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Record W2071961632 · doi:10.1186/1471-2458-13-63

Community-level risk factors for notifiable gastrointestinal illness in the Northwest Territories, Canada, 1991-2008

2013· article· en· W2071961632 on OpenAlexafffundabout
Aliya Pardhan-Ali, Jeff Wilson, Victoria L. Edge, Chris Furgal, Richard J. Reid‐Smith, Maria Santos, Scott A. McEwen

Bibliographic record

VenueBMC Public Health · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsGovernment of Northwest TerritoriesTrent UniversityEcoMetrixUniversity of Guelph
FundersNasivvik Centre for Inuit Health and Changing Environments
KeywordsMedicineCampylobacteriosisEnvironmental healthPublic healthEpidemiologyIncidence (geometry)PopulationDemographyCampylobacterInternal medicineBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Enteric pathogens are an important cause of illness, however, little is known about their community-level risk factors (e.g., socioeconomic, cultural and physical environmental conditions) in the Northwest Territories (NWT) of Canada. The objective of this study was to undertake ecological (group-level) analyses by combining two existing data sources to examine potential community-level risk factors for campylobacteriosis, giardiasis and salmonellosis, which are three notifiable (mandatory reporting to public health authorities at the time of diagnosis) enteric infections. METHODS: The rate of campylobacteriosis was modeled using a Poisson distribution while rates of giardiasis and salmonellosis were modeled using a Negative Binomial distribution. Rate ratios (the ratio of the incidence of disease in the exposed group to the incidence of disease in the non-exposed group) were estimated for infections by the three major pathogens with potential community-level risk factors. RESULTS: Significant (p≤0.05) associations varied by etiology. There was increased risk of infection with Salmonella for communities with higher proportions of 'households in core need' (unsuitable, inadequate, and/or unaffordable housing) up to 42% after which the rate started to decrease with increasing core need. The risk of giardiasis was significantly higher both with increased 'internal mobility' (population moving between communities), and also where the community's primary health facility was a health center rather than a full-service hospital. Communities with higher health expenditures had a significantly decreased risk of giardiasis. Results of modeling that focused on each of Giardia and Salmonella infections separately supported and expanded upon previous research outcomes that suggested health disparities are often associated with socioeconomic status, geographical and social mobility, as well as access to health care (e.g. facilities, services and professionals). In the campylobacteriosis model, a negative association was found between food prices in communities and risk of infection. There was also a significant interaction between trapping and consumption of traditional foods in communities. Higher rates of community participation in both activities appeared to have a protective effect against campylobacteriosis. CONCLUSIONS: These results raise very interesting questions about the role that traditional activities might play in infectious enteric disease incidence in the NWT, but should be interpreted with caution, recognizing database limitations in collection of case data and risk factor information (e.g. missing data). Given the cultural, socioeconomic, and nutritional benefits associated with traditional food practices, targeted community-based collaborative research is necessary to more fully investigate the statistical correlations identified in this exploratory research. This study demonstrates the value of examining the role of social determinants in the transmission and risk of infectious diseases.

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 score0.022
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.091
GPT teacher head0.269
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

Citations25
Published2013
Admission routes3
Has abstractyes

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