ASSESSING THE USE AND COST OF HEALTHCARE SERVICES AND CATASTROPHIC EXPENDITURES IN ENUGU AND ANAMBRA STATES, NIGERIA
Bibliographic record
Abstract
Healthcare in Nigeria is financed from a mixture of budgetary allocations, out-of-pocket spending, development funding from external donors and a small pool of social health insurance contributions. About 70% of the total health expenditure is out-of-pocket which places a financial burden on poorer households and individuals. The National Health Insurance Scheme is currently limited to the formal public sector. The 2004 National Living Standard Survey, a representative sample of more than 19,000 households, indicated that outof-pocket expenditure on out-patient care was about US$22.5 per capita, which accounted for about 9% of total household expenditure. On average, about 4% of households are estimated to spend more than half of their total household expenditure on healthcare and 12% of them are estimated to spend more than a quarter. Health expenditures are said to be “catastrophic” when they risk sending a household into, or further into, poverty. The purpose of health financing schemes and targets is to protect the poor from shocks associated with severe illness and to ensure equitable access to services. However, this can only be achieved if healthcare planners are well-informed about the financial burden of paying for health services. This research sought to fill gaps in the information currently available on what health services are being accessed in Enugu and Anambra states. It explored whether public or private services were being used, the financing incidence (based on socioeconomic group and rural-urban location) of out-ofpocket spending and the incidence of catastrophic healthcare payments. It is hoped that the information in this policy brief will help guide decision makers in their efforts to protect the poor from over burdensome and damaging healthcare expenditures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".