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Health and economic impact of rotavirus vaccination in GAVI-eligible countries

BMC Public Health201010:253

DOI: 10.1186/1471-2458-10-253

Received: 22 December 2009

Accepted: 14 May 2010

Published: 14 May 2010

Abstract

Background

Rotavirus infection is responsible for about 500,000 deaths annually, and the disease burden is disproportionately borne by children in low-income countries. Recently the World Health Organization (WHO) has released a global recommendation that all countries include infant rotavirus vaccination in their national immunization programs. Our objective was to provide information on the expected health, economic and financial consequences of rotavirus vaccines in the 72 GAVI support-eligible countries.

Methods

We synthesized population-level data from various sources (primarily from global-level databases) for the 72 countries eligible for the support by the GAVI Alliance (GAVI-eligible countries) in order to estimate the health and economic impact associated with rotavirus vaccination programs. The primary outcome measure was incremental cost (in 2005 international dollars [I$]) per disability-adjusted life year (DALY) averted. We also projected the expected reduction in rotavirus disease burden and financial resources required associated with a variety of scale-up scenarios.

Results

Under the base-case assumptions (70% coverage), vaccinating one single birth cohort would prevent about 55% of rotavirus associated deaths in the 72 GAVI-eligible countries. Assuming I$25 per vaccinated child (~$5 per dose), the number of countries with the incremental cost per DALY averted less than I$200 was 47. Using the WHO's cost-effectiveness threshold based on per capita GDP, the vaccines were considered cost-effective in 68 of the 72 countries (~94%). A 10-year routine rotavirus vaccination would prevent 0.9-2.8 million rotavirus associated deaths among children under age 5 in the poorest parts of the world, depending on vaccine scale-up scenarios. Over the same intervention period, rotavirus vaccination programs would also prevent 4.5-13.3 million estimated cases of hospitalization and 41-107 million cases of outpatient clinic visits in the same population.

Conclusions

Our findings suggest that rotavirus vaccination would be considered a worthwhile investment for improving general development as well as childhood health level in most low-income countries, with a favorable cost-effectiveness profile even under a vaccine price ($1.5-$5.0 per dose) higher than those of traditional childhood vaccines.

Background

While childhood diarrhea can be caused by multiple pathogens, including both bacteria and viruses, rotavirus is the most common cause of severe diarrhea leading to hospitalization or disease-specific death among children under 5 years of age [1, 2]. Responsible for more than 2 million hospitalizations and 500,000 deaths annually (as of 2004), disease mortality is disproportionately borne by children in low-income countries in Africa and Asia [3]. Since almost all children are infected by age 5, and improved hygiene, water and sanitation measures have had little impact on disease burden, vaccination is considered among the most promising of strategies to prevent mortality [2].

Rotavirus has diverse genotypes that vary geographically and over time [46], with clinical manifestations of infection depending heavily on the patient's age [7]. Rotavirus is also characterized by frequent reinfection, with developed natural immunity depending on the number of prior infections. In the last 5 years, two new oral, live attenuated rotavirus vaccines, Rotarix® (GlaxoSmithKline) [a two-dose monovalent live attenuated human rotavirus vaccine] and RotaTeq® (Merck & Co., Inc.) [a three-dose human-bovine reassortant pentavalent rotavirus vaccine], have been licensed and are now available in many countries [8, 9]. In 2005, the World Health Organization (WHO) Strategic Advisory Group of Experts (SAGE) on Immunization, recommended the introduction of these vaccines in Europe, the United States, and Latin America, on the basis of results of phase III clinical trials [8]; both vaccines appeared to have partial efficacy of a similar magnitude as that conferred by a single natural infection. However, SAGE initially withheld a global recommendation until clinical trials could show satisfactory efficacy in Africa and Asia [8, 9]. Clinical trials of Rotarix® (completed in 2008) demonstrated a reduction of rotavirus disease burden in South Africa and Malawi [10]. Based on this newer evidence, in June 2009, SAGE recommended that all countries include infant rotavirus vaccination in their national immunization programs [10].

Especially in light of the Millennium Development Goal (MDG) 4 of child mortality reduction, there has been an effort at the global level to accelerate introduction of rotavirus vaccines in developing countries. However, in addition to the uncertainties about ultimate efficacy in Africa and Asia, as well as uncertainties about the likelihood of uptake, the current vaccines are costly relative to traditional childhood vaccines. Undoubtedly, the poorest countries will require financial assistance. The GAVI Alliance has promised to provide financial support for rotavirus vaccination programs to developing countries, and the WHO and its partners have established global networks for surveillance of rotavirus, providing valuable data on the burden of severe types of acute rotavirus gastroenteritis [11]. However, given that the countries eligible for support from the GAVI Alliance (GAVI-eligible countries) face numerous challenges, not the least of which is the need to fund other children's health initiatives within constrained budgets, countries are being encouraged to consider the potential health impact, cost-effectiveness, and financial requirements carefully, before choosing to prioritize rotavirus vaccines.

The objective of this analysis is to use population-based data for 72 GAVI-eligible countries and provide information on the expected health, economic, and financial consequences of rotavirus vaccines.

Methods

Analytic overview

We synthesized population-level data on demographic structure, country- or region-specific disease burden, medical utilization patterns for treating rotavirus diseases, and costs in order to estimate the health and economic consequences associated with rotavirus vaccination programs in the 72 GAVI-eligible countries. (The data used for our study is openly available upon request.) Model-projected clinical outcomes include intermediate health outcomes (e.g., hospitalizations), deaths averted, years of life saved (YLS), and disability-adjusted life years (DALYs) averted. The primary outcome measure for the cost-effectiveness analysis was incremental cost (expressed in 2005 international dollars [I$] adjusted for price differences across countries using purchasing power parity estimates) per DALY averted. For the base-case we adopted a modified societal perspective and discounted future costs and disability-adjusted and unadjusted life years by 3% annually [1215]. We have also conducted a cost-effectiveness analysis from the perspective of local government. Sensitivity analyses were conducted to explore the influence of both uncertain parameters and assumptions on results. We projected the expected reduction in rotavirus disease burden and budget impact associated with a variety of scale-up scenarios.

Model

Simulation model of the natural history of rotavirus infection

We have previously described a model of the natural history of rotavirus infection using TreeAge 2008 software (Williamstown, MA), which captures details such as the age-specific risk, probability of asymptomatic cases, rate of reinfection, correlation between strength of natural immunity and the total number of previous infections, and waning of vaccine efficacy [16]. We applied the model to Vietnam to reevaluate the cost-effectiveness of a rotavirus vaccination program [16]. We leveraged this work to develop a series of rotavirus disease models to explore uncertainties associated with analytic choices about model structure [17], and conducted corroboration exercises by comparing projected model outcomes to those obtained using the companion model described below.

Excel-based companion model

The companion rotavirus model has been developed to reflect the main features of rotavirus infection and vaccination, and to project the health and economic consequences at the population level, in settings where data are limited. The model is constructed as a static cohort simulation model and is programmed using Microsoft® Excel 2003 and Visual Basic for Applications 6.3 (Microsoft Corporation, Redmond, Washington). The model employs simplifying assumptions that rely on insights from more complex natural history models [16, 17]. The upper panel in Figure 1 shows the simplified schematic of natural history represented in the Excel-based companion model. Severe cases of rotavirus gastroenteritis are considered those which are symptomatic and requiring outpatient clinic visit or hospitalization or those resulting in disease-specific deaths [18].
https://static-content.springer.com/image/art%3A10.1186%2F1471-2458-10-253/MediaObjects/12889_2009_Article_2164_Fig1_HTML.jpg
Figure 1

Schematic of the companion rotavirus model. Upper panel shows the schematic of the natural history model of rotavirus infection the companion model is based on. The model assumes one episode of rotavirus diarrhea at maximum and full protection against subsequent rotavirus diarrhea during the first 5 years of life of a vaccinated child. Lower panel presents the schematic of the companion model.

The Excel-based companion model is applied to birth cohorts (born in 2010) in the 72 GAVI-eligible countries. The model tracks the cohorts up to age 5, incorporating background mortality and recording population-level health and cost outcomes with and without vaccination programs. Combining country-specific or region-specific data on the incidence of rotavirus deaths, ratios of outpatient visits or hospitalizations to rotavirus deaths, and serotype distribution, the model generates estimates for the reduction in rotavirus related events requiring medical treatments (i.e., outpatient clinic visits and hospitalizations) or leading to deaths. By applying the estimates to each country's age-structured population, the model transforms them into aggregated population health outcome measures, YL and DALYs (Figure 1, Lower Panel). DALYs are calculated using the approach recommended by the Global Burden of Disease (GBD) study [19] but are not age-weighted (age weight modulating factor K = 0) [20]. We assumed that the average mean duration of a diarrheal episode was 7 days and that each episode is associated with a disability weight of 0.119 [19]. The model also tracks direct medical and non-medical costs associated with rotavirus disease events and the costs of the vaccine program from the societal perspective. The primary measure for the cost-effectiveness analysis is expressed as the incremental costs (2005 I$) per DALY averted. The model is also used to project the financial costs and disease burden reduction at the population level for a multi-year rotavirus vaccination program, varying assumptions to simulate different demand forecasting and scale-up scenarios.

Model input and assumptions

Intervention: vaccine types and vaccination schedule

The number of doses (and schedule) recommended by the vaccine manufacturers are 2 (at 2 and 4 months) for Rotarix® and 3 (at 2,4, and 6 months) for Rotateq®. Because this analysis is intended to provide a broad policy overview of the expected health and economic outcomes of rotavirus vaccination, and not to contextualize a detailed situation of a single specific country, we chose not to distinguish the two vaccines in terms of dose and schedule or vaccine efficacy. This decision was based in large part on the WHO SAGE recommendations and in part based on published evidence: 1) a 3-dose schedule is recommended in any setting where the two vaccines, Rotarix® and Rotateq® are interchangeably used [21]; 2) efficacy data for a rotavirus vaccine obtained in one population can be extrapolated to other populations if the two populations belong to the same under 5 mortality category [10]; and 3) in a phase III Rotarix® trial performed in Malawi, the overall efficacy against severe rotavirus gastroenteritis of a 2-dose schedule (49.2%) showed little difference compared to the corresponding value with a 3-dose schedule (49.7%) [10]. To first generate an estimate of the potential avertable global burden with rotavirus vaccination, we standardized an initial set of assumptions: 1) infants are given 3 doses of a rotavirus vaccine (Rotarix® or Rotateq®) at ages aligned with each countries EPI visits (or DTP schedule) (6,10, and 14 weeks or 2,4, and 6 months of age); 2) coverage is 70%, (purposefully assumed in order to illustrate differences in country-specific outcomes related to variations in epidemiology); 3) vaccine-induced immunity is durable over 5 years without waning; and 4), for both vaccines, side effects are considered negligible as long as the first and last dose of either vaccine is given at the recommended age ranges (6-15 weeks for the first dose and 8 months or 32 weeks for the last dose) [21]. We then conducted additional analyses using a range of alternative assumptions (e.g., waning of vaccine-acquired immunity).

