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Record W4400487914 · doi:10.1093/ofid/ofae384

Neighborhood-Level Burden of Social Risk Factors on Respiratory Syncytial Virus Hospitalization in Ontario, Canada, 2016–2019

2024· article· en· W4400487914 on OpenAlexafffundabout
Kitty Chen, Trevor van Ingen, Brendan T. Smith, Tiffany Fitzpatrick, Michael Whelan, Alyssa S. Parpia, Jenna Alessandrini, Sarah A. Buchan

Bibliographic record

VenueOpen Forum Infectious Diseases · 2024
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsInstitute for Clinical Evaluative SciencesPublic Health OntarioUniversity of Toronto
FundersPublic Health Ontario
KeywordsMedicineDemographicsDemographyIncidence (geometry)PopulationCensusPediatricsEnvironmental health

Abstract

fetched live from OpenAlex

Background: Beyond clinical risk factors, little is known about the impact of social determinants on respiratory syncytial virus (RSV) burden. Our study aimed to estimate RSV-related hospitalization rates across sociodemographic and housing characteristics. Methods: We conducted a population-based study of all RSV-related hospitalizations in Ontario, Canada, between September 1, 2016, and August 31, 2019, using validated hospital discharge codes and census data. Crude and age-standardized annualized RSV incidence rates and rate ratios (RRs) were estimated for a range of individual-level demographics and neighborhood-level measures of marginalization and housing characteristics. Results: Overall, the annual RSV-related hospitalization rate was 27 per 100 000, with the highest rates observed in children age <12 months (1049 per 100 000) and 12-23 months (294 per 100 000) and adults age ≥85 years (155 per 100 000). Higher RSV-related hospitalization rates were associated with increasing marginalization quintile (Q) of material resources (RR, 1.4; Q5: 33 per 100 000 vs Q1: 24 per 100 000) and household instability (RR, 1.5; Q5: 31 per 100 000 vs Q1: 22 per 100 000). Conclusions: The burden of RSV-related hospitalization was greatest in young children and older adults, with variation by sociodemographic and housing factors. Understanding the role of these social factors is crucial for informing equitable preventive program delivery.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.322
Teacher spread0.294 · 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

Citations5
Published2024
Admission routes3
Has abstractyes

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