Burgess COMMENTARY

Peter Burgess

Bulletin of the World Health Organization

Estimated global resources needed to attain international malaria control goals

Anthony Kiszewski,a Benjamin Johns,b,c Allan Schapira,d Charles Delacollette,e Valerie Crowell,f Tessa Tan-Torres,b Birkinesh Ameneshewa,g Awash Teklehaimanoth & Fatoumata Nafo-Traoréi

Introduction

Globally, there are more than a million malaria-related deaths each year. About four-fifths of these are in Africa.1

Effective interventions that reduce death and illness from malaria are still not widely accessible in most malaria-endemic countries. The World Health Assembly in 2005 urged Member States to establish policies and operational plans to ensure that at least 80% of those at risk of, or suffering from, malaria benefit by 2010 from major preventive and curative interventions, so as to ensure a reduction in the burden of malaria of at least 50% by 2010 and 75% by 2015.2 These targets are echoed in the Roll Back Malaria Partnership Global Strategic Plan 2005-2015.3 The United Nations Millennium Declaration set a target to halt and begin to reverse the global incidence of malaria by 2015.4

Achieving these targets will require additional financial resources. Comparison of estimated costs with present investments should help accelerate mobilization of funds and identify important country-level gaps.

This paper presents the methods used to construct a model for estimating the total financial costs of scaling up malaria control over 2006-2015 to achieve internationally agreed objectives and targets for the 81 most heavily affected malaria-endemic countries of the world’s 107 malaria-endemic countries and territories. Pessimistic and optimistic scenarios with different assumptions about the effect of interventions on the needs for diagnosis and treatment provide upper and lower bounds of the estimation.

The exercise includes a set of widely recommended interventions. Besides commodities and distribution costs, we included costs for necessary health system strengthening activities (programme costs in Figures 1-4), especially for community health workers, training, communication, operational research and monitoring and evaluation. We did not include costs for running health facilities since the bulk of interventions will be delivered at the peripheral level, and effective prevention and treatment of malaria should reduce the number of severe malaria cases requiring hospitalization. While we included the costs of technical assistance for national programmes, we did not consider those required at international level for managing such assistance, monitoring and evaluation, and research and development.

Fig. 1. Estimated global malaria control intervention and programme costs from 2006–2015 according to the pessimistic scenario^a Fig. 1. Estimated global malaria control intervention and programme costs from 2006–2015 according to the pessimistic scenarioa

The analysis estimates the total cost of scaling up malaria control in each country, including the costs of existing levels of interventions. The needs calculated are then compared to current health expenditures and funding for malaria control by country.

Methods

A detailed description, including assumptions and calculations, is available in the working paper Methodology for estimating the costs of global malaria control (2006-15), at http://www.who.int/malaria/costing.

To arrive at the cost estimates, we selected countries for the analysis, estimated the population in need of each intervention, prepared scale-up scenarios, and calculated country-specific costs. All costs are calculated in 2006 US$.

Countries

The 81 countries included (listed in Table 3, available at: http://www.who.int/bulletin) are those which have significant populations at risk of Plasmodium falciparum malaria. The remaining malaria-endemic countries in the world are mainly affected by vivax malaria. The malaria risk there is highly variable, making the estimation of needs for prevention difficult. The inclusion of these countries could skew the estimates towards addressing problems which are not central to achieving the Millennium Development Goals. While the importance of vivax malaria should not be underestimated and its control may be challenging, these countries, with few exceptions, do not need external financial support for malaria control. Based on these criteria, all endemic countries in Africa south of the Sahara (but no country in North Africa) have been included. In the following, therefore, “Africa” refers to sub-Saharan Africa.

Table 3. Average estimated per-capita needs for malaria control in 2006 versus most recent per-capita total and government expenditure on health (US$) html, 19kb

Epidemiological estimates

The proportion of people in each country exposed to a particular class of endemicity was assigned using sources ranging from climatic/environmental modelling5 to clinical reporting of incidence (see http://www.mara.org.za/; http://www.paho.org/english/hcp/hct/mal/malaria.htm; http://www.searo.who.int/EN/Section10/Section21.htm; http://www.wpro.who.int/sites/mvp/epidemiology/malaria/). In countries where epidemiological data was unavailable, estimates were prepared using data from countries with similar epidemiological conditions but better health reporting. Population data and growth rates were obtained from United Nations Population Division 2004 projections, interpolated to yearly estimates using MortPack software.6