Vaccine efficacy

We used two different approaches for estimating vaccine efficacy. For the base-case analysis, recognizing that differences in rotavirus serotype distributions would affect the overall efficacy against severe types of rotavirus, we calculated a weighted average of serotype-specific vaccine efficacy using the country- or region-specific serotype distribution data. Data for rotavirus serotype distributions were obtained from published epidemiological studies. When country-specific data were not available, region-specific estimates reported by the global networks for rotavirus surveillance were used [11] (see Additional file 1 for details).

For a secondary analysis, based on the SAGE recommendation that efficacy data of a rotavirus vaccine can be extrapolated to other populations that have the same under 5 child mortality stratum, we divided the GAVI countries into the three categories (i.e., "high" mortality for the highest quartile, "intermediate" mortality for the second highest quartile, and "low" mortality for the lowest two quartiles [10]). We then extrapolated the pooled efficacy for a 2- or 3-dose schedule obtained from Malawi (49.5%) [10] to GAVI-eligible countries that belong to the "high" child mortality stratum and the corresponding data from South Africa (76.9%) [10] to the countries that fall on "intermediate" strata. Currently, there are no corresponding efficacy data obtained in low income countries with "low" under 5 mortality. Rather than using the data from industrialized countries with "low" under 5 mortality, we conservatively chose to use the data from South Africa for the low-child mortality GAVI-eligible countries. Under 5 mortality data to stratify countries for efficacy data extrapolation were obtained from the WHO World Health Statistics database [22] (Table 1).
Table 1

Country-specific profiles of the 72 GAVI-eligible countries

Countrya

GNI per capita (2008 US$)

GAVI grouping for co-financingb

DTP3 coverage (2008)

Under 5 child mortality (per 1,000 live births) (2006)

Under 5 rotavirus mortality (per 100000 children <5) (2004)

Percentage of death due to diarrhea among children under 5 (2000)

AFR D

      

   Angola

3,450

4

81%

260

389

19.1

   Benin

690

1

93%

148

182

17.1

   Burkina Faso

480

1

99%

204

256

18.8

   Cameroon

1,150

3

84%

149

179

17.3

   Chad

530

1

43%

209

266

18.1

   Comoros

750

1

81%

68

64

13.6

   Ghana

670

2

87%

120

92

12.2

   Guinea

390

1

70%

161

188

16.5

   Guinea-Bissau

250

1

79%

200

283

18.6

   Liberia

170

4

92%

235

331

17.3

   Madagascar

410

1

88%

115

141

16.9

   Mali

580

1

99%

217

307

18.3

   Mauritania

840

1

74%

125

153

16.2

   Niger

330

1

89%

253

392

19.8

   Nigeria

1,160

2

57%

191

228

15.7

   Sao Thome

1,020

1

99%

96

129

16.0

   Senegal

970

1

88%

116

158

17.1

   Sierra Leone

320

4

87%

269

439

19.7

   The Gambia

390

1

96%

114

107

12.2

   Togo

400

1

89%

107

134

13.8

AFR E

      

   Burundi

140

4

92%

181

255

18.2

   Central African Republic

410

4

51%

174

210

14.7

   Congo

1,970

4

89%

126

86

11.2

   Cote d'Ivoire

980

4

74%

127

223

14.8

   Democratic Republic of the Congo

150

4

83%

205

281

18.1

   Eritrea

300

4

85%

74

84

15.6

   Ethiopia

280

1

81%

123

213

17.3

   Kenya

770

2

85%

121

135

16.5

   Lesotho

1,080

1

91%

132

25

3.9

   Malawi

290

1

91%

120

225

18.1

   Mozambique

370

1

80%

138

183

16.5

   Rwanda

410

1

97%

160

272

18.5

   Tanzania

430

1

84%

118

147

16.8

   Uganda

420

1

79%

134

165

17.2

   Zambia

950

1

95%

182

227

17.5

   Zimbabwe

c

2

75%

85

106

12.1

AMR A, B & D

      

   Cuba

c

2

99%

7

1

1.3

   Guyana

1,420

3

93%

62

119

21.4

   Honduras

1,800

3

93%

27

43

12.2

   Bolivia

1,460

3

83%

61

66

14.3

   Haiti

660

4

53%

80

133

16.5

   Nicaragua

1,080

2

96%

26

30

12.2

EMR D

      

   Afghanistan

c

4

85%

257

338

18.9

   Djibouti

1,130

3

89%

130

145

16.6

   Pakistan

980

2

73%

97

95

14.0

   Somalia

c

4

31%

145

315

18.7

   Sudan

1,130

4

93%

89

79

12.9

   Yemen

950

1

87%

100

108

16.1

EUR B & C

      

   Armenia

3,350

3

89%

24

29

10.5

   Azerbaijan

3,830

3

95%

89

125

15.3

   Georgia

2,470

3

92%

32

42

11.5

   Kyrgyzstan

740

2

95%

41

86

14.1

   Tajikistan

600

2

86%

68

177

16.4

   Uzbekistan

910

2

98%

44

88

14.8

   Moldova

1,470

2

95%

19

5

2.0

   Ukraine

3,210

3

90%

24

2

1.2

SEAR B & D

      

   Indonesia

2,010

3

77%

34

60

18.3

   Korea, Democratic Republic

c

2

92%

55

56

18.9

   Sri Lanka

1,790

3

98%

13

16

13.5

   Timor Leste

2,460

4

79%

55

115

21.9

   Bangladesh

520

1

87%

69

89

20.0

   Bhutan

1,900

1

96%

70

98

20.9

   India

1,070

2

84%

76

102

20.3

   Myanmar

c

1

85%

104

128

21.1

   Nepal

400

1

82%

59

91

20.5

WPR B

      

   Cambodia

600

1

91%

82

226

16.6

   Kiribati

2,000

3

82%

64

127

21.9

   Lao People Democratic Republic

750

1

61%

75

122

15.6

   Mongolia

1,680

2

96%

42

67

14.5

   Papua New Guinea

1,010

2

52%

73

128

15.3

   Solomon Islands

1,180

1

78%

72

45

8.8

   Viet Nam

890

2

93%

17

21

10.4

a The six WHO geographical regions are the African Region (AFR), Eastern Mediterranean Region (EMR), Region of the Americas (AMR), European Region (EUR), South-East Asian Region (SEAR), and Western Pacific Region (WPR). Categories for mortality rates include very low child, very low adult mortality (A), low child, low adult mortality (B), low child, high adult mortality (C), high child, high adult mortality (D), and high child and very high adult mortality (E). Combining the two dimensions, we modeled 36 countries from AFR (with 20 in AFR D and 16 in AFR E); 6 countries from EMR D; 6 countries from AMR A, B & D; 8 countries from EUR B & C; 9 countries from SEAR B & D; and 7 countries from WPR B.

b The GAVI Alliance new vaccine co-financing policy (which came into effect in 2008) requires GAVI-eligible countries to share new vaccine costs at one of four levels depending on the country's ability to pay: (1) Poorest group countries will pay $0.20/dose of the first vaccine and $0.15/dose for the 2nd and 3rd vaccines; (2) Intermediate group countries will pay $0.30/dose of the first vaccine and $0.15/dose for the 2nd and 3rd vaccines; (3) Least Poor group countries will pay $0.20/dose of the first vaccine and $0.15/dose for the 2nd and 3rd vaccines for the first year, but will increase their copayment by 15% annually; and (4) Fragile group countries will pay $0.10/dose of the first vaccine and $0.15/dose for the 2nd and 3rd vaccines.

c Data are not available.

Demographic data and assumptions

For country-specific age-structure that incorporates background mortality, we used the estimates of population size (1-year interval) from UN World Population Prospects 2006 Revision [23]. Age-specific life expectancy data (grouped in five-year intervals) for calculating years of life lost as a component of DALYs were obtained from WHO life tables (for year 2006) [24]. To facilitate comparison of the results across regions, for the present analysis, the 72 GAVI-eligible countries were clustered into 11 groups according to the WHO classification system, which is based on geographical location and mortality rates (see footnotes to Table 1).

Incidence of rotavirus disease events

We estimated age-specific incidence rates of rotavirus-associated deaths by applying regional estimates of age distribution (across ages 0, 1, 2, 3, and 4) of severe rotavirus gastroenteritis cases obtained from published literature [16, 2528] to the WHO estimates of the country-specific cumulative number of rotavirus deaths among children under 5 years of age [29] (Table 1). Incidence rates of outpatient visits or hospitalization vary widely depending on the health care infrastructure and resources available in a country. Estimates of country-specific ratios of cumulative incidence of outpatient visits or hospitalizations to deaths were obtained from published literature; when country-specific data were not available, regional estimates or neighboring countries' values were used. The age-specific incidence rates for non-fatal outcomes were then estimated by adjusting cumulative incidence of each outcome by the same age distribution data mentioned above.

Costs

Costs included both direct medical costs (vaccination program costs plus medical treatment costs) and indirect costs (travel costs and caregivers' time costs). Since the price of a rotavirus vaccine and program costs to deliver the vaccine in local settings are not known, we use a composite cost approach to estimate the vaccination program costs among the direct, medical costs. In doing so, we distinguish cost items that are tradable (e.g., vaccine wastage, insurance and security fees associated with freight into the country) from those that are typically non-tradable (e.g., administration, immunization support and monitoring/programmatic expenses). We conduct analyses assuming a 3-dose vaccination schedule with total cost per vaccinated child of I$10 and of I$25, implying vaccine prices per dose of approximately US$1.50 and US$5.00 respectively. For a composite cost of I$25 per vaccinated child, we assume the following breakdown of the total costs: rotavirus vaccine purchase of US$15.00 (3 doses at US$5.00 each); vaccine wastage of approximately US$2.65 (~15% of vaccine price); freight and insurance costs of approximately US$0.90 (~6% of vaccine price); administrative costs of I$1.50; immunization support (including cold chain, training, and operational costs) of I$2.95; and other programmatic costs (including surveillance and monitoring and social mobilization) of I$2.00. The lower cost of I$10 was derived by assuming 3 doses of vaccine at US$1.50 each, wastage of approximately US$0.79, freight and insurance costs of approximately US$0.27, administrative costs of I$0.50, immunization support of I$2.00, and programmatic costs of I$1.94. For both composite cost estimates, we assumed that the costs reflect any initial start-up cost and that the non-tradable components (such as administration, immunization support and other programmatic costs) would reflect incremental costs associated with introducing rotavirus vaccines into a national immunization programs to varying extents, depending on countries' health system infrastructure.

Costs for hospitalization were based on country-specific data. When data were not available, we based estimates on WHO-CHOICE data for the cost of a bed-day at a secondary hospital, conservatively assuming one pre-hospitalization consultation in an outpatient setting, one-time hospitalization, and an average length of stay of 3 days for hospitalization (see also Additional file 1). Similarly, costs for outpatient visits were based on country-specific data when available, and, when data were not available, were based on the use of WHO-CHOICE data for a 20-minute visit at health center (80% coverage) assuming one visit per episode. We calculated caregiver's time for hospitalization or outpatient visit by multiplying an average length of stay for hospitalization (3 days × 8 hours/day) or an average length of an outpatient visit (6 hours) by an estimated country-specific average hourly wage. The average hourly wage data for each country were estimated by analyzing the data from the Labor Statistics database [30] and the World Development Indicators [31], taking into account unemployment and under-employment rate and distribution of the main industries in a country (Additional file 1). We obtained estimates of average transport costs for a visit to medical facilities from published studies for some countries and extrapolated the data to countries in the same region for which country-specific estimates were not available (Additional file 1).

