Leveraging Health Administrative Data to Investigate Maternal Vulnerabilities in Early Life Respiratory Syncytial Virus (RSV) Hospitalizations in Ontario, Canada
Bibliographic record
Abstract
IntroductionRespiratory syncytial virus (RSV) is the leading cause of hospitalization among infants globally. Several RSV vaccine candidates are currently in trial and the severity of RSV-related illness can be reduced with prophylaxis. To optimize delivery of these programs and reduce inequities, a comprehensive understanding of risk factors for severe RSV-related illness is required. Objectives and ApproachThe objective of this study was to quantify the risks of severe, early life RSV-related illness in terms of medical conditions, birth characteristics and novel socio-economic factors. We used linked population-based health and socio-demographic administrative data for all children born in Ontario (Apr 1st, 2012-March 31st, 2018), including laboratory viral testing data for a subset of children, to identify all RSV-related hospitalizations occurring in Ontario before a child’s third birthday or end of follow-up (March 31st, 2019). We calculated the relative risk of RSV-related admission, adjusted for several medical complexity and transmission factors. Critically, we leveraged these routinely collected health administrative data to determine the admission risks associated with various novel measures indicative of maternal social vulnerability, such as homelessness and involvement with the criminal justice system. Results11,279 RSV-related hospitalizations were identified among 789,484 children; 57% of admissions occurred before 6 months of age. We identified several socio-economic factors independently associated with increased risk of severe RSV-related illness, including several maternal factors: young age at first delivery (Relative risk (RR) <20 vs 40+ years: 2.27, 95%CI:(1.89,2.73)), involvement with the criminal justice system [RR:1.34 (1.16,1.55)], social assistance use (RR:1.53 (1.45,1.62)), homelessness [RR:1.69 (1.01,2.83)], mental health/addictions concerns [RR:1.51 (1.36,1.67)] and child apprehension [RR:1.59 (1.29,1.96)]. Conclusion / ImplicationsWe identified several socio-economic factors independently associated with increased risk of severe RSV-related illness, including several novel factors related to maternal vulnerability. This information could inform the selection of high-risk groups for RSV prophylaxis or immunization programs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".