Rural and Urban Disparity in Health Services Utilization in China
Bibliographic record
Abstract
OBJECTIVES: To describe patterns in physician and hospital utilization among rural and urban populations in China and to determine factors associated with any differences. METHODS: In 2003, the Third National Health Services Survey in China was conducted to collect information about health services utilization from randomly selected residents. Of the 193,689 respondents to the survey (response rate, 77.8%), 6429 urban and 16,044 rural respondents who were age 18 or older and reported an illness within the last 2 weeks before the survey were analyzed. Generalized estimating equations with a log link were used to assess the relationship between rural/urban residence and physician visit/hospitalization to adjust for respondents clustered at the household level. RESULTS: About half of respondents did not see a physician when they were ill. Rural respondents used physicians more than urban respondents (52.0% vs. 43.0%, P < 0.001) and used hospitals less (7.6% vs. 11.1%, P < 0.001). Factor associated with increased physician utilization included residing in rural areas among majority Chinese (ie, Han) [rate ratio (RR), 1.21; 95% confidence interval (95% CI), 1.16-1.26], residing <3 km away from the medical center (RR, 1.16; 95% CI, 1.12-1.21), or being uninsured (RR, 1.38; 95% CI, 1.30-1.46). Rural minority Chinese visited physicians significantly less than urban minority Chinese (RR, 0.90; 95% CI, 0.83-0.98). Hospital utilization was significantly lower among rural males (RR, 0.84; 95% CI, 0.72-0.98), rural seniors (age, > or =65; RR, 0.64; 95% CI, 0.53-0.77), rural respondents with low education (RR, 0.70; 95% CI, 0.57-0.86 for illiterate), or rural insured respondents (RR, 0.86; 95% CI, 0.69-0.99) than hospitalization among urban counterparts. CONCLUSIONS: Three national approaches should be considered in reforming the healthcare system in China: universal insurance coverage, higher amounts of insurance coverage, and increasing the population's level of education. In addition, access issues in remote areas and by rural minority Chinese population should be addressed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".