Calculating country-specific costs

Commodity prices were derived primarily from “Sources and prices of selected products for the prevention, diagnosis and treatment of malaria.”7 We did not take into consideration the future price reductions likely to occur as a result of increased demand and production, nor possible increases due to the need to deploy novel medicines and insecticides because of resistance. Costs for malaria control interventions per country in a given year are estimated as unit cost (commodity plus delivery) multiplied by target population living in endemic areas for prevention, and by incidence of clinical episodes, for curative care. The scale and costs of other inputs were derived from typical programmes and budgets, including those described in successful proposals to the Global Fund to Fight AIDS, Tuberculosis and Malaria (see http://www.theglobalfund.org). Other expenses were based on country-specific estimates or were derived independently8 (see http://www.dcp2.org/file/24/wp9.pdf).

Interventions and services

Vector control

We estimated the costs for provision of long-lasting insecticidal nets (LLINs) to all people living in endemic areas9 at the rate of one net per two people, with replacement after three years. Other vector control methods, especially indoor residual spraying, may be substituted in certain areas, using the LLIN cost estimate as a rough equivalent in cost per person protected. Actual cost differences may vary in either direction;10 however, the long-term cost of LLINs is lower than that determined for conventional insecticide-treated mosquito nets in comparative studies.

Intermittent preventive therapy (IPT)

We costed provision of IPT using sulfadoxine-pyrimethamine (SP), distributed by ante-natal care services, with three treatment courses (see http://www.afro.who.int/malaria/publications/malaria_in_pregnancy_092004.pdf) given to all pregnant women living in Africa in regions with moderate to intense transmission. Age-specific fertility rates reported by the UN Population Division in the 2003 World Fertility Report were used to determine the number of pregnancies expected annually.

Rapid diagnostic tests (RDTs)

We assumed that RDTs would be used for all patients with malaria-like illness to detect P. falciparum in all areas with significant transmission of the parasite, except in children under five years in Africa up to 2010. WHO currently does not recommend using RDTs in this age-group in areas of intense transmission (see http://www.who.int/malaria/docs/ReportLABdiagnosis-web.pdf).11

Artemisinin-based combination therapies (ACTs)

ACTs were assumed to be the first-line treatment. The average cost of treatment was calculated for each of three age groups and multiplied by the annual expected number of fevers suspected to be malaria. In hyper- and holo-endemic areas: 0-4 years: 4, 5-14 years: 2, above 14 years: 1 episode per person; in meso-endemic areas, the corresponding rates were 2, 1 and 1; and in hypo-endemic areas, 1, 0.5 and 0.5.

Severe and complicated malaria

We assumed incidence rates of severe malaria ranging from 0.005 to 0.04 per person per year depending on endemicity and age-group. A median cost of US$ 29.50 for managing a single severe malaria case was derived from surveys in Africa (see http://www.who.int/malaria/cmc_upload/0/000/016/330/multicenter.pdf). This cost includes therapeutics and laboratory tests, but not transport and pre- and post-hospitalization costs.

Epidemic prevention and response

Resources for malaria epidemic prevention and control were estimated for areas with unstable P. falciparum malaria. In sub-Saharan Africa, the MARA-linked datasets were consulted to determine countries and populations at epidemic risk. To identify countries beyond Africa, we used reports in peer-reviewed journals12 as well as government and WHO regional office sources.

Costs were estimated for a “surveillance package” including training, computers and software, and for an “intervention package” including supplies, equipment and IRS operations to prevent or curb epidemics. Also costed were supplemental supplies of ACTs as well as the increased need for management of severe malaria.

Strengthening health infrastructure

We grouped countries according to the need for augmentation of infrastructure, based on the classifications described by the WHO Commission on Macroeconomics and Health.13 For each group, we defined sets of trained personnel and equipment necessary for management, monitoring and evaluation, improvement of microscopy services, enhancement of transport capacity and strengthening supply management and logistics.

Training for staff and community health workers

Many of the interventions represent new policies and procedures that will require training in treatment, diagnosis, delivery of preventive interventions, supervision, management and operational research. Estimates include costs of training of epidemiologists and entomologists, health service staff and community health workers.

Communication

We provide estimates for producing and communicating information to communities on malaria prevention, early recognition of symptoms and the need to seek prompt treatment.

Monitoring, evaluation and operational research

Estimates of the cost of monitoring and evaluation include routine assessment of surveillance data captured through health information systems, periodic surveys of health facilities in some countries, population surveys and studies on drug and insecticide resistance.

