Stockpiling drugs to prevent and treat influenza would be economically effective.
We compared strategies for stockpiling neuraminidase inhibitors to treat and prevent influenza in Singapore. Cost-benefit and cost-effectiveness analyses, with Monte Carlo simulations, were used to determine economic outcomes. A pandemic in a population of 4.2 million would result in an estimated 525–1,775 deaths, 10,700–38,600 hospitalization days, and economic costs of $0.7 to $2.2 billion Singapore dollars. The treatment-only strategy had optimal economic benefits: stockpiles of antiviral agents for 40% of the population would save an estimated 418 lives and $414 million, at a cost of $52.6 million per shelf-life cycle of the stockpile. Prophylaxis was economically beneficial in high-risk subpopulations, which account for 78% of deaths, and in pandemics in which the death rate was >0.6%. Prophylaxis for pandemics with a 5% case-fatality rate would save 50,000 lives and $81 billion. These models can help policymakers weigh the options for pandemic planning.
Ten percent of the world's population and 20% of the population of tropical Singapore are infected with influenza virus annually (
Studies have compared the cost-effectiveness of vaccination versus treatment with antiviral agents (
This study used a decision-based model (
Decision-based model for strategies during pandemic influenza.
Cost-benefit analyses were used to compare treatment-only and prophylaxis strategies to taking no action. These analyses included direct and indirect economic costs, such as the cost of death. However, quantifying the societal cost of death is difficult, and cost-effectiveness analyses based on cost per life saved by treatment only and prophylaxis, compared to no action, were included. The model was run by using Excel spreadsheets (Microsoft Corp, Redmond, WA, USA); details are shown in the Appendix and on Tan Tock Seng Hospital's website (
Pandemic influenza is unpredictable: uncertainties surround its occurrence and outcomes (
| Input variables | Age ranges, y | ||||
|---|---|---|---|---|---|
| <19 | 20–64 | >65 | Sources | ||
| Average age | 10 | 40 | 73 | ||
| Population, ×1,000 persons | 999.2 | 2,962.5 | 278.6 | ||
| Low risk, % | 90 | 89.7 | 63.3 | ||
| High risk, %‡ | 10 | 10.3 | 36.7 | ||
| Baseline influenzalike illness rate, cases/wk | 7,686 | 19,940 | 750 | ||
| Influenza clinical attack rate, % (range) | 30 (10–50) | 30 (10–50) | 30 (10–50) | ||
| Case-fatality rate/100,000§ | Ministry of Health | ||||
| Low risk | 5 (1–12.5) | 6 (1–9) | 340 (28–680) | ||
| High risk | 137 (12.6–765) | 149 (10–570) | 1,700 (276–3,400) | ||
| Earnings lost per death, $¶ | 1,909,092 | 1,780,027 | 187,301 | ||
| Hospitalization rate/100,000 infected# | Ministry of Health | ||||
| Low risk | 210 (42–525) | 72 (12–108) | 1,634 (135–3,268) | ||
| High risk | 210 (100–1,173) | 234 (16–895) | 2,167 (352–4,334) | ||
| Average length of hospital stay, d | 3.88 (2.3–9.2) | 4.61 (3.2–11.8) | 6.20 (4.6–13.4) | ||
| Average additional days lost | 2 (1–3) | 2 (1–3) | 2 (1–3) | Local physicians | |
| Hospital cost, $/d | 342 | 342 | 342 | Ministry of Health | |
| Value of 1 lost day, $** | 108 | 166/108 | 108 | Ministry of Health, | |
| Outpatient | |||||
| Days lost from outpatient influenza | 3 (1–5) | 3 (1–5) | 3 (1–5) | ||
| Consultation and outpatient treatment cost, $ | 40 | 40 | 40 | Local physicians | |
| Value of 1 lost day, $** | 108 | 166 | 108 | Ministry of Health | |
| Treatment with oseltamivir | |||||
| Sought early medical care, % | 70 (50–90) | 70 (50–90) | 70 (50–90) | ||
| Case-fatality rate reduction, % | 70 (50–90) | 70 (50–90) | 30 (20–90) | ||
| Hospitalization rate reduction, % | 60 (50–90) | 60 (50–90) | 30 (20–90) | ||
| Lost days gained, d | 1.0 (0.1–2.0) | 1.0 (0.1–2.0) | 1.0 (0.1–2.0) | ||
| Treatment cost, $ per course | 31 | 31 | 31 | Ministry of Health | |
| Prophylaxis with oseltamivir | |||||
| Efficacy of prophylaxis, % | 70 (50–90) | 70 (50–90) | 70 (50–90) | ||
| Immunity after prophylaxis, % | 35 (20–50) | 35 (20–50) | 35 (20–50) | ||
| Prophylaxis cost, $/wk | 21.7 | 21.7 | 21.7 | Ministry of Health | |
| No. stockpile cycles to pandemic | 2.25 (1–3.5) | 2.25 (1–3.5) | 2.25 (1–3.5) | ||
| Pandemic duration, wk | 12 (6–24) | ||||
| Treatment stockpile, % of population†† | 10–100 | ||||
| Prophylaxis stockpile, wk†† | 2–24 | ||||
*All healthcare costs are in 2004 Singapore dollars and were compounded by using the consumer price index for Singapore (
Treatment stockpiles, based on proportions of the population, are used on all influenzalike-illness cases, from pandemic plan activation until the pandemic ceases or the stockpile is depleted, whichever comes first. Analysis was conducted to determine the proportion of untreated influenza patients and simulation iterations with complete coverage, by stockpile levels. Further analysis was then performed for prophylaxis stockpiles where prophylaxis, by weeks, is given to the population over and above treatment requirements.
