We analyzed strategies for the use of stockpiled antiviral drugs in the context of a future influenza pandemic and estimated cost-benefit ratios. Current stockpiling of oseltamivir appears to be cost-saving to the economy under several treatment strategies, including therapeutic treatment of patients and postexposure prophylactic treatment of patients' close contacts.

The widespread epidemic of highly pathogenic avian influenza that emerged in east Asia continues today. As the epidemic grows, so does the probability that this virulent virus will acquire genetic traits for increased person-to-person transmissibility, potentially setting the stage for the next global influenza pandemic (

The next pandemic will be associated with major adverse health and economic outcomes, with estimated costs reaching US$166 billion in the United States alone (

We estimated the health-related impact of pandemic influenza on the Israeli population, by using rates (illness, physician visits, hospitalizations, and deaths) derived from previous pandemics, according to Meltzer et al. (

Variable | Point estimate | Range |
---|---|---|

Overall attack rate, %† | 25 | 15–35 |

Probability of pandemic (per year) | 3 | 1–10 |

Adult workdays lost, by age, y | ||

<1–≤18 | 3.7 | 2–5 |

19–64 | 4.9 | 3–7 |

≥65 | 0.5 | 0.25–2 |

Average hospital stay (days) by age, y | ||

<1–≤18 | 4.0 | 2–5 |

19–64 | 5.8 | 2–7 |

≥65 | 7.0 | 4-9 |

Patients seeking medical care within 48 h, % | 80 | 70–90 |

Efficacy of antiviral prophylaxis, % | ||

Preexposure prophylaxis (50 days) | 71 | 57–85 |

Postexposure prophylaxis (7 days) | 36 | 25–47 |

Efficacy of antiviral therapy, % | ||

Reduction in hospitalizations | 59 | 30–70 |

Reduction in antimicrobial drug use | 63 | 40–80 |

Reduction in lost workdays under treatment | 1 | 0.5–1.5 |

*Complete list and references available in online Appendix. †Population attack rate was calculated by stratifying the population by age and risk and applying age- and risk-specific attack rates and ranges (Appendix).

According to base-case assumptions, a pandemic would result in an estimated 1,618,200 patients (≈25% of the Israeli population), 781,921 physician visits, 10,334 hospitalizations, 2,855 deaths, and 6,536,240 lost workdays. These outcomes would result in an excess of $55.4 million in health-related costs and in overall costs to the economy of $523.5 million (≈0.5% of the Israeli gross domestic product).

We defined 3 strategies for the use of antiviral drugs during a pandemic: therapeutic use, long-term preexposure prophylaxis, and short-term postexposure prophylaxis for close contacts of influenza patients (with index patients under treatment). The first 2 strategies could target either the entire population or only those at high risk for complications. The efficacy of therapeutic treatment was based on currently available evidence regarding epidemic influenza (Appendix). Systematic review and meta-analysis were used to estimate the efficacy of preexposure prophylaxis, while the expected efficacy of postexposure prophylaxis and the number of persons treated under this strategy were estimated by using the results of a recently published stochastic simulation model (

The impact of each strategy on health-related outcomes was analyzed in a spreadsheet model by using the formulas summarized in

We compared the economic outcomes of each of the 5 strategies with nonintervention, estimated stockpiling costs, and calculated cost-benefit ratios. Based on the historic incidence of 3 influenza pandemics over the last century, we adjusted all cost-benefit outcomes for a conservatively estimated probability of 3 pandemics every 100 years and applied a wide range of estimates for sensitivity analyses (Appendix).

Strategy | Cost-benefit ratio, relative to nonintervention | ||
---|---|---|---|

All costs to economy | Direct healthcare costs | ||

NI | No intervention (base case) | Ref. | Ref. |

1a | Therapeutic use (all patients) | 2.44 | 0.30 |

1b | Therapeutic use (limited to patients at high risk) | 3.68 | 1.51 |

2a | Preexposure long-term prophylaxis of entire population | 0.38 | 0.04 |

2b | Preexposure long-term prophylaxis, limited to high-risk population | 0.37 | 0.10 |

3a | Postexposure short-term prophylaxis for all close contacts ("ring prophylaxis"), including treatment of index patients | 2.49 | 0.27 |

*Ref., reference value of zero divided by zero.

