Evaluation of the impact of a chronic disease scheme reimbursing medical costs of patients with diabetes in Anhui province, China: a follow-up study
Bibliographic record
Abstract
BACKGROUND: Although many studies have investigated the relationship between the introduction of the New Cooperative Medical Scheme (NCMS) in rural China in 2003 and increased use of medical services, the effect on health status, objectively measured, is seldom reported. In Anhui Province a chronic disease scheme (CDS) for reimbursing part of the cost of outpatient care is designed to improve management of those with chronic conditions, including diabetes. METHODS: A follow-up study was designed in which patients with diabetes aged 40-70 years who had recently (in 2010) been granted a chronic disease card were individually matched on age, sex and village with a patient with diabetes not yet in the scheme. Each subject gave a fingertip sample of blood to give the concentration of glycosylated hemoglobin (HbA1c), a measure indicating blood glucose control during the previous 3 months. This measure was made on recruitment and at 12 month follow-up: information on use of health services, quality of life and financial burden was also collected at the two contacts. RESULTS: Of 602 pairs initially recruited, 528 pairs were contacted at follow-up and are the subject of this report. To distinguish between outcomes associated with application and those of membership of the scheme, the primary analysis was of 256 pairs in which one had been a member of the CDS throughout and the other never applied. No difference between pairs on HbA1c was found either at recruitment or follow-up but those in the CDS reported more hospital visits, more tests and more use of high level hospitals. However they had poorer scores on quality of life scales (SF-12, EQ-5D) and were more likely to report that the financial costs were very burdensome. Those recently applying for the scheme, or being accepted since recruitment, had lower HbA1c scores. CONCLUSIONS: On-going membership of the CDS was associated with increased use of services but this did not appear to result in better management of blood glucose or improved quality of life. Those who had recently joined the scheme had signs of improvement, suggesting a need for active follow-up to maintain and reinforce early gains.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".