Ageing-related functional and cognitive impairments and cold mortality risk: a longitudinal cohort study in China
Bibliographic record
Abstract
BACKGROUND: Cold-related mortality is the leading contributor to the disease burden from non-optimal temperatures in China. In this study, we aimed to explore ageing-related risk factors, advance our understanding of temperature-related mortality, and enhance the accuracy of relative risk measures. METHODS: We assessed daily cold spell exposure individually for 13 527 participants with a median age of 89 years (IQR 78-96) and 4659 winter mortalities during follow-up from 2008 to 2018 in the Chinese Longitudinal Healthy Longevity Survey. We used a time-varying Cox proportional hazards model to capture daily variations in cold spell exposure, adjusting for individual-level demographics, multidimensional measures of functional status, and socioeconomic factors. We also examined effect modification by demographics, functional status measures, comorbidities, and socioeconomic factors. The primary outcome is all-cause mortality recorded from 2008 to 2018. FINDINGS: Cold spell days were associated with a near-doubling of mortality risk (hazard ratio [HR] 1·87-2·08). Individuals with impaired physical functional status, particularly those dependent on activities of daily living (eg, bathing, dressing, eating, toileting, cooking, carrying weights of 5 kg, doing laundry, and taking public transportation), exhibited significantly higher vulnerability (HR 2·23-3·74). Cognitive impairments, notably in attention and calculation, orientation, and short-term memory, also increased risk (HR 2·23-2·41). Women faced greater mortality risk than men (HR 2·17-2·27 vs 1·50-1·89). Self-reported chronic disease comorbidities did not significantly modify these associations. INTERPRETATION: Beyond age as a general risk factor, to reduce cold-related mortality in China, interventions should prioritise older adults with impaired activities of daily living (eg, bathing or dressing) and cognitive deficits (eg, attention and calculation or short-term memory), particularly women. Community-based programmes, such as subsidised heating and real-time cold alert systems, combined with targeted caregiver support for functionally dependent individuals, could mitigate risks. FUNDING: Data collection was jointly supported by the National Key R&D Program of China (number 2018YFC2000400), the National Natural Science Foundation of China (number 72061137004), the US National Institute of Aging, National Institutes of Health (number P01AG031719), and Beijing TaiKang YiCai Public Welfare Foundation. The study was supported by the National Natural Science Foundation of China (number 82422064, 82250610230), the Natural Science Foundation of Beijing (number IS23105), and the Tsinghua University Vanke School of Public Health Research Fund (number 2021PY001).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".