Association between area-level material deprivation and incidence of hospitalization among children with SARS-CoV-2 in Montreal
Bibliographic record
Abstract
Background: Although sociodemographic factors have been linked with SARS-CoV-2 infection and hospitalizations in adults, there are little data on the association between sociodemographic characteristics and SARS-CoV-2-related hospitalization in children. The objective of this study was to determine the association between area-level material deprivation and incidence of hospitalization with SARS-CoV-2 among children. Methods: We conducted a retrospective cohort study of all children (0 to 17 years of age) with a PCR-confirmed SARS-CoV-2 infection March 1, 2020 through May 31, 2021 at a tertiary-care paediatric hospital, in Montreal, Canada. Data were collected through chart review and included age, sex, and postal code, allowing linkage to dissemination area-level material deprivation, measured with the Pampalon Material Deprivation Index (PMDI) quintiles. We examined the association between PMDI quintiles and hospitalization using Poisson regression. Results: During the study period, 964 children had a positive PCR-confirmed SARS-CoV-2 test and 124 were hospitalized. Children living in the most deprived quintile of PMDI represented 40.7% of hospitalizations. Incidence rate ratio of hospitalization for this group compared to the most privileged quintile was 2.42 (95%CI: 1.33; 4.41). Conclusion: Children living in the most materially deprived areas had more than twice the rate of hospitalizations for COVID-19 than children living in most privileged areas. Special efforts should be deployed to protect children who live in disadvantaged areas, especially pending vaccination of younger children.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".