Healthcare Service Use for Mood and Anxiety Disorders Following Acute Myocardial Infarction: A Cohort Study of the Role of Neighbourhood Socioenvironmental Characteristics in a Largely Rural Population
Bibliographic record
Abstract
Depression and other mood and anxiety disorders are recognized as common complications following cardiac events. Some studies report poorer cardiac outcomes among patients in socioeconomically marginalized neighbourhoods. This study aimed to describe associations between socioeconomic and built environment characteristics of neighbourhood environments and mental health service contacts following an acute myocardial infarction (AMI or heart attack) among adults in the province of New Brunswick, Canada. This province is characterized largely by residents in small towns and rural areas. A cohort of all adults aged 45 and over surviving AMI and without a recent record of mental disorders was identified by linking provincial medical-administrative datasets. Residential histories were tracked over time to assign neighbourhood measures of marginalization, local climate zones, and physical activity friendliness (i.e., walkability). Cox models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the risk of healthcare use for mood and anxiety disorders over the period 2003/04-2015/16 by neighbourhood characteristics. The baseline cohort included 13,330 post-AMI patients, among whom 32.5% were found to have used healthcare services for a diagnosed mood or anxiety disorder at least once during the period of observation. Among men, an increased risk of mental health service use was found among those living in areas characterized by high ethnic concentration (HR: 1.14 (95%CI: 1.03-1.25)). Among women, the risk was significantly higher among those in materially deprived neighbourhoods (HR: 1.16 (95%CI: 1.01-1.33)). We found no convincing evidence of associations between this outcome and the other neighbourhood characteristics considered here. These results suggest that selected features of neighbourhood environments may increase the burden on the healthcare system for mental health comorbidities among adults with cardiovascular disease. Further research is needed to understand the differing needs of socioeconomically marginalized populations to improve mental health outcomes following an acute cardiac event, specifically in the context of smaller and rural communities and of universal healthcare coverage.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".