From fever to anti-malarial: the treatment-seeking process in rural Senegal
Bibliographic record
Abstract
BACKGROUND: Currently less than 15% of children under five with fever receive recommended artemisinin-combination therapy (ACT), far short of the Roll Back Malaria target of 80%. To understand why coverage remains low, it is necessary to examine the treatment pathway from a child getting fever to receiving appropriate treatment and to identify critical blockages. This paper presents the application of such a diagnostic approach to the coverage of prompt and effective treatment of children with fever in rural Senegal. METHODS: A two-stage cluster sample household survey was conducted in August 2008 in Tambacounda, Senegal, to investigate treatment behaviour for children under five with fever in the previous two weeks. The treatment pathway was divided in to five key steps; the proportion of all febrile children reaching each step was calculated. Results were stratified by sector of provider (public, community, and retail). Logistic regression was used to determine predictors of treatment seeking. RESULTS: Overall 61.6% (188) of caretakers sought any advice or treatment and 40.3% (123) sought any treatment promptly within 48 hours. Over 70% of children taken to any provider with fever did not receive an anti-malarial. The proportion of febrile children receiving ACT within 48 hours was 6.2% (19) from any source; inclusion of correct dose and duration reduced this to 1.3%. The proportion of febrile children receiving ACT within 48 hours (not including dose & duration) was 3.0% (9) from a public provider, 3.0% (9) from a community source and 0.3% (1) from the retail sector. Inclusion of confirmed diagnosis within the public sector treatment pathway as per national policy increases the proportion of children receiving appropriate treatment with ACT in this sector from 9.4% (9/96) to an estimated 20.0% (9/45). CONCLUSIONS: Process analysis of the treatment pathway for febrile children must be stratified by sector of treatment-seeking. In Tambacounda, Senegal, interventions are needed to increase prompt care-seeking for fever, improve uptake of rapid diagnostic tests at the public and community levels and increase correct treatment of parasite-positive patients with ACT. Limited impact will be achieved if interventions to improve prompt and effective treatment target only one step in the treatment pathway in any sector.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".