Effectiveness of rotavirus vaccine in preventing severe gastroenteritis in young children according to socioeconomic status
Bibliographic record
Abstract
In 2011, the monovalent rotavirus vaccine was introduced into a universal immunization program in Quebec (Canada). This retrospective cohort study assessed vaccine effectiveness (VE) in preventing acute gastroenteritis (AGE) and rotavirus gastroenteritis (RVGE) hospitalizations among children <3 y living in the Quebec Eastern Townships region according to socioeconomic status (SES). Data were gathered from a tertiary hospital database paired with a regional immunization registry. Three cohorts of children were followed: (1) vaccinated children born in post-universal vaccination period (2011-2013, n = 5,033), (2) unvaccinated children born in post-universal vaccination period (n = 1,239), and (3) unvaccinated children born in pre-universal vaccination period (2008-2010, n = 6,436). In each cohort, AGE and RVGE hospitalizations were identified during equivalent follow-up periods to calculate VE globally and according to neighborhood-level SES. Using multivariable logistic regression, adjusted odds ratios (OR) were computed to obtain VE (1-OR). Adjusted VE of 2 doses was 62% (95% confidence interval [CI]: 37%-77%) and 94% (95%CI: 52%-99%) in preventing AGE and RVGE hospitalization, respectively. Stratified analyses according to SES showed that children living in neighborhoods with higher rates of low-income families had significantly lower VE against AGE hospitalizations compared to neighborhoods with lower rates of low-income families (30% vs. 78%, p = 0.027). Our results suggest that the rotavirus vaccine is highly effective in preventing severe gastroenteritis in young children, particularly among the most well-off. SES seems to influence rotavirus VE, even in a high-income country like Canada. Further studies are needed to determine factors related to lower rotavirus VE among socioeconomically disadvantaged groups.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".