Cost of delivering primary healthcare services through public sector in India
Bibliographic record
Abstract
Background & objectives: Public health spending on primary healthcare has increased by four times (in real terms) over the last decade and continues to constitute more than half of the total public health expenditure. The present study estimated the cost of providing healthcare services at sub centre (SC) and primary health centre (PHC) level in four selected States of India. Methods: A total of 51 SCs and 33 PHCs were selected across the four States (Himachal Pradesh, Odisha, Kerala and Tamil Nadu) of India. The economic cost of delivering health services at these facilities was assessed using bottom-up costing methodology during the reference year of 2014-2015. The cost of capital items was annualized and allocation of shared resources was based on appropriate apportioning statistics. Results: The mean annual cost of providing health services at SC and PHC was JOURNAL/ijmer/04.03/02223309-202209000-00003/372FF01/v/2023-02-02T183910Z/r/image-tiff 0.69 million (US$ 11,392) and JOURNAL/ijmer/04.03/02223309-202209000-00003/372FF01/v/2023-02-02T183910Z/r/image-tiff 5.1 million (US$ 83,837), respectively. Nearly 3/4 th and 2/3 rd of this cost at the level of SC (74%) and PHC (63%) were spent on salaries. In terms of unit cost, the costs per antenatal care and postnatal care visit were JOURNAL/ijmer/04.03/02223309-202209000-00003/372FF01/v/2023-02-02T183910Z/r/image-tiff 221 (173-276) and JOURNAL/ijmer/04.03/02223309-202209000-00003/372FF01/v/2023-02-02T183910Z/r/image-tiff 333 (244-461), respectively, at SCs. Similarly, the costs of per patient outpatient consultation and per bed day hospitalization at PHC level were JOURNAL/ijmer/04.03/02223309-202209000-00003/372FF01/v/2023-02-02T183910Z/r/image-tiff 121 (91-155) and JOURNAL/ijmer/04.03/02223309-202209000-00003/372FF01/v/2023-02-02T183910Z/r/image-tiff 1168 (955-1468), respectively. Interpretation & conclusions: The cost estimates from the present study can be used in economic evaluations, assessing technical efficiency and also for providing valuable information during scale-up of health facilities.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".