Influence of socioeconomic events on cause-specific mortality in urban Shanghai, China, from 1974 to 2015: a population-based longitudinal study
Bibliographic record
Abstract
BACKGROUND: Understanding how socioeconomic events influence cause-specific mortality is essential for optimizing disease-control strategies. We characterized long-term trends in cause-specific mortality in a stable population from a very large urban centre. METHODS: We derived population data from 1974 to 2015 on vital status, demographics and causes of death from the death registration system in Yangpu District, Shanghai, China. We examined temporal trends in mortality and assessed the effects of age, period and birth cohort. RESULTS: Over 41 879 864 person-years of follow-up, we analyzed 290 332 deaths: 3.80% from communicable conditions (group 1), 86.50% from noncommunicable diseases (group 2), and 5.56% from injuries (group 3). Age-standardized mortality decreased after 1988 for group 1 (average annual percentage change [AAPC] -6.7, 95% confidence interval [CI] -9.3 to -4.1), after 1995 for group 2 (AAPC -2.9, 95% CI -3.5 to -2.3), and after 1994 for group 3 (AAPC -5.4, 95% CI -6.3 to -4.5), after improvements in public health and clinical service infrastructure and the removal of polluting industries during the 1980s. We observed increased mortality from group 2 and group 3 causes in those born between 1955 and 1965, a period that included the Great Chinese Famine. Cause-specific mortality risks increased in those born after 1949 for cancer and diabetes only. INTERPRETATION: Birth cohorts exposed to extreme starvation in early life had increased premature cause-specific mortality in later life. Decreased cause-specific mortality followed improvements in public health, medical infrastructure and pollution control, but not for cancer or diabetes, likely because of exposure to new risk factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".