Cost of Delivering Child Immunization Services in Urban Bangladesh: A\nStudy Based on Facility-level Surveys
Bibliographic record
Abstract
This facility-based study estimated the costs of providing child immunization services in Dhaka, Bangladesh, from the perspective of healthcare providers.About a quarter of all immunization (EPI) delivery sites in Dhaka city were surveyed during 1999.The EPI services in urban Dhaka are delivered through a partnership of the Government of Bangladesh (GoB) and non-governmental organizations (NGOs).About 77% of the EPI delivery sites in Dhaka were under the management of NGOs, and 62% of all vaccinations were provided through these sites.The outreach facilities (both GoB and NGO) provided immunization services at a much lower cost than the permanent static facilities.The average cost per measles-vaccinated child (MVC), an indirect measure of number of children fully immunized (FIC __ the number of children immunized by first year of life), was US$ 11.61.If all the immunization doses delivered by the facilities were administered to children who were supposed to be immunized (FVC), the cost per child would have been US$ 6.91.The wide gap between the cost per MVC and the cost per FVC implies that the cost of immunizing children can be reduced significantly through better targeting of children.The incremental cost of adding new services or interventions with current EPI was quite low, not significantly higher than the actual cost of new vaccines or drugs to be added.NGOs in Dhaka mobilized about US$ 15,000 from the local community to support the immunization activities.Involving local community with EPI activities not only will improve the sustainability of the programme but will also increase the immunization coverage.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".