A model to estimate the cost of the National Essential Public Health Services Package in Beijing, China
Bibliographic record
Abstract
BACKGROUND: In order to address several health challenges, the Chinese government issued the National Essential Public Health Services Package (NEPHSP) in 2009. In China's large cities, the lack of funding for community health centers and consequent lack of comprehensive services and high quality care has become a major challenge. However, no study has been carried out to estimate the cost of delivering the services in the package. This project was to develop a cost estimation approach appropriate to the context and use it to calculate the cost of the NEPHSP in Beijing in 2011. METHODS: By adjusting models of cost analysis of primary health care and workload indicators of staffing need developed by the World Health Organization, a model was developed to estimate the cost of the services in the package through an intensive interactive process. A total of 17 community health centers from eight administrative districts in Beijing were selected. Their service volume and expenditure data in 2010 were used to evaluate the costs of providing the NEPHSP in Beijing based on the applied model. RESULTS: The total workload of all types of primary health care in 17 sample centers was equivalent to the workload requirement for 14,056,402 standard clinic visits. The total expenditure of the 17 sample centers was 26,329,357.62 USD in 2010. The cost of the workload requirement of one standard clinic visit was 1.87 USD. The workload of the NEPHSP was equivalent to 5,514,777 standard clinic visits (39.23 % of the total workload). The model suggests that the cost of the package in Beijing was 7.95 USD per capita in 2010. The cost of the NEPHSP in urban areas was lower than suburban areas: 7.31 and 8.65 USD respectively. CONCLUSIONS: The average investment of 3.97 USD per capita in NEPHSP was lower than the amount needed to meet its running costs. NEPHSP in Beijing is therefore underfunded. Additional investment is needed, and a dynamic cost estimate mechanism should be introduced to ensure services remain adequately funded.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".