Future mortality burden attributable to non-optimal temperatures under the dual threats from climate change and population aging
Bibliographic record
Abstract
Background and Aim: Elderly population subgroups are amongst the most vulnerable to heat and cold. While temperature-related health impacts are projected to increase with progressive global warming, the influence of population aging on these trends remains unclear. Here we quantify the contribution of population aging to future temperature-related mortality in 729 locations across 42 countries at 1.5 °C, 2 °C, and 3 °C of warming using climate models of the latest Coupled Model Inter-comparison Project (CMIP6). Methods: We first estimated the location-age-specific temperature-mortality associations in a two-stage time-series analysis using quasi-Poisson regression with distributed lag nonlinear models and multivariate dose-response meta-regression using data from the Multi-Country Multi-City (MCC) Collaborative Research Network. We then combined the association estimates with future temperature series (18 models from CMIP6) to derive excess temperature-mortality projections at three warming levels. We derived age group-specific population and baseline mortality projections according to the Shared Socio-economic Pathway 5-8.5 to derive the corresponding excess mortality. Finally, we quantified the impact of population aging as the difference in the change in temperature-related mortality fractions (warming target minus historical period) between "climate-population" and "climate-only" scenarios (i.e., either accounting or not accounting for changes in population demographics). Results: Future heat-related mortality will increase by 0.8%-7.0% at 1.5-3.0 °C warming, among which 1 in 2 to 5 in 7 deaths would be attributable to population aging. Population aging would mostly offset the decrease of future cold-related mortality driven by climate only, leading to a net increase of 1.5%-5.3% at 1.5-3 ºC warming. Countries in Middle-East Asia, East-Asia, South-East Asia, and South America that have larger increases in population aging generally will face substantially elevated temperature-related mortality. Conclusions: Our findings suggest that population aging would constitute a crucial driver for a larger impact of non-optimal temperatures under a warming climate.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".