Cost-effectiveness analysis

For the base-case, we estimated the cost-effectiveness of a rotavirus program in the 72 GAVI-eligible countries from the societal perspective, following a single birth cohort over a 5-year time horizon. The primary outcome measure of the analysis was incremental cost-effectiveness ratio (expressed in 2005 I$ per DALY averted). The incremental cost is the sum of vaccination program costs, averted medical treatment costs for children, and averted caregivers' time and travel costs. We have also evaluated the cost-effectiveness of rotavirus vaccination from the local government perspective, including direct medical costs only and adjusting vaccine costs using the GAVI's co-financing scheme for new vaccines [32] (see footnotes to Table 1 for details). The government savings from the averted medical treatment costs were calculated by adjusting the total averted medical costs for the public sector's percentages of total health expenditure [31].

Sensitivity analysis

In order to explore uncertainties surrounding model projected outcomes, we performed univariate sensitivity analyses, varying key parameter values and assumptions one at a time. For example, in a sensitivity analysis, we assumed that vaccine efficacy against severe rotavirus diseases would decline over a 5-year time horizon by ~14% annually, based on 2-year efficacy data obtained from a published study [33]. We have also conducted multivariate sensitivity analyses by developing multiple scenarios based on different combinations of analytic choices (e.g., discount rate or perspective), efficacy levels, vaccination program costs, etc.

Budget impact analysis and scale-up scenarios

When considering whether to introduce a new vaccine into a national immunization program, local policy makers need information on total cost as well as value for money, in order to assess the affordability and sustainability of a vaccination program. To provide policymakers with more practical budgetary information, we conducted a budget impact analysis over a 10-year time horizon (2010-2019) from the perspective of budget holders (i.e., local governments) [34, 35] by extending the base-case analysis using the same Excel-based model. We first developed alternative roll-out scenarios (i.e., vaccination introduced at different coverage levels), in which the year of rotavirus vaccine introduction, maximum achievable coverage rate, and years to full coverage were varied across countries. The financial forecasts are based on the program cost per child of I$25 (i.e., US$5 per vaccine dose). We calculated the vaccine costs to local governments according to the GAVI's co-financing scheme [32] (Table 1), but used the same composite approach as the base-case cost-effectiveness analysis in estimating the incremental costs of delivering rotavirus vaccines. The financial forecasts were expressed in US$ because budgets are typically expressed in nominal rather than real terms. Note that the financial requirements are not discounted, based on the published guidelines that discounting is not necessary for budget impact analysis [34, 35].

For the base-case roll-out scenario, building upon the two previous approaches-an explicit 2-Phase scenario envisioned by Artherly et al. [36] and Wolfson et al.'s approach [37], we categorized the 72 GAVI-eligible countries into 10 groups according to each country's national income level (2008) (Table 1), the most recent year's DPT3 (the third dose of Diptheria-Pertussis-Tetanus vaccine) coverage (Table 1), past history of new vaccine (i.e., Hib or HepB vaccines) introduction, and Artherly and colleagues' demand forecasting results [36]. Table 2 presents the details of the 10 categories and rotavirus vaccine demand forecasting for 10 consecutive cohorts being introduced between 2010-2019 for each country. Following the approach used by Wolfson et al. [37], we used DTP3 coverage from 2008 (the most recent year available) as a proxy for a full coverage. We also used the past trends in the coverage of Hib, HepB, and DTP3 to forecast years of first introduction and years to full coverage. We then assumed that 1) countries in Category 1 roll out in Year 1 (2010), and countries in Category 2 roll out in 2011, etc., with countries in Category 10 rolling out in Year 10 (2019). (Note that, because Nicaragua has already introduced Rotarix®, we use 2006 as their start date.); 2) it takes 2 or 3 years after implementation to reach full coverage for Category 1, 4 years for Categories 2 and 3, 5 years for Categories 4-6, 6 years for Categories 7-8, and 7 years for Categories 9-10; and 3) once full coverage is achieved, the coverage level would be maintained (Table 2). We also evaluated alternative, more optimistic roll-out scenarios by using Wolfson et al.'s original assumptions adjusted with the most recent year's data for GNI per capita and DTP3 coverage [37] and by assuming a flat coverage of 70% over a 10-year period.
Table 2

Implied coverage rates for a base-case scale-up scenario (%)

Country

Categorya

Year 1

Year 2

Year 3

Year 4

Year 5

Year 6

Year 7

Year 8

Year 9

Year 10

  

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

AFR D

           

   Angola

10

         

31.0

   Benin

5

    

15.0

30.6

46.2

61.8

77.4

93.0

   Burkina Faso

5

    

57.0

65.4

73.8

82.2

90.6

99.0

   Cameroon

3

  

53.0

60.8

68.5

76.3

84.0

84.0

84.0

84.0

   Chad

8

       

28.0

30.5

33.0

   Comoros

7

      

27.0

36.0

45.0

54.0

   Ghana

6

     

80.0

81.4

82.8

84.2

85.6

   Guinea

8

       

57.0

59.2

61.3

   Guinea-Bissau

9

        

47.0

51.6

   Liberia

5

    

48.0

56.8

65.6

74.4

83.2

92.0

   Madagascar

6

     

61.0

66.4

71.8

77.2

82.6

   Mali

5

    

54.0

63.0

72.0

81.0

90.0

99.0

   Mauritania

8

       

31.0

38.2

45.3

   Niger

9

        

25.0

34.1

   Nigeria

4

   

41.0

44.2

47.4

50.6

53.8

57.0

57.0

   Sao Tome

3

  

43.0

57.0

71.0

85.0

99.0

99.0

99.0

99.0

   Senegal

6

     

54.0

60.8

67.6

74.4

81.2

   Sierra Leone

6

     

24.0

36.6

49.2

61.8

74.4

   The Gambia

5

    

84.0

86.4

88.8

91.2

93.6

96.0

   Togo

6

     

50.0

57.8

65.6

73.4

81.2

AFR E

           

   Burundi

10

         

74.0

   Central African Republic

10

         

29.0

   Congo

10

         

33.0

   Cote d'Ivoire

4

   

10.0

22.8

35.6

48.4

61.2

74.0

74.0

   Democratic Republic of the Congo

10

         

40.0

   Eritrea

6

     

52.0

58.6

65.2

71.8

78.4

   Ethiopia

6

     

42.0

49.8

57.6

65.4

73.2

   Kenya

6

     

72.0

74.6

77.2

79.8

82.4

   Lesotho

3

  

14.0

33.3

52.5

71.8

91.0

91.0

91.0

91.0

   Malawi

5

    

64.0

69.4

74.8

80.2

85.6

91.0

   Mozambique

8

       

25.0

34.2

43.3

   Rwanda

5

    

88.0

89.8

91.6

93.4

95.2

97.0

   Tanzania

7

      

79.0

79.8

80.7

81.5

   Uganda

8

       

29.0

37.3

45.7

   Zambia

5

    

80.0

83.0

86.0

89.0

92.0

95.0

   Zimbabwe

8

       

9.0

20.0

31.0

AMR A, B & D

           

   Cuba

1

93.0

95.0

97.0

99.0

99.0

99.0

99.0

99.0

99.0

99.0

   Guyana

1

89.0

93.0

93.0

93.0

93.0

93.0

93.0

93.0

93.0

93.0

   Honduras

1

84.0

93.0

93.0

93.0

93.0

93.0

93.0

93.0

93.0

93.0

   Bolivia

1

82.0

83.0

83.0

83.0

83.0

83.0

83.0

83.0

83.0

83.0

   Haiti

10

         

39.0

   Nicaragua

1b

96.0

96.0

96.0

96.0

96.0

96.0

96.0

96.0

96.0

96.0

EMR D

           

   Afghanistan

10

         

31.0

   Djibouti

2

 

46.0

56.8

67.5

78.3

89.0

89.0

89.0

89.0

89.0

   Pakistan

4

   

63.0

65.0

67.0

69.0

71.0

73.0

73.0

   Somalia

9

        

33.0

32.7

   Sudan

3

  

22.0

39.8

57.5

75.3

93.0

93.0

93.0

93.0

   Yemen

6

     

9.0

24.6

40.2

55.8

71.4

EUR B & C

           

   Armenia

1

55.0

66.3

77.7

89.0

89.0

89.0

89.0

89.0

89.0

89.0

   Azerbaijan

1

95.0

95.0

95.0

95.0

95.0

95.0

95.0

95.0

95.0

95.0

   Georgia

1

55.0

67.3

79.7

92.0

92.0

92.0

92.0

92.0

92.0

92.0

   Kyrgyzstan

1

10.0

38.3

66.7

95.0

95.0

95.0

95.0

95.0

95.0

95.0

   Tajikistan

1

39.0

54.7

70.3

86.0

86.0

86.0

86.0

86.0

86.0

86.0

   Uzbekistan

1

5.0

36.0

67.0

98.0

98.0

98.0

98.0

98.0

98.0

98.0

   Moldova

1

81.0

85.7

90.3

95.0

95.0

95.0

95.0

95. 0

95.0

95.0

   Ukraine

1

4.0

32.7

61.3

90.0

90.0

90.0

90.0

90.0

90.0

90.0

SEAR B & D

           

   Indonesia

4

   

42.0

49.0

56.0

63.0

70.0

77.0

77.0

   Korea, Democratic Republic

10

         

27.0

   Sri Lanka

3

  

62.0

71.0

80.0

89.0

98.0

98.0

98.0

98.0

   Timor Leste

4

   

57.0

61.4

65.8

70.2

74.6

79.0

79.0

   Bangladesh

7

      

5.0

18.7

32.3

46.0

   Bhutan

3

  

90.0

91.5

93.0

94.5

96.0

96.0

96.0

96.0

   India

3

  

6.0

25.5

45.0

64.5

84.0

84.0

84.0

84.0

   Myanmar

7

      

8.0

20.8

33.7

46.5

   Nepal

7

      

2.0

15.3

28.7

42. 0

WPR B

           

   Cambodia

5

    

50.0

58.2

66.4

74.6

82.8

91.0

   Kiribati

2

 

36.0

47.5

59.0

70.5

82.0

82.0

82.0

82.0

82.0

   Lao People Democratic Republic

8

       

50.0

51.8

53.7

   Mongolia

2

 

95.0

95.3

95.5

95.8

96.0

96.0

96.0

96.0

96.0

   Papua New Guinea

4

   

60.0

58.4

56.8

55.2

53.6

52.0

52.0

   Solomon Islands

2

 

53.0

59.3

65.5

71.8

78.0

78.0

78.0

78.0

78.0

   Viet Nam

5

    

78.0

81.0

84.0

87.0

90.0

93.0

a The profile of each category is as follows:

Category 1: 13 countries classified by Atherly et al.'s demand forecasting study [36] as "Phase 1" countries that are likely to introduce rotavirus vaccines earlier than other countries.

Category 2: Lower middle per capita income ($976-3,705) and introduction of both Hib and HepB vaccines.

Category 3: Lower middle per capita income ($976-3,705), either Hib or HepB vaccine introduction, and high (>80%) to very high (>90%) DTP3 coverage.

