Putting a Population Health Lens to Multimorbidity in Ontario
Bibliographic record
Abstract
Almost all Ontarians die with multimorbidity, and most accumulate more than five conditions over their lifetime.Our health system is still largely focused on specialties and treating one disease at a time -an approach that is incompatible with the healthcare needs of patients with multiple and often complex chronic conditions.This burden requires a health system that recognizes that patients will more likely live and die with multiple chronic conditions than not (i.e., multimorbidity management versus specialized care).There are important and meaningful differences in the types and numbers of conditions that patients die with.In particular, increases in the most preventable conditions are greater among the most deprived members of our society.To address the worrying trends seen here, chronic disease prevention -not only management -must be a priority, with a strong focus on health equity.Chronic disease prevention and a strong focus on equity are signatures of a population health approach.This work echoes calls for a stronger emphasis on population health in the health system. The IssueThe co-occurrence of multiple chronic conditions -known as multimorbidity -is now the typical trajectory for patients in a health system.In high-income countries, the burden of chronic diseases on healthcare systems is increasingly exacerbated by a growing proportion of the population having multiple chronic conditions (Fortin et al. 2012;McPhail 2016;Violan et al. 2014).For instance, in 2009, it was estimated that a quarter of Putting a Population Health Lens to Multimorbidity in Ontario ICES Report Laura Rosella and Kathy Kornas Percentage of decedents with degree of multimorbidity 0 2 0 4 0 6 0 8 0 1 00 5+ conditions 4 conditions 3 conditions 2 conditions No multimorbidity (0-1 condition) 0 2 0 4 0 6 0 8 0 1 00 5+ conditions 4 conditions 3 conditions 2 conditions No multimorbidity (0-1 condition) 0 2 0 4 0 6 0 8 0 1 00 5+ conditions 4 conditions 3 conditions 2 conditions No multimorbidity (0-1 condition) 1994 (N = 74,227) 2004 (N = 80,725) 2013 N = 91,312) 0% 20% 40% 6 0% 80% 1 00% Percentage of decedents with degree of multimorbidity 5+ conditions 4 conditions 3 conditions 2 conditions No multimorbidity (0-1 condition) 1994
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".