Top Ten Causes of Death and Life Expectancy in Zahedan (South-East Iran) in 2014
Bibliographic record
Abstract
INTRODUCTION: Life expectancy is an overall measure of population health. Sistan and Balouchestan is the biggest in terms of geographical size and one of the least developed provinces in Iran. We tried to determine the top 10 causes of death and life expectancy in Zahedan in 2014 and compare them with other parts of Iran and global pattern.METHODS: It is a cross-sectional study. Our population included residents of Zahedan in 2014. We used data from the death registration system according to ICD10 codes to determine the top 10 causes of death in Zahedan. We tried in this study to apply William Brass method to modify mortalities undercount. Then we estimated the life expectancy for men and women in Zahedan by providing lifetime tables.RESULTS: Thetop ten causes of death in Zahedan were, respectively: 1- injury, poisoning and other certain consequences of external causes, 2- certain conditions originating in the prenatal period, 3-strokes, 4- ischemic heart diseases, 5-neoplasm, 6- Alzheimer disease, 7- hypertension-related diseases, 8- congenital malformations, deformations and chromosomal abnormalities, 9- certain infectious and parasitic diseases and 10- respiratory system infections. The death registration coverage in Zahedanhad been 83% for men and 73% for women in 2014 based on William Brass method. After applying undercount deaths and based on life tablescalculation we determined life expectancy 64.39 years and 67.51 years respectively for men and women in Zahedan city in 2014.CONCLUSION: In regard to ranks of strokes, ischemic heart disease, road accidents and cancers in our study; our resultsare similar to countries with middle income and higher income. Also, it is pertinent to also state it that thesecond rank of prenatal disease in our population is similar to that of the low-income countries.The relatively low life expectancy in Zahedan indicates the less developed condition and a purposeful intervention to improve the development condition and the prevention of the main causes of death in Zahedan are necessary to improve life expectancy in this region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".