Category 4: Lower middle per capita income ($976-3,705), either Hib or HepB vaccine introduction, and low (<70%) to moderate (>70%) DTP3 coverage.

Category 5: Low income (<$975), Hib or HepB vaccine introduction, and very high (>90%) DTP3 coverage.

Category 6: Low income (<$975), both Hib and HepB vaccine introduction, and high (>80%) DTP3 coverage.

Category 7: Low income (<$975), either Hib or HepB vaccine introduction, and high (>80%) DTP3 coverage.

Category 8: Low income (<$975), either Hib or HepB vaccine introduction, and low (<70%) to moderate (>70%) DTP3 coverage.

Category 9: Low income (<$975), neither Hib nor HepB vaccine introduction.

Category 10: 8 countries judged by Atherly et al. [36] to be countries that are not likely to introduce rotavirus vaccines.

b Introduced rotavirus vaccine in 2006.

Results

Model validation

To ensure that the companion population-based model produced results consistent with our state-transition model when both were subject to our simplifying assumptions, we compared projected health outcomes and cost-effectiveness from the two different models for Vietnam. While the absolute averted deaths reflects both epidemiological differences in the incidence and proportion of vaccine-targeted serotypes, the cost-effectiveness ratios estimated using the two models differ by less than 5% (data not shown). Our previous work [17], which explored model uncertainty by comparing results from five different models of the natural history of rotavirus infection, also reaffirmed that our companion model yielded results consistent with those from more complex natural history models. According to the previous analysis [17], within the category of static, deterministic, aggregate-level models, different choices in model structure lead to relatively modest differences in the estimated cost-effectiveness of rotavirus vaccination while intermediate "non-severe" epidemiologic outcomes vary more substantially depending on choices of model structure.

Health outcomes

Under the base-case assumptions (70% coverage, a limit of one rotavirus disease event per child over the first 5 years of life, and vaccine efficacy against severe rotavirus cases adjusted for country-specific serotype distribution), vaccinating one single birth cohort would prevent approximately 52% (range: 50%-59%) of severe rotavirus disease events across the 72 GAVI-eligible countries compared to no vaccination (Table 3). However, the distribution of the absolute numbers of the reduced rotavirus disease burden would vary across regions or countries according to the varying levels of risk of rotavirus death. The number of rotavirus deaths averted per 1,000 vaccinated children varied from near 0 (Cuba) to 16 (Sierra Leone) (Table 3). The upper panel of Figure 2 shows that more than a half of the absolute global reduction (in terms of DALYs) would occur in the African region, and that the South-East Asian Region is another region that would benefit greatly from rotavirus vaccine introduction. The lower panel of Figure 2 indicates that the highest reduction in burden would be achieved in countries with a high disease burden (≥200 rotavirus deaths per 100,000 children under 5 years of age) but a similar reduction would be achieved in countries with a medium burden (100-200 rotavirus deaths per 100,000 children under 5 years of age) because reduction in disease burden also depends heavily on population size and country-specific vaccine efficacy adjusted for local rotavirus serotype distributions.
Table 3

Health outcomes of rotavirus vaccination in the GAVI-eligible countries

Country

Base-case vaccine efficacy (adjusted for serotype distribution)

Vaccine efficacy based on SAGE recommendation

 

Reduction in risk of severe rotavirus disease events

Rotavirus deaths averted (per 1000 vaccinated children)

DALYs averted

Reduction in risk of severe rotavirus disease events

Rotavirus deaths averted (per 1000 vaccinated children)

DALYs averted

AFR D

      

   Angola

55%

14.2

176,385

35%

9.0

111,341

   Benin

55%

6.7

42,644

35%

4.2

26,918

   Burkina Faso

50%

8.6

92,836

35%

5.9

63,999

   Cameroon

54%

6.6

67,042

35%

4.2

42,761

   Chad

55%

9.8

76,358

35%

6.2

48,200

   Comoros

55%

2.4

1,245

54%

2.3

1,221

   Ghana

59%

3.7

44,965

35%

2.2

26,291

   Guinea

55%

6.9

43,736

35%

4.3

27,608

   Guinea-Bissau

50%

9.5

13,602

35%

6.5

9,366

   Liberia

55%

12.1

38,173

35%

7.6

24,096

   Madagascar

55%

5.2

68,520

35%

3.3

43,252

   Mali

55%

11.1

110,369

35%

7.0

69,669

   Mauritania

55%

5.7

10,359

35%

3.6

6,539

   Niger

55%

14.4

160,612

35%

9.1

101,384

   Nigeria

51%

7.7

733,421

35%

5.3

498,170

   Sao Thome

55%

4.7

430

35%

3.0

272

   Senegal

55%

5.8

45,473

35%

3.7

28,704

   Sierra Leone

55%

15.9

62,305

35%

10.1

39,329

   The Gambia

55%

4.0

4,250

35%

2.5

2,682

   Togo

55%

5.0

21,192

35%

3.1

13,377

AFR E

      

   Burundi

55%

9.3

67,442

35%

5.8

42,572

   Central African Republic

55%

7.7

19,559

35%

4.8

12,346

   Congo

55%

3.2

7,180

35%

2.0

4,532

   Cote d'Ivoire

51%

7.6

85,210

35%

5.2

57,935

   Democratic Republic of the Congo

55%

10.3

541,455

35%

6.5

341,786

   Eritrea

55%

3.1

11,263

54%

3.0

11,045

   Ethiopia

55%

7.8

443,905

35%

4.9

280,209

   Kenya

55%

5.0

132,053

35%

3.1

83,357

   Lesotho

55%

0.9

838

35%

0.6

529

   Malawi

57%

8.6

81,905

35%

5.2

50,027

   Mozambique

55%

6.8

90,101

35%

4.3

56,875

   Rwanda

55%

9.9

75,890

35%

6.2

47,905

   Tanzania

57%

5.5

146,305

35%

3.4

89,138

   Uganda

55%

6.0

153,907

35%

3.8

97,151

   Zambia

51%

7.8

56,160

35%

5.3

37,952

   Zimbabwe

52%

3.6

21,407

35%

2.4

14,392

AMR A, B & D

      

   Cuba

57%

0.0

63

54%

0.0

60

   Guyana

57%

4.8

969

54%

4.6

923

   Honduras

57%

1.7

6,710

54%

1.6

6,386

   Bolivia

57%

2.6

12,692

54%

2.5

12,080

   Haiti

57%

5.2

26,275

35%

3.2

16,098

   Nicaragua

57%

1.2

3,384

54%

1.1

3,195

EMR D

      

   Afghanistan

53%

12.5

254,469

35%

8.2

166,606

   Djibouti

53%

5.4

2,234

35%

3.5

1,463

   Pakistan

53%

3.5

315,915

35%

2.3

206,836

   Somalia

55%

12.1

78,642

35%

7.7

49,641

   Sudan

53%

2.9

65,913

35%

1.9

43,155

   Yemen

53%

4.0

67,530

35%

2.6

44,213

EUR B & C

      

   Armenia

59%

1.1

907

54%

1.0

834

   Azerbaijan

59%

4.7

14,086

35%

2.8

8,329

   Georgia

59%

1.7

1,471

54%

1.6

1,352

   Kyrgyzstan

59%

3.3

7,922

54%

3.1

7,277

   Tajikistan

59%

7.1

24,217

54%

6.5

22,246

   Uzbekistan

59%

3.6

41,188

54%

3.3

37,836

   Moldova

59%

0.2

159

54%

0.2

146

   Ukraine

59%

0.1

590

54%

0.1

542

SEAR B & D

      

   Indonesia

57%

2.3

187,993

54%

2.2

176,588

   Korea, Democratic Republic

57%

2.1

12,982

54%

2.0

12,194

   Sri Lanka

57%

0.6

3,424

54%

0.6

3,216

   Timor Leste

57%

4.3

4,470

54%

4.1

4,199

   Bangladesh

55%

3.4

231,585

54%

3.3

225,739

   Bhutan

57%

3.8

858

54%

3.6

806

   India

53%

3.6

1,777,110

54%

3.7

1,802,809

   Myanmar

57%

4.9

79,028

35%

3.0

47,784

   Nepal

58%

3.5

52,750

54%

3.2

48,998

WPR B

      

   Cambodia

57%

8.6

63,250

35%

5.2

38,244

   Kiribati

57%

4.9

118

54%

4.6

111

   Lao People Democratic Republic

57%

4.6

14,067

54%

4.3

13,213

   Mongolia

57%

2.6

2,316

54%

2.5

2,176

   Papua New Guinea

57%

5.0

16,493

54%

4.7

15,492

   Solomon Islands

57%

1.7

493

54%

1.6

463

   Viet Nam

58%

0.8

26,089

54%

0.8

24,032

I$ = International dollars; DALY = Disability-adjusted life year; ICER = Incremental cost-effectiveness ratio.

AFR = African Region; EMR = Eastern Mediterranean Region; EUR = European Region; AMR = Region of the Americas; WPR = Western Pacific Region; SEAR = South-East Asian Region.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2458-10-253/MediaObjects/12889_2009_Article_2164_Fig2_HTML.jpg
Figure 2

Distribution of averted rotavirus disease burden (DALYs) in the GAVI-eligible countries. Upper panel shows the distribution of averted DALYs associated with rotavirus infection by region. Lower panel shows the corresponding distribution by the level of rotavirus mortality burden (among children under age 5).

Cost-effectiveness

Table 4 shows the incremental cost-effectiveness ratios of a rotavirus vaccination program in each of the 72 countries for the two different estimated total costs per vaccinated child of I$10 and I$25, which correspond to the per dose vaccine costs of US$1.5 and US$5. Assuming I$25 per vaccinated child for the base-case, the number of countries with the incremental cost per DALY averted less than I$100 was 23. Among the rest of the countries, the corresponding ratios of 24 countries fell between I$100-I$200. It should be noted that the incremental cost per DALY averted was relatively high in four countries (Cuba, I$28,440; Moldova, I$4,500; Sri Lanka, I$1,300; and Ukraine, I$12,160), mainly due to their relatively low rotavirus mortality (Cuba, 1; Moldova, 5; Sri Lanka, 16; and Ukraine, 2 per 100,000 children under age 5). Assuming I$10 per vaccinated child, rotavirus infant vaccination would be cost-saving in 10 countries, and, in another 55 countries, the incremental cost per DALY averted would be less than I$200. However, in the four countries with relatively low rotavirus disease burden, the ratios would still be relatively high, with I$11,330, I$1,760, I$490, and I$4,820, respectively, for Cuba, Moldova, Sri Lanka, and Ukraine. Table 4 also presents the cost-effectiveness results from the local government perspective, in which the per-child vaccination program costs accrued to local governments range between I$10.3-I$10.6 (depending on the level of national income and the history of other new vaccine introduction) when assuming US$5 per vaccine dose in the market. The results suggest that, under this alternative government perspective, rotavirus vaccination would be considered more cost-effective than for the base-case societal perspective, presumably due to the financial support for vaccine purchase from GAVI.
Table 4

Cost-effectiveness of rotavirus vaccination in the GAVI-eligible countries

Country

Base-case vaccine efficacy (adjusted for serotype distribution)

Vaccine efficacy based on SAGE recommendation

 

