Bibliographic record
Abstract
In the current global economy, chronic non-communicable diseases (NCDs) have become the leading cause of death and a major health concern for both developed and developing countries. Among other factors, the worldwide spread of NCDs is driven by the globalisation of unhealthy habits. The purpose of this paper is to develop a simple statistic to measure, at the national level, the average population's exposure to the main NCDs modifiable risk factors. The approach and methodology followed by the United Nations Development Programme to compute the Human Development Index (HDI) is applied to four basic indicators of NCD-related preventable risk factors (alcohol consumption, excess caloric intake, non-balanced diet and tobacco use) in 112 countries worldwide in 2012-14. We obtain a summary composite index, which we call the Unhealthy Behaviour Index (UBI), which ranks countries by the average level of the unhealthy habits (drinking, eating and smoking) of their populations. We find that Belarus and Russian federation are the two countries with the unhealthiest NCD-related lifestyle. With the exception of Canada, the first twenty populations more exposed to the main NCDs preventable risk factors all live in European countries, and mainly in countries of Eastern Europe. Overall, the UBI tends to increase along with the level of human development. In medium, high and very high HDI countries, however, the same level of human development may be associated with very different kinds of NCD-related lifestyles. Finally, economic growth may push populations toward either more unhealthy or healthy habits, depending on the countries' level of development; the elasticity of unhealthy habits with respect to income per capita is positive (but less than one: on average 0.6) until $30,000, decreases as income rises, and becomes negative (around -0.3) in very high income countries.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".