Trend of Socio-Demographic Index and Mortality Estimates in Iran and its Neighbors, 1990-2015; Findings of the Global Burden of Diseases 2015 Study.
Bibliographic record
Abstract
BACKGROUND: The Global burden of disease and injuries study (GBD 2015) reports expected measures for years of life lost (YLL) based on socio-demographic index (SDI) of countries, as well as the observed measures. In this extended GBD 2015 report, we reviewed total and cause-specific deaths and YLL for Iran and all its neighboring countries between 1990 and 2015. METHODS: We extracted data from the GBD 2015 database. Observed YLL measures were calculated by multiplying the number of deaths by standard life expectancy at each age. SDI was a composite index, calculated based on income per capita, average years of schooling, and total fertility rate. The GBD world population was used for age standardization. RESULTS: All-ages crude death rate in Iran reduced from 665.6 per 100,000 population (95% uncertainty interval: 599.3-731.6) in 1990 to 487.2 (414.9-566.1) in 2015. The ratio of observed to expected YLL (O/E ratio) for all-causes ranged between 0.54 (Turkey) and 1.95 (Russia) in 2015. For Iran, the all-causes O/E ratio was less than 1 in all years (1990-2015), except 2003. However, cause-specific O/E ratio was more than 1 for some causes, including the top leading causes of YLL (ischemic heart disease, road injuries, and cerebrovascular disorders). Ischemic heart disease was the first or second cause of YLL in all comparator countries except Afghanistan. CONCLUSION: The leading YLL causes with high O/E ratios should be prioritized in public health efforts. In addition to research evidence, countries with low O/E ratios should be scrutinized to find feasible innovative interventions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".