Scale-up and impact of implementation on costs

Coverage of most interventions is expected to increase gradually to 95% or 100% in 2015 in accordance with internationally agreed targets. For severe malaria management, “coverage” was considered to be 100% throughout, because an episode of severe disease almost inevitably incurs costs on families and/or health services. For programme costs, complete coverage was assumed from the outset, reflecting the need for staff and infrastructure for scale up of control. Consideration was given to supply chain constraints affecting ACTs in the first two years.

Costs were evaluated in two scenarios: one with a pessimistic set of assumptions, in which the effect of interventions on malaria incidence and thereby the needs for diagnosis and treatment is less than would be expected from field trials, and one with an optimistic set of assumptions, where needs for diagnosis and treatment decrease to a greater extent. Estimates of impact in the two scenarios were based on evidence where available,14 and on consensus among the authors. In the pessimistic scenario, vector control (exemplified by LLINs) at 80% coverage would reduce the need for RDTs, ACTs and severe malaria management by 50%, and in the optimistic scenario, by 75%. In the pessimistic scenario, 100% coverage with RDTs would reduce the need for ACTs by 25% in Africa and 50% elsewhere; in the optimistic scenario, the corresponding reductions would be 50% and 75%. In both scenarios, 100% coverage with ACTs would reduce severe malaria costs by 50%. For all these interventions, lower coverage levels would result in proportionally lower impacts.

Data on malaria financing

We extracted data on domestic annual funding for malaria control15 and on annual per capita total and government expenditure on health16 by country, where this information was available. These figures were then compared to the average estimated needs for funding for malaria control in each country.

Results

The summation of the baseline estimates for the 81 countries for 2005 resulted in 660 million persons in falciparum malaria-endemic areas in Africa and 1.240 billion in Asia and the Americas. The annual number of malaria-like fever episodes was 1.064 billion for Africa and 399 million for Asia and the Americas; severe episodes were estimated at 10.7 million a year for Africa and 3.3 million for Asia and the Americas.

Table 1 shows the cost of scaling up malaria control programmes worldwide to reach internationally agreed targets for coverage of malaria control. A total of US$ 38 billion (optimistic scenario) to US$ 45 billion (pessimistic scenario) will be required from 2006 to 2015; on average, US$ 3.8 to US$ 4.5 billion per year. The average annual costs for Africa are US$ 1.7 billion and US$ 2.2 billion in the optimistic and pessimistic scenarios, respectively; outside Africa, the corresponding costs are US$ 2.1 billion and US$ 2.4 billion.

Figures 1 and 2 show the costs of specific interventions and programme costs over the 10-year period. In the two scenarios, the initial costs are identical. Vector control costs are dominant, increasing over time as a result of increasing coverage and population growth. While in the pessimistic scenario (Figure 1) case management costs are relatively constant after initial scale-up, in the optimistic scenario (Figure 2) they undergo a marked decline, especially after 2010. In both scenarios, the largest costs occur in 2012 and 2015. These peaks are mainly due to the periodic replacement cycles for LLINs. In reality, they would probably be smoothed by variable rates of scale up in individual countries.

Fig. 2. Estimated global malaria control intervention and programme costs from 2006–2015 according to the optimistic scenario^a

Figures 3 and 4 compare the distribution of expenditures by intervention and type of programme cost in Africa and the rest of the world. Outside Africa, vector control costs are more dominant relative to case management costs because of the larger populations and lower malaria incidence rates. Infrastructure and institutional strengthening costs are higher in Africa, while training costs are higher outside Africa due to large populations needing interventions and higher human resource costs.

Fig. 3. Allocation to different interventions and types of programme costs in the optimistic scenario in Africa, averaged over the years 2006–2015

Country-by-country comparisons of resources needed for malaria control and those available from national sources demonstrate large gaps in nearly all countries (see Table 2, available at: http://www.who.int/bulletin). Only approximately 4.6% of estimated needed resources are available from domestic sources in the African countries, and 9.2% in the countries outside Africa. Estimates of available resources should be treated with caution, however, due to the difficulty of isolating malaria funding within the government health budget and of estimating malaria funding from nongovernment sources.

Table 3 (available at: http://www.who.int/bulletin) shows that, in some countries, particularly in Asia, the Americas and southern Africa, current levels of health expenditure could, with some adjustment, cover malaria control needs. In others, mainly in Africa, estimated needs constitute over two-thirds of total annual health expenditures; much greater external funding will be necessary to fill these gaps.

