Association of neighbourhood-level poverty with outcomes and clinical care following atrial fibrillation diagnosis in a universal healthcare system
Bibliographic record
Abstract
Abstract Background There are limited data on the association of poverty with outcomes and care patterns after an atrial fibrillation (AF) diagnosis in jurisdictions with universal healthcare. The Canadian province of Ontario provides publicly funded healthcare and prohibits private payment for medically necessary physician and in-hospital care. It also covers prescription medications for residents aged >65 years. Purpose Determine the association of neighbourhood-level poverty with outcomes and processes of care after AF diagnosis in older people within a universal healthcare system. Methods Using linked administrative databases, we conducted a population-based cohort study of community-dwelling adults aged ≥66 years who were newly diagnosed with AF in Ontario between April 1, 2007 and March 31, 2019. The primary exposure was material deprivation of patients' neighborhood of residence. This metric is derived using Canadian census data to estimate inability to access and attain basic material needs. Neighborhoods were categorized by quintile of material deprivation from Q1 (wealthiest) to Q5 (poorest). We used cause-specific hazards regression models to study the association of deprivation quintile with time to the following outcomes over one year from AF diagnosis: death, ischemic stroke, bleeding, heart failure (HF) hospitalization, cardiology services, and AF-specific treatments. Models accounted for clustering by region of residence and adjusted for age, sex, diabetes, HF, stroke/transient ischemic attack, vascular disease, hypertension, bleeding history, rural residence, renal function, and setting of AF diagnosis (hospital, emergency department [ED] or outpatient). Results We studied 350,353 patients with AF (median age 78 years, 48.9% female). People from neighborhoods in higher deprivation quintiles (poorer) were more likely to be diagnosed in hospital/ED than outpatient settings. Relative to people from the wealthiest neighbourhoods (Q1), patients in the poorest neighbourhoods (Q5) had higher prevalence of baseline hypertension, diabetes, HF, vascular disease and other comorbidities. In adjusted analyses (Figure), higher quintiles of neighborhood poverty were associated with greater rates of death, ischemic stroke, bleeding, and HF hospitalization, but lower rates of cardiology visits, cardiac testing, anticoagulation, anti-arrhythmic medications, cardioversion, or AF ablation. Conclusions In a setting of universal healthcare and prescription medication coverage, people living in poorer neighbourhoods had worse baseline health and higher rates of adverse outcomes after an AF diagnosis. Despite this, people in poorer neighbourhoods had less cardiology visits and diagnostic tests and were less likely to receive anticoagulation and rhythm control interventions. This shows that universal healthcare and medication coverage are insufficient to achieve equitable health care and outcomes for people with AF. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): This research was funded by a Canadian Institutes of Health Research Foundation grant; and is supported by ICES (formerly the Institute for Clinical Evaluative Sciences), which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC).
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".