title: Modelling the returns on options for improving malaria case management in Ethiopia.
abstract: BACKGROUND
  Diverse opinions have emerged about the best way to scale up malaria interventions. Three controversies seem most important: (1) should the scale-up focus on a broader target of febrile illness (including infectious disease and pneumonia)? (2) should the scale-up feature a single intervention or be targeted to the situation? (3) should scale-up have a preference for one kind of delivery mechanism or another?
  
  
  METHODS
  A decision model of 576 nodes describes the patterns of access, treatment and outcomes of an episode of febrile illness for a child below 5 years. Incremental costs and outcomes relative to baseline (2010) are computed for particular scenarios for Ethiopia using data from the literature. Two perspectives define the relevant costs: society at large and financiers (government and donors) where the costs borne by households are not included.
  
  
  RESULTS
  Scaling up malaria interventions by one means or another is a very inexpensive way of saving young lives in poor countries. The low cost per life saved stems from two main reasons: the excessive baseline costs of presumptive use of antimalarial drugs for non-malaria cases, and the excessive costs of delayed treatment of pneumonia. A very limited policy of supplying antibiotics to facilities to eliminate stockouts would save 2100 lives, at a cost of only $615 a life. A much broader programme option, bundling malaria and pneumonia together for patients presenting with febrile illness [including rapid diagnostic test (RDT) for malaria, respiratory rate timers (RRTs) and free antibiotics], would save tens of thousands of young lives at and still cost society less than child fever management in the baseline situation! It is not clear that scale-up via community health workers (CHWs) is to be preferred to a facility-based intervention. The delivery through CHWs allows for a broader coverage of using RDT and RRT, but with limited effectiveness due to limited skills of CHWs in treating and managing patients.
authors.affiliation: Simmons College, Boston, MA, 02115, USA, Brandeis University, Waltham MA, 02454, USA, Futures Group International, Washington DC, 20005, USA and Deloitte LLP, Boston, MA, 02116, USA Simmons College, Boston, MA, 02115, USA, Brandeis University, Waltham MA, 02454, USA, Futures Group International, Washington DC, 20005, USA and Deloitte LLP, Boston, MA, 02116, USA gaumer@simmons.edu.
authors.forename: Gary
authors.initials: G
authors.lastname: Gaumer
authors.affiliation: Simmons College, Boston, MA, 02115, USA, Brandeis University, Waltham MA, 02454, USA, Futures Group International, Washington DC, 20005, USA and Deloitte LLP, Boston, MA, 02116, USA Simmons College, Boston, MA, 02115, USA, Brandeis University, Waltham MA, 02454, USA, Futures Group International, Washington DC, 20005, USA and Deloitte LLP, Boston, MA, 02116, USA.
authors.forename: Wu
authors.initials: W
authors.lastname: Zeng
authors.affiliation: Simmons College, Boston, MA, 02115, USA, Brandeis University, Waltham MA, 02454, USA, Futures Group International, Washington DC, 20005, USA and Deloitte LLP, Boston, MA, 02116, USA.
authors.forename: Allyala Krishna
authors.initials: AK
authors.lastname: Nandakumar
mesh_terms: D000900:Anti-Bacterial Agents / Q000627:therapeutic use; D000962:Antimalarials / Q000627:therapeutic use; D019090:Case Management; D002675:Child, Preschool; D003661:Decision Support Techniques; D005002:Ethiopia / Q000453:epidemiology; D005260:Female; D006302:Health Services Research; D006801:Humans; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D008288:Malaria / Q000453:epidemiology / Q000517:prevention & control*; D008297:Male; D011014:Pneumonia / Q000453:epidemiology / Q000517:prevention & control
chemical_list: D000900:Anti-Bacterial Agents; D000962:Antimalarials
keywords: Ethiopia; Malaria; cost effectiveness; decision analytic model; scale-up