Evidence map›Paper›PMID 34983558›Full record

ArticleMalaria journal2022

A practical approach for geographic prioritization and targeting of insecticide-treated net distribution campaigns during public health emergencies and in resource-limited settings.

Alyssa J Young, Will Eaton, Matt Worges, Honelgn Hiruy, Kolawole Maxwell, Bala Mohammed Audu, Madeleine Marasciulo, Charles Nelson, James Tibenderana, Tarekegn A Abeku

Abstract read
In one paragraph

Article in Malaria journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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

10 authors.

Alyssa J YoungTulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA. ayoung1@tulane.edu.ORCID http://orcid.org/0000-0002-1703-6929
Will EatonTulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
Matt WorgesTulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
Honelgn HiruyTulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
Kolawole MaxwellMalaria Consortium, Abuja, Nigeria.
Bala Mohammed AuduNational Malaria Elimination Programme, Abuja, Nigeria.
Madeleine MarasciuloMalaria Consortium, Raleigh, USA.
Charles NelsonMalaria Consortium, London, UK.
James TibenderanaMalaria Consortium, London, UK.
Tarekegn A AbekuMalaria Consortium, London, UK.

Funding

Ethiopia Global Infectious Diseases Training ProgramD43TW011386 · FIC · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI BERHANE, YEMANE, FAWZI, WAFAIE W · 2020 to 2024
$1.2M
FIC NIH HHS D43 TW011386
6 · The paper itself

Abstract

backgroundThe use of data in targeting malaria control efforts is essential for optimal use of resources. This work provides a practical mechanism for prioritizing geographic areas for insecticide-treated net (ITN) distribution campaigns in settings with limited resources.

methodsA GIS-based weighted approach was adopted to categorize and rank administrative units based on data that can be applied in various country contexts where Plasmodium falciparum transmission is reported. Malaria intervention and risk factors were used to rank local government areas (LGAs) in Nigeria for prioritization during mass ITN distribution campaigns. Each factor was assigned a unique weight that was obtained through application of the analytic hierarchy process (AHP). The weight was then multiplied by a value based on natural groupings inherent in the data, or the presence or absence of a given intervention. Risk scores for each factor were then summated to generate a composite unique risk score for each LGA. This risk score was translated into a prioritization map which ranks each LGA from low to high priority in terms of timing of ITN distributions.

resultsA case study using data from Nigeria showed that a major component that influenced the prioritization scheme was ITN access. Sensitivity analysis results indicate that changes to the methodology used to quantify ITN access did not modify outputs substantially. Some 120 LGAs were categorized as 'extremely high' or 'high' priority when a spatially interpolated ITN access layer was used. When prioritization scores were calculated using DHS-reported state level ITN access, 108 (90.0%) of the 120 LGAs were also categorized as being extremely high or high priority. The geospatial heterogeneity found among input risk factors suggests that a range of variables and covariates should be considered when using data to inform ITN distributions.

conclusionThe authors provide a tool for prioritizing regions in terms of timing of ITN distributions. It serves as a base upon which a wider range of vector control interventions could be targeted. Its value added can be found in its potential for application in multiple country contexts, expediated timeframe for producing outputs, and its use of systematically collected malaria indicators in informing prioritization.

Indexed as

Spatial AnalysisChild, PreschoolEmergenciesHumansInfantInsecticide-Treated BednetsMosquito ControlNigeriaPublic HealthAnalytic hierarchy processGIS-AHPINLA-SPDEInsecticide-treated netsIntervention targetingITN distribution campaignMalariaMCDAPlasmodium falciparumPrioritization schemeVector control

Identifiers

PMID34983558
PMCPMC8724754

What OpenQuestion holds

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LicenceCC BY
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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.