Input variables are shown in
The clinical attack rates during the 1918 and 1957 pandemics were 29.4% and 24%, respectively (
Case-fatality rates were derived from Singapore's excess deaths from interpandemic influenza; hospitalization and death were assumed to occur only in clinical influenza. To reflect hospitalization rates in relation to case-fatality rates, both rates were correlated. For outpatient visits, clinical influenza patients were assumed to seek medical care and take medical leave. However, some patients may not be treated effectively within 48 hours of infection, and they were assumed not to benefit from treatment.
For pandemic duration, influenza activity in tropical climates commonly rises above the baseline for >12 weeks (
Individual economic value was calculated from the net present value of future earnings for average-aged persons in the respective age groups, adjusted for age. Other costs included were hospitalizations and work days lost; all costs were standardized to 2004 Singapore dollars.
This study relied on international studies on oseltamivir. Oseltamivir has a good safety profile with insignificant rates of severe adverse events and drug withdrawal (
Stockpile use depends on the probability of an influenza pandemic occurring. Antigenic shifts and reappearances of past variants were estimated to have pandemic potential every 8–10 years (
If no action were taken during a pandemic, the mean number of simulated deaths in Singapore would be 1,105 (5th and 95th percentiles of 525 and 1,775), with mean hospital days of 23,098 (10,736, 38,638). The mean economic cost would exceed SGD$1.43 billion (0.73, 2.19), and 78% of all deaths would occur in groups at high risk. From the sensitivity analyses, the outcome was most sensitive to changes in attack rate and case-fatality rate reduction with treatment and was sensitive to the variables of treatment and prophylaxis stockpiles.
| % stockpile | Cost of stockpile (1 cycle, million $) | Overall % untreated influenza cases | % iterations with complete treatment | Lives saved | Overall benefit over no action (million $) |
|---|---|---|---|---|---|
| No action | NA | 100 | 0 | Deaths: 1,105 (525, 1,775) | Cost: 1,430 (730, 2,193) |
| 10 | 13.1 | 89.1 | 0 | 49 (18, 108) | 24 (–4, 73) |
| 20 | 26.3 | 42.0 | 0 | 249 (128, 412) | 224 (103, 385) |
| 30 | 39.4 | 9.0 | 15 | 386 (185, 645) | 385 (165, 619) |
| 40 | 52.6 | 0.01 | 55 | 418 (185, 730) | 414 (145, 759) |
| 50 | 65.7 | <0.01 | 90 | 422 (185, 744) | 399 (122, 761) |
| 60 | 78.9 | 0 | 100 | 422 (185, 744) | 376 (98, 743) |
| 70 | 92.0 | 0 | 100 | 422 (185, 744) | 353 (76, 721) |
| 80 | 105.2 | 0 | 100 | 422 (185, 744) | 330 (52, 700) |
| 90 | 118.3 | 0 | 100 | 422 (185, 744) | 307 (26, 676) |
| 100 | 131.4 | 0 | 100 | 422 (185, 744) | 285 (4, 654) |
*Mean values are shown with 5th and 95th percentiles in parentheses; NA, not available. †All healthcare costs are in 2004 Singapore dollars.
