Measuring Burden of Diseases in a Rapidly Developing Economy: State of Qatar
Bibliographic record
Abstract
BACKGROUND: The Global Burden of Disease (GBD) study has provided a conceptual and methodological framework to quantify and compare the health of populations. AIM: The objective of the study was to assess the national burden of disease in the population of Qatar using the disability-adjusted life year (DALYs) as a measure of disability. METHODS: We adapted the methodology described by the World Health Organization for conducting burden of disease to calculate years of life lost due to premature mortality (YLL), years lived with disability (YLD) and disability adjusted life years (DALYs). The study was conducted during the period from November 2011 to October 2012. RESULTS: The study findings revealed that ischemic heart disease (11.8%) and road traffic accidents (10.3%) were the two leading causes of burden of diseases in Qatar in 2010. The burden of diseases among men (222.04) was found three times more than of women's (71.85). Of the total DALYs, 72.7% was due to non fatal health outcomes and 27.3% was due to premature death. For men, chronic diseases like ischemic heart disease (15.7%) and road traffic accidents (13.7%) accounted great burden and an important source of lost years of healthy life. For women, birth asphyxia and birth trauma (12.6%) and abortion (4.6%) were the two leading causes of disease burden. CONCLUSION: The results of the study have shown that the national health priority areas should cover cardiovascular diseases, road traffic accidents and mental health. The burden of diseases among men was three times of women's.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".