Examining Public Sector Availability and Supply Chain Management Practices for Malaria Commodities: Findings From Northern Nigeria
Bibliographic record
Abstract
BACKGROUND: Nigeria accounts for substantial proportions of global malaria infections and deaths, with children aged younger than 5 years being the most affected group. This suggests that access to lifesaving malaria interventions could be suboptimal, especially at public health facilities where most rural dwellers seek health care. We conducted this study to ascertain if public health facilities have the commodities and the robust supply chain management (SCM) system required to deliver malaria interventions to children younger than 5 years. METHOD: We conducted a cross-sectional survey in 1,858 health facilities across 7 states in Nigeria. Using structured questionnaires, we assessed the availability of selected malaria commodities required by children aged younger than 5 years. We also interviewed health workers to evaluate other core SCM activities. RESULT: More than 50% of health facilities in 5 states were stocked out of malaria rapid diagnostic tests (mRDTs), and stock-out rates for artemisinin-based combination therapies (ACTs) were over 50% for almost all assessed ACTs across all states. The percentage of health facilities that received malaria commodities within the recommended lead time was below average across most states (71%). States with a higher percentage of health workers who were aware of and placed orders following the national reporting timeline and those that delivered commodities to the last mile predominantly through third-party logistics service providers tended to have higher availability of mRDTs and artemether/lumefantrine combinations. The top 2 logistics challenges were insecurity and inadequate funding. CONCLUSION: The availability of lifesaving malaria commodities across the health facilities visited was suboptimal, possibly due to several SCM challenges. The results from this study underscore the urgent need to implement effective interventions to address the observed gaps. This will contribute to reducing malaria morbidity and mortality among children aged younger than 5 years in Nigeria.
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 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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".