The population cost-benefit and cost-effectiveness outcomes from the Monte Carlo simulation analyses are shown in
| Strategy option | Stockpile cost (1 cycle, million $) | Lives saved compared with no action | Cost per life saved compared with no action ($100,000) | Benefit compared with no action (million $) |
|---|---|---|---|---|
| No action | Not applicable | Deaths: 1,105 (525, 1,775) | Not applicable | Cost: 1,430 (730, 2,193) |
| Only Rx‡ | 79 | 423 (183, 756) | 38 (dominates§, 395) | 379 (89, 734) |
| 6 wk¶ | 631 | 492 (216, 870) | 2,246 (811, 4,676) | –487 (–925, 48) |
| 12 wk¶ | 1183 | 684 (286, 1,264) | 3,193 (1,008, 6,788) | –1,188 (–1,934, –265) |
| 18 wk¶ | 1735 | 850 (377, 1,442) | 3,668 (1,358, 7,363) | –1,920 (–2,941, –783) |
| 24 wk¶ | 2,287 | 903 (425, 1,509) | 4,516 (1,828, 9,022) | –2,811 (–4,070, –1,384) |
*Mean values are shown with 5th and 95th percentiles in parentheses. †All healthcare costs are in 2004 Singapore dollars. ‡Only Rx refers to treatment only, without prophylaxis. §Treatment-only dominates no action because treatment-only saves lives and is less costly overall. ¶No. of weeks of prophylaxis for the respective risk and age groups.
Lives saved compared with no action, by prophylaxis levels. Mean, 5th, and 95th percentiles based on Monte Carlo simulations are shown.
| Risk and age group, y | Strategy option | Stockpile cost (1 cycle, million $) | Mean lives saved compared with no action | Mean cost per life saved compared with no action (million $) | Mean benefit compared with no action (million $) |
|---|---|---|---|---|---|
| Low risk, age <1–19 | No action | NA | Deaths: 17 | NA | Cost: 122 |
| Only Rx † | 17 | 8 | Dominates§ | 87 | |
| 12 wk ‡ | 251 | 11 | 41 | –315 | |
| 24 wk ‡ | 485 | 14 | 70 | –717 | |
| Low risk, age 20–64 | No action | N/A | Deaths: 42 | N/A | Cost: 507 |
| Only Rx | 49 | 21 | Dominates§ | 382 | |
| 12 wk | 741 | 29 | 40 | –808 | |
| 24 wk | 1,433 | 36 | 73 | –1,999 | |
| Low risk, age >65 | No action | NA | Deaths: 185 | NA | Cost: 57 |
| Only Rx | 3 | 60 | Dominates§ | 28 | |
| 12 wk | 49 | 108 | 0.9 | –43 | |
| 24 wk | 95 | 148 | 1.3 | –115 | |
| High risk, age >1–19 | No action | NA | Deaths: 92 | NA | Cost: 186 |
| Only Rx | 2 | 45 | Dominates§ | 94 | |
| 12 wk | 28 | 63 | 1.0 | 83 | |
| 24 wk | 54 | 78 | 1.8 | 66 | |
| High risk, age 20–64 | No action | NA | Deaths: 220 | NA | Cost: 443 |
| Only Rx | 6 | 109 | Dominates§ | 235 | |
| 12 wk | 85 | 153 | 1.1 | 175 | |
| 24 wk | 165 | 189 | 2.0 | 100 | |
| High risk, age >65 | No action | NA | Deaths: 547 | NA | Cost: 117 |
| Only Rx | 2 | 179 | Dominates§ | 44 | |
| 12 wk | 29 | 321 | 0.17 | 24 | |
| 24 wk | 55 | 438 | 0.25 | 0.1 |
*Mean values are shown, with all costs in 2004 Singapore dollars; NA, not applicable. †Only Rx refers to treatment-only, without prophylaxis. ‡12 and 24 wk refer to number of weeks of prophylaxis for the respective risk and age groups. §Treatment-only dominates no action because treatment-only saves lives and is less costly overall.
The simulated proportion of decisions with treatment only or 24 weeks' prophylaxis as the optimal outcome is shown in
Proportion of decisions for treatment or 24 weeks prophylaxis, by case-fatality rate.
The analyses suggest that treatment is always beneficial compared to no action and that the optimal treatment stockpile is 40%–60%: 40% maximizes economic benefits, while 60% maximizes treatment benefits. Compared to other strategies, treatment-only was the optimal economic strategy, while no action was always the least desirable option. Although treatment-only saved fewer lives than prophylaxis, stockpiling costs for treatment were lower. Prophylaxis was only economically beneficial compared with no action in subpopulations at high risk.
Substantial outcomes with prophylaxis occurred with durations of >4 weeks because shorter durations prolonged the pandemic, were insufficient for immunity, and did not cover the pandemic's peak. Increasing duration improved outcomes because it covered the pandemic's peak, but the improved outcomes tapered off after 20 weeks, resulting in a sigmoid curve (
In low-risk groups with low death and hospitalization rates, increasing prophylaxis duration decreased economic benefit and increased cost per life saved. In contrast, groups at high risk, who had higher death and hospitalization rates, were affected substantially by prophylaxis, resulting in overall benefits compared to taking no action. Elderly groups had the smallest populations but the highest risk levels and most deaths. However, their lower average future earnings compared to those of younger age groups resulted in lower overall benefits.
