Country Factors Dominate Global Trial Participation Inequality
Across 78,117 randomized trials, country-level factors explain 88.9% of variation in participation, outweighing disease burden and temporal effects.
Underlying Paper
Global Inequalities in Clinical Trials Participation
Clinical trials are fundamental to the production of medical evidence and determine who gains access to experimental therapies. Although prior work has long documented inequalities in global clinical trial participation, a systematic quantification of the relative contributions of country-level factors and disease burden remains absent. This paper analyzes inequality in participation across 78,117 randomized controlled trials (RCTs) spanning 16 major disease categories from 2000 to 2024. Linking 185.6 million RCTs participants to country-level disease burden, the paper shows that inequalities in RCTs participation are predominantly explained by country-level factors rather than disease burden. Country-level factors account for 88.9% of the variation in participation, whereas disease and temporal effects contribute marginally. Removing entire disease categories, including those traditionally underfunded, has little effect on overall inequality. Instead, participation is highly concentrated geographically, with a small group of Global North countries enrolling a disproportionate share of participants across nearly all diseases. These patterns persist despite decades of disease-targeted funding and increasing alignment between research efforts and disease burden. Findings also show that without parallel horizontal investments in research capacity, health infrastructure, and governance, even well-funded disease programs are insufficient to reduce inequality in clinical trial participation.
Clinical trials shape both the medical evidence base and access to experimental therapies, yet participation has long been unevenly distributed across countries. This study asks whether those disparities are driven primarily by disease burden or by country-level conditions. Across a large longitudinal sample of randomized controlled trials, it finds that the latter accounts for far more of the variation in participation.
Core Contribution
The authors link 185.6 million participants from 78,117 randomized controlled trials conducted from 2000 to 2024 to country-level disease burden across 16 major disease categories. Their country-disease analysis compares participation with burden using a participation-to-burden ratio, distinguishing proportional representation from under- and over-representation.
The main result is that country-level factors account for 88.9% of variation in trial participation, while disease and temporal effects contribute comparatively little. Participation is geographically concentrated, with a small group of Global North countries enrolling disproportionate shares of participants across many diseases.
Figure 1 illustrates these disparities through disease-specific maps, specialization patterns, and comparisons between disease burden and annual participant counts across income groups.
Technical Approach
The study decomposes inequality in participation across countries, disease categories, and time. It examines whether removing disease categories with high contributions to inequality substantially changes the overall distribution, and separately tests the sensitivity of inequality to removing high-volume countries.
It also evaluates country-level factors associated with participation patterns, including research capacity, health infrastructure, governance, and socioeconomic conditions. The paper presents simulated alignment scenarios to assess how changes in poorly aligned country-factor relationships could affect inequality measures and network structure. These simulations are analytical scenarios rather than observed policy interventions.
Results and Analysis
The findings suggest that global trial-participation inequality is not principally a matter of redistributing attention among disease categories. Excluding high-contribution disease categories produces little change in overall inequality, whereas excluding high-volume countries has a more substantial effect. This is consistent with the paper's conclusion that participation is shaped predominantly by country-level conditions.
That distinction matters for policy. Disease-focused programs may improve attention to particular conditions, but the paper argues that reducing participation inequality also requires parallel investments in the broader conditions that support clinical research, including capacity, infrastructure, and governance.
The evidence supports a descriptive account of where participation is concentrated and how country-level conditions relate to that concentration. It does not establish that changing any one national factor will directly produce the participation shifts represented in the simulations.
Caveats in Practice
The analysis is observational, so its country-level associations should not be interpreted as causal estimates. Measures of participant geography and enrollment depend on information reported in trial publications, which can be incomplete or unevenly available. Country-level indicators may also have missing data, particularly in settings where research infrastructure is less well documented. These limitations are important because they may affect measurement precisely in the countries most underrepresented in clinical research.
Evidence Box
moderateKey Claims
- •Country-level factors account for most variation in global RCT participation
- •Disease-category differences contribute relatively little to overall participation inequality
- •Reducing inequality likely requires disease programs to be paired with broader investments in research capacity, infrastructure, and governance
Key Results
- •88.9% of participation variation attributed to country-level factors
- •78,117 RCTs and 185.6 million participants analyzed from 2000–2024
- •16 major disease categories linked to country-level burden
- •Removing high-contribution disease categories has little effect on overall inequality, while removing high-volume countries has a larger effect
Limitations & Caveats
- •Observational design cannot establish causal effects of country-level factors
- •Participant location and enrollment depend on information reported in published trial text
- •Country-level indicators may contain missing or unevenly measured data
- •Intervention results are model-based scenarios rather than observed policy effects
