ArticleAmerican journal of epidemiology2021
Human Mobility Associated With Risk of Schistosoma japonicum Infection in Sichuan, China.
Article in American journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- One health at the last mile: Multi-scale predictors of Schistosoma japonicum infection in southwest China across two decades of control.PLoS neglected tropical diseases · 2026Article
- Effect of environmental factors in reducing the prevalence of schistosomiasis in schoolchildren: An analysis of three extensive national prevalence surveys in Brazil (1950-2018).PLoS neglected tropical diseases · 2023Article
- Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination.International journal of health geographics · 2023Article
- Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination.Research square · 2023Article
- Integrating genomic and epidemiologic data to accelerate progress toward schistosomiasis elimination.eLife · 2022Review
- Identifying the Determinants of Distribution ofPathogens (Basel, Switzerland) · 2022Article
- Urinary Schistosomiasis in the Urban Community of Zinder (Niger): The Permanent Pond of In'birgui, an Epidemic Focus.Iranian journal of parasitologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Urbanization increases human mobility in ways that can alter the transmission of classically rural, vector-borne diseases like schistosomiasis. The impact of human mobility on individual-level Schistosoma risk is poorly characterized. Travel outside endemic areas may protect against infection by reducing exposure opportunities, whereas travel to other endemic regions may increase risk. Using detailed monthly travel- and water-contact surveys from 27 rural communities in Sichuan, China, in 2008, we aimed to describe human mobility and to identify mobility-related predictors of S. japonicum infection. Candidate predictors included timing, frequency, distance, duration, and purpose of recent travel as well as water-contact measures. Random forests machine learning was used to detect key predictors of individual infection status. Logistic regression was used to assess the strength and direction of associations. Key mobility-related predictors include frequent travel and travel during July-both associated with decreased probability of infection and less time engaged in risky water-contact behavior, suggesting travel may remove opportunities for schistosome exposure. The importance of July travel and July water contact suggests a high-risk window for cercarial exposure. The frequency and timing of human movement out of endemic areas should be considered when assessing potential drivers of rural infectious diseases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.