DDT versus Non-DDT Malaria Control Table 1. Assumptions used for the mix of IVM interventions in the policies. ITN IRS EM Research question addressed Current policy mix 55% 0.5% 44.5% of which 85% DDT 1: amount of resources necessary to achieve malaria elimination with current mix of IVM interventions; 2: costs compared to gains in GDP Current policy mix without DDT Policy mix focusing on ITN 49.5% 50% 0.5% 3: costs and benefits associated with continued use of DDT vs. its rapid phase out 66% 0.5% 33.5% of which 85% DDT 66% of which 85% DDT 35% of which 85% DDT 4: amount of resources necessary to achieve malaria elimination with alternative mixes of IVM interventions; 5: costs compared to gains in GDP Policy mix focusing on IRS 33.5% 0.5% Policy mix focusing on EM 55% 10% Notes: - The current policy mix is based on the costs per person covered listed in Table S1, on [48] for total quantities of each insecticide used for IRS, on [4] for ITN coverage and on Table S1 for EM coverage and resulting percentage in policy mix. - The percentages in each intervention category in the current policy mix without DDT differs from the current policy mix scenario because we assume the same degree of coverage with IRS than in the current mix scenario. As the direct costs for non-DDT chemicals are higher than the direct costs of DDT, more money needs to be allocated to IRS so that less can be allocated to ITN and EM (not visible in the case of EM due to rounding effects). doi:10.1371/journal.pone.0027771.t001 for the specification and calibration of malaria sectors in the MMM model. Data used in the model is based on available statistical data and on relevant literature (values and references indicated in Table S1). The structure of the simulation model was validated in a series of expert interviews. The most prominent data source for the population, education, health and production sectors are the United Nations Population Division, The World Bank’s Education Statistics Database, and The World Bank’s World Development Indicators. The technical appendix Text S1 lists all the equations, initial values and parameter values of the simulation model. The simulation model itself is available as online supporting information (Dataset S1 and Dataset S2). Policies and scenarios For testing different policies and scenarios, a baseline has to be established. The baseline scenario reproduces historical behavior as closely as possible. For the time period in the future it assumes no fundamental shifts in malaria policies, and no major external shocks. More specifically, regarding malaria policies, we assume that malaria expenditure (measured as its share on the total gross domestic product GDP in the SSA region) grows by 1 percent per year. Health and education expenditures remain a fixed share of GDP. After establishment of the baseline simulations, the MMM model can be used to address the research questions formulated in the introduction section of this paper. The key question underlying this paper concerns the costs and benefits of a continued use of DDT for IRS versus its rapid phase out. For comparison purposes, we calculate the same costs and benefits for alternative combinations of IVM interventions. For all calculations we consider the costs necessary for eliminating malaria as well as the benefits resulting from malaria elimination. Costs and benefits are calculated on the basis of a 3% discount rate. In the MMM model, malaria elimination becomes possible if and when the entire vulnerable population is effectively covered by IVM interventions. The model calculates the coverage of each IVM intervention resulting from the expenditures for this intervention. To achieve effective coverage of the entire vulnerable population, the model assumes an overlapping factor of 50% between the different IVM interventions. In addition to calculating the costs of elimination, we compare these costs to the gain in GDP that could be achieved through malaria elimination. Focusing on the gain in GDP integrates a series of other benefits gained from malaria elimination such as saved lives. Saved lives, in the MMM model, translate into a healthier and more productive workforce that increases the overall level of GDP. After calculating the baseline as well as the economic costs of malaria (potential gain in GDP), we define a series of policies that we test with the MMM model and that we compare to the baseline. The policies differ in their combination of IVM interventions (Table 1). The policies are subjected to two different scenarios. The first scenario aims at eliminating malaria in SSA by 2025, the second by 2035. Table 1 details the assumptions underlying the different policies, i.e., the different combinations of IVM interventions and specifications of Indoor Residual Spraying (IRS), Insecticide Treated Bed Nets (ITN), and Environmental Management (EM). The table also lists the research questions that can be answered with such analyses. The percentages of the interventions in the table relate to the percentage of the corresponding intervention category on total malaria prevention expenditure. The choice of policies is based on two criteria. First, we define policies such that they test combinations of IVM interventions with different emphases. Second, the combinations need to be a realistic possibility of allocating the different IVM interventions to the aggregated SSA region. The results obtained from these policies should give a sense of the magnitude and direction of the change in costs necessary for eliminating malaria. Further emphasis in one direction in the mix will cause even more changes in the costs (in the same direction). For assessing the costs and benefits of a policy-scenario combination, we calculate the malaria expenditures necessary to gradually scale up the specific combination of IVM interventions for achieving malaria elimination in 2025/35. We implement expenditure policies by setting different levels of malaria expenditure as fixed shares of SSA GDP. This implies that we assume stable financial support from development partners (77% of total malaria expenditure). This assumption might not be realistic as international financial support is likely to fluctuate. However, it demonstrates the need for stable financial support over extended periods of time if malaria elimination is to be reached at all. The current mix of interventions in terms of malaria expenditure consists of 55% ITN, 44.5% IRS and 0.5% EM PLoS ONE | www.plosone.org 5 November 2011 | Volume 6 | Issue 11 | e27771