Population Aging and the Determinants of Healthcare Expenditures: The Case of Hospital, Medical and Pharmaceutical Care in British Columbia, 1996 to 2006
Bibliographic record
Abstract
THERE IS A GAP BETWEEN RHETORIC AND REALITY CONCERNING HEALTHCARE EXPENDITURES AND POPULATION AGING: although decades-old research suggests otherwise, there is widespread belief that the sustainability of the healthcare system is under serious threat owing to population aging. To shed new empirical light on this old debate, we used population-based administrative data to quantify recent trends and determinants of expenditure on hospital, medical and pharmaceutical care in British Columbia. We modelled changes in inflation-adjusted expenditure per capita between 1996 and 2006 as a function of two demographic factors (population aging and changes in age-specific mortality rates) and three non-demographic factors (age-specific rates of use of care, quantities of care per user and inflation-adjusted costs per unit of care). We found that population aging contributed less than 1% per year to spending on medical, hospital and pharmaceutical care. Moreover, changes in age-specific mortality rates actually reduced hospital expenditure by -0.3% per year. Based on forecasts through 2036, we found that the future effects of population aging on healthcare spending will continue to be small. We therefore conclude that population aging has exerted, and will continue to exert, only modest pressures on medical, hospital and pharmaceutical costs in Canada. As indicated by the specific non-demographic cost drivers computed in our study, the critical determinants of expenditure on healthcare stem from non-demographic factors over which practitioners, policy makers and patients have discretion.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".