Do expenditures on public health reduce preventable mortality in the long run? Evidence from the Canadian provinces
Bibliographic record
Abstract
BACKGROUND: Investments in public health - prevention of illnesses, and promotion, surveillance, and protection of population health - may improve population health, however, effects may only be observed over a long period of time. OBJECTIVE: To investigate the potential long-run relationship between expenditures on public health and avoidable mortality from preventable causes. METHODS: We focused on the country spending the most on public health in the OECD, Canada. We constructed a longitudinal dataset on mortality, health care expenditures and socio-demographic information covering years 1979-2017 for the ten Canadian provinces. We estimated error correction models for panel data to disentangle short-from long-run relationships between expenditures on public health and avoidable mortality from preventable causes. We further explored some specific causes of mortality to understand potential drivers. For comparison, we also estimated the short-run relationship between curative expenditures and avoidable mortality from treatable causes. RESULTS: We find evidence of a long-run relationship between expenditures on public health and preventable mortality, and no consistent short-run associations between these two variables. Findings suggest that a 1% increase in expenditures on public health could lead to 0.22% decrease in preventable mortality. Reductions in preventable mortality are greater for males (-0.29%) compared to females (-0.09%). These results are robust to different specifications. Reductions in some cancer and cardiovascular deaths are among the probable drivers of this overall decrease. By contrast, we do not find evidence of a consistent short-run relationship between curative expenditures and treatable mortality, except for males. CONCLUSION: This study supports the argument that expenditures on public health reap health benefits primarily in the long run, which, in this case, represents a reduction in avoidable mortality from preventable causes. Reducing public health expenditures on the premise that they have no immediate measurable benefits might thus harm population health outcomes in the long run.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".