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Modelling the returns on options for improving malaria case management in Ethiopia†

Overview of attention for article published in Health Policy & Planning, November 2013
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Article details
Title
Modelling the returns on options for improving malaria case management in Ethiopia†
Published in
Health Policy & Planning, November 2013
DOI 10.1093/heapol/czt081
Pubmed ID
Authors
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.Findings 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.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
United Kingdom 1 <1%
Unknown 103 99%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Master 18 17%
Researcher 17 16%
Student > Ph. D. Student 11 11%
Student > Doctoral Student 8 8%
Other 7 7%
Other 19 18%
Unknown 24 23%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 30 29%
Social Sciences 12 12%
Nursing and Health Professions 11 11%
Economics, Econometrics and Finance 6 6%
Agricultural and Biological Sciences 4 4%
Other 12 12%
Unknown 29 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 November 2013.
All research outputs
#22,038,777
of 34,358,242 outputs
Outputs from Health Policy & Planning
#2,333
of 2,601 outputs
Outputs of similar age
#168,916
of 264,994 outputs
Outputs of similar age from Health Policy & Planning
#29
of 34 outputs
Altmetric has tracked 34,358,242 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,601 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.6. This one is in the 10th percentile – i.e., 10% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,994 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.