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Table 2 Parameters and input data used by LiST and Spectrum-Malaria in projections for DRC and Zambia

From: Impact of malaria interventions on child mortality in endemic African settings: comparison and alignment between LiST and Spectrum-Malaria model

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

  1. 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
  2. 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)