Stack ML, Ozawa S, Bishai DM, Mirelman A, Tam Y, Niessen, et al. Estimated economic benefits during the Decade of Vaccines include treatment savings, gains in labor productivity. Health Aff (Millwood). 2011;30(6):1012-8. ONLINE APPENDIX Additional Methods Lives Saved Tool Analysis The Lives Saved Tool (LiST) (Version 4.2 Beta 7) was used to project the number of child deaths that would be averted between the years 2011-2020 due to vaccine scale-up (coverage assumptions below). These projections can be seen in Exhibit F. LiST models the impact of health interventions by applying the change in intervention coverage and effectiveness to countryspecific cause-specific mortality, assuming country-specific population growth. Each child can be saved from a given cause only once, but then has the population risk of dying of other causes in subsequent age categories. The underlying burden of disease modeling used to determine the number of deaths averted due to each vaccine is based on extensive data over a large number of countries and up to 20 years of observations, allowing it to capture and mitigate underlying seasonality and annual variation of disease incidence. In the analysis, vaccination coverage levels in 2010 were taken from WHO/UNICEF 2009 estimates of national immunization coverage(1). Countries with vaccination coverage above 90% were 1
held constant through 2020. For each country, we used WHO published estimates of the numbers of deaths in 2008 due to each disease for children under five years old(2). The effectiveness of Hib, pneumococcal, measles, and rotavirus vaccines used for the analysis was published(3, 4, 5). The effectiveness of DTP vaccine was estimated to be 85%. LiST also estimated yearly deaths due to malaria in children under five years of age. This number was then used to calculate the number of lives saved due to increased coverage of a malaria vaccine. With 45% efficacy(6), a vaccine against malaria was assumed to be introduced in all malaria-affected countries in 2015 with coverage increasing to 90% by 2020. Economic Analysis Treatment costs averted Case fatality ratios (CFR) were used to calculate the number of cases that would be averted from the projected number of averted deaths. Country-specific CFRs were applied to project pneumonia and meningitis cases averted. Due to lack of available data, a single CFR was used for all countries except India to calculate cases of measles, malaria, pertussis and rotavirus. A surveillance based case fatality ratio for rotavirus in India was available in the literature and on account of its size and impact on our analysis it was used for India only. All other 2
case fatality ratios were estimated from an extensive literature review of surveillance studies in developing countries. The estimates and their uncertainty ranges can be seen in Exhibit G. To calculate the treatment costs averted, care seeking behavior was then applied to the estimated number of averted cases to determine how many cases would have sought care and encountered expenses. Country and symptom-specific care seeking behavior data were gathered from Demographic Health Surveys and UNICEF s State of the World s Children Report (depending on symptom) when available(7, 8). For countries that did not have data, the median of countries with data in the same sub-region was used. The rate of hospitalization depending on severity of disease was then applied to determine how many averted cases who sought care would have been admitted and how many would have been outpatients. Finally, population rates on if outpatients seek care from health centers or hospitals were applied to determine how many outpatients would have been treated in each type of facility. The cost of care was calculated using country-specific WHO- CHOICE estimates for hospital and outpatient fees (9), average bed days per hospitalization by syndrome (10-14) and a proportion of visit costs for medication and diagnostics based on studies from 10 countries (10, 14, 15). These were then 3