Since many characteristics of the next pandemic viral strain remain unknown, our modeling methods and parameter estimates were designed to consistently underestimate intervention-related benefits, thus yielding minimum estimates of the true cost-benefit ratios. In a series of multivariate sensitivity analyses that used the variable ranges detailed in

In light of recent episodes of human infection with avian influenza, the World Health Organization reiterated its 1997 call for all countries to prepare for the next "inevitable, and possibly imminent" pandemic (

Our model suggests that prepandemic stockpiling of oseltamivir is cost-saving to the economy over a wide range of treatment strategies. Stockpiling is also directly cost-saving to the healthcare system, if oseltamivir use is limited to treating patients at high risk. Investment in stockpiling remains cost-saving to the economy as long as the estimated annual pandemic risk remains >1 pandemic every 80 years. In the last 400 years, at least 31 pandemics have been recorded (

This favorable cost-benefit ratio can be achieved if stockpiled antiviral drugs are administered either solely as a therapeutic measure or as short-term prophylaxis for exposed contacts, a strategy termed "ring prophylaxis" (

When one considers a ring prophylaxis strategy, the risk of "strategy failure" due to early antiviral stockpile depletion must be considered. If postexposure prophylaxis does not confer sufficient immunity upon exposed contacts who underwent prophylaxis, and if vaccines or additional antiviral agents do not become available, rapid consumption of available stocks may leave the population vulnerable to additional outbreak waves, potentially caused by influx of new cases. The probabilities of similar "failure" scenarios are difficult to assess and were not included in our analysis. Application of this strategy for the entire population without surplus antiviral reserves should therefore be considered cautiously and monitored closely.

This study aimed to elicit minimum cost-benefit estimates for investment in a national antiviral stockpile. Among our conservative assumptions, we chose to exclude indirect costs of preventable deaths, which, if added, would have increased cost-benefit ratios up to 6-fold (Appendix). Furthermore, in view of recent events in east Asia, the probability of a pandemic has probably risen to >3 per 100 years, and new strains may prove more pathogenic than previous pandemic strains. In modeling the benefits of therapeutic strategies, we omitted the beneficial effects of decreased viral shedding afforded by neuraminidase inhibitors (

The conclusions of this study must be considered carefully during the planning of antiviral stockpiling. Drug prices can be expected to change substantially as a result of contractual negotiations with manufacturers (although our results indicate stockpiling may remain cost-saving even if drug costs are more than tripled, as would be the case if preprepared capsules are purchased). Powder-form antiviral drugs have considerable advantages in terms of cost and shelf life, but the logistical aspects of their preparation and distribution should be further assessed to confirm feasibility. Finally, we assumed that strain-specific vaccine would not be available in sufficient quantities during the first stages of the pandemic. Efforts are currently being directed towards shortening this delay. Once available, strain-specific vaccines would likely be the favored intervention, with antiviral agents serving as adjunct treatment.

In summary, prepandemic stockpiling of antiviral drugs can be expected to prove cost-saving. Cost-beneficial strategies for their use may involve treatment of patients, and, if backed by adequate antiviral stockpiles, short-term postexposure prophylaxis of close contacts. These strategies should be considered when planning stockpiling efforts.

Several countries have already begun active stockpiling efforts (

We used a static spreadsheet model (Microsoft Excel 2000, Microsoft Corporation, Redmond, WA, USA) to estimate the effect of pandemic influenza on the Israeli population. We defined 3 separate strategies for the use of antiviral drugs during a pandemic, analyzed the effect of these strategies on pandemic outcomes, and estimated the economic consequences of each scenario.

We divided the population (6,748,000 in Israel at the end of 2003) into 3 age categories: ≤18 years, 19–64 years, and ≥65 years (

We constructed a baseline nonintervention scenario by using age- and risk-specific rates to estimate the expected numbers of patients, physician visits, hospitalizations, deaths, and lost work days (

Economic Outcomes

Point estimates of economic variables used in the base-case model are detailed in

According to previous assessments by Meltzer et al. that addressed a similar scenario (

Drug resistance may appear in approximately one third of patients treated with the M2 inhibitors amantadine or rimantadine (

We adjusted all cost-benefit outcomes for the estimated probability of a pandemic occurring per year. We based our calculations on the recent historic incidence of 3 influenza pandemics over the last century, thus adopting a conservative point estimate of 1 pandemic every 33 years for the base-case and applied a wide range of estimates for sensitivity analyses.

