Barriers to cancer treatment for people experiencing socioeconomic disadvantage in high-income countries: a scoping review
Bibliographic record
Abstract
BACKGROUND: Despite advances in cancer research and treatment, the burden of cancer is not evenly distributed. People experiencing socioeconomic disadvantage have higher rates of cancer, later stage at diagnoses, and are dying of cancers that are preventable and screen-detectable. However, less is known about barriers to accessing cancer treatment. METHODS: We conducted a scoping review of studies examining barriers to accessing cancer treatment for populations experiencing socioeconomic disadvantage in high-income countries, searched across four biomedical databases. Studies published in English between 2008 and 2021 in high-income countries, as defined by the World Bank, and reporting on barriers to cancer treatment were included. RESULTS: A total of 20 studies were identified. Most (n = 16) reported data from the United States, and the remaining included publications were from Canada (n = 1), Ireland (n = 1), United Kingdom (n = 1), and a scoping review (n = 1). The majority of studies (n = 9) focused on barriers to breast cancer treatment. The most common barriers included: inadequate insurance and financial constraints (n = 16); unstable housing (n = 5); geographical distribution of services and transportation challenges (n = 4); limited resources for social care needs (n = 7); communication challenges (n = 9); system disintegration (n = 5); implicit bias (n = 4); advanced diagnosis and comorbidities (n = 8); psychosocial dimensions and contexts (n = 6); and limited social support networks (n = 3). The compounding effect of multiple barriers exacerbated poor access to cancer treatment, with relevance across many social locations. CONCLUSION: This review highlights barriers to cancer treatment across multiple levels, and underscores the importance of identifying patients at risk for socioeconomic disadvantage to improve access to treatment and cancer outcomes. Findings provide an understanding of barriers that can inform future, equity-oriented policy, practice, and service innovation.
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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 | 0.014 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".