Inequalities in changing mortality and life expectancy in Jiading District, Shanghai, 2002–2018
Bibliographic record
Abstract
BACKGROUND: Improvements of population health in China have been unevenly distributed among different sexes and regions. Mortality Registration System provides an opportunity for timely assessments of mortality trend and inequalities. METHODS: Causes of death were reclassified following the method of Global Burden of Disease Study (GBD). Age-standardized mortality rate (ASMR) and ring-map of the rate by town were used to describe inequalities in changing mortality. Life expectancy (LE) and cause-deleted LE were calculated on the basis of life table technique. RESULTS: The burden of death from 2002 to 2018 was dominated by cardiovascular diseases (CVD), neoplasms, chronic respiratory diseases and injuries in Jiading district, accounting for almost 80% of total deaths. The overall ASMR dropped from 407.6/100000 to 227.1/100000, and LE increased from 77.86 years to 82.31 years. Women lived about 3.0-3.5 years longer than men. Besides, a cluster of lower LE was found for CVD in the southeast corner and one cluster for neoplasms in the southern corner of the district. The largest individual contributor to increment in LE was neoplasms, ranged from 2.41 to 3.63 years for males, and from 1.60 to 2.36 years for females. CONCLUSIONS: Improvement in health was mainly attributed to the decline of deaths caused by CVD and neoplasms, but was distributed with sex and town. This study served as a reflection of health inequality, is conducive to formulate localized health policies and measures.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".