NON-COMMUNICABLE DISEASE PREVENTION: EVIDENCE FOR DIABETES AND TOBACCO CONTROL POLICIES IN ECUADOR
Bibliographic record
Abstract
Non-communicable diseases (NCDs) are a leading cause of mortality and morbidity worldwide, disproportionately affecting low-and middle-income countries (LMICs). In Latin America, NCDs account for more than 80% of all deaths, with a significant share occurring in the young population, thereby creating an economic impact. Within this context, tobacco use continues to be a modifiable risk factor, closely linked to NCDs such as diabetes. The burden of NCDs is further exacerbated by the region's reliance on out-of-pocket expenditures, which deepens income inequalities in access to prevention and disease management. Ecuador is a clear example of these challenges. Diabetes has become one of the leading causes of mortality and hospitalization in the country. Out-of-pocket expenditure in Ecuador remains high, representing nearly a third of the total health expenditure. Tobacco use is an important contributor to the increasing burden of NCDs, with a current smoking prevalence of 10% in the adult population. Within this context, this dissertation aims to examine how individual behaviours and systemic factors interact to influence health outcomes and policy effectiveness. The first study analyzes how prevalence and management vary with socio-economic characteristics. The second study uses a Discrete Choice Experiment (DCE) to understand the potential impact of plain packaging, health warnings, price changes and illicit cigarette availability in the adult population of Ecuador. The last study examines consumer preferences in Canada (province of Ontario), Ecuador, Chile and Colombia, using the Becker-DeGroot-Marschak (BDM) auction mechanism to measure the willingness to pay (WTP) for plain packaging, stick warning, branded packs and illicit cigarettes. Together, these three studies offer new empirical evidence that is not only relevant for Ecuador but for similar LMICs.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".