Political democracy, economy, and cancer risk: A comparative analysis of 170 countries
Bibliographic record
Abstract
Abstract Much literature acknowledges the importance of political systems for population health. People living in democratic countries tend to have higher life expectancies and lower rates of infant mortality compared to those in other countries. However, few quantitative comparative studies explore the political origins of chronic disease. To address this gap, this study examines the impact of political democracy on cancer risk. Using data from the Global Cancer Observatory (GLOBOCAN), regression models test differences in age‐adjusted cancer rates across 170 countries. Counter to study hypotheses, overall incidence of cancer is not any lower in democratic countries. This is evident even when removing the confounding influence of economic and several other factors. However, among children and adolescents, cancer mortality rates and leukemia incidence are exceptions since these are lower in democratic countries in some models. Results otherwise do not support the view that political regime type alone prevents cancer. Overall findings appear robust to threats of endogeneity, higher average age in developed countries, comparative differences in the ability to diagnose cases, and several other threats. The broader literature indicates that democratic countries have better health overall. However, findings are that democratic countries have higher cancer incidence on average, which is likely due to having higher levels of economic prosperity compared to more autocratic countries. Economic policy is likely to be an important consideration for preventing cancer. Longitudinal analysis was not possible for these data, which is reason for caution when interpreting these findings.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".