Neighborhood-Level Burden of Social Risk Factors on Respiratory Syncytial Virus Hospitalization in Ontario, Canada, 2016–2019
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".