Global disparities in cancer supportive care: An international survey
Bibliographic record
Abstract
BACKGROUND: The global cancer burden is rising, particularly in low- and middle-income countries (LMIC), highlighting a critical research gap in understanding disparities in supportive care access. To address this, the Multinational Association of Supportive Care in Cancer (MASCC) Health Disparities Committee initiated a global survey to investigate and delineate these disparities. This study aims to explore and compare supportive care access disparities between LMIC and High-Income Countries (HIC). METHODS: An online cross-sectional survey was conducted among active members of MASCC. Members, representing diverse healthcare professions received email invitations. The survey, available for 3 weeks, comprised sections covering (1) sociodemographic information; (2) clinical service/practice-related disparities in their region/nation; (3) population groups facing disparities within their region or country. Chi-squared or Fisher's exact test for cross-sectional analyses, and a multivariable logistic regression model was employed for statistical analysis. RESULTS: A total of 218 active members participated, with one-quarter (26.6%) from LMIC and 18.4% ethnic minorities, timely cancer care (43.7%) and timely supportive care (45.0%) emerged as the most pressing disparities globally. Notably, participants from LMIC underscored cancer drug affordability (56.4%) and supportive care guideline implementation (56.4%) as critical issues. Economically disadvantaged populations were noted as more likely to face disparities by both LMIC and HIC (non-US-based) respondents, while US-based respondents identified racial/ethnic minorities as facing more disparities. CONCLUSION: This global survey reveals significant disparities in cancer supportive care between LMIC and HIC, with a particular emphasis on medication affordability and guideline implementation in LMIC. Addressing these disparities requires targeted intervention, considering specific regional priorities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".