Long-Run Macroeconomic Determinants of Cancer Incidence
Bibliographic record
Abstract
Background: Understanding how cancer incidence evolves during economic growth is useful for forecasting the economic impact of cancerous diseases, and for governing the process of resources allocation in planning health services. We analyse the relationship between economic growth and cancer incidence in order to describe and measure the influence of an increasing real per capita income on the overall rate of cancer incidence. Method: We test the relationship between real per capita income and the overall rate of cancer incidence with a cross-sectional analysis, using data from the World Bank and the World Health Organization databases, for 165 countries in 2008. We measure the elasticity of cancer incidence with respect to per capita income, and we decompose the elasticities coefficients into two components: age-effect and lifestyle-effect. Results: An Engel’s model, in a double-log quadratic specification, explains about half of the variations in the age-standardised rates and nearly two thirds of the variations in the incidence crude rates. All the elasticities of the crude rates are positive, but less than one. The income elasticity of the age-standardised rates are negative in lower income countries, and positive (around 0.25 and 0.32) in upper middle and high income countries, respectively. Conclusions: These results are used to develop a basic framework in order to explain how demand-side economic structural changes may affect the long run evolution of cancer incidence. At theoretical level, a J-Curve is a possible general model to represents, other things being equal, how economic growth influence cancer incidence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".