Bridging the Access Gap for Comprehensive Sickle Cell Disease Management Across Sub-Saharan Africa: Learnings for Other Global Health Interventions?
Bibliographic record
Abstract
Background: Sickle cell disease (SCD) is a major unresolved global health issue, with the highest disease burden in sub-Saharan African countries; yet, SCD care has not proportionally reached patients in these regions, and the disease has received limited attention in the past. Addressing the burden of SCD in sub-Saharan Africa requires a holistic, collaborative approach to ensure solutions are both comprehensive - i.e., cover the entire continuum of care from early diagnosis to treatment - and sustainable - i.e., are co-created and co-owned with local partners and integrated into existing local systems to enable long-term independence without the need for continuous external support. Objective: We outline a set of recommendations for enhancing the provision of comprehensive healthcare for prevalent diseases in resource-constraint settings, gathered from the Novartis Africa SCD Program, that could serve as 'blueprint' for public-private partnerships to tackle global health priorities. Methods: The Novartis Africa SCD program was initiated with the aim to bridge access gaps to SCD care and provide comprehensive and innovative treatment solutions for SCD, especially in SSA where the disease burden is highest. The Program was first inaugurated in 2019 in Ghana through a public-private partnership with the Ministry of Health of the Government of Ghana, the Ghana Health Service, and the Sickle Cell Foundation of Ghana. Through engagement with these partners, as well as with support from other organizations with complementary competencies and resources, several targeted solutions were implemented to help strengthen the healthcare ecosystem to allow for comprehensive SCD management. Learnings from these interventions are highlighted as best practice consideration as a catalyst and to activate more public-private actors for this neglected global health issue. Findings and Conclusions: A solid understanding of the access barriers to comprehensive care has to be acquired by listening to and learning from patients, civil society, and local experts. Access barriers need to be addressed at multiple levels, i.e., by not only making medicines available and affordable, but also by strengthening healthcare systems, building capacity, and fostering local research and development. Partnerships across governmental, public, academic, non-profit, and private organizations are needed to secure political will, pool resources, gather expertise with understanding of the local context, and allow integration into all levels of existing local healthcare structures and the wider society.
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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 | 0.030 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
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".