End-of-life healthcare utilization and costs according to cause of death: a retrospective study
Bibliographic record
Abstract
CONTEXT: Aging individuals are more likely to experience multiple health issues, resulting in increased and complex healthcare needs, particularly at the end-of-life (EoL). While several studies have shown that healthcare costs rose substantially during the final year and months of life, there is limited research on the EoL utilization and cost in Quebec, specifically using population-based data that span multiple healthcare settings and different causes of death. OBJECTIVE: To examine healthcare and social care utilization and cost during the last year of life of people who died in Quebec at the age of 66 and over, according to their cause of death. METHODS: We conducted a retrospective quantitative longitudinal study to analyze individuals' healthcare utilization and cost during their last year of life between 2014 and 2018. Individuals were retrieved from the death registry and classified according to their cause of death as: cancer, organ failure, frailty (physical or cognitive) and other or unknown. Healthcare and social care utilization included: medical visits, emergency department visits, hospitalizations, homecare and long-term care (LTC). Costs were attributed to services either directly from administrative databases or estimated from public financial reports. We used generalized linear models (GLMs) to assess variations in service utilization and costs: Poisson or negative binomial models were employed for count outcomes based on data dispersion, and Gamma log link for costs. RESULTS: The cohort included 21,255 individuals (52.6% women) with ~ 40% of deaths occurring among individuals aged 80-89 years. Nearly 50% of the cohort died from an organ failure, 30% of cancer and 15% of frailty. Cancer and organ failure were associated with high acute care use and costs, while frailty led to greater use of LTC and social care. Overall, the EoL the individual average cost was 34,467$ ($25,000-$53,000), depending on cause of death and type of services used. Notably, social services cost exceeds healthcare cost, especially for people who died from frailty, highlighting the financial burden of LTC accommodation. CONCLUSION: This study highlights distinct EoL healthcare and social services utilization and related costs among older adults, depending on the cause of death, age, and sex. These findings underscore the need for tailored care strategies that reflect different EoL trajectories.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".