Table 1. Estimated costs for scaling up malaria control interventions, 2006–2015 html, 6kb

Table 2. Comparison of available and needed domestic funding for malaria control (US$ million), for countries for which data is available html, 14kb

Discussion

Considering population growth, our estimate for populations in endemic areas in Africa is close to other recent estimates,17 which are based on the same climate-based distribution model. The estimate of malaria-like fever episodes for Africa is lower than that of Snow et al., especially for adults,18 but it is higher than that of a field study in southern Ghana19 and is based on a model which has proved useful for WHO’s country-level work for supply planning in Africa. Outside Africa, estimation is fraught with greater uncertainty, because of the enormous epidemiological variability. Our estimate of population in areas endemic for P. falciparum outside Africa is about two-thirds of that of Snow et al.20 This is not surprising, because we have used a more eclectic approach to identify populations that need protection by continuous vector control. Our calculation of malaria-like fevers is also more uncertain beyond Africa, where widely applicable data are scarce. We estimated that 75% of all severe cases occur in Africa, which corresponds well to current estimates of the distribution of falciparum malaria,15 but our total estimate of severe cases is high (3-5%) compared to global estimates of falciparum malaria (see http://www.who.int/malaria/docs/incidence_estimations2.pdf), pointing to the need for population-based studies of this problem.

Although Africa’s malaria burden is higher than that of the rest of the world, the total costs are higher for Asia and the Americas due to the enormous size of the populations estimated to need vector control coverage. In many countries effective control over some years may interrupt transmission in areas with low transmission potential so that vector control could be replaced by surveillance, greatly reducing costs. Likewise, in countries with intense malaria transmission, increasing urbanization, combined with integrated vector management, could lead to reductions in malaria burden and thus in both preventive and curative expenditures. Especially in areas of low to moderate transmission, the widespread use of ACTs could help reduce transmission. We have not attempted to model this due to lack of good data.

The high allocation to RDTs is meaningful, because as malaria incidence decreases, the costs of diagnosis relative to those of treatment should increase.

Some limitations of our analysis deserve mention. The numbers reported for the optimistic and pessimistic scenarios are not intended to represent an absolute “ceiling” or “floor” for the cost of malaria control. Synergistic interactions could reduce the amount of resources required to achieve goals. In areas of particular vulnerability or opportunity, it may be possible to adopt a more accelerated and costly programme, while in other locales, the targets assumed in this analysis may be too ambitious.

For country-level planning, it is essential to assess systemic strengths and weaknesses, and to regularly review performance to adjust the rhythm of financial inputs. Our projected allocations to health system strengthening constitute 16-21% of total costs. The real needs would vary greatly by country depending on health system characteristics. For example, where there is high coverage of government services, the substantial financing estimated for community workers could be allocated instead to support delivery through public health facilities.

The exclusion of vector source reduction methods from this analysis does not reflect their value, but rather their complexity. The training component in this costing exercise is intended in part to build the capacity of managers and entomologists to develop locally appropriate long-term strategies.

Our results highlight the incongruity between goals and targets for malaria control set by the international community and the resources that are available to combat the disease. International funding has increased in recent years, with estimated annual contributions to malaria control from development agencies rising to US$ 600 million in 2004 from less than US$ 50 million in 2000 (see http://www.rbm.who.int/docs/hlsp_report.pdf). In 2005, estimated disbursements for malaria from bilateral donors, WHO and the Global Fund were approximately US$ 841 million. New major funding initiatives launched by the World Bank and the United States of America in 2005 suggest that resources for malaria control will continue to increase.

However, current international funding for malaria control represents approximately 20% of estimated total needs for gradual scale up. The continuity of funding is also of concern. It is unlikely that malaria control efforts will lead to the elimination of malaria in the countries included in this analysis. Therefore, high levels of coverage of curative and particularly preventive interventions will need to be maintained beyond 2015 in most places.

It is also important to monitor funding for malaria from all sources, including the private sector. To ensure long-term sustainability and national ownership of malaria control programmes, domestic funding should account for an ever-increasing proportion of total malaria spending.