The baseline scenario, modeled by using estimates derived by Meltzer et al. from previous pandemics (

We assumed that strain-specific vaccine would be unavailable during the initial months of a pandemic. If appropriately stockpiled, antiviral drugs can be directed either at therapy or prophylaxis. Prophylactic strategies can be divided into long-term preexposure prophylaxis and short-term, epidemiologically directed postexposure prophylaxis. We compared each of 3 strategies with nonintervention, alternately targeting either the entire population or only populations at high risk for complications.

When given therapeutically to influenza patients within 48 hours of symptom onset and if continued for 5 days, neuraminidase inhibitors can reduce the duration of clinical symptoms by an average of 1 day (

We evaluated 2 variations of therapeutic antiviral use: nonselective treatment available to all patients (strategy 1A) and selective treatment limited to use in patients at high risk (strategy 1B). We adjusted this model for treatment initiation rates, since not all patients would be expected to reach a physician and initiate treatment within the 48-hour window of therapeutic opportunity. The proportion of patients likely to seek physician care was estimated by using data available from studies of interpandemic influenza in Israel (

We evaluated 2 variations of preexposure antiviral use: mass prophylaxis made available to the entire population (strategy 2A) and selective prophylaxis, limited to groups at increased risk for complications (strategy 2B). We assumed a pandemic duration of 50 days (

We performed a systematic review and meta-analysis of studies evaluating the protective efficacy of neuraminidase inhibitors when used for preexposure (seasonal) prophylaxis. The methods and results of this meta-analysis are described below.

A computerized search was conducted by using MEDLINE (January 1966–December 2004) and Embase (January 1980–December 2004) databases. The following combination of keywords was used: (influenza) and ([oseltamivir or Tamiflu] or [zanamivir or Relenza]) and (prevention or prophylaxis or chemoprophylaxis). This search was limited to articles published in English. In addition, we searched these databases using the names of authors of studies identified in the primary search and in the studies' reference sections. We also contacted drug companies for information on unpublished trials.

Our search identified 24 candidate papers. These papers were then independently reviewed by

The 2 authors who reviewed the papers also abstracted information from each of the selected studies. In cases in which >1 article was published on the same study, all articles were assessed for data consistency. All data were abstracted by using a standardized protocol and computerized report form. Reported relative risk (RR), or incidence data necessary for computing RR, were abstracted based on intent-to-treat analysis.

For all studies included in the analysis, cases were defined as clinical influenza confirmed by isolation of influenza virus, by reverse-transcriptase polymerase chain reaction (RT-PCR), or by testing paired serum samples for rise in antibody titer against circulating influenza virus. For each study, we calculated the RR and 95% confidence interval (CI) for influenza infection in the intervention group compared to the control group. We calculated the overall RR for preexposure prophylaxis. Since oseltamivir and zanamivir are both neuraminidase inhibitors and the results of the studies that evaluated these drugs were comparable, we combined the results of these studies in our overall estimate.

In each study, 95% CIs of the RR were calculated by using Breslow's method (

Heterogeneity of RRs across n studies was tested with the formula

^{2}heterogeneity = ΣW

_{i}M

_{i}

^{2}– (ΣW

_{i}M

_{i})

^{2}/ ΣW

_{i}

where M_{i} is an individual measure of association and W_{i} is a weighting factor equal to the reciprocal of the squared individual variance. Significance was evaluated with n – 1 degrees of freedom.

Since only 2 studies of preexposure prophylaxis were included, no sensitivity analysis was applied. All computations were performed by using PEPI software for epidemiologic analysis (

The results of each study and overall estimate of preexposure studies are presented in

In this scenario, stockpiled antiviral agents are administered as short-term prophylaxis to exposed close contacts in addition to treating the index patients, a strategy termed "ring prophylaxis" (

While these estimates are the only ones published to date on the efficacy of this strategy during an influenza pandemic, the inherent limitations of stochastic models such as the one used by Longini et al. must be acknowledged when this strategy is considered in practice. The authors of that study modeled communities of 2000 people, with predefined mixing patterns between subpopulations and a relatively limited number of "outsiders" entering the population. The paucity of evidence-based data on contact and transmission probabilities regarding both epidemic and pandemic influenza, as well as the complexity of real-life mixing patterns within a large population, may lead to substantial alterations in the efficacy of targeted prophylaxis, which cannot be modeled. Longini et al. have shown that these estimates are sensitive to various (currently unpredictable) parameters of the population and the pandemic strain, such as population compliance, delay in treatment initiation, and the outbreak basic reproductive number (

