Decomposing the causes of socioeconomic-related health inequality among urban and rural populations in China: a new decomposition approach
Bibliographic record
Abstract
BACKGROUND: In recent decades, China has experienced tremendous economic growth and also witnessed growing socioeconomic-related health inequality. The study aims to explore the potential causes of socioeconomic-related health inequality in urban and rural areas of China over the past two decades. METHODS: This study used six waves of the China Health and Nutrition Survey (CHNS) from 1991 to 2006. The recentered influence function (RIF) regression decomposition method was employed to decompose socioeconomic-related health inequality in China. Health status was derived from self-rated health (SRH) scores. The analyses were conducted on urban and rural samples separately. RESULTS: We found that the average level of health status declined from 1989 to 2006 for both urban and rural populations. Average health scores were greater for the rural population compared with those for the urban population. We also found that there exists pro-rich health inequality in China. While income and secondary education were the main factors to reduce health inequality, older people, unhealthy lifestyles and a poor home environment increased inequality. Health insurance had the opposite effects on health inequality for urban and rural populations, resulting in lower inequality for urban populations and higher inequality for their rural counterparts. CONCLUSION: These findings suggest that an effective way to reduce socioeconomic-related health inequality is not only to increase income and improve access to health care services, but also to focus on improvements in the lifestyles and the home environment. Specifically, for rural populations, it is particularly important to improve the design of health insurance and implement a more comprehensive insurance package that can effectively target the rural poor. Moreover, it is necessary to comprehensively promote the flush toilets and tap water in rural areas. For urban populations, in addition to promoting universal secondary education, healthy lifestyles should be promoted, including measures such as alcohol control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".