The Impact of Neighborhood Material and Social Disadvantage on Respiratory Health Across Canada
Bibliographic record
Abstract
Abstract Rationale: While inhalational exposures and genetic predisposition confer an increased risk of lung diseases, less is known about the socioeconomic determinants of respiratory health. Both material and social disadvantages experienced at the neighborhood level may conceivably contribute to poor lung health outcomes. We aimed to quantify the impact of neighborhood disadvantage on lung function, exercise capacity, and respiratory symptoms in a general population cohort. Methods: Participants were enrolled in the Canadian Cohort Obstructive Lung Disease (CanCOLD) study and followed for three years with repeat pulmonary function, cardiopulmonary exercise testing, and assessment of respiratory symptoms using the St. George's Respiratory Questionnaire (SGRQ). The Material and Social Deprivation Index (MSDI) was used as a measure of neighborhood disadvantage, with the cohort separated into five groups (C1 to C5, with C1 representing the least materially and socially disadvantaged and C5 representing the most). Multivariable linear regression models were used to quantify the association between MSDI groups and baseline pulmonary function (post-bronchodilator FEV1/FVC, FEV1 and FVC %predicted), peak exercise capacity (V'O2 [mL/kg/min], V'O2 %predicted), ventilatory response to exercise (nadir V'E/V'CO2), and SGRQ score adjusting for possible covariates such as age, sex, height, weight, race, cigarette and cannabis smoking. We also used multivariable linear regression models to assess the association between MSDI group and rate of FEV1 and FVC change from baseline to 36 months. Results: There were 1,449 participants with baseline MSDI enrolled. Compared to those in the least disadvantaged neighborhoods, individuals living in the most disadvantaged neighborhoods were younger (median age 65 vs. 67 years), more likely to be of non-White race (9% vs. 3%), and more likely to currently smoke cigarettes (32% vs. 6%) or cannabis (12% vs. 4%). Compared to the least disadvantaged, the most disadvantaged had lower FEV1/FVC (p=0.002), FEV1 %predicted (p<0.001), FVC %predicted (p=0.006), peak V'O2 in ml/kg/min and %predicted (both p<0.001), higher exercise ventilatory inefficiency as measured by nadir V'E/V'CO2 (p<0.001), and worse SGRQ scores (p=0.002) (Table 1). The most disadvantaged also had faster decline of FEV1 (p=0.035) and FVC (p=0.029) compared to the least disadvantaged. Conclusions: Living in the most materially and socially disadvantaged neighborhoods has a detrimental impact on lung function and its decline, exercise capacity, and respiratory symptoms in older Canadians. This may indicate that these individuals are at greater risk for developing chronic lung diseases over time. Increased screening of individuals living in disadvantaged neighborhoods may be warranted for lung disease prevention and treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| 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".