Evidence map›Paper›PMID 28677517›Full record

ArticleEpidemiology and infection2017

Estimating age-time-dependent malaria force of infection accounting for unobserved heterogeneity.

L Mugenyi, S Abrams, N Hens

Abstract read
In one paragraph

Article in Epidemiology and infection, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

3 authors.

L MugenyiInfectious Diseases Research Collaboration,Plot 2C Nakasero Hill Road,Kampala,Uganda.
S AbramsCenter for Statistics, Interuniversity Institute for Biostatistics and statistical Bioinformatics, UHasselt (Hasselt University),Diepenbeek,Belgium.
N HensCenter for Statistics, Interuniversity Institute for Biostatistics and statistical Bioinformatics, UHasselt (Hasselt University),Diepenbeek,Belgium.

Funding

Transmission ProjectU19AI089674 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MATTHEW G DORSEY · 2010 to 2026
$29.3M
NIAID NIH HHS U19 AI089674
6 · The paper itself

Abstract

Despite well-recognized heterogeneity in malaria transmission, key parameters such as the force of infection (FOI) are generally estimated ignoring the intrinsic variability in individual infection risks. Given the potential impact of heterogeneity on the estimation of the FOI, we estimate this quantity accounting for both observed and unobserved heterogeneity. We used cohort data of children aged 0·5-10 years evaluated for the presence of malaria parasites at three sites in Uganda. Assuming a Susceptible-Infected-Susceptible model, we show how the FOI relates to the point prevalence, enabling the estimation of the FOI by modelling the prevalence using a generalized linear mixed model. We derive bounds for varying parasite clearance distributions. The resulting FOI varies significantly with age and is estimated to be highest among children aged 5-10 years in areas of high and medium malaria transmission and highest in children aged below 1 year in a low transmission setting. Heterogeneity is greater between than within households and it increases with decreasing risk of malaria infection. This suggests that next to the individual's age, heterogeneity in malaria FOI may be attributed to household conditions. When estimating the FOI, accounting for both observed and unobserved heterogeneity in malaria acquisition is important for refining malaria spread models.

Indexed as

Age FactorsChildChild, PreschoolCohort StudiesHumansInfantLinear ModelsMalariaPrevalenceUgandaClearance rate distributiongeneralized linear mixed modelpoint prevalenceSIS compartmental model

Identifiers

PMID28677517
PMCPMC9148793

What OpenQuestion holds

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Registered trials

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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.