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Diversity in clinical trials is an important part of developing new medications that are safe and effective for all potential patients, understanding the demographic disparities within them will provide several benefits to the field.
It is widely known that diversity in clinical trials is an important part of developing new medications that are safe and effective for all potential patients in need of treatment. While researchers have spent more than 20 years investigating this aspect of clinical trials, the majority of studies are anecdotal; focus narrowly on a single therapeutic area, or a short list of therapeutic areas; or are drawn from a convenience sample.1,2,3
There have been a few studies, however, which attempt to look at the issue at a more macro level. In 2009, the Tufts Center for the Study of Drug Development (Tufts CSDD) used FDA’s New Drug Approval Database (drugs@FDA) to collect data on patient demographic data for studies on 27 drugs approved in 2007. These demographic data were then compared to U.S. census data and found that participants identifying as Black or of African descent, and Hispanic/Latinx participants were significantly underrepresented.4
In an effort to help address the need for diversity in clinical trials, Congress enacted FDASIA (the FDA Safety and Innovation Act) in 2012, which requires the agency to publish publicly available reports on trial participant demographics. Additionally, in 2014 the FDA began publishing the Drug Trial Snapshot, an annual 12–16-page report which highlights the distribution of clinical trial participant for the drugs approved during that year. Unfortunately, the Snapshot falls short in a few areas. Most importantly are the facts that it is a static report which makes evaluating trends difficult, and the report does not assess disparities in demographic diversity. The report presents what distribution looks like but gives no indication of where it should be.
Improving our understanding of the demographic disparities in clinical trials will provide several benefits to the field. The information can be used to identify the areas of greatest need-which demographic subgroup disparities are the greatest, both overall and within specific therapeutic areas or disease conditions. The information can be used to assess whether participant diversity has changed over time and how it has done so. It can also be used to assess whether new diversity programs and policies are having an impact, allowing the effective programs to continue and the resources dedicated to ineffective programs to be reallocated to other programs that may have a greater impact.
In 2018, the Tufts CSDD was awarded a grant from Merck to investigate, measure, and quantify the level of clinical trial participant diversity and the disparities therein. This research had three goals. The first was to assess the availability of participant demographic data. The second goal was to determine a baseline assessment of participant diversity and disparities among demographic subgroups. The third was to develop an approach that could be used by FDA and others to improve the clarity and value of the Drug Trial Snapshot and other publications reporting on clinical trial participant diversity. While data was collected for all trials when available, Tufts CSDD’s analysis focused mainly on disparity in pivotal trials since these are the trials FDA uses when considering the approval of a new drug application.
In 2018, Tufts CSDD compiled data on all new drugs and biologics approved by the FDA from publicly available data on the agency website. Data were gathered on a total of 341 NDAs and BLAs including Trade Name, Generic Name, Approval Date, Application Number, Classification (NDA or BLA), Sponsor, Indication, Therapeutic Area, FDA Designations (First-in-Class, Orphan, Fast Track, Breakthrough, Priority, Accelerated Approval, or 505b2). Participant demographic data including sex, race, ethnicity, and age were also gathered for all new drug and biologic approvals. Supplemental data were collected from the Medical Reviews and Printed Labeling for each approved drug, as well as ClinicalTrials.gov, and additional peer-reviewed publications.If demographic data were not found on these sources, the data were considered “not available.”
To assess participant demographic subgroup disparities, clinical trial demographic data were compared with corresponding disease prevalence rates or U.S. census data. Tufts CSDD searched for and collected prevalence rates for each of the disease conditions for which a new drug or biologic was approved. Data were collected from publicly available sources such as government websites, national health organizations, and peer-reviewed literature. When disease prevalence data were not available, it was assumed that prevalence rates were equal between racial and demographic subgroups, and Tufts CSDD referred to US Census Data to impute participant demographic subgroup distributions.
Tufts CSDD found disease prevalence rates for 193 drugs (56.6%) out of the total 341 approved, of which 114 (33.4%) target diseases where prevalence varied by race and ethnicity. U.S. Census data were collected for each year from 2007 to 2017. A 10-year census average was calculated for each demographic category by calculating the mean percentage across all years.
