Evidence map›Paper›PMID 38106223›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Characterizing mobility patterns and malaria risk factors in semi-nomadic populations of Northern Kenya.

Hannah R Meredith, Amy Wesolowski, Dennis Okoth, Linda Maraga, George Ambani, Tabitha Chepkwony, Lucy Abel, Joseph Kipkoech, Gilchrist Lokoel, Daniel Esimit and 4 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 4 institutions in 2 countries.

Hannah R MeredithDuke Global Health Institute, Durham, North Carolina, USA.
Amy WesolowskiJohns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Dennis OkothDepartment of Health Services and Sanitation, Turkana County, Kenya.
Linda MaragaAcademic Model Providing Access to Healthcare, Eldoret, Uasin Gishu, Kenya.
George AmbaniAcademic Model Providing Access to Healthcare, Eldoret, Uasin Gishu, Kenya.
Tabitha ChepkwonyAcademic Model Providing Access to Healthcare, Eldoret, Uasin Gishu, Kenya.
Lucy AbelAcademic Model Providing Access to Healthcare, Eldoret, Uasin Gishu, Kenya.
Joseph KipkoechAcademic Model Providing Access to Healthcare, Eldoret, Uasin Gishu, Kenya.
Gilchrist LokoelDepartment of Health Services and Sanitation, Turkana County, Kenya.
Daniel EsimitDepartment of Health Services and Sanitation, Turkana County, Kenya.ORCID 0000-0001-5279-5085
Samuel LokemerDepartment of Health Services and Sanitation, Turkana County, Kenya.
James MaragiaDepartment of Health Services and Sanitation, Turkana County, Kenya.
Wendy Prudhomme O'MearaDuke Global Health Institute, Durham, North Carolina, USA.ORCID 0000-0001-9455-3965
Andrew A ObalaSchool of Medicine, Moi University College of Health Sciences, Eldoret, Uasin Gishu, Kenya.
AMPATH · KEMoi University · KEDuke Institute for Health Innovation · USJohns Hopkins University · US

Funding

Disease Emergence and Elimination: Using Mobility Data to Inform Spatial Disease DynamicsDP2LM013102 · NLM · JOHNS HOPKINS UNIVERSITY · PI WESOLOWSKI, AMY · 2018 to 2018
$2.5M
NLM NIH HHS DP2 LM013102
6 · The paper itself

Abstract

While many studies have characterized mobility patterns and disease dynamics of individuals from settled populations, few have focused on more mobile populations. Highly mobile groups are often at higher disease risk due to their regular movement that may increase the variability of their environments, reduce their access to health care, and limit the number of intervention strategies suitable for their lifestyles. Quantifying the movements and their associated disease risks will be key to developing intervention strategies more suitable for mobile populations. Here, we worked with four semi-nomadic communities in Central Turkana, Kenya to 1) characterize mobility patterns of travelers from semi-nomadic communities and 2) test the hypothesis that semi-nomadic individuals are at greater risk of exposure to malaria during seasonal migrations than when staying at their semi-permanent settlements. From March-October, 2021, we conducted a study in semi-nomadic households (n=250) where some members traveled with their herd while others remained at the semi-permanent settlement. Participants provided medical and travel histories, demographics, and a dried blood spot for malaria testing before and after the travel period. Further, a subset of travelers was given GPS loggers to document their routes. Four travel patterns emerged from the logger data, Long Term, Transient, Day trip, and Static, with only Long Term and Transient trips being associated with malaria cases detected in individuals who carried GPS devices. After completing their trips, travelers had a higher prevalence of malaria than those who remained at the household (9.2% vs 4.4%), regardless of gender, age group, and catchment area. These findings highlight the need to develop intervention strategies amenable to mobile lifestyles that can ultimately help prevent the transmission of malaria.

Identifiers

PMID38106223
PMCPMC10723563
OpenAlexW4389515132

What OpenQuestion holds

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LicenceCC BY
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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.