multiplied by the number of averted cases that would have sought treatment from each facility and the number who would have been admitted to the hospital. Productivity Losses Caretaker productivity loss from seeking care for a sick child was calculated by multiplying an estimate of a caretaker s daily productivity by the number of days lost due to care seeking. It was assumed that caretakers lost 50% of one day s productivity for seeking outpatient care and 100% of the total number of bed days productivity when a sick child was admitted to a hospital (See Exhibit B for a diagram of the analysis). Daily wages used in the analysis for caretaker productivity loss were based on the IMF s 2010 World Economic Outlook projected GDP per capita for the years 2011-2015(16) and extrapolated for years 2016-2020 based on projected GDP per capita growth during the years 2011-2015. These annual projections were then divided by 365 for a daily productivity estimate. To calculate productivity loss due to meningitis disability, regional ratios of survived cases that result in severe sequelae were applied to our estimated number of survived meningitis cases that would be averted (17). Age-specific survival rates 4
were then applied to estimate how many survivors would reach productive age and account for competing risks of mortality(18). The number of productive life years lost was then calculated by multiplying the number of children with disability that would live to productive age (age 15) by the life expectancy at age 15 and discounting it 50%. (It was assumed that severe disability, as defined by Edmond (2010) in accordance with the Global Burden of Disease - Disease Control Priorities Project, would reduce productive life years by 50% either through premature death or long-term disability.) Using the human capital approach, we multiplied the number of productive life years lost from disability by projected annual GDP/capita(16) to obtain an estimate of productivity lost due to severe meningitis sequelae. These results were discounted 3% and listed in 2009 US dollars. The same approach was used to determine productivity loss due to premature deaths. Age specific survival rates were applied to the number of averted deaths projected by LiST to calculate the number of children that would have reached productive age due to competing risks. This was then multiplied by country-specific life expectancy at age 15 to determine the number of productive life years lost due to premature death. Our estimate of productive life years lost was then multiplied by the projected 5
annual GDP/capita for each country. These results were discounted 3% and listed in 2009 US dollars. Sensitivity Analysis Sensitivity analyses were first conducted for key parameters associated with the LiST model. Estimated plausibility ranges around the deaths averted values were calculated for four of the largest GAVI countries (Democratic Republic of Congo, India, Nigeria and Pakistan). Confidence bounds were developed around seven parameters (mortality rates (19), cause of death (2), Hib, Pneumococcal (3) and rotavirus vaccine effectiveness (42-79%) and proportion of diarrhea deaths due to rotavirus (+- 10%)). The ratio of the upper and lower bounds to the standard value were estimated and averaged from these four countries. The ratios associated with all combinations of varying three parameters were estimated to derive a plausibility range of 30% to 297%. The next set of sensitivity analyses were conducted to present uncertainty ranges around the treatment cost savings and lost productivity estimates in the analysis. Monte Carlo simulations were carried out varying 13 key variables, such as case fatality ratios for pneumonia, meningitis, and rotavirus, productivity loss of long-term sequelae as a percentage of per capita GDP, 6
and the plausibility range from the LiST mortality estimates. Beta or uniform distributions were applied to each variable. See Exhibit G for baseline estimates and ranges of variables. Besides the LiST estimates, all other variables were tested for their effect on the final outcome if they were to be off by half (50%) or doubled (200%). The analysis involved running 10,000 iterations of the model using the PALISADES @RISK software (version 5.7, Palisades Corporation, New Field, NY) to obtain the uncertainty ranges. See Exhibit E for the overall distribution of the cost of illness analysis. 7