For each new drug and biologic approved between 2007 and 2017, Tufts CSDD calculated an expected or “Predicted” distribution of participants by demographic subgroup based on either the known disease prevalence or overall US population distributions. Tufts CSDD derived a “Disparity Percentage”-a summary statistic to characterize underrepresentation-for all approved drugs. This percentage is the difference between the “Actual” number of participants by demographic subgroup and the expected or “Predicted” level (i.e., determined by the identified prevalence rate or US census data in the approval year) divided by the “Predicted” level. An example of the Disparity percentage is given below. Means were calculated for this statistic for each participant subgroup by disease condition, therapeutic area, and year.
Disease Condition for Approved Drug:
Peripheral T-Cell Lymphoma
Total Clinical Trial Participants:
“Actual” Distribution of Participants who are Black or of African Descent:
3.7% (29 participants)
Expected or “Predicted” Distribution of Participants who are Black or of African Descent:
13.5% (106 participants)
One of the more surprising findings of this study was the general lack of availability of demographic data. While disclosure of this data is an FDA requirement, only 83% (282) of the drugs approved during the 2007 to 2017 time period had data on participant race readily available for at least one trial referenced in the Medical Review, and only half (171) of the drugs provided ethnicity data on participants for at least one trial (Table 1). When looking at pivotal trials, the availability of race data drops to 73% (551 of 757 pivotal trials) and only 37% of pivotal trials (278) had ethnicity data available. Availability of age and sex data were relatively high for pivotal trials, with 83% and 90% (630 and 679) of pivotal trials having data available, respectively. The availability of data for all demographics plummets dramatically when looking at all trials. Forty-six percent (2,148) of all trials had sex data readily available, and 38% (1,781) had age data available. Availability of data regarding participant race drops to 27% (1,296) of trials, and the availability of data regarding participant ethnicity was even lower-13% (614) of trials (Table 1).
The extreme lack of ethnicity data speaks to a great need for increased diligence in the collection of these data, while observations made during the data collection process speak to a need for a standardization of the process. Ethnicity data were rarely collected, yet in the instances they were collected, the process frequently varied by study. In many cases race and ethnicity were conflated and treated as the same variable. Even within the CSDD’s study, keeping these variables separate became a challenge as a result of this conflation within the data. When data on participant ethnicity were collected, the ethnicities available were rarely the same. For many studies, the only ethnicities listed were Hispanic or Latinx, and non-Hispanic or Latinx. In rare studies a more extensive list was made available, and this list varied between studies. While an entirely comprehensive list of ethnicities may not be a realistic aspiration, a standardized list of ethnicities that industry researchers can default to may help increase collection of this data, and in turn contribute to an improvement in diversity inclusion in clinical trials.
Of the racial and ethnic demographics examined in this study, participants identifying as Black or of African descent were the most underrepresented. Pivotal studies for drugs approved between 2007 and 2017 under enrolled these participants by more than 46,000-an under-enrollment disparity of -65.4% (Table 2). Further, pivotal trials averaged an underrepresentation of greater than 20% for this demographic for 80% (198) of the drugs in the sample, and 86% (19) of the therapeutic areas where demographic data were available had an average underrepresentation of greater than 20% (Table 3).
