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Record W4387737034 · doi:10.1371/journal.pntd.0011341

Environmental sampling for typhoidal Salmonellas in household and surface waters in Nepal identifies potential transmission pathways

2023· article· en· W4387737034 on OpenAlexaff
Christopher LeBoa, Sneha Shrestha, Jivan Shakya, Shiva Ram Naga, Sony Shrestha, Mudita Shakya, Alexander T. Yu, Rajeev Shrestha, Krista Vaidya, Nishan Katuwal, Kristen Aiemjoy, Isaac I. Bogoch, Christopher B. Uzzell, Denise O. Garrett, Stephen P. Luby, Jason R. Andrews, Dipesh Tamrakar

Bibliographic record

VenuePLoS neglected tropical diseases · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsToronto General HospitalUniversity of Toronto
FundersStanford University Center for Innovation in Global HealthFogarty International CenterBill and Melinda Gates Foundation
KeywordsSalmonellaSalmonella typhiTyphoid feverVeterinary medicineTransmission (telecommunications)FecesPopulationBiologyMicrobiologyMedicineEnvironmental healthBacteriaEscherichia coli

Abstract

fetched live from OpenAlex

INTRODUCTION: Salmonella Typhi and Salmonella Paratyphi, fecal-oral transmitted bacterium, have temporally and geographically heterogeneous pathways of transmission. Previous work in Kathmandu, Nepal implicated stone waterspouts as a dominant transmission pathway after 77% of samples tested positive for Salmonella Typhi and 70% for Salmonella Paratyphi. Due to a falling water table, these spouts no longer provide drinking water, but typhoid fever persists, and the question of the disease's dominant pathway of transmission remains unanswered. METHODS: We used environmental surveillance to detect Salmonella Typhi and Salmonella Paratyphi A DNA from potential sources of transmission. We collected 370, 1L drinking water samples from a population-based random sample of households in the Kathmandu and Kavre Districts of Nepal between February and October 2019. Between November 2019 and July 2021, we collected 380, 50mL river water samples from 19 sentinel sites on a monthly interval along the rivers leading through the Kathmandu and Kavre Districts. We processed drinking water samples using a single qPCR and processed river water samples using differential centrifugation and qPCR at 0 and after 16 hours of liquid culture enrichment. A 3-cycle threshold (Ct) decrease of Salmonella Typhi or Salmonella Paratyphi, pre- and post-enrichment, was used as evidence of growth. We also performed structured observations of human-environment interactions to understand pathways of potential exposure. RESULTS: Among 370 drinking water samples, Salmonella Typhi was detected in 7 samples (1.8%) and Salmonella Paratyphi A was detected in 4 (1.0%) samples. Among 380 river water samples, Salmonella Typhi was detected in 171 (45%) and Salmonella Paratyphi A was detected in 152 (42%) samples. Samples located upstream of the Kathmandu city center were positive for Salmonella Typhi 12% of the time while samples from locations in and downstream were positive 58% and 67% of the time respectively. Individuals were observed bathing, washing clothes, and washing vegetables in the rivers. IMPLICATIONS: These results suggest that drinking water was not the dominant pathway of transmission of Salmonella Typhi and Salmonella Paratyphi A in the Kathmandu Valley in 2019. The high degree of river water contamination and its use for washing vegetables raises the possibility that river systems represent an important source of typhoid exposure in Kathmandu.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.044
GPT teacher head0.222
Teacher spread0.178 · 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

Citations13
Published2023
Admission routes1
Has abstractyes

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