Family and Child Risk Factors for Early-Life RSV Illness
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Most infants hospitalized with respiratory syncytial virus (RSV) do not meet common "high-risk" criteria and are otherwise healthy. The objective of this study was to quantify the risks and relative importance of socioeconomic factors for severe, early-life RSV-related illness. We hypothesized several of these factors, particularly those indicating severe social vulnerability, would have statistically significant associations with increased RSV hospitalization rates and may offer impactful targets for population-based RSV prevention strategies, such as prophylaxis programs. METHODS: We used linked health, laboratory, and sociodemographic administrative data for all children born in Ontario (2012-2018) to identify all RSV-related hospitalizations occurring before the third birthday or end of follow-up (March 31, 2019). We estimated rate ratios and population attributable fractions using a fully adjusted model. RESULTS: A total of 11 782 RSV-related hospitalizations were identified among 789 484 children. Multiple socioeconomic factors were independently associated with increased RSV-related admissions, including young maternal age, maternal criminal involvement, and maternal history of serious mental health and/or addiction concerns. For example, an estimated 4.1% (95% confidence interval: 2.2 to 5.9) of RSV-related admissions could be prevented by eliminating the increased admissions risks among children whose mothers used welfare-based drug insurance. Notably, 41.6% (95% confidence interval: 39.6 to 43.5) of admissions may be prevented by targeting older siblings (eg, through vaccination). CONCLUSIONS: Many social factors were independently associated with early-life RSV-related hospitalization. Existing RSV prophylaxis and emerging vaccination programs should consider the importance of both clinical and social risk factors when determining eligibility and promoting compliance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".