References 1. WHO. WHO/UNICEF estimates of national immunization coverage. 2009 [cited 2010 October]; Available from: http://apps.who.int/immunization_monitoring/en/globalsummary/tim eseries/tswucoveragedtp3.htm 2. Black RE, Cousens S, Johnson HL, Lawn JE, Rudan I, Bassani DG, et al. Global, regional, and national causes of child mortality in 2008: a systematic analysis. Lancet. 2010 Jun 5;375(9730):1969-87. 3. Theodoratou E, Johnson S, Jhass A, Madhi SA, Clark A, Boschi-Pinto C, et al. The effect of Haemophilus influenzae type b and pneumococcal conjugate vaccines on childhood pneumonia incidence, severe morbidity and mortality. Int J Epidemiol. 2010 Apr;39 Suppl 1:i172-85. 4. Sudfeld CR, Navar AM, Halsey NA. Effectiveness of measles vaccination and vitamin A treatment. Int J Epidemiol. 2010 Apr;39 Suppl 1:i48-55. 5. Fischer-Walker C, Black RE. Rotavirus vaccine and diarrhea mortality: quantifying regional variation in effect size. BMC Public Health. 2011;11(Supplement 3). 6. Olotu A, Lusingu J, Leach A, Lievens M, Vekemans J, Msham S, et al. Efficacy of RTS,S/AS01E malaria vaccine and exploratory analysis on anti-circumsporozoite antibody titres and protection in children aged 5-17 months in Kenya and 8
Tanzania: a randomised controlled trial. The Lancet Infectious Diseases. 2011;11(2):102-9. 7. UNICEF. State of the World's Children. 2010 [cited 2010 November 10]; Available from: http://www.unicef.org/sowc/. 8. MACRO. Demographic and Health Surveys, 45 countries. 1997-2009 [cited 2010 November 14]; Available from: http://www.measuredhs.com/countries/. 9. WHO. Country specific unit costs. 2005 [cited 2010 November 15]; Available from: http://www.who.int/choice/costs/en/. 10. Ayieko P, Akumu AO, Griffiths UK, English M. The economic burden of inpatient paediatric care in Kenya: household and provider costs for treatment of pneumonia, malaria and meningitis. Cost Eff Resour Alloc. 2009;7:3. 11. Broughton EI. Economic evaluation of Haemophilus influenzae type B vaccination in Indonesia: a cost-effectiveness analysis. J Public Health (Oxf). 2007 Dec;29(4):441-8. 12. Bassat Q, Guinovart C, Sigauque B, Aide P, Sacarlal J, Nhampossa T, et al. Malaria in rural Mozambique. Part II: children admitted to hospital. Malar J. 2008;7:37. 13. Adams Iea. Malaria: A burden explored. Bulletin of Health Information. 2004;1(1):28-34. 14. Rheingans RD, Constenla D, Antil L, Innis BL, Breuer T. Economic and health burden of rotavirus gastroenteritis for the 9
2003 birth cohort in eight Latin American and Caribbean countries. Rev Panam Salud Publica. 2007 Apr;21(4):192-204. 15. Hussain H, Waters H, Omer SB, Khan A, Baig IY, Mistry R, et al. The cost of treatment for child pneumonias and meningitis in the Northern Areas of Pakistan. Int J Health Plann Manage. 2006 Jul-Sep;21(3):229-38. 16. World Economic Outlook [database on the Internet]2010 [cited November 25, 2010]. Available from: http://www.imf.org/external/pubs/ft/weo/2010/01/weodata/index.as px. 17. Edmond K, Clark A, Korczak VS, Sanderson C, Griffiths UK, Rudan I. Global and regional risk of disabling sequelae from bacterial meningitis: a systematic review and meta-analysis. Lancet Infect Dis. 2010 May;10(5):317-28. 18. World Health Statistics [database on the Internet]2010 [cited November 1, 2010]. Available from: http://www.who.int/gho/database/en/. 19. Levels and Trends in Child Mortality, Report 2010 [database on the Internet]. UN Inter-agency Group on Child Mortality Estimation. 2010. Available from: http://www.childmortality.org/cmemain.html. 10
