Population health and burden of disease profile of Iran among 20 countries in the region: from Afghanistan to Qatar and Lebanon.
Bibliographic record
Abstract
BACKGROUND: Population health and disease profiles are diverse across Iran's neighboring countries. Borrowing the results of the country-level Global Burden of Diseases, Injuries, and Risk Factors 2010 Study (GBD 2010), we aim to compare Iran with 19 countries in terms of an important set of population health and disease metrics. These countries include those neighboring Iran and a few other countries from the Middle East and North Africa (MENA) region. METHODS: We show the pattern of health transition across the comparator countries from 1990 through 2010. We use classic GBD metrics measured for the year 2010 to indicate the rank of Iran among these nations. The metrics include disability-adjusted life years (DALYs), years of life lost as a result of premature death (YLLs), years of life lost due to disability (YLDs), health-adjusted life expectancy (HALE), and age-standardized death rate (ASD). RESULTS: Considerable and uniform transition from communicable, maternal, neonatal, and nutritional (CMMN) conditions to non-communicable diseases (NCDs) was seen between 1990 and 2010. On average, ischemic heart disease, lower respiratory infections, and road injuries were the three principal causes of YLLs, while low back pain and major depressive disorders were the top causes of YLDs in these countries. Iran ranked 13th in HALE and 12th in ASD. The function of Iran's health care, measured by DALYs, was somewhat in the middle of the HALE spectrum for the comparator countries. This intermediate position becomes rather highlighted when Afghanistan, as outlier, is taken out of the comparison. CONCLUSION: Effective policies to reduce NCDs need to be formulated and implemented through an integrated health care system. Our comparison shows that Iran can learn from the experience of a number of these countries to devise and execute the required strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".