MétaCan
Menu
Back to cohort
Record W2595065451 · doi:10.1186/s40249-017-0281-7

Fish sharing as a risk factor for Opisthorchis viverrini infection: evidence from two villages in north-eastern Thailand

2017· article· en· W2595065451 on OpenAlexaff
Parichat Saenna, Cameron Hurst, Pierre Echaubard, Bruce A. Wilcox, Banchob Sripa

Bibliographic record

VenueInfectious Diseases of Poverty · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsLaurentian University
FundersOffice of the Higher Education CommissionNational Institutes of HealthThailand Research FundHigher Education Research PromotionKhon Kaen UniversityNational Institute of Allergy and Infectious DiseasesDivision of Intramural Research, National Institute of Allergy and Infectious Diseases
KeywordsOpisthorchis viverriniOpisthorchisFish <Actinopterygii>OpisthorchiasisPublic healthRisk factorEnvironmental healthTropical medicineVeterinary medicineMedicineHelminthsGeographyFisheryLiver flukeBiologyZoologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Foodborne trematodiasis (FBT) is a significant global health problem, with the liver flukes Opisthorchis viverrini, O. felineus, and Clonorchis sinensis contributing to half of the global burden of FBT. North-eastern Thailand where O. viverrini is endemic and un-cooked fish dishes remain an integral part of the food culture has the highest reported incidence of opisthorchiasis, including associated cholangiocarcinoma. Both food sharing and eating practices are potentially important factors in FTB, suggesting an important role for the social ecology of disease transmission in these rural communities. METHODS: Two rural Thai-Lao villages that were part of a 12-village project in Northeastern Thailand were selected for detailed investigation of O. viverrini infection risk associated with sharing of raw fish dishes among households. The project included screening individuals for infection and cholangiocarcinoma, a household questionnaire, and offering treatment options for positive individuals. Social network mapping was used to construct raw fish dish-sharing networks and create a proxy variable capturing variability in the degree of food sharing (DFS), measured as the number of different households with which each household shared fish dishes. Measures of associations between DFS, O. viverrini infection, the frequency of raw fish consumption, and the number of raw fish dishes consumed were generated using binary logistic regression, proportional odds ordinal logistic regression, and Poisson regression. RESULTS: The results showed that the probability that a household has members infected with O. viverrini increased by ~7% (P < 0.01) for each additional household included in its network. Moreover, the frequency and number of types of raw fish dishes consumed increased significantly as the DFS increased. Of the two villages, that with the highest infection prevalence (48% versus 34.6%) had significantly higher social connectivity overall (P < 0.001). CONCLUSIONS: Our findings suggest that the social ecology of human settlements may be key to understanding the transmission dynamics of some FBT. In the case of O. viverrini in Thai-Lao communities, for which food sharing is a traditional practice supporting social cohesion, food sharing network mapping should be incorporated into community-based interventions. These should encourage fish dish preparation methods that minimize infection risk by targeting households with high DFS values.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.327
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations56
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInfectious Diseases of PovertySame topicParasites and Host InteractionsFrench-language works237,207