Due to the generalizations needed to execute such a broad global costing, these estimates should not be used as a template for country-level planning. Nor are our estimates of commodity needs meant to be used as forecasting figures for industry. However, the estimates may be useful as benchmarks against which to assess planned inputs or global commodity need estimations. ■

References

• AK Rowe, SY Rowe, RW Snow, EL Korenromp, JR Schellenberg, C Stein, et al. The burden of malaria mortality among African children in the year 2000. Int J Epidemiol 2006; 35: 691-704.

• Resolution WHA. 58.2. Malaria control. In: Fifty-eighth World Health Assembly, Resolutions and Decisions Annex. Geneva: WHO; 2005. Available at: http://www.who.int/gb/ebwha/pdf_files/WHA58/WHA58_2-en.pdf

• Global Partnership to Roll Back Malaria. The African Summit on Roll Back Malaria, Abuja, Nigeria, 25 April 2000. Geneva: WHO; 2000 (WHO/CDS/RBM/2000.17). Available at: http://whqlibdoc.who.int/hq/2000/WHO_CDS_RBM_2000.17.pdf

• Millennium Declaration. New York: United Nations; 2000 (A/RES/55/2). Available at: http://www.un.org/millennium/declaration/ares552e.htm

• MH Craig, RW Snow, D le Sueur. A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitol Today 1999; 15: 105-11.

• World population prospects: the 2004 revision. New York: United Nations; 2005.

• Sources and prices of selected products for the prevention, diagnosis and treatment of malaria. Geneva: WHO, UNICEF, Population Services International, Management Sciences for Health; 2004.

• B Johns, T Adam, DB Evans. Enhancing the comparability of costing methods: cross-country variability in the prices of non-traded inputs to health programmes. Cost Eff Resour Alloc 2006; 4: 8-.

• Resolution WHA. 58.2. Malaria control. In: Fifty-eighth World Health Assembly, Resolutions and Decisions Annex. Geneva: WHO; 2005.

• HL Guyatt, J Kinnear, M Burini, RW Snow. A comparative cost analysis of insecticide-treated nets and indoor residual spraying in highland Kenya. Health Policy Plan 2002; 17: 144-53.

• Guidelines for the treatment of malaria. Geneva: WHO; 2006.

• AE Kiszewski, A Teklehaimanot. A review of the clinical and epidemiological burdens of epidemic malaria. Am J Trop Med Hyg 2004; 71: 128-35.

• Commission on Macroeconomics and Health. Macroeconomics and health: investing in health for economic development. Geneva: WHO; 2001.

• Murphy C, Ringheim K, Woldehanna S, Volmink J, editors. Reducing malaria’s burden: evidence of effectiveness for decision-makers. Washington: Global Health Council; 2003.

• World malaria report. Geneva: WHO/UNICEF; 2005.

• World health report 2006: Working together for health. Geneva: WHO; 2006.

• SI Hay, CA Guerra, AJ Tatem, PM Atkinson, RW Snow. Urbanization, malaria transmission and disease burden in Africa. Nat Rev Microbiol 2005; 3: 84-93.

• RW Snow, E Eckert, A Teklehaimanot. Estimating the needs for artesunate-based combination therapy for malaria case-management in Africa. Trends Parasitol 2003; 19: 363-9.

• IA Agyepong, J Kangeya-Kayonda. Providing practical estimates of malaria for health planners in resource-poor countries. Am J Trop Med Hyg 2004; 71: 162-7.

• RW Snow, CA Guerra, AM Noor, HY Myint, SI Hay. The global distribution of clinical episodes of Plasmodium falciparum malaria. Nature 2005; 434: 214-7.

Affiliations

• Harvard School of Public Health, Boston, MA, USA.

• Health System Financing, Expenditure and Resource Allocation Department, WHO, Geneva, Switzerland.

• Office of WHO Representative, Jakarta, Indonesia.

• Department of Public Health and Epidemiology, Swiss Tropical Institute, Basel, Switzerland.

• WHO Mekong Malaria Programme, Bangkok, Thailand.

• Global Malaria Programme, WHO, Geneva, Switzerland.

• WHO Regional Office for Africa, Brazzaville, Congo.

• Earth Institute, Columbia University, New York, NY, USA.

• WHO Representative, Brazzaville, Congo.

