Table 2.
Parameters and input data used by LiST and Spectrum-Malaria in projections for DRC and Zambia
| Parameter (2015, unless otherwise indicated) | DRC | Zambia | Data sources & definitions | |||
|---|---|---|---|---|---|---|
| Spectrum-Malaria | LiST | Spectrum-Malaria | LiST | Spectrum-Malaria | LiST | |
| Population at malaria riska | 97.1% | 91% | 100% | 98% | All-age population living where PfPR among 2–9 years >0 [2, 19] | Women exposed to P. falciparum malaria [50] |
| Population 0–4 years (including children living not at malaria risk) | 12,373,927 | 13,682,392 | 2,848,069 | 2,888,817 | United Nations Population Division [51] | |
| Index for seasonality in malaria transmission | 0.36 | NA | 1.64 | NA | Coefficient of variation in EIR over a year, defined as the standard deviation divided by the year-average of monthly EIR [34]; country estimates from Malaria Atlas Project [2, 19] | |
| Prevalence of P. falciparum infection in children 2–9 years, average over 2000–2002 | 64% | NA | 35% | NA | Malaria Atlas Project [19] | |
| Malaria deaths in children 0–59 months (% of all-cause deaths) | 33,038 | 47,473 (16%) | 2734 | 2723 (5.9%) | WHO (http://www.who.int/healthinfo/global_burden_disease/estimates/en/index3.html and [1]) | WHO |
| All-cause under-5 deaths | NA | 298,200 | NA | 45,916 | NA | UN Inter-Agency Group for Child Mortality Estimation [20] |
| Malaria deaths in 5–14 years | 3258 | NA | 2226 | NA | WHO [1]; share of 5–14 versus 15+ years as for malaria cases [2, 19] | NA |
| Malaria deaths in 15+ years | 2936 | NA | 2074 | NA | ||
| Malaria cases i.e. disease episodes in 0–4 years | 8,231,156 | NA | 1,188,935 | NA | WHO [1]; the share of 0–4 years in WHO’s all age estimate taken from Malaria Atlas Project [2, 19] | NA |
Abbreviations: EIR Entomological Inoculation Rate, LiST Lives Saved Tool, DRC Democratic Republic of the Congo, MAP Malaria Atlas Project, P. falciparum Plasmodium falciparum, NA not available
aThe population at risk of malaria does not influence impact calculations, but it is used in the OneHealth Tool costing as the ‘Population in Need’ (PIN) that would need to get various services like ITNs, IRS spraying, etc. (Equation for number of services: Target Population * PIN * Coverage)