Evidence map›Paper›PMID 31426819›Full record

ArticleInternational journal of health geographics2019

The use of GPS data loggers to describe the impact of spatio-temporal movement patterns on malaria control in a high-transmission area of northern Zambia.

Marisa Hast, Kelly M Searle, Mike Chaponda, James Lupiya, Jailos Lubinda, Jay Sikalima, Tamaki Kobayashi, Timothy Shields, Modest Mulenga, Justin Lessler and 2 more

Open access · goldAbstract read
In one paragraph

Article in International journal of health geographics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
5.8field-weighted citation impact, top 4% 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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.

  1. Pooled it
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  4. Human mobility and the infectious disease transmission: A systematic review.Geo-spatial information science = Diqui kongjian xinxi kexue xuebao · 2024
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  6. Understanding Spatiotemporal Human Mobility Patterns for Malaria Control Using a Multiagent Mobility Simulation Model.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023
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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

12 authors at 4 institutions in 3 countries.

Marisa HastJohns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. mhast2@jhu.edu.ORCID 0000-0003-2471-6700
Kelly M SearleUniversity of Minnesota, School of Public Health, Minneapolis, MN, USA.
Mike ChapondaThe Tropical Diseases Research Centre, Ndola, Zambia.
James LupiyaThe Tropical Diseases Research Centre, Ndola, Zambia.
Jailos LubindaMacha Research Trust, Choma District, Choma, Zambia.
Jay SikalimaThe Tropical Diseases Research Centre, Ndola, Zambia.
Tamaki KobayashiJohns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Timothy ShieldsJohns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Modest MulengaThe Tropical Diseases Research Centre, Ndola, Zambia.
Justin LesslerJohns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
William J MossJohns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Southern and Central Africa International Centers of Excellence for Malaria Research
Johns Hopkins University · USTropical Diseases Research Centre · ZMUniversity of Minnesota · USUniversity of Ulster · GB

Funding

TransmissionU19AI089680 · NIAID · JOHNS HOPKINS UNIVERSITY · PI WILLIAM J MOSS · 2010 to 2026
$25.6M
NIAID NIH HHS U19 AI089680NIH HHS U19AI089680
6 · The paper itself

Abstract

backgroundHuman movement is a driver of malaria transmission and has implications for sustainable malaria control. However, little research has been done on the impact of fine-scale movement on malaria transmission and control in high-transmission settings. As interest in targeted malaria control increases, evaluations are needed to determine the appropriateness of these strategies in the context of human mobility across a variety of transmission settings.

methodsA human mobility study was conducted in Nchelenge District, a high-transmission setting in northern Zambia. Over 1 year, 84 participants were recruited from active malaria surveillance cohorts to wear a global positioning system data logger for 1 month during all daily activity. Participants completed a survey questionnaire and underwent malaria testing and treatment at the time of logger distribution and at collection 1 month later. Incident malaria infections were identified using polymerase chain reaction. Participant movement was characterized throughout the study area and across areas targeted for an indoor residual spraying (IRS) intervention. Participant movement patterns were compared using movement intensity maps, activity space plots, and statistical analyses. Malaria risk was characterized across participants using spatial risk maps and time spent away from home during peak vector biting hours.

resultsMovement data were collected from 82 participants, and 63 completed a second study visit. Participants exhibited diverse mobility patterns across the study area, including movement into and out of areas targeted for IRS, potentially mitigating the impact of IRS on parasite prevalence. Movement patterns did not differ significantly by season or age, but male participants traveled longer distances and spent more time away from home. Monthly malaria incidence was 22%, and malaria risk was characterized as high across participants. Participants with incident parasitemia traveled a shorter distance and spent more time away from home during peak biting hours; however, these relationships were not statistically significant, and malaria risk score did not differ by incident parasitemia.

conclusionsIndividual movement patterns in Nchelenge District, Zambia have implications for malaria control, particularly the effectiveness of targeted IRS strategies. Large and fine-scale population mobility patterns should be considered when planning intervention strategies across transmission settings.

Indexed as

Geographic Information SystemsMovementSpatial BehaviorAdolescentAdultAgedCross-Sectional StudiesData Interpretation, StatisticalFemaleFollow-Up StudiesHumansLongitudinal StudiesMalariaMaleMiddle AgedMosquito ControlGPSMalariaPopulation movementZambia

Identifiers

PMID31426819
PMCPMC6701131
OpenAlexW2969576209

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.