This study of pandemic outcomes in a tropical climate is similar to an Israeli study that compared treatment and prophylaxis strategies (
Limitations of this study include the disregard for intangible costs, such as societal value of health; cost-utility analyses could address these costs. Also, indirect effects on national economy and world trade were not considered. For comparability, neither treatment nor prophylaxis was assumed to alter the pandemic's transmission dynamics. This assumption may be true if therapy is limited to small subpopulations, but it understates the benefits if infection is delayed until the pandemic is resolved or vaccine becomes available; it overestimates the benefits if the pandemic continues (
Stockpiling is insurance in planning for pandemics with high case-fatality rates, in which more severe outcomes and higher risks demand higher premiums. Policymakers should consider lives saved even if economic costs outweigh incremental benefits. Prophylaxis of high-risk groups balances saving lives with economic benefits. Prophylaxis also reduces hospitalizations, which may otherwise overwhelm the healthcare system. Analysis of peak pandemic healthcare use is required to determine the effects of prophylaxis. Other options to reduce a pandemic's impact, including reducing influenza attack rates by quarantine or closing borders, should be considered as alternative strategies.
The current avian influenza (H5N1) outbreak in Asia, which has a high case-fatality rate, indicates the need for decisive action. Oseltamivir is effective against H5N1 and is used as treatment in Vietnam (
The decision to stockpile requires predetermined objectives; noneconomic, moral, and ethical implications should be considered. Treatment-only maximizes economic benefits, while prophylaxis saves most lives. Policymakers have to act decisively, and determine the subpopulations to be given priority, to enable preparedness plans to succeed.
Antiviral stockpiles will be used on clinical influenza cases according to the pandemic distribution curve, assumed to be normally distributed (
The population proportion with clinical influenza left untreated because of treatment stockpile deficiencies is calculated as follows:
No. of doses required = (influenzalike illness per week × pandemic duration) + no. of clinical influenza cases
Shortfall of doses for treatment = no. of doses required – no. of doses available
The proportion untreated is the shortfall of treatment doses matched to the number of case-patients who require treatment, according to the pandemic distribution curve.
The cost of treatment was calculated as follows:
Total cost of treatment agerisk group = cost of treatment per course × stockpile percentage × populationage, risk group
The cost of prophylaxis for 1 stockpile cycle was calculated as follows:
Total cost of prophylaxisage, risk group = cost of prophylaxis per week × no. weeks of prophylaxis × populationage, risk group
The medical cost of outpatient clinical influenza was calculated as follows:
Outpatient medical costsage, risk group = populationage, risk group × attack rate × consultation and treatment cost
The cost of outpatient lost days was calculated by using work days lost for the adult population and unspecified days lost for the young and elderly populations, as follows:
Economic cost of outpatient lost daysage, risk group = populationage, risk group × attack rate × outpatient days lost × value of a day lostage, risk group
The hospitalization cost for influenza-related complications was calculated by summing direct hospitalization cost with cost of additional days lost after hospitalization.
The direct hospitalization cost was calculated as follows:
Economic cost of hospitalizationage, risk group = populationage, risk group × attack rate × hospitalization rate age, risk group × length of stay age, risk group × (hospitalization cost + value of a day lost age, risk group)
The cost from additional days lost was calculated as follows:
Economic cost of additional days lost after hospitalization = population age, risk group × attack rate × hospitalization rateage, risk group × additional days lostage, risk group × value of a day lostage, risk group
The cost from influenza deaths is calculated as follows:
Economic cost from influenza deaths = populationage, risk group × attack rate × case-fatality rateage, risk group × net present value of future earningsage, risk group
For cost-benefit comparisons, the following equation is used:
Overall benefit = overall costtreatment only or prophylaxis – overall costno action
For the cost-effectiveness comparisons, the following equation is used:
Cost per-life-saved compared to no action = (cost excluding cost per lifetreatment-only or prophylaxis – cost excluding cost per lifeno action) / (deathsno action – deathstreatment-only or prophylaxis)
The individual costs that constitute the total costs are calculated for the strategies of no action, treatment-only, and prophylaxis as follows:
Overall costno action, treatment-only, prophylaxis = Σ (populationage, risk group × probability of outcomeclinical influenza, hospitalization, death × cost of outcomeclinical influenza, hospitalization, death × effectivenesstreatment-only, prophylaxis) + cost of strategytreatment-only, prophylaxis
"Dominate" is a term used in cost-effectiveness analyses and refers to a strategy that is both more efficacious and less costly than another strategy.
We thank K. Satku, Director of Medical Services, the staff at the Ministry of Health, and A. Earnest for their kind assistance.
Dr Lee is a preventive medicine physician with the Singapore Ministry of Defence, currently working at the Communicable Disease Centre, Tan Tock Seng Hospital, Singapore. His research interests include clinical cost-effectiveness, emerging infectious diseases management, and clinical process improvement.