Economic evaluation of severe malaria in children under 14 years in Zambia
Bibliographic record
Abstract
INTRODUCTION: Malaria exerts a significant economic burden on health care providers and households and our study attempts to make claims on the cost effectiveness of artesunate against quinine in patients under 14 years of age in Zambia. Also, to find the average total costs involved in the treatment of severe malaria in children and their impact on household expenditure. METHODS: Cost-effectiveness analysis of severe malaria treatment was conducted from a healthcare provider perspective using a Markov model. Standard costing was performed for the identification, measurement and assessment phases with data from quantification reports for anti-malaria commodities as these documents provides drug procurement costs from suppliers and freight costs. Average and incremental cost-effectiveness ratio were estimated and uncertainties were assessed through probabilistic sensitivity analysis. RESULTS: In Zambia severe malaria in children has been shown to account for over 45% of the total monthly curative healthcare costs incurred by households compared to the mean per capita monthly income. The cost of treating severe malaria depleted 7.67% of the monthly average household income. According, to the cost effectiveness analysis the of artesunate with quinine the ICER was $105 per death averted. CONCLUSION: The use of artesunate over quinine in the treatment of severe malaria in children under 14 years is a highly cost-effective strategy for the healthcare provider in Zambia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".