Prevalence of Cardiovascular Disease in a Population-Based Cohort of High-Cost Healthcare Services Users
Bibliographic record
Abstract
BACKGROUND: Data are limited data on the prevalence of cardiovascular disease (CVD) and multimorbidity in contemporary cohorts of high-cost users (HCUs) in Canada.We examined the following: (i) the prevalence of CVD, with a comparison of total healthcare costs among HCUs with vs without CVD; (ii) the contribution of other comorbidities to costs among HCUs with CVD; and (iii) the trajectory of healthcare costs in the years before and after becoming an HCU. METHODS: The study included adult Alberta patients in the Canadian Institutes of Health Research/Canadian Institute for Health Information Dynamic Cohort of Complex, High System Users from 2011-2012 through 2014-2015. We examined total healthcare costs, including hospital, ambulatory care, physician services, and drugs. RESULTS: Among 88,536 HCUs, 23.4% had no CVD, 28.9% were hospitalized with a primary diagnosis of CVD, and 47.7% were hospitalized with a secondary diagnosis of CVD. Total healthcare costs were $2.0 billion (20.4% non-hospital costs), $2.8 billion (24.1% non-hospital costs), and $4.9 billion (19.8% non-hospital costs), respectively, in the 3 groups. Many HCUs with CVD were frail (74.2%) and many had diabetes (33.8%) or chronic obstructive pulmonary disease (27.9%), which contributed to higher costs and mortality. Healthcare expenditures in HCUs with CVD were several times higher than per capita health expenditures in the years prior to, and following, their inclusion in the dynamic HCU cohort. CONCLUSIONS: CVD is very common in HCUs of healthcare. HCUs with CVD have high rates of frailty and multimorbidity. Further research is needed to identify and intervene earlier, in order to flatten the cost curve in these complex patients.
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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 | 0.002 |
| 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 | 0.001 |
| 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".