Neighbourhood disadvantage and behavioural problems during childhood and the risk of cardiovascular disease risk factors and events from a prospective cohort
Bibliographic record
Abstract
Both low socioeconomic status (SES) and behavioural problems in childhood are associated with cardiovascular disease (CVD) in adulthood, but their combined effects on CVD are unknown. Study objectives were to investigate the effect of neighbourhood level SES and behavioural problems during childhood on the development of CVD risk factors and events during adulthood. Participants were from a longitudinal cohort (n = 3792, baseline: 6–13 years of age) of Montreal children, followed from 1976 to 2010. SES was a composite measure of neighbourhood income, employment, education, and single-parent households separately assessed from census micro data sets in 1976, 2001, and 2006. Behavioural problems were assessed based on sex-specific peer assessments. CVD events were from medical records. Sex-stratified multivariable Cox regression models adjusted for age, frequency of medical visits, and parental history of CVD. Males from disadvantaged neighbourhoods during childhood were 2.06 (95% CI: 1.09–3.90, p = 0.03) and 2.51 (95% CI: 1.49–4.22, p = 0.0005) times more likely to develop a CVD risk factor or an event, respectively, than males not from disadvantaged neighbourhoods. Aggressive males were also 50% more likely to develop a CVD risk factor or event. Females from disadvantaged neighbourhoods during childhood were 1.85 (95% CI: 1.33–2.59, p = 0.0003) times more likely to develop a CVD risk factor. Future studies should aim to disentangle the interpersonal from the socioeconomic effects on CVD incidence.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".