ICERa(I$/DALY averted) I$10 per vaccinated child

ICERa(I$/DALY averted) I$25 per vaccinated child

ICERb(I$/DALY averted) based on GAVI's co-financing scheme

ICERa(I$/DALY averted) I$10 per vaccinated child

ICERa(I$/DALY averted) I$25 per vaccinated child

ICERb(I$/DALY averted) based on GAVI's co-financing scheme

AFR D

      

   Angola

saving

26

4

0.2

68

22

   Benin

25

108

52

57

190

87

   Burkina Faso

22

90

37

42

141

59

   Cameroon

22

108

57

54

190

93

   Chad

saving

54

33

17

113

59

   Comoros

119

341

150

122

348

153

   Ghana

71

221

100

142

398

176

   Guinea

18

99

54

49

178

88

   Guinea-Bissau

7

68

37

26

113

57

   Liberia

17

67

32

37

114

53

   Madagascar

20

124

65

60

225

108

   Mali

5

58

29

26

109

51

   Mauritania

21

116

52

58

209

92

   Niger

saving

36

23

10

78

42

   Nigeria

27

102

48

51

162

74

   Sao Thome

40

152

65

83

262

112

   Senegal

32

125

56

68

215

95

   Sierra Leone

2

41

21

17

79

37

   The Gambia

59

196

87

112

328

145

   Togo

46

155

75

88

262

121

AFR E

      

   Burundi

22

84

44

46

145

70

   Central African Republic

20

96

46

50

171

78

   Congo

74

250

111

143

422

185

   Cote d'Ivoire

21

95

47

44

153

72

   Democratic Republic of the Congo

19

76

39

41

131

62

   Eritrea

95

266

117

97

272

120

   Ethiopia

28

98

45

55

166

74

   Kenya

37

150

68

81

260

115

   Lesotho

397

1,061

459

655

1,707

737

   Malawi

26

93

40

55

165

71

   Mozambique

31

115

48

64

198

83

   Rwanda

1

58

30

23

114

54

   Tanzania

42

147

66

87

258

114

   Uganda

31

127

62

68

220

102

   Zambia

23

103

45

49

166

72

   Zimbabwe

78

251

110

134

391

170

AMR A, B & D

      

   Cuba

11,332

28,443

12,240

11,909

29,888

12,862

   Guyana

4

113

40

7

122

44

   Honduras

85

386

190

95

411

201

   Bolivia

47

246

120

53

262

128

   Haiti

4

105

56

46

212

102

   Nicaragua

217

646

289

234

688

307

EMR D

      

   Afghanistan

saving

47

29

15

90

48

   Djibouti

saving

90

54

25

179

92

   Pakistan

42

192

102

95

325

159

   Somalia

saving

39

32

11

83

51

   Sudan

84

266

121

148

426

190

   Yemen

26

160

82

73

276

132

EUR B & C

      

   Armenia

227

685

312

254

753

341

   Azerbaijan

saving

95

68

35

223

123

   Georgia

131

429

206

148

473

225

   Kyrgyzstan

59

215

99

68

239

109

   Tajikistan

6

80

48

10

91

53

   Uzbekistan

saving

122

58

saving

144

67

   Moldova

1,763

4,497

1,949

1,925

4,900

2,122

   Ukraine

4,816

12,161

5,243

5,249

13,245

5,710

SEAR B & D

      

   Indonesia

80

302

136

90

326

146

   Korea, Democratic Republic

118

363

154

129

390

165

   Sri Lanka

487

1,303

565

522

1,391

603

   Timor Leste

41

161

67

46

174

73

   Bangladesh

50

209

104

52

216

106

   Bhutan

38

175

75

44

190

81

   India

54

201

97

53

198

96

   Myanmar

28

137

74

75

256

126

   Nepal

62

215

102

70

235

110

WPR B

      

   Cambodia

saving

44

34

10

111

63

   Kiribati

4

111

50

9

122

54

   Lao People Democratic Republic

15

132

77

20

144

83

   Mongolia

77

274

114

85

296

123

   Papua New Guinea

saving

84

36

saving

95

41

   Solomon Islands

143

444

186

156

476

200

   Viet Nam

193

799

390

228

885

427

I$ = International dollars; DALY = Disability-adjusted life year; ICER = Incremental cost-effectiveness ratio.

AFR = African Region; EMR = Eastern Mediterranean Region; EUR = European Region; AMR = Region of the Americas; WPR = Western Pacific Region; SEAR = South-East Asian Region.

a ICERs are for a strategy of vaccinating 70% of a single birth cohort born in 2010 assuming a vaccination program cost of I$10 and I$25 per vaccinated child compared to no vaccination.

b ICERs are for a strategy of vaccinating 70% of a single birth cohort born in 2010, but vaccination program costs vary across countries according to the GAVI's co-financing scheme [32].

While there is no consensus on a threshold cost-effectiveness ratio under which an intervention is cost-effective, a useful proxy suggested by WHO to compare different public health interventions is per capita GDP [38]. In this context, to facilitate comparison of the cost-effectiveness of rotavirus vaccines in different settings, we compared the base-case results with each country's per capita GDP and found that a rotavirus vaccination program would be cost-effective in all GAVI countries except the four with relatively low rotavirus death rates (Table 4).

Sensitivity analyses

Results were most sensitive to vaccination cost per child, rotavirus-associated mortality, ratios of hospitalizations and outpatient visits to deaths, vaccine efficacy against severe gastroenteritis, and the discount rate. For example, at a total cost per vaccinated child of I$5, rotavirus vaccination was shown to be cost-saving in 54 countries and for the rest of the countries (except for Cuba, Moldova, Sri Lanka, and Ukraine) the incremental cost-effectiveness ratios ranged between I$1-I$170 per DALY averted. With a total cost per vaccinated child of I$50, rotavirus vaccines were not cost-saving in any GAVI-eligible countries. Results were moderately sensitive to hospitalization, outpatient visit costs, and level of vaccine immunity waning. When we decreased age-specific vaccine efficacy by ~14% annually over a 5-year time horizon in India, for instance, the incremental cost per DALY averted (at I$25 per child) increased from I$200 to I$220. The results were robust to duration of the disease, disability weight for diarrhea, transportation cost per visit, length of stay for hospitalization, and time per outpatient visit.

When we used standard age weighting (K = 1) [19] instead of uniform age weighting (K = 0), outcomes looked more favorable. For example, using K = 1, the total numbers of DALYs averted (discounted at 3%) among the 72 GAVI-eligible countries increased to ~8.1 million (compared to ~7.1 million with K = 0), and the incremental costs per DALY averted decreased by approximately 9-13% for each of the GAVI-eligible countries.

We conducted analyses using alternative vaccine efficacy data by following the WHO SAGE recommendation for efficacy data extrapolation across countries. The average rotavirus death reduction for the GAVI-eligible countries was reduced to ~41% (compared to ~52%), since the average vaccine efficacy against severe rotavirus diseases was lower using this approach than calculating based on country- or region-specific serotype distribution and serotype-specific vaccine efficacy. Accordingly, rotavirus vaccination appeared less cost-effective; for instance, assuming this alternate efficacy and I$25 per vaccinated child, only seven countries had an incremental cost per DALY averted under I$100 (Table 4).

Budget impact: Forecasting disease burden reduction and financial costs

Table 5 summarizes the impact of alternative scale-up scenarios (combined with different vaccine efficacy estimates) on the overall disease burden reduction aggregated across the GAVI countries over a 10-year period. The first four rows in Table 5 present the results based on the base-case rollout scenarios. The results from the first scenario (assuming the base-case rollout scenario, vaccine efficacy based on the SAGE approach, and vaccine immunity waning) show that a 10-year effort for rotavirus vaccine scale-up would prevent about 41 million cases of outpatient visits, 4.5 million hospitalizations, and 0.9 million deaths caused by rotavirus in the GAVI countries. Without assuming immunity waning, the second scenario projects higher levels (~10%) of reduction in disease burden. The other alternative scenarios combined with different assumptions show very similar patterns. Similarly, Table 6 shows the aggregate financial requirements in the GAVI countries using the same set of scale-up scenarios. Figure 3 presents the incremental budget impact for each year of the 10-year time horizon, using Djibouti as an example. In our base-case rollout scenario, the country is assumed to provide rotavirus vaccines starting in 2011 (Table 2), achieve full coverage (89%) in 2015, and maintain full coverage for the rest of the time horizon. For each year, the left-hand bars in the graph indicate the financial costs of sustaining a rotavirus program without considering the savings to the government from averted treatment costs, while the right-hand bars indicate the financial costs when including such savings. (See Additional file 1 for country-specific results not considering the savings to the government.)
Table 5

Health impact of alternative scale-up scenarios in the GAVI-eligible countries

No

Scale-up scenarios

No. of children vaccinated

(in million)

No. of outpatient visits averted

(r = 0%) (in million)

No. of hospitalization averted

(r = 0%) (in million)

No. of deaths averted

(r = 0%) (in million)

YL saved

(r = 3%) (in million)

DALYs averted

(r = 3%) (in million)

1

Base-case rollout scenario (Table 2)

      
 

Vaccine efficacy based on the SAGE approach

      
 

Vaccine immunity waning (14% annually)

281.8

40.7

4.5

0.9

20.3

20.4

2

Base-case rollout scenario (Table 2)

      
 

Vaccine efficacy based on the SAGE approach

      
 

No vaccine immunity waning

281.8

44.1

4.9

1.0

22.0

22.1

3

Base-case rollout scenario (Table 2)

      
 

Vaccine efficacy adjusted for serotype distribution

      
 

Vaccine immunity waning (14% annually)

281.8

47.8

5.6

1.2

25.3

25.4

4a

Base-case rollout scenario (Table 2)

      
 

Vaccine efficacy adjusted for serotype distribution

      
 

No vaccine immunity waning

281.8

51.7

6.0

1.3

27.3

27.4

5

(Modified) Wolfson et al. scenario [37]

      
 

Vaccine efficacy based on the SAGE approach

      
 

No vaccine immunity waning

410.5

64.2

7.7

1.6

35.6

35.7

6

(Modified) Wolfson et al. scenario [37]

      
 

Vaccine efficacy adjusted for serotype distribution

      
 

No vaccine immunity waning

410.5

81.4

10.4

2.2

48.1

48.3

7

A flat coverage of 70%

      
 

Vaccine efficacy based on the SAGE approach

      
 

No vaccine immunity waning

537.0

85.9

10.1

2.1

48.9

49.1

8

A flat coverage of 70%

      
 

Vaccine efficacy adjusted for serotype distribution

      
 

No vaccine immunity waning

537.0

106.6

13.3

2.8

64.5

64.7

r = discount rate; YL = years of life; DALYs = disability-adjusted life-years; I$ = international dollars; SAGE = Strategic Advisory Group of Experts on Immunization.

a Base-case analysis.

