Variations in Lifetime Healthcare Costs
Bibliographic record
Abstract
The mean costs of providing healthcare increase with age, but within every age/sex cohort there is substantial variation.Moreover, this variation does not disappear over the users' lifetime.This study applies Markov modelling to administrative data to examine the variability of healthcare costs currently covered under the Canada Health Act across a population and over the lifespan.Policy initiatives that ignore individual variability across the lifespan yield inequitable results.For example, age-specific policies that exempt seniors from costs charged to the rest of the population will transfer healthcare resources to healthy low-cost seniors from younger individuals with higher needs. RésuméLe coût moyen de prestations de services de santé accroît avec l' âge, mais au sein de toute cohorte âge/sexe il existe des variations substantielles.Or, ces variations ne s' estompent pas au cours de la vie des utilisateurs.La présente étude applique le modèle de Markov aux données administratives pour étudier la variabilité des coûts de services de santé présentement couverts par la Loi canadienne sur la santé pour une population donnée au cours de la vie de l'utilisateur.Les initiatives politiques qui ne tiennent pas compte des variabilités individuelles au cours de la vie mènent à des résultats inéquitables.Par exemple, les politiques fondées sur l' âge qui accordent aux aînés une exemption de coûts par rapport au reste de la population conduiront à un transfert des ressources en faveur d' aînés sains et peu coûteux au détriment de jeunes personnes dont les besoins sont importants.T
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".