Patterns and Trends in Mortality Associated With and Due to Diabetes Mellitus in a Transitioning Region With 3.17 Million People: Observational Study
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus (DM) imposes a significant disease burden in economically transitioning regions. Most transitioning regions share similar experience in urbanization processes. Shanghai's Pudong district serves as a representative area of such regions. OBJECTIVE: We aimed to assess the burden of and trends in DM mortality in Shanghai's Pudong district and analyze the impact of aging and multimorbidity. METHODS: A longitudinal, population-based study was conducted to analyze DM mortality in Pudong from 2005 to 2020. We used joinpoint regression to analyze epidemiological features and long-term trends in crude mortality rate (CMR), age-standardized mortality rate worldwide (ASMRW), and years of life lost (YLL). Furthermore, the decomposition method was used to evaluate the contribution of demographic and nondemographic factors associated with mortality. RESULTS: person-years, respectively. Among the mortality associated with and due to DM, the total annual ASMRW increased by 3.65% (95% CI 3.25%-4.06%) and 1.38% (95% CI 0.74%-2.02%), respectively. Additionally, the total annual YLL rate increased by 4.98% (95% CI 3.92%-6.05%) and 2.68% (95% CI 1.34%-4.04%). The rates of YLL increase in persons aged 30 to 44 years (3.98%, 95% CI 0.32%-7.78%) and 45 to 59 years (4.31%, 95% CI 2.95%-5.69%) were followed by the increase in persons aged 80 years and older (10.53%, 95% CI 9.45%-11.62%) for deaths associated with DM. The annual CMR attributable to demographic factors increased by 41.9% (95% CI 17.73%-71.04%) and 36.72% (95% CI 16.69%-60.2%) for deaths associated with and due to DM, respectively. Hypertension, cerebrovascular disease, and ischemic heart disease were the top 3 comorbidities. CONCLUSIONS: Aging and multimorbidity played essential roles in changing the burden of DM in an urbanizing and transitioning region. There is an increasing disease burden among young and middle-aged people, emphasizing the need for greater attention to these groups. Health management is an emerging method that holds important implications for alleviating the future burden of DM.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".