EXHIBIT LIST Exhibit A. (table) Caption/headline: GAVI eligible countries (2010) Source: www.gavialliance.org Exhibit B (Figure) Headline: Treatment Cost Analysis Diagram SOURCE Authors analysis. Exhibit C. (figure) Headline: Annual cost of illness averted by syndrome SOURCE Authors analysis. Exhibit D. (figure) Headline: Sensitivity analysis: Tornado diagram of input effects SOURCE Authors analysis. Exhibit E. (figure) Headline: Sensitivity analysis: Distribution of total cost of illness (72 GAVI countries) SOURCE Authors analysis. Exhibit F. (table) Headline: LiST mortality projections by disease and country Source: Johns Hopkins University. Lives Saved Tool (Version 4.2 Beta 7). 2010. Exhibit G. (table) Headline: Sensitivity analysis: Inputs and ranges SOURCE Authors analysis. Notes: *Country specific CFRs for pneumonia and meningitis were varied by multiplying them by the above weighting factor. 11
Exhibit A. GAVI eligible countries (2010) Afghanistan, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bhutan, Bolivia, Burkina Faso, Burundi, Cambodia, Cameroon, Central African Republic, Chad, Comoros, Democratic Republic of Congo, Republic of Congo, Cote d Ivoire, Cuba, Djibouti, Eritrea, Ethiopia, The Gambia, Georgia, Ghana, Guinea, Guinea- Bissau, Guyana, Haiti, Honduras, India, Indonesia, Kenya, Kiribati, North Korea, Kyrgyz Republic, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Mozambique, Myanmar, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Rwanda, Sao Tome and Principe, Senegal, Sierra Leone, Solomon Islands, Somalia, Sri Lanka, Sudan, Tajikistan, Tanzania, Timor- Leste, Togo, Uganda, Ukraine, Uzbekistan, Vietnam, Yemen, Zambia, Zimbabwe 13
Exhibit B Treatment Cost Analysis Diagram LiST projected deaths with vaccine scale- up Difference LiST projected deaths without scale- up Deaths averted due to vaccine scale- up CFR Cases averted due to scale- up % with ARI/Fever/Diarrhea who seek care from health facility # Cases who would seek Care # Cases who wouldn t seek care Rate of admittance among all cases who seek care # Inpatients # Outpatients No Treatment Costs % outpatients seen at hospital $ per Bed Day # of Bed Days % of total for Medication & Diagnostics # of outpatients who sought care from a hospital $ per hospital outpatient % of total for Medication & Diagnostics # of outpatients who sought care from a health center $ per health center outpatient % of total for Medication & Diagnostics Treatment Costs Averted Treatment Costs Averted Treatment Costs Averted 14
Exhibit C. Annual Cost of Illness Averted by Syndrome 15
Exhibit D. Sensitivity analysis: Tornado diagram of input effects 16
Exhibit E. Sensitivity analysis: Distribution of total cost of illness 17
Exhibit F. LiST mortality projections by disease and country Baseline vaccine coverage* Deaths averted in Children < 5 years of age, by Cause, 2010-2020 Country Pneumo- coccal Hib Rotavirus Measles DTP Malaria Pneumonia Meningitis Diarrhea Measles Pertussis Malaria Total Afghanistan 0 83 0 76 83 0 124,371 12,933 90,802 4,152 907 44 233,209 Angola 0 73 0 77 73 0 46,896 4,190 40,779 2,203 3,321 14,836 112,225 Armenia 0 0 0 96 93 0 1,459 784 170 0 0 0 2,413 Azerbaijan 0 0 0 67 73 0 3,657 585 891 19 76 0 5,228 Bangladesh 0 0 0 89 94 0 59,827 11,781 20,502 380-61 4,365 96,794 Benin 0 83 0 72 83 0 12,562 2,253 7,792 305 1,345 15,000 39,257 Bhutan 0 0 0 98 96 0 33,897 6,567 8,806 0-20 34 49,284 Bolivia (Plurinational State of) 0 85 0 86 85 0 3,795 601 1,231 0 133 4 5,764 Burkina Faso 0 81 0 75 82 0 40,375 2,917 30,902 101 882 35,554 110,731 Burundi 0 92 0 91 92 0 13,938 2,126 15,928-5 - 3 6,511 38,495 Cambodia 0 0 0 92 94 0 29,687 4,008 2,808 2 1 396 36,902 Cameroon 0 80 0 74 80 0 39,115 6,566 26,694 3,294 1,243 32,478 109,390 18
Central African Republic 0 54 0 62 54 0 10,269 1,153 6,001 0 2,660 5,264 25,347 Chad 0 22 0 23 23 0 55,676 5,193 34,260 66 22,618 31,764 149,577 Comoros 0 38 0 79 83 0 38,889 8,129 20,925 0 799 0 68,742 Congo 0 91 0 76 91 0 4,416 623 3,248 0-2 5,708 13,993 Côte d'ivoire 0 81 0 67 81 0 21,087 4,487 16,081 0 1,130 27,966 70,751 Cuba 0 96 0 96 96 0 61 12 17 0 0 0 90 Democratic People's Republic of Korea 0 0 0 98 93 0 3,419 718 865 0-2 0 5,000 Democratic Republic of the Congo 0 77 0 76 77 0 216,598 26,012 162,802 10,772 17,925 163,003 597,112 Djibouti 0 89 0 73 89 0 680 69 588 15 9 6 1,367 Eritrea 0 99 0 95 99 0 3,331 364 3,125 1 2 43 6,866 Ethiopia 0 79 0 75 79 0 86,539 14,928 104,650 568 5,314 33,969 245,968 Gambia 0 98 0 96 98 0 1,397 133 1,233 0 0 2,199 4,962 Georgia 0 0 0 83 88 0 2,338 283 351 0 0 0 2,972 Ghana 0 94 0 93 94 0 7,089 1,026 6,247-11 - 9 19,146 33,488 Guinea 0 58 0 51 57 0 17,521 2,657 11,184 9,675 5,573 21,137 67,747 Guinea- Bissau 0 68 0 76 68 0 6,851 1,231 3,422 855 837 3,433 16,629 19
