Associations between Area-Level Unemployment, Body Mass Index, and Risk Factors for Cardiovascular Disease in an Urban Area
Bibliographic record
Abstract
Introduction:Cardiovascular Disease (CVD) has been linked to “neighbourhood” socioeconomic status (nSES), often operationalized as a composite index of aggregate income, occupation and education within predefined administrative boundaries. The role of specific, non-composite socioeconomic markers has not been clearly explained. It is also unclear whether the relationship between nSES and CVD varies according to sex. We sought to determine whether area-level unemployment (ALU) was associated with CVD risk, and whether this association differed by sex.Methods:342 individuals from the Montreal Neighbourhood Survey of Lifestyle and Health provided self-reported behavioural and socioeconomic information. A nurse collected biochemical and anthropometric data. ALU, a weighted average of the proportion of persons 15-years and older available for but without work, was measured using a Geographic Information System for a 250 m buffer centred on individual residence. Generalized Estimating Equations were used to estimate the associations between ALU, body mass index (BMI) and a cumulative score for total cardiometabolic risk (TCR).Results:After confounder adjustments, the mean 4th minus 1st quartile difference in BMI was 3.19 kg/m2 (95% CI: 2.39, 3.99), while the prevalence ratio for the 4th relative to 1st quartile for TCR was 2.20 (95 % CI: 1.53, 3.17). Sex interacted with ALU; women relative to men had greater mean 3.97 kg/m2 (95% CI: 2.08, 5.85) BMI and greater mean TCR 1.51 (95% CI: 0.78, 2.90), contrasted at mean ALU.Conclusions:Area-level unemployment is associated with greater CVD risk, and this association is stronger for women.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".