The Impact of Environmental Pollution on the Health of Middle-Aged and Older Adults in China
Bibliographic record
Abstract
Abstract While several studies have demonstrated the negative impacts of environmental pollution on population health, in general, few studies have examined the potential differential effects on the health of middle-aged and older populations, i.e. 45 years and older. Given the twin concerns of environmental pollution and population aging in China, this article employed a fixed effects model to infer the impact of environmental pollution on public health with a particular focus on middle-aged and older adults. The analyses were based on data from the 2011–2018 waves of the CHARLS and pollutant data from prefecture-level cities. The results showed that environmental pollution significantly increased the risk of chronic diseases and negatively impacted the health of middle-aged and older adults. Environmental pollution had its greatest negative effect on the health of the elderly, women, urban residents and those with lower incomes than for their counterparts. We further found that the main channels of effect were through reduced physical exercise and an increase in depressive symptoms, and the pollution prevention actions alleviated the health deterioration of environmental pollution for the middle-aged and elderly. It is imperative for the government to urgently reinforce policy's enforcement to decrease air and water pollution, and enhance the ability to circumvent pollution for the lower socioeconomic groups.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".