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Record W4283749942 · doi:10.1186/s12879-022-07553-6

Risk factors for campylobacteriosis in Australia: outcomes of a 2018–2019 case–control study

2022· article· en· W4283749942 on OpenAlexaff
Danielle M. Cribb, Liana Varrone, Rhiannon L. Wallace, Angus McLure, James J. Smith, Russell Stafford, Dieter Bulach, Linda Selvey, Simon M. Firestone, Nigel French, Mary Valcanis, Emily Fearnley, Timothy Sloan-Gardner, Trudy Graham, Kathryn Glass, Martyn Kirk

Bibliographic record

VenueBMC Infectious Diseases · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Health and Medical Research CouncilAgrifutures AustraliaMedical Research CouncilNSW Ministry of HealthQueensland HealthMeat and Livestock AustraliaUniversity of QueenslandDepartment of Health, State Government of VictoriaUniversity of MelbourneAustralian GovernmentMassey UniversityHunter New England Local Health DistrictAustralian National UniversityU.S. Department of Health and Human ServicesUniversity of FloridaEli Lilly and Company
KeywordsCampylobacteriosisOdds ratioMedicineCampylobacterMedical microbiologyPopulationCampylobacter jejuniCase-control studyAttributable riskInternal medicineVeterinary medicineEpidemiologyEnvironmental healthBiologyImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: We aimed to identify risk factors for sporadic campylobacteriosis in Australia, and to compare these for Campylobacter jejuni and Campylobacter coli infections. METHODS: In a multi-jurisdictional case-control study, we recruited culture-confirmed cases of campylobacteriosis reported to state and territory health departments from February 2018 through October 2019. We recruited controls from notified influenza cases in the previous 12 months that were frequency matched to cases by age group, sex, and location. Campylobacter isolates were confirmed to species level by public health laboratories using molecular methods. We conducted backward stepwise multivariable logistic regression to identify significant risk factors. RESULTS: We recruited 571 cases of campylobacteriosis (422 C. jejuni and 84 C. coli) and 586 controls. Important risk factors for campylobacteriosis included eating undercooked chicken (adjusted odds ratio [aOR] 70, 95% CI 13-1296) or cooked chicken (aOR 1.7, 95% CI 1.1-2.8), owning a pet dog aged < 6 months (aOR 6.4, 95% CI 3.4-12), and the regular use of proton-pump inhibitors in the 4 weeks prior to illness (aOR 2.8, 95% CI 1.9-4.3). Risk factors remained similar when analysed specifically for C. jejuni infection. Unique risks for C. coli infection included eating chicken pâté (aOR 6.1, 95% CI 1.5-25) and delicatessen meats (aOR 1.8, 95% CI 1.0-3.3). Eating any chicken carried a high population attributable fraction for campylobacteriosis of 42% (95% CI 13-68), while the attributable fraction for proton-pump inhibitors was 13% (95% CI 8.3-18) and owning a pet dog aged < 6 months was 9.6% (95% CI 6.5-13). The population attributable fractions for these variables were similar when analysed by campylobacter species. Eating delicatessen meats was attributed to 31% (95% CI 0.0-54) of cases for C. coli and eating chicken pâté was attributed to 6.0% (95% CI 0.0-11). CONCLUSIONS: The main risk factor for campylobacteriosis in Australia is consumption of chicken meat. However, contact with young pet dogs may also be an important source of infection. Proton-pump inhibitors are likely to increase vulnerability to infection.

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)0.003
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 score0.064
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.033
GPT teacher head0.280
Teacher spread0.247 · 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

Citations22
Published2022
Admission routes1
Has abstractyes

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