Global disparities in immunotherapy clinical trials: A comprehensive analysis of low- and middle-income countries over the past decade.
Bibliographic record
Abstract
1606 Background: Immunotherapy (IO) has largely impacted cancer treatment over the last decade, yet its accessibility and representation in clinical trials remain severely limited in low- and middle- income countries. We investigated the involvement of these countries in IO cancer trials, analyzing country-specific rates and influencing factors. Methods: Data was obtained from clinicaltrials.gov. Advanced search focused on cancer interventional studies from 12/31/2013 to 01/01/2024 with specific IO treatments. Only completed trials were included. Studies unrelated to immunotherapy or cancer were excluded. Country trial rates were calculated per 100k population. Statistical analysis used Chi-square test for nominal data. Results: Of 1593 trials, 1282 were included in the final analysis. Of world’s 217 countries and regions (World Bank) 72 (33.2%) participated in IO cancer trials in the last decade. Zero country from low-income group was involved in the trials. Only 2.4% (31) of all trials, included lower-middle-income countries (LMICs). Out of 54 LMICs, only 8 were represented: Ukraine, Philippines, India, Guatemala, Egypt, Vietnam, Morocco, and Eswatini (Swaziland). Ukraine led this group with 16 trials, while the remaining 7 were included in less than 10 trials each. Upper-middle-income countries (UMICs) were included in 21.3% (273) of trials. Out of 54 UMICs, 22 were involved in the trials; China led this group (174), followed by Russia (69), Brazil (52), Mexico and Turkey (41 each). Among the top 30 countries with the highest trial participation, none included LMICs, and only 5 were UMICs. Of 81 high-income countries (HICs) 42 participated in IO trials. USA was involved in over 65% of all trials (840), followed by Spain (222), France (193), and Canada (180). However, population-adjusted rates varied, with European HICs like Latvia (2.16), Belgium (1.09), Norway (0.87) among 25 other countries, showing higher rates than USA (0.25). Majority of trials done solely in one country were also from USA (65%), and China (79%). A significant link was found between the economic status of participating countries and the funding source of trials (p<0.00001). Industry funded 28 of 31 (90%) trials in LMICs, indicating a reliance on industry funding in these regions. Industry favored adult-only over pediatric-inclusive trials (p<0.0001). Out of 80 trials (6.24%) including children, 22 were industry-funded. Exclusively pediatric IO trials were only 4 (0.3%) of all IO trials. LMICs participated in 3.75% (3) of all pediatric-inclusive trials. Conclusions: Countries with low economic status and children with cancer from both low- and high-income areas are largely underrepresented in IO cancer trials globally. More inclusive IO trials across age groups and LMICs are vital to implement trial results in real-world settings and close the care gap.
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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 | 0.016 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".