Evidence map›Paper›PMID 38478562›Full record

ArticlePLOS global public health2024

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 · goldAbstract read
In one paragraph

Article in PLOS global public health, 2024. 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
2.8field-weighted citation impact, top 11% 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

6 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Characterization of Malaria Outbreak in Marsabit County, Kenya, March 2024.The American journal of tropical medicine and hygiene · 2025
    Article
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, United States of America.
Amy WesolowskiJohns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Dennis OkothDepartment of Health Services and Sanitation, Lodwar, Turkana County, Kenya.
Linda MaragaAcademic Model Providing Access to Healthcare, Eldoret, Kenya.
George AmbaniAcademic Model Providing Access to Healthcare, Eldoret, Kenya.
Tabitha ChepkwonyAcademic Model Providing Access to Healthcare, Eldoret, Kenya.
Lucy AbelAcademic Model Providing Access to Healthcare, Eldoret, Kenya.
Joseph KipkoechAcademic Model Providing Access to Healthcare, Eldoret, Kenya.
Gilchrist LokoelDepartment of Health Services and Sanitation, Lodwar, Turkana County, Kenya.
Daniel EsimitDepartment of Health Services and Sanitation, Lodwar, Turkana County, Kenya.ORCID https://orcid.org/0000-0001-5279-5085
Samuel LokemerDepartment of Health Services and Sanitation, Lodwar, Turkana County, Kenya.
James MaragiaDepartment of Health Services and Sanitation, Lodwar, Turkana County, Kenya.
Wendy Prudhomme O'MearaDuke Global Health Institute, Durham, North Carolina, United States of America.ORCID https://orcid.org/0000-0001-9455-3965
Andrew A ObalaSchool of Medicine, Moi University College of Health Sciences, Eldoret, Kenya.
AMPATH · KEMoi University · KEDuke Institute for Health Innovation · USJohns Hopkins University · US

Funding

Human mobility models to forecast disease dynamics and the effectiveness of public health interventionsR01AI160780 · NIAID · JOHNS HOPKINS UNIVERSITY · PI CUMMINGS, DEREK A, WESOLOWSKI, AMY · 2021 to 2025
$3.3M
Disease Emergence and Elimination: Using Mobility Data to Inform Spatial Disease DynamicsDP2LM013102 · NLM · JOHNS HOPKINS UNIVERSITY · PI WESOLOWSKI, AMY · 2018 to 2018
$2.5M
NIAID NIH HHS R01 AI160780NLM NIH HHS DP2 LM013102
6 · The paper itself

Abstract

While many studies have characterized mobility patterns and disease dynamics of 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 interventions more suitable for mobile populations. Turkana, Kenya is an ideal setting to characterize these relationships. While the vast, semi-arid county has a large mobile population (>60%) and was recently shown to have endemic malaria, the relationship between mobility and malaria risk in this region has not yet been defined. Here, we worked with 250 semi-nomadic households from four communities in Central Turkana to 1) characterize mobility patterns of travelers and 2) test the hypothesis that semi-nomadic individuals are at greater risk of malaria exposure when migrating with their herds than when staying at their semi-permanent settlements. 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 and age. These findings highlight the need to develop intervention strategies amenable to mobile lifestyles that can ultimately help prevent the transmission of malaria.

Identifiers

PMID38478562
PMCPMC10936864
OpenAlexW4392763148

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.