Evidence map›Paper›PMID 38076857›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Network Profile: Improving Response to Malaria in the Amazon through Identification of Inter-Community Networks and Human Mobility in Border Regions of Ecuador, Peru, and Brazil.

Mark M Janko, Andrea L Araujo, Edson J Ascencio, Gilvan R Guedes, Luis E Vasco, Reinaldo A Santos, Camila P Damasceno, Perla G Medrano, Pamela R Chacón-Uscamaita, Annika K Gunderson and 11 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

21 authors at 7 institutions in 4 countries.

Mark M JankoDuke Global Health Institute, Duke University, Durham, North Carolina, USA.
Andrea L AraujoInstituto de Geografía, Universidad San Francisco de Quito, Quito, Ecuador.
Edson J AscencioEmerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru.
Gilvan R GuedesCenter for Regional Development and Planning (Cedeplar), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.
Luis E VascoInstituto de Geografía, Universidad San Francisco de Quito, Quito, Ecuador.
Reinaldo A SantosCenter for Regional Development and Planning (Cedeplar), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.
Camila P DamascenoCenter for Regional Development and Planning (Cedeplar), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.
Perla G MedranoDuke Global Health Institute, Duke University, Durham, North Carolina, USA.
Pamela R Chacón-UscamaitaEmerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru.
Annika K GundersonDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Sara O'MalleyDuke Global Health Institute, Duke University, Durham, North Carolina, USA.
Prakrut H KansaraDepartment of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA.
Manuel B NarvaezInstituto de Geografía, Universidad San Francisco de Quito, Quito, Ecuador.
Carolina S CoombesEmerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru.
Francesco PizzituttiInstituto de Geografía, Universidad San Francisco de Quito, Quito, Ecuador.
Gabriela Salmon-MulanovichDepartamento Académico Ingeniería, Pontifica Universidad Catolica del Peru, Lima, Peru.
Benjamin F ZaitchikDepartment of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA.
Carlos F MenaInstituto de Geografía, Universidad San Francisco de Quito, Quito, Ecuador.
Andres G LescanoEmerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru.
Alisson F BarbieriCenter for Regional Development and Planning (Cedeplar), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.
William K PanDuke Global Health Institute, Duke University, Durham, North Carolina, USA.
Universidad San Francisco de Quito · ECDuke University · USUniversidade Federal de Minas Gerais · BRUniversidad Peruana Cayetano Heredia · PEJohns Hopkins University · USPontificia Universidad Católica del Perú · PEUniversity of North Carolina at Chapel Hill · US

Funding

Peru Infectious Diseases Epidemiology Research Training ConsortiumD43TW007393 · FIC · UNIVERSIDAD PERUANA CAYETANO HEREDIA · PI Andres G Lescano · 2005 to 2026
$5.9M
FIC NIH HHS D43 TW007393
6 · The paper itself

Abstract

Objectives: Understanding human mobility's role on malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission. Design: A community-level network survey. Setting: We collect data on community connectivity along three river systems in the Amazon basin: the Pastaza river corridor spanning the Ecuador-Peru border; and the Amazon and Javari river corridors spanning the Brazil-Peru border. Participants: We interviewed key informants in Brazil, Ecuador, and Peru, including from indigenous communities: Shuar, Achuar, Shiwiar, Kichwa, Ticuna, and Yagua. Key informants are at least 18 years of age and are considered community leaders. Primary outcome: Weekly, community-level malaria incidence during the study period. Methods: We measure community connectivity across the study area using a respondent driven sampling design. Forty-five communities were initially selected: 10 in Brazil, 10 in Ecuador, and 25 in Peru. Participants were recruited in each initial node and administered a survey to obtain data on each community's mobility patterns. Survey responses were ranked and the 2-3 most connected communities were then selected and surveyed. This process was repeated for a third round of data collection. Community network matrices will be linked with eadch country's malaria surveillance system to test the effects of mobility on disease risk. Findings: To date, 586 key informants were surveyed from 126 communities along the Pastaza river corridor. Data collection along the Amazon and Javari river corridors is ongoing. Initial results indicate that network sampling is a superior method to delineate migration flows between communities. Conclusions: Our study provides measures of mobility and connectivity in rural settings where traditional approaches are insufficient, and will allow us to understand mobility's effect on malaria transmission.

Identifiers

PMID38076857
PMCPMC10705622
OpenAlexW4389197627

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.