The cost of Child Health Days: a case study of Ethiopia's Enhanced Outreach Strategy (EOS)
Bibliographic record
Abstract
Child Health Days (CHDs) are twice-annual campaign-style events designed to increase the coverage of vitamin A and one or more other child health services. Although more than two dozen countries have had a CHD, little has been published about them. This paper presents an activity-based costing study of Ethiopia's version of CHDs, the Enhanced Outreach Strategy (EOS). The December 2006 round reached more than 10 million beneficiaries at an average cost per beneficiary of US$0.56. When measles is added, the cost of the package doubles. Given the way the distribution day delivery system and the service package are structured, there are economies of scope. Because most of the costs are determined by the number of delivery sites and are independent of the number of beneficiaries, other things equal, increasing the beneficiaries would reduce the average cost per beneficiary. Taking into account only the mortality impact of vitamin A, EOS saved 20,200 lives and averted 230,000 DALYs of children 6-59 months. The average cost per life saved was US$228 and the cost per DALY averted was equivalent to 6% of per capita GDP (US$9), making the EOS cost-effective, according to WHO criteria. While CHDs are generally construed as a temporary strategy for improving coverage of supply-constrained systems, inadequate attention has been paid to demand-side considerations that suggest CHDs have an important role to play in changing care-seeking behaviour, in increasing community organization and participation, and in promoting district autonomy and capacity. Recognition of these effects suggests the need for decisions about where and when to introduce, and when to end, a CHD to take into account more than 'just' health sector considerations: they are more broadly about community development. UNICEF played a key role in initiating the EOS and finances 68% of costs, raising concern about the programme's long-term sustainability.
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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 | 0.001 | 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".