Assessing the burden of medical impoverishment by cause: a systematic breakdown by disease in Ethiopia
Bibliographic record
Abstract
BACKGROUND: Out-of-pocket (OOP) medical expenses often lead to catastrophic expenditure and impoverishment in low- and middle-income countries. Yet, there has been no systematic examination of which specific diseases and conditions (e.g., tuberculosis, cardiovascular disease) drive medical impoverishment, defined as OOP direct medical costs pushing households into poverty. METHODS: We used a cost and epidemiological model to propose an assessment of the burden of medical impoverishment in Ethiopia, i.e., the number of households crossing a poverty line due to excessive OOP direct medical expenses. We utilized disease-specific mortality estimates from the Global Burden of Disease study, epidemiological and cost inputs from surveys, and secondary data from the literature to produce a count of poverty cases due to OOP direct medical costs per specific condition. RESULTS: In Ethiopia, in 2013, and among 20 leading causes of mortality, we estimated the burden of impoverishment due to OOP direct medical costs to be of about 350,000 poverty cases. The top three causes of medical impoverishment were diarrhea, lower respiratory infections, and road injury, accounting for 75 % of all poverty cases. CONCLUSIONS: We present a preliminary attempt for the estimation of the burden of medical impoverishment by cause for high mortality conditions. In Ethiopia, medical impoverishment was notably associated with illness occurrence and health services utilization. Although currently used estimates are sensitive to health services utilization, a systematic breakdown of impoverishment due to OOP direct medical costs by cause can provide important information for the promotion of financial risk protection and equity, and subsequent design of health policies toward universal health coverage, reduction of direct OOP payments, and poverty alleviation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".