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Record W4308787923 · doi:10.1089/fpd.2022.0045

Estimated Reduction in the Burden of Nontyphoidal <i>Salmonella</i> Illness in Canada Circa 2019

2022· article· en· W4308787923 on OpenAlexaffabout
Shiona K Glass-Kaastra, Brendan Dougherty, Andrea Nesbitt, Mythri Viswanathan, Nadia Ciampa, Stephen Parker, Céline Nadon, Diane Macdonald, M. Kate Thomas

Bibliographic record

VenueFoodborne Pathogens and Disease · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsMedicinePsychological interventionPublic healthGovernment (linguistics)Environmental healthIncidence (geometry)Economic impact analysisCausality (physics)Demography

Abstract

fetched live from OpenAlex

(NTS) is a leading cause of acute gastrointestinal illness in Canada, and reported cases have been on the rise since the early 2000s. To address this trend, agri-food industry partners and government have worked to identify and implement interventions, guided by the enhanced information provided by whole-genome sequencing, to reduce the incidence of NTS. A substantial reduction in the number of NTS cases reported occurred in 2019. Due to underreporting and underdiagnosis factors, the observed decrease in the number of reported cases represents a fraction of the true number of illnesses averted in the community. The objective of this study was to: (1) use burden of illness estimation methodologies to estimate the true number of NTS illnesses, hospitalizations, and deaths prevented, and (2) estimate the economic savings associated with the prevention of these cases. Compared with the previous 5 years, there were an estimated 25,821 fewer illnesses, 213 fewer hospitalizations, and 2 fewer deaths attributable to NTS in 2019. This corresponds to an estimated reduction of 26.9 million Canadian dollars in the economic burden of NTS. Although causality cannot be proven by this study, the findings are suggestive that the strategically implemented suite of public health actions, including genomic-based surveillance, policy changes, and interventions by the government and industry, were successful in reducing the economic and health burden of NTS infections in Canada.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.432
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.207
Teacher spread0.191 · 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 teacher head, 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

Citations6
Published2022
Admission routes2
Has abstractyes

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