Clinical Research: Phase 1 - Phase 4

Advancing Health Outcomes for All: Addressing Diversity and Ethnicity Data Challenges in Global Clinical Research

In the business of pharmaceutical and MedTech clinical development, the significance of robust data cannot be overstated. It serves as the cornerstone for informed decision-making and cultivates an environment where innovation thrives. However, when confronted with the task of comparing ethnicity data across diverse countries, a myriad of challenges and intricacies surface, warranting a thorough examination.

Beyond the imperative of regulatory compliance, the embrace of diversity and inclusivity within clinical research represents a momentous stride towards enhancing healthcare outcomes for all, transcending the constraints of ethnicity and geography. As global clinical trials expand in 2024 and beyond, the adept management of cross-country ethnicity data becomes paramount for clinical sponsors.

This is our third installment in a blog series focused on diversity, equity, and inclusion (DEI) in clinical research (see part one here and part two here). In this piece, we highlight the subtleties of managing ethnicity data across international borders to foster a more comprehensive understanding of its pivotal role in advancing global healthcare.

Unraveling Challenges and Diverse Perspectives

Clinical development professionals often grapple with the need to compare data on health outcomes and patient demographics internationally. This is essential for assessing how healthcare systems and interventions perform on a global scale. While certain data standards, such as those defined by the International Labour Organization for labor market statistics, are widely accepted, the landscape becomes more complex when dealing with ethnicity data in healthcare.

Four key challenges exist in comparing ethnicity data across different countries:

  • Variations in Population Makeup: Each country defines and classifies ethnicity differently, making direct comparisons challenging. Diverse ethnic compositions in various regions play a crucial role in shaping these classifications. For example, according to the United States Census Bureau, the U.S. has a notably diverse population, consisting of approximately 60.1% non-Hispanic White, 18.5% Hispanic or Latino, 13.4% Black or African American, 5.9% Asian, and 1.3% American Indian or Alaska Native, among others. In contrast, a country like Japan has a more homogeneous population, primarily consisting of Japanese individuals.
  • Ethnicity in Policymaking: Ethnicity often holds significance in healthcare policymaking, and its management can differ greatly from one country to another. Some countries prioritize healthcare policies targeting minority ethnic groups to address health disparities. For example, Canada aims to address the healthcare needs of Indigenous peoples, recognizing their unique health challenges, while Sweden may focus on broader public health measures without explicitly targeting specific ethnic groups.
  • Subjective Nature of Ethnicity: Ethnicity is subjective, with individuals self-identifying differently based on cultural, historical, or societal factors. Individuals from the same ethnic background might identify themselves differently in different countries. For example, individuals with South Asian ancestry might identify as “Sindhi” in one country but “Punjabi” in another, even though their ancestral roots are similar.
  • Terminology Differences: The terms used to describe ethnic groups can vary significantly from one country to another, introducing ambiguity when attempting to align data. Comparing terminology used in different countries, such as “African American” in the U.S., “Black British” in the United Kingdom (UK), and “Afro-Caribbean” in some Caribbean nations, illustrates the diversity of descriptors employed to categorize similar ethnic groups.

Data Issues in Cross-Country Ethnicity Comparison

Additionally, three key issues are essential for clinical development professionals to be aware of when comparing ethnicity data globally:

  • Missing Data: In some countries, ethnicity data may not be collected at all due to legal restrictions. France and Germany, for instance, do not collect ethnicity information and rely on alternative data sources, such as a person’s country of birth.
  • Diverse Classification Systems: Different countries employ diverse classification systems for ethnicity. Some opt for detailed lists of ethnic groups, while others prefer concise classifications that efficiently capture the main population groups.
  • Data Minimization Principles in Europe: Data protection authorities place significant emphasis on the principle of data minimization, a critical consideration for clinical development professionals. This principle underlines the utmost importance of precise data collection and processing, aligning each piece of information with the research’s specific objectives. The fundamental goal here is to ensure that no extraneous or unnecessary data finds its way into the research process. Although data minimization is a guiding principle, data protection authorities make exceptions for data necessary for regulatory or legal compliance, particularly safety information. Demonstrating the need for certain data (e.g., race/ethnicity or genetic information) and ensuring the drug’s safety across populations can usually justify its collection, although challenges may persist in some countries due to varying national regulations.

Case Study: Contrasting Approaches in Europe

To illustrate the complexities of cross-country ethnicity data comparison within Europe, consider the case of France and the UK. In the UK, the census includes specific categories like ‘White British’ and ‘White Irish’ to classify individuals of European descent. However, in France, ethnicity data collection is restricted by law, and detailed ethnic classifications are generally avoided. Instead, studies may be conducted based on objective data such as a person’s name, geographic origin, or nationality before adopting French nationality. This approach differs significantly from the UK’s more specific ethnic classifications.

These variations highlight the challenges of comparing ethnicity data even within Europe, where countries have distinct approaches to data collection and classification. It underscores the need for clinical development professionals to approach cross-country ethnicity data comparison with caution and transparency.

Fostering Global Collaborations for Effective Research Solutions

To navigate these complexities effectively and foster meaningful change in the field of clinical trials, it is essential to cultivate long-term global relationships and collaborations that span beyond individual trials. This approach requires substantial commitment and cooperation across the clinical trial ecosystem, involving patient groups, community members, clinical research sites, contract research organizations, academia, nonprofit and advocacy organizations, government agencies, and the pharmaceutical and MedTech industries.

Premier Research is well-equipped to assist clinical trial sponsors in this endeavor. Our dedicated team of professionals understands the nuances involved in international data comparisons, and we offer tailored solutions to ensure accurate, compliant, and transparent assessments. From designing comprehensive data collection strategies that account for regional variations to providing expert guidance on interpreting and integrating diverse ethnicity data, Premier Research empowers sponsors to make informed decisions and foster equitable access to healthcare solutions on a global scale.

By partnering with Premier Research, sponsors can access the necessary support and expertise to drive diversity and inclusivity in clinical trials, ultimately advancing healthcare outcomes for all, regardless of their ethnicity or geographic location.

To learn more about managing diversity in your global clinical studies, contact us.