The trend in quality of life of Chinese population: analysis based on population health surveys from 2008 to 2020
Bibliographic record
Abstract
Abstract Background Quality of life (QoL) is one of the most important indicators for evaluating an individual’s overall health status. However, evidence exploring the trend in QoL of the Chinese population is still lacking. This study aimed to investigate the trend in QoL of the Chinese population measured by the EQ-5D from 2008 to 2020, as well as compare the changing trends in QoL categorized by populations with different socio-demographic characteristics. Methods Data were obtained from the 2008, 2013, and 2020 waves of the Health Services Surveys conducted in Tianjin, China. Respondents completed the EQ-5D (EQ-5D-3L in 2008 and 2013 and EQ-5D-5L in 2020) through face-to-face interviews or self-administration. Responses of the EQ-5D-3L in 2008 and 2013 were mapped onto the EQ-5D-5L responses, and then converted to utility values using the Chinese value set. The trend in QoL was explored by comparing the percentage of any reported problems on each EQ-5D dimension and the corresponding utility values across the three waves. Subgroup analyses were performed to compare trends in utility values stratified by socio-demographic indicators. The effect of the time variable (year) on utility values was assessed by multiple linear regression analyses using the pooled data. Results By analyzing and comparing the three waves of the data (N = 25,939 in the 2008 wave, N = 22,138 in 2013, and N = 19,177 in 2020), an upward trend was observed in the percentages of reporting problems on all five dimensions (p < 0.001), resulting in a decreasing trend in utility values (2008: 0.948, 2013: 0.942, 2020: 0.939, p < 0.001). Utility values declined more over time among the female, the elder, the recipients of medical assistance, the widowed, the unemployed, and respondents with primary or lower education. The effect of the year (Coef. for 2013 = − 0.009, p < 0.001; Coef. for 2020 = − 0.010, p < 0.001) confirmed the downward trend in the utility values. Conclusions The overall QoL of the Chinese population decreased over the period from 2008 to 2020. The QoL of the disadvantaged or vulnerable populations in terms of socioeconomic characteristics declined more over time.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".