Evidence map›Paper›PMID 29864165›Full record

ArticlePLoS neglected tropical diseases2018

Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles.

Alexandra V Kulinkina, Yvonne Walz, Magaly Koch, Nana-Kwadwo Biritwum, Jürg Utzinger, Elena N Naumova

Open access · goldAbstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.3field-weighted citation impact, top 14% 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, 50 citations in OpenAlex.

  1. Article
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  7. Journal of parasitology research · 2023
    Article
  8. Article
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  11. Article
  12. Screening forPathogens (Basel, Switzerland) · 2022
    Article
  13. Schistosomiasis with a Focus on Africa.Tropical medicine and infectious disease · 2021
    Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
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

6 authors at 5 institutions in 4 countries.

Alexandra V KulinkinaDepartment of Civil and Environmental Engineering, Tufts University, Medford, Massachusetts, United States of America.ORCID 0000-0002-4777-1254
Yvonne WalzInstitute for Environment and Human Security, United Nations University, Bonn, Germany.
Magaly KochDepartment of Civil and Environmental Engineering, Tufts University, Medford, Massachusetts, United States of America.
Nana-Kwadwo BiritwumNeglected Tropical Diseases Program, Ghana Health Service, Accra, Ghana.
Jürg UtzingerSwiss Tropical and Public Health Institute, Basel, Switzerland.
Elena N NaumovaDepartment of Civil and Environmental Engineering, Tufts University, Medford, Massachusetts, United States of America.
Tufts University · USBoston University · USGhana Health Service · GHSwiss Tropical and Public Health Institute · CHUnited Nations University · DE

Funding

Integrated Primary Prevention & Chemotherapy: Urogenital Schistosomiasis ControlR34AI097083 · NIAID · TUFTS UNIVERSITY MEDFORD · PI GRIFFITHS, JEFFREY K, GUTE, DAVID MUELLER · 2013 to 2013
$234k
NIAID NIH HHS R34 AI097083NIH HHS R34 AI097083-01A1
6 · The paper itself

Abstract

backgroundSchistosomiasis is a water-related neglected tropical disease. In many endemic low- and middle-income countries, insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases. Hence, modeling is relied upon to predict areas of high transmission and to inform control strategies. We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches. METHODOLOGY: Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites, including applying the models to known human water contact locations. Lastly, measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables. PRINCIPAL

findingsPredictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured. A water index (MNDWI) and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated in the combined model that included WASH variables. CONCLUSIONS/SIGNIFICANCE: The study helps to understand localized drivers of schistosomiasis transmission. Specifically, unsatisfactory water quality in boreholes perpetuates reliance on surface water bodies, indirectly increasing schistosomiasis risk and resulting in rapid reinfection (up to 40% prevalence six months following preventive chemotherapy). Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure.

Indexed as

Models, StatisticalAnimalsChildCross-Sectional StudiesFemaleGeographyGhanaHumansHygieneMalePrevalenceRemote Sensing TechnologySanitationSchistosoma haematobiumSchistosomiasis haematobiaSchoolsWater

Identifiers

PMID29864165
PMCPMC6014678
OpenAlexW2806181484

What OpenQuestion holds

Textmetadata
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.