Issue with Evaluating Costs Over Time in a Context of Medical Guideline Changes: An Example in Myocardial Infarction Care Based on a Longitudinal Study from 1997 to 2018
Bibliographic record
Abstract
BACKGROUND: Cost studies appear sporadically in the scientific literature and are rarely revised unless drastic technological advancements occur. However, health technologies and medical guidelines evolve over time. It is unclear if these changes render obsolete prior estimates. We examined this issue in a cost study in the context of patients' first myocardial infarction (MI), a clinical area prone to such continuous evolution in care. METHODS: We conducted a longitudinal cost analysis based on a Quebec cohort. Quebec health administrative databases were used to identify incident MI cases using diagnostic codes from the international classification of diseases (ICD-9 and ICD-10). Physician fees and hospitalization costs (ie, costs incurred by the hospital center) were derived from administrative databases and a university hospital's finance department. All costs were converted to 2019 Canadian dollars. Nonparametric bootstraps were used to estimate 95% confidence intervals (CI) of the average costs of an episode of care. Generalized linear regressions were used to examine temporal trends of cost. RESULTS: Our study sample consists of 261 patients hospitalized for a first MI. The average total cost for this first event was estimated at $5782 (95% CI: $5293 - $6373). Though total costs remained stable over time, physician fees increased by 123% ($1240 vs $2761) whereas total hospital length of stay dropped by 17% (6.6 vs 5.5 days) over the 21-year period. CONCLUSION: Patients' first MI hospitalization impose an economic burden on the healthcare system. Though overall costs remained stable, our results suggest that some cost components varied over time.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".