Spatiotemporal heterogeneity in associations of national population ageing with socioeconomic and environmental factors at the global scale
Bibliographic record
Abstract
Global concerted and sustained action is required under a rapid population ageing trend, while global ageing varies across countries in space and time. To support global action on sustainable development and healthy ageing, we investigated the spatiotemporal heterogeneity toward associations between national population ageing (the share of the population aged 65 and older) and various socioeconomic and environmental factors for 189 countries and territories from 2001 to 2020. We adopted Bayesian Spatiotemporally Varying Coefficients (STVC) model to fit the spatial and temporal heterogeneous associations among variables. The concept of variance partitioning was innovatively integrated into Bayesian STVC modeling to propose a spatiotemporal variance partitioning index (STVPI) for identifying the explainable percentage of influencing factors considering their spatiotemporal heterogeneous impacts. The results showed that global ageing had increased rapidly over the past 20 years, especially after 2009, and exhibited a clear geospatial agglomeration, with Europe and Africa possessing the highest and lowest regional ageing levels. The total explainable percentages of socioeconomic and environmental factors for global ageing were 61.85% [95% credible intervals (CIs): 58.57%–64.9%] and 37.40% (95% CIs: 34.38%–40.65%), respectively. Specifically, the cumulative explainable percentage of the five factors, male-to-female ratio, gross national income (GNI), particulate matter 2.5 (PM2.5), normalized difference vegetation index (NDVI), and temperature, exceeded 90%. Over time, the annual impacts of education, male-to-female ratio, and physicians were increasing year by year; in contrast, the annual impacts of hospital beds, GNI, NDVI, PM2.5, and precipitation showed downward trends. Geospatially, the country-scale impacts of all factors showed substantial geographical disparities globally but significant clusters regionally. According to country subgroups (not-ageing, ageing, aged, and hyper-aged society), sex ratio, national income, air quality, greenness, and climate consistently played essential roles across the subgroups of four ageing stages. Our findings focusing on spatiotemporal disparities toward ageing and its influencing factors are expected to inform the formation of differentiated policies tailored for different national contexts in response to global ageing. The STVC-based STVPI is promising to be used in broader natural and social sciences to determine the relative importance of potential influencing factors within spatiotemporal dimensions to real-world phenomena.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".