Projected extreme temperature event-attributable dementia deaths in China: a climate–ageing–adaptation framework
Bibliographic record
Abstract
BACKGROUND: Climate change is increasing health risks worldwide, but how extreme temperature events affect Alzheimer's disease and other dementias (ADD) remains poorly understood. We aimed to project future extreme temperature event-attributable ADD deaths in China, accounting for the combined effects of climate change, population ageing, and adaptation methods. METHODS: We analysed 399,036 ADD deaths among adults aged ≥60 years in China during 2013-2020, using a time-stratified case-crossover design to estimate ADD mortality risks associated with extreme temperature events. We subsequently developed an integrated climate-ageing-adaptation framework that combined exposure-response functions, high-resolution climate projections, and demographic forecasts. Using this framework, we projected county-level ADD deaths attributable to heatwaves and cold spells among individuals aged ≥60 years across China under multiple SSP-RCP and adaptation scenarios for the 2030s, 2050s, and 2080s FINDINGS: Without adaptation, ADD heatwave-attributable deaths are projected to rise sharply, especially under rapid socioeconomic development with high greenhouse gas emissions scenario (SSP5-RCP8.5) in the 2080s, which reach 59,088-an 11-fold (+1003%) increase comparing to 2010s levels. Cold spell-attributable deaths generally decline but reductions are insufficient to offset the sharp rise in heatwave mortality, leading to a net increase in total attributable deaths. Adaptation strategies could avert up to 76.4% of heatwave-attributable deaths compared to no-adaptation scenario. INTERPRETATION: The convergence of climate change and population ageing is projected to substantially magnify dementia-related mortality in China. Under SSP5-RCP8.5, deaths attributable to extreme temperature events are projected to reach unprecedented levels by the end of the 21st century, especially without adaptation measures. These findings underscore the urgent need for massive greenhouse gases emission reductions alongside balanced, region-specific adaptation measures, and highlight dementia care as an essential component of climate resilience planning. Our study provides guidance for designing climate-resilient public health policies, particularly for ageing populations in climate-vulnerable counties and regions. FUNDING: This work was supported by the Science and Technology Fundamental Resources Investigation Program of China [2017FY101201, 2017FY101206], and the National Foundation for Australia-China Relations of Australia [6016294].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 teacher head, 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".