Evidence map›Paper›PMID 39495810›Full record

ArticlePLoS neglected tropical diseases2024

Mapping schistosomiasis risk landscapes and implications for disease control: A case study for low endemic areas in the Middle Paranapanema river basin, São Paulo, Brazil.

Vivian Alessandra Ferreira da Silva, Milton Kampel, Rafael Silva Dos Anjos, Raquel Gardini Sanches Palasio, Maria Isabel Sobral Escada, Roseli Tuan, Alyson Singleton, Caroline Kate Glidden, Andrew Chamberlin, Giulio Alessandro De Leo and 2 more

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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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.

No citing paper in PubMed yet.

4 · The record

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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.

Vivian Alessandra Ferreira da SilvaRemote Sensing Postgraduate Program (PGSER), Coordination of Teaching, Research and Extension (COEPE), National Institute for Space Research (INPE), São José dos Campos, São Paulo, Brazil.ORCID 0000-0003-0714-9097
Milton KampelRemote Sensing Postgraduate Program (PGSER), Coordination of Teaching, Research and Extension (COEPE), National Institute for Space Research (INPE), São José dos Campos, São Paulo, Brazil.
Rafael Silva Dos AnjosLiSS -Laboratory for investigation of Socio Environmental Systems, National Institute for Space Research (INPE), São José dos Campos, SP, Brazil.
Raquel Gardini Sanches PalasioLaboratory of Spatial Analysis in Health (LAES), Department of Epidemiology, School of Public Health, University of Sao Paulo (FSP/USP), São Paulo, SP, Brazil.
Maria Isabel Sobral EscadaRemote Sensing Postgraduate Program (PGSER), Coordination of Teaching, Research and Extension (COEPE), National Institute for Space Research (INPE), São José dos Campos, São Paulo, Brazil.
Roseli TuanPasteur Institute, São Paulo, SP, Brazil.
Alyson SingletonEmmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, California, United States of America.
Caroline Kate GliddenDepartment of Biology, Stanford University, Stanford, California, United States of America.
Andrew ChamberlinDepartment of Oceans, Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America.
Giulio Alessandro De LeoDepartment of Oceans, Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America.
Adriano Pinter Dos SantosPasteur Institute, São Paulo, SP, Brazil.
Antônio Miguel Vieira MonteiroRemote Sensing Postgraduate Program (PGSER), Coordination of Teaching, Research and Extension (COEPE), National Institute for Space Research (INPE), São José dos Campos, São Paulo, Brazil.

Funding

NSF DEB – 2011179NSF ICER-2024383
6 · The paper itself

Abstract

backgroundSchistosomiasis, a chronic parasitic disease, remains a public health issue in tropical and subtropical regions, especially in low and moderate-income countries lacking assured access to safe water and proper sanitation. A national prevalence survey carried out by the Brazilian Ministry of Health from 2011 to 2015 found a decrease in human infection rates to 1%, with 19 out of 26 states still classified as endemic areas. There is a risk of schistosomiasis reemerging as a public health concern in low-endemic regions. This study proposes an integrated landscape-based approach to aid surveillance and control strategies for schistosomiasis in low-endemic areas. METHODOLOGY/PRINCIPAL

findingsIn the Middle Paranapanema river basin, specific landscapes linked to schistosomiasis were identified using a comprehensive methodology. This approach merged remote sensing, environmental, socioeconomic, epidemiological, and malacological data. A team of experts identified ten distinct landscape categories associated with varying levels of schistosomiasis transmission potential. These categories were used to train a supervised classification machine learning algorithm, resulting in a 92.5% overall accuracy and a 6.5% classification error. Evaluation revealed that 74.6% of collected snails from water collections in five key municipalities within the basin belonged to landscape types with higher potential for S. mansoni infection. Landscape connectivity metrics were also analysed. CONCLUSIONS/SIGNIFICANCE: This study highlights the role of integrated landscape-based analyses in informing strategies for eliminating schistosomiasis. The methodology has produced new schistosomiasis risk maps covering the entire basin. The region's low endemicity can be partly explained by the limited connectivity among grouped landscape-units more prone to triggering schistosomiasis transmission. Nevertheless, changes in social, economic, and environmental landscapes, especially those linked to the rising pace of incomplete urbanization processes in the region, have the potential to increase risk of schistosomiasis transmission. This study will help target interventions to bring the region closer to schistosomiasis elimination.

Indexed as

RiversAnimalsBrazilEndemic DiseasesHumansPrevalenceSchistosoma mansoniSchistosomiasisSchistosomiasis mansoniSnails

Identifiers

PMID39495810
PMCPMC11563476

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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.