Participants identifying as other racial identities (e.g. Native American, Native Alaskan, Native Hawaiian, or Pacific Islander, etc.) were also significantly underrepresented. Participants with other racial identities were under enrolled in pivotal trials by nearly 12,000 (-46.1%) (Table 2). They were underrepresented in pivotal trials for 76% (157) of drugs approved in the time period, and 76% (16) of therapeutic areas (Table 3). Hispanic or Latinx participants showed a less dramatic, but still significant, underrepresentation, being under enrolled in pivotal trials by nearly 5,000 participants, an under-enrollment disparity of -12.4% (Table 2). Although the overall under-enrollment of Hispanic or Latinx participants was less dramatic, pivotal trials for 62% (87) of the approved drugs which collected ethnicity data under-enrolled participant of this demographic group by greater than 20%, and they were underrepresented in 62% (13) of therapeutic areas (Table 3). When examining all trials together, Asian participants were over enrolled by more than 23,000 participants in pivotal trials, a disparity of +148.9% (Table 2). However, when examining the pivotal trials for individual drugs, Asian participants were underrepresented in pivotal trials for 59% (115) of drugs approved during the time period, and in 46% (10) of therapeutic areas (Table 3). This apparent contradiction stems from the fact that certain drugs recruited large numbers of Asian participants in their pivotal trials. These trials resulted in very high overrepresentation disparities and raised both the total number of Asian participants and the average disparity when examining all trials together, while the majority of individual trials continued to show an underrepresentation for this demographic. Further complicating the analysis of this demographic is the fact that researchers in some countries included participants of Indian descent as Asian, while researchers in other countries did not. This inconsistency may have also contributed to the inflation of the demographic subgroup when looking at all trials together. For comparison, White participants were over-enrolled in pivotal trials by more than 41,000 (+13.6%) (Table 2) and underrepresented in pivotal trials for 9% (22) of drugs, and in 5% (1) of therapeutic areas (Table 3).
It is clear that the issue of underrepresentation of racial and ethnic minorities is a consistent problem in clinical trials, however there are some areas in particular that show a dramatic lack of representation.
Table 4 lists the therapeutic areas with the highest percentage of drugs that underrepresented demographics by at least 20% within their pivotal trials, as well as the average disparity percentage when examining all of the drugs within the TA (to ensure that therapeutic areas were fairly represented, only those with 10 or more drugs were included). One TA from this list that stands out is Neurology as it was among the top five for each of the racial or ethnic subgroups. Among Neurology drugs, 89% underrepresented participants with other racial identities, 88% underrepresented participants that are Black or of African descent in pivotal trials, 85% underrepresented Hispanic or Latinx participants, and 71% underrepresented Asian participants. Similarly, dermatology was among the top five therapeutic areas for three of the demographic subgroups. Eighty-one percent of the drugs in this TA underrepresented participants who are Black or of African descent, and Asian and participants with other racial identities were both underrepresented by 69% of the drugs in this TA. Cardiology/vascular diseases, gastroenterology, immunology, infections and infectious diseases, oncology, and pulmonary/respiratory diseases were each among the top five for two demographic subgroups.
Literature from the past two decades has attempted to identify the causes of underrepresentation of demographic subgroups, as well as offer potential solutions to these problems.5,6 The results of this Tufts CSDD study indicate that continued efforts to resolve these barriers is required; however, there are indications that at least some of these efforts are being made. One source of these efforts is the patient engagement movement, which has inspired clinical researchers to improve their efforts to increase diversity in clinical trials.7 As a result, many large and mid-sized pharmaceutical and biotechnology companies have publicly committed themselves to improving diversity in clinical trials in recent years.
While this study goes a long way toward quantifying the problem of racial and ethnic underrepresentation in clinical trials, it is not without limitations, leaving opportunities for further research. First, the availability of demographic data for clinical trials. Although quantifying this problem was one of the goals of this research, the low availability of data indicates that if a more complete sample were collected, the findings may vary. Additionally, even though using the Medical Reviews for each drug as a basis for trials can reasonably be assumed to capture all pivotal trials, it is known that not all trials conducted for a drug are included in the Medical Review. Our conclusions can only be applied to the trials reported and it is likely that many of the drugs on our list have unreported trials. Further, no data were collected on trials conducted for drugs that failed to receive FDA approval, another sizeable set of clinical trials, which may have different disparities. Finally, the use of U.S. Census data in instances where disease prevalence rates were unavailable means that disparity rates cannot necessarily be generalized outside of the US. Demographic makeup varies by country, as do incidence and prevalence rates for many diseases, so future research will require applying regional population census and prevalence data to ensure that results are applicable in countries other than the US. Additional research into these areas, and demographic disparity in general, is required.
Zachary P. Smith, MA*, email: firstname.lastname@example.org; Kenneth A. Getz, MBA, Yaritza Peña, BA, all with Tufts Center for the Study of Drug Development, Tufts University School of Medicine,