Under strategies 2 and 3, clinical influenza would develop in a varying proportion of participants who received prophylaxis despite treatment (breakthrough cases). We assumed that these patients, although becoming ill, would nonetheless benefit from the neuraminidase inhibitors that they had been receiving, and they were credited with the effects of therapeutic neuraminidase inhibitor treatment, such as shorter duration of illness and fewer hospitalizations and deaths (see strategy 1).

Very little evidence-based data are currently available to allow accurate predictions regarding the effect of the next influenza pandemic. This model, as well as similar published models that attempted to make inferences regarding an impending pandemic, is based on estimates derived mainly from sparse data on previous pandemics and on the characteristics of interpandemic influenza. We have systematically chosen the most conservative estimates available in the literature regarding the different parameters, but such estimates are still associated with much uncertainty. A series of sensitivity analyses was therefore conducted to establish the robustness of the various outcomes of this model. These analyses applied a wide range of values for the parameters relating to pandemic probability, health-related pandemic outcomes, and antiviral drug efficacy. We considered the economic parameters as relatively stable, as they are based on verifiable data.

The sensitivity analysis examined the effect that the modification of each parameter had on the main outcomes (cost-benefit ratios), thus assessing to which of the variables the model was most sensitive. This analysis showed that the most important parameters were the severity of the pandemic (illness and death rates) and the annual probability of a pandemic. Reducing the annual probability of a pandemic to 1 every 100 years, only the strategy of treating patients at high risk represented a cost savings, with a cost-benefit ratio of 1.23. When a combination of low-range estimates (according to Meltzer et al.) of the various health-related pandemic outcomes modeled were assumed, the cost-benefit ratios of therapeutic treatment and postexposure prophylaxis were decreased, but these strategies remained cost-saving, with cost-benefit ratios of >2.27 and 1.47, respectively. We then analyzed our data to define the ranges of cost-benefit ratios when applying various combinations of the parameters in

We thank Alex Leventhal and Shmuel Reznikovitch for their support and contribution to this work.