Bulletin of the World Health Organization Past issues Volume 85: 2007 Volume 85, Number 8, August 2007, 569-648

Bulletin of the World Health Organization Estimated global resources needed to attain international malaria control goals Anthony Kiszewski, Benjamin Johns, Allan Schapira, Charles Delacollette, Valerie Crowell, Tessa Tan-Torres, Birkinesh Ameneshewa, Awash Teklehaimanot, Fatoumata Nafo-Traoré Volume 85, Number 8, August 2007, 623-630 Table 3. Average estimated per-capita needs for malaria control in 2006 versus most recent per-capita total and government expenditure on health (US$) Country Average estimated needs for malaria control per capita, 2006 Per-capita total expenditure on health at average exchange rate, 2003 Per-capita government expenditure on health at average exchange rate, 2003 Africa Angola 3.74 26 22 Benin 2.77 20 9 Botswana 1.79 232 135 Burkina Faso 2.20 19 9 Burundi 2.32 3 1 Cameroon 2.27 37 11 Cape Verde 1.35 78 57 Central African Republic 2.46 12 5 Chad 2.27 16 7 Comoros 2.49 11 6 Congo 3.15 19 12 Côte d’Ivoire 7.10 28 8 Democratic Republic of the Congo 0.71 4 1 Djibouti 5.39 47 31 Equatorial Guinea 3.53 96 65 Eritrea 2.47 8 4 Ethiopia 1.84 5 3 Gabon 4.12 196 130 Gambia 2.19 21 8 Ghana 2.22 16 5 Guinea 2.37 22 4 Guinea-Bissau 2.43 9 4 Kenya 2.52 20 8 Liberia 2.35 6 4 Madagascar 2.86 8 5 Malawi 2.32 13 5 Mali 2.51 16 9 Mauritania 4.41 17 13 Mozambique 2.42 12 7 Namibia 2.58 145 101 Niger 2.60 9 5 Nigeria 2.33 22 6 Rwanda 1.77 7 3 Sao Tome and Principe 4.37 34 29 Senegal 2.82 29 12 Sierra Leone 2.16 7 4 Somalia 3.84 n/a n/a South Africa 1.25 295 114 Sudan 2.62 21 9 Swaziland 2.17 107 61 Togo 2.67 16 4 Uganda 2.13 18 5 United Rep. of Tanzania 2.08 12 7 Zambia 2.64 21 11 Zimbabwe 2.02 40 14 Median 2.43 19 8 Asia and Oceania Afghanistan 1.09 11 4 Bangladesh 1.43 14 4 Bhutan 1.21 10 9 Cambodia 0.44 33 6 China 0.07 61 22 India 0.67 27 7 Indonesia 1.14 30 11 Iran 0.16 131 62 Lao People’s Dem. Rep. 1.49 11 4 Malaysia 0.45 163 95 Myanmar 1.17 394 77 Nepal 1.91 12 3 Pakistan 0.33 13 4 Papua New Guinea 2.43 23 20 Philippines 0.22 31 14 Solomon Islands 4.41 28 26 Sri Lanka 0.81 31 14 Thailand 1.23 76 47 Timor-Leste 3.21 39 30 Vanuatu 4.69 54 40 Viet Nam 0.61 26 7 Yemen 1.57 32 13 Median 1.16 30.5 13.5 Americas Bolivia 1.28 61 39 Brazil 0.50 212 96 Colombia 0.74 138 116 Dominican Republic 1.22 132 44 Ecuador 0.50 109 42 El Salvador 1.93 183 84 Guatemala 0.84 112 44 Guyana 0.87 53 44 Haiti 1.83 26 10 Honduras 1.61 72 41 Nicaragua 0.82 60 29 Paraguay 0.78 75 24 Peru 0.76 98 47 Suriname 2.32 182 83 Median 0.86 104 44 Global median 2.16 26.5 11 Population figures for 2006 were calculated using the United Nations Population Division medium variants estimates of total population in 2003 and annual population growth rate over 2000–2005. [an error occurred while processing this directive]