Table 6

Budget impact of alternative scale-up scenarios in the GAVI-eligible countries

   

Financial costsa

No

Scale-up scenarios

No. of children vaccinated

Global society perspective (Total vaccination program costs)

GAVI Alliance perspective (Vaccine cost support)

Local government perspective (not including medical cost savings)b

Local government perspective (including medical cost savings)b

  

(in million)

(US$, million)

(US$, million)

(US$, million)

(US$, million)

1

Base-case rollout scenario (Table 2)

     
 

Vaccine efficacy based on the SAGE approach

     
 

Vaccine immunity waning (14% annually)

281.8

5,879

4,079

1,800

1,714

2

Base-case rollout scenario (Table 2)

     
 

Vaccine efficacy based on the SAGE approach

     
 

No vaccine immunity waning

281.8

5,879

4,079

1,800

1,707

3

Base-case rollout scenario (Table 2)

     
 

Vaccine efficacy adjusted for serotype distribution

     
 

Vaccine immunity waning (14% annually)

281.8

5,879

4,079

1,800

1,695

4c

Base-case rollout scenario (Table 2)

     
 

Vaccine efficacy adjusted for serotype distribution

     
 

No vaccine immunity waning

281.8

5,879

4,079

1,800

1,686

5

(Modified) Wolfson et al. scenario [30]

     
 

Vaccine efficacy based on the SAGE approach

     
 

No vaccine immunity waning

410.5

8,573

5,943

2,630

2,472

6

(Modified) Wolfson et al. scenario [30]

     
 

Vaccine efficacy adjusted for serotype distribution

     
 

No vaccine immunity waning

410.5

8,573

5,943

2,630

2,414

7

A flat coverage of 70%

     
 

Vaccine efficacy based on the SAGE approach

     
 

No vaccine immunity waning

537.0

11,222

7,785

3,437

3,236

8

A flat coverage of 70%

     
 

Vaccine efficacy adjusted for serotype distribution

     
 

No vaccine immunity waning

537.0

11,222

7,785

3,437

3,170

r = discount rate; YL = years of life; DALYs = disability-adjusted life-years; I$ = international dollars; SAGE = Strategic Advisory Group of Experts on Immunization.

a All 10-year forecasts are based on the composite program cost of I$25 (corresponding to US$5 per vaccine dose) per vaccinated child and are not discounted.

b The vaccine costs to local governments are based on the GAVI's co-financing scheme [32].

c Base-case analysis.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2458-10-253/MediaObjects/12889_2009_Article_2164_Fig3_HTML.jpg
Figure 3

Budget impact of rotavirus vaccination in Djibouti. This figure shows the budget impact of a rotavirus program in Djibouti for each year of a 10-year time horizon. For each year, the difference between the bars indicates the savings to the government from averted treatment costs.

Discussion

While child mortality is reported to have decreased in general since 1990, mortality among young children remains high in many developing countries. The most recent World Health Statistics show that diarrhea makes up the second or third cause of childhood deaths in most low-income countries [22]; in 68 of the 72 GAVI-countries, diarrhea accounts for more than 10% of deaths among young children. Since rotavirus is the most common cause of severe gastroenteritis among young children, any interventions to control rotavirus infection have the potential to greatly improve childhood mortality, contributing to achieving one of the MDG targets.

Our findings show that routine infant vaccination using either of the two new recent vaccines would prevent 0.9-2.8 million deaths associated with rotavirus infection among children under age 5 in the poorest parts of the world over the next decade (2010-2019), depending on vaccine scale-up scenarios. Rotavirus vaccination programs would also prevent 4.5-13.3 million estimated cases of hospitalization and 41-107 million cases of outpatient clinic visits in the same population over the same intervention period.

Our results also show that rotavirus vaccine programs would be considered a cost-effective public health intervention in most GAVI countries; using the WHO's cost-effectiveness threshold based on per capita GDP, the vaccines were considered cost-effective in 68 of the 72 countries (~94%). It should be noted that a rotavirus vaccination program might not prove cost-effective in countries with a relatively low rotavirus mortality rate like Cuba, Ukraine, and Moldova. Although the likelihood that a country will introduce a new vaccine or scale-up rate is not always proportionate to the level of disease burden, these results may affect policy makers' decisions whether or not to introduce a vaccine. For example, although Cuba is classified into a high demand region in a previous study [36] and in the present study, Cuba's rotavirus associated mortality rate is less than 1 per 100,000 children under age 5 and estimated incremental cost-effectiveness is about I$30,000 per DALY averted (at the composite vaccination cost of I$25 per vaccinated child). It is conceivable that policy makers might want to consider using resources to introduce other childhood vaccines or to extend coverage of existing health interventions that target a higher burden of disease.

In the present analysis, the most influential factor in the cost-effectiveness results was per child vaccination costs, which includes rotavirus vaccine costs, vaccine wastage, and any activities to support the delivery of the vaccines. Since the data on country-specific incremental costs for introducing a new childhood vaccine into a national immunization program are rarely available for low-income countries, we chose to use a composite cost approach, assuming a total program cost of I$10 or I$25 per vaccinated child. Accordingly, our base-case cost-effectiveness results use standardized vaccination program costs across the 72 GAVI countries, so the main differences in the incremental cost-effectiveness ratios may be attributable to the differences in disease burden (i.e., rotavirus associated mortality rates, ratios of rotavirus associated hospitalizations or outpatient visits to rotavirus deaths) and vaccine efficacy adjusted for serotype distributions. Note that, although the discount rate was still relatively influential, it was not as important as it is with vaccines where benefits are realized in the distant future (e.g., HPV or HBV vaccines). The results were robust to country-specific hourly wage rates or transportation costs per medical visit, which were used in calculating direct non-medical costs such as caregivers' time costs or travel costs. The results were not sensitive to assumptions about medical utilization such as average length of hospitalization or average time spent per outpatient clinic visit.

Note that our base-case estimates of the main health outcome (i.e. DALYs averted) are based on uniform age weights (K = 0), primarily for normative reasons, while a majority of previous similar studies use the standard age weights (K = 1) used by the WHO GBD study [19]. Uniform age weighting leads to relatively lower years of life lost (YLL) compared with those associated with standard age weighting (K = 1) over the age range of age 0-4 years [20]. Accordingly, when assuming similar model structure and parameter values, our results are more conservative since our base-case analysis (K = 0) yields a lower number of DALYs averted than other similar studies that evaluate the impact of rotavirus vaccination in terms of DALYs incorporating non-uniform age weights (K = 1) (as also shown in the sensitivity analysis results).

Although the WHO SAGE recommended global use of rotavirus vaccines based on the evidence from recent clinical trials, there is a high level of uncertainty about short-term and longer-term vaccine efficacy in local settings given the variability of rotavirus serotype distributions across regions and over time. Our analysis shows the implication of following SAGE's recommendation to transfer vaccine efficacy by using data from other countries with comparable levels of childhood mortality. In a majority of the GAVI countries, the extended vaccine efficacy following the SAGE approach was lower than the weighted average of serotype-specific vaccine efficacy used in our base-case analysis. Correspondingly, the results of a secondary analysis using the SAGE approach showed the estimated avertable burden to be about 30% (0-70%) lower.

Recently an increasing number of published studies have evaluated the health and economic consequences of rotavirus vaccines in different settings, using various modeling approaches [16, 25, 26, 36, 3959]. Ten studies [16, 25, 36, 3944, 52] have assessed cost-effectiveness of rotavirus vaccination in low-income countries. When subject to the same currency type, inflation and vaccine price, our results were comparable to those from the studies performed in low-income country settings. For example, when we roughly adjusted Rose et al.'s results for India that are obtained using an individual-level Markov model [39] for currency type and inflation, the adjusted incremental cost-effectiveness ratio of ~I$140 per DALY averted was comparable to our result for India, I$200 per DALY averted (at I$25 per vaccinated child). When we assumed similar program costs, the discrepancy in the two studies' results was further reduced.

The cost-effectiveness profiles of rotavirus vaccines from the present study are also very similar to those for HPV vaccines reported in a previous study [60], which was performed using a similar approach in the 72 GAVI-eligible countries. That study reported that at a cost per vaccinated girl of I$10 ($2 per dose), for 49 of 72 countries, the cost per DALY averted was less than I$100 (or the program was cost-saving), and for an additional 12 countries, less than I$200. At higher costs per vaccinated girl (e.g. I$25), for 33 of 72 countries, the cost per DALY averted was less than I$300, and for an additional 23 countries, less than I$500 [60]. However, provided vaccine prices are similar, the financial resources required are very different, in large part due to differences in the size of the target populations (i.e., infants of both genders versus adolescent girls). Interestingly, although not immediately intuitive, the avertable mortality burden from the two vaccines is also similar. For example, while annual deaths among the 72 GAVI countries are estimated to be ~466,000 from rotavirus [29] and ~161,000 from cervical cancer [61], if we restrict the comparison to female gender, the annual deaths are ~230,000 versus ~161,000. Further, these estimates represent deaths based on a snapshot of a population structure for a particular calendar year, and do not reflect a future population dynamics. Consider the present day (2010) cohort of newborn infants and the present day cohort of 9-year old girls (2010) in the GAVI-eligible countries. Modeled projections show approximately 508,000 of the infants will die from rotavirus and 580,000 of the 9-year old girls will eventually die from cervical cancer. Clearly both vaccines are priorities.

Although a rotavirus program proves cost-effective over a wide range of parameter values and alternative assumptions, countries need information on affordability of the vaccines as well. That is, in most low-income countries with severe resource constraints, multiple public health priorities compete for a limited shared budget; policy makers need to know the financial costs required to implement any program as well as its value for money. In the present study, in addition to the secondary cost-effectiveness analysis from the local government perspective, we performed a budget impact analysis based on simplifying assumptions, taking into account the 'new vaccine co-financing policy' recently announced by the GAVI Alliance, in order to project financial requirements and potential savings from averted medical costs for each year of the 10-year period. Our findings suggest that, with GAVI's support, the costs to each GAVI country would be reduced to approximately I$10-I$11 when assuming a composite program cost of I$25, but that covering this would still be a substantial burden to the lowest-income GAVI countries. This type of analysis may be useful to policy makers seeking to address the question of affordability of new vaccines by comparing the budget impact forecast with their own projected total immunization budget. Assessment of affordability is not complicated when there is a single earmarked budget for a new vaccine of interest. However, if there are multiple other immunization (or public health) programs competing for a shared budget, choosing an optimal set of immunization programs under a budget constraint can be challenging. Recently there have been a number of studies aiming to help policy makers with resource allocation, taking into account multiple criteria such as ethical or political considerations in addition to technical efficiency [6265].

Note that the results of our financial forecasting and overall disease burden reduction are, of course, sensitive to the forecasted level of demand for rotavirus vaccine. We base our base-case scale-up scenario on local experience with other childhood vaccines and national income levels. However, there are reasons why this forecast may be over- or under-estimated. For instance, there may be discrepancies between reported and actual uptake rates in local settings simply due to the challenges of collecting health information across entire populations. Further, while we assumed that all children covered would receive a full 3 doses of vaccine, in practice dose completion rates may vary, affecting the total cost and altering cost-effectiveness (due to both changes in cost and changes in impact if vaccine efficacy with fewer than 3 doses is lower than that under a full 3-dose course). Thus, to come up with more reliable forecasts for financial requirements associated with new vaccine introduction, more research is needed to improve the quality of coverage data and to conceptualize an approach to forecasting future uptake rate more realistically. Also, some operational research is needed to identify factors that might affect the sustainability of scale-up scenarios.