Variable by age | Low risk | High risk | |||
---|---|---|---|---|---|

Point estimate | Range | Point estimate | Range | Reference | |

Attack rates (%) | |||||

Overall | 25 | 15–35 | 25 | 15–35 | |

≤18 | 38.50 | 23–54 | 38.50 | 23–54 | |

19–64 | 17.50 | 11–25 | 17.50 | 11–25 | |

≥65 | 15.50 | 9–22 | 15.50 | 9–22 | |

Outpatient visits (per person) | |||||

≤18 | 0.1975 | 0.165–0.23 | 0.346 | 0.259–0.403 | |

19–64 | 0.0625 | 0.04–0.085 | 0.1095 | 0.07–0.149 | |

≥65 | 0.0595 | 0.045–0.074 | 0.1045 | 0.079–0.13 | |

Hospitalizations (per 1,000) | |||||

≤18 | 0.5 | 0.2–2.9 | 2.9 | 2.1–9.0 | |

19–64 | 1.465 | 0.18–2.75 | 2.985 | 0.83–5.14 | |

≥65 | 2.25 | 1.5–3.0 | 8.5 | 4.0–13.0 | |

Deaths (per 1,000) | |||||

≤18 | 0.024 | 0.014–0.125 | 0.22 | 0.126–7.65 | |

19–64 | 0.037 | 0.025–0.09 | 2.91 | 0.1–5.73 | |

≥65 | 0.42 | 0.28–0.54 | 4.195 | 2.76–5.63 | |

Adult workdays lost | |||||

≤18 | 3.7 | 2–5 | 3.7 | 2–5 | |

19–64 | 4.9 | 3–7 | 4.9 | 3–7 | |

≥65 | 0.5 | 0.25–2 | 0.5 | 0.25–2 | |

Average hospital stay (d) | |||||

≤18 | 4.0 | 2–5 | 4.0 | 2–5 | |

19–64 | 5.8 | 2–7 | 5.8 | 2–7 | |

≥65 | 7.0 | 4–9 | 7.0 | 4–9 |

Variable | Point estimate (US$) | Reference |
---|---|---|

Average daily wage* | 71.6 | |

Cost of hospital day* | 317.8 | |

Cost of physician visit*† | 45.3 | |

Antiviral (NAI) drug costs‡ | ||

5-day therapeutic course | 8.9 | Roche, pers. comm. |

7-day prophylactic course | 6.2 | Roche, pers. comm |

*Converted from original prices in NIS (new Israeli shekels) at the rate of US$1 = 4.50 NIS. †Physician costs include prescription drugs and diagnostic tests. ‡NAI, neuraminidase inhibitors. Based on manufacturer's quoted price for oseltamivir.

Variable | Point estimate | Range | Reference |
---|---|---|---|

Efficacy of antiviral prophylaxis* | |||

Preexposure prophylaxis† | 71 | 57–85 | |

Postexposure prophylaxis† | 36 | 25–47 | |

Efficacy of antiviral therapy | |||

% reduction in hospitalization | 59 | 30–70 | |

% reduction in antimicrobial drug use | 63 | 40–80 | |

Reduction in lost work days under treatment | 1 | 0.5–1.5 | |

Annual probability of pandemic (%) | 3 | 1–10 | Assumed |

% patients seeking medical care within 48 hours | 80 | 70–90 |

*Efficacy presented as percent reduction of attack rate. †Based on a meta-analysis of 2 studies (see text for details).

Trial | Age group, y | Intervention (no. participants) | Treatment duration |
---|---|---|---|

Hayden et al., 1999 (11) | 18–65 | Oseltamivir 75 mg 1×/d (520) | 6 weeks |

Oseltamivir 75 mg 2×/d (520) | |||

Placebo (519) | |||

Monto et al., 1999 (15) | 18–69 | Zanamivir 10 mg 1×/d (553) | 4 weeks |

Placebo (554) |

Trial | Drug | RR (95% CI) | Protective efficacy (%) | p value for heterogeneity |
---|---|---|---|---|

Hayden et al. (11) | Oseltamivir | 0.26 (0.13–0.50) | 74 | |

Monto et al. (15) | Zanamivir | 0.32 (0.17–0.63) | 68 | |

Overall | 0.29 (0.20–0.43) | 71 | 0.643 |

Probability of pandemic | Estimates of health-related pandemic outcomes* | ||||||||
---|---|---|---|---|---|---|---|---|---|

Minimum | Base case | Maximum | |||||||

1/100 y | 1/33 y | 1/10 y | 1/100 y | 1/33 y | 1/10 y | 1/100 y | 1/33 y | 1/10 y | |

Strategy | |||||||||

Therapeutic use | |||||||||

All patients | 0.71–0.79 | 2.13–2.37 | 7.10–7.92 | 0.73–0.87 | 2.18–2.62 | 7.25–8.74 | 0.74–0.96 | 2.23–2.88 | 7.42–9.61 |

Patients at high risk | 0.80–1.21 | 2.40–3.63 | 7.99–12.09 | 0.84–1.50 | 2.51–4.49 | 8.36–14.96 | 0.88–1.74 | 2.63–5.23 | 8.76–17.42 |

Preexposure long-term prophylaxis | |||||||||

Entire population | 0.04–0.11 | 0.13–0.34 | 0.42–1.13 | 0.07–0.19 | 0.21–0.58 | 0.71–1.92 | 0.10–0.28 | 0.30–0.83 | 1.00–2.76 |

Patients at high risk | 0.04–0.10 | 0.11–0.30 | 0.38–1.02 | 0.07–0.18 | 0.20–0.55 | 0.67–1.85 | 0.10–0.28 | 0.30–0.83 | 0.99–2.77 |

Postexposure short-term prophylaxis | 0.18–1.14 | 0.54–3.42 | 1.82–11.40 | 0.30–1.94 | 0.91–5.81 | 3.04–19.37 | 0.43–2.78 | 1.30–8.35 | 4.32–27.83 |

*Base-case estimates refer to the point estimates detailed in

Formulas used to analyze the impact of each strategy on health-related outcomes.

Dr. Balicer is a public health physician in the Israeli Defense Force Medical Corps, currently working for the Israeli Ministry of Health. He serves as co-editor of the Israeli preparedness plan for pandemic influenza and is affiliated with Ben-Gurion University of the Negev.