Guyana 0 98 0 97 98 0 77 8 90 0 0 3 178 Haiti 0 0 0 59 59 0 16,133 2,257 12,604 0 6,628 340 37,962 Honduras 0 98 82 99 98 0 1,741 296 133 0 0 3 2,173 India 0 0 0 71 66 0 637,925 93,849 234,665 262,208 211,732 8,828 1,449,207 Indonesia 0 0 0 82 82 0 87,536 8,644 27,495 896 250 1,749 126,570 Kenya 0 75 0 74 75 0 32,672 4,254 28,092 2,272 1,550 15,964 84,804 Kiribati 0 86 0 82 86 0 3,625 400 1,881 0 22 0 5,928 Kyrgyzstan 0 0 0 99 95 0 1,444 277 762 0 0 0 2,483 Lao People's Democratic Republic 0 0 0 59 57 0 8,531 1,410 883 1,217 142 28 12,211 Lesotho 0 83 0 85 83 0 1,175 291 666 0 62 0 2,194 Liberia 0 64 0 64 64 0 3,679 451 2,594 850 971 2,211 10,756 Madagascar 0 78 0 64 78 0 7,618 1,144 5,935 0 298 1,025 16,020 Malawi 0 93 0 92 93 0 17,937 4,569 12,686 0-2 21,051 56,241 Mali 0 74 0 71 74 0 29,884 2,340 26,316 33 4,005 31,287 93,865 Mauritania 0 64 0 59 64 0 4,194 642 2,646 0 466 2,450 10,398 Mongolia 0 97 0 94 95 0 506 75 59 0 0 0 640 Mozambique 0 74 0 77 76 0 37,245 6,863 21,434 0 2,578 24,963 93,083 20
Myanmar 0 0 0 87 90 0 13,424 1,837 5,065 78-8 898 21,294 Nepal 0 0 0 79 82 0 11,492 2,353 5,629 158 741 11 20,384 Nicaragua 0 98 94 99 98 0 1,095 196 1 0 0 0 1,292 Niger 0 70 0 73 70 0 56,760 7,155 38,855 295 8,414 38,203 149,682 Nigeria 0 0 0 41 42 0 343,883 37,336 176,961 2,561 82,983 208,430 852,154 Pakistan 0 85 0 80 85 0 109,677 13,076 85,411 713 4,308 867 214,052 Papua New Guinea 0 64 0 58 64 0 6,527 1,542 815 1,909 151 1,510 12,454 Republic of Moldova 0 47 0 90 85 0 11,489 1,237 1,589 0 0 0 14,315 Rwanda 0 97 0 92 97 0 12,187 2,526 14,720-7 - 1 4,071 33,496 Sao Tome and Principe 0 0 0 90 98 0 38,343 6,131 10,087-19 - 5 625 55,162 Senegal 0 86 0 79 86 0 9,532 705 7,567 2,473 152 10,365 30,794 Sierra Leone 0 75 0 71 75 0 14,538 1,286 11,950 5,847 1,736 7,778 43,135 Solomon Islands 0 77 0 60 81 0 3,265 734 357 272 14 654 5,296 Somalia 0 0 0 24 31 0 39,774 4,011 20,176 21,184 12,250 6,342 103,737 Sri Lanka 0 0 0 96 97 0 768 69 329 0 0 0 1,166 Sudan 0 76 0 82 84 0 27,433 5,177 19,135 23 971 49,079 101,818 Tajikistan 0 93 0 89 93 0 4,603 739 2,852 8 0 0 8,202 21
Timor- Leste 0 0 0 70 72 0 451 84 206 285 9 250 1,285 Togo 0 89 0 84 89 0 3,985 774 3,397 25 24 8,017 16,222 Uganda 0 64 0 68 64 0 59,808 10,395 45,756 15,918 18,919 69,249 220,045 Ukraine 0 81 0 94 90 0 314 853 316 0 0 0 1,483 United Republic of Tanzania 0 85 0 91 85 0 55,187 13,547 34,316 0 1,345 54,155 158,550 Uzbekistan 0 98 0 95 98 0 1,378 248 629 0 0 0 2,255 Viet Nam 0 0 0 97 96 0 11,405 8,324 4,282 29 5 146 24,191 Yemen 0 67 0 58 66 0 17,822 2,717 14,056 2,276 9,256 412 46,539 Zambia 0 81 0 85 81 0 20,506 3,072 15,021 483 651 16,848 56,581 Zimbabwe 0 73 0 76 73 0 8,133 1,547 3,879 5,526 3,266 1,453 23,804 Total NA NA NA NA NA NA 2,661,441 377,770 1,524,602 359,905 438,561 1,012,123 6,374,402 22
Exhibit G. Sensitivity analysis: Inputs and ranges All Diseases Base Value Low (50%) High (200%) Distribution % of total bed days for meds and diagnosis 0.2 0.1 0.4 Beta % of outpatients who sought care from hospital 0.25 0.125 0.5 Beta % days productivity loss for caretaker of outpatient 0.5 0.25 1 Uniform % days productivity loss for caretaker of inpatient 1 0.5 1 Uniform LiST mortality range 1 0.3 2.97 Beta Specific Diseases Base Value Low (50%) High (200%) Distribution % of GDP for productivity loss of long- term sequelae 0.5 0.25 1 Beta Measles CFR 0.03 0.015 0.06 Beta Malaria CFR 0.04 0.02 0.08 Beta Pertussis CFR 0.028 0.014 0.056 Beta Rotavirus CFR 0.005 0.0025 0.01 Beta Rotavirus India CFR 0.004 0.002 0.008 Beta Pneumonia CFR 1 0.5 2 Beta Weighting factor a Meningitis CFR Weighting factor a 1 0.5 2 Beta a Country specific case fatality ratios for pneumonia and meningitis were varied by multiplying them by the above weighting factor 23