Housing tenure and acute lower respiratory tract infection admissions in two Scottish birth cohorts
Bibliographic record
Abstract
Abstract Introduction Early-life acute lower respiratory tract infections (LRTI) have been associated with increased morbidity and mortality. Despite young children spending much of their time at home, the contribution of home ownership status, on LRTI hospital admissions is unknown. Objectives To estimate the association between housing tenure and the odds of LRTI hospitalization in children <2 years in two birth cohorts. Methods De-identified Scottish birth records were linked to maternal Census data (2001 and 2011) and to hospital admission data to construct two birth cohorts (Cohort 1 (C1), born 2000-2002; Cohort 2 (C2), 2010-2012). Using logistic regression we estimated the association of housing tenure (owned, social rented, private rented, rent-free) with the odds of LRTI hospital admission, before and after adjustment for maternal age, residential area, and maternal qualification level. Results Over the 2-year follow-up, there were 14,833 LRTI admissions in 12,527 children (10,832 children had 1 LRTI admission and 1,695 >1 LRTI admission). 75.6 % of all LRTI admissions were due to bronchiolitis. In C1 and C2, 4.0% and 5.3% children, respectively, had one or more LRTI admission. Compared to children living in owned housing, the odds of LRTI admission were higher in children living in social rented housing (C1: Odds ratio=1.40, 95% confidence interval: 1.31-1.49; C2: 1.23, 1.16-1.31), private rented (C1:1.24, 1.11,1.39; C2: 1.14, 1.06-1.21), and rent-free housing (C1: 1.53, 1.35,1.74; C2: 1.04, 0.80-1.36) after confounder adjustment. Conclusions We found an association between non-owned housing and higher odds of LRTI admission which was more marked in C1 than in C2. Further research is warranted to unpick the mechanisms underlying the association between housing tenure and LRTI admission. Interventions to prevent LRTI admissions could usefully target children living in non-owned housing. Key messages • This study demonstrates the value of administrative and health data linkage in improving our understanding of how socio-environmental factors might impact children’s respiratory health. • Children living in social and private rented housing (compared to owned housing) have a higher risk of LRTI hospital admission and could benefit the most from LRTI-lowering interventions.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".