WHO | World Health Organization Bulletin of the World Health Organization Estimated global resources needed to attain international malaria control goals Anthony Kiszewski, Benjamin Johns, Allan Schapira, Charles Delacollette, Valerie Crowell, Tessa Tan-Torres, Birkinesh Ameneshewa, Awash Teklehaimanot, Fatoumata Nafo-Traoré Volume 85, Number 8, August 2007, 623-630 Table 1. Estimated costs for scaling up malaria control interventions, 2006–2015 Year Estimated cost (US$ billion) Pessimistic scenario Optimistic scenario Africa Asia, Oceania, Americas Total Africa Asia, Oceania, Americas Total 2006 1.689 1.842 3.531 1.671 1.835 3.506 2007 1.774 2.045 3.819 1.686 1.972 3.658 2008 1.854 2.018 3.872 1.657 1.857 3.514 2009 2.076 2.440 4.516 1.724 2.159 3.883 2010 1.991 2.263 4.254 1.576 1.932 3.508 2011 2.151 2.338 4.489 1.687 1.973 3.661 2012 2.575 2.760 5.335 1.990 2.389 4.380 2013 2.445 2.497 4.942 1.797 2.092 3.889 2014 2.362 2.430 4.792 1.662 2.000 3.662 2015 2.700 2.960 5.660 1.957 2.511 4.468 Total 21.617 23.593 45.210 17.407 20.720 38.129 Average/year 2.162 2.359 4.521 1.741 2.072 3.813 Percent 47.8 52.2 100 45.7 54.3 100 [an error occurred while processing this directive]

WHO | World Health Organization Bulletin of the World Health Organization Estimated global resources needed to attain international malaria control goals Anthony Kiszewski, Benjamin Johns, Allan Schapira, Charles Delacollette, Valerie Crowell, Tessa Tan-Torres, Birkinesh Ameneshewa, Awash Teklehaimanot, Fatoumata Nafo-Traoré Volume 85, Number 8, August 2007, 623-630 Table 2. Comparison of available and needed domestic funding for malaria control (US$ million), for countries for which data is available Country Domestic annual funding,latest year for which data is available (2000–2003)15 Estimated annual funding needs 2006–2010 (average of pessimistic and optimistic scenarios) Estimated funding gap – domestic funding Africa Angola 1.080 54.484 53.404 Botswana 0.432 3.218 2.786 Burkina Faso 0.096 33.467 33.371 Burundi 0.030 18.444 18.414 Cameroon 9.678 40.442 30.764 Central African Republic 0.179 10.359 10.180 Chad 0.028 24.101 24.073 Comoros 0.104 2.188 2.084 Côte d’Ivoire 0.167 42.984 42.817 Eritrea 0.098 11.855 11.757 Ethiopia 4.971 151.319 146.348 Kenya 0.082 89.910 89.828 Madagascar 5.358 56.114 50.756 Malawi 22.238 31.764 9.526 Mali 1.007 38.200 37.193 Mauritania 0.132 14.618 14.486 Mozambique 0.256 50.449 50.193 Namibia 0.573 5.159 4.586 Nigeria 3.530 323.381 319.851 Rwanda 0.120 17.033 16.913 Sao Tome & Principe 0.039 0.784 0.745 Senegal 2.100 31.347 29.247 Somalia 0.160 45.141 44.981 South Africa 8.300 59.057 50.757 Swaziland 0.450 2.463 2.013 Sudan 2.600 100.439 97.839 Togo 0.100 12.808 11.808 Uganda 0.385 64.868 64.483 United Republic of Tanzania 0.500 87.160 86.660 Subtotal 64.793 1423.556 1358.763 Percent of estimated need 4.6% 100% 95.4% Asia, Oceania and Americas Bangladesh 0.232 233.829 233.597 Bolivia 0.918 11.315 10.397 Brazil 40.696 68.946 28.25 Colombia 13.050 32.294 19.244 Dominican Republic 1.221 11.596 10.375 El Salvador 4.555 12.108 7.553 Ecuador 3.816 6.237 2.421 Guatemala 0.703 9.737 9.034 Guyana 0.800 0.673 -0.127 Honduras 0.081 11.227 11.146 India 49.100 802.709 753.609 Indonesia 0.045 278.458 278.413 Islamic Republic of Iran 6.206 10.055 3.849 Lao People’s Democratic Republic 0.369 8.846 8.477 Malaysia 0.927 11.971 11.044 Myanmar 23.041 63.772 40.731 Nicaragua 0.333 4.258 3.925 Pakistan 0.492 60.538 60.046 Papua New Guinea 1.450 15.341 13.891 Paraguay 5.412 4.292 -1.120 Peru 4.110 128.450 124.340 Philippines 0.062 19.866 19.804 Sri Lanka 1.481 16.691 15.210 Suriname 0.161 0.761 0.600 Thailand 18.700 80.399 61.699 Viet Nam 4.537 54.581 50.044 Yemen 2.000 36.454 34.454 Subtotal 184.498 1995.404 1810.906 Percent of estimated need 9.2% 100% 90.8% [an error occurred while processing this directive]