Evidence map›Paper›PMID 28196105›Full record

Trial reportPloS one2017

Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi.

Alinune N Kabaghe, Michael G Chipeta, Robert S McCann, Kamija S Phiri, Michèle van Vugt, Willem Takken, Peter Diggle, Anja D Terlouw

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 2 pooled it
7.7field-weighted citation impact, top 3% of its field
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

43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 84 citations in OpenAlex.

  1. Pooled it
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  12. Geospatial joint modeling of vector and parasite serology to microstratify malaria transmission.Proceedings of the National Academy of Sciences of the United States of America · 2024
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  15. Sampling for malaria molecular surveillance.Trends in parasitology · 2023
    Review
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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 at 5 institutions in 3 countries.

Alinune N KabagheCenter of Tropical Medicine and Travel Medicine, Department of Infectious Diseases, Division of Internal Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands.ORCID http://orcid.org/0000-0001-8517-1586
Michael G ChipetaPublic Health Department, College of Medicine, University of Malawi, Blantyre, Malawi.
Robert S McCannPublic Health Department, College of Medicine, University of Malawi, Blantyre, Malawi.
Kamija S PhiriPublic Health Department, College of Medicine, University of Malawi, Blantyre, Malawi.
Michèle van VugtCenter of Tropical Medicine and Travel Medicine, Department of Infectious Diseases, Division of Internal Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands.
Willem TakkenLaboratory of Entomology, Wageningen University and Research, Wageningen, Netherlands.
Peter DiggleLancaster Medical School, Lancaster University, Lancaster, United Kingdom.
Anja D TerlouwMalaria Theme, Malawi-Liverpool Wellcome Trust, Blantyre, Malawi.
University of Malawi · MWUniversity of Liverpool · GBAmsterdam UMC Location University of Amsterdam · NLLancaster University · GBWageningen University & Research · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIn the context of malaria elimination, interventions will need to target high burden areas to further reduce transmission. Current tools to monitor and report disease burden lack the capacity to continuously detect fine-scale spatial and temporal variations of disease distribution exhibited by malaria. These tools use random sampling techniques that are inefficient for capturing underlying heterogeneity while health facility data in resource-limited settings are inaccurate. Continuous community surveys of malaria burden provide real-time results of local spatio-temporal variation. Adaptive geostatistical design (AGD) improves prediction of outcome of interest compared to current random sampling techniques. We present findings of continuous malaria prevalence surveys using an adaptive sampling design.

methodsWe conducted repeated cross sectional surveys guided by an adaptive sampling design to monitor the prevalence of malaria parasitaemia and anaemia in children below five years old in the communities living around Majete Wildlife Reserve in Chikwawa district, Southern Malawi. AGD sampling uses previously collected data to sample new locations of high prediction variance or, where prediction exceeds a set threshold. We fitted a geostatistical model to predict malaria prevalence in the area.

findingsWe conducted five rounds of sampling, and tested 876 children aged 6-59 months from 1377 households over a 12-month period. Malaria prevalence prediction maps showed spatial heterogeneity and presence of hotspots-where predicted malaria prevalence was above 30%; predictors of malaria included age, socio-economic status and ownership of insecticide-treated mosquito nets.

conclusionsContinuous malaria prevalence surveys using adaptive sampling increased malaria prevalence prediction accuracy. Results from the surveys were readily available after data collection. The tool can assist local managers to target malaria control interventions in areas with the greatest health impact and is ready for assessment in other diseases.

Indexed as

Rural PopulationAdolescentAdultChild, PreschoolCross-Sectional StudiesFemaleHumansInfantMalariaMalawiMaleMiddle AgedPrevalenceSocioeconomic Factors

Identifiers

PMID28196105
PMCPMC5308819
OpenAlexW2588272301

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.