Our study has several limitations. First, we synthesized available data using an Excel-based model that relies on simplifying assumptions; this was a purposeful tradeoff in that, unlike our past detailed models of a single country [16], here we sought to provide a broad perspective on avertable burden and cost-effectiveness of a global investment to vaccinate children against rotavirus. As such, this simple model cannot capture indirect effects that might result from a large-scale routine immunization program. If a rotavirus program can provide such benefits among non-vaccinated subpopulations, as suggested in recent published studies that used a dynamic mathematical model to estimate the cost-effectiveness of rotavirus vaccines in the United States [58, 66], we may be underestimating benefits. Second, unlike our previously published model applied to a single country [16], this static model applied to 72 countries is implicitly based on a decision-tree structure, and thus has a limited capacity to explore uncertainty surrounding natural history of rotavirus infection and potential serotype replacement that might occur in the long term after vaccine introduction [17, 67]. Third, our analysis assumes composite costs rather than estimating individual country-specific vaccine delivery costs. Accordingly, our results should be carefully interpreted by donors or global policy makers interested in the relative cost-effectiveness of the rotavirus vaccines conditional on alternative strategies to achieve coverage. We emphasize the need for future research to estimate the incremental costs of delivering new rotavirus vaccines in local settings. Lastly, while we tried to estimate country-specific data for the ratios of hospitalizations and outpatient visits to deaths and for serotype distributions of rotavirus, country-specific data were often not available, leaving us to use data from neighboring countries or regional average.

Conclusion

The limitations of our study suggest that our findings should be considered for only what they are intended-broad estimate of the potential impact and value of rotavirus vaccination. Our main findings suggest that rotavirus vaccine introduction would be considered a worthwhile investment for improving general development as well as public health level in most low-income countries, with a favorable cost-effectiveness profile even under a vaccine price ($1.5-$5.0 per dose) higher than those of traditional childhood vaccines. In addition, our study provides information on an approximate future stream of financial costs that need to be included in health budgets. Further, as more evidence on vaccine efficacy and safety, particularly associated with vaccination schedule in local settings [68], and more country-specific data (e.g., data from the cMYP immunization financing database) become available, our analysis can serve as baseline for any future refinement of country-specific results.

Declarations

Acknowledgements

SG and SK are funded in part by the Bill and Melinda Gates Foundation for work on HPV vaccines; to the extent the study methodology and decision modeling approach overlaps with our HPV vaccine research, we would like to acknowledge our support from the Foundation. The funding source did not have any influence on the design and conduct of the study. This study is part of a broader set of efforts to inform vaccine policy in low and middle income countries, supported in part by Harvard Initiative for Global Health (Decision Science Working Group). The authors are very grateful to Joshua Chang from the Center for Health Decision Science at Harvard School of Public Health for his technical assistance. We also thank the reviewers for extremely helpful comments during the revision process of the manuscript.

Authors’ Affiliations

(1)
Center for Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health
(2)
Harvard Initiative for Global Health

