Abstract B042: Socioeconomic and racial disparities in pediatric cancer outcomes in the Ghanaian health sector: Challenges and strategies for equitable care
Bibliographic record
Abstract
Abstract Objectives This study aims to investigate the socioeconomic and racial disparities in pediatric cancer outcomes within the Ghanaian health sector. The objectives are to identify the specific challenges faced by different socioeconomic and racial groups, assess the impact of these disparities on diagnosis and treatment outcomes, and propose strategies for achieving more equitable care for pediatric cancer patients in Ghana. Methods A mixed-methods approach was employed, combining quantitative analysis of patient data from national cancer registries and hospitals with qualitative interviews of healthcare providers and affected families. The quantitative component focused on variables such as socioeconomic status, race, stage of cancer at diagnosis, treatment received, and survival rates. The qualitative component explored personal experiences, barriers to care, and perceptions of the healthcare system. Data were collected from multiple regions to ensure a representative sample of the diverse population in Ghana. Results The study found significant disparities in pediatric cancer outcomes based on socioeconomic status and race. Children from lower socioeconomic backgrounds and minority racial groups were more likely to be diagnosed at later stages and had lower survival rates compared to their higher-income and majority-race counterparts. Key challenges identified included limited access to specialized oncology care, financial barriers, inadequate health insurance coverage, and cultural beliefs affecting treatment-seeking behavior. Additionally, there was a shortage of trained healthcare professionals and insufficient infrastructure in rural areas. Conclusion Socioeconomic and racial disparities significantly impact pediatric cancer outcomes in Ghana. Addressing these disparities requires a multifaceted approach, including policy interventions to improve healthcare access and affordability, targeted outreach programs to educate communities, and investment in healthcare infrastructure and workforce training. Implementing these strategies can promote more equitable care and improve survival rates for all pediatric cancer patients in Ghana. Collaboration between government, healthcare providers, and non-governmental organizations is essential to overcome these challenges and ensure that every child has the opportunity for a healthy future. Citation Format: Mavis Agyapomaa. Socioeconomic and racial disparities in pediatric cancer outcomes in the Ghanaian health sector: Challenges and strategies for equitable care [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B042.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".