Evidence map›Paper›PMID 30932867›Full record

ArticleJMIR public health and surveillance2019

Wet Markets and Food Safety: TripAdvisor for Improved Global Digital Surveillance.

Nicole E Kogan, Isabelle Bolon, Nicolas Ray, Gabriel Alcoba, Jose L Fernandez-Marquez, Martin M Müller, Sharada P Mohanty, Rafael Ruiz de Castañeda

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.6field-weighted citation impact, top 11% 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

11 citing papers in PubMed, 27 citations in OpenAlex.

  1. Review
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  8. Review
  9. Evaluation of Tourism Food Safety and Quality with Neural Networks.Computational intelligence and neuroscience · 2022
    Article
  10. Review
  11. 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

8 authors at 3 institutions in 2 countries.

Nicole E KoganMassachusetts Institute of Technology, Cambridge, MA, United States.ORCID http://orcid.org/0000-0003-1622-0537
Isabelle BolonInstitute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland.ORCID http://orcid.org/0000-0001-5940-2731
Nicolas RayInstitute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland.ORCID http://orcid.org/0000-0002-4696-5313
Gabriel AlcobaDivision of Tropical and Humanitarian Medicine, University Hospitals of Geneva, Geneva, Switzerland.ORCID http://orcid.org/0000-0002-9423-1533
Jose L Fernandez-MarquezCitizen Cyberlab, Centre Universitaire d'Informatique, University of Geneva, Carouge, Switzerland.ORCID http://orcid.org/0000-0002-0254-0794
Martin M MüllerDigital Epidemiology Lab, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.ORCID http://orcid.org/0000-0003-1909-8194
Sharada P MohantyDigital Epidemiology Lab, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.ORCID http://orcid.org/0000-0003-4578-0170
Rafael Ruiz de CastañedaInstitute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland.ORCID http://orcid.org/0000-0002-2287-0985
University of Geneva · CHÉcole Polytechnique Fédérale de Lausanne · CHUniversity Hospital of Geneva · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWet markets are markets selling fresh meat and produce. Wet markets are critical for food security and sustainable development in their respective regions. Due to their cultural significance, they attract numerous visitors and consequently generate tourist-geared information on the Web (ie, on social networks such as TripAdvisor). These data can be used to create a novel, international wet market inventory to support epidemiological surveillance and control in such settings, which are often associated with negative health outcomes.

objectiveUsing social network data, we aimed to assess the level of wet markets' touristic importance on the Web, produce the first distribution map of wet markets of touristic interest, and identify common diseases facing visitors in these settings.

methodsA Google search was performed on 31 food market-related keywords, with the first 150 results for each keyword evaluated based on their relevance to tourism. Of all these queries, wet market had the highest number of tourism-related Google Search results; among these, TripAdvisor was the most frequently-occurring travel information aggregator, prompting its selection as the data source for this study. A Web scraping tool (ParseHub) was used to extract wet market names, locations, and reviews from TripAdvisor. The latter were searched for disease-related content, which enabled assignment of GeoSentinel diagnosis codes to each. This syndromic categorization was overlaid onto a mapping of wet market locations. Regional prevalence of the most commonly occurring symptom group - food poisoning - was then determined (ie, by dividing the number of wet markets per continent with more than or equal to 1 review containing this syndrome by the total number of wet markets on that continent with syndromic information).

resultsOf the 1090 hits on TripAdvisor for wet market, 36.06% (393/1090) conformed to the query's definition; wet markets were heterogeneously distributed: Asia concentrated 62.6% (246/393) of them, Europe 19.3% (76/393), North America 7.9% (31/393), Oceania 5.1% (20/393), Africa 3.1% (12/393), and South America 2.0% (8/393). Syndromic information was available for 14.5% (57/393) of wet markets. The most frequently occurring syndrome among visitors to these wet markets was food poisoning, accounting for 54% (51/95) of diagnoses. Cases of this syndrome were identified in 56% (22/39) of wet markets with syndromic information in Asia, 71% (5/7) in Europe, and 71% (5/7) in North America. All wet markets in South America and Oceania reported food poisoning cases, but the number of reviews with syndromic information was very limited in these regions (n=2).

conclusionsThe map produced illustrates the potential role of touristically relevant social network data to support global epidemiological surveillance. This includes the possibility to approximate the global distribution of wet markets and to identify diseases (ie, food poisoning) that are most prevalent in such settings.

Indexed as

agricultureepidemiologyfoodborne diseasesmapssocial networkingtravel

Identifiers

PMID30932867
PMCPMC6462893
OpenAlexW2911065313

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.