Malaria control in Northern Ghana: a scoping review
Bibliographic record
Abstract
BACKGROUND: Despite the achievements of the Ghana Health Service and its partners in reducing malaria morbidity and mortality in Ghana, the disease still poses a significant public health problem and a huge expenditure on the National Health Insurance Scheme and the economy. This review examined the strategies employed to control malaria in northern Ghana, explored the progress made, and discussed the challenges that persist. METHODS AND RESULTS: A literature search was conducted across four databases-Medline, Embase, Scopus, and Cochrane Library-to identify studies conducted in Northern Ghana on malaria control interventions between 1st January 2013 and 10th June 2023. The data were handled using Covidence; of the 1782 studies identified, 38 met the eligibility criteria and were included in this review. The data were analysed with MS Excel and presented in tables. Most of the studies (44.7%) were conducted in the Upper East region and mostly included pregnant women (19%), children (14%) and malaria patients (14.3%). Most of the studies used quantitative methodology and were mostly concentrated on malaria case management (28.9%), Intermittent preventive treatment (IPTp, 23.7%), and Long-Lasting Insecticidal Nets (LLINs, 15.8%). The least common researched area was the malaria vaccine (2.6%). CONCLUSIONS: The most common strategies used were case management, LLINs, IPTp and LLITNs. However, there was no regional or intervention balance in the number of studies conducted. Further studies are required on the combined effects of these interventions as well as larval source management and malaria vaccines.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".