References

  1. Kosek M, Bern C, Guerrant RL: The global burden of diarrhoeal disease, as estimated from studies published between 1992 and 2000. Bull World Health Organ. 2003, 81 (3): 197-204.PubMedPubMed CentralGoogle Scholar
  2. Parashar UD, Hummelman EG, Bresee JS, Miller MA, Glass RI: Global illness and deaths caused by rotavirus disease in children. Emerg Infect Dis. 2003, 9 (5): 565-72.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Parashar UD, Burton A, Lanata C, Boschi-Pinto C, Shibuya K, Steele D, Birmingham M, Glass RI: Global Mortality Associated with Rotavirus Disease among Children in 2004. Journal of Infectious Diseases. 2009, 200 (s1): S9-S15. 10.1086/605025.View ArticlePubMedGoogle Scholar
  4. Santos N, Hoshino Y: Global distribution of rotavirus serotypes/genotypes and its implication for the development and implementation of an effective rotavirus vaccine. Rev Med Virol. 2005, 15: 29-56. 10.1002/rmv.448.View ArticlePubMedGoogle Scholar
  5. Arista S, Giammanco GM, De Grazia S, Ramirez S, Lo Biundo C, Colomba C, Cascio A, Martella V: Heterogeneity and temporal dynamics of evolution of G1 human rotaviruses in a settled population. J Virol. 2006, 80 (21): 10724-33. 10.1128/JVI.00340-06.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Trinh QD, Nguyen TA, Phan TG, Khamrin P, Yan H, Hoang PL, Maneekarn N, Li Y, Yagyu F, Okitsu S, Ushijima H: Sequence analysis of the VP7 gene of human rotavirus G1 isolated in Japan, China, Thailand, and Vietnam in the context of changing distribution of rotavirus G-types. J Med Virol. 2007, 79 (7): 1009-16. 10.1002/jmv.20920.View ArticlePubMedGoogle Scholar
  7. Zahn M, Marshall GS: Clinical and epidemiological aspects of rotavirus infection. Pediatr Ann. 2006, 35 (1): 23-8.View ArticlePubMedGoogle Scholar
  8. WHO: Rotavirus Vaccines. Weekly Epidemiological Record. 2007, 82 (32): 285-296.Google Scholar
  9. Dennehy PH: Rotavirus vaccines--an update. Vaccine. 2007, 25 (16): 3137-41. 10.1016/j.vaccine.2007.01.102.View ArticlePubMedGoogle Scholar
  10. WHO: Meeting of the immunization Strategic Advisory Group of Experts, April 2009 - conclusions and recommendations. Weekly Epidemiological Record. 2009, 84 (23): 220-236.Google Scholar
  11. WHO: Global networks for surveillance of rotavirus gastroenteritis, 2001-2008. Weekly Epidemiological Record. 2008, 83 (47): 421-5.Google Scholar
  12. Jamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, Evans DB, Jha P, Mills A, Musgrove P: Disease Control Priorities in Developing Countries. 2006, New York: Oxford University Press, 2Google Scholar
  13. Tan-Torres Edejer T, Baltussen R, Adam T, Hutubessy R, Acharya A, Evans DB, Murray CJL: Making choices in health: WHO guide to cost-effectiveness analysis. 2003, Geneva: World Health OrganizationGoogle Scholar
  14. Drummond MF, Sculpher MJ, Torrance GW, O'Brian BJ, Stoddart GL: Methods for the economic evaluation of health care programmers. 2005, New York, NY: Oxford University PressGoogle Scholar
  15. WHO: WHO guide for standardization of economic evaluations of immunization programmes. 2008, Geneva: World Health OrganizationGoogle Scholar
  16. Kim S, Goldie SJ, Salomon JS: Cost-effectiveness of Rotavirus Vaccination in Vietnam. BMC Public Health. 2009, 9: 29-10.1186/1471-2458-9-29.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Kim SY, Goldie SJ, Salomon JA: Exploring Model Uncertainty in Economic Evaluation of Health Interventions. 2009, Society for Medical Decision Making Annual Meeting, Hollywood, CAGoogle Scholar
  18. Ruiz-Palacios GM, Perez-Schael I, Velazquez FR, Abate H, Breuer T, Clemens SC, Human Rotavirus Vaccine Study Group, et al: Safety and efficacy of an attenuated vaccine against severe rotavirus gastroenteritis. N Engl J Med. 2006, 354 (1): 11-22. 10.1056/NEJMoa052434.View ArticlePubMedGoogle Scholar
  19. Murray CJL, Lopez AD: The Global Burden of Disease, Vol. 1 of Global Burden of Disease and Injury Series. 1996, Cambridge, MA: Harvard University PressGoogle Scholar
  20. Mathers CD, Salomon JA, Ezzati M, Begg S, Hoorn SV, Lopez AD: Sensitivity an uncertainty analyses for burden of disease and risk factor estimates. Global Burden of Disease and Risk Factors. Edited by: Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJL. 2006, Washington: Oxford University Press, 399-426.Google Scholar
  21. GAVI Alliance: Candidate Rotavirus Vaccine Recommendations for Consideration by the WHO Strategic Advisory Group of Experts (SAGE) on Immunization. 2009, GenevaGoogle Scholar
  22. WHO: World Health Statistics. 2008, WHO, GenevaGoogle Scholar
  23. Nations United Department of Economic and Social Affairs Division Population: World Population Prospects: The 2006 Revision. CD-ROM Edition - Extended Dataset in Excel and ASCII formats (United Nations publication, Sales No. E.07.XIII.7). 2007Google Scholar
  24. WHO: Life tables for WHO member states: 2006. [http://www.who.int/whosis/database/life_tables/life_tables.cfm]
  25. Flem ET, Latipov R, Nurmatov ZS, Xue Y, Kasymbekova KT, Rheingans RD: Costs of diarrheal disease and the cost-effectiveness of a rotavirus vaccination program in kyrgyzstan. J Infect Dis. 2009, 200 (Suppl 1): S195-202. 10.1086/605040.View ArticlePubMedGoogle Scholar
  26. Rheingans RD, Constenla D, Antil L, Innis BL, Breuer T: Potential cost-effectiveness of vaccination for rotavirus gastroenteritis in eight Latin American and Caribbean countries. Rev Panam Salud Publica. 2007, 21 (4): 205-216.View ArticlePubMedGoogle Scholar
  27. Arvay ML, Curns AT, Terp S, Armah G, Wontuo P, Parashar UD, Binka F, Glass RI, Widdowson MA: How much could rotavirus vaccines reduce diarrhea-associated mortality in northern Ghana? A model to assess impact. J Infect Dis. 2009, 200 (Suppl 1): S85-S91. 10.1086/605062.View ArticlePubMedGoogle Scholar
  28. Benhafid M, Youbi M, Klena JD, Gentsch JR, Teleb N, Widdowson MA: Epidemiology of rotavirus gastroenteritis among children <5 years of age in Morocco during 1 year of sentinel hospital surveillance, June 2006-May 2007. J Infect Dis. 2009, 200: S70-5. 10.1086/605048.View ArticlePubMedGoogle Scholar
  29. WHO: Estimated rotavirus deaths for children under 5 years of age: 2004. [http://www.who.int/immunization_monitoring/burden/rotavirus_estimates/en/index.html]
  30. International Labour Office: Database on labour statistics (LABORSTA). [http://laborsta.ilo.org/]
  31. Bank World: World Development Indicators Online. [http://web.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:20398986~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html]
  32. GAVI Alliance: Policy Brief: GAVI Alliance New Vaccine Co-financing Policy. 2009, GenevaGoogle Scholar
  33. Linhares AC, Velázquez FR, Pérez-Schael I, Sáez-Llorens X, Abate H, Espinoza F, López P, Macías-Parra M, Ortega-Barría E, Rivera-Medina DM, Rivera L, Pavía-Ruz N, Nuñez E, Damaso S, Ruiz-Palacios GM, De Vos B, O'Ryan M, Gillard P: Efficacy and safety of an oral live attenuated human rotavirus vaccine against rotavirus gastroenteritis during the first 2 years of life in Latin American infants: a randomised, double-blind, placebo-controlled phase III study. Lancet. 2008, 371 (9619): 1181-9. 10.1016/S0140-6736(08)60524-3.View ArticlePubMedGoogle Scholar
  34. Mauskopf JA, Sullivan SD, Annemans L, Caro J, Mullins CD, Nuijten M, Orlewska E, Watkins J, Trueman P: Principles of good practice for budget impact analysis: Report of the ISPOR task force on good research practices-Budget impact analysis. Value in Health. 2007, 10 (5): 336-47. 10.1111/j.1524-4733.2007.00187.x.View ArticlePubMedGoogle Scholar
  35. Marshall DA, Douglas PR, Drummond MF, Torrance GW, MacLeod S, Manti O, Cheruvu L, Corvari R: Guidelines for conducting pharmaceutical budget impact analyses for submission to public drug plans in Canada. Pharmacoeconomics. 2008, 26 (6): 477-95. 10.2165/00019053-200826060-00003.View ArticlePubMedGoogle Scholar
  36. Atherly D, Dreibelbis R, Parashar UD, Levin C, Wecker J, Rheingans RD: Rotavirus vaccination: cost-effectiveness and impact on child mortality in developing countries. J Infect Dis. 2009, 200 (Suppl 1): S28-38. 10.1086/605033.View ArticlePubMedGoogle Scholar
  37. Wolfson LJ, Gasse F, Lee-martin S, Lydon P, Magan A, Tibouti A, Johns B, Hutubessy R, Salama P, Okwo-Bele JM: Estimating the Costs of Achieving the WHO-UNICEF Global Immunization Vision and Strategy, 2006-2015. Bulletin of the World Health Organization. 2008, 86 (1): 27-39. 10.2471/BLT.07.045096.View ArticlePubMedPubMed CentralGoogle Scholar
  38. WHO: The World Health Report 2002: Reducing Risks, Promoting Healthy Life. 2002, WHO, GenevaGoogle Scholar
  39. Rose J, Hawthorn RL, Watts B, Singer ME: Public health impact and cost effectiveness of mass vaccination with live attenuated human rotavirus vaccine (RIX4414) in India: model based analysis. BMJ. 2009, 339: b3653-10.1136/bmj.b3653.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Fischer TK, Anh DD, Antil L, Cat ND, Kilgore PE, Thiem VD, Rheingans R, Tho le H, Glass RI, Bresee JS: Health care costs of diarrheal disease and estimates of the cost-effectiveness of rotavirus vaccination in Vietnam. J Infect Dis. 2005, 192 (10): 1720-6. 10.1086/497339.View ArticlePubMedGoogle Scholar
  41. Podewils LJ, Antil L, Hummelman E, Bresee J, Parashar UD, Rheingans R: Projected cost-effectiveness of rotavirus vaccination for children in Asia. J Infect Dis. 2005, 192 (Suppl 1): S133-45. 10.1086/431513.View ArticlePubMedGoogle Scholar
  42. Isakbaeva ET, Musabaev E, Antil L, Rheingans R, Juraev R, Glass RI, Bresee JS: Rotavirus disease in Uzbekistan: Cost-effectiveness of a new vaccine. Vaccine. 2007, 25: 373-380. 10.1016/j.vaccine.2006.07.029.View ArticlePubMedGoogle Scholar
  43. Tate JE, Rheingans RD, O'Reilly CE, Obonyo B, Burton DC, Tornheim JA, Adazu K, Jaron P, Ochieng B, Kerin T, Calhoun L, Hamel M, Laserson K, Breiman RF, Feikin DR, Mintz ED, Widdowson MA: Rotavirus disease burden and impact and cost-effectiveness of a rotavirus vaccination program in kenya. J Infect Dis. 2009, 200 (Suppl 1): S76-84. 10.1086/605058.View ArticlePubMedGoogle Scholar
  44. Flem ET, Musabaev E, Juraev R, Kerin T, Gentsch J, Glass RI, Bresee JS: Rotavirus gastroenteritis in uzbekistan: implications for vaccine policy in central Asia. J Infect Dis. 2009, 200 (Suppl 1): S114-24.Google Scholar
  45. Clark AD, Walker DG, Mosqueira NR, Penny ME, Lanata CF, Fox-Rushby J, Sanderson CF: Cost-effectiveness of rotavirus vaccination in peru. J Infect Dis. 2009, 200 (Suppl 1): S114-24. 10.1086/605043.View ArticlePubMedGoogle Scholar
  46. Ortega O, El-Sayed N, Sanders JW, Abd-Rabou Z, Antil L, Bresee J, Mansour A, Adib I, Nahkla I, Riddle MS: Cost-benefit analysis of a rotavirus immunization program in the Arab Republic of Egypt. J Infect Dis. 2009, 200 (Suppl 1): S92-8. 10.1086/605057.View ArticlePubMedGoogle Scholar
  47. Wang XY, Riewpaiboon A, von Seidlein L, Chen XB, Kilgore PE, Ma JC, Qi SX, Zhang ZY, Hao ZY, Chen JC, Xu ZY: Potential cost-effectiveness of a rotavirus immunization program in rural China. Clin Infect Dis. 2009, 49 (8): 1202-10. 10.1086/605632.View ArticlePubMedGoogle Scholar
  48. Soarez PC, Valentim J, Sartori AMC, Novaes HMD: Cost-effectiveness análisis of routine rotavirus vaccination in Brazil. Rev Panam Salud Publica. 2008, 23 (4): 221-230.View ArticlePubMedGoogle Scholar
  49. Valencia-Mendoza A, Bertozzi SM, Gutierrez JP, Itzler R: Cost-effectiveness of introducing a rotavirus vaccine in developing countries: The case of Mexico. BMC Infectious Diseases. 2008, 8: 103-10.1186/1471-2334-8-103.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Constenla DO, Linhares AC, Rheingans RD, Antil LR, Waldman EA, da Silva LJ: Economic Impact of a Rotavirus Vaccine in Brazil. J HEALTH POPUL NUTR. 2008, 26 (4): 388-396.PubMedPubMed CentralGoogle Scholar
  51. Constenla D, Velázquez FR, Rheingans RD, Antil L, Cervantes Y: Economic impact of a rotavirus vaccination program in Mexico. Rev Panam Salud Publica. 2009, 25 (6): 481-90. 10.1590/S1020-49892009000600003.View ArticlePubMedGoogle Scholar
  52. Rheingans RD, Antil L, Dreibelbis R, Podewils LJ, Bresee JS, Parashar UD: Economic Costs of Rotavirus Gastroenteritis and Cost-Effectiveness of Vaccination in Developing Countries. Journal of Infectious Diseases. 2009, 200: S16-27. 10.1086/605026.View ArticlePubMedGoogle Scholar
  53. Jit M, Edmunds WJ: Evaluating rotavirus vaccination in England and Wales: Part II. The potential cost-effectiveness of vaccination. Vaccine. 2007, 25: 3971-3979. 10.1016/j.vaccine.2007.02.070.View ArticlePubMedGoogle Scholar
  54. Lorgelly PK, Joshi D, Iturriza Gómara M, Gray J, Mugford M: Exploring the cost effectiveness of an immunization programme for rotavirus gastroenteritis in the United Kingdom. Epidemiol Infect. 2008, 136 (1): 1-12.Google Scholar
  55. Widdowson MA: Cost-effectiveness and Potential Impact of Rotavirus Vaccination in the United States. Pediatrics. 2007, 119 (4): 684-697. 10.1542/peds.2006-2876.View ArticlePubMedGoogle Scholar
  56. Melliez H, Levybruhl D, Boelle PY, Dervaux B, Baron S, Yazdanpanah Y: Cost and cost-effectiveness of childhood vaccination against rotavirus in France. Vaccine. 2008, 26 (5): 706-15. 10.1016/j.vaccine.2007.11.064.View ArticlePubMedGoogle Scholar
  57. Newall AT, Beutels P, Macartney K, Wood J, MacIntyre CR: The cost-effectiveness of rotavirus vaccination in Australia. Vaccine. 2007, 25 (52): 8851-60. 10.1016/j.vaccine.2007.10.009.View ArticlePubMedGoogle Scholar
  58. Shim E, Galvani AP: Impact of transmission dynamics on the cost-effectiveness of rotavirus vaccination. Vaccine. 2009, 27 (30): 4025-30. 10.1016/j.vaccine.2009.04.030.View ArticlePubMedGoogle Scholar
  59. Jit M, Bilcke J, Mangen MJ, Salo H, Melliez H, Edmunds WJ, Yazdan Y, Beutels P: The cost-effectiveness of rotavirus vaccination: Comparative analyses for five European countries and transferability in Europe. Vaccine. 2009, 27 (44): 6121-8. 10.1016/j.vaccine.2009.08.030.View ArticlePubMedGoogle Scholar
  60. Goldie SJ, O'Shea M, Campos NG, Diaz M, Sweet S, Kim S: Health and economic outcomes of HPV 16,18 Vaccination in 72 GAVI-eligible countries. Vaccine. 2008, 26 (32): 4080-93. 10.1016/j.vaccine.2008.04.053.View ArticlePubMedGoogle Scholar
  61. Ferlay J, Bray F, Pisani P, Parkin DM: GLOBOCAN 2002. Cancer Incidence, Mortality and Prevalence Worldwide. IARC Cancer Base No. 5 Version 2.0. 2004, Lyon, France: IARCPressGoogle Scholar
  62. Jehu-Appiah C, Baltussen R, Acquah C, Aikins M, d'Almeida SA, Bosu WK, Koolman X, Lauer J, Osei D, Adjei S: Balancing equity and efficiency in health priorities in Ghana: the use of multicriteria decision analysis. Value in Health. 2008, 11: 1081-87. 10.1111/j.1524-4733.2008.00392.x.View ArticlePubMedGoogle Scholar
  63. Baltussen R, Niessen L: Priority setting of health interventions: the need for multi-criteria decision analysis. Cost Effectiveness and Resource Allocation. 2006, 4 (14):
  64. Baltussen R, Asbroek AHA, Koolman X, Shrestha N, Bhattarai P, Niessen LW: Priority Setting using multiple criteria: should a lung health programme be implemented in Nepal?. Health Policy and Planning. 2007, 22: 178-185. 10.1093/heapol/czm010.View ArticlePubMedGoogle Scholar
  65. Epstein DM, Chalabi Z, Claxton K, Sculpher M: Efficiency, Equity, and Budgetary Policies: Informing Decisions Using Mathematical Programming. Medical Decision Making. 2007, 27: 128-137. 10.1177/0272989X06297396.View ArticlePubMedGoogle Scholar
  66. Pitzer VE, Viboud C, Simonsen L, Steiner C, Panozzo CA, Alonso WJ, Miller MA, Glass RI, Glasser JW, Parashar UD, Grenfell BT: Demographic Variability, Vaccination, and the Spatiotemporal Dynamics of Rotavirus Epidemics. Science. 2009, 35 (5938): 290-294. 10.1126/science.1172330.View ArticleGoogle Scholar
  67. Kim S, Goldie SJ: Cost-effectiveness analyses of vaccination programmes: a focused review of modelling approaches. Pharmacoeconomics. 2008, 26 (3): 191-215. 10.2165/00019053-200826030-00004.View ArticlePubMedGoogle Scholar
  68. Danchin MH, Bines JE: Defeating Rotavirus? The Global Recommendation for Rotavirus Vaccination. N Engl J Med. 2009, 361 (20): 1919-1921. 10.1056/NEJMp0905091.View ArticlePubMedGoogle Scholar
  69. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/10/253/prepub

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