Housing tenure and early-life acute lower respiratory tract infection admissions in two national Scottish birth cohorts
Bibliographic record
Abstract
Introduction: Early-life acute lower respiratory tract infections (LRTIs) have been associated with subsequent wheezing, asthma and mortality. Despite infants and young children spending much of their time at home, the association of housing circumstances, including housing ownership status, on acute LRTI hospital admissions is not well explored. Objective: To assess the association between housing tenure and the odds of hospital admission for acute LRTIs in children aged <2 years in Scotland. Methods: Scottish birth records were linked to maternal census data (2001 and 2011) to construct two birth cohorts: cohort 1 (C1; born 2000-2002) and cohort 2 (C2; 2010-2012). Linkage to hospital records provided information on acute LRTI admissions. Using multivariable logistic regression models, we estimated the association of housing tenure with the odds of ≥1 hospital admission for LRTI before the second birthday with adjustment for area of residence (urban/rural), maternal highest qualification level and maternal age. Results: There were 14 833 LRTI admissions in 12 527 children across both cohorts. 4.0% and 5.3% children in C1 and C2, respectively, had ≥1 LRTI admission. Compared with living in owned housing, the odds of LRTI admission were higher in children living in social rented housing (C1: OR 1.40, 95% CI 1.31 to 1.49; C2: 1.23, 1.16 to 1.31), private rented (C1: 1.24, 1.11 to 1.39; C2: 1.14, 1.06 to 1.21) and rent-free housing (C1: 1.53, 1.35 to 1.74; C2: 1.04, 0.80 to 1.36). Conclusion: Children living in rented housing had higher odds of early-childhood LRTI admission compared with those living in owned housing in Scotland. Further linkage to residential-level data could inform the design of LRTI prevention policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".