriving at an exact figure is complicated by the fact that many investments in building basic health system capacity also support core dimensions of pandemic preparedness. An analysis of global health spending found that roughly 1 percent of global ODA spending on health in 2013 (approximately US$204 million) focused specifically on pandemic preparedness (Schäferhoff and others 2015). Other, non-ODA spending on pandemic preparedness is similarly difficult to measure but likely to be significant; in 2013, the U.S. Department of Defense spent roughly US$256 million on efforts to build global biosurveillance and response capacities (KFF 2014).
Globally, the current funding for pandemic preparedness and response falls short of what is needed. In 2016, the international Commission on a Global Health Risk Framework for the Future recommended an additional US$4.5 billion annual global investment for upgrading pandemic preparedness at the country level, for funding infectious disease research and development efforts, and for establishing or replenishing rapid-response financing mechanisms such as the World Bank’s PEF (Sands, Mundaca-Shah, and Dzau 2016).
Costs for efforts associated with prepandemic preparedness activities also are not well quantified, although investment in One Health activities is likely to be cost-effective (World Bank 2012). The USAID PREDICT project has estimated that discovery and detection of the majority of zoonotic viruses would cost US$1.6 billion (Anthony and others 2013). The Global Virome Project, a more comprehensive study aiming to characterize more than 99 percent of the world’s viruses, is estimated to cost US$3.4 billion over 10 years (Daszak and others 2016). Building on efforts to identify and describe the ecology of potential pandemic viruses, the Coalition for Epidemic Preparedness Innovations (CEPI) estimated a cost of US$1 billion over five years to develop vaccine candidates against known emerging infectious diseases (for example, Ebola virus) and to build technology platforms and production facilities to accelerate vaccine response to outbreaks of known or unknown pathogens (Brende and others 2017).
Instituting response measures after a pandemic has begun can be expensive, with most of the direct cost borne by the health care sector, although response costs typically are not reported in a cohesive manner. As noted, the response to the 2014 West Africa Ebola epidemic cost more than US$3.8 billion, including donations from several countries (USAID and CDC 2016). Additionally, the World Bank Group mobilized US$1.6 billion from the International Development Association and the International Finance Corporation to stimulate economic recovery in the three worst-affected countries of Guinea, Liberia, and Sierra Leone (World Bank 2016). Taken together, at US$5.4 billion, these values amount to a cost of US$235 per capita for these three countries.
When total costs for response are not available, unit costs for response activities provide valuable insights. Figure 17.4 shows estimated unit costs for selected response measures, based on modeling studies for pandemic influenza in HICs. Vaccinations and medicines have the lowest unit costs; in LMICs, large-scale purchasing and subsidies could push drug costs down even more. Conversely, hospital care has the highest unit costs. Costs per day of hospitalization (especially those with ICU involvement) can add up quickly when aggregated at the national level. However, these medical care costs are potentially bounded by capacity limits (such as a finite number of hospital beds), especially during more severe pandemics.
Pandemic severity itself can play a role in the drivers of cost and the effects of mitigation efforts. One study based on modeling simulations in an Australian population found that, in low-severity pandemics, most costs borne by the larger economy (not just the health care system) come from productivity losses related to illness and social distancing. In higher-severity pandemics, the largest drivers of costs are hospitalization costs and productivity loss because of deaths (Milne, Halder, and Kelso 2013).
Costs per Death Prevented
Figure 17.5 depicts a compilation of data from 18 scientific publications that examined costs and benefits associated with response during the 2009 influenza pandemic. The lowest costs per deaths prevented were found for contact tracing, face masks, and surveillance. Pharmaceutical interventions such as vaccines and antiviral therapies were in the midrange.
Measures that decreased person-to-person contact, including social distancing, quarantine, and school closures, had the greatest cost per death prevented, most likely because of the amount of economic disruption caused by those measures. Social distancing includes avoidance of large gatherings and public places where economic activities occur. School closures often lead to lost productivity because they cause workplace absenteeism among caretakers of school-age children. Macroeconomic model simulations also have identified school closures as a potential source of GDP loss during a moderately severe pandemic (Smith and others 2009).
The information shown in figure 17.5 is subject to several caveats:
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The data come from only a few studies covering a handful of countries.
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Cost-utility analyses of pandemic preparedness and response for LMICs are rare. Because the underlying data for these studies were drawn primarily from HICs, the estimates may not accurately represent the relative benefit-cost of interventions in LMICs. For example, in countries with high unemployment and underemployment, school closures may not lead to increased workforce absenteeism and thus might have a lower cost per death prevented.
