Recurrent costs in primary health care in Ethiopia: facility and disease specific unit costs and their components in government primary hospitals and health centers
Bibliographic record
Abstract
BACKGROUND: Continued investment, especially from domestic financing, is needed for Ethiopia to achieve universal health coverage and a sustainable health system over time. Understanding costs of providing health services will assist the government to mobilize adequate resources for health, and to understand future costs of changes in quality of care, service provision scope, and potential decline in external resources. This study assessed costs per unit of service output, "unit costs", for government primary hospitals and health centers, and disease-specific services within each facility. METHODS: Quantitative and qualitative data were collected from 25 primary hospitals and 47 health centers across eight of the eleven regions of Ethiopia for 2013/14, and 2014/15 and 2015/16 but only for primary hospitals, and supplemented by other related health and financial institutions records. A top-down costing approach was used to estimate unit costs for each facility by department - inpatient, outpatient, maternal and child health, and delivery. A mixed-method approach was used for the disease-specific unit costs exempt from fees. RESULTS: Health center median unit cost was 146 Ethiopian birr (ETB) (17 PPP$, 2012), the Delivery department had the highest median unit cost (647 ETB; 76 PPP$, 2012) and Outpatient department (OPD) had the lowest (124 ETB; 14 PPP$, 2012). Primary hospital median unit cost was 339 ETB (40 PPP$, 2012), with Inpatient department having the highest median unit cost (1288 ETB; 151 PPP$, 2012), while OPD was the lowest (252 ETB; 29 PPP$, 2012). Drugs and pharmaceutical supplies accounted for most of the costs for both facilities. Among the exempted services offered, tuberculosis and antiretroviral treatment are the costliest with median unit costs from 1091 to 1536 ETB (128-180 PPP$, 2012), with drugs and supplies accounting for almost 90% of the costs. CONCLUSIONS: High unit costs of service provision could be indicative of underutilization of the primary health care system, coupled with inefficiencies associated with organization and delivery of health services. Data from this study are being used to assess efficiency and productivity among primary care facilities, facilitate premium setting for health insurance, and improve budgeting and allocating health resources for a more sustainable and effective primary health care system.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".