Severe RSV-related IIlness among Ontario Children: Using Population-based, Administrative Data to Comprehensively Identify High-risk Children and Evaluate Population Interventions
Bibliographic record
Abstract
Respiratory syncytial virus (RSV) is the most common cause of respiratory tract infection and hospitalization among children globally. Early-life infection with RSV has long-term impacts, including increased risk of developing asthma, and there is increasing recognition of the burden RSV imposes upon the health of older adults. In this dissertation, I present three studies pertaining to the epidemiology and prevention of severe RSV-related illness, particularly among young children, using unique multi-linked health and socio-demographic administrative data for the population of Ontario, Canada. Using a quasi-experimental design, I evaluated the effectiveness of RSV prophylaxis programs for high-risk infants. Severe RSV-related illness substantially declined over the 24-year study period, particularly among high-risk children. While we cannot firmly attribute causality, the magnitude and timing of changes, along with diminished social inequities, among prophylaxis-eligible infants are noteworthy and suggest these programs have noticeable real-world effectiveness, albeit to a lesser degree than observed in efficacy studies. In a birth cohort study, I investigated the contribution of multiple socio-demographic factors to early-life RSV admissions. To date, these factors have received little attention relative to medical factors, despite the known role they have in influencing the transmission, severity and uptake of interventions for other common respiratory viruses. Multiple novel socio-economic factors were independently associated with significantly greater RSV-related admission rates, including young maternal age, maternal involvement with the criminal system, and maternal mental health and/or addiction concerns. I developed the first RSV transmission model for any Canadian population, which also uniquely captures the entire population age spectrum. Relative to other common communicable diseases, few RSV transmission models exist. Our model accurately captured the seasonal RSV epidemic curves observed among infants and suggests RSV burden is greatest at the extremes of the age spectrum. This calibrated base model can be adapted to investigate the potential impacts of emerging RSV vaccination strategies in Ontario and could be further calibrated for use in other populations. Overall, these results contribute to our understanding of the real-world impact of RSV prophylaxis programs and highlight impactful targets for revisions to prophylaxis guidelines and emerging vaccination strategies. These results may inform the selection of optimal policies and interventions which not only reduce RSV burden but also minimize social inequities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".