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Record W6907388629 · doi:10.20381/ruor-24432

The association between climate, geography and respiratory syncitial virus hospitalizations among children in Ontario, Canada: a population-based study

2020· other· en· W6907388629 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Ottawa - Library · 2020
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsOddsLogistic regressionSocioeconomic statusOdds ratioNeighbourhood (mathematics)Respiratory systemEpidemiologyVirusAssociation (psychology)

Abstract

fetched live from OpenAlex

Abstract Background Respiratory syncytial virus (RSV) infection is a major cause of hospitalization in young children in Canada, despite routine immunoprophylaxis in those with medical risk factors. We aimed to determine if cold temperatures are associated with RSV hospitalization. Methods We conducted a population-based nested case–control study of children in Ontario, Canada, using health administrative data. We compared children hospitalized for RSV between September 1, 2011 and August 31, 2012 to age and sex matched controls. We used multivariable logistic regression to identify associations between minimum daily temperature and RSV hospitalizations with adjustment for sociodemographic and environmental factors. Results We identified 1670 children with RSV hospitalizations during the study period and 6680 matched controls. Warmer temperatures (OR = 0.94, 95%CI: 0.93, 0.95) were associated with lower odds of RSV hospitalization. Southern ecozone (OR = 1.6, 95%CI: 1.2, 2.1), increased ozone concentration (OR = 1.03, 95%CI: 1.01, 1.06) and living in a lower income neighbourhood (OR = 1.3, 95%CI: 1.1, 1.5) significantly increased the odds of RSV hospitalization, as did living in a household with a larger number of siblings in a sub-cohort of children (OR = 1.34, 95%CI: 1.26, 1.41). Conclusions In Ontario, the likelihood of having an RSV hospitalization is associated with colder temperature exposures and socioeconomic factors.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.005
GPT teacher head0.165
Teacher spread0.161 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueUniversity of Ottawa - LibraryFrench-language works237,207