Evidence map›Paper›PMID 26437798›Full record

ArticleMalaria journal2015

Distribution of malaria exposure in endemic countries in Africa considering country levels of effective treatment.

Melissa A Penny, Nicolas Maire, Caitlin A Bever, Peter Pemberton-Ross, Olivier J T Briët, David L Smith, Peter W Gething, Thomas A Smith

Abstract read
In one paragraph

Article in Malaria journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

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  3. Comparative malaria impact of chlorfenapyr-α-cypermethrin netsCurrent research in parasitology & vector-borne diseases · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Melissa A PennyDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, 4051, Basel, Switzerland. melissa.penny@unibas.ch.
Nicolas MaireDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, 4051, Basel, Switzerland. nicolas.maire@unibas.ch.
Caitlin A BeverDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, 4051, Basel, Switzerland. cbever@intven.com.
Peter Pemberton-RossDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, 4051, Basel, Switzerland. Peter.Pemberton-Ross@unibas.ch.
Olivier J T BriëtDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, 4051, Basel, Switzerland. olivier.briet@unibas.ch.
David L SmithDepartment of Zoology, University of Oxford, Tinbergen Building, South Parks Road, Oxford, OX1 3PS, UK. david.smith@zoo.ox.ac.uk.
Peter W GethingDepartment of Zoology, University of Oxford, Tinbergen Building, South Parks Road, Oxford, OX1 3PS, UK. peter.gething@zoo.ox.ac.uk.
Thomas A SmithDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, 4051, Basel, Switzerland. thomas-a.smith@unibas.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMalaria prevalence, clinical incidence, treatment, and transmission rates are dynamically interrelated. Prevalence is often considered a measure of malaria transmission, but treatment of clinical malaria reduces prevalence, and consequently also infectiousness to the mosquito vector and onward transmission. The impact of the frequency of treatment on prevalence in a population is generally not considered. This can lead to potential underestimation of malaria exposure in settings with good health systems. Furthermore, these dynamical relationships between prevalence, treatment, and transmission have not generally been taken into account in estimates of burden.

methodsUsing prevalence as an input, estimates of disease incidence and transmission [as the distribution of the entomological inoculation rate (EIR)] for Plasmodium falciparum have now been made for 43 countries in Africa using both empirical relationships (that do not allow for treatment) and OpenMalaria dynamic micro-simulation models (that explicitly include the effects of treatment). For each estimate, prevalence inputs were taken from geo-statistical models fitted for the year 2010 by the Malaria Atlas Project to all available observed prevalence data. National level estimates of the effectiveness of case management in treating clinical attacks were used as inputs to the estimation of both EIR and disease incidence by the dynamic models. RESULTS AND

conclusionsWhen coverage of effective treatment is taken into account, higher country level estimates of average EIR and thus higher disease burden, are obtained for a given prevalence level, especially where access to treatment is high, and prevalence relatively low. These methods provide a unified framework for comparison of both the immediate and longer-term impacts of case management and of preventive interventions.

Indexed as

AfricaAntimalarialsChildChild, PreschoolCommunicable Disease ControlDisease Transmission, InfectiousHumansIncidenceMalaria, FalciparumPrevalenceAntimalarials

Identifiers

PMID26437798
PMCPMC4595196

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.