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The 2009 influenza pandemic is considered a relatively mild pandemic. In a more severe influenza pandemic, the cost per death prevented could decrease for some interventions, such as school closures.
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Results are sensitive to assumptions about the value of a prevented death and estimated costs of different interventions.
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The data cover only pandemics caused by influenza. For pandemics caused by other types of pathogens, the cost-utility values may be different, and not all intervention measures may be available.
Data on antiviral stockpiles provide some insight into how the cost utility of pandemic preparedness efforts may vary by country income level. Figure 17.6 shows the cost utility of antiviral stockpiling by country income level, based on simulation studies.
A more recent study found that antiviral stockpiling in Cambodia (a lower-middle-income country) would cost between US$3,584 and US$115,168 per death prevented; however, this result is highly sensitive to assumptions about the timing between pandemics (Drake, Chalabi, and Coker 2015).
Although based only on a handful of countries, the results suggest that antiviral stockpiling in LICs has an extremely high cost per death prevented, whereas countries at other income levels are clustered within much lower ranges. Antiviral stockpiling is not cost-effective or feasible for LICs, primarily because of the high cost of antiviral agents. For stockpiling to be a cost-effective strategy for LICs, almost all of the costs would have to be subsidized. The associated costs also may be reduced by the increased availability of generic antiviral drugs. Additionally, the efficacy of antivirals is not assured, particularly for LICs, which may not be able to identify cases early enough to administer antivirals efficaciously.
Cost-Effectiveness
Pérez Velasco and others (2012) synthesized information from 44 studies that contained economic evaluations of influenza pandemic preparedness and response strategies in HICs (figure 17.7). In their analysis, the following interventions among the general population had the potential to provide cost savings: vaccines, antiviral treatment, social distancing, antiviral prophylaxis plus antiviral treatment, and vaccines plus antiviral treatment. The cost savings from antiviral drugs found in this study are likely to be diminished in LMICs, as inability to deploy antivirals in a timely manner poses a serious challenge to their efficacious use.
Depending on the characteristics of a pandemic and how mitigation efforts are implemented, some mitigation strategies could become highly cost-ineffective. For example, a costly vaccination campaign that is carried out in an area well after a pandemic peaks is not nearly as effective in reducing transmission as having vaccines available and distributed earlier in the pandemic.
Allocation of limited resources (by creating priority groups for vaccines and antivirals) is an important consideration during a pandemic. Modeling studies from the 2009 influenza pandemic investigated the most cost-effective strategies for allocating vaccines. Those studies found that vaccinating high-risk individuals was more cost-effective than prioritizing children. Favoring children decreased the overall infection rate, but high-risk individuals were the predominant drivers of direct costs during the pandemic, because they were more likely to be hospitalized (Lee and others 2010). However, these studies did not account for the indirect costs of school closures and absenteeism. Consideration of these factors could reveal increased cost savings from vaccinating children.
Another key question for benefit-cost analyses related to pandemics is the extent to which stockpiles of vaccines, antiviral drugs, and protective equipment should be assembled in advance of a pandemic. Vaccines for a novel influenza virus can take several months to develop, and vaccines for other pathogens (for example, Ebola and Zika) can take even longer to develop. Studies have examined the cost-effectiveness of stockpiling prepandemic vaccines that have lower efficacy than reactive vaccines but can be deployed more quickly. One study found that cost savings can be obtained as long as prepandemic vaccines have at least 30 percent efficacy. However, cost-effectiveness differs by pandemic severity and the percentage of the population that receives the vaccine during the vaccination campaign (Halder, Kelso, and Milne 2014).
Antiviral drugs to fight pandemic influenza also can be stockpiled ahead of time. However, the optimal number of doses to stockpile depends on factors including the effectiveness of concurrent interventions and the likelihood of antiviral wastage on noninfluenza respiratory infections (Greer and Schanzer 2013).
Most pandemic-related benefit-cost studies focus on pharmaceutical interventions for high-income and upper-middle-income countries. The studies have largely neglected the question of how to allocate strained resources in low- and lower-middle-income countries. Furthermore, few evaluations have been conducted of the cost-effectiveness of general investment in health systems, infrastructure, and capacity building as a means to achieve pandemic preparedness